diff --git a/Documentation/FS-Rules_2022_v1.0.pdf b/Documentation/FS-Rules_2022_v1.0.pdf
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341M64.sys b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341M64.sys
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341SER.CAT b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341SER.CAT
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341SER.INF b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341SER.INF
deleted file mode 100644
index 3da3a76d3afa30e1ef7bfed2dbcd059e37b49839..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341SER.INF
+++ /dev/null
@@ -1,282 +0,0 @@
-; CH341SER.INF
-; Driver for CH340/CH341 (USB=>SERIAL chip) V3.7
-; WDM&VXD for Windows 98/Me/2000/XP/Vista/7/8/8.1/10/11/SERVER 2003/2008/2012/2016/2019/2022
-; Copyright (C) W.ch 2001-2022
-;
-
-[Version]
-Signature = "$Chicago$"
-Class = Ports
-ClassGuid = {4D36E978-E325-11CE-BFC1-08002BE10318}
-Provider = %WinChipHead%
-DriverVer = 01/18/2022, 3.7.2022.01
-CatalogFile =CH341SER.CAT
-
-
-[ControlFlags]
-ExcludeFromSelect = USB\VID_1A86&PID_7523
-ExcludeFromSelect = USB\VID_1A86&PID_5523
-ExcludeFromSelect = USB\VID_1A86&PID_7522
-ExcludeFromSelect = USB\VID_1A86&PID_E523
-ExcludeFromSelect = USB\VID_4348&PID_5523
-ExcludeFromSelect = USB\VID_4348&PID_5523&REV_0250
-ExcludeFromSelect = USBSERPORT\SER5523
-ExcludeFromSelect = CH341PORT\SER5523
-
-[Manufacturer]
-%WinChipHead% = WinChipHead,NT,NTamd64,NTia64,NTARM64
-
-[WinChipHead]
-%CH340SER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Install, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Install, USB\VID_4348&PID_5523&REV_0250
-%CH341S98.DeviceDesc% = CH341S98_Install, USBSERPORT\SER5523
-%CH341S98.DeviceDesc% = CH341S98_Install, CH341PORT\SER5523
-
-[WinChipHead.NT]
-%CH340SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_4348&PID_5523&REV_0250
-
-[WinChipHead.NTamd64]
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_4348&PID_5523&REV_0250
-
-[WinChipHead.NTia64]
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_4348&PID_5523&REV_0250
-
-[WinChipHead.NTARM64]
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_4348&PID_5523&REV_0250
-
-[CH341SER_Install]
-DelFiles = CH341S98.DelFiles.SYS
-CopyFiles = CH341SER.CopyFiles.SYS, CH341SER.CopyFiles.DLL
-AddReg = CH341SER.9X.AddReg, CH341SER.AddReg
-
-[CH341SER_Install.NT]
-CopyFiles = CH341SER.NT.CopyFiles.SYS, CH341SER.CopyFiles.DLL
-AddReg = CH341SER.NT.AddReg, CH341SER.AddReg
-
-[CH341SER_Install.NT.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341SER_Inst.NTamd64]
-CopyFiles = CH341SER.NT.CopyFiles.SYSA64, CH341SER.CopyFiles.DLLA64, CH341SER.CopyFiles.WOWDLL
-AddReg = CH341SER.NTAMD64.AddReg, CH341SER.AddReg
-
-[CH341SER_Inst.NTamd64.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341SER_Inst.NTia64]
-CopyFiles = CH341SER.NT.CopyFiles.SYSI64,CH341SER.CopyFiles.DLLA64
-AddReg = CH341SER.NT.AddReg, CH341SER.AddReg
-
-[CH341SER_Inst.NTia64.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341SER_Inst.NTARM64]
-CopyFiles = CH341SER.NT.CopyFiles.SYSM64,CH341SER.CopyFiles.DLLA64, CH341SER.CopyFiles.WOWDLL
-AddReg = CH341SER.NTARM64.AddReg, CH341SER.AddReg
-
-[CH341SER_Inst.NTARM64.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341S98_Install]
-DelFiles = CH341S98.DelFiles.SYS
-CopyFiles = CH341S98.CopyFiles.VXD, CH341SER.CopyFiles.SYS
-AddReg = CH341S98.9X.AddReg, CH341S98.AddReg
-
-;[CH341S98_Install.NT]
-
-[CH341S98.DelFiles.SYS]
-CH341S98.SYS, , , 1
-
-[CH341SER.CopyFiles.SYS]
-CH341S98.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYS]
-CH341SER.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYSA64]
-CH341S64.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYSI64]
-;CH341I64.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYSM64]
-CH341M64.SYS, , , 2
-
-[CH341S98.CopyFiles.VXD]
-CH341SER.VXD, , , 2
-
-[CH341SER.CopyFiles.DLL]
-CH341PT.DLL, , , 2
-CH341PORTS.DLL, , , 2
-
-[CH341SER.CopyFiles.DLLA64]
-CH341PTA64.DLL, , , 2
-CH341PORTSA64.DLL, , , 2
-
-[CH341SER.CopyFiles.WOWDLL]
-CH341PT.DLL, , , 2
-;��װDLL�ǿ�ѡ��,DLL��������ʶ��CH341�˿ںͼ���CH341�˿ڵIJ���¼�
-
-[CH341SER.9X.AddReg]
-HKR, , DevLoader, , *NTKERN
-HKR, , NTMPDriver, , CH341S98.SYS
-
-[CH341SER.NT.AddReg]
-HKR,,EnumPropPages32,,"CH341PORTS.dll,SerialPortPropPageProvider"
-
-[CH341SER.NTAMD64.AddReg]
-HKR,,EnumPropPages32,,"CH341PORTSA64.dll,SerialPortPropPageProvider"
-
-[CH341SER.NTARM64.AddReg]
-HKR,,EnumPropPages32,,"CH341PORTSA64.dll,SerialPortPropPageProvider"
-
-[CH341SER.NT.HW.AddReg]
-;HKR,,"EnableBRA",0x00010001,1
-;HKR,,"BRAVal",0x00010001,1
-;�����������ڲ����ʵ����������������ɽ��������зֺ�ȥ��
-;HKR,,"UpperFilters",0x00010000,"serenum"
-;������������ö�ٽ��ڴ��ڵļ��弴���豸,����ʱ������DTR��RTS�ź�,�����Ҫö��,�뽫�������еķֺ�ȥ��
-
-[CH341S98.9X.AddReg]
-HKR, , DevLoader, , *vcomm
-HKR, , PortDriver, , CH341SER.VXD
-HKR, , Contention, , *vcd
-HKR, , ConfigDialog, , serialui.dll
-HKR, , DCB, 3, 1C,00,00,00, 80,25,00,00, 11,00,00,00, 00,00,0A,00, 0A,00,08,00, 00,11,13,00, 00,00,00,00
-HKR, , PortSubClass, 1, 01
-HKR, , EnumPropPages, , "serialui.dll,EnumPropPages"
-;HKR, , Enumerator, , serenum.vxd
-;������������ö�ٽ��ڴ��ڵļ��弴���豸,����ʱ������DTR��RTS�ź�,�����Ҫö��,�뽫�������еķֺ�ȥ��
-
-[CH341SER.AddReg]
-HKLM, SOFTWARE\WinChipHead\IC\CH341SER, WDM, 0x00010001, 0x00000034
-HKLM, SOFTWARE\WinChipHead\IC\CH341PORT, DLL, 0x00010001, 0x00000010
-HKLM, SOFTWARE\WinChipHead\IC\CH341SER, Function, , "USB=>Serial"
-;HKLM, SYSTEM\CurrentControlSet\Services\CH341SER, UserRemoval, 0x00010001, 0x00000001
-;��������������ϵͳ��������ʾ����ȫɾ��USBתSERIALӲ���豸���������û��ֹ�ɾ��Ӳ��
-
-[CH341S98.AddReg]
-HKLM, SOFTWARE\WinChipHead\IC\CH341SER, VXD, 0x00010001, 0x00000023
-
-[CH341SER_Install.NT.Services]
-AddService = CH341SER, 2, CH341SER.Service
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER_Inst.NTamd64.Services]
-AddService = CH341SER_A64, 2, CH341SER.ServiceA64
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER_Inst.NTia64.Services]
-AddService = CH341SER_I64, 2, CH341SER.ServiceI64
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER_Inst.NTARM64.Services]
-AddService = CH341SER_M64, 2, CH341SER.ServiceM64
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER.Service]
-DisplayName = "CH341SER"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341SER.SYS
-
-[CH341SER.ServiceA64]
-DisplayName = "CH341SER_A64"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341S64.SYS
-
-[CH341SER.ServiceI64]
-DisplayName = "CH341SER_I64"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341I64.SYS
-
-[CH341SER.ServiceM64]
-DisplayName = "CH341SER_M64"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341M64.SYS
-
-[Serenum_Service_Inst]
-DisplayName = "SerEnum"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %12%\serenum.sys
-LoadOrderGroup = PNP Filter
-
-[DestinationDirs]
-DefaultDestDir = 10, System32\Drivers
-CH341S98.DelFiles.SYS = 11
-CH341SER.CopyFiles.SYS = 10, System32\Drivers
-CH341SER.NT.CopyFiles.SYS = 10, System32\Drivers
-CH341S98.CopyFiles.VXD = 11
-CH341SER.CopyFiles.DLL = 11
-CH341SER.CopyFiles.DLLA64 = 11
-CH341SER.CopyFiles.WOWDLL = 10,SysWOW64
-CH341SER.NT.CopyFiles.SYSA64 = 10, System32\Drivers
-CH341SER.NT.CopyFiles.SYSM64 = 10, System32\Drivers
-;CH341SER.NT.CopyFiles.SYSI64 = 10, System32\Drivers
-
-[SourceDisksFiles]
-CH341M64.SYS = 1
-CH341PT.DLL = 1
-CH341PTA64.DLL= 1
-CH341SER.SYS = 1
-CH341S98.SYS = 1
-CH341SER.VXD = 1
-CH341S64.SYS = 1
-CH341PORTS.DLL = 1
-CH341PORTSA64.DLL = 1
-;CH341I64.SYS = 1
-
-[SourceDisksNames]
-1 = %DISK_NAME%, , ,
-
-[SourceDisksNames.amd64]
-1 = %DISK_NAME%, , ,
-
-[SourceDisksNames.ia64]
-1 = %DISK_NAME%, , ,
-
-[SourceDisksNames.arm64]
-1 = %DISK_NAME%, , ,
-
-[Strings]
-WinChipHead = "wch.cn"
-CH341SER.DeviceDesc = "USB-SERIAL CH341"
-CH341S98.DeviceDesc = "USB-SERIAL CH341"
-CH340SER.DeviceDesc = "USB-SERIAL CH340"
-CH341ASER.DeviceDesc = "USB-SERIAL CH341A"
-CH340KSER.DeviceDesc = "USB-SERIAL CH340K"
-CH330SER.DeviceDesc = "USB-SERIAL CH330"
-DISK_NAME = "CH341 Serial Installation Disk"
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341SER.SYS b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/CH341SER.SYS
deleted file mode 100644
index a4c846c89272555409034bb91cd0ee11a4fc796d..0000000000000000000000000000000000000000
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deleted file mode 100644
index 1c04d3dfafbdc94db4a03e4163022449f1ddf21f..0000000000000000000000000000000000000000
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/DRVSETUP64/DRVSETUP64.exe b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/DRVSETUP64/DRVSETUP64.exe
deleted file mode 100644
index 826fdc342edac270e6e7d2eabd803b15fa3645f4..0000000000000000000000000000000000000000
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deleted file mode 100644
index bf142f96c4ca7e3ba3a1528de304da231b074d15..0000000000000000000000000000000000000000
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341M64.sys b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341M64.sys
deleted file mode 100644
index cf588a3023f27e5634c61c3617ee7e419374146f..0000000000000000000000000000000000000000
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341PORTS.DLL b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341PORTS.DLL
deleted file mode 100644
index d040b9c9bc84e99ea78c9f1b9791c5eecb37cb57..0000000000000000000000000000000000000000
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341PORTSA64.DLL b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341PORTSA64.DLL
deleted file mode 100644
index 785cd64231c6752a0963d5c6f1655ed42cef5505..0000000000000000000000000000000000000000
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diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341PT.DLL b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341PT.DLL
deleted file mode 100644
index 37cadfc6b084e984ace1fc87a61f51da7a4d8982..0000000000000000000000000000000000000000
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deleted file mode 100644
index c5c69afbe150b7cc6327185bbdc8845417de35d2..0000000000000000000000000000000000000000
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index 440e9e5cc10ef257fc3ad53036f662e11a51fa28..0000000000000000000000000000000000000000
Binary files a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341S64.SYS and /dev/null differ
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341S98.SYS b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341S98.SYS
deleted file mode 100644
index c2939dde68294e972cbef2f583bafe912dae877e..0000000000000000000000000000000000000000
Binary files a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341S98.SYS and /dev/null differ
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.CAT b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.CAT
deleted file mode 100644
index a79e9f5cfd66af8e128d38ba158febe6ab19b067..0000000000000000000000000000000000000000
Binary files a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.CAT and /dev/null differ
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.INF b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.INF
deleted file mode 100644
index 3da3a76d3afa30e1ef7bfed2dbcd059e37b49839..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.INF
+++ /dev/null
@@ -1,282 +0,0 @@
-; CH341SER.INF
-; Driver for CH340/CH341 (USB=>SERIAL chip) V3.7
-; WDM&VXD for Windows 98/Me/2000/XP/Vista/7/8/8.1/10/11/SERVER 2003/2008/2012/2016/2019/2022
-; Copyright (C) W.ch 2001-2022
-;
-
-[Version]
-Signature = "$Chicago$"
-Class = Ports
-ClassGuid = {4D36E978-E325-11CE-BFC1-08002BE10318}
-Provider = %WinChipHead%
-DriverVer = 01/18/2022, 3.7.2022.01
-CatalogFile =CH341SER.CAT
-
-
-[ControlFlags]
-ExcludeFromSelect = USB\VID_1A86&PID_7523
-ExcludeFromSelect = USB\VID_1A86&PID_5523
-ExcludeFromSelect = USB\VID_1A86&PID_7522
-ExcludeFromSelect = USB\VID_1A86&PID_E523
-ExcludeFromSelect = USB\VID_4348&PID_5523
-ExcludeFromSelect = USB\VID_4348&PID_5523&REV_0250
-ExcludeFromSelect = USBSERPORT\SER5523
-ExcludeFromSelect = CH341PORT\SER5523
-
-[Manufacturer]
-%WinChipHead% = WinChipHead,NT,NTamd64,NTia64,NTARM64
-
-[WinChipHead]
-%CH340SER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Install, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Install, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Install, USB\VID_4348&PID_5523&REV_0250
-%CH341S98.DeviceDesc% = CH341S98_Install, USBSERPORT\SER5523
-%CH341S98.DeviceDesc% = CH341S98_Install, CH341PORT\SER5523
-
-[WinChipHead.NT]
-%CH340SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Install.NT, USB\VID_4348&PID_5523&REV_0250
-
-[WinChipHead.NTamd64]
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTamd64, USB\VID_4348&PID_5523&REV_0250
-
-[WinChipHead.NTia64]
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTia64, USB\VID_4348&PID_5523&REV_0250
-
-[WinChipHead.NTARM64]
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_7523
-%CH341ASER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_5523
-%CH340KSER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_7522
-%CH330SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_1A86&PID_E523
-%CH341SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_4348&PID_5523
-%CH340SER.DeviceDesc% = CH341SER_Inst.NTARM64, USB\VID_4348&PID_5523&REV_0250
-
-[CH341SER_Install]
-DelFiles = CH341S98.DelFiles.SYS
-CopyFiles = CH341SER.CopyFiles.SYS, CH341SER.CopyFiles.DLL
-AddReg = CH341SER.9X.AddReg, CH341SER.AddReg
-
-[CH341SER_Install.NT]
-CopyFiles = CH341SER.NT.CopyFiles.SYS, CH341SER.CopyFiles.DLL
-AddReg = CH341SER.NT.AddReg, CH341SER.AddReg
-
-[CH341SER_Install.NT.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341SER_Inst.NTamd64]
-CopyFiles = CH341SER.NT.CopyFiles.SYSA64, CH341SER.CopyFiles.DLLA64, CH341SER.CopyFiles.WOWDLL
-AddReg = CH341SER.NTAMD64.AddReg, CH341SER.AddReg
-
-[CH341SER_Inst.NTamd64.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341SER_Inst.NTia64]
-CopyFiles = CH341SER.NT.CopyFiles.SYSI64,CH341SER.CopyFiles.DLLA64
-AddReg = CH341SER.NT.AddReg, CH341SER.AddReg
-
-[CH341SER_Inst.NTia64.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341SER_Inst.NTARM64]
-CopyFiles = CH341SER.NT.CopyFiles.SYSM64,CH341SER.CopyFiles.DLLA64, CH341SER.CopyFiles.WOWDLL
-AddReg = CH341SER.NTARM64.AddReg, CH341SER.AddReg
-
-[CH341SER_Inst.NTARM64.HW]
-AddReg = CH341SER.NT.HW.AddReg
-
-[CH341S98_Install]
-DelFiles = CH341S98.DelFiles.SYS
-CopyFiles = CH341S98.CopyFiles.VXD, CH341SER.CopyFiles.SYS
-AddReg = CH341S98.9X.AddReg, CH341S98.AddReg
-
-;[CH341S98_Install.NT]
-
-[CH341S98.DelFiles.SYS]
-CH341S98.SYS, , , 1
-
-[CH341SER.CopyFiles.SYS]
-CH341S98.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYS]
-CH341SER.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYSA64]
-CH341S64.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYSI64]
-;CH341I64.SYS, , , 2
-
-[CH341SER.NT.CopyFiles.SYSM64]
-CH341M64.SYS, , , 2
-
-[CH341S98.CopyFiles.VXD]
-CH341SER.VXD, , , 2
-
-[CH341SER.CopyFiles.DLL]
-CH341PT.DLL, , , 2
-CH341PORTS.DLL, , , 2
-
-[CH341SER.CopyFiles.DLLA64]
-CH341PTA64.DLL, , , 2
-CH341PORTSA64.DLL, , , 2
-
-[CH341SER.CopyFiles.WOWDLL]
-CH341PT.DLL, , , 2
-;��װDLL�ǿ�ѡ��,DLL��������ʶ��CH341�˿ںͼ���CH341�˿ڵIJ���¼�
-
-[CH341SER.9X.AddReg]
-HKR, , DevLoader, , *NTKERN
-HKR, , NTMPDriver, , CH341S98.SYS
-
-[CH341SER.NT.AddReg]
-HKR,,EnumPropPages32,,"CH341PORTS.dll,SerialPortPropPageProvider"
-
-[CH341SER.NTAMD64.AddReg]
-HKR,,EnumPropPages32,,"CH341PORTSA64.dll,SerialPortPropPageProvider"
-
-[CH341SER.NTARM64.AddReg]
-HKR,,EnumPropPages32,,"CH341PORTSA64.dll,SerialPortPropPageProvider"
-
-[CH341SER.NT.HW.AddReg]
-;HKR,,"EnableBRA",0x00010001,1
-;HKR,,"BRAVal",0x00010001,1
-;�����������ڲ����ʵ����������������ɽ��������зֺ�ȥ��
-;HKR,,"UpperFilters",0x00010000,"serenum"
-;������������ö�ٽ��ڴ��ڵļ��弴���豸,����ʱ������DTR��RTS�ź�,�����Ҫö��,�뽫�������еķֺ�ȥ��
-
-[CH341S98.9X.AddReg]
-HKR, , DevLoader, , *vcomm
-HKR, , PortDriver, , CH341SER.VXD
-HKR, , Contention, , *vcd
-HKR, , ConfigDialog, , serialui.dll
-HKR, , DCB, 3, 1C,00,00,00, 80,25,00,00, 11,00,00,00, 00,00,0A,00, 0A,00,08,00, 00,11,13,00, 00,00,00,00
-HKR, , PortSubClass, 1, 01
-HKR, , EnumPropPages, , "serialui.dll,EnumPropPages"
-;HKR, , Enumerator, , serenum.vxd
-;������������ö�ٽ��ڴ��ڵļ��弴���豸,����ʱ������DTR��RTS�ź�,�����Ҫö��,�뽫�������еķֺ�ȥ��
-
-[CH341SER.AddReg]
-HKLM, SOFTWARE\WinChipHead\IC\CH341SER, WDM, 0x00010001, 0x00000034
-HKLM, SOFTWARE\WinChipHead\IC\CH341PORT, DLL, 0x00010001, 0x00000010
-HKLM, SOFTWARE\WinChipHead\IC\CH341SER, Function, , "USB=>Serial"
-;HKLM, SYSTEM\CurrentControlSet\Services\CH341SER, UserRemoval, 0x00010001, 0x00000001
-;��������������ϵͳ��������ʾ����ȫɾ��USBתSERIALӲ���豸���������û��ֹ�ɾ��Ӳ��
-
-[CH341S98.AddReg]
-HKLM, SOFTWARE\WinChipHead\IC\CH341SER, VXD, 0x00010001, 0x00000023
-
-[CH341SER_Install.NT.Services]
-AddService = CH341SER, 2, CH341SER.Service
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER_Inst.NTamd64.Services]
-AddService = CH341SER_A64, 2, CH341SER.ServiceA64
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER_Inst.NTia64.Services]
-AddService = CH341SER_I64, 2, CH341SER.ServiceI64
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER_Inst.NTARM64.Services]
-AddService = CH341SER_M64, 2, CH341SER.ServiceM64
-;AddService = Serenum, , Serenum_Service_Inst
-
-[CH341SER.Service]
-DisplayName = "CH341SER"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341SER.SYS
-
-[CH341SER.ServiceA64]
-DisplayName = "CH341SER_A64"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341S64.SYS
-
-[CH341SER.ServiceI64]
-DisplayName = "CH341SER_I64"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341I64.SYS
-
-[CH341SER.ServiceM64]
-DisplayName = "CH341SER_M64"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %10%\System32\Drivers\CH341M64.SYS
-
-[Serenum_Service_Inst]
-DisplayName = "SerEnum"
-ServiceType = 1
-StartType = 3
-ErrorControl = 1
-ServiceBinary = %12%\serenum.sys
-LoadOrderGroup = PNP Filter
-
-[DestinationDirs]
-DefaultDestDir = 10, System32\Drivers
-CH341S98.DelFiles.SYS = 11
-CH341SER.CopyFiles.SYS = 10, System32\Drivers
-CH341SER.NT.CopyFiles.SYS = 10, System32\Drivers
-CH341S98.CopyFiles.VXD = 11
-CH341SER.CopyFiles.DLL = 11
-CH341SER.CopyFiles.DLLA64 = 11
-CH341SER.CopyFiles.WOWDLL = 10,SysWOW64
-CH341SER.NT.CopyFiles.SYSA64 = 10, System32\Drivers
-CH341SER.NT.CopyFiles.SYSM64 = 10, System32\Drivers
-;CH341SER.NT.CopyFiles.SYSI64 = 10, System32\Drivers
-
-[SourceDisksFiles]
-CH341M64.SYS = 1
-CH341PT.DLL = 1
-CH341PTA64.DLL= 1
-CH341SER.SYS = 1
-CH341S98.SYS = 1
-CH341SER.VXD = 1
-CH341S64.SYS = 1
-CH341PORTS.DLL = 1
-CH341PORTSA64.DLL = 1
-;CH341I64.SYS = 1
-
-[SourceDisksNames]
-1 = %DISK_NAME%, , ,
-
-[SourceDisksNames.amd64]
-1 = %DISK_NAME%, , ,
-
-[SourceDisksNames.ia64]
-1 = %DISK_NAME%, , ,
-
-[SourceDisksNames.arm64]
-1 = %DISK_NAME%, , ,
-
-[Strings]
-WinChipHead = "wch.cn"
-CH341SER.DeviceDesc = "USB-SERIAL CH341"
-CH341S98.DeviceDesc = "USB-SERIAL CH341"
-CH340SER.DeviceDesc = "USB-SERIAL CH340"
-CH341ASER.DeviceDesc = "USB-SERIAL CH341A"
-CH340KSER.DeviceDesc = "USB-SERIAL CH340K"
-CH330SER.DeviceDesc = "USB-SERIAL CH330"
-DISK_NAME = "CH341 Serial Installation Disk"
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.SYS b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.SYS
deleted file mode 100644
index 75c3d073f04b6b08f063eeaca8019144047250d6..0000000000000000000000000000000000000000
Binary files a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.SYS and /dev/null differ
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.VXD b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.VXD
deleted file mode 100644
index 1c04d3dfafbdc94db4a03e4163022449f1ddf21f..0000000000000000000000000000000000000000
Binary files a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/CH341SER/WIN 1X/CH341SER.VXD and /dev/null differ
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/Code_Dongle_FASTbot.ino b/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/Code_Dongle_FASTbot.ino
deleted file mode 100644
index e9227eb858e3ee9672492e270e00b28b94ee64a1..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Custom Dongle/Arduino code/Code_Dongle_FASTbot/Code_Dongle_FASTbot.ino
+++ /dev/null
@@ -1,148 +0,0 @@
-/*
- See documentation at https://nRF24.github.io/RF24
- See License information at root directory of this library
- Author: Brendan Doherty (2bndy5)
-*/
-
-/**
- A simple example of sending data from 1 nRF24L01 transceiver to another.
-
- This example was written to be used on 2 devices acting as "nodes".
- Use the Serial Monitor to change each node's behavior.
-*/
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-
-RF24 radio( 9,8);
-
-uint8_t address[][6] = { "1Node", "2Node" };
-
-bool radioNumber = 1; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-float payload[3] = {0.0, 0.0, 0.0};
-
-//#define DEBUG
-
-void setup() {
-
- Serial.begin(9600);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
-#ifdef DEBUG
-Serial.println("Dongle Par FastBot");
-#endif
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
-
- //radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- radio.setDataRate(RF24_250KBPS);
-
- radio.setPayloadSize(sizeof(payload));
-
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- radio.startListening();
-
- Serial.println("angle commande output"); // réglage pid
-
-} // setup
-
-unsigned long Timeout_time = 200;
-unsigned long Timeout = 0;
-
-bool Ping = true;
-
-void loop() {
-
- // Il faut faire un ping pong le dongle possède un timer de reset le dongle envoie les trois valeurs et l'autre renvoie
-
- if (Serial.available()>0) {
-
- int space = 0;
- Serial.print("Données reçu");
- while (Serial.available()>0 && space < 3) {
- payload[space] = Serial.parseFloat();
- #ifdef DEBUG
- Serial.print(payload[space]);
- Serial.print(':');
- #endif
- space++;
-
- }
-// Serial.print('\n');
-
- while (Serial.available())
- {
- Serial.read();
- }
-
-
- }
-//
- unsigned long actualTime = millis();
-
- if (((actualTime - Timeout) > Timeout_time) & (Ping == false) )
- {
-// Serial.println("Timeout reach");
- Ping = true;
- }
-//
- if (Ping == true)
- {
- Ping = false;
-
- Timeout = millis();
-
- radio.stopListening();
-
- bool report =radio.write(&payload, sizeof(payload));
- #ifdef DEBUG
- if (report) {
- Serial.print(F("Transmission successful! ")); // payload was delivered
- } else {
- Serial.println(F("Transmission failed or timed out")); // payload was not delivered
- }
- #endif
-
- radio.startListening();
-
- }
- else
- {
-
- uint8_t pipe;
-
- float receive[3];
-
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&receive, bytes); // fetch payload from FIFO
- //Serial.print("Received package \n");
- Ping = true;
-
- Serial.print(receive[0]);
- Serial.print(",");
- Serial.print(receive[1]);
- Serial.print(",");
- Serial.print(receive[2]);
- Serial.println();
-
-
- }
-
- }
-
-
-} // loop
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/GettingStarted/GettingStarted.ino b/Documentation/Pre-projet/Custom Dongle/Arduino code/GettingStarted/GettingStarted.ino
deleted file mode 100644
index 33478dd19e0246adcc61dbbfd7b6113832a16af3..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Custom Dongle/Arduino code/GettingStarted/GettingStarted.ino
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * See documentation at https://nRF24.github.io/RF24
- * See License information at root directory of this library
- * Author: Brendan Doherty (2bndy5)
- */
-
-/**
- * A simple example of sending data from 1 nRF24L01 transceiver to another.
- *
- * This example was written to be used on 2 devices acting as "nodes".
- * Use the Serial Monitor to change each node's behavior.
- */
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-// instantiate an object for the nRF24L01 transceiver
-RF24 radio(10, 9); // using pin 7 for the CE pin, and pin 8 for the CSN pin
-
-// Let these addresses be used for the pair
-uint8_t address[][6] = { "1Node", "2Node" };
-// It is very helpful to think of an address as a path instead of as
-// an identifying device destination
-
-// to use different addresses on a pair of radios, we need a variable to
-// uniquely identify which address this radio will use to transmit
-bool radioNumber = 1; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-// Used to control whether this node is sending or receiving
-bool role = true; // true = TX role, false = RX role
-
-// For this example, we'll be using a payload containing
-// a single float number that will be incremented
-// on every successful transmission
-float payload = 0.0;
-
-void setup() {
-
- Serial.begin(115200);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
- // print example's introductory prompt
- Serial.println(F("RF24/examples/GettingStarted"));
-
-// // To set the radioNumber via the Serial monitor on startup
-// Serial.println(F("Which radio is this? Enter '0' or '1'. Defaults to '0'"));
-// while (!Serial.available()) {
-// // wait for user input
-// }
-// char input = Serial.parseInt();
-// radioNumber = input == 1;
- Serial.print(F("radioNumber = "));
- Serial.println((int)radioNumber);
-
- // role variable is hardcoded to RX behavior, inform the user of this
- Serial.println(F("*** PRESS 'T' to begin transmitting to the other node"));
-
- // Set the PA Level low to try preventing power supply related problems
- // because these examples are likely run with nodes in close proximity to
- // each other.
- radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- // save on transmission time by setting the radio to only transmit the
- // number of bytes we need to transmit a float
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- // set the TX address of the RX node into the TX pipe
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- // set the RX address of the TX node into a RX pipe
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- // additional setup specific to the node's role
- if (role) {
- radio.stopListening(); // put radio in TX mode
- } else {
- radio.startListening(); // put radio in RX mode
- }
-
- // For debugging info
- // printf_begin(); // needed only once for printing details
- // radio.printDetails(); // (smaller) function that prints raw register values
- // radio.printPrettyDetails(); // (larger) function that prints human readable data
-
-} // setup
-
-void loop() {
-
- if (role) {
- // This device is a TX node
-
- unsigned long start_timer = micros(); // start the timer
- bool report = radio.write(&payload, sizeof(float)); // transmit & save the report
- unsigned long end_timer = micros(); // end the timer
-
- if (report) {
- Serial.print(F("Transmission successful! ")); // payload was delivered
- Serial.print(F("Time to transmit = "));
- Serial.print(end_timer - start_timer); // print the timer result
- Serial.print(F(" us. Sent: "));
- Serial.println(payload); // print payload sent
- payload += 0.01; // increment float payload
- } else {
- Serial.println(F("Transmission failed or timed out")); // payload was not delivered
- }
-
- // to make this example readable in the serial monitor
- delay(1000); // slow transmissions down by 1 second
-
- } else {
- // This device is a RX node
-
- uint8_t pipe;
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
- Serial.print(F("Received "));
- Serial.print(bytes); // print the size of the payload
- Serial.print(F(" bytes on pipe "));
- Serial.print(pipe); // print the pipe number
- Serial.print(F(": "));
- Serial.println(payload); // print the payload's value
- }
- } // role
-
- if (Serial.available()) {
- // change the role via the serial monitor
-
- char c = toupper(Serial.read());
- if (c == 'T' && !role) {
- // Become the TX node
-
- role = true;
- Serial.println(F("*** CHANGING TO TRANSMIT ROLE -- PRESS 'R' TO SWITCH BACK"));
- radio.stopListening();
-
- } else if (c == 'R' && role) {
- // Become the RX node
-
- role = false;
- Serial.println(F("*** CHANGING TO RECEIVE ROLE -- PRESS 'T' TO SWITCH BACK"));
- radio.startListening();
- }
- }
-
-} // loop
diff --git a/Documentation/Pre-projet/Custom Dongle/Arduino code/GettingStarted_dongle/GettingStarted_dongle.ino b/Documentation/Pre-projet/Custom Dongle/Arduino code/GettingStarted_dongle/GettingStarted_dongle.ino
deleted file mode 100644
index 0ef56f0264bbdce88039af74a73e276b5a0977a1..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Custom Dongle/Arduino code/GettingStarted_dongle/GettingStarted_dongle.ino
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * See documentation at https://nRF24.github.io/RF24
- * See License information at root directory of this library
- * Author: Brendan Doherty (2bndy5)
- */
-
-/**
- * A simple example of sending data from 1 nRF24L01 transceiver to another.
- *
- * This example was written to be used on 2 devices acting as "nodes".
- * Use the Serial Monitor to change each node's behavior.
- */
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-// instantiate an object for the nRF24L01 transceiver
-RF24 radio(9, 8); // using pin 7 for the CE pin, and pin 8 for the CSN pin
-
-// Let these addresses be used for the pair
-uint8_t address[][6] = { "1Node", "2Node" };
-// It is very helpful to think of an address as a path instead of as
-// an identifying device destination
-
-// to use different addresses on a pair of radios, we need a variable to
-// uniquely identify which address this radio will use to transmit
-bool radioNumber = 1; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-// Used to control whether this node is sending or receiving
-bool role = true; // true = TX role, false = RX role
-
-// For this example, we'll be using a payload containing
-// a single float number that will be incremented
-// on every successful transmission
-float payload= 0.0;
-
-void setup() {
-
- Serial.begin(115200);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
- // print example's introductory prompt
- Serial.println(F("RF24/examples/GettingStarted"));
-
-// // To set the radioNumber via the Serial monitor on startup
-// Serial.println(F("Which radio is this? Enter '0' or '1'. Defaults to '0'"));
-// while (!Serial.available()) {
-// // wait for user input
-// }
-// char input = Serial.parseInt();
-// radioNumber = input == 1;
- Serial.print(F("radioNumber = "));
- Serial.println((int)radioNumber);
-
- // role variable is hardcoded to RX behavior, inform the user of this
- Serial.println(F("*** PRESS 'T' to begin transmitting to the other node"));
-
- // Set the PA Level low to try preventing power supply related problems
- // because these examples are likely run with nodes in close proximity to
- // each other.
- //radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- // save on transmission time by setting the radio to only transmit the
- // number of bytes we need to transmit a float
- radio.setDataRate(RF24_250KBPS);
-
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- // set the TX address of the RX node into the TX pipe
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- // set the RX address of the TX node into a RX pipe
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- // additional setup specific to the node's role
- if (role) {
- radio.stopListening(); // put radio in TX mode
- } else {
- radio.startListening(); // put radio in RX mode
- }
-
- // For debugging info
- // printf_begin(); // needed only once for printing details
- // radio.printDetails(); // (smaller) function that prints raw register values
- // radio.printPrettyDetails(); // (larger) function that prints human readable data
-
-} // setup
-
-void loop() {
-
- if (role) {
- // This device is a TX node
-
- unsigned long start_timer = micros(); // start the timer
- bool report = radio.write(&payload, sizeof(float)); // transmit & save the report
- unsigned long end_timer = micros(); // end the timer
-
- if (report) {
- Serial.print(F("Transmission successful! ")); // payload was delivered
- Serial.print(F("Time to transmit = "));
- Serial.print(end_timer - start_timer); // print the timer result
- Serial.print(F(" us. Sent: "));
- Serial.println(payload); // print payload sent
- payload += 0.01; // increment float payload
- } else {
- Serial.println(F("Transmission failed or timed out")); // payload was not delivered
- }
-
- // to make this example readable in the serial monitor
- delay(1000); // slow transmissions down by 1 second
-
- } else {
- // This device is a RX node
-
- uint8_t pipe;
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
- Serial.print(F("Received "));
- Serial.print(bytes); // print the size of the payload
- Serial.print(F(" bytes on pipe "));
- Serial.print(pipe); // print the pipe number
- Serial.print(F(": "));
- Serial.println(payload); // print the payload's value
- }
- } // role
-
- if (Serial.available()) {
- // change the role via the serial monitor
-
- char c = toupper(Serial.read());
- if (c == 'T' && !role) {
- // Become the TX node
-
- role = true;
- Serial.println(F("*** CHANGING TO TRANSMIT ROLE -- PRESS 'R' TO SWITCH BACK"));
- radio.stopListening();
-
- } else if (c == 'R' && role) {
- // Become the RX node
-
- role = false;
- Serial.println(F("*** CHANGING TO RECEIVE ROLE -- PRESS 'T' TO SWITCH BACK"));
- radio.startListening();
- }
- }
-
-} // loop
diff --git a/Documentation/Pre-projet/Custom Dongle/Wiring.png b/Documentation/Pre-projet/Custom Dongle/Wiring.png
deleted file mode 100644
index 6eb7f5b45c2fdbe89eb07a3b5d3603065c1c1cb3..0000000000000000000000000000000000000000
Binary files a/Documentation/Pre-projet/Custom Dongle/Wiring.png and /dev/null differ
diff --git a/Documentation/Pre-projet/Custom Dongle/dongle-presentation.aux b/Documentation/Pre-projet/Custom Dongle/dongle-presentation.aux
deleted file mode 100644
index 7569d6e25bcd06dee2566cfca08a538fc9b044f2..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,34 +0,0 @@
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diff --git a/Documentation/Pre-projet/Custom Dongle/dongle-presentation.log b/Documentation/Pre-projet/Custom Dongle/dongle-presentation.log
deleted file mode 100644
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-\titre{Dongle de contrôle}
-
-\departement{Recherche}
-
-\auteurs{Eymeric \textbf{Chauchat}}
-
-\version{V1.0}
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-\versionnement{
-\ver{V1.0}{25 aout 2022}{ ECT }{Rédaction initiale.}{1}
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-\begin{document}
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-\tableofcontents
-
-\section{Présentation}
-Ce document va présenter la réalisation du Dongle de contrôle de la voiture RC et du script permettant son arrêt d'urgence et sa commande si l'on choisit une intelligence déporté.
-
-Le Dongle est composé d'une arduino Nano, d'une carte nrf24l01 et d'un bouton poussoir. Il va permettre de contrôler en temps réel la voiture et de récupérer des informations des différents capteurs.
-
-\section{Construction}
-
-
-Le câblage suit celui d'une carte nrf24l01 basique (Figure \ref{fig:wiring}). La carte se branche ensuite directement à l'ordinateur qui va la contrôler soit directement grâce à l'invité port série d'arduino ou au travers d'un script python.
-
-\begin{figure}
-\centering
-\includegraphics[width=0.4\textwidth]{Wiring.png}
-\caption{Câblage possible d'une nrf24l01 avec une arduino nano}
-\label{fig:wiring}
-\end{figure}
-
-\section{Script de contrôle}
-
-
-
-
-\end{document}
\ No newline at end of file
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-\documentclass{EPSA-rap-template}
-
-\type{Présentation}
-
-\titresize{\Large} % ne pas hésiter a changer la taille :
-%\normalsize
-%\large
-%\Large
-%\LARGE
-%\huge
-%\Huge
-%\HUGE
-
-
-\titresh{ Démonstrateur FASTBOT-AutoDrive}
-
-\titre{ Démonstrateur FASTBOT-AutoDrive v1.0}
-
-\departementsh{ R\& I}
-
-\departement{Direction Recherche \& Innovation EPSA}
-
-
-
-\auteurs{Eymeric \textbf{Chauchat}}
-
-\version{V1.0}
-
-\versionnement{
-\ver{V2.0}{15 Septembre 2022}{ ECT } {Ajustement pour Par}{3}
-\ver{V1.1}{4 Septembre 2022}{ ECT }{Changement de titre.}{2}
-\ver{V1.0}{31 août 2022}{ ECT }{Rédaction initiale.}{1}
-}
-
-\setuppack
-
-\begin{document}
-
-\fairepagedegarde
-\newpage
-\tableofcontents
-
-
-\section{Mise en contexte}
-
-En 2022 les règles du concours de formula student ont évolué et ont pour la première fois dans l'histoire du FS rendu obligatoire les épreuves driverless (conduite autonome) pour la catégorie de véhicule électrique.
-Ce changement à pour conséquence de donner un très fort malus aux équipes FS ne possèdent pas de Driverless sur leur véhicule (0 points sur le DV-skidpad et sur le DV-acceleration). L'EPSA faisant parti de cette catégorie, il faut trouver au plus vite une solution pour permettre de développer en parallèle de voiture FS un système de conduite autonome pour voiture.
-
-L'EPSA dans son projet principal à pour l'instant énormément de mal à réunir des élèves ingénieurs talentueux en électronique et en programmation du à sa propension à la réalisation mécanique et à son passé dans les FS thermiques.
-
-\section{Introduction}
-
-L'idée a donc émergé d'essayer d'organiser deux PaR satellites composés exclusivement de passionné d'informatique et d'électronique pour permettre un travail sur le sujet. Les nouveaux membres de ce projet auront pour exclusif but de préparer un système de détection et un algorithme permettant la conduite autonome d'un véhicule radio commandé.
-
-Ce véhicule radio commandé taille un seizième possède des performances se rapprochant à son échelle d'une Formula Student électrique.
-
-Pour bien sûre garder le lien total avec le Formula Student, la voiture devra être autonome uniquement et spécifiquement sur des répliques des épreuves de Formula Student à l'échelle un seizième et devra respecter le cahier des charges du Formula Student à son échelle.
-
-Sachant que le but du projet est de principalement programmer et de développer l'électronique de communication, la voiture va être pré-préparer pour être commandable par des cartes de contrôles modernes.
-
-\section{Organisation Macro}
-
-Le projet bien que dirigé par l'EPSA sera de la forme de deux PaR classiques centralien : il sera composé de 4 membres maximum. Il possèdera en plus des revues centralienne, son propre système de jalonnement TOP avec de plus une liaison hebdomadaire avec la direction EPSA (lors de la séance de PaR).
-
-Nous sommes ainsi en recherche active de deux tuteurs un spécialisé en computer vision et un spécialisé en intelligence artificiel et électronique embarquée.
-
-\section{Déroulé macro de l'année}
-
-Le projet sera donc décomposé en deux groupes qui bien que lié dans la finalité travailleront de manière semi-indépendante.
-
-
-Durant la première phase de l'année la première tâche sera de trouver pour la première équipe de trouver un moyen d'acquérir le flux vidéo sur l'intelligence (qui pourra être ou non embarqué) et pour l'autre de développer une simulation de comportement de la voiture pour pouvoir entrainer les différents algorithmes dessus (espace 3D où peut évoluer une réplique numérique de la voiture). (jusqu'à fin Octobre)
-
-
-La deuxième phase de l'année sera principalement axée sur le développement de la solution. La première équipe devra réussir à extraire du flux vidéo la position de la voiture dans l'espace et par rapport aux obstacles, tandis que la seconde mettra en place un algorithme qui permettra à partir de la position de la voiture par rapport aux obstacles, de la contrôler de manière autonome dans la simulation. (jusqu'à mi-février)
-
-
-La troisième phase du projet est la plus courte. Elle aura pour but de réunir les deux parties du projet pour pouvoir réaliser une série de test de l'algorithme de détection et de contrôle sur la voiture radio-commandé. C'est en ce sens que les deux groupes restent lié car il est nécessaire que à l'aube de la troisième phase le résultat de l'algorithme de détection soit l'entrée de l'algorithme de contrôle
-
-
-\end{document}
\ No newline at end of file
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-\babel@toc {french}{}\relax
-\contentsline {section}{\numberline {1}Mise en contexte}{1}{section.1}%
-\contentsline {section}{\numberline {2}Introduction}{1}{section.2}%
-\contentsline {section}{\numberline {3}Organisation Macro}{2}{section.3}%
-\contentsline {section}{\numberline {4}Déroulé macro de l'année}{2}{section.4}%
diff --git a/Documentation/Pre-projet/Resume/Arduino Nano scheme.png b/Documentation/Pre-projet/Resume/Arduino Nano scheme.png
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diff --git a/Documentation/Pre-projet/Resume/_minted-resume/8738AEBF99E5B84BD07FB03F4080BAFE101492748D21B1CFCE480A8C1C03A2DB.pygtex b/Documentation/Pre-projet/Resume/_minted-resume/8738AEBF99E5B84BD07FB03F4080BAFE101492748D21B1CFCE480A8C1C03A2DB.pygtex
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--- a/Documentation/Pre-projet/Resume/_minted-resume/8738AEBF99E5B84BD07FB03F4080BAFE101492748D21B1CFCE480A8C1C03A2DB.pygtex
+++ /dev/null
@@ -1,156 +0,0 @@
-\begin{Verbatim}[commandchars=\\\{\}]
-\PYG{c+cm}{/*}
-\PYG{c+cm}{ * See documentation at https://nRF24.github.io/RF24}
-\PYG{c+cm}{ * See License information at root directory of this library}
-\PYG{c+cm}{ * Author: Brendan Doherty (2bndy5)}
-\PYG{c+cm}{ */}
-
-\PYG{c+cm}{/**}
-\PYG{c+cm}{ * A simple example of sending data from 1 nRF24L01 transceiver to another.}
-\PYG{c+cm}{ *}
-\PYG{c+cm}{ * This example was written to be used on 2 devices acting as \PYGZdq{}nodes\PYGZdq{}.}
-\PYG{c+cm}{ * Use the Serial Monitor to change each node\PYGZsq{}s behavior.}
-\PYG{c+cm}{ */}
-\PYG{c+cp}{\PYGZsh{}include}\PYG{+w}{ }\PYG{c+cpf}{\PYGZlt{}SPI.h\PYGZgt{}}
-\PYG{c+cp}{\PYGZsh{}include}\PYG{+w}{ }\PYG{c+cpf}{\PYGZdq{}printf.h\PYGZdq{}}
-\PYG{c+cp}{\PYGZsh{}include}\PYG{+w}{ }\PYG{c+cpf}{\PYGZdq{}RF24.h\PYGZdq{}}
-
-\PYG{c+c1}{// instantiate an object for the nRF24L01 transceiver}
-\PYG{n}{RF24}\PYG{+w}{ }\PYG{n+nf}{radio}\PYG{p}{(}\PYG{l+m+mi}{10}\PYG{p}{,}\PYG{+w}{ }\PYG{l+m+mi}{9}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// using pin 10 for the CE pin, and pin 9 for the CSN pin}
-
-\PYG{c+c1}{// Let these addresses be used for the pair}
-\PYG{k+kr}{uint8\PYGZus{}t}\PYG{+w}{ }\PYG{n}{address}\PYG{p}{[][}\PYG{l+m+mi}{6}\PYG{p}{]}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{p}{\PYGZob{}}\PYG{+w}{ }\PYG{l+s}{\PYGZdq{}1Node\PYGZdq{}}\PYG{p}{,}\PYG{+w}{ }\PYG{l+s}{\PYGZdq{}2Node\PYGZdq{}}\PYG{+w}{ }\PYG{p}{\PYGZcb{};}
-\PYG{c+c1}{// It is very helpful to think of an address as a path instead of as}
-\PYG{c+c1}{// an identifying device destination}
-
-\PYG{c+c1}{// to use different addresses on a pair of radios, we need a variable to}
-\PYG{c+c1}{// uniquely identify which address this radio will use to transmit}
-\PYG{k+kr}{bool}\PYG{+w}{ }\PYG{n}{radioNumber}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{l+m+mi}{1}\PYG{p}{;}\PYG{+w}{ }\PYG{c+c1}{// 0 uses address[0] to transmit, 1 uses address[1] to transmit}
-
-\PYG{c+c1}{// Used to control whether this node is sending or receiving}
-\PYG{k+kr}{bool}\PYG{+w}{ }\PYG{n}{role}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{k+kr}{false}\PYG{p}{;}\PYG{+w}{ }\PYG{c+c1}{// true = TX role, false = RX role}
-
-\PYG{c+c1}{// For this example, we\PYGZsq{}ll be using a payload containing}
-\PYG{c+c1}{// a single float number that will be incremented}
-\PYG{c+c1}{// on every successful transmission}
-\PYG{k+kr}{float}\PYG{+w}{ }\PYG{n}{payload}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{l+m+mf}{0.0}\PYG{p}{;}
-
-\PYG{k+kr}{void}\PYG{+w}{ }\PYG{n+nb}{setup}\PYG{p}{()}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{begin}\PYG{p}{(}\PYG{l+m+mi}{115200}\PYG{p}{);}
-\PYG{+w}{ }\PYG{k}{while}\PYG{+w}{ }\PYG{p}{(}\PYG{o}{!}\PYG{n+nf}{Serial}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// some boards need to wait to ensure access to serial over USB}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{c+c1}{// initialize the transceiver on the SPI bus}
-\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{o}{!}\PYG{n}{radio}\PYG{p}{.}\PYG{n+nf}{begin}\PYG{p}{())}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}radio hardware is not responding!!\PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{k}{while}\PYG{+w}{ }\PYG{p}{(}\PYG{l+m+mi}{1}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}\PYGZcb{}}\PYG{+w}{ }\PYG{c+c1}{// hold in infinite loop}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{c+c1}{// print example\PYGZsq{}s introductory prompt}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}RF24/examples/GettingStarted\PYGZdq{}}\PYG{p}{));}
-
-\PYG{+w}{ }\PYG{c+c1}{// To set the radioNumber via the Serial monitor on startup}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}Which radio is this? Enter \PYGZsq{}0\PYGZsq{} or \PYGZsq{}1\PYGZsq{}. Defaults to \PYGZsq{}0\PYGZsq{}\PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{k}{while}\PYG{+w}{ }\PYG{p}{(}\PYG{o}{!}\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{available}\PYG{p}{())}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// wait for user input}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-\PYG{+w}{ }\PYG{k+kr}{char}\PYG{+w}{ }\PYG{n}{input}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{parseInt}\PYG{p}{();}
-\PYG{+w}{ }\PYG{n}{radioNumber}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n}{input}\PYG{+w}{ }\PYG{o}{==}\PYG{+w}{ }\PYG{l+m+mi}{1}\PYG{p}{;}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}radioNumber = \PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{((}\PYG{k+kr}{int}\PYG{p}{)}\PYG{n}{radioNumber}\PYG{p}{);}
-
-\PYG{+w}{ }\PYG{c+c1}{// role variable is hardcoded to RX behavior, inform the user of this}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}*** PRESS \PYGZsq{}T\PYGZsq{} to begin transmitting to the other node\PYGZdq{}}\PYG{p}{));}
-
-\PYG{+w}{ }\PYG{c+c1}{// Set the PA Level low to try preventing power supply related problems}
-\PYG{+w}{ }\PYG{c+c1}{// because these examples are likely run with nodes in close proximity to}
-\PYG{+w}{ }\PYG{c+c1}{// each other.}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{setPALevel}\PYG{p}{(}\PYG{n}{RF24\PYGZus{}PA\PYGZus{}LOW}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// RF24\PYGZus{}PA\PYGZus{}MAX is default.}
-
-\PYG{+w}{ }\PYG{c+c1}{// save on transmission time by setting the radio to only transmit the}
-\PYG{+w}{ }\PYG{c+c1}{// number of bytes we need to transmit a float}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{setPayloadSize}\PYG{p}{(}\PYG{k+kr}{sizeof}\PYG{p}{(}\PYG{n}{payload}\PYG{p}{));}\PYG{+w}{ }\PYG{c+c1}{// float datatype occupies 4 bytes}
-
-\PYG{+w}{ }\PYG{c+c1}{// set the TX address of the RX node into the TX pipe}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{openWritingPipe}\PYG{p}{(}\PYG{n}{address}\PYG{p}{[}\PYG{n}{radioNumber}\PYG{p}{]);}\PYG{+w}{ }\PYG{c+c1}{// always uses pipe 0}
-
-\PYG{+w}{ }\PYG{c+c1}{// set the RX address of the TX node into a RX pipe}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{openReadingPipe}\PYG{p}{(}\PYG{l+m+mi}{1}\PYG{p}{,}\PYG{+w}{ }\PYG{n}{address}\PYG{p}{[}\PYG{o}{!}\PYG{n}{radioNumber}\PYG{p}{]);}\PYG{+w}{ }\PYG{c+c1}{// using pipe 1}
-
-\PYG{+w}{ }\PYG{c+c1}{// additional setup specific to the node\PYGZsq{}s role}
-\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{n}{role}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{stopListening}\PYG{p}{();}\PYG{+w}{ }\PYG{c+c1}{// put radio in TX mode}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}\PYG{+w}{ }\PYG{k}{else}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{startListening}\PYG{p}{();}\PYG{+w}{ }\PYG{c+c1}{// put radio in RX mode}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{c+c1}{// For debugging info}
-\PYG{+w}{ }\PYG{c+c1}{// printf\PYGZus{}begin(); // needed only once for printing details}
-\PYG{+w}{ }\PYG{c+c1}{// radio.printDetails(); // (smaller) function that prints raw register values}
-\PYG{+w}{ }\PYG{c+c1}{// radio.printPrettyDetails(); // (larger) function that prints human readable data}
-
-\PYG{p}{\PYGZcb{}}\PYG{+w}{ }\PYG{c+c1}{// setup}
-
-\PYG{k+kr}{void}\PYG{+w}{ }\PYG{n+nb}{loop}\PYG{p}{()}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-
-\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{n}{role}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// This device is a TX node}
-
-\PYG{+w}{ }\PYG{k+kr}{unsigned}\PYG{+w}{ }\PYG{k+kr}{long}\PYG{+w}{ }\PYG{n}{start\PYGZus{}timer}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n+nf}{micros}\PYG{p}{();}\PYG{+w}{ }\PYG{c+c1}{// start the timer}
-\PYG{+w}{ }\PYG{k+kr}{bool}\PYG{+w}{ }\PYG{n}{report}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n+nf}{write}\PYG{p}{(}\PYG{o}{\PYGZam{}}\PYG{n}{payload}\PYG{p}{,}\PYG{+w}{ }\PYG{k+kr}{sizeof}\PYG{p}{(}\PYG{k+kr}{float}\PYG{p}{));}\PYG{+w}{ }\PYG{c+c1}{// transmit \PYGZam{} save the report}
-\PYG{+w}{ }\PYG{k+kr}{unsigned}\PYG{+w}{ }\PYG{k+kr}{long}\PYG{+w}{ }\PYG{n}{end\PYGZus{}timer}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n+nf}{micros}\PYG{p}{();}\PYG{+w}{ }\PYG{c+c1}{// end the timer}
-
-\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{n}{report}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}Transmission successful! \PYGZdq{}}\PYG{p}{));}\PYG{+w}{ }\PYG{c+c1}{// payload was delivered}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}Time to transmit = \PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{end\PYGZus{}timer}\PYG{+w}{ }\PYG{o}{\PYGZhy{}}\PYG{+w}{ }\PYG{n}{start\PYGZus{}timer}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// print the timer result}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{} us. Sent: \PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{payload}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// print payload sent}
-\PYG{+w}{ }\PYG{n}{payload}\PYG{+w}{ }\PYG{o}{+=}\PYG{+w}{ }\PYG{l+m+mf}{0.01}\PYG{p}{;}\PYG{+w}{ }\PYG{c+c1}{// increment float payload}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}\PYG{+w}{ }\PYG{k}{else}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}Transmission failed or timed out\PYGZdq{}}\PYG{p}{));}\PYG{+w}{ }\PYG{c+c1}{// payload was not delivered}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{c+c1}{// to make this example readable in the serial monitor}
-\PYG{+w}{ }\PYG{n+nf}{delay}\PYG{p}{(}\PYG{l+m+mi}{1000}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// slow transmissions down by 1 second}
-
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}\PYG{+w}{ }\PYG{k}{else}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// This device is a RX node}
-
-\PYG{+w}{ }\PYG{k+kr}{uint8\PYGZus{}t}\PYG{+w}{ }\PYG{n}{pipe}\PYG{p}{;}
-\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{n}{radio}\PYG{p}{.}\PYG{n+nf}{available}\PYG{p}{(}\PYG{o}{\PYGZam{}}\PYG{n}{pipe}\PYG{p}{))}\PYG{+w}{ }\PYG{p}{\PYGZob{}}\PYG{+w}{ }\PYG{c+c1}{// is there a payload? get the pipe number that recieved it}
-\PYG{+w}{ }\PYG{k+kr}{uint8\PYGZus{}t}\PYG{+w}{ }\PYG{n}{bytes}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{getPayloadSize}\PYG{p}{();}\PYG{+w}{ }\PYG{c+c1}{// get the size of the payload}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n+nf}{read}\PYG{p}{(}\PYG{o}{\PYGZam{}}\PYG{n}{payload}\PYG{p}{,}\PYG{+w}{ }\PYG{n}{bytes}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// fetch payload from FIFO}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}Received \PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{bytes}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// print the size of the payload}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{} bytes on pipe \PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{pipe}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// print the pipe number}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{print}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}: \PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{payload}\PYG{p}{);}\PYG{+w}{ }\PYG{c+c1}{// print the payload\PYGZsq{}s value}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}\PYG{+w}{ }\PYG{c+c1}{// role}
-
-\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{available}\PYG{p}{())}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// change the role via the serial monitor}
-
-\PYG{+w}{ }\PYG{k+kr}{char}\PYG{+w}{ }\PYG{n}{c}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n}{toupper}\PYG{p}{(}\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{read}\PYG{p}{());}
-\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{n}{c}\PYG{+w}{ }\PYG{o}{==}\PYG{+w}{ }\PYG{l+s+sc}{\PYGZsq{}T\PYGZsq{}}\PYG{+w}{ }\PYG{o}{\PYGZam{}\PYGZam{}}\PYG{+w}{ }\PYG{o}{!}\PYG{n}{role}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// Become the TX node}
-
-\PYG{+w}{ }\PYG{n}{role}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{k+kr}{true}\PYG{p}{;}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}*** CHANGING TO TRANSMIT ROLE \PYGZhy{}\PYGZhy{} PRESS \PYGZsq{}R\PYGZsq{} TO SWITCH BACK\PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{stopListening}\PYG{p}{();}
-
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}\PYG{+w}{ }\PYG{k}{else}\PYG{+w}{ }\PYG{k}{if}\PYG{+w}{ }\PYG{p}{(}\PYG{n}{c}\PYG{+w}{ }\PYG{o}{==}\PYG{+w}{ }\PYG{l+s+sc}{\PYGZsq{}R\PYGZsq{}}\PYG{+w}{ }\PYG{o}{\PYGZam{}\PYGZam{}}\PYG{+w}{ }\PYG{n}{role}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// Become the RX node}
-
-\PYG{+w}{ }\PYG{n}{role}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{k+kr}{false}\PYG{p}{;}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{F}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}*** CHANGING TO RECEIVE ROLE \PYGZhy{}\PYGZhy{} PRESS \PYGZsq{}T\PYGZsq{} TO SWITCH BACK\PYGZdq{}}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n}{radio}\PYG{p}{.}\PYG{n}{startListening}\PYG{p}{();}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{p}{\PYGZcb{}}\PYG{+w}{ }\PYG{c+c1}{// loop}
-\end{Verbatim}
diff --git a/Documentation/Pre-projet/Resume/_minted-resume/953A0B2C4B8DD42FC38318DE0A83EE11101492748D21B1CFCE480A8C1C03A2DB.pygtex b/Documentation/Pre-projet/Resume/_minted-resume/953A0B2C4B8DD42FC38318DE0A83EE11101492748D21B1CFCE480A8C1C03A2DB.pygtex
deleted file mode 100644
index 1e9b34baa1e368bcd09f90228b38d0c583f05bc6..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Resume/_minted-resume/953A0B2C4B8DD42FC38318DE0A83EE11101492748D21B1CFCE480A8C1C03A2DB.pygtex
+++ /dev/null
@@ -1,44 +0,0 @@
-\begin{Verbatim}[commandchars=\\\{\}]
-\PYG{c+cp}{\PYGZsh{}define DirPin A6}
-\PYG{c+c1}{// Droite 176 Gauche 850}
-\PYG{c+cp}{\PYGZsh{}define PontG 5}
-\PYG{c+cp}{\PYGZsh{}define PontD 6}
-
-\PYG{k+kr}{int}\PYG{+w}{ }\PYG{n}{val}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{l+m+mi}{0}\PYG{p}{;}
-\PYG{k+kr}{int}\PYG{+w}{ }\PYG{n}{aim}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{l+m+mi}{500}\PYG{p}{;}
-
-\PYG{k+kr}{void}\PYG{+w}{ }\PYG{n+nb}{setup}\PYG{p}{()}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{begin}\PYG{p}{(}\PYG{l+m+mi}{9600}\PYG{p}{);}
-\PYG{p}{\PYGZcb{}}
-
-\PYG{k+kr}{void}\PYG{+w}{ }\PYG{n+nb}{loop}\PYG{p}{()}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-
-
-\PYG{+w}{ }\PYG{k}{if}\PYG{p}{(}\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{available}\PYG{p}{())\PYGZob{}}
-\PYG{+w}{ }\PYG{n}{aim}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{readString}\PYG{p}{().}\PYG{n}{toInt}\PYG{p}{();}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{aim}\PYG{p}{);}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{k}{if}\PYG{p}{(}\PYG{n}{val}\PYG{+w}{ }\PYG{o}{\PYGZlt{}}\PYG{+w}{ }\PYG{n}{aim}\PYG{l+m+mi}{\PYGZhy{}50}\PYG{p}{)}
-\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontG}\PYG{p}{,}\PYG{l+m+mi}{100}\PYG{p}{);}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontD}\PYG{p}{,}\PYG{l+m+mi}{0}\PYG{p}{);}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{k}{else}\PYG{+w}{ }\PYG{k}{if}\PYG{p}{(}\PYG{n}{val}\PYG{+w}{ }\PYG{o}{\PYGZgt{}}\PYG{+w}{ }\PYG{n}{aim}\PYG{o}{+}\PYG{l+m+mi}{50}\PYG{p}{)}
-\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontD}\PYG{p}{,}\PYG{l+m+mi}{100}\PYG{p}{);}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontG}\PYG{p}{,}\PYG{l+m+mi}{0}\PYG{p}{);}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-\PYG{+w}{ }\PYG{k}{else}
-\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontD}\PYG{p}{,}\PYG{l+m+mi}{0}\PYG{p}{);}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontG}\PYG{p}{,}\PYG{l+m+mi}{0}\PYG{p}{);}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-
-\PYG{+w}{ }\PYG{n}{val}\PYG{o}{=}\PYG{+w}{ }\PYG{n+nf}{analogRead}\PYG{p}{(}\PYG{n}{DirPin}\PYG{p}{);}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{val}\PYG{p}{);}
-
-\PYG{p}{\PYGZcb{}}
-\end{Verbatim}
diff --git a/Documentation/Pre-projet/Resume/_minted-resume/A27053242F9901AA52BB0C45E52D419D109BC8D619DBDEE45AC6AD3984E95C55.pygtex b/Documentation/Pre-projet/Resume/_minted-resume/A27053242F9901AA52BB0C45E52D419D109BC8D619DBDEE45AC6AD3984E95C55.pygtex
deleted file mode 100644
index 97e34df2d0bd4b51862f4aa662527d50308f3238..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Resume/_minted-resume/A27053242F9901AA52BB0C45E52D419D109BC8D619DBDEE45AC6AD3984E95C55.pygtex
+++ /dev/null
@@ -1,40 +0,0 @@
-\begin{Verbatim}[commandchars=\\\{\}]
-
-\PYG{c+cp}{\PYGZsh{}define PontHRouge 10}
-\PYG{c+cp}{\PYGZsh{}define PontHNoir 9}
-\PYG{k+kr}{int}\PYG{+w}{ }\PYG{n}{val}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{l+m+mi}{0}\PYG{p}{;}
-
-\PYG{k+kr}{void}\PYG{+w}{ }\PYG{n+nb}{setup}\PYG{p}{()}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-
-\PYG{+w}{ }\PYG{n+nf}{pinMode}\PYG{p}{(}\PYG{n}{PontHRouge}\PYG{p}{,}\PYG{k+kr}{OUTPUT}\PYG{p}{);}
-\PYG{+w}{ }\PYG{n+nf}{pinMode}\PYG{p}{(}\PYG{n}{PontHNoir}\PYG{p}{,}\PYG{k+kr}{OUTPUT}\PYG{p}{);}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{begin}\PYG{p}{(}\PYG{l+m+mi}{115200}\PYG{p}{);}
-
-\PYG{+w}{ }\PYG{k}{while}\PYG{+w}{ }\PYG{p}{(}\PYG{o}{!}\PYG{n+nf}{Serial}\PYG{p}{)}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{c+c1}{// some boards need to wait to ensure access to serial over USB}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{l+s}{\PYGZdq{}Test des commandes PWM voiture\PYGZdq{}}\PYG{p}{);}
-
-\PYG{p}{\PYGZcb{}}
-
-\PYG{k+kr}{void}\PYG{+w}{ }\PYG{n+nb}{loop}\PYG{p}{()}\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-
-\PYG{+w}{ }\PYG{k}{if}\PYG{p}{(}\PYG{n}{val}\PYG{+w}{ }\PYG{o}{\PYGZgt{}=}\PYG{+w}{ }\PYG{l+m+mi}{0}\PYG{+w}{ }\PYG{p}{)}
-\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontHRouge}\PYG{p}{,}\PYG{n+nf}{abs}\PYG{p}{(}\PYG{n}{val}\PYG{p}{));}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontHNoir}\PYG{p}{,}\PYG{n+nf}{abs}\PYG{p}{(}\PYG{n}{val}\PYG{p}{));}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-\PYG{+w}{ }\PYG{k}{if}\PYG{p}{(}\PYG{n}{val}\PYG{+w}{ }\PYG{o}{\PYGZlt{}}\PYG{+w}{ }\PYG{l+m+mi}{0}\PYG{p}{)}
-\PYG{+w}{ }\PYG{p}{\PYGZob{}}
-\PYG{+w}{ }\PYG{n+nf}{analogWrite}\PYG{p}{(}\PYG{n}{PontHNoir}\PYG{p}{,}\PYG{n+nf}{abs}\PYG{p}{(}\PYG{n}{val}\PYG{p}{));}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-
-\PYG{+w}{ }\PYG{k}{if}\PYG{p}{(}\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{available}\PYG{p}{())\PYGZob{}}
-
-\PYG{+w}{ }\PYG{n}{val}\PYG{+w}{ }\PYG{o}{=}\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{readString}\PYG{p}{().}\PYG{n}{toInt}\PYG{p}{();}
-\PYG{+w}{ }\PYG{n+nf}{Serial}\PYG{p}{.}\PYG{n+nf}{println}\PYG{p}{(}\PYG{n}{val}\PYG{p}{);}
-\PYG{+w}{ }\PYG{p}{\PYGZcb{}}
-\PYG{p}{\PYGZcb{}}
-
-\end{Verbatim}
diff --git a/Documentation/Pre-projet/Resume/_minted-resume/D39AAC58234C1264F11CFBFA0DA2541F101492748D21B1CFCE480A8C1C03A2DB.pygtex b/Documentation/Pre-projet/Resume/_minted-resume/D39AAC58234C1264F11CFBFA0DA2541F101492748D21B1CFCE480A8C1C03A2DB.pygtex
deleted file mode 100644
index 0e2f9222938cc4b87dc88bce8b7cb511ad64560f..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Resume/_minted-resume/D39AAC58234C1264F11CFBFA0DA2541F101492748D21B1CFCE480A8C1C03A2DB.pygtex
+++ /dev/null
@@ -1,3 +0,0 @@
-\begin{Verbatim}[commandchars=\\\{\}]
-\PYG{n}{RF24}\PYG{+w}{ }\PYG{n+nf}{radio}\PYG{p}{(}\PYG{l+m+mi}{10}\PYG{p}{,}\PYG{+w}{ }\PYG{l+m+mi}{9}\PYG{p}{);}\PYG{+w}{ }
-\end{Verbatim}
diff --git a/Documentation/Pre-projet/Resume/_minted-resume/default.pygstyle b/Documentation/Pre-projet/Resume/_minted-resume/default.pygstyle
deleted file mode 100644
index 211763d1d4e4c090ba3a286634a9a96c7fae303b..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Resume/_minted-resume/default.pygstyle
+++ /dev/null
@@ -1,101 +0,0 @@
-
-\makeatletter
-\def\PYG@reset{\let\PYG@it=\relax \let\PYG@bf=\relax%
- \let\PYG@ul=\relax \let\PYG@tc=\relax%
- \let\PYG@bc=\relax \let\PYG@ff=\relax}
-\def\PYG@tok#1{\csname PYG@tok@#1\endcsname}
-\def\PYG@toks#1+{\ifx\relax#1\empty\else%
- \PYG@tok{#1}\expandafter\PYG@toks\fi}
-\def\PYG@do#1{\PYG@bc{\PYG@tc{\PYG@ul{%
- \PYG@it{\PYG@bf{\PYG@ff{#1}}}}}}}
-\def\PYG#1#2{\PYG@reset\PYG@toks#1+\relax+\PYG@do{#2}}
-
-\@namedef{PYG@tok@w}{\def\PYG@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
-\@namedef{PYG@tok@c}{\let\PYG@it=\textit\def\PYG@tc##1{\textcolor[rgb]{0.24,0.48,0.48}{##1}}}
-\@namedef{PYG@tok@cp}{\def\PYG@tc##1{\textcolor[rgb]{0.61,0.40,0.00}{##1}}}
-\@namedef{PYG@tok@k}{\let\PYG@bf=\textbf\def\PYG@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
-\@namedef{PYG@tok@kp}{\def\PYG@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
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-\documentclass{EPSA-rap-template}
-
-\usepackage{eurosym}
-\usepackage{amssymb}
-\usepackage{minted}
-
-\type{Rapport de travaux}
-
-\titresize{\large} % ne pas hésiter a changer la taille :
-%\normalsize
-%\large
-%\Large
-%\LARGE
-%\huge
-%\Huge
-%\HUGE
-
-\titre{Travail Préliminaire Démonstrateur AutoDrive V1.0}
-
-\titresh{ Travail Préliminaire AutoDrive}
-
-\departement{Direction Recherche \& Innovation EPSA}
-
-\departementsh{ R\& I}
-
-\auteurs{Eymeric \textbf{Chauchat}}
-
-\version{V1.0}
-
-\versionnement{
-\ver{V1.1}{4 Septembre 2022}{ ECT }{Changement de titre.}{2}
-\ver{V1.0}{27 aout 2022}{ ECT }{Rédaction initiale.}{1}
-}
-
-\setuppack
-
-\begin{document}
-
-\fairepagedegarde
-\newpage
-\tableofcontents
-
-\section{Introduction}
-
-Ce document a pour but de présenter tout le travail préliminaire qui a été fait pour pouvoir lancer le projet satellite epsa sur la conduite autonome. Il rend compte de la remise en l'état du véhicule ciblé pour le projet et du développement de cartes de contrôle.
-
-\newpage
-
-\section{Voiture RC}
-
-Cette partie va présenter la voiture radio commandé qui va être utilisé pour les projets de PaR cette année et la manière dont elle a été préparé. Pour permettre un contrôle par un ordinateur ou plus largement par n'importe quel type de microprocesseur ou processeur, nous avons remplacé la carte de réception par une arduino allié d'une nrf24l01 et de quelques composants annexes.
-
-\bigskip
-{
-\centering
-\includegraphics[width=\textwidth]{carte-pae-6766.jpg}
-\par
-}
-
-\subsection{Fonctionnement de la voiture}
-
-La voiture possède deux systèmes principaux, la direction et la propulsion. La direction est composée d'une nappe de 6 câbles :
-
-\begin{itemize}
-
-\item Noir : moteur -
-\item Orange : moteur +
-\item Vert pomme : Potentiomètre +
-\item Blanc : Potentiomètre signal
-\item Vert : Potentiomètre -
-\item Bleu : isolation unused
-
-\end{itemize}
-
-à savoir que moteur - et moteur + sont contrôlé grâce à l'intermédiaire d'un pont en H sur la nappe. Le contrôle de la partie direction se fait simplement à partir de la donnée en angle du potentiomètre et de la sortie moteur.
-
-le deuxième système, la propulsion est composé de 4 câbles :
-
-\begin{itemize}
-\item Blanc : Ground
-\item Jaune : +8 V continu
-\item Noir : première entrée pont en H
-\item Rouge : deuxième entrée pont en H
-\end{itemize}
-
-Le contrôle de la propulsion se fait au travers du câble Rouge et Noir. Pour avancer il faut mettre un signal sur le câble noir et pour reculer il faut mettre un signal sur le câble rouge et le câble noir.
-
-\subsection{Montage électrique}
-
-la nrf24l01 est montée de manière analogue au dongle usb sur l'arduino de la voiture (Figure \ref{fig:wiringcar}). Cependant nous avons aussi besoin de connection avec la distribution en puissance de la voiture.
-\bigskip
-
-{
-\centering
-\includegraphics[width=0.45\textwidth]{carte-pae-6753.jpg}
-\includegraphics[width=0.45\textwidth]{carte-pae-6755.jpg}
-\par
-}
-
-On peut remarquer l'ajout d'un pont en H au dessus de l'arduino qui va nous permettre de contrôler le moteur de direction.
-
-\begin{figure}[hbt!]
-\centering
-\includegraphics[width=0.4\textwidth]{Arduino Nano scheme.png}
-\caption{Câblage de l'arduino nano}
-\label{fig:wiringcar}
-\end{figure}
-
-\subsection{Codes de test}
-
-Pour tester les différentes fonctions de la carte de contrôle, nous avons élaboré 3 codes.
-
-\subsubsection{Test Puissance}
-
-Un premier code a été écrit pour tester la communication avec le pont en H principal de la voiture.
-
-\begin{minted}{Arduino}
-
-#define PontHRouge 10
-#define PontHNoir 9
-int val = 0;
-
-void setup() {
-
- pinMode(PontHRouge,OUTPUT);
- pinMode(PontHNoir,OUTPUT);
- Serial.begin(115200);
-
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- Serial.println("Test des commandes PWM voiture");
-
-}
-
-void loop() {
-
- if(val >= 0 )
- {
- analogWrite(PontHRouge,abs(val));
- analogWrite(PontHNoir,abs(val));
- }
- if(val < 0)
- {
- analogWrite(PontHNoir,abs(val));
- }
-
- if(Serial.available()){
-
- val = Serial.readString().toInt();
- Serial.println(val);
- }
-}
-
-\end{minted}
-
-\subsubsection{Test direction}
-
-De manière analogue, nous avons développé un script permettant de tester que la direction de la voiture marchait correctement.
-
-\begin{minted}{arduino}
-#define DirPin A6
-// Droite 176 Gauche 850
-#define PontG 5
-#define PontD 6
-
-int val = 0;
-int aim = 500;
-
-void setup() {
- Serial.begin(9600);
-}
-
-void loop() {
-
-
- if(Serial.available()){
- aim = Serial.readString().toInt();
- Serial.println(aim);
- }
-
- if(val < aim-50)
- {
- analogWrite(PontG,100);
- analogWrite(PontD,0);
- }
-
- else if(val > aim+50)
- {
- analogWrite(PontD,100);
- analogWrite(PontG,0);
- }
- else
- {
- analogWrite(PontD,0);
- analogWrite(PontG,0);
- }
-
-
- val= analogRead(DirPin);
- Serial.println(val);
-
-}
-\end{minted}
-
-\newpage
-
-\section{Dongle de contrôle}
-Cette partie va présenter la réalisation du Dongle de contrôle de la voiture RC et du script permettant son arrêt d'urgence et sa commande si l'on choisit une intelligence déporté.
-
-Le Dongle est composé d'une arduino Nano, d'une carte nrf24l01 et d'un bouton poussoir. Il va permettre de contrôler en temps réel la voiture et de récupérer des informations des différents capteurs :
-
-\bigskip
-
-{
-\centering
-\includegraphics[width=0.45\textwidth]{carte-pae-6770.jpg}
-\includegraphics[width=0.45\textwidth]{carte-pae-6771.jpg}
-\par
-}
-
-\subsection{Construction}
-
-
-Le câblage suit celui d'une carte nrf24l01 basique (Figure \ref{fig:wiringdongle}). La carte se branche ensuite directement à l'ordinateur qui va la contrôler soit directement grâce à l'invité port série d'arduino ou au travers d'un script python ou directement si on rajoute un contrôleur sur les ports vacants de la carte arduino.
-
-\begin{figure}[hbt!]
-\centering
-\includegraphics[width=0.4\textwidth]{Wiring.png}
-\caption{Câblage possible d'une nrf24l01 avec une arduino nano}
-\label{fig:wiringdongle}
-\end{figure}
-
-\subsection{Script de contrôle}
-
-Pour tester le branchement de la carte nous avons pour l'instant uniquement télé-verser un code permettant de tester que la nrf24l01+ est bien branchée. (Section \ref{sec:nrf24l01}) à savoir que nous avons initialiser les pins radio de la manière suivantes : \mint{arduino}|RF24 radio(10, 9); | \noindent 10 pour le pin CE et 9 pour le pin CSN.
-
-
-
-\newpage
-
-\section{Solution caméra}
-
-En plus du contrôle à distance de la voiture (qui est obligatoire pour permettre un arrêt d'urgence), le projet nécessite que l'on traite des données vidéos pour permettre la conduite autonome de la voiture. En ce sens plusieurs solutions s'offrent à nous pour permettre l'acquisition et le traitement de ces données. (ce travail préliminaire a été fait pour soulager les membres du projet qui vont devoir trancher la solution rapidement pour ne pas s'éterniser sur le choix de concept )
-
-\subsection{Description des solutions}
-
-L'intelligence de la voiture peut se trouver à deux endroits :
-
-\begin{itemize}
-\item A l'intérieur de la voiture télécommandée
-\item sur l'ordinateur qui possède le dongle d'arret d'urgence / commande
-\end{itemize}
-
-Ces deux possibilités ouvrent un grand champ d'alternative pour arriver à faire l'acquisition vidéo et le traitement / commande.
-
-\subsection{Intelligence embarqué}
-
-Dans le cas d'une intelligence embarquée, l'acquisition vidéo se fera directement grâce à des caméras branchées sur la carte de contrôle.
-
-La question est alors : quelle doit être la plateforme qui va nous permettre de faire les calculs et en déduire la commande de notre voiture ?
-
-La plateforme choisit doit être robuste (résistante au probable choc sur le corps de la voiture), doit permettre une liaison avec l'arduino de contrôle de la voiture et doit avoir une assez grande puissance de calcul pour traiter les images (qui peuvent provenir de différentes caméras HD).
-
-A savoir que toute les plateformes se rapprochant de Arduino, Rasberry Pi, ou tournant sur ATmega328P, PIC ou autre microprocesseur cadencé en dessous du gigahertz ne pourra pas convenir dû au manque de puissance de calcul que nécessite le traitement vidéo.
-
-Voici alors une première liste de plateforme :
-
-
-\begin{itemize}
-
-\item Téléphone Android haut de gamme :
-
-\textbf{Avantages : }
-\begin{itemize}
-\item disponible auprès des membres du Pae
-\item Aucun coût
-\item Pas de consommation
-\item Puissance suffisante
-\item taille petite
-\end{itemize}
-
-\textbf{Inconvénients : }
-\begin{itemize}
-\item Obligation de développement en .apk
-\item Puissance de calcul limité
-\item difficulté pour gérer les différentes entrée vidéo
-\end{itemize}
-
-\item \href{https://www.nvidia.com/fr-be/autonomous-machines/jetson-store/}{Nvidia Jetson}
-
-\textbf{Avantages : }
-\begin{itemize}
-\item très forte puissance de calcul
-\item basse consommation
-\item Carte qui pourrait être intégré sur le véhicule à grande échelle
-\item architecture faite pour le calcul d'IA
-\item Programmation en CUDA / C++
-\end{itemize}
-\textbf{Inconvénients : }
-\begin{itemize}
-\item prix 109 \euro{}
-\item besoin de fournir une protection
-\item Assez encombrant sur la voiture (103mm x 90.5mm x 34mm)
-\end{itemize}
-
-\end{itemize}
-
-\subsection{Intelligence déporté}
-
-La deuxième solution est donc de déporter le calcul et la commande à l'extérieur de la voiture sur un ordinateur distant. Il faut alors trouver un moyen de recevoir le flux vidéo de plusieurs caméras à distance. La réception doit être de bonne qualité et avec une faible latence ($\leqslant 500ms$).
-
-On ne peut donc pas utiliser de système analogique (beaucoup de bruit à cause de la nature analogique du transfert). Il faut alors trouver un système numérique peu onéreux ($\leqslant 200 $\euro{}) qui permettent le transfert.
-
-Voici la liste préliminaire des solutions :
-
-\begin{itemize}
-
-\item ESP32-CAM
-
-\textbf{Avantages : }
-
-\begin{itemize}
-\item Système compact
-\item Système autonome
-\item Bon marché ($30$\euro{})
-\item faible latence ($\leqslant 100$ms)
-\item Petit encombrement
-\end{itemize}
-\textbf{Inconvénients : }
-
-\begin{itemize}
-\item Faible qualité vidéo (max 800x600 en 30 fps)
-\item multiplication des émissions wifi (comment gérer la réception)
-\end{itemize}
-
-
-\end{itemize}
-
-\newpage
-
-\section{Annexe}
-
-\subsection{Code de test nrf24l01+}
-\label{sec:nrf24l01}
-\begin{minted}{arduino}
-/*
- * See documentation at https://nRF24.github.io/RF24
- * See License information at root directory of this library
- * Author: Brendan Doherty (2bndy5)
- */
-
-/**
- * A simple example of sending data from 1 nRF24L01 transceiver to another.
- *
- * This example was written to be used on 2 devices acting as "nodes".
- * Use the Serial Monitor to change each node's behavior.
- */
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-// instantiate an object for the nRF24L01 transceiver
-RF24 radio(10, 9); // using pin 10 for the CE pin, and pin 9 for the CSN pin
-
-// Let these addresses be used for the pair
-uint8_t address[][6] = { "1Node", "2Node" };
-// It is very helpful to think of an address as a path instead of as
-// an identifying device destination
-
-// to use different addresses on a pair of radios, we need a variable to
-// uniquely identify which address this radio will use to transmit
-bool radioNumber = 1; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-// Used to control whether this node is sending or receiving
-bool role = false; // true = TX role, false = RX role
-
-// For this example, we'll be using a payload containing
-// a single float number that will be incremented
-// on every successful transmission
-float payload = 0.0;
-
-void setup() {
-
- Serial.begin(115200);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
- // print example's introductory prompt
- Serial.println(F("RF24/examples/GettingStarted"));
-
- // To set the radioNumber via the Serial monitor on startup
- Serial.println(F("Which radio is this? Enter '0' or '1'. Defaults to '0'"));
- while (!Serial.available()) {
- // wait for user input
- }
- char input = Serial.parseInt();
- radioNumber = input == 1;
- Serial.print(F("radioNumber = "));
- Serial.println((int)radioNumber);
-
- // role variable is hardcoded to RX behavior, inform the user of this
- Serial.println(F("*** PRESS 'T' to begin transmitting to the other node"));
-
- // Set the PA Level low to try preventing power supply related problems
- // because these examples are likely run with nodes in close proximity to
- // each other.
- radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- // save on transmission time by setting the radio to only transmit the
- // number of bytes we need to transmit a float
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- // set the TX address of the RX node into the TX pipe
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- // set the RX address of the TX node into a RX pipe
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- // additional setup specific to the node's role
- if (role) {
- radio.stopListening(); // put radio in TX mode
- } else {
- radio.startListening(); // put radio in RX mode
- }
-
- // For debugging info
- // printf_begin(); // needed only once for printing details
- // radio.printDetails(); // (smaller) function that prints raw register values
- // radio.printPrettyDetails(); // (larger) function that prints human readable data
-
-} // setup
-
-void loop() {
-
- if (role) {
- // This device is a TX node
-
- unsigned long start_timer = micros(); // start the timer
- bool report = radio.write(&payload, sizeof(float)); // transmit & save the report
- unsigned long end_timer = micros(); // end the timer
-
- if (report) {
- Serial.print(F("Transmission successful! ")); // payload was delivered
- Serial.print(F("Time to transmit = "));
- Serial.print(end_timer - start_timer); // print the timer result
- Serial.print(F(" us. Sent: "));
- Serial.println(payload); // print payload sent
- payload += 0.01; // increment float payload
- } else {
- Serial.println(F("Transmission failed or timed out")); // payload was not delivered
- }
-
- // to make this example readable in the serial monitor
- delay(1000); // slow transmissions down by 1 second
-
- } else {
- // This device is a RX node
-
- uint8_t pipe;
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
- Serial.print(F("Received "));
- Serial.print(bytes); // print the size of the payload
- Serial.print(F(" bytes on pipe "));
- Serial.print(pipe); // print the pipe number
- Serial.print(F(": "));
- Serial.println(payload); // print the payload's value
- }
- } // role
-
- if (Serial.available()) {
- // change the role via the serial monitor
-
- char c = toupper(Serial.read());
- if (c == 'T' && !role) {
- // Become the TX node
-
- role = true;
- Serial.println(F("*** CHANGING TO TRANSMIT ROLE -- PRESS 'R' TO SWITCH BACK"));
- radio.stopListening();
-
- } else if (c == 'R' && role) {
- // Become the RX node
-
- role = false;
- Serial.println(F("*** CHANGING TO RECEIVE ROLE -- PRESS 'T' TO SWITCH BACK"));
- radio.startListening();
- }
- }
-
-} // loop
-\end{minted}
-
-
-\end{document}
\ No newline at end of file
diff --git a/Documentation/Pre-projet/Resume/resume.toc b/Documentation/Pre-projet/Resume/resume.toc
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-\contentsline {section}{\numberline {3}Dongle de contrôle}{7}{section.3}%
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-\contentsline {subsection}{\numberline {3.2}Script de contrôle}{7}{subsection.3.2}%
-\contentsline {section}{\numberline {4}Solution caméra}{8}{section.4}%
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-\contentsline {section}{\numberline {5}Annexe}{11}{section.5}%
-\contentsline {subsection}{\numberline {5.1}Code de test nrf24l01+}{11}{subsection.5.1}%
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- 8 named destinations out of 1000 (max. 500000)
- 65 words of extra memory for PDF output out of 10000 (max. 10000000)
-
diff --git "a/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.out" "b/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.out"
deleted file mode 100644
index 5c210de6a31fa035ad7fc4cd2260c45fd4d69632..0000000000000000000000000000000000000000
--- "a/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.out"
+++ /dev/null
@@ -1,4 +0,0 @@
-\BOOKMARK [1][-]{section.1}{\376\377\000I\000n\000t\000r\000o\000d\000u\000c\000t\000i\000o\000n}{}% 1
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diff --git "a/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.pdf" "b/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.pdf"
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index b635622bf86664c9593e808f381fd6e2a993eacc..0000000000000000000000000000000000000000
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diff --git "a/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.tex" "b/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.tex"
deleted file mode 100644
index 3c9e1421a8b987869dbddfe2d6c173ee48628b3c..0000000000000000000000000000000000000000
--- "a/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.tex"
+++ /dev/null
@@ -1,135 +0,0 @@
-\documentclass{EPSA-rap-template}
-
-\usepackage{eurosym}
-\usepackage{amssymb}
-
-\type{Présentation}
-
-\titresize{\LARGE} % ne pas hésiter a changer la taille :
-%\normalsize
-%\large
-%\Large
-%\LARGE
-%\huge
-%\Huge
-%\HUGE
-
-\titre{Intelligence embarqué}
-
-\departement{Recherche}
-
-\auteurs{Eymeric \textbf{Chauchat}}
-
-\version{V1.0}
-
-\versionnement{
-\ver{V1.0}{25 aout 2022}{ ECT }{Rédaction initiale.}{1}
-}
-
-\setuppack
-
-\begin{document}
-
-\fairepagedegarde
-\newpage
-\tableofcontents
-
-\section{Introduction}
-
-En plus du contrôle à distance de la voiture (qui est obligatoire pour permettre un arrêt d'urgence), le projet nécessite que l'on traite des données vidéos pour permettre la conduite autonome de la voiture. En ce sens plusieurs solutions s'offrent à nous pour permettre l'acquisition et le traitement de ces données.
-
-\section{Description des solutions}
-
-L'intelligence de la voiture peut se trouver à deux endroits :
-
-\begin{itemize}
-\item A l'intérieur de la voiture télécommandée
-\item sur l'ordinateur qui possède le dongle d'arret d'urgence / commande
-\end{itemize}
-
-Ces deux possibilités ouvrent un grand champ d'alternative pour arriver à faire l'acquisition vidéo et le traitement / commande.
-
-\subsection{Intelligence embarqué}
-
-Dans le cas d'une intelligence embarquée, l'acquisition vidéo se fera directement grâce à des caméras branchées sur la carte de contrôle.
-
-La question est alors : quelle doit être la plateforme qui va nous permettre de faire les calculs et en déduire la commande de notre voiture ?
-
-La plateforme choisit doit être robuste (résistante au probable choc sur le corps de la voiture), doit permettre une liaison avec l'arduino de contrôle de la voiture et doit avoir une assez grande puissance de calcul pour traiter les images (qui peuvent provenir de différentes caméras HD).
-
-A savoir que toute les plateformes se rapprochant de Arduino, Rasberry Pi, ou tournant sur ATmega328P, PIC ou autre microprocesseur cadencé en dessous du gigahertz ne pourra pas convenir dû au manque de puissance de calcul que nécessite le traitement vidéo.
-
-Voici alors une première liste de plateforme :
-
-
-\begin{itemize}
-
-\item Téléphone Android haut de gamme :
-
-\textbf{Avantages : }
-\begin{itemize}
-\item disponible auprès des membres du Pae
-\item Aucun coût
-\item Pas de consommation
-\item Puissance suffisante
-\item taille petite
-\end{itemize}
-
-\textbf{Inconvénients : }
-\begin{itemize}
-\item Obligation de développement en .apk
-\item Puissance de calcul limité
-\item difficulté pour gérer les différentes entrée vidéo
-\end{itemize}
-
-\item \href{https://www.nvidia.com/fr-be/autonomous-machines/jetson-store/}{Nvidia Jetson}
-
-\textbf{Avantages : }
-\begin{itemize}
-\item très forte puissance de calcul
-\item basse consommation
-\item Carte qui pourrait être intégré sur le véhicule à grande échelle
-\item architecture faite pour le calcul d'IA
-\item Programmation en CUDA / C++
-\end{itemize}
-\textbf{Inconvénients : }
-\begin{itemize}
-\item prix 109 \euro{}
-\item besoin de fournir une protection
-\item Assez encombrant sur la voiture (103mm x 90.5mm x 34mm)
-\end{itemize}
-
-\end{itemize}
-
-\subsection{Intelligence déporté}
-
-La deuxième solution est donc de déporter le calcul et la commande à l'extérieur de la voiture sur un ordinateur distant. Il faut alors trouver un moyen de recevoir le flux vidéo de plusieurs caméras à distance. La réception doit être de bonne qualité et avec une faible latence ($\leqslant 500ms$).
-
-On ne peut donc pas utiliser de système analogique (beaucoup de bruit à cause de la nature analogique du transfert). Il faut alors trouver un système numérique peu onéreux ($\leqslant 200 $\euro{}) qui permettent le transfert.
-
-Voici la liste préliminaire des solutions :
-
-\begin{itemize}
-
-\item ESP32-CAM
-
-\textbf{Avantages : }
-
-\begin{itemize}
-\item Système compact
-\item Système autonome
-\item Bon marché ($30$\euro{})
-\item faible latence ($\leqslant 100$ms)
-\item Petit encombrement
-\end{itemize}
-\textbf{Inconvénients : }
-
-\begin{itemize}
-\item Faible qualité vidéo (max 800x600 en 30 fps)
-\item multiplication des émissions wifi (comment gérer la réception)
-\end{itemize}
-
-
-\end{itemize}
-
-\end{document}
\ No newline at end of file
diff --git "a/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.toc" "b/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.toc"
deleted file mode 100644
index dac91c9d932446edc3bd112a1c141dfaa30c9374..0000000000000000000000000000000000000000
--- "a/Documentation/Pre-projet/Solution cam\303\251ra/choix streaming camera.toc"
+++ /dev/null
@@ -1,5 +0,0 @@
-\babel@toc {french}{}\relax
-\contentsline {section}{\numberline {1}Introduction}{1}{section.1}%
-\contentsline {section}{\numberline {2}Description des solutions}{1}{section.2}%
-\contentsline {subsection}{\numberline {2.1}Intelligence embarqué}{1}{subsection.2.1}%
-\contentsline {subsection}{\numberline {2.2}Intelligence déporté}{2}{subsection.2.2}%
diff --git a/Documentation/Pre-projet/Voiture RC/Arduino Nano scheme.jpg b/Documentation/Pre-projet/Voiture RC/Arduino Nano scheme.jpg
deleted file mode 100644
index 62675312a3cf9c06d8fc77995a7538ffcc7a7a5f..0000000000000000000000000000000000000000
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index 99b780602be86afab3b73a931358ecd9603a9d0c..0000000000000000000000000000000000000000
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diff --git a/Documentation/Pre-projet/Voiture RC/Arduino Nano scheme.psd b/Documentation/Pre-projet/Voiture RC/Arduino Nano scheme.psd
deleted file mode 100644
index 069520fe5af68d947beba564bb88847f2aa044c4..0000000000000000000000000000000000000000
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diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/Code_Voiture_RC/Code_Voiture_RC.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/Code_Voiture_RC/Code_Voiture_RC.ino
deleted file mode 100644
index d2df72a44d8d2b5519c7c86461a3dc82c9cf83ce..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/Code_Voiture_RC/Code_Voiture_RC.ino
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * See documentation at https://nRF24.github.io/RF24
- * See License information at root directory of this library
- * Author: Brendan Doherty (2bndy5)
- */
-
-/**
- * A simple example of sending data from 1 nRF24L01 transceiver to another.
- *
- * This example was written to be used on 2 devices acting as "nodes".
- * Use the Serial Monitor to change each node's behavior.
- */
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-// instantiate an object for the nRF24L01 transceiver
-RF24 radio(8, 7); // using pin 7 for the CE pin, and pin 8 for the CSN pin
-
-// Let these addresses be used for the pair
-uint8_t address[][6] = { "1Node", "2Node" };
-// It is very helpful to think of an address as a path instead of as
-// an identifying device destination
-
-// to use different addresses on a pair of radios, we need a variable to
-// uniquely identify which address this radio will use to transmit
-bool radioNumber = 1; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-// Used to control whether this node is sending or receiving
-bool role = false; // true = TX role, false = RX role
-
-// For this example, we'll be using a payload containing
-// a single float number that will be incremented
-// on every successful transmission
-float payload = 0.0;
-
-void setup() {
-
- Serial.begin(115200);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
- // print example's introductory prompt
- Serial.println(F("RF24/examples/GettingStarted"));
-
- // To set the radioNumber via the Serial monitor on startup
- Serial.println(F("Which radio is this? Enter '0' or '1'. Defaults to '0'"));
- while (!Serial.available()) {
- // wait for user input
- }
- char input = Serial.parseInt();
- radioNumber = input == 1;
- Serial.print(F("radioNumber = "));
- Serial.println((int)radioNumber);
-
- // role variable is hardcoded to RX behavior, inform the user of this
- Serial.println(F("*** PRESS 'T' to begin transmitting to the other node"));
-
- // Set the PA Level low to try preventing power supply related problems
- // because these examples are likely run with nodes in close proximity to
- // each other.
- radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- // save on transmission time by setting the radio to only transmit the
- // number of bytes we need to transmit a float
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- // set the TX address of the RX node into the TX pipe
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- // set the RX address of the TX node into a RX pipe
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- // additional setup specific to the node's role
- if (role) {
- radio.stopListening(); // put radio in TX mode
- } else {
- radio.startListening(); // put radio in RX mode
- }
-
- // For debugging info
- // printf_begin(); // needed only once for printing details
- // radio.printDetails(); // (smaller) function that prints raw register values
- // radio.printPrettyDetails(); // (larger) function that prints human readable data
-
-} // setup
-
-void loop() {
-
- if (role) {
- // This device is a TX node
-
- unsigned long start_timer = micros(); // start the timer
- bool report = radio.write(&payload, sizeof(float)); // transmit & save the report
- unsigned long end_timer = micros(); // end the timer
-
- if (report) {
- Serial.print(F("Transmission successful! ")); // payload was delivered
- Serial.print(F("Time to transmit = "));
- Serial.print(end_timer - start_timer); // print the timer result
- Serial.print(F(" us. Sent: "));
- Serial.println(payload); // print payload sent
- payload += 0.01; // increment float payload
- } else {
- Serial.println(F("Transmission failed or timed out")); // payload was not delivered
- }
-
- // to make this example readable in the serial monitor
- delay(1000); // slow transmissions down by 1 second
-
- } else {
- // This device is a RX node
-
- uint8_t pipe;
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
- Serial.print(F("Received "));
- Serial.print(bytes); // print the size of the payload
- Serial.print(F(" bytes on pipe "));
- Serial.print(pipe); // print the pipe number
- Serial.print(F(": "));
- Serial.println(payload); // print the payload's value
- }
- } // role
-
- if (Serial.available()) {
- // change the role via the serial monitor
-
- char c = toupper(Serial.read());
- if (c == 'T' && !role) {
- // Become the TX node
-
- role = true;
- Serial.println(F("*** CHANGING TO TRANSMIT ROLE -- PRESS 'R' TO SWITCH BACK"));
- radio.stopListening();
-
- } else if (c == 'R' && role) {
- // Become the RX node
-
- role = false;
- Serial.println(F("*** CHANGING TO RECEIVE ROLE -- PRESS 'T' TO SWITCH BACK"));
- radio.startListening();
- }
- }
-
-} // loop
diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/Code_Voiture_RC_tot/Code_Voiture_RC_tot.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/Code_Voiture_RC_tot/Code_Voiture_RC_tot.ino
deleted file mode 100644
index 6e06d7926e6de193fdc72449fe7190f568e3846a..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/Code_Voiture_RC_tot/Code_Voiture_RC_tot.ino
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * See documentation at https://nRF24.github.io/RF24
- * See License information at root directory of this library
- * Author: Brendan Doherty (2bndy5)
- */
-
-/**
- * A simple example of sending data from 1 nRF24L01 transceiver to another.
- *
- * This example was written to be used on 2 devices acting as "nodes".
- * Use the Serial Monitor to change each node's behavior.
- */
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-
-
-void setup() {
-
- Serial.begin(115200);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
-
-} // setup
-
-void loop() {
-
-
-
-} // loop
diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/Comande_preli/Comande_preli.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/Comande_preli/Comande_preli.ino
deleted file mode 100644
index 5e4d554a93ab07109794c02db22f94c8181c5cac..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/Comande_preli/Comande_preli.ino
+++ /dev/null
@@ -1,70 +0,0 @@
-#define PontHRouge 10
-#define PontHNoir 9
-
-int val = 0;
-
-#define frequencePWMde30hz 0b00000111
-
-// Variables internes à ce programme
-int valeurMaxRegistreOCR1x = 0;
-int valeurRapportCycliquePwmPinD9;
-int valeurRapportCycliquePwmPinD10;
-
-void setup() {
-
- pinMode(PontHRouge,OUTPUT);
- pinMode(PontHNoir,OUTPUT);
-
- // Sélection du mode "Phase Correct PWM 10 bits"
- bitClear(TCCR1B, WGM13); // Mise à 0 de WGM13
- bitClear(TCCR1B, WGM12); // Mise à 0 de WGM12
- bitSet(TCCR1A, WGM11); // Mise à 1 de WGM11
- bitSet(TCCR1A, WGM10); // Mise à 1 de WGM10
- // Valeur max registre OCR1x
- valeurMaxRegistreOCR1x = pow(2,10) - 1;
-
- TCCR1B &= 0b11111000; // <===== à ne pas toucher
- TCCR1B |= frequencePWMde30hz;
-
-
-
- Serial.begin(115200);
-
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- Serial.println("Test des commandes PWM voiture");
-
-}
-
-void loop() {
-
- bitSet(TCCR1A, COM1A1);
- bitClear(TCCR1A, COM1A0); // Mise en marche PWM D9
-
- bitSet(TCCR1A, COM1B1);
- bitClear(TCCR1A, COM1B0); // Mise en marche PWM D10
-
- if(val >= 0 )
- {
- OCR1A = val * valeurMaxRegistreOCR1x;
- OCR1B = val * valeurMaxRegistreOCR1x;
- }
- if(val < 0)
- {
- OCR1A = 0 * valeurMaxRegistreOCR1x;
- OCR1B = val * valeurMaxRegistreOCR1x;
- }
-
- if(Serial.available()){
-
- val = Serial.readString().toInt();
-
- Serial.println(val);
- }
-
-
-
-
-}
diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/Commande_first/Commande_first.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/Commande_first/Commande_first.ino
deleted file mode 100644
index 0e97fe3c7d3ff7fd01f299f35ad8552238b61f83..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/Commande_first/Commande_first.ino
+++ /dev/null
@@ -1,108 +0,0 @@
-#define PontHRouge 10
-#define PontHNoir 9
-
-int val = 0;
-
-#define frequencePWMde31372hz 0b00000001
-#define frequencePWMde3921hz 0b00000010
-#define frequencePWMde980hz 0b00000011
-#define frequencePWMde490hz 0b00000100
-#define frequencePWMde245hz 0b00000101
-#define frequencePWMde122hz 0b00000110
-#define frequencePWMde30hz 0b00000111
-
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-// instantiate an object for the nRF24L01 transceiver
-RF24 radio(8, 7); // using pin 7 for the CE pin, and pin 8 for the CSN pin
-
-// Let these addresses be used for the pair
-uint8_t address[][6] = { "1Node", "2Node" };
-// It is very helpful to think of an address as a path instead of as
-// an identifying device destination
-
-bool radioNumber = 1; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-// For this example, we'll be using a payload containing
-// a single float number that will be incremented
-// on every successful transmission
-float payload[3];
-
-void setup() {
-
- pinMode(PontHRouge,OUTPUT);
- pinMode(PontHNoir,OUTPUT);
-
- TCCR1B &= 0b11111000; // <===== à ne pas toucher
- TCCR1B |= frequencePWMde245hz; // <===== à changer, selon la fréquence que vous souhaitez en sortie
-
-
- Serial.begin(9600);
-
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- Serial.println("Test des commandes PWM voiture");
-
- radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- // save on transmission time by setting the radio to only transmit the
- // number of bytes we need to transmit a float
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- // set the TX address of the RX node into the TX pipe
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- // set the RX address of the TX node into a RX pipe
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- radio.startListening();
-
-}
-
-void loop() {
-
- if(val >= 0 )
- {
- analogWrite(PontHRouge,abs(val));
- analogWrite(PontHNoir,abs(val));
- }
- if(val < 0)
- {
- analogWrite(PontHNoir,abs(val));
- }
-
- if(Serial.available()){
-
- val = Serial.readString().toInt();
-
- Serial.println(val);
- }
-
- uint8_t pipe;
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
-
- Serial.print(payload[0]);
- Serial.print(":");
- Serial.print(payload[1]);
- Serial.print(":");
- Serial.print(payload[2]);
- Serial.print("\n");
-
-
-// radio.stopListening();
-// float payload[] = {1, 1 ,1};
-// radio.write(&payload,sizeof(payload));
-//
-// radio.startListening();
-
-
- }
-
-
-}
diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/GettingStarted/GettingStarted.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/GettingStarted/GettingStarted.ino
deleted file mode 100644
index 82ceeec5ce44f285f96113de79b9a0f984a2e27b..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/GettingStarted/GettingStarted.ino
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * See documentation at https://nRF24.github.io/RF24
- * See License information at root directory of this library
- * Author: Brendan Doherty (2bndy5)
- */
-
-/**
- * A simple example of sending data from 1 nRF24L01 transceiver to another.
- *
- * This example was written to be used on 2 devices acting as "nodes".
- * Use the Serial Monitor to change each node's behavior.
- */
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-// instantiate an object for the nRF24L01 transceiver
-RF24 radio(8, 7); // using pin 7 for the CE pin, and pin 8 for the CSN pin
-
-// Let these addresses be used for the pair
-uint8_t address[][6] = { "1Node", "2Node" };
-// It is very helpful to think of an address as a path instead of as
-// an identifying device destination
-
-// to use different addresses on a pair of radios, we need a variable to
-// uniquely identify which address this radio will use to transmit
-bool radioNumber = 0; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-// Used to control whether this node is sending or receiving
-bool role = false; // true = TX role, false = RX role
-
-// For this example, we'll be using a payload containing
-// a single float number that will be incremented
-// on every successful transmission
-float payload = 0.0;
-
-void setup() {
-
- Serial.begin(115200);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
- // print example's introductory prompt
- Serial.println(F("RF24/examples/GettingStarted"));
-
-// // To set the radioNumber via the Serial monitor on startup
-// Serial.println(F("Which radio is this? Enter '0' or '1'. Defaults to '0'"));
-// while (!Serial.available()) {
-// // wait for user input
-// }
-// char input = Serial.parseInt();
-// radioNumber = input == 1;
- Serial.print(F("radioNumber = "));
- Serial.println((int)radioNumber);
-
- // role variable is hardcoded to RX behavior, inform the user of this
- Serial.println(F("*** PRESS 'T' to begin transmitting to the other node"));
-
- // Set the PA Level low to try preventing power supply related problems
- // because these examples are likely run with nodes in close proximity to
- // each other.
- radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- // save on transmission time by setting the radio to only transmit the
- // number of bytes we need to transmit a float
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- // set the TX address of the RX node into the TX pipe
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- // set the RX address of the TX node into a RX pipe
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- // additional setup specific to the node's role
- if (role) {
- radio.stopListening(); // put radio in TX mode
- } else {
- radio.startListening(); // put radio in RX mode
- }
-
- // For debugging info
- // printf_begin(); // needed only once for printing details
- // radio.printDetails(); // (smaller) function that prints raw register values
- // radio.printPrettyDetails(); // (larger) function that prints human readable data
-
-} // setup
-
-void loop() {
-
- if (role) {
- // This device is a TX node
-
- unsigned long start_timer = micros(); // start the timer
- bool report = radio.write(&payload, sizeof(float)); // transmit & save the report
- unsigned long end_timer = micros(); // end the timer
-
- if (report) {
- Serial.print(F("Transmission successful! ")); // payload was delivered
- Serial.print(F("Time to transmit = "));
- Serial.print(end_timer - start_timer); // print the timer result
- Serial.print(F(" us. Sent: "));
- Serial.println(payload); // print payload sent
- payload += 0.01; // increment float payload
- } else {
- Serial.println(F("Transmission failed or timed out")); // payload was not delivered
- }
-
- // to make this example readable in the serial monitor
- delay(1000); // slow transmissions down by 1 second
-
- } else {
- // This device is a RX node
-
- uint8_t pipe;
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
- Serial.print(F("Received "));
- Serial.print(bytes); // print the size of the payload
- Serial.print(F(" bytes on pipe "));
- Serial.print(pipe); // print the pipe number
- Serial.print(F(": "));
- Serial.println(payload); // print the payload's value
- }
- } // role
-
- if (Serial.available()) {
- // change the role via the serial monitor
-
- char c = toupper(Serial.read());
- if (c == 'T' && !role) {
- // Become the TX node
-
- role = true;
- Serial.println(F("*** CHANGING TO TRANSMIT ROLE -- PRESS 'R' TO SWITCH BACK"));
- radio.stopListening();
-
- } else if (c == 'R' && role) {
- // Become the RX node
-
- role = false;
- Serial.println(F("*** CHANGING TO RECEIVE ROLE -- PRESS 'T' TO SWITCH BACK"));
- radio.startListening();
- }
- }
-
-} // loop
diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/GettingStarted_voiture/GettingStarted_voiture.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/GettingStarted_voiture/GettingStarted_voiture.ino
deleted file mode 100644
index a97f8ffe9bb233d45aa34b56422588391ea69779..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/GettingStarted_voiture/GettingStarted_voiture.ino
+++ /dev/null
@@ -1,159 +0,0 @@
-/*
- * See documentation at https://nRF24.github.io/RF24
- * See License information at root directory of this library
- * Author: Brendan Doherty (2bndy5)
- */
-
-/**
- * A simple example of sending data from 1 nRF24L01 transceiver to another.
- *
- * This example was written to be used on 2 devices acting as "nodes".
- * Use the Serial Monitor to change each node's behavior.
- */
-#include <SPI.h>
-#include "printf.h"
-#include "RF24.h"
-
-// instantiate an object for the nRF24L01 transceiver
-RF24 radio( 8,7); // using pin 7 for the CE pin, and pin 8 for the CSN pin
-
-// Let these addresses be used for the pair
-uint8_t address[][6] = { "1Node", "2Node" };
-// It is very helpful to think of an address as a path instead of as
-// an identifying device destination
-
-// to use different addresses on a pair of radios, we need a variable to
-// uniquely identify which address this radio will use to transmit
-bool radioNumber = 0; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-// Used to control whether this node is sending or receiving
-bool role = false; // true = TX role, false = RX role
-
-// For this example, we'll be using a payload containing
-// a single float number that will be incremented
-// on every successful transmission
-float payload = 0.0;
-
-void setup() {
-
- TCCR1B &= 0b11111000; // <===== à ne pas toucher
- TCCR1B |= 0b00000101; // <===== à changer, selon la fréquence que vous souhaitez en sortie
-
- Serial.begin(115200);
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
- // print example's introductory prompt
- Serial.println(F("RF24/examples/GettingStarted"));
-
-// // To set the radioNumber via the Serial monitor on startup
-// Serial.println(F("Which radio is this? Enter '0' or '1'. Defaults to '0'"));
-// while (!Serial.available()) {
-// // wait for user input
-// }
-// char input = Serial.parseInt();
-// radioNumber = input == 1;
- Serial.print(F("radioNumber = "));
- Serial.println((int)radioNumber);
-
- // role variable is hardcoded to RX behavior, inform the user of this
- Serial.println(F("*** PRESS 'T' to begin transmitting to the other node"));
-
- // Set the PA Level low to try preventing power supply related problems
- // because these examples are likely run with nodes in close proximity to
- // each other.
- //radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- radio.setDataRate(RF24_250KBPS);
-
- // save on transmission time by setting the radio to only transmit the
- // number of bytes we need to transmit a float
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- // set the TX address of the RX node into the TX pipe
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- // set the RX address of the TX node into a RX pipe
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- // additional setup specific to the node's role
- if (role) {
- radio.stopListening(); // put radio in TX mode
- } else {
- radio.startListening(); // put radio in RX mode
- }
-
- // For debugging info
- // printf_begin(); // needed only once for printing details
- // radio.printDetails(); // (smaller) function that prints raw register values
- // radio.printPrettyDetails(); // (larger) function that prints human readable data
-
-} // setup
-
-void loop() {
-
- if (role) {
- // This device is a TX node
-
- unsigned long start_timer = micros(); // start the timer
- bool report = radio.write(&payload, 3* sizeof(float)); // transmit & save the report
- unsigned long end_timer = micros(); // end the timer
-
- if (report) {
- Serial.print(F("Transmission successful! ")); // payload was delivered
- Serial.print(F("Time to transmit = "));
- Serial.print(end_timer - start_timer); // print the timer result
- Serial.print(F(" us. Sent: "));
- Serial.println(payload); // print payload sent
- payload += 0.01; // increment float payload
- } else {
- Serial.println(F("Transmission failed or timed out")); // payload was not delivered
- }
-
- // to make this example readable in the serial monitor
- delay(1000); // slow transmissions down by 1 second
-
- } else {
- // This device is a RX node
-
- uint8_t pipe;
- if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
- Serial.print(F("Received "));
- Serial.print(bytes); // print the size of the payload
- Serial.print(F(" bytes on pipe "));
- Serial.print(pipe); // print the pipe number
- Serial.print(F(": "));
- Serial.println(payload); // print the payload's value
- }
- } // role
-
- if (Serial.available()) {
- // change the role via the serial monitor
-
- char c = toupper(Serial.read());
- if (c == 'T' && !role) {
- // Become the TX node
-
- role = true;
- Serial.println(F("*** CHANGING TO TRANSMIT ROLE -- PRESS 'R' TO SWITCH BACK"));
- radio.stopListening();
-
- } else if (c == 'R' && role) {
- // Become the RX node
-
- role = false;
- Serial.println(F("*** CHANGING TO RECEIVE ROLE -- PRESS 'T' TO SWITCH BACK"));
- radio.startListening();
- }
- }
-
-} // loop
diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/Test_Direction/Test_Direction.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/Test_Direction/Test_Direction.ino
deleted file mode 100644
index 8e496d04d02ae8871621a88b907e06e222785b57..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/Test_Direction/Test_Direction.ino
+++ /dev/null
@@ -1,55 +0,0 @@
-#define DirPin A6
-// Droite 176 Gauche 850
-
-#define PontG 5
-
-#define PontD 6
-
-
-
-int val = 0;
-
-int aim = 500;
-
-void setup() {
- // put your setup code here, to run once:
- Serial.begin(9600);
-
-}
-
-void loop() {
- // put your main code here, to run repeatedly:
-
-if(Serial.available()){
-
- aim = Serial.readString().toInt();
- Serial.println(aim);
-}
-
- if(val < aim-50)
- {
-
- analogWrite(PontG,100);
- analogWrite(PontD,0);
- }
-
- else if(val > aim+50)
- {
-
- analogWrite(PontD,100);
- analogWrite(PontG,0);
- }
- else
- {
- analogWrite(PontD,0);
- analogWrite(PontG,0);
- }
-
-
- val= analogRead(DirPin);
- Serial.println(val);
-
- //analogWrite(PontG,100);
-
-
-}
diff --git a/Documentation/Pre-projet/Voiture RC/Code Arduino/Voiture_Finale/Voiture_Finale.ino b/Documentation/Pre-projet/Voiture RC/Code Arduino/Voiture_Finale/Voiture_Finale.ino
deleted file mode 100644
index 03283ea4265df4910a8983424d7e42dd64fd8958..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/Code Arduino/Voiture_Finale/Voiture_Finale.ino
+++ /dev/null
@@ -1,254 +0,0 @@
-#include <SPI.h>
-#include <PID_v2.h>
-#include "printf.h"
-#include "RF24.h"
-
-#define PontHRouge 10
-#define PontHNoir 9
-
-#define DirPin 4
-// Droite 180 Gauche 850
-
-//double Kp=3,Ki=0,Kd=0.3;//ces valeurs ne sont pas les bonnes
-//double Kp=0.97,Ki=1.3,Kd=0.0955; //marche bien pour discrétisation
-//double Kp=1.78,Ki=0.41,Kd=0.61; broida ok tier
-double Kp=1,Ki=0.2,Kd=0.133;
-int aggressif = 40;
-//double Kpd=2.3,Kid=8,Kdd=0.2;//ces valeurs ne sont pas les bonnes
-double Kpd=1,Kid=0.2,Kdd=0.133;
-//double Kpd=0.5,Kid=0.1,Kdd=0;
-
-double Setpoint, Input, Output; //valeur visé pour les roues dans [-127,127], valeur réelle des roues dans [-127,127] et valeur de la commande des roues
-PID myPID(&Input, &Output, &Setpoint,Kp,Ki,Kd, DIRECT);
-//PID myPID(&Input, &Output, &Setpoint,1,0,0, DIRECT);
-bool turning;
-int zone_morte=5;
-
-
-#define PontG 5
-#define PontD 6
-
-int val = 0;
-
-#define frequencePWMde31372hz 0b00000001
-#define frequencePWMde3921hz 0b00000010
-#define frequencePWMde980hz 0b00000011
-#define frequencePWMde490hz 0b00000100
-#define frequencePWMde245hz 0b00000101
-#define frequencePWMde122hz 0b00000110
-#define frequencePWMde30hz 0b00000111
-
-
-RF24 radio(8, 7);
-
-uint8_t address[][6] = { "1Node", "2Node" };
-
-
-bool radioNumber = 0; // 0 uses address[0] to transmit, 1 uses address[1] to transmit
-
-float payload[3] = {0.0,0.0,0.0};
-float sending[3] = {0.0, 0.0 ,0.0};
-
-void setup() {
-
- Serial.begin(9600);
-
- while (!Serial) {
- // some boards need to wait to ensure access to serial over USB
- }
-
- // initialize the transceiver on the SPI bus
- if (!radio.begin()) {
- Serial.println(F("radio hardware is not responding!!"));
- while (1) {} // hold in infinite loop
- }
-
- pinMode(PontHRouge,OUTPUT);
- pinMode(PontHNoir,OUTPUT);
-
- delay(10);
-
- TCCR1B &= 0b11111000; // <===== à ne pas toucher
- TCCR1B |= frequencePWMde245hz; // <===== à changer, selon la fréquence que vous souhaitez en sortie
-
-
-
- //Serial.println("Test des commandes PWM voiture");
- Serial.println("angle commande");
-
- //radio.setPALevel(RF24_PA_LOW); // RF24_PA_MAX is default.
-
- radio.setDataRate(RF24_250KBPS);
-
- radio.setPayloadSize(sizeof(payload)); // float datatype occupies 4 bytes
-
- radio.openWritingPipe(address[radioNumber]); // always uses pipe 0
-
- radio.openReadingPipe(1, address[!radioNumber]); // using pipe 1
-
- radio.startListening();
-
- //configurer le PID
- //configurer le PID
- myPID.SetMode(AUTOMATIC);
- myPID.SetOutputLimits(-255,255);
- //demander de tourner
- demarrer_virage(0);
-
-}
-
-
-void loop() {
-
- if(val >= 0 )
- {
- analogWrite(PontHRouge,abs(val));
- analogWrite(PontHNoir,abs(val));
- }
- if(val < 0)
- {
- analogWrite(PontHNoir,abs(val));
- }
-
- if(Serial.available()){
-
- val = Serial.readString().toInt();
-
- Serial.println(val);
- }
-
- uint8_t pipe;
- if (radio.available(&pipe)) {
- // is there a payload? get the pipe number that recieved it
- uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
- radio.read(&payload, bytes); // fetch payload from FIFO
-
-// Serial.print(bytes);
-// Serial.print(";");
-// Serial.print(payload[0]);
-// Serial.print(":");
-// Serial.print(payload[1]);
-// Serial.print(":");
-// Serial.print(payload[2]);
-// Serial.print("\n");
-
- val = payload[0];
- demarrer_virage(payload[1]);
-
- radio.stopListening();
-
- delay(0.2);
-
- bool report = radio.write(&sending,sizeof(sending));
-
-// if (report) {
-// Serial.print(F("Transmission successful! ")); // payload was delivered
-// } else {
-// Serial.println(F("Transmission failed or timed out")); // payload was not delivered
-// }
-
- radio.startListening();
-
-
- }
-
-// uint8_t pipe;
-// if (radio.available(&pipe)) { // is there a payload? get the pipe number that recieved it
-// uint8_t bytes = radio.getPayloadSize(); // get the size of the payload
-// radio.read(&payload, bytes); // fetch payload from FIFO
-// Serial.print(F("Received "));
-// Serial.print(bytes); // print the size of the payload
-// Serial.print(F(" bytes on pipe "));
-// Serial.print(pipe); // print the pipe number
-// Serial.print(F(": "));
-// Serial.println(payload[0]); // print the payload's value
-// }
- if (turning) {
- //on utilise un PID pour tourner
- //Input = 127.0/335.0*(analogRead(DirPin)-180)-127;//on ramène l'angle du potentiomètre à l'interval [-127,127]
- Input = 127.0/335.0*(analogRead(DirPin)-180)-127;//on ramène l'angle du potentiomètre à l'interval [-127,127]
-
-
- if(abs(Input-Setpoint)>zone_morte)
- {
-
-
-
- if ( abs(Input-Setpoint)<aggressif ) {
- myPID.SetTunings(Kpd, Kid, Kdd);
- //myPID.SetTunings(1,0,0);
- }
- else
- {
- myPID.SetTunings(Kp, Ki, Kd);
- //myPID.SetTunings(1,0,0);
- }
-
-
- myPID.Compute();//on calcul l'output pour notre PID
-
- //si on tombe dans la zone morte et que le virage est presque fini, on s'arrête là
-// if (abs(Output)<zone_morte && abs(Input-Setpoint)<marge) {
-// turning = false;
-// stop_direction();
-// }//sinon on tourne très légèrement pour atteindre la valeur souhaitée
-// else if (abs(Output)<zone_morte) {
-// Output = 1.2*zone_morte*sign(Output);
-// }
-
- //on applique la commande
- if (Output>0) {
- Output = Output/255*(255-40)+40;
- droite(abs(Output));
- }
- else {
- Output = Output/255*(255-40)-40;
- gauche(abs(Output));
- }
-
- }
- else
- {
- stop_direction();
- }
- }
-
- Serial.print(Input);
- Serial.print(",");
- Serial.print(Setpoint);
- Serial.print(",");
- Serial.print(Output);
- Serial.println();
- sending[0]=Input;
- sending[1]=Setpoint;
- sending[2]=Output;
-
-}
-
-void demarrer_virage(int value) {//value est dans [-127,127]
- turning=true;
- Setpoint=value;
-}
-
-//pour value<85, ça ne tourne plus, même quand les roues ne touchent pas le sol
-void gauche(int value) {
- analogWrite(PontG, value);
- analogWrite(PontD, 0);
-}
-
-void droite(int value) {
- analogWrite(PontD, value);
- analogWrite(PontG, 0);
-}
-
-void stop_direction() {
- analogWrite(PontD, 0);
- analogWrite(PontG, 0);
-}
-
-int sign(int value) {
- if (value>=0) {
- return 1;
- }
- return -1;
-}
diff --git a/Documentation/Pre-projet/Voiture RC/RC-presentation.aux b/Documentation/Pre-projet/Voiture RC/RC-presentation.aux
deleted file mode 100644
index 5364ac8c270ea7888f87917e723fcf007e1518e7..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/RC-presentation.aux
+++ /dev/null
@@ -1,36 +0,0 @@
-\relax
-\providecommand\hyper@newdestlabel[2]{}
-\providecommand\babel@aux[2]{}
-\@nameuse{bbl@beforestart}
-\catcode `:\active
-\catcode `;\active
-\catcode `!\active
-\catcode `?\active
-\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
-\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
-\global\let\oldcontentsline\contentsline
-\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
-\global\let\oldnewlabel\newlabel
-\gdef\newlabel#1#2{\newlabelxx{#1}#2}
-\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
-\AtEndDocument{\ifx\hyper@anchor\@undefined
-\let\contentsline\oldcontentsline
-\let\newlabel\oldnewlabel
-\fi}
-\fi}
-\global\let\hyper@last\relax
-\gdef\HyperFirstAtBeginDocument#1{#1}
-\providecommand\HyField@AuxAddToFields[1]{}
-\providecommand\HyField@AuxAddToCoFields[2]{}
-\pgfsyspdfmark {pgfid2}{0}{38412394}
-\pgfsyspdfmark {pgfid3}{0}{37462122}
-\babel@aux{french}{}
-\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}\protected@file@percent }
-\@writefile{toc}{\contentsline {section}{\numberline {2}Construction}{1}{section.2}\protected@file@percent }
-\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Montage électrique}{1}{subsection.2.1}\protected@file@percent }
-\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Câblage de l'arduino nano\relax }}{1}{figure.caption.2}\protected@file@percent }
-\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
-\newlabel{fig:wiring}{{1}{1}{Câblage de l'arduino nano\relax }{figure.caption.2}{}}
-\@writefile{toc}{\contentsline {section}{\numberline {3}Fonctionnement de la voiture}{2}{section.3}\protected@file@percent }
-\@writefile{toc}{\contentsline {section}{\numberline {4}Script de contrôle}{2}{section.4}\protected@file@percent }
-\gdef \@abspage@last{3}
diff --git a/Documentation/Pre-projet/Voiture RC/RC-presentation.log b/Documentation/Pre-projet/Voiture RC/RC-presentation.log
deleted file mode 100644
index 3a753b3f19ad0e743842560840ac4319f935e99c..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/RC-presentation.log
+++ /dev/null
@@ -1,1401 +0,0 @@
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- \write18 enabled.
- %&-line parsing enabled.
-**./RC-presentation.tex
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-\c@subsection=\count187
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-\c@figure=\count191
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-\abovecaptionskip=\skip47
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-\bibindent=\dimen138
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-\babel@savecnt=\count193
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diff --git a/Documentation/Pre-projet/Voiture RC/RC-presentation.pdf b/Documentation/Pre-projet/Voiture RC/RC-presentation.pdf
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diff --git a/Documentation/Pre-projet/Voiture RC/RC-presentation.tex b/Documentation/Pre-projet/Voiture RC/RC-presentation.tex
deleted file mode 100644
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+++ /dev/null
@@ -1,85 +0,0 @@
-\documentclass{EPSA-rap-template}
-
-\type{Présentation}
-
-\titresize{\LARGE} % ne pas hésiter a changer la taille :
-%\normalsize
-%\large
-%\Large
-%\LARGE
-%\huge
-%\Huge
-%\HUGE
-
-\titre{Voiture RC}
-
-\departement{Recherche}
-
-\auteurs{Eymeric \textbf{Chauchat}}
-
-\version{V1.0}
-
-\versionnement{
-\ver{V1.0}{26 aout 2022}{ ECT }{Rédaction initiale.}{1}
-}
-
-\setuppack
-
-\begin{document}
-
-\fairepagedegarde
-\newpage
-\tableofcontents
-
-\section{Introduction}
-
-Ce document va présenter la voiture radio commandé qui va être utilisé pour les projets de PaR cette année et la manière dont elle a été préparé. Pour permettre un contrôle par un ordinateur ou plus largement par n'importe quel type de microprocesseur ou processeur, nous avons remplacé la carte de réception par une arduino allié d'une nrf24l01 et de quelques composants annexes.
-
-\section{Construction}
-
-
-\subsection{Montage électrique}
-
-la nrf24l01 est montée de manière analogue au dongle usb sur l'arduino de la voiture (Figure \ref{fig:wiring}). Cependant nous avons aussi besoin de connection avec la distribution en puissance de la voiture.
-
-\begin{figure}
-\centering
-\includegraphics[width=0.4\textwidth]{Arduino Nano scheme.jpg}
-\caption{Câblage de l'arduino nano}
-\label{fig:wiring}
-\end{figure}
-
-\section{Fonctionnement de la voiture}
-
-La voiture possède deux systèmes principaux, la direction et la propulsion. La direction est composée d'une nappe de 6 câbles :
-
-\begin{itemize}
-
-\item Noir : moteur -
-\item Orange : moteur +
-\item Vert pomme : Potentiomètre +
-\item Blanc : Potentiomètre signal
-\item Vert : Potentiomètre -
-\item Bleu : isolation unused
-
-\end{itemize}
-
-à savoir que moteur - et moteur + sont contrôlé grâce à l'intermédiaire d'un pont en H sur la nappe. Le contrôle de la partie direction se fait simplement à partir de la donnée en angle du potentiomètre et de la sortie moteur.
-
-le deuxième système, la propulsion est composé de 4 câbles :
-
-\begin{itemize}
-\item Blanc : Ground
-\item Jaune : +8 V continu
-\item Noir : première entrée pont en H
-\item Rouge : deuxième entrée pont en H
-\end{itemize}
-
-Le contrôle de la propulsion se fait au travers du câble Rouge et Noir. Pour avancer il faut mettre un signal sur le câble noir et pour reculer il faut mettre un signal sur le câble rouge et le câble noir.
-
-\section{Script de contrôle}
-
-
-
-
-\end{document}
\ No newline at end of file
diff --git a/Documentation/Pre-projet/Voiture RC/RC-presentation.toc b/Documentation/Pre-projet/Voiture RC/RC-presentation.toc
deleted file mode 100644
index 76be5556496780be0f9eb7b509b1620f5c78cce1..0000000000000000000000000000000000000000
--- a/Documentation/Pre-projet/Voiture RC/RC-presentation.toc
+++ /dev/null
@@ -1,6 +0,0 @@
-\babel@toc {french}{}\relax
-\contentsline {section}{\numberline {1}Introduction}{1}{section.1}%
-\contentsline {section}{\numberline {2}Construction}{1}{section.2}%
-\contentsline {subsection}{\numberline {2.1}Montage électrique}{1}{subsection.2.1}%
-\contentsline {section}{\numberline {3}Fonctionnement de la voiture}{2}{section.3}%
-\contentsline {section}{\numberline {4}Script de contrôle}{2}{section.4}%
diff --git a/Documentation/Pre-projet/presentation-pae.pdf b/Documentation/Pre-projet/presentation-pae.pdf
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diff --git "a/Documentation/Pre-projet/travail-pr\303\251liminaire.pdf" "b/Documentation/Pre-projet/travail-pr\303\251liminaire.pdf"
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diff --git a/Documentation/Projet/Arduino/PID_direction/PID_direction.ino b/Documentation/Projet/Arduino/PID_direction/PID_direction.ino
deleted file mode 100644
index 8eb36225f031c55690cc78ab2cce6cf20c86d6d5..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Arduino/PID_direction/PID_direction.ino
+++ /dev/null
@@ -1,86 +0,0 @@
-#include <PID_v2.h>
-
-#define DirPin A6
-// Droite 180 Gauche 850
-
-#define PontG 5
-#define PontD 6
-
-double Kp=1,Ki=0,Kd=0;//ces valeurs ne sont pas les bonnes
-
-double Setpoint, Input, Output; //valeur visé pour les roues dans [-127,127], valeur réelle des roues dans [-127,127] et valeur de la commande des roues
-PID myPID(&Input, &Output, &Setpoint,Kp,Ki,Kd, DIRECT);
-bool turning;
-int zone_morte=150;
-int marge = 5;
-
-void setup() {
- // put your setup code here, to run once:
- Serial.begin(9600);
-
- //configurer le PID
- myPID.SetMode(AUTOMATIC);
- myPID.SetOutputLimits(-255,255);
-
- //demander de tourner
- demarrer_virage(100);
-}
-
-void loop() {
-
-
- if (turning) {
- //on utilise un PID pour tourner
- Input = 127.0/335.0*(analogRead(DirPin)-180)-127;//on ramène l'angle du potentiomètre à l'interval [-127,127]
- myPID.Compute();//on calcul l'output pour notre PID
- Serial.println(Input);
-
-
-
- //si on tombe dans la zone morte et que le virage est presque fini, on s'arrête là
- if (abs(Output)<zone_morte && abs(Input-Setpoint)<marge) {
- turning = false;
- stop_direction();
- }//sinon on tourne très légèrement pour atteindre la valeur souhaitée
- else if (abs(Output)<zone_morte) {
- Output = 1.2*zone_morte*sign(Output);
- }
-
- //on applique la commande
- if (Output<0) {
- droite(abs(Output));
- }
- else {
- gauche(abs(Output));
- }
- }
-}
-
-
-void demarrer_virage(int value) {//value est dans [-127,127]
- turning=true;
- Setpoint=value;
-}
-
-//pour value<85, ça ne tourne plus, même quand les roues ne touchent pas le sol
-void gauche(int value) {
- analogWrite(PontG, value);
- analogWrite(PontD, 0);
-}
-
-void droite(int value) {
- analogWrite(PontD, value);
- analogWrite(PontG, 0);
-}
-
-void stop_direction() {
- analogWrite(PontD, 0);
- analogWrite(PontG, 0);
-}
-
-int sign(int value) {
- if (value>=0) {
- return 1;
- }
- return -1;
-}
\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/BlueCheckerPatternPaper.png b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/BlueCheckerPatternPaper.png
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/README.md b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/README.md
deleted file mode 100644
index cc865b58a14038820881b8c2ab703fe995e802c0..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# 2D-car-dynamics-simulation
-2D car simulator
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/__pycache__/car_data_display.cpython-38.pyc b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/__pycache__/car_data_display.cpython-38.pyc
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/analysis.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/analysis.py
deleted file mode 100644
index f1ed06de08bbcb7d8a3230582a9be209f2c2faaa..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/analysis.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import xml.etree.ElementTree as ET
-from track_2_generator import create_track
-from shapely.geometry import Polygon, Point, LineString
-import numpy as np
-
-from matplotlib.pyplot import plot, legend
-import json
-
-"""loading track"""
-track_points = create_track(0.5,0,0, (0,0))[:-1]+[(1,0.5)]
-track = Polygon(track_points)
-#outside_l = create_double_circles(0.59375, (0.5,0.5),(1.5,0.5))
-
-"""
-x_shift, y_shift = 1433.0000000000002, -406.0866103896103*2#Tom: 1433.0000000000002, -406.0866103896103*2 / Damien: -540, 960
-R_simu = 150#Tom: 150 / Damien: 350
-R_reel = 0.5
-x_shift, y_shift = x_shift/R_simu-4.77, y_shift/R_simu+2.7 #Tom:-4.77,2.7 / Damien: 1.88, -2.5
-"""
-
-def get_error(name, R_simu, center_x, center_y, start=-1, end=-1):
-
- """loading trajectory"""
- with open(name) as json_file:
- data = json.load(json_file)
-
- x_shift, y_shift = center_x/R_simu, center_y/R_simu
-
- time = data["time"]
- x_l = [(i/R_simu-x_shift)/2+1 for i in data["x"]]
- y_l = [(i/R_simu-y_shift)/2+0.5 for i in data["y"]]
- e_l = []
-
- if start>=0:
- x_l = x_l[start:]
- y_l = y_l[start:]
- time = time[start:]
- if end>=0:
- x_l = x_l[0:end]
- y_l = y_l[0:end]
- time = time[0:end]
-
- for x,y in zip(x_l,y_l):
- front_center = Point(np.array([x,y]))
- e = track.distance(front_center)
- e_l.append(e)
-
- return x_l, y_l, time, e_l
-
-#plot([x for x,y in track_points], [y for x,y in track_points])
-#plot([i[0] for i in outside_l], [i[1] for i in outside_l])
-
-prefix = 'paths/'
-suffix = '_normal.json'
-Tom = get_error(prefix+'errorT'+suffix, 150, 1733, -666, 70, 450)
-Damien = get_error(prefix+'errorD'+suffix, 350, 930, 510, 25)
-plot(Damien[2],Damien[3])
-plot(*Tom[2:])
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_data_display.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_data_display.py
deleted file mode 100644
index bf1612ced527b9c1829c40807a6fc49f0e5de188..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_data_display.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import pygame
-
-
-class CarDataDisplay:
- def __init__(self, car, track):
- self.car = car
- self.track = track
- pygame.font.init()
-
- def display_data(self, screen, position=(0, 0), font='Verdana', size=20):
- font_color = (255, 255, 255)
- font = pygame.font.SysFont(font, size)
- vel_long = font.render('Long velocity: ' + str(round(self.car.velocity.x * 3.6, 2)), False, font_color)
- vel_lat = font.render("Lat velocity: " + str(round(self.car.velocity.y * 3.6, 2)), False, font_color)
- rpm = font.render('Engine RPM: ' + str(round(self.car.wheel_rpm * self.car.diff_ratio * self.car.gears[self.car.gear], 2)),
- False, font_color)
- gear = font.render('Gear: ' + str(self.car.gear), False, font_color)
- steering = font.render('Steering: ' + str(self.car.steering), False, font_color)
- throttle = font.render('Throttle: ' + str(self.car.throttle), False, font_color)
- brake = font.render('brake: ' + str(self.car.brakes), False, font_color)
- velocity = font.render('Velocity: ' + str(round((self.car.velocity.x**2+self.car.velocity.y**2)**0.5 * 3.6, 2)), False, font_color)
- pos_x = font.render('position.x: ' + str(self.track.car_pos[0]), False, font_color)
- pos_y = font.render('position.y: ' + str(self.track.car_pos[1]), False, font_color)
- track_phase = font.render('phase: ' + str(self.track.track_phase), False, font_color)
- lap_number = font.render('lap: ' + str(self.track.lap_nb), False, font_color)
-
- #screen.blit(vel_long, position)
- #screen.blit(vel_lat, (position[0], position[1] + (size * 5 / 4)))
- #screen.blit(rpm, (position[0], position[1] + 2 * (size * 5 / 4)))
- #screen.blit(gear, (position[0], position[1] + 3 * (size * 5/4)))
- screen.blit(velocity, (position[0], position[1] + 1 * (size * 5/4)))
- screen.blit(steering, (position[0], position[1] + 2 * (size * 5/4)))
- screen.blit(throttle, (position[0], position[1] + 3 * (size * 5/4)))
- screen.blit(pos_x, (position[0], position[1] + 4 * (size * 5/4)))
- screen.blit(pos_y, (position[0], position[1] + 5 * (size * 5/4)))
- screen.blit(track_phase, (position[0], position[1] + 6 * (size * 5/4)))
- screen.blit(lap_number, (position[0], position[1] + 7 * (size * 5/4)))
- #screen.blit(brake, (position[0], position[1] + 6 * (size * 5/4)))
\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_drawer.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_drawer.py
deleted file mode 100644
index 5e1de8e141d1bacf0a3c7e9c3046fb1f6aadcec5..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_drawer.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from shapely.geometry.polygon import Polygon
-from shapely.affinity import rotate
-import pygame
-
-
-class CarDrawer:
- def __init__(self, length=55, width=20, init_position=(1366/2, 768/2)):
- self.length = length
- self.width = width
- self.trace = []
- self.init_position = init_position
- pos_x = self.init_position[0]
- pos_y = self.init_position[1]
- self.r_center = (pos_x, pos_y)
-
- self.car_model = Polygon([(pos_x - self.length * 2 / 10, pos_y - self.width / 2),
- (pos_x - self.length * 2 / 10, pos_y + self.width / 2),
- (pos_x + self.length * 8 / 10, pos_y + self.width / 2),
- (pos_x + self.length * 8 / 10, pos_y - self.width / 2)])
-
- self.f_axle = Polygon([(pos_x + self.length * 6 / 10, pos_y + self.width * 5 / 6),
- (pos_x + self.length * 6 / 10, pos_y - self.width * 5 / 6),
- (pos_x + self.length * 6 / 10, pos_y + self.width * 5 / 6)])
- self.f_tire_1 = Polygon([(pos_x + self.length * 4.5 / 10, pos_y + self.width * 5 / 6),
- (pos_x + self.length * 4.5 / 10, pos_y + self.width * 7 / 6),
- (pos_x + 7.5 / 10 * self.length, pos_y + self.width * 7 / 6),
- (pos_x + 7.5 / 10 * self.length, pos_y + self.width * 5 / 6)])
- self.f_tire_2 = Polygon([(pos_x + self.length * 4.5 / 10, pos_y - self.width * 5 / 6),
- (pos_x + self.length * 4.5 / 10, pos_y - self.width * 7 / 6),
- (pos_x + 7.5 / 10 * self.length, pos_y - self.width * 7 / 6),
- (pos_x + 7.5 / 10 * self.length, pos_y - self.width * 5 / 6)])
- self.r_axle = Polygon([(pos_x, pos_y + self.width * 5 / 6),
- (pos_x, pos_y - self.width * 5 / 6),
- (pos_x, pos_y + self.width * 5 / 6)])
- self.r_tire_1 = Polygon([(pos_x + self.length * 1.5 / 10, pos_y + self.width * 5 / 6),
- (pos_x + self.length * 1.5 / 10, pos_y + self.width * 7 / 6),
- (pos_x - 1.5 / 10 * self.length, pos_y + self.width * 7 / 6),
- (pos_x - 1.5 / 10 * self.length, pos_y + self.width * 5 / 6)])
- self.r_tire_2 = Polygon([(pos_x + self.length * 1.5 / 10, pos_y - self.width * 5 / 6),
- (pos_x + self.length * 1.5 / 10, pos_y - self.width * 7 / 6),
- (pos_x - 1.5 / 10 * self.length, pos_y - self.width * 7 / 6),
- (pos_x - 1.5 / 10 * self.length, pos_y - self.width * 5 / 6)])
-
- def draw(self, screen, car):
- car_color = (181, 25, 253)
- angle = -car.angle
- steering = car.steering
-
- car_model = rotate(self.car_model, angle, self.r_center)
- x, y = car_model.exterior.xy
- pygame.draw.polygon(screen, (0, 0, 255), [(xx, yy) for xx, yy in zip(x, y)])
- f_axle = rotate(self.f_axle, angle, self.r_center)
- x_axle, y_axle = f_axle.exterior.xy
- pygame.draw.polygon(screen, car_color, [(xx, yy) for xx, yy in zip(x_axle, y_axle)], 2)
- f_tire_1 = rotate(self.f_tire_1, angle, self.r_center)
- f_tire_1 = rotate(f_tire_1, - steering, (x_axle[0], y_axle[0]))
- x, y = f_tire_1.exterior.xy
- pygame.draw.polygon(screen, (0, 0, 0), [(xx, yy) for xx, yy in zip(x, y)])
- f_tire_2 = rotate(self.f_tire_2, angle, self.r_center)
- f_tire_2 = rotate(f_tire_2, - steering, (x_axle[1], y_axle[1]))
- x, y = f_tire_2.exterior.xy
- pygame.draw.polygon(screen, (0, 0, 0), [(xx, yy) for xx, yy in zip(x, y)])
- r_axle = rotate(self.r_axle, angle, self.r_center)
- x, y = r_axle.exterior.xy
- pygame.draw.polygon(screen, car_color, [(xx, yy) for xx, yy in zip(x, y)], 2)
- r_tire_1 = rotate(self.r_tire_1, angle, self.r_center)
- x, y = r_tire_1.exterior.xy
- pygame.draw.polygon(screen, (0, 0, 0), [(xx, yy) for xx, yy in zip(x, y)])
- r_tire_2 = rotate(self.r_tire_2, angle, self.r_center)
- x, y = r_tire_2.exterior.xy
- pygame.draw.polygon(screen, (0, 0, 0), [(xx, yy) for xx, yy in zip(x, y)])
- rect = pygame.Rect(self.r_center[0], self.r_center[1], 5, 5)
- pygame.draw.rect(screen, (255, 0, 0), rect)
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_model.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_model.py
deleted file mode 100644
index f58ba58cc2791045b091fbf9989148258ce5ad84..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/car_model.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from pygame.math import Vector2
-from math import tan, radians, degrees, pi, atan2, sin, cos
-import numpy as np
-import random
-
-class Car:
- def __init__(self, x, y, angle=0.0, length=5):
- self.position = Vector2(x, y)
- self.velocity = Vector2(0.0, 0.0)
- self.angle = angle
- self.length = length
- self.width = 2
- self.brake_deceleration = 30000
- self.free_deceleration = 2
-
- self.acceleration = Vector2(0, 0)
- self.steering = 0.0
- self.gear = 0
- self.throttle = 0
- self.brakes = 0
- self.wheel_rpm = 0
- self.rpm = 2000
- self.angular_velocity = 0
- self.force = Vector2(0, 0)
-
- # Parts:
- self.engine = Engine()
-
- # Characteristics:
- self.gears = {0: 0, 1: 3, 2: 2, 3: 1.5, 4: 1.25, 5: 1, 6: 0.75, -1: -2.9}
- self.diff_ratio = 3.42
- self.n = 0.8 # power transfer efficiency
- self.wheel_radius = 0.35
- self.mass = 1100
- self.rear_wheels_mass = 100
- self.c_drag = 0.4257
- self.cornering_stiffness_f = -5.0
- self.cornering_stiffness_r = -5.2
- self.max_grip = 2.0
- self.max_steering = 20
-
- def get_driver_input(self, throttle, gear, brakes, steering_angle):
- self.brakes = brakes
- self.gear = gear
- #on ajoute des vibrations
- #self.steering+=random.randint(-5,5)
- self.steering = steering_angle
- self.throttle = abs(throttle)
-
- def update(self, dt):
- self.rpm = self.wheel_rpm * self.diff_ratio * self.gears[self.gear]
-
- if self.rpm < 2000:
- self.rpm = 2000
-
- traction_force = self.engine.get_torque(self.rpm, self.throttle) * self.diff_ratio * \
- self.gears[self.gear] * (self.n / self.wheel_radius) - \
- self.brake_deceleration * self.brakes * np.sign(self.velocity.x)
-
- resistance_force = Vector2(- self.c_drag * self.velocity.x * abs(self.velocity.x) - 12.8 * self.velocity.x,
- - self.c_drag * self.velocity.y * abs(self.velocity.y) - 12.8 * self.velocity.y)
-
- if self.velocity.x > 5.55:
- yawspeed = 2 * self.angular_velocity
-
- if self.velocity.x == 0:
- rot_angle = 0
- sideslip = 0
-
- else:
- rot_angle = atan2(yawspeed, self.velocity.x)
- sideslip = atan2(self.velocity.y, self.velocity.x)
-
- slipanglefront = sideslip + rot_angle - radians(self.steering)
- slipanglerear = sideslip - rot_angle
-
- flatf = Vector2(0, 0)
- flatr = Vector2(0, 0)
-
- flatf.x = 0
- flatf.y = self.cornering_stiffness_f * slipanglefront
- flatf.y = min(self.max_grip, flatf.y)
- flatf.y = max(-self.max_grip, flatf.y)
- flatf.y *= self.mass * 4.9
-
- flatr.x = 0
- flatr.y = self.cornering_stiffness_r * slipanglerear
- flatr.y = min(self.max_grip, flatr.y)
- flatr.y = max(- self.max_grip, flatr.y)
- flatr.y *= self.mass * 4.9
-
- self.force.x = traction_force + sin(radians(self.steering)) * flatf.x + flatr.x + resistance_force.x
- self.force.y = cos(radians(self.steering)) * flatf.y + flatr.y + resistance_force.y
-
- torque = 1.5 * (flatf.y - flatr.y)
-
- self.acceleration = self.force / self.mass
- self.wheel_rpm = self.velocity.x / self.wheel_radius * 30 / pi
-
- self.velocity += self.acceleration * dt
- self.position += self.velocity.rotate(-self.angle) * dt
-
- angular_acceleration = torque / 1000
- self.angular_velocity += angular_acceleration * dt
- self.angle += degrees(self.angular_velocity) * dt
-
- else:
- self.force.x = traction_force + resistance_force.x
- self.force.y = resistance_force.y
- self.acceleration = self.force / self.mass
- self.wheel_rpm = self.velocity.x / self.wheel_radius * 30 / pi
-
- self.velocity += self.acceleration * dt
- if self.steering:
- turning_radius = self.length / tan(radians(self.steering))
- angular_velocity = self.velocity.x / turning_radius
- else:
- angular_velocity = 0
-
- self.position += self.velocity.rotate(-self.angle) * dt
- self.angle += degrees(angular_velocity) * dt
-
-
-class Engine:
- def __init__(self):
- self.rpm_lut = np.array([1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000])
- self.torque_lut = np.array([200, 325, 475, 550, 550, 500, 375, 300, 0])
-
- def get_torque(self, rpm, throttle):
- if rpm < 1000:
- rpm = 1000
- torque = np.interp(rpm, self.rpm_lut, self.torque_lut)
- return torque * throttle
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/division_circuit.pdn b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/division_circuit.pdn
deleted file mode 100644
index b3ff0a77d6bc13b5959208afab95d8eafb430331..0000000000000000000000000000000000000000
Binary files a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/division_circuit.pdn and /dev/null differ
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/error.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/error.json
deleted file mode 100644
index 21505d068ebb6e1995948f960d3a9999052e9309..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/error.json
+++ /dev/null
@@ -1,1928 +0,0 @@
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/errorFPS.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/errorFPS.json
deleted file mode 100644
index df18a428ab0a3d8502122ba2b561510423ee0fdc..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/errorFPS.json
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/error_location_perturbation.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/error_location_perturbation.json
deleted file mode 100644
index 128cb09bf4203165765718ebfa9cd7c5b32a499d..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/error_location_perturbation.json
+++ /dev/null
@@ -1,2141 +0,0 @@
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- ]
-}
\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/etude_error.txt b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/etude_error.txt
deleted file mode 100644
index 4dd26eedb518ec2b061f16424fc6017a57756852..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/etude_error.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-dimensions circuits:
-Simulation:
- centre: x=1725, y=
- sommet gauche: x=1632, y=-832
- bas gauche: x=1565, y=-505
- sommet droit: x=1855, y=-830
- bas droit: x=1925, y=-512
- droite droite: x=2040, y=-675
- gauche gauche: x=1420 , y=-700
-
- => R/2 = -93, -160, 130, 200
- => R = 327, 318, 315, 305
-
- Rmoy = 300
-
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/game.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/game.py
deleted file mode 100644
index f66964acc146f0e1d300240de5044bb17b87c2fb..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/game.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import pygame
-from track import *
-from car_drawer import CarDrawer
-from car_model import Car
-from input_providers import *
-from car_data_display import CarDataDisplay
-from pygame.math import Vector2
-import json
-
-from shapely.geometry import Point
-from random import randint
-
-class Game:
- def __init__(self, width, height):
- pygame.init()
- pygame.display.set_caption("Car model")
- self.window_width = width
- self.window_height = height
- self.screen = pygame.display.set_mode((width, height))
- self.fps = 1
- self.background = Background('BlueCheckerPatternPaper.png', [-500, -500])
-
- self.clock = pygame.time.Clock()
- self.ticks = 20
- self.exit = False
- self.x, self.y = [], []
- self.time = []
-
- def run(self):
-
- car = Car(self.window_width / 20, self.window_height / 20)
- track = Track('track3.svg')
- track_drawer = TrackDrawer(track)
- car.position.x, car.position.y = track.full_path[0][0] / 10 - 1366 / 20, track.full_path[0][1] / 10 - 768 / 20
- car_drawer = CarDrawer()
- input_provider = JoystickInputProvider()
- if not input_provider.joysticks:
- input_provider = KeyboardInputProvider()
- car_data_display = CarDataDisplay(car)
- trace = [(car.position.x * 10 + 1366/2, car.position.y * 10 + 768/2) for _ in range(3)]
-
- while not self.exit:
- dt = self.clock.tick(self.fps) / 1000
-
- # Event queue
- for event in pygame.event.get():
- if event.type == pygame.QUIT:
- self.exit = True
-
- trace.pop(2)
- trace.insert(0, (car.position.x * 10 + 1366/2, car.position.y * 10 + 768/2))
-
- # User input
- car_input = input_provider.get_input()
- car.get_driver_input(car_input[0], car_input[1], car_input[2], car_input[3])
- car.update(dt)
-
- # Drawing
- self.screen.fill((0, 0, 0))
- track_drawer.draw(self.screen, car.position * 10, trace)
- car_drawer.draw(self.screen, car)
- car_data_display.display_data(self.screen)
- pygame.display.flip()
- pygame.quit()
-
- def run_pid_controller(self, track_path, solution_path='solutionOpt.csv', dt=10):
- car = Car(self.window_width / 20, self.window_height / 20, angle=90)
- track = Track(track_path)
- track_drawer = TrackDrawer(track)
- solution = pd.read_csv(solution_path, index_col=0)
- track.apply_deformations(list(solution.Deformation))
- car.position.x, car.position.y = track.full_path[0][0] / 10 - 1366 / 20, track.full_path[0][1] / 10 - 768 / 20
- car_drawer = CarDrawer()
- car_data_display = CarDataDisplay(car, track)
- input_provider = AutonomousDriver(solution)
- time = 0
- trace = [(car.position.x * 10 + 1366/2, car.position.y * 10 + 768/2) for _ in range(3)]
- last_u = -1
- while not self.exit:
- """fps perturbation"""
- fps = 20
- u_dt = 1/fps
-
- # Handling time
- self.clock.tick(1 / dt)
- time += dt
-
- # Event queue
- for event in pygame.event.get():
- if event.type == pygame.QUIT:
- self.exit = True
-
- # Input from solution file and PID controller
- input_provider.index = track_drawer.chunk_indexes[-1] # Incrementing row index of solution matrix
- car_input = input_provider.get_input(track.track_phase)
-
- turning=car_input[3]
-
- """perturbations"""
- incert_turning = 0 #5°
- turning += randint(-incert_turning, incert_turning)
-
- """limit turning"""
- max_steering = 30
- if turning<0:
- turning = max(turning, -max_steering)
- else:
- turning= min(turning, max_steering)
-
- if abs(time-last_u)>u_dt:
- last_u=time
- car.get_driver_input(car_input[0], car_input[1], car_input[2], turning)
- car.update(dt)
-
- # Calculating position of a front center of a car
- vector = Vector2(40, 0).rotate(-car.angle)
- vector = np.array((vector.x + 1366 / 2, vector.y + 768 / 2))
- front_center = Point(np.array((car.position.x * 10, car.position.y * 10)) + vector)
-
-
- """perturbations"""
- R_simu = 150
- R_reel = 8
- incert_reel = 0 #0.2m
- incert_simu = incert_reel*R_simu/R_reel
-
- incert_simu = int(incert_simu)
-
- error_x = randint(-incert_simu, incert_simu)
- error_y = randint(-incert_simu, incert_simu)
- error_point = Point(front_center.x+error_x, front_center.y+error_y)
-
- # Calculating line error as a distance from front center to a given line (path)
- i = 1# check if mid is past, if yes, track is on the left so we need to adapt
- if track.is_starting or track.is_ending:
- input_provider.line_error = track.mid_track-error_point.x
- else:
- if not track.is_right:
- i=-1
- e = LineString(track.visible_path).distance(error_point)
- #print(e)
- if Polygon(track.visible_path).contains(error_point):
- input_provider.line_error = - i * e
- else:
- input_provider.line_error = i * e
-
- self.x.append(front_center.x)
- self.y.append(front_center.y)
- self.time.append(time)
-
- # Updating trace
- trace.pop(2)
- trace.insert(0, (car.position.x * 10 + 1366 / 2, car.position.y * 10 + 768 / 2))
-
- # Drawing
- self.screen.fill((0, 0, 0))
- self.background.set_location([- car.position.x * 10, 768 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([1200 - car.position.x * 10, 768 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([2400 - car.position.x * 10, 768 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([- car.position.x * 10, 768 + 1200 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([1200 - car.position.x * 10, 768 + 1200 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([2400 - car.position.x * 10, 768 + 1200 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([- car.position.x * 10, 768 + 2400 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([1200 - car.position.x * 10, 768 + 2400 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- self.background.set_location([2400 - car.position.x * 10, 768 + 2400 - car.position.y * 10])
- self.screen.blit(self.background.image, self.background.rect)
- track_drawer.draw(self.screen, car.position * 10, trace)
- car_drawer.draw(self.screen, car)
- car_data_display.display_data(self.screen)
- rect = pygame.Rect(front_center.x - car.position.x * 10, front_center.y - car.position.y * 10, 5, 5)
- pygame.draw.rect(self.screen, (0, 0, 255), rect)
- pygame.display.flip()
-
- #check if track is done
- completed = track.max_y<front_center.y
- if completed:
- break
- # Checking if car has passed finishing-line
- if input_provider.index == len(track.track_chunks) - 1:
- break
-
-
- # Checking if car completed track
- # completed = True
- # for chunk in track.track_chunks:
- # if not chunk.is_active:
- # completed = False
- # break
-
- print("Time:", time, "Completed:", completed)
- data = {"time":self.time, "x":self.x, "y":self.y}
- """saving data"""
- """
- with open('error.json', 'w') as outfile:
- json.dump(data, outfile, indent=4)
- print('SAVED')
- """
- pygame.quit()
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/input_providers.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/input_providers.py
deleted file mode 100644
index 789de013229715b99d6e94fca1f65f4829439259..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/input_providers.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import pandas as pd
-import pygame
-from pid_controller import PidController
-
-
-class InputProvider:
- def get_input(self):
- pass
-
-
-class RecordedInputProvider(InputProvider):
- def __init__(self, csv_path):
- self.input_dataframe = pd.read_csv(csv_path, index_col=0)
- self.index = self.input_dataframe.first_valid_index()
-
- def get_input(self):
- if self.index <= self.input_dataframe.last_valid_index():
- throttle = self.input_dataframe.Throttle[self.index]
- brakes = self.input_dataframe.Brakes[self.index]
- steering = self.input_dataframe.Steering[self.index]
- gear = self.input_dataframe.Gear[self.index]
- self.index += 1
- return throttle, gear, brakes, steering
-
-
-class JoystickInputProvider(InputProvider):
- def __init__(self):
- self.gear = 0
- self.gear_timer = 0
- self.clock = pygame.time.Clock()
- self.joysticks = []
- for i in range(pygame.joystick.get_count()):
- self.joysticks.append(pygame.joystick.Joystick(i))
- self.joysticks[-1].init()
- print("Detected pad:", self.joysticks[-1].get_name())
-
- def get_input(self):
- self.gear_timer += self.clock.tick()
- throttle = - self.joysticks[-1].get_axis(3)
- steering = - self.joysticks[-1].get_axis(4) * 30
- if self.joysticks[-1].get_axis(2) < - 0.5:
- brakes = - self.joysticks[-1].get_axis(2) * 5000
- else:
- brakes = 0
-
- if - self.joysticks[-1].get_axis(1) > 0.9 and self.gear < 6 and self.gear_timer > 300:
- self.gear += 1
- self.gear_timer = 0
-
- elif - self.joysticks[-1].get_axis(1) < -0.9 and self.gear > -1 and self.gear_timer > 300:
- self.gear -= 1
- self.gear_timer = 0
-
- return throttle, self.gear, brakes, steering
-
-
-class KeyboardInputProvider(InputProvider):
- def __init__(self):
- self.gear = 0
- self.gear_timer = 0
- self.clock = pygame.time.Clock()
-
- def get_input(self):
- steering = 0
- self.gear_timer += self.clock.tick()
-
- if pygame.key.get_pressed()[pygame.K_UP]:
- throttle = 1
- else:
- throttle = 0
-
- if pygame.key.get_pressed()[pygame.K_DOWN]:
- brakes = 1
- else:
- brakes = 0
-
- if pygame.key.get_pressed()[pygame.K_LEFT]:
- steering = 20
-
- if pygame.key.get_pressed()[pygame.K_RIGHT]:
- steering = -20
-
- if pygame.key.get_pressed()[pygame.K_q] and self.gear < 6 and self.gear_timer > 300:
- self.gear += 1
- self.gear_timer = 0
-
- if pygame.key.get_pressed()[pygame.K_a] and self.gear > -1 and self.gear_timer > 300:
- self.gear -= 1
- self.gear_timer = 0
-
- return throttle, self.gear, brakes, steering
-
-
-class AutonomousDriver(InputProvider):
- def __init__(self, input_file=None):
- self.input = input_file
- self.index = self.input.first_valid_index()
- self.pid_controller = PidController(0, 0, 0)
- self.line_error = 0
-
- def get_input(self,phase):
- if phase=='Loop':
- self.index=1#testing purposes
- elif phase=='Beginning':
- self.index=0
- else:
- self.index=2
- self.pid_controller.p_gain = self.input.P[self.index]
- self.pid_controller.i_gain = self.input.I[self.index]
- self.pid_controller.d_gain = self.input.D[self.index]
- throttle = self.input.Throttle[self.index]
- gear = self.input.Gear[self.index]
- brakes = self.input.Brakes[self.index]
-
- return throttle, gear, brakes, self.pid_controller.get_control(self.line_error)
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/direction.png b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/direction.png
deleted file mode 100644
index 2c8b1ddbee5232fc060037563e0b005c22f38ee3..0000000000000000000000000000000000000000
Binary files a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/direction.png and /dev/null differ
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_direction.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_direction.json
deleted file mode 100644
index 4e8d2f4ebac98bdaf64990798ee58f3e8a837106..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_direction.json
+++ /dev/null
@@ -1,9614 +0,0 @@
-{
- "time": [
- 0.005555555555555556,
- 0.011111111111111112,
- 0.016666666666666666,
- 0.022222222222222223,
- 0.02777777777777778,
- 0.03333333333333333,
- 0.03888888888888889,
- 0.044444444444444446,
- 0.05,
- 0.05555555555555556,
- 0.061111111111111116,
- 0.06666666666666667,
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_normal.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_normal.json
deleted file mode 100644
index 3ae7933365811d94b671f9c2574f4e6079863943..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_normal.json
+++ /dev/null
@@ -1,14410 +0,0 @@
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_position.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_position.json
deleted file mode 100644
index 88776f876f7eda940de7550b40b5edd9c744cbf9..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorD_position.json
+++ /dev/null
@@ -1,4822 +0,0 @@
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_direction.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_direction.json
deleted file mode 100644
index 4f70cf23e1a41cfa8302b9104f9c1d4d9c41f573..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_direction.json
+++ /dev/null
@@ -1,1976 +0,0 @@
-{
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\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_normal.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_normal.json
deleted file mode 100644
index e227a8135fcb26cd38dc17c4bb3c926bde367834..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_normal.json
+++ /dev/null
@@ -1,1988 +0,0 @@
-{
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\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_position.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_position.json
deleted file mode 100644
index 21505d068ebb6e1995948f960d3a9999052e9309..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_position.json
+++ /dev/null
@@ -1,1928 +0,0 @@
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_position_old.json b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_position_old.json
deleted file mode 100644
index 7e152b108cf6425d0be161464e9aad8aa5e8ddcb..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/errorT_position_old.json
+++ /dev/null
@@ -1,2072 +0,0 @@
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-}
\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_direction.png b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_direction.png
deleted file mode 100644
index 4ff981304572dadb6f601825da074125b87ffff1..0000000000000000000000000000000000000000
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_normal.png b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_normal.png
deleted file mode 100644
index 25f1f5aa217588a0fb1472a3dbbc76873c77d969..0000000000000000000000000000000000000000
Binary files a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_normal.png and /dev/null differ
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_position.png b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_position.png
deleted file mode 100644
index e600bc0bbee3ae5994805367bc3cf8be18ee86de..0000000000000000000000000000000000000000
Binary files a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/error_position.png and /dev/null differ
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/normal.png b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/normal.png
deleted file mode 100644
index 7cdef6693c3051965563bc2716f60f4331d946cd..0000000000000000000000000000000000000000
Binary files a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/normal.png and /dev/null differ
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/position.png b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/position.png
deleted file mode 100644
index 16386b29d399fe59dd65f85c27dd83b7b7f44358..0000000000000000000000000000000000000000
Binary files a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/paths/position.png and /dev/null differ
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/pid_controller.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/pid_controller.py
deleted file mode 100644
index 74483f308415ef31a1bf51c068dc8cd413b9b2f4..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/pid_controller.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import numpy as np
-
-
-class PidController:
- def __init__(self, p_gain, i_gain, d_gain, set_point=0):
- self.p_gain = p_gain
- self.i_gain = i_gain
- self.d_gain = d_gain
- self.set_point = set_point
- self.integrated_error = 0
- self.previous_error = 0
-
- def get_control(self, process_value):
- error = self.set_point - process_value
- control = self.p_gain * error + self.i_gain * self.integrated_error + self.d_gain * (error - self.previous_error)
- self.previous_error = error
- self.integrated_error += error
- return np.sign(control) * abs(control)
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/run.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/run.py
deleted file mode 100644
index 2381f540eca0ea4e3cbb6f1757d45700a6fdbdb7..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/run.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from input_providers import *
-from tqdm import tqdm
-from tabu_search import Search, PointSolution
-from simulator import Simulator
-from game import Game
-
-
-if __name__ == '__main__':
- game = Game(1366, 768)
- game.run_pid_controller('track_11.svg', 'test_solution.csv', 0.05)
- #game.run()
- #sim = Simulator('track6.svg')
- #solution = pd.read_csv('solutionOpt.csv', index_col=0)
- #print(sim.run(0.05, solution))
-
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/simulator.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/simulator.py
deleted file mode 100644
index aaa8782a32e2f6bbf537c02439101a3ef89f0dbb..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/simulator.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from track import *
-from car_model import Car
-from input_providers import *
-from pygame.math import Vector2
-
-
-class Simulator:
- def __init__(self, track_path, timeout=30):
- self.track = Track(track_path)
- self.timeout = timeout
-
- def run(self, dt, solution, car=None):
- if car is None:
- car = Car(1366 / 20, 768 / 20)
-
- car.position.x, car.position.y = self.track.path[0][0] / 10 - 1366 / 20, self.track.path[0][1] / 10 - 768 / 20
- self.track.apply_deformations(list(solution.Deformation))
- input_provider = AutonomousDriver(solution)
- time = 0
- trace = [(car.position.x * 10 + 1366 / 2, car.position.y * 10 + 768 / 2) for _ in range(3)]
-
- # Indicators
-
- track_path_length = LineString(self.track.path).length / 10 # length of path loaded from solution
- regulation_quality_indicator_square = 0 # integral of squared error values
- regulation_quality_indicator_abs = 0 # integral of abs values
- max_line_error = 0 # error in m
- average_slip = 0 # average of abs lateral speed in m / s
- slip_indicator = 0 # integral of squared speed in m / s^2
- max_speed_long = 0 # m / s^2
- max_speed_lat = 0 # m / s^2
- average_speed_long = 0 # average speed longitudinal in m
- average_rpm = 0
- max_rpm = 0
- number_of_iterations = 0 # helper variable for computing averages
-
-
- while True:
- time += dt
- if time > self.timeout:
- break
-
- # User input
- indexes = self.track.check_car_position(trace)
- input_provider.index = indexes[-1]
-
- car_input = input_provider.get_input()
- car.get_driver_input(car_input[0], car_input[1], car_input[2], car_input[3])
- car.update(dt)
-
- vector = Vector2(40, 0).rotate(-car.angle)
- vector = np.array((vector.x + 1366 / 2, vector.y + 768 / 2))
- front_center = Point(np.array((car.position.x * 10, car.position.y * 10)) + vector)
- if Polygon(self.track.path).contains(front_center):
- input_provider.line_error = - LineString(self.track.path).distance(front_center)
- else:
- input_provider.line_error = LineString(self.track.path).distance(front_center)
-
- # Update solution indicators
-
- number_of_iterations += 1
- average_rpm = average_rpm * (number_of_iterations - 1) / number_of_iterations + car.rpm / number_of_iterations
- average_speed_long = average_speed_long * (number_of_iterations - 1) / number_of_iterations + car.velocity.x / number_of_iterations
- average_slip = average_slip * (number_of_iterations - 1) / number_of_iterations + abs(car.velocity.y) / number_of_iterations
-
- regulation_quality_indicator_square += (input_provider.line_error / 10) ** 2
- regulation_quality_indicator_abs += abs(input_provider.line_error / 10)
- if abs(input_provider.line_error) / 10 > max_line_error:
- max_line_error = abs(input_provider.line_error) / 10
- slip_indicator += car.velocity.y ** 2
- if car.velocity.x > max_speed_long:
- max_speed_long = car.velocity.x
- if abs(car.velocity.y) > max_speed_lat:
- max_speed_lat = abs(car.velocity.y)
- if car.rpm > max_rpm:
- max_rpm = car.rpm
-
- trace.pop(2)
- trace.insert(0, (car.position.x * 10 + 1366 / 2, car.position.y * 10 + 768 / 2))
-
- if input_provider.index == len(self.track.track_chunks) - 1:
- break
-
- finished = True
- for chunk in self.track.track_chunks:
- if not chunk.is_active:
- finished = False
- break
-
- for index in range(1, len(self.track.track_chunks)):
- self.track.track_chunks[index].is_active = False
- if finished and time < self.timeout:
- return time, (track_path_length, average_rpm, average_speed_long, average_slip,
- regulation_quality_indicator_abs, regulation_quality_indicator_square,
- max_line_error, slip_indicator, max_speed_long, max_speed_lat, max_rpm)
- else:
- return 99999, (track_path_length, average_rpm, average_speed_long, average_slip,
- regulation_quality_indicator_abs, regulation_quality_indicator_square,
- max_line_error, slip_indicator, max_speed_long, max_speed_lat, max_rpm)
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution.csv b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution.csv
deleted file mode 100644
index 1d5c21eaa7660469eb966d2cd103d371ea4eda27..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution.csv
+++ /dev/null
@@ -1,299 +0,0 @@
-,P,I,D,Throttle,Gear,Brakes,Deformation
-0,0.5,0.005,10,1,1,0,0.5
-1,0.5,0.005,10,1,1,0,0.5
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution2.csv b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution2.csv
deleted file mode 100644
index 388edc5d569e0d20dd68e746a35168a14875fd46..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution2.csv
+++ /dev/null
@@ -1,299 +0,0 @@
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-283,0.5,0.005,10,1,1,0,0.5
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution3.csv b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution3.csv
deleted file mode 100644
index 1bf11f5f495662cb1ea1e6edcf1dd29820df0241..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solution3.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-,P,I,D,Throttle,Gear,Brakes,Deformation
-0,0.5,0.005,10,0.5,1,0,0.5
-1,0.5,0.005,10,0.5,1,0,0.5
-2,0.5,0.005,10,0.5,1,0,0.5
-3,0.5,0.005,10,0.5,1,0,0.5
-4,0.5,0.005,10,0.5,1,0,0.5
-5,0.5,0.005,10,0.5,1,0,0.5
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-11,0.5,0.005,10,0.5,1,0,0.5
-12,0.5,0.005,10,0.5,1,0,0.5
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solutionOpt.csv b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solutionOpt.csv
deleted file mode 100644
index d0ca20665155c32fc4d72d6a59a7cdbc6e8a822a..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/solutionOpt.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-,P,I,D,Throttle,Gear,Brakes,Deformation
-0,0.5,0.005,10.0,0.5,1.0,0.0,0.5
-1,0.5,0.005,10.0,1.0,1.0,0.0,0.5
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/tabu_search.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/tabu_search.py
deleted file mode 100644
index 77cf2280c531a60556067970a73e9ed1f7caae56..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/tabu_search.py
+++ /dev/null
@@ -1,510 +0,0 @@
-import numpy as np
-from operator import itemgetter
-import bisect
-import matplotlib.pyplot as plt
-from simulator import Simulator
-from input_providers import *
-from tqdm import tqdm
-from math import floor, e
-
-class Search:
- def __init__(self, init_solution, track):
- self.solution = init_solution # podajemy rozwiązanie początkowe
-
- self.candidates_list = [] #lista sąsiedztwa z której wybieramy następne rozwiązanie
- #postać: (rozwiązanie PointSolution, pozycja_w_rozwiązaniu, czas_przejazdu)
- # postać gauss: (rozwiązanie PointSolution, pozycja_w_rozwiązaniu, czas_przejazdu, ktory element zostal zmieniony)
- self.tabu_list = [] #lista zabronień
- #postać: (rozwiązanie PointSolution, pozycja_w_rozwiązaniu, ilość_iteracji)
-
- #wartosci kroku parametrów
- self.dP = 0.5
- self.dI = 0.5
- self.dD = 1
- self.dthrottle = 0.5
- self.dgear = 1
- self.dbrakes = 0.5
- self.ddeformation = 0.15
-
- #ograniczenia na parametry
- self.maxP = 5
- self.minP = 0
-
- self.maxI = 5
- self.minI = 0
-
- self.maxD = 20
- self.minD = 0
-
- self.maxthrottle = 1
- self.minthrottle = 0
-
- self.maxbrakes = 1
- self.minbrakes = 0
-
- self.maxgear = 6
- self.mingear = 0 #wsteczny nie potrzebny
-
- self.maxdeformation = 1
- self.mindeformation = 0
-
- self.stop_num_of_iterations = 100 # warunek stopu liczba iteracji
- self.stop_time_change = 3 # warunek stopu - poprawa czasu o _ sek
- self.stop_best_time = -5 # warunek stopu - jesli czasu będzie poniżej wartości
-
- # symulator do pobierania czasów przejazdu
- self.sim = Simulator(track)
-
- self.plot_simulation_indicators = []
-
- self.first_time = self.simulate(self.solution, True) #czas dla rozwiązania początkowego
- self.current_time = self.first_time #przechowywany aktualny czas (można zmienić na tablice żeby zapisywać jak sie zmienialy czasy)
- self.best_time = self.first_time
-
- self.num_of_iterations_tabu = 40 #ile iteracji ma zostac na liscie tabu
-
- self.f0, self.ax0 = plt.subplots(1)
- self.f1, self.ax1 = plt.subplots(1)
- self.f2, self.ax2 = plt.subplots(1)
- self.f3, self.ax3 = plt.subplots(1)
-
- self.ax0.set_xlim(0, self.stop_num_of_iterations)
- self.ax0.set_ylim(0, 10)
- self.ax0.set_title("Current Time")
- self.liTime, = self.ax0.plot([], [])
-
- self.ax1.set_xlim(0, self.stop_num_of_iterations)
- self.ax1.set_ylim(0, 10)
- self.ax1.set_title("Candidates Times")
- self.liCandiTime, = self.ax1.plot([], [])
-
- self.ax2.set_xlim(0, self.stop_num_of_iterations)
- self.ax2.set_ylim(0, 100)
- self.ax2.set_title("Tabu size")
- self.liTabuSize, = self.ax2.plot([], [])
-
- self.ax3.set_xlim(0, self.stop_num_of_iterations)
- self.ax3.set_ylim(0, 30)
- self.ax3.set_title("Tabu usage")
- self.liTabuUsage, = self.ax3.plot([], [])
-
- self.plot_times = []
- self.plot_tabu_used = []
- self.plot_tabu_size = []
- self.plot_candidates_times_min = []
- self.plot_candidates_times_max = []
- self.plot_candidates_times_mean = []
-
- self.use_gaussian = False
- self.use_changes = False
- self.solutionSize = len(self.solution.values)
-
- self.changes = [self.dP, self.dI, self.dD, self.dthrottle, self.dgear, self.dbrakes]
- self.maxVals = [self.maxP, self.maxI, self.maxD, self.maxthrottle, self.maxgear, self.maxbrakes]
- self.minVals = [self.minP, self.minI, self.minD, self.minthrottle, self.mingear, self.minbrakes]
-
- self.aspiration_time = 0.2
-
-
- def search(self):
- # najpierw generujemy początkową listę sąsiedztwa
- if(self.use_gaussian):
- self.generate_candidates_gaussian()
- elif self.use_changes:
- self.generate_candidates_changes()
- else:
- self.generate_candidates()
-
- # póżniej tylko aktualizujemy
- iterations = 0
- self.plot_times.append(self.current_time)
- self.plot_tabu_size.append(len(self.tabu_list))
- self.plot_candidates_times_min.append(self.candidates_list[0][2])
- max = 0
- for j in range(len(self.candidates_list) - 1, 0, -1):
- if self.candidates_list[j][2] < 99999:
- max = self.candidates_list[j][2]
- self.plot_candidates_times_max.append(max)
- self.plot_candidates_times_mean.append(np.median([x[2] for x in self.candidates_list]))
- time_change = 0
- while iterations < self.stop_num_of_iterations and time_change < self.stop_time_change and self.current_time > self.stop_best_time: #warunki stopu
- self.iterate()
- time_change = self.first_time - self.current_time
- iterations += 1
- if self.current_time < self.best_time:
- self.best_time = self.current_time
- self.solution.to_csv("solutionOpt.csv")
-
- self.plot_times.append(self.current_time)
- self.plot_tabu_size.append(len(self.tabu_list))
- self.plot_candidates_times_min.append(self.candidates_list[0][2])
- #self.plot_candidates_times_max.append(self.candidates_list[len(self.candidates_list) - 1][2])
- max = 0
- for j in range(len(self.candidates_list) - 1, 0, -1):
- if self.candidates_list[j][2] < 99999:
- max = self.candidates_list[j][2]
- self.plot_candidates_times_max.append(max)
- self.plot_candidates_times_mean.append(np.median([x[2] for x in self.candidates_list]))
-
-
- #self.liTime.set_xdata(np.arange(iterations))
- #self.liTabuSize.set_xdata(np.arange(iterations))
- #self.liTabuUsage.set_xdata(np.arange(iterations))
-
- #self.liTime.set_ydata(self.plot_times)
- #self.liTabuSize.set_ydata(self.plot_tabu_size)
- #self.liTabuUsage.set_ydata(self.plot_tabu_used)
-
- print(self.current_time)
- print([x[2] for x in self.candidates_list])
- #plt.pause(0.01)
-
-
- self.liTime.set_xdata(np.arange(iterations+1))
- self.liTabuSize.set_xdata(np.arange(iterations+1))
- self.liTabuUsage.set_xdata(np.arange(iterations+1))
-
- self.liTime.set_ydata(self.plot_times)
- self.liTabuSize.set_ydata(self.plot_tabu_size)
- self.liTabuUsage.set_ydata(self.plot_tabu_used)
- self.ax1.plot(np.arange(iterations+1), self.plot_candidates_times_min, 'r', np.arange(iterations+1), self.plot_candidates_times_max, 'g', np.arange(iterations+1), self.plot_candidates_times_mean, 'b')
- print(self.plot_candidates_times_min, self.plot_candidates_times_max, self.plot_candidates_times_mean)
- #self.ax1.plot(np.arange(iterations+1), self.plot_candidates_times_max)
- #self.ax1.plot(np.arange(iterations+1), self.plot_candidates_times_mean)
- #plt.plot(np.arange(iterations+1), self.plot_candidates_times_min, np.arange(iterations+1), self.plot_candidates_times_max, np.arange(iterations+1), self.plot_candidates_times_mean)
-
- self.f4, self.ax4 = plt.subplots(1)
- self.ax4.set_xlim(0, self.stop_num_of_iterations)
- self.ax4.set_ylim(0, 200)
- self.ax4.set_title("Path length")
- self.ax4.plot(np.arange(iterations+1), [x[0] for x in self.plot_simulation_indicators])
-
- self.f5, self.ax5 = plt.subplots(1)
- self.ax5.set_xlim(0, self.stop_num_of_iterations)
- self.ax5.set_ylim(2000, 12000)
- self.ax5.set_title("RPM")
- self.ax5.plot(np.arange(iterations + 1), [x[1] for x in self.plot_simulation_indicators], np.arange(iterations + 1), [x[10] for x in self.plot_simulation_indicators])
-
- self.f6, self.ax6 = plt.subplots(1)
- self.ax6.set_xlim(0, self.stop_num_of_iterations)
- self.ax6.set_ylim(0, 120)
- self.ax6.set_title("Speed")
- self.ax6.plot(np.arange(iterations + 1), [x[2] for x in self.plot_simulation_indicators], np.arange(iterations + 1), [x[8] for x in self.plot_simulation_indicators])
-
- self.f7, self.ax7 = plt.subplots(1)
- self.ax7.set_xlim(0, self.stop_num_of_iterations)
- self.ax7.set_ylim(0, 10)
- self.ax7.set_title("Side slip")
- self.ax7.plot(np.arange(iterations + 1), [x[3] for x in self.plot_simulation_indicators], np.arange(iterations + 1), [x[9] for x in self.plot_simulation_indicators])
-
- self.f8, self.ax8 = plt.subplots(1)
- self.ax8.set_xlim(0, self.stop_num_of_iterations)
- self.ax8.set_ylim(0, 300)
- self.ax8.set_title("Regulator indicators")
- self.ax8.plot(np.arange(iterations + 1), [x[4] for x in self.plot_simulation_indicators], np.arange(iterations + 1), [x[5] for x in self.plot_simulation_indicators])
-
- self.f10, self.ax10 = plt.subplots(1)
- self.ax10.set_xlim(0, self.stop_num_of_iterations)
- self.ax10.set_ylim(0, 10)
- self.ax10.set_title("Max Line Error")
- self.ax10.plot(np.arange(iterations + 1), [x[6] for x in self.plot_simulation_indicators])
-
- self.f9, self.ax9 = plt.subplots(1)
- self.ax9.set_xlim(0, self.stop_num_of_iterations)
- self.ax9.set_ylim(0, 1000)
- self.ax9.set_title("Slip indicator")
- self.ax9.plot(np.arange(iterations + 1), [x[7] for x in self.plot_simulation_indicators])
- print(self.candidates_list)
- plt.pause(6000)
-
-
- def iterate(self):
- on_tabu_list = True
- i = 0
-
- while on_tabu_list and i < len(self.candidates_list):
- best_change = self.candidates_list[i]
- on_tabu_list = self.check_tabu_list(best_change[0], best_change[1])
- #kryterium aspiracji
- if self.current_time - best_change[2] > self.aspiration_time and on_tabu_list:
- on_tabu_list = False
- print("Uzyte kryterium aspiracji. Poprawa czasu: ", self.current_time - best_change[2])
- i += 1
-
- self.plot_tabu_used.append(i-1)
- self.add_to_tabu(PointSolution(self.solution.values[best_change[1]].copy()), best_change[1])
-
-
- if(self.use_gaussian):
- self.modify_gaussian(best_change[0].to_list(), best_change[1], best_change[3])
- self.update_candidates_gaussian(best_change[1])
- elif self.use_changes:
- self.modify_changes(best_change[0].to_list(), best_change[1], best_change[3])
- self.update_candidates_changes(best_change[1])
- else:
- self.solution.iloc[best_change[1]] = best_change[0].to_list()
- self.update_candidates(best_change[1])
- self.current_time = self.simulate(self.solution, True)
- #self.current_time = best_change[2]
- self.update_tabu()
-
-
-
-
- def simulate(self, solution, save_indicators=False):
- t, indicators = self.sim.run(0.05, solution)
- if save_indicators:
- self.plot_simulation_indicators.append(indicators)
- return t
-
- def generate_candidates(self):
- for i in tqdm(range(0, len(self.solution.values)-1)):
-
- #zmieniane po jednej wartosci - mniej przypadkow i zmiana 2 mozna rozbic na 2 zmiany po jednej zmiennej
- #zmieniac nie tylko o dt - teraz strasznie wolno zmierza
- init_value = self.solution.values[i].copy()
- x = self.solution.values[i].copy()
-
- parameters = x.copy()
- changes = [self.dP, self.dI, self.dD, self.dthrottle, self.dgear, self.dbrakes, self.ddeformation]
- maxVals = [self.maxP, self.maxI, self.maxD, self.maxthrottle, self.maxgear, self.maxbrakes, self.maxdeformation]
- minVals = [self.minP, self.minI, self.minD, self.minthrottle, self.mingear, self.minbrakes, self.mindeformation]
-
- for j in range(0, 7):
- if parameters[j] + changes[j] <= maxVals[j]:
- parameters[j] += changes[j]
- self.solution.iloc[i] = parameters.copy()
- t = self.simulate(self.solution)
- self.candidates_list.append([PointSolution(parameters), i, t])
-
- parameters = x.copy()
-
- if parameters[j] - changes[j] >= minVals[j]:
- parameters[j] -= changes[j]
- self.solution.iloc[i] = parameters.copy()
- t = self.simulate(self.solution)
- self.candidates_list.append([PointSolution(parameters), i, t])
-
- parameters = x.copy()
-
- self.solution.iloc[i] = init_value.copy()
-
- self.candidates_list.sort(key=itemgetter(2))
-
-
-
- def update_candidates(self, i): #i - gdzie zmiana wystapila
- init_value = self.solution.values[i].copy()
- x = self.solution.values[i].copy()
-
- parameters = x.copy()
- changes = [self.dP, self.dI, self.dD, self.dthrottle, self.dgear, self.dbrakes, self.ddeformation]
- maxVals = [self.maxP, self.maxI, self.maxD, self.maxthrottle, self.maxgear, self.maxbrakes, self.maxdeformation]
- minVals = [self.minP, self.minI, self.minD, self.minthrottle, self.mingear, self.minbrakes, self.mindeformation]
-
- #aktualizacja czasow - trzeba bo jak sie zmienia rozwiazanie to sie wszystko zmienia
- for j in tqdm(range(len(self.candidates_list))):
- index = self.candidates_list[j][1]
- x = self.solution.values[index].copy()
- self.solution.iloc[index] = self.candidates_list[j][0].to_list()
- self.candidates_list[j][2] = self.simulate(self.solution)
- self.solution.iloc[index] = x.copy()
-
-
- for j in range(0, 7):
- if parameters[j] + changes[j] <= maxVals[j]:
- parameters[j] += changes[j]
- self.solution.iloc[i] = parameters.copy()
- t = self.simulate(self.solution) # ZAMIENIC NA DOBRA SYMULACJE
- self.candidates_list.append([PointSolution(parameters), i, t]) #DODAWANIE DO POSORTOWANEJ LISTY
-
- parameters = x.copy()
-
- if parameters[j] - changes[j] >= minVals[j]:
- parameters[j] -= changes[j]
- self.solution.iloc[i] = parameters.copy()
- t = self.simulate(self.solution)
- self.candidates_list.append([PointSolution(parameters), i, t])
-
- parameters = x.copy()
- self.solution.iloc[i] = init_value.copy()
- self.candidates_list.sort(key=itemgetter(2))
-
- def generate_candidates_gaussian(self):
- for i in tqdm(range(0, len(self.solution.values)-1)):
-
- #zmieniane po jednej wartosci - mniej przypadkow i zmiana 2 mozna rozbic na 2 zmiany po jednej zmiennej
- #zmieniac nie tylko o dt - teraz strasznie wolno zmierza
- parameters = [0, 0, 0, 0, 0, 0, 0]
-
- for j in range(0, 5):
- parameters[j] = self.changes[j]
- self.modify_gaussian(parameters, i, j)
- t = self.simulate(self.solution)
- self.candidates_list.append([PointSolution(parameters), i, t, j])
- self.modify_gaussian((-1)*parameters, i, j)
-
- parameters[j] = -self.changes[j]
- self.modify_gaussian(parameters, i, j)
- t = self.simulate(self.solution)
- self.candidates_list.append([PointSolution(parameters), i, t, j])
- self.modify_gaussian((-1)*parameters, i, j)
-
- parameters = [0, 0, 0, 0, 0, 0, 0]
-
- self.candidates_list.sort(key=itemgetter(2))
-
-
-
- def update_candidates_gaussian(self, i): #i - gdzie zmiana wystapila
-
- #aktualizacja czasow - trzeba bo jak sie zmienia rozwiazanie to sie wszystko zmienia
- for j in tqdm(range(len(self.candidates_list))):
- index = self.candidates_list[j][1]
- self.modify_gaussian(self.candidates_list[j][0].to_list(), index, self.candidates_list[j][3])
- self.candidates_list[j][2] = self.simulate(self.solution)
- self.modify_gaussian((-1)*self.candidates_list[j][0].to_list(), index, self.candidates_list[j][3])
-
- self.candidates_list.sort(key=itemgetter(2))
-
- def modify_gaussian(self, parameters, position, changePosition):
- factors = generate_list_of_factors(7)
- if len(parameters) == 0:
- return
- for i in range(-3, 3):
- newPosition = i + position
- if newPosition < 0 or newPosition >= self.solutionSize:
- continue
- newParameters = parameters.copy()
- if(changePosition != 4):
- newParameters[changePosition] *= factors[i+3]
- solutionSum = self.solution.iloc[newPosition][changePosition] + newParameters[changePosition]
- if solutionSum > self.maxVals[changePosition]:
- self.solution.iloc[newPosition, changePosition] = self.maxVals[changePosition]
- elif solutionSum < self.minVals[changePosition]:
- self.solution.iloc[newPosition, changePosition] = self.minVals[changePosition]
- else:
- self.solution.iloc[newPosition, changePosition] = solutionSum
-
- def generate_candidates_changes(self):
- for i in tqdm(range(0, len(self.solution.values)-1)):
-
- #zmieniane po jednej wartosci - mniej przypadkow i zmiana 2 mozna rozbic na 2 zmiany po jednej zmiennej
- #zmieniac nie tylko o dt - teraz strasznie wolno zmierza
- parameters = [0, 0, 0, 0, 0, 0, 0]
-
- for j in range(0, 5):
- parameters[j] = self.changes[j]
- modified = self.modify_changes(parameters, i, j)
- t = self.simulate(self.solution)
- self.candidates_list.append([PointSolution(parameters), i, t, j])
- if modified:
- self.modify_changes((-1)*parameters, i, j)
-
- parameters[j] = -self.changes[j]
- modified = self.modify_changes(parameters, i, j)
- t = self.simulate(self.solution)
- self.candidates_list.append([PointSolution(parameters), i, t, j])
- if modified:
- self.modify_changes((-1)*parameters, i, j)
-
- parameters = [0, 0, 0, 0, 0, 0, 0]
-
- self.candidates_list.sort(key=itemgetter(2))
-
-
-
- def update_candidates_changes(self, i): #i - gdzie zmiana wystapila
-
- #aktualizacja czasow - trzeba bo jak sie zmienia rozwiazanie to sie wszystko zmienia
- for j in tqdm(range(len(self.candidates_list))):
- index = self.candidates_list[j][1]
- modified = self.modify_changes(self.candidates_list[j][0].to_list(), index, self.candidates_list[j][3])
- self.candidates_list[j][2] = self.simulate(self.solution)
- if modified:
- self.modify_changes((-1)*self.candidates_list[j][0].to_list(), index, self.candidates_list[j][3])
-
- self.candidates_list.sort(key=itemgetter(2))
-
- def modify_changes(self, parameters, position, changePosition):
- if len(parameters) == 0:
- return False
- solutionSum = self.solution.iloc[position][changePosition] + parameters[changePosition]
- if solutionSum < self.maxVals[changePosition] and solutionSum > self.minVals[changePosition]:
- self.solution.iloc[position, changePosition] = solutionSum
- return True
- else:
- return False
-
-
- def update_tabu(self):
- for i in range(0, len(self.tabu_list)-2): #dla -1 czasem przekracza - jedno rozwiązanie puste jest
- self.tabu_list[i][2] += 1
- if self.tabu_list[i][2] > self.num_of_iterations_tabu:
- del(self.tabu_list[i])
-
-
-
- def add_to_tabu(self, value, position):
- self.tabu_list.append([value, position, 1])
-
- def check_tabu_list(self, value, position):
- for x in self.tabu_list:
- if x[1] == position:
- if x[0] == value:
- return True
- return False
-
-
-
-
-class PointSolution:
- def __init__(self, list):
- self.P = list[0]
- self.I = list[1]
- self.D = list[2]
- self.throttle = list[3] #gaz
- self.gear = list[4] #bieg
- self.brakes = list[5] #hamulec
- self.deformation = list[6] #deformacja
-
- def __eq__(self, solution):
- if self.P != solution.P:
- return False
- if self.I != solution.I:
- return False
- if self.D != solution.D:
- return False
- if self.throttle != solution.throttle:
- return False
- if self.gear != solution.gear:
- return False
- if self.brakes != solution.brakes:
- return False
- if self.deformation != solution.deformation:
- return False
- return True
-
- def __str__(self):
- return str(self.to_list())
-
- def to_list(self):
- return [self.P, self.I, self.D, self.throttle, self.gear, self.brakes, self.deformation]
-
-
-def generate_list_of_factors(size):
- deviation = (size - 1) / 4
- list_of_factors = []
- for index in range(size):
- list_of_factors.append(e**(- (index - floor(size / 2)) ** 2 / (2 * deviation ** 2)))
- return list_of_factors
-
-
-if __name__ == '__main__':
- #plt.plot(list(range(21)), generate_list_of_factors(21))
- #plt.show()
- #solution1 = [PointSolution([1,0,0,0.3,0,0,0.5]) for i in range(100)]
- solution1 = pd.read_csv('solution3.csv', index_col=0)
- tabu1 = Search(solution1, 'track6.svg')
- tabu1.search()
- #plt.pause(5)
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/test_solution.csv b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/test_solution.csv
deleted file mode 100644
index f44301fd912007bb21f92ccd33b55d26e774ebf6..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/test_solution.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-,P,I,D,Throttle,Gear,Brakes,Deformation
-0,1.0,0.010,5,0.5,1,0,0.5
-1,10.0,0.005,5,0.035,1,0,0.5
-2,1.0,0.010,5,0,1,0.15,0.5
\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track.py
deleted file mode 100644
index 4ad8d9283d67af3ce6734ae018f219bd78b66af3..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import xml.etree.ElementTree as ET
-import pygame
-import numpy as np
-from math import sqrt, sin, cos, e, floor
-import matplotlib.pyplot as plt
-from shapely.geometry import Polygon, Point, LineString
-import copy
-
-
-CIRCLE_TAG_NAME = '{http://www.w3.org/2000/svg}circle'
-
-
-def generate_list_of_factors(size, deviation):
- list_of_factors = []
- for index in range(size):
- list_of_factors.append(e**(- (index - floor(size / 2)) ** 2 / (2 * deviation ** 2)))
- return list_of_factors
-
-
-class TrackChunk:
- def __init__(self, point_1=(0, 0), point_2=(0, 0), point_3=(0, 0), point_4=(0, 0)):
- self.polygon = Polygon([point_1, point_2, point_3, point_4])
- self.is_active = False
-
-
-class Track:
- def __init__(self, track_file_path='track3.svg', resize_factor=2.5, width=65):
- tree = self.read_svg_file(track_file_path)
- self.width = width
-
- self.track_points = [(resize_factor * x, -resize_factor * y) for x, y in self.get_all_points(tree)]
- self.track_polygon = Polygon(self.track_points)
- self.visible_path = []
- self.full_path = []
-
- self.inner_edge = []
- self.outer_edge = []
-
- self.end_first_line = 3
- self.mid = 39
- self.up_right = (self.mid-self.end_first_line)//4+self.end_first_line
- self.down_right = 3*(self.mid-self.end_first_line)//4+self.end_first_line
- self.start_last_line = len(self.track_points)-3
- self.up_left = (self.start_last_line-self.mid)//4+self.mid
- self.down_left = 3*(self.start_last_line-self.mid)//4+self.mid
-
- self.miny = -410
- self.maxy = -930
- self.minx = 1420
- self.maxx = 1920
-
- self.line_width = 100
-
- self.lap_nb=0
- self.total_lap = 2
- self.is_right = True
- self.is_starting = True
- self.is_ending = False
- self.mid_track = 1730
- self.track_phase = "Beginning"#Beginning, LR, Right, RL, Left
- self.x1, self.x2, self.y1, self.y2 =1850,1580, -600, -580
- self.current = "0"
- #self.right = {"xl":1740, "xr":1840, "yu":-650, "yd":-550}
- #self.left = {"xl":1650,"xr":1720,"yu":-650,"yd":-550}
- #self.entered_right_cp = False
- #self.entered_left_cp = False
-
- self.car_pos = [0,0]
-
- for index in range(len(self.track_points) - 1):
- a1 = self.track_points[index + 1][1] - self.track_points[index][1]
- b1 = self.track_points[index + 1][0] - self.track_points[index][0]
- if abs(b1) == 0:
- vector = np.array((self.width, 0))
- #continue
- elif abs(a1) == 0:
- vector = np.array((0, self.width))
- continue
- else:
- a = b1 / a1
- x = self.width / sqrt(1 + a ** 2)
- vector = np.array((x, - a * x))
- if self.track_polygon.contains(Point(self.track_points[index] + vector)):
- self.inner_edge.append(self.track_points[index] + vector + [1366/2, 768/2])
- self.outer_edge.append(self.track_points[index] - vector + [1366/2, 768/2])
- else:
- self.inner_edge.append(self.track_points[index] - vector + [1366/2, 768/2])
- self.outer_edge.append(self.track_points[index] + vector + [1366/2, 768/2])
-
- self.full_path.append((np.array(self.inner_edge[- 1]) + np.array(self.outer_edge[- 1])) / 2)
-
- a1 = self.track_points[0][1] - self.track_points[-1][1]
- b1 = self.track_points[0][0] - self.track_points[-1][0]
-
- if abs(b1) == 0:
- vector = np.array((self.width, 0))
- elif abs(a1) == 0:
- vector = np.array((0, self.width))
- else:
- a = b1 / a1
- x = self.width / sqrt(1 + a ** 2)
- vector = np.array((x, - a * x))
- if self.track_polygon.contains(Point(self.track_points[-1] + vector)):
- self.inner_edge.append(self.track_points[-1] + vector + [1366/2, 768/2])
- self.outer_edge.append(self.track_points[-1] - vector + [1366/2, 768/2])
- else:
- self.inner_edge.append(self.track_points[-1] - vector + [1366/2, 768/2])
- self.outer_edge.append(self.track_points[-1] + vector + [1366/2, 768/2])
-
- self.full_path.append((np.array(self.inner_edge[- 1]) + np.array(self.outer_edge[- 1])) / 2)
- self.path_deformations = [0.5 for _ in range(len(self.full_path))]
- self.track_chunks = []
- for index in range(len(self.outer_edge) - 1, -1, -1):
- self.track_chunks.append(TrackChunk(self.inner_edge[index - 1], self.outer_edge[index - 1],
- self.outer_edge[index], self.inner_edge[index]))
- self.track_chunks = list(reversed(self.track_chunks))
- self.track_chunks[0].is_active = True
- self.visible_path = self.full_path[0:self.end_first_line]
-
-
- def apply_deformations(self, deformations):
- #if len(deformations) != len(self.path_deformations):
- # print("Wrong solution vector length.", len(deformations), " ", len(self.path_deformations))
- # return
- self.path_deformations = [deformations[0] for i in range(len(self.path_deformations))]
- for j in range(len(self.full_path)):
- self.full_path[j] = self.path_deformations[j] * self.outer_edge[j] + (1 - self.path_deformations[j]) * self.inner_edge[j]
-
- def modify_path(self, index, deformation, number_of_neighbours, deviation):
- if index + number_of_neighbours / 2 > len(self.full_path) - 1:
- index = index - len(self.full_path) - 1
- factors = generate_list_of_factors(number_of_neighbours, deviation)
- for i, factor in enumerate(factors):
- self.path_deformations[index + i - floor(len(factors) / 2)] += factor * deformation / 20
- if self.path_deformations[index + i - floor(len(factors) / 2)] > 0.9:
- self.path_deformations[index + i - floor(len(factors) / 2)] = 0.9
- if self.path_deformations[index + i - floor(len(factors) / 2)] < 0.1:
- self.path_deformations[index + i - floor(len(factors) / 2)] = 0.1
-
- for j in range(len(self.full_path)):
- self.full_path[j] = self.path_deformations[j] * self.outer_edge[j] + (1 - self.path_deformations[j]) * self.inner_edge[j]
-
- def update_visible_path(self, pos):
- #print(pos)
- if self.current=="0":
- self.visible_path = self.full_path[:self.end_first_line]
- if pos[1]<self.y1:
- self.current="1"
- self.track_phase = "Loop"
- self.is_starting=False
- elif self.current=="1":
- self.visible_path = self.full_path[self.down_left:self.start_last_line]+self.full_path[self.end_first_line:self.up_right+2]+[np.array([self.maxx, self.miny,]), np.array([self.minx,self.miny])]
- if pos[0]>self.x1:
- self.current="2"
- elif self.current=="2":
- self.visible_path = self.full_path[self.up_right:self.down_right+10]
- if pos[0]<self.x1:
- self.current="3"
- elif self.current=="3":
- self.visible_path = self.full_path[self.down_right:self.up_left+10]+[np.array([self.minx, self.maxy,]), np.array([self.maxx,self.maxy])]
- if pos[0]<self.x2:
- self.current="4"
- #if pos[1]<self.mid_track:
- #self.is_right = False
- elif self.current=="4":
- self.visible_path = self.full_path[self.up_left:self.down_left+10]
- self.is_right = False
- if pos[0]>self.x2:
- self.current="5"
- self.is_right = True
- elif self.current=="5":
- self.visible_path = self.full_path[self.down_left:self.start_last_line]+self.full_path[self.end_first_line:self.up_right]+[np.array([self.maxx, self.miny,]), np.array([self.minx,self.miny])]
- if pos[1]<self.y2:
- self.lap_nb+=1
- if self.lap_nb>=self.total_lap:
- self.current = "6"
- self.is_ending = True
- self.track_phase = "End"
- else:
- self.current="1"
- elif self.current=="6":
- self.visible_path = self.full_path[self.down_left:]
- self.is_ending = self.lap_nb>=self.total_lap
- self.max_y = max([i[1] for i in self.full_path])
-
- @staticmethod
- def circle_to_point(circle):
- const = 6
- return float(circle.attrib['cx']) * const, float(circle.attrib['cy']) * const
-
- @staticmethod
- def read_svg_file(svg_path):
- return ET.parse(svg_path)
-
- def get_all_points(self, tree):
- circles = []
- for circle in tree.iter(CIRCLE_TAG_NAME):
- circles.append(self.circle_to_point(circle))
- return circles
-
- def check_car_position(self, trace):
- indexes = []
- trace_linestring = LineString(trace)
- pos = np.mean(trace_linestring.xy[0]), np.mean(trace_linestring.xy[1])
- self.update_visible_path(pos)
- self.car_pos = pos
- for chunk in self.track_chunks:
- if not chunk.is_active:
- if chunk.polygon.intersects(trace_linestring):
- chunk.is_active = True
- if chunk.is_active:
- indexes.append(self.track_chunks.index(chunk))
-
- return indexes
-
-
-class TrackDrawer:
- def __init__(self, track):
- self.track = track
- self.path_for_drawing = copy.copy(self.track.visible_path)
- self.inner_edge_for_drawing = copy.copy(self.track.inner_edge)
- self.outer_edge_for_drawing = copy.copy(self.track.outer_edge)
- self.chunk_indexes = [0]
-
- def draw(self, screen, car_position, trace):
- self.path_for_drawing = copy.copy(self.track.visible_path)
- for ind, point in enumerate(self.track.visible_path):
- self.path_for_drawing[ind] = np.array(point) - np.array([car_position.x, car_position.y])
- for ind, point in enumerate(self.track.inner_edge):
- self.inner_edge_for_drawing[ind] = np.array(point) - np.array([car_position.x, car_position.y])
- for ind, point in enumerate(self.track.outer_edge):
- self.outer_edge_for_drawing[ind] = np.array(point) - np.array([car_position.x, car_position.y])
-
- self.chunk_indexes = self.track.check_car_position(trace)
- chunk_xy = {}
- for index in self.chunk_indexes:
- chunk_xy[index] = self.track.track_chunks[index].polygon.exterior.xy
- chunk_xy[index] = [np.array((x, y)) for x, y in zip(chunk_xy[index][0], chunk_xy[index][1])]
- for ind, point in enumerate(chunk_xy[index]):
- chunk_xy[index][ind] = point - np.array([car_position.x, car_position.y])
- #pygame.draw.polygon(screen, (7, 215, 247), chunk_xy[index])
-
- pygame.draw.lines(screen, (0, 0, 255), False, [(x - car_position.x, y - car_position.y) for x, y in trace], 2)
- pygame.draw.polygon(screen, (0, 255, 0), self.path_for_drawing, 2)
- #pygame.draw.rect(screen, (255,0,0), pygame.Rect(self.track.right["xl"],
- # self.track.right["yu"],
- # abs(self.track.right["xl"]-self.track.right["xr"]),
- # abs(self.track.right["yu"]-self.track.right["yd"])),1)
- #pygame.draw.polygon(screen, (255, 153, 51), self.inner_edge_for_drawing, 10)
- #pygame.draw.polygon(screen, (255, 153, 51), self.outer_edge_for_drawing, 10)
-
-class Background(pygame.sprite.Sprite):
- def __init__(self, image_file, location):
- pygame.sprite.Sprite.__init__(self) #call Sprite initializer
- self.image = pygame.image.load(image_file)
- self.rect = self.image.get_rect()
- self.rect.left, self.rect.top = location
-
- def set_location(self, location):
- self.rect.left, self.rect.top = location
\ No newline at end of file
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deleted file mode 100644
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diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track_2_generator.py b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track_2_generator.py
deleted file mode 100644
index 856c4102ced928fb58baf29dcc8e43b35182e7f5..0000000000000000000000000000000000000000
--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track_2_generator.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from math import cos, sin, pi
-def create_track(r, start_point_distance_y, end_point_distance_y, tot_shift):
- laps = 1 #nombre de tours
- resolution_circle = 10 #resolution du cercle en degre
- resolution_line = 10
-
- #contruire les points qui vont former le chemin
- points = [(0, start_point_distance_y)]
- current_x, current_y = 0, start_point_distance_y
- if current_y<0:
- #faire les points de la première ligne droite
- while current_y<0:
- current_y += resolution_line
- points.append((current_x, current_y))
-
- #faire les points des cercles
- current_angle = 0 #angle en degree
- for i in range(laps):
- while current_angle<720:
- rad = current_angle/360*2*pi
- if 0<current_angle<360:
- current_x, current_y = r-r*cos(rad), r*sin(rad)
- else:
- current_x, current_y = -(r-r*cos(rad)), r*sin(rad)
- current_angle+=resolution_circle
- points.append((current_x, current_y))
-
- if current_y<end_point_distance_y:
- current_x=0
- while current_y<end_point_distance_y:
- current_y += resolution_line
- points.append((current_x, current_y))
-
-
- #on decale les points
- shift_x = -min([x[0] for x in points])
- shift_y = -min([x[1] for x in points])
-
- points = [(x+shift_x+tot_shift[0],y+shift_y+tot_shift[1]) for x,y in points]
-
- #print(points)
- return points
-
-def create_double_circles(r, left_center, right_center):
- resolution_circle = 10 #resolution du cercle en degre
- current_angle=0
- l_l = []
- l_r = []
- while current_angle<360:
- rad = current_angle/360*2*pi
- current_x_left, current_y_left = left_center[0]+r*cos(rad), left_center[1]+r*sin(rad)
- current_x_right, current_y_right = right_center[0]+r*cos(rad), right_center[1]+r*sin(rad)
- #if current_x_left<(left_center[0]+right_center[0])/2:
- l_l.append((current_x_left, current_y_left))
- #if current_x_right>(left_center[0]+right_center[0])/2:
- l_r.append((current_x_right, current_y_right))
- current_angle+=resolution_circle
-
- return l_l+l_r
-
-if __name__ =="__main__":
- points = create_track(1, -20, 20)
- #construire le svg
- mid = ""
- #prendre le code autour du chemin
- f = open("basic_track.svg", "r")
- t = f.read().split('CUT_HERE')
- for i,point in enumerate(points):
- mid += f'<circle \n id="path{i}" \n style="fill:#000000;stroke:none" \n cx="{point[0]}" \n cy="{point[1]}" \n r="0.1" /> \n'
- t = t[0]+mid+t[1]
-
- f = open("track_11.svg", "w")
- f.write(t)
- f.close()
-
\ No newline at end of file
diff --git a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track_8.svg b/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track_8.svg
deleted file mode 100644
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--- a/Documentation/Projet/Simulation/2D-car-dynamics-simulation-master/track_8.svg
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-# Created date: 2020-09-27
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : YOLO detection to XML example script
-#
-#================================================================
-import os
-import subprocess
-import time
-from datetime import datetime
-import cv2
-import mss
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import *
-from yolov3.configs import *
-from yolov3.yolov4 import read_class_names
-from tools.Detection_to_XML import CreateXMLfile
-import random
-
-def draw_enemy(image, bboxes, CLASSES=YOLO_COCO_CLASSES, show_label=True, show_confidence = True, Text_colors=(255,255,0), rectangle_colors='', tracking=False):
- NUM_CLASS = read_class_names(CLASSES)
- num_classes = len(NUM_CLASS)
- image_h, image_w, _ = image.shape
- hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
- colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
- colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
-
- random.seed(0)
- random.shuffle(colors)
- random.seed(None)
-
- detection_list = []
-
- for i, bbox in enumerate(bboxes):
- coor = np.array(bbox[:4], dtype=np.int32)
- score = bbox[4]
- class_ind = int(bbox[5])
- bbox_color = rectangle_colors if rectangle_colors != '' else colors[class_ind]
- bbox_thick = int(0.6 * (image_h + image_w) / 1000)
- if bbox_thick < 1: bbox_thick = 1
- fontScale = 0.75 * bbox_thick
- (x1, y1), (x2, y2) = (coor[0], coor[1]), (coor[2], coor[3])
-
- # put object rectangle
- cv2.rectangle(image, (x1, y1), (x2, y2), bbox_color, bbox_thick*2)
-
- x, y = int(x1+(x2-x1)/2), int(y1+(y2-y1)/2)
-
- if show_label:
- # get text label
- score_str = " {:.2f}".format(score) if show_confidence else ""
-
- if tracking: score_str = " "+str(score)
-
- label = "{}".format(NUM_CLASS[class_ind]) + score_str
-
- # get text size
- (text_width, text_height), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_COMPLEX_SMALL,
- fontScale, thickness=bbox_thick)
- # put filled text rectangle
- cv2.rectangle(image, (x1, y1), (x1 + text_width, y1 - text_height - baseline), bbox_color, thickness=cv2.FILLED)
-
- # put text above rectangle
- cv2.putText(image, label, (x1, y1-4), cv2.FONT_HERSHEY_COMPLEX_SMALL, fontScale, Text_colors, bbox_thick, lineType=cv2.LINE_AA)
-
- return image
-
-def detect_enemy(Yolo, original_image, input_size=416, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors=''):
- image_data = image_preprocess(original_image, [input_size, input_size])
- image_data = image_data[np.newaxis, ...].astype(np.float32)
-
- if YOLO_FRAMEWORK == "tf":
- pred_bbox = Yolo.predict(image_data)
-
- elif YOLO_FRAMEWORK == "trt":
- batched_input = tf.constant(image_data)
- result = Yolo(batched_input)
- pred_bbox = []
- for key, value in result.items():
- value = value.numpy()
- pred_bbox.append(value)
-
- pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
- pred_bbox = tf.concat(pred_bbox, axis=0)
-
- bboxes = postprocess_boxes(pred_bbox, original_image, input_size, score_threshold)
- bboxes = nms(bboxes, iou_threshold, method='nms')
-
- image = draw_enemy(original_image, bboxes, CLASSES=CLASSES, rectangle_colors=rectangle_colors)
-
- return image, bboxes
-
-offset = 30
-times = []
-sct = mss.mss()
-yolo = Load_Yolo_model()
-while True:
- t1 = time.time()
- img = np.array(sct.grab({"top": 87-offset, "left": 1920, "width": 1280, "height": 720, "mon": -1}))
- img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
- image, bboxes = detect_enemy(yolo, np.copy(img), input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
- if len(bboxes) > 0:
- CreateXMLfile("XML_Detections", str(int(time.time())), img, bboxes, read_class_names(TRAIN_CLASSES))
- print("got it")
- time.sleep(2)
-
- t2 = time.time()
- times.append(t2-t1)
- times = times[-20:]
- ms = sum(times)/len(times)*1000
- fps = 1000 / ms
- print("FPS", fps)
-
- #cv2.imshow("Detection image", img)
- #if cv2.waitKey(25) & 0xFF == ord("q"):
- #cv2.destroyAllWindows()
- #break
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/LICENSE b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/LICENSE
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 pythonlessons
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/MCL.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/MCL.py
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/MCL.py
+++ /dev/null
@@ -1,451 +0,0 @@
-# import os
-# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-# import cv2
-# import tensorflow as tf
-# from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
-# from yolov3.configs import *
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import axes3d
-from matplotlib import cm
-
-import numpy as np
-import random as rd
-from math import cos, sin, tan, atan
-
-
-#yolo = Load_Yolo_model()
-
-### Carte ###
-
-# pixel_x_ext, pixel_y_ext = [242, 220, 165, 110, 63, 33, 22, 34, 63, 110, 165, 220, 243, 310, 334, 388, 443, 490, 521, 531, 520, 489, 443, 388, 333, 310], [76, 64, 52, 64, 95, 141, 196, 252, 298, 330, 340, 328, 318, 316, 328, 339, 329, 298, 251, 196, 142, 95, 64, 53, 64, 77]
-# pixel_x_int, pixel_y_int = [245, 238, 222, 196, 166, 134, 108, 91, 85, 90, 109, 134, 165, 196, 222, 239, 308, 314, 332, 358, 388, 419, 445, 462, 468, 462, 445, 419, 388, 359, 332, 314], [201, 167, 140, 123, 116, 123, 140, 165, 195, 228, 253, 270, 277, 270, 253, 227, 200, 226, 253, 270, 277, 270, 253, 228, 197, 166, 140, 122, 117, 123, 140, 166]
-# diametre = 225
-# centre_x, centre_y = 278, 200
-# coord_x_ext, coord_y_ext = [i/diametre for i in pixel_x_ext], [i/diametre for i in pixel_y_ext]
-# coord_x_int, coord_y_int = [i/diametre for i in pixel_x_int], [i/diametre for i in pixel_y_int]
-
-#coord_ext = [(i/diametre , j/diametre) for i,j in zip(pixel_x_ext, pixel_y_ext)]
-#coord_int = [(i/diametre , j/diametre) for i,j in zip(pixel_x_int, pixel_y_int)]
-
-
-coord_x_int = []
-coord_y_int = []
-
-coord_x_ext = []
-coord_y_ext = []
-
-r_in = 1-0.14/2
-r_ext = r_in + 0.394 + 0.14
-
-for i in range(16):
- theta = 2*3.1415*i/16
- coord_x_int.append(-1.2+r_in*cos(theta))
- coord_y_int.append(r_in*sin(theta))
-
- coord_x_int.append(1.2+r_in*cos(theta))
- coord_y_int.append(r_in*sin(theta))
-
- if 1<i<15:
- coord_x_ext.append(-1.2+r_ext*cos(theta))
- coord_y_ext.append(r_ext*sin(theta))
-
- if not 7<=i<=9:
- coord_x_ext.append(1.2+r_ext*cos(theta))
- coord_y_ext.append(r_ext*sin(theta))
-
-coord_ext = [(i,j) for i,j in zip(coord_x_ext, coord_y_ext)]
-coord_int = [(i,j) for i,j in zip(coord_x_int, coord_y_int)]
-
-### Paramètres ###
-sigma_position = 0.05
-sigma_direction = 8*3.1415/180
-seuil_cone = 0.6
-
-
-dep_x, dep_y, dep_theta = 1.314, 1.162, 3.14/3.6
-nb_particule = 30
-pos = [[dep_x, dep_y, dep_theta] for i in range(nb_particule)]
-
-F = 145.31 #194.97 #1/0.0051/0.8 #3957/3 #156.25 #Focale camera
-h_reel = 1 #Hauteur d'un plot
-y_0 = 3264/2 #Milieu de l'image
-
-dist_roue = 0.25 #Distance entre les roues avant et les roues arrieres
-
-
-# class position:
-
-# def __init__(self, pos_x, pos_y, nb_particule):
-# self.__pos = [pos_x, pos_y]*nb_particule
-
-
-def normalisation(W):
- a = sum(W)
- return [w/a for w in W]
-
-def distance(x_1, y_1, x_2, y_2):
- return np.sqrt((x_1-x_2)**2 + (y_1-y_2)**2)
-
-def boite2coord(x1,y1,x2,y2):
-
- d = F*h_reel/abs(y1-y2) - 0.3982
-
- ypmax=960/2
- ymax=0.6
- #y_r = -((x1+x2)/2 - ypmax)*d/F
- tan_theta=((x1+x2)/2-ypmax)/ypmax*ymax
- theta = -atan(tan_theta)
- x_r = d*cos(theta)
- y_r = d*sin(theta)
-
- #print(sin_theta)
- #x_r = d*(1-sin_theta**2)**0.5
-
- return x_r,y_r
-
-def rotation(point, centre, angle):
-
- x1,y1 = point
- x2,y2 = centre
-
- new_x = (x1-x2)*cos(angle) - (y1-y2)*sin(angle) + x2
- new_y = (x1-x2)*sin(angle) + (y1-y2)*cos(angle) + y2
-
- return new_x, new_y
-
-def distance_Chamfer(A_x, A_y, B_x, B_y):
- m = len(A_x)
- n = len(B_x)
-
- if m==0 or n==0:
- return 0.0001
-
- res = 0
-
- tab = [[distance(A_x[i], A_y[i], B_x[j], B_y[j]) for i in range(m)] for j in range(n)]
- tab = np.array(tab)
- for i in range(m):
- res += np.min(tab[:,i])
- for j in range(n):
- res += np.min(tab[j,:])
-
- return res
-
-
-# def orientation_voiture(x,y):
-# x1,y1 = x-centre_x, y-centre_y
-# if x<0:
-# x2,y2 = x1+diametre/2, y1
-# theta = atan(y2/x2)
-# return theta-3.1415/2
-# else:
-# x2,y2 = x1-diametre/2, y1
-# theta = atan(y2/x2)
-# return theta-3.1415/2
-
-
-def motion_update(commande, position):
- vitesse, direction, FPS = commande
- x,y,theta = position
- dt = 1/FPS
-
-
- if direction ==0:
- new_x = x + dt*vitesse*cos(theta)
- new_y = y + dt*vitesse*sin(theta)
- new_theta = theta
-
- else:
- R = dist_roue/tan(direction)
- angle_rotation = dt*vitesse/R
-
- if direction>0:
- centre_rotation_x = x + R*sin(theta)
- centre_rotation_y = y - R*cos(theta)
- else:
- centre_rotation_x = x - R*sin(theta)
- centre_rotation_y = y + R*cos(theta)
-
- new_x, new_y = rotation((x,y),(centre_rotation_x, centre_rotation_y), angle_rotation)
- new_theta = theta + angle_rotation #orientation_voiture(new_x, new_y)
-
- new_x += rd.gauss(0, sigma_position)
- new_y += rd.gauss(0, sigma_position)
- new_theta += rd.gauss(0, sigma_direction)
-
- return (new_x, new_y, new_theta)
-
-
-
-def sensor_update(observation, position):
- x,y,theta = position
- vision_x = []
- vision_y = []
- vision_x_ext = []
- vision_y_ext = []
- for pt in coord_int:
- vision_x.append((pt[0]-x)*cos(theta) + (pt[1]-y)*sin(theta))
- vision_y.append(-(pt[0]-x)*sin(theta) + (pt[1]-y)*cos(theta))
-
-
- for pt in coord_ext:
- vision_x_ext.append((pt[0]-x)*cos(theta) + (pt[1]-y)*sin(theta))
- vision_y_ext.append(-(pt[0]-x)*sin(theta) + (pt[1]-y)*cos(theta))
-
- cones_vu_x = []
- cones_vu_y = []
- for i in range(len(vision_x)):
- if vision_x[i]>0 and abs(vision_y[i])<1.15*vision_x[i]:
- cones_vu_x.append(vision_x[i])
- cones_vu_y.append(vision_y[i])
-
- for i in range(len(vision_x_ext)):
- if vision_x_ext[i]>0 and abs(vision_y_ext[i])<0.6*vision_x_ext[i]:
- cones_vu_x.append(vision_x_ext[i])
- cones_vu_y.append(vision_y_ext[i])
-
- if len(cones_vu_x) == 0:
- return 0
- else:
- obs_x = []
- obs_y = []
- for i in observation:
- obs_x.append(i[0])
- obs_y.append(i[1])
-
- return 1/distance_Chamfer(cones_vu_x, cones_vu_y, obs_x, obs_y)**4
-
-
-
-
-
-
-def particle_filter(pos,u_t,z_t): #Position, commande, observation
- X_t_barre, X_t = [], []
- M = len(pos)
- for m in range(M):
- x = motion_update(u_t, pos[m])
- w = sensor_update(z_t, x)
- X_t_barre.append((x,w))
-
- X = [X_t_barre[i][0] for i in range(M)]
- W = [X_t_barre[i][1] for i in range(M)]
- W = normalisation(W)
- X_t = low_variance_resampling(X, W)
-
- return X_t,W
-
-
-def low_variance_resampling(X,W):
- X_t = []
- J = len(X)
- r = rd.random()/J
- c = W[0]
- i=0
- for j in range(J):
- U = r + (j-1)/J
- while U > c:
- i += 1
- c += W[i]
- X_t.append(X[i])
- return X_t
-
-
-def get_position(boxes,commande,pos):
-
-
- # Positionnement des plots à partir des boites
-
- liste_x =[]
- liste_y =[]
- for i in boxes:
- if i[4] >= seuil_cone:
- x,y = boite2coord(i[0],i[1],i[2],i[3])
- liste_x.append(x)
- liste_y.append(y)
-
- z_t = []
- cone_x = []
- cone_y = []
-
-
- for i in range(len(liste_x)):
- z_t.append((liste_x[i], liste_y[i]))
-
-
- #Positionnement
- pos, W = particle_filter(pos, commande, z_t)
-
- pos_calc = [0,0,0]
- for i in range(len(pos)):
- pos_calc[0] += pos[i][0]*W[i]
- pos_calc[1] += pos[i][1]*W[i]
- pos_calc[2] += pos[i][2]*W[i]
-
- for i in range(len(liste_x)):
- x,y = rotation((liste_x[i] ,liste_y[i]), (0,0), pos_calc[2])
- cone_x.append(x+pos_calc[0])
- cone_y.append(y+pos_calc[1])
-
- return pos_calc, pos, cone_x, cone_y
-
-
-
-if __name__ == "__main__":
-
- detection = [np.array([115.43045807, 210.114151 , 394.80041504, 559.36151123,
- 0.95864254, 0. ]), np.array([515.29907227, 304.63769531, 671.36590576, 497.72848511,
- 0.93361914, 0. ]), np.array([7.85924133e+02, 3.29671387e+02, 9.00264526e+02, 4.77042267e+02,
- 7.98994482e-01, 0.00000000e+00])]
-
-
- z_t = []
- liste_x = []
- liste_y = []
- nb_particle = 100
- #pos = [(2.5*rd.random(), 1.6*rd.random(), 2*3.14*rd.random()) for i in range(nb_particle)]
- pos = [(1.2,1.19,-0.3) for i in range(nb_particle)]
-
- for i in detection:
- if i[4] >= seuil_cone:
- x,y = boite2coord(i[0],i[1],i[2],i[3])
- liste_x.append(x)
- liste_y.append(y)
-
- for i in range(len(liste_x)):
- z_t.append((liste_x[i], liste_y[i]))
-
-
- # x = np.linspace(0,2.5,100)
- # y = np.linspace(0,1.6,100)
- # X, Y = np.meshgrid(x,y)
- # Z = np.zeros((100,100))
- # for i in range(100):
- # for j in range(100):
- # #Z[i,j] = sensor_update(z_t, (X[i,j],Y[i,j], 3.14/3))
- # Z[i,j] = orientation_voiture(X[i,j], Y[i,j])
-
- # fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
- # surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
- # linewidth=0, antialiased=False)
-
-
- plt.ion()
- fig = plt.figure()
- ax = fig.add_subplot(111)
- exterieur = ax.plot(coord_x_ext, coord_y_ext,'+')
- interieur = ax.plot(coord_x_int, coord_y_int,'+')
-
-
- pos_x = [elt[0] for elt in pos]
- pos_y = [elt[1] for elt in pos]
- line1, = ax.plot(pos_x, pos_y, '.')
- line2, = ax.plot(0,0,'o')
- line3, = ax.plot(0,0,'o')
-
- for i in range(100):
-
-
- pos, W = particle_filter(pos, (0,0,1), z_t)
- #W = [0.01 for i in range(100)]
-
- pos_x = [0.0 for i in range(nb_particle)]
- pos_y = [0.0 for i in range(nb_particle)]
- pos_moy_x, pos_moy_y, pos_moy_theta = 0,0,0
- cone_x, cone_y = [], []
-
- for i in range(nb_particle):
- pos_x[i] = pos[i][0]
- pos_y[i] = pos[i][1]
- pos_moy_x += W[i]*pos[i][0]
- pos_moy_y += W[i]*pos[i][1]
- pos_moy_theta += W[i]*pos[i][2]
-
- for i in range(len(liste_x)):
- x,y = rotation((liste_x[i] ,liste_y[i]), (0,0), pos_moy_theta)
- cone_x.append(x+pos_moy_x)
- cone_y.append(y+pos_moy_y)
-
- line1.set_xdata(pos_x)
- line1.set_ydata(pos_y)
- line2.set_xdata(pos_moy_x)
- line2.set_ydata(pos_moy_y)
- line3.set_xdata(cone_x)
- line3.set_ydata(cone_y)
-
- fig.canvas.draw()
-
- plt.pause(0.1)
-'''
-if __name__ == "__main__":
- # pos = [(1.314, 1.162, 3.14/3.6) for i in range(10)]
- # #pos = [(2.5*rd.random(), 1.5*rd.random(), 6.28*rd.random()) for i in range(50)]
-
- # liste_x = [0.37409758380473157,
- # 0.6517064494114153,
- # 0.23060853761333963,
- # 0.5278583908503303,
- # 0.14161368355256793,
- # 0.5134652832573952]
-
- # liste_y = [0.14021924676581576,
- # -0.3119493901540909,
- # -0.3464004029844368,
- # 0.01390277627039628,
- # -0.2754514724880131,
- # -0.5902545559074325]
-
- # observation = []
- # for i in range(len(liste_x)):
- # observation.append((liste_x[i], liste_y[i]))
-
- commande = (0.138841, -pi/6)
-
- # a,W = particle_filter(pos, commande, observation)
- #print(a,W)
-
-
-
-
-
-
- pos_initiale = (1.314, 1.162, 3.14/3.6)
- pos_finale = (1.4, 1.271, 3.14/5.5)
-
- pos = [pos_initiale for i in range(30)]
-
- pos_calc,pos = get_position(commande,pos)
-
-
- plt.figure(1)
- plt.plot(pos_initiale[0], pos_initiale[1], 'o', label='Position initale')
- plt.arrow(pos_initiale[0], pos_initiale[1], 0.07*cos(pos_initiale[2]), 0.07*sin(pos_initiale[2]))
- plt.plot(pos_finale[0], pos_finale[1], 'o', label='Position finale')
- plt.arrow(pos_finale[0], pos_finale[1], 0.07*cos(pos_finale[2]), 0.07*sin(pos_finale[2]))
-
- plt.plot(coord_x_ext, coord_y_ext,'+')
- plt.plot(coord_x_int, coord_y_int,'+')
-
- pos_x = []
- pos_y = []
- for k in range(len(pos)):
- i = pos[k]
- pos_x.append(i[0])
- pos_y.append(i[1])
-
- plt.plot(pos_x,pos_y,'x', label='Positions possibles')
-
- # pos_calc = [0,0,0]
- # for i in range(len(a)):
- # pos_calc[0] += a[i][0]*W[i]
- # pos_calc[1] += a[i][1]*W[i]
- # pos_calc[2] += a[i][2]*W[i]
-
- plt.plot(pos_calc[0], pos_calc[1], 'o', label='Moyenne pondérée des positions calculées')
- plt.arrow(pos_calc[0], pos_calc[1], 0.07*cos(pos_calc[2]), 0.07*sin(pos_calc[2]))
-
-
- plt.legend()
- plt.show()
-
- '''
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Output.mp4 b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Output.mp4
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/README.md b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/README.md
deleted file mode 100644
index 9e2e6b4d8764ea3123a5f916d8a0d6c8d5e00bb6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/README.md
+++ /dev/null
@@ -1,170 +0,0 @@
-# TensorFlow-2.x-YOLOv3 and YOLOv4 tutorials
-
-YOLOv3 and YOLOv4 implementation in TensorFlow 2.x, with support for training, transfer training, object tracking mAP and so on...
-Code was tested with following specs:
-- i7-7700k CPU and Nvidia 1080TI GPU
-- OS Ubuntu 18.04
-- CUDA 10.1
-- cuDNN v7.6.5
-- TensorRT-6.0.1.5
-- Tensorflow-GPU 2.3.1
-- Code was tested on Ubuntu and Windows 10 (TensorRT not supported officially)
-
-## Installation
-First, clone or download this GitHub repository.
-Install requirements and download pretrained weights:
-```
-pip install -r ./requirements.txt
-
-# yolov3
-wget -P model_data https://pjreddie.com/media/files/yolov3.weights
-
-# yolov3-tiny
-wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights
-
-# yolov4
-wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
-
-# yolov4-tiny
-wget -P model_data https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights
-```
-
-## Quick start
-Start with using pretrained weights to test predictions on both image and video:
-```
-python detection_demo.py
-```
-
-<p align="center">
- <img width="100%" src="IMAGES/city_pred.jpg" style="max-width:100%;"></a>
-</p>
-
-## Quick training for custom mnist dataset
-mnist folder contains mnist images, create training data:
-```
-python mnist/make_data.py
-```
-`./yolov3/configs.py` file is already configured for mnist training.
-
-Now, you can train it and then evaluate your model
-```
-python train.py
-tensorboard --logdir=log
-```
-Track training progress in Tensorboard and go to http://localhost:6006/:
-<p align="center">
- <img width="100%" src="IMAGES/tensorboard.png" style="max-width:100%;"></a>
-</p>
-
-Test detection with `detect_mnist.py` script:
-```
-python detect_mnist.py
-```
-Results:
-<p align="center">
- <img width="40%" src="IMAGES/mnist_test.jpg" style="max-width:40%;"></a>
-</p>
-
-## Custom YOLOv3 & YOLOv4 object detection training
-Custom training required to prepare dataset first, how to prepare dataset and train custom model you can read in following link:<br>
-https://pylessons.com/YOLOv3-TF2-custrom-train/<br>
-More about YOLOv4 training you can read [on this link](https://pylessons.com/YOLOv4-TF2-training/). I didn’t have time to implement all YOLOv4 Bag-Of-Freebies to improve the training process… Maybe later I’ll find time to do that, but now I leave it as it is. I recommended to use [Alex's Darknet](https://github.com/AlexeyAB/darknet) to train your custom model, if you need maximum performance, otherwise, you can use my implementation.
-
-## Google Colab Custom Yolo v3 training
-To learn more about Google Colab Free gpu training, visit my [text version tutorial](https://pylessons.com/YOLOv3-TF2-GoogleColab/)
-
-## Yolo v3 Tiny train and detection
-To get detailed instructions how to use Yolov3-Tiny, follow my text version tutorial [YOLOv3-Tiny support](https://pylessons.com/YOLOv3-TF2-Tiny/). Short instructions:
-- Get YOLOv3-Tiny weights: ```wget -P model_data https://pjreddie.com/media/files/yolov3-tiny.weights```
-- From `yolov3/configs.py` change `TRAIN_YOLO_TINY` from `False` to `True`
-- Run `detection_demo.py` script.
-
-## Yolo v3 Object tracking
-To learn more about Object tracking with Deep SORT, visit [Following link](https://pylessons.com/YOLOv3-TF2-DeepSort/).
-Quick test:
-- Clone this repository;
-- Make sure object detection works for you;
-- Run object_tracking.py script
-<p align="center">
- <img src="IMAGES/tracking_results.gif"></a>
-</p>
-
-## YOLOv3 vs YOLOv4 comparison on 1080TI:
-
-YOLO FPS on COCO 2017 Dataset:
-| Detection | 320x320 | 416x416 | 512x512 |
-|--------------|---------|---------|---------|
-| YoloV3 FPS | 24.38 | 20.94 | 18.57 |
-| YoloV4 FPS | 22.15 | 18.69 | 16.50 |
-
-TensorRT FPS on COCO 2017 Dataset:
-| Detection | 320x320 | 416x416 | 512x512 | 608x608 |
-|-----------------|---------|---------|---------|---------|
-| YoloV4 FP32 FPS | 31.23 | 27.30 | 22.63 | 18.17 |
-| YoloV4 FP16 FPS | 30.33 | 25.44 | 21.94 | 17.99 |
-| YoloV4 INT8 FPS | 85.18 | 62.02 | 47.50 | 37.32 |
-| YoloV3 INT8 FPS | 84.65 | 52.72 | 38.22 | 28.75 |
-
-mAP on COCO 2017 Dataset:
-| Detection | 320x320 | 416x416 | 512x512 |
-|------------------|---------|---------|---------|
-| YoloV3 mAP50 | 49.85 | 55.31 | 57.48 |
-| YoloV4 mAP50 | 48.58 | 56.92 | 61.71 |
-
-TensorRT mAP on COCO 2017 Dataset:
-| Detection | 320x320 | 416x416 | 512x512 | 608x608 |
-|-------------------|---------|---------|---------|---------|
-| YoloV4 FP32 mAP50 | 48.58 | 56.92 | 61.71 | 63.92 |
-| YoloV4 FP16 mAP50 | 48.57 | 56.92 | 61.69 | 63.92 |
-| YoloV4 INT8 mAP50 | 40.61 | 48.36 | 52.84 | 54.53 |
-| YoloV3 INT8 mAP50 | 44.19 | 48.64 | 50.10 | 50.69 |
-
-## Converting YOLO to TensorRT
-I will give two examples, both will be for YOLOv4 model,quantize_mode=INT8 and model input size will be 608. Detailed tutorial is on this [link](https://pylessons.com/YOLOv4-TF2-TensorRT/).
-### Default weights from COCO dataset:
-- Download weights from links above;
-- In `configs.py` script choose your `YOLO_TYPE`;
-- In `configs.py` script set `YOLO_INPUT_SIZE = 608`;
-- In `configs.py` script set `YOLO_FRAMEWORK = "trt"`;
-- From main directory in terminal type `python tools/Convert_to_pb.py`;
-- From main directory in terminal type `python tools/Convert_to_TRT.py`;
-- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}'`;
-- Now you can run `detection_demo.py`, best to test with `detect_video` function.
-
-### Custom trained YOLO weights:
-- Download weights from links above;
-- In `configs.py` script choose your `YOLO_TYPE`;
-- In `configs.py` script set `YOLO_INPUT_SIZE = 608`;
-- Train custom YOLO model with instructions above;
-- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f"{YOLO_TYPE}_custom"`;
-- In `configs.py` script make sure that `TRAIN_CLASSES` is with your custom classes text file;
-- From main directory in terminal type `python tools/Convert_to_pb.py`;
-- From main directory in terminal type `python tools/Convert_to_TRT.py`;
-- In `configs.py` script set `YOLO_FRAMEWORK = "trt"`;
-- In `configs.py` script set `YOLO_CUSTOM_WEIGHTS = f'checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}–{YOLO_INPUT_SIZE}'`;
-- Now you can run `detection_custom.py`, to test custom trained and converted TensorRT model.
-
-What is done:
---------------------
-- [x] Detection with original weights [Tutorial link](https://pylessons.com/YOLOv3-TF2-introduction/)
-- [x] Mnist detection training [Tutorial link](https://pylessons.com/YOLOv3-TF2-mnist/)
-- [x] Custom detection training [Tutorial link1](https://pylessons.com/YOLOv3-TF2-custrom-train/), [link2](https://pylessons.com/YOLOv3-TF2-custrom-images/)
-- [x] Google Colab training [Tutorial link](https://pylessons.com/YOLOv3-TF2-GoogleColab/)
-- [x] YOLOv3-Tiny support [Tutorial link](https://pylessons.com/YOLOv3-TF2-Tiny/)
-- [X] Object tracking [Tutorial link](https://pylessons.com/YOLOv3-TF2-DeepSort/)
-- [X] Mean Average Precision (mAP) [Tutorial link](https://pylessons.com/YOLOv3-TF2-mAP/)
-- [X] Yolo v3 on Raspberry Pi [Tutorial link](https://pylessons.com/YOLOv3-TF2-RaspberryPi/)
-- [X] YOLOv4 and YOLOv4-tiny detection [Tutorial link](https://pylessons.com/YOLOv4-TF2-introduction/)
-- [X] YOLOv4 and YOLOv4-tiny detection training (Not fully) [Tutorial link](https://pylessons.com/YOLOv4-TF2-training/)
-- [X] Convert to TensorRT model [Tutorial link](https://pylessons.com/YOLOv4-TF2-TensorRT/)
-- [X] Add multiprocessing after detection (drawing bbox) [Tutorial link](https://pylessons.com/YOLOv4-TF2-multiprocessing/)
-- [X] Generate YOLO Object Detection training data from its own results [Tutorial link](https://pylessons.com/YOLOv4-TF2-CreateXML/)
-- [X] Counter-strike Global Offensive realtime YOLOv4 Object Detection aimbot [Tutorial link](https://pylessons.com/YOLOv4-TF2-CSGO-aimbot/)
-
-To be continued... (not anytime soon)
---------------------
-- [ ] Converting to TensorFlow Lite
-- [ ] YOLO on Android (Leaving it for future, will need to convert everythin to java... not ready for this)
-- [ ] Generating anchors
-- [ ] YOLACT: Real-time Instance Segmentation
-- [ ] Model pruning (Pruning is a technique in deep learning that aids in the development of smaller and more efficient neural networks. It's a model optimization technique that involves eliminating unnecessary values in the weight tensor.)
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/YOLOv3_colab_training.ipynb b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/YOLOv3_colab_training.ipynb
deleted file mode 100644
index c61593049a347b3ec4b1ed55447f57b7ba83aebc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/YOLOv3_colab_training.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"YOLOv3_colab_training.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.9"}},"cells":[{"cell_type":"markdown","metadata":{"id":"twluDsqOaqRW"},"source":["\n","========================================================<br>\n","<br>\n"," File name : YOLOv3_colab_training.ipynb<br>\n"," Author : PyLessons<br>\n"," Created date: 2020-09-30<br>\n"," Website : https://pylessons.com/YOLOv3-TF2-GoogleColab<br>\n"," GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3<br>\n"," Description : Train custom model on Google colab tutorial<br>\n","<br>\n","================================================================\n","\n","\n","**Open this notebook from google drive**<br>\n","**Go to \"Edit\" -> \"Notebook settings\" and enable GPU.**\n"]},{"cell_type":"code","metadata":{"id":"srBiJiFEaKl1","executionInfo":{"status":"ok","timestamp":1601446581065,"user_tz":-180,"elapsed":1009,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"b3a94fa4-c78c-4db9-d400-f14bf19732e0","colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["# Check if NVIDIA GPU is enabled\n","!nvidia-smi"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Wed Sep 30 06:16:20 2020 \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 455.23.05 Driver Version: 418.67 CUDA Version: 10.1 |\n","|-------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|===============================+======================+======================|\n","| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n","| N/A 41C P0 28W / 250W | 0MiB / 16280MiB | 0% Default |\n","| | | ERR! |\n","+-------------------------------+----------------------+----------------------+\n"," \n","+-----------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=============================================================================|\n","| No running processes found |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"sj3eAw1OXOnB"},"source":["**Connect and authorize google drive with google colab:**"]},{"cell_type":"code","metadata":{"id":"PjjcQSpya_FR","executionInfo":{"status":"ok","timestamp":1601446598010,"user_tz":-180,"elapsed":17936,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"c6e2509b-27ea-45c0-e923-a8fc89e52f3d","colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')\n","!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n","gdrive\tsample_data\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"h5GywMhIKCd0"},"source":["**Open our project \"TensorFlow-2.x-YOLOv3\" direcotry in google drive:**"]},{"cell_type":"code","metadata":{"id":"iYM4wmy-cFlK","executionInfo":{"status":"ok","timestamp":1601446598379,"user_tz":-180,"elapsed":3922,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"d7879aa3-ca3c-4e1f-f7c5-6685f56f07cf","colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["%cd gdrive/My\\ Drive/TensorFlow-2.x-YOLOv3/\n","!ls"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3\n","CAPTCHA_solver_v3.h5 IMAGES\t\t __pycache__\n","checkpoints\t log\t\t requirements.txt\n","custom_dataset\t mAP\t\t save_program_screen.py\n","deep_sort\t mnist\t\t tools\n","detection_custom.py model_data\t train.py\n","detection_demo.py mp_test.py\t yolov3\n","detect_mnist.py multiprc.py\t YOLOv3_colab_training.ipynb\n","evaluate_mAP.py object_tracker.py\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"24u5FY2ZKbrc"},"source":["**Install all required libraries for our project:**"]},{"cell_type":"code","metadata":{"id":"adhpOaKT9lWC"},"source":["!pip install -r ./requirements.txt"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1DMiQtY7Koy7"},"source":["**Download yolov3.weights if you don't have it:**"]},{"cell_type":"code","metadata":{"id":"UX-LG3R6An5U"},"source":["!wget -P model_data https://pjreddie.com/media/files/yolov3.weights"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DDmBbAUKLUkB"},"source":["**Test if TensorFlow works with gpu for you, in output should see similar results:**\n","```\n","2.3.0\n","'/device:GPU:0'\n","```"]},{"cell_type":"code","metadata":{"id":"M3cWo7hhc-qO","executionInfo":{"status":"ok","timestamp":1601446634334,"user_tz":-180,"elapsed":7965,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"1a7edf1a-a3a9-450e-ace2-34bf1ad2c6a8","colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["import tensorflow as tf\n","print(tf.__version__)\n","tf.test.gpu_device_name()"],"execution_count":5,"outputs":[{"output_type":"stream","text":["2.3.0\n"],"name":"stdout"},{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'/device:GPU:0'"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"sX0TGlJhMGd_"},"source":["**Test by loading trained model:**"]},{"cell_type":"code","metadata":{"id":"NUKLydfYCo4r","executionInfo":{"status":"ok","timestamp":1601446652047,"user_tz":-180,"elapsed":16498,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}}},"source":["import cv2\n","import numpy as np\n","import matplotlib\n","import matplotlib.pyplot as plt\n","%matplotlib inline \n","import tensorflow as tf\n","from yolov3.yolov4 import Create_Yolo\n","from yolov3.utils import load_yolo_weights, detect_image\n","from yolov3.configs import *\n","\n","if YOLO_TYPE == \"yolov4\":\n"," Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS\n","if YOLO_TYPE == \"yolov3\":\n"," Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS\n","\n","yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE)\n","load_yolo_weights(yolo, Darknet_weights) # use Darknet weights"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qdpGgKUUMJOe"},"source":["**Test by testing detection on original model:**"]},{"cell_type":"code","metadata":{"id":"PcNcPmvLC5fl","executionInfo":{"status":"ok","timestamp":1601446675762,"user_tz":-180,"elapsed":19238,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"52d4b8fa-aa52-47a4-a835-5342342e073c","colab":{"base_uri":"https://localhost:8080/","height":884,"output_embedded_package_id":"1yJ7Wt-vDQ42A4wLCjMsACdBP8uCGWsSG"}},"source":["image_path = \"./IMAGES/street.jpg\"\n","\n","image = detect_image(yolo, image_path, '', input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0))\n","image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n","\n","plt.figure(figsize=(30,15))\n","plt.imshow(image)"],"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"f81_fxA5Naqm"},"source":["**Run `XML_to_YOLOv3.py` script to convert XML files to YOLOv3 annotations files:**"]},{"cell_type":"code","metadata":{"id":"pXlFGBAp7Ibg","executionInfo":{"status":"ok","timestamp":1596814215969,"user_tz":-180,"elapsed":428158,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"340f3942-c2b3-4e2a-bf5f-267e12d696fb","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["!python tools/XML_to_YOLOv3.py"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/train\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/train/1.jpg 650,576,959,749,0\n","/content/gdrive/My Drive/TensorFlow-2.x-YOLOv3/custom_dataset/train/2.jpg 215,190,409,294,0\n","/content/gdrive/My 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['license-plate']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5CYGwaPfV3H6"},"source":["**Start training custom model:**"]},{"cell_type":"code","metadata":{"id":"rUxAdSEQEdpG"},"source":["from train import *\n","tf.keras.backend.clear_session()\n","main()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7YNbZxosPRNw"},"source":["**Create Yolo v3 custom model and load custom trained weights**"]},{"cell_type":"code","metadata":{"id":"W5CuaoSI3KRm","executionInfo":{"status":"ok","timestamp":1601446697304,"user_tz":-180,"elapsed":13637,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"684fc60e-fd68-4378-9e3d-7f2ab599e439","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)\n","yolo.load_weights(\"./checkpoints/yolov3_custom\") # use keras weights"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f9d9124e128>"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"dwqeCYh_PTuw"},"source":["**Test the detection with `IMAGES/plate_2.jpg` image**"]},{"cell_type":"code","metadata":{"id":"bx94uGmLPJz5","executionInfo":{"status":"ok","timestamp":1601446712563,"user_tz":-180,"elapsed":9187,"user":{"displayName":"Python Lessons","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMQmMhFapKcavl337-vY17yrbowBHBlZQ5qYQv=s64","userId":"12382394757900236362"}},"outputId":"64ed8c06-805e-40cd-8df2-792b043d5a74","colab":{"base_uri":"https://localhost:8080/","height":883}},"source":["image_path = \"./IMAGES/plate_1.jpg\"\n","image = detect_image(yolo, image_path, \"\", input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))\n","image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n","\n","plt.figure(figsize=(30,15))\n","plt.imshow(image)"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f9d90e9e6d8>"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 2160x1080 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7dKA8DYOj8d-"},"source":["# **You just trained your first Yolo v3 custom object detector on google colab, GOOD JOB!!**"]},{"cell_type":"code","metadata":{"id":"LQHYhLbEkZxh"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/MCL.cpython-38.pyc b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/MCL.cpython-38.pyc
deleted file mode 100644
index a67d2f76d8ea432cd871518cde5ab2c70f8b5dd6..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/MCL.cpython-38.pyc and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/evaluate_mAP.cpython-38.pyc b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/evaluate_mAP.cpython-38.pyc
deleted file mode 100644
index 6155ea7d3952101c53ad13549fd112f254956f4c..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/evaluate_mAP.cpython-38.pyc and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/calibration.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/calibration.py
deleted file mode 100644
index b0e1e48caed045b23b0cc06f003b689a600f1220..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/calibration.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Mar 6 08:43:27 2023
-
-@author: paull
-"""
-
-import matplotlib.pyplot as plt
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import cv2
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
-from yolov3.configs import *
-from MCL import boite2coord
-image_path = "./test/webcam chalon/" #".jpg"
-video_path = "./IMAGES/test.mp4"
-
-
-yolo = Load_Yolo_model()
-
-distances = [0.5, 1, 2]
-h_pixel = []
-F = 1/0.0051
-h_cone = 0.27
-calib=False
-
-
-if calib:
- for dist in distances:
- path = image_path + str(dist) + "m.jpg"
-
- print(path)
-
- _, boxes = detect_image(yolo, path, "./Calibration_cam/out.jpg", input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
-
- print("Distance : ", dist,", taille : ", abs(boxes[0][1] - boxes[0][3]))
-else:
- #path = "./test/tel grand angle/test.jpg"
- path = image_path + '1.jpg'
- _, boxes = detect_image(yolo, path, "./Calibration_cam/out.jpg", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
- print(boxes)
- plot_x = []
- plot_y = []
- for i in boxes:
- if i[4] >= 0.7:
- x,y = boite2coord(i[0],i[1],i[2],i[3])
- plot_x.append(x)
- plot_y.append(y)
- print("Position du plot : x={}, y={}".format(x,y))
- plt.plot(plot_x, plot_y,'.')
- plt.plot(0,0,'+')
- plt.show()
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/checkpoint b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/checkpoint
deleted file mode 100644
index a1f094eed2d8217798a1d52515992c9cfa809ff3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/checkpoint
+++ /dev/null
@@ -1,2 +0,0 @@
-model_checkpoint_path: "yolov3_custom"
-all_model_checkpoint_paths: "yolov3_custom"
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/yolov3_custom.data-00000-of-00002 b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/yolov3_custom.data-00000-of-00002
deleted file mode 100644
index 07f3e1d2dcaf9d4a96ca8d51a983b5e8922a64b8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/yolov3_custom.data-00000-of-00002
+++ /dev/null
@@ -1,2173 +0,0 @@
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- layer_with_weights-4
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-
-layer_with_weights-5
-
-layer-9
- layer-10
- layer_with_weights-6
- layer-11
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-
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- layer-30
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- !layer-32
- "layer-33
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-$layer_with_weights-19
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- %layer-36
-&layer_with_weights-20
- &layer-37
-'layer_with_weights-21
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-)layer_with_weights-22
- )layer-40
-*layer_with_weights-23
- *layer-41
- +layer-42
- ,layer-43
--layer_with_weights-24
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- .layer-45
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/yolov3_custom.data-00001-of-00002 b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/checkpoints/yolov3_custom.data-00001-of-00002
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/1.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/1.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/10.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/10.xml
deleted file mode 100644
index 97cce271f9014d863bade26e13d8ffe88d572943..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/10.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.xml
deleted file mode 100644
index a4f1e281d26fbb19a991c5b4a2aec1439a86652f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.xml
deleted file mode 100644
index 958c11046fa5c40a05c221f6776edf732e33ef6a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.xml
deleted file mode 100644
index 1c2a4ab2cd64de1abb20dbe9000820543b202a39..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.xml
deleted file mode 100644
index fd46a3df4dea17af6e9cc0341e47b4caa4151dfe..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>108.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\108.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.xml
deleted file mode 100644
index 7c8b5113f509fd1910898a775309dbd7e37b5a93..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>109.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\109.jpg</path>
- <source>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.xml
deleted file mode 100644
index 4f2ab8385285dd4e1750c7607e44353d17e93df8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.xml
+++ /dev/null
@@ -1,134 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>11.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\11.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.xml
deleted file mode 100644
index ca3674f25b2a0174db035fe033d5cf1191c0bf64..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>110.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\110.jpg</path>
- <source>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.xml
deleted file mode 100644
index c193e113c287d37b2e3e40ae61d3e357e2c7b69e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>111.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\111.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.xml
deleted file mode 100644
index 6b5ae328e4255250e9da0f97637d182e5bdaa2dd..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>112.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.xml
deleted file mode 100644
index f215a8926395304615b57553496ff2916527559e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>113.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.xml
deleted file mode 100644
index 63c7af1c30678797a8487a0db6c79f503546bbf2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.xml
deleted file mode 100644
index bf71ccf9a473f81f5cb5cd4938f212e8ddfbd312..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.xml
deleted file mode 100644
index ccb5344abb84b37663afcb63de06581f18f280e3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.xml
deleted file mode 100644
index 2f72f24c9191dcead939676baa69cd3f7f328c9b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.xml
deleted file mode 100644
index 68e0cefa006ddf1881ea87bedb7a289fe153eeb3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/12.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/120.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/121.xml
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/122.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/123.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/124.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/125.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/126.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/127.xml
+++ /dev/null
@@ -1,122 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/128.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/129.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/13.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/130.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/131.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/132.xml
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/133.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/136.xml
+++ /dev/null
@@ -1,110 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.xml
deleted file mode 100644
index 5bc04c6a295eeb36113a32e575defe7db9ea83dc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/137.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/138.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/139.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/14.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/140.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/141.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/142.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/143.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/144.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/145.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/146.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/147.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/148.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/149.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/150.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/150.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/151.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/151.xml
deleted file mode 100644
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/152.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/154.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/155.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/156.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/157.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/158.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/16.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/160.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/161.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.xml
deleted file mode 100644
index a9a71716e4d3ae0c327b3a79fb905601a9d949ed..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/162.xml
+++ /dev/null
@@ -1,110 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.xml
deleted file mode 100644
index bbb8815c44081a3f5f46509800216c10ce99cff7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/163.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/164.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/165.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/166.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/167.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/168.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/169.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/17.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/170.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/171.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/172.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/173.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/174.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/175.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/178.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/18.xml
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/180.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/181.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/182.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/185.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/186.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/187.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/188.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/190.xml
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deleted file mode 100644
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/192.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/192.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/192.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/195.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/196.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/197.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/198.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/199.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/2.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/20.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/200.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.xml
deleted file mode 100644
index 097e67470ec816cc3265961bd71ba480116908bc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/201.xml
+++ /dev/null
@@ -1,86 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/202.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/203.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/204.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/205.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/206.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.xml
deleted file mode 100644
index 31b466c59f6b9d6c1d0591cf48f97cb58687d2ab..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/207.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\207.jpg</path>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.xml
deleted file mode 100644
index eaea94d9dfeb48df25755c1f913666ae6693c2f4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/208.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>208.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\208.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.xml
deleted file mode 100644
index 7ec9a42458eb7b3406ae89335df6d0cad6c4a601..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/209.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>209.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\209.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.xml
deleted file mode 100644
index ecca5bab9afbe365f9f650d7884c5fcee8ec5f4e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/21.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>21.jpg</filename>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.xml
deleted file mode 100644
index 43c7ce58b63137a2404f720ee833d879d0e14690..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/210.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>210.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\210.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.xml
deleted file mode 100644
index a68e768a75174393828432202b8f90095f5ef2e1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/211.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.xml
deleted file mode 100644
index 0fd6509eec8077a0567167eab9795c40e310e932..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/212.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.xml
deleted file mode 100644
index f8f6777cbe07ce75ecc9d15f7ba2e8d3956e1d14..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/213.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/214.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/215.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/216.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/217.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/218.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/219.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/22.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/220.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/221.xml
+++ /dev/null
@@ -1,170 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\221.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.xml
deleted file mode 100644
index 0d82b5c3ed1365cd03f0ee7e759f9710b873d871..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/222.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>222.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.xml
deleted file mode 100644
index 25a28926da320788a8e1e7645fa5080192ec6276..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/223.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.xml
deleted file mode 100644
index 2b117f04131ab5d1bb6a790b3a85f1977c305ef4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/224.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.xml
deleted file mode 100644
index 57289b095fc96c0af8251229bd3fdaa9355cb484..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/225.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/226.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/227.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/228.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/229.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/23.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.xml
deleted file mode 100644
index d056eb59e926bbd8f90485ee74a71c63d09617d8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/230.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/231.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/232.xml
+++ /dev/null
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+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/234.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/235.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/236.xml
+++ /dev/null
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-<annotation>
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/237.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/238.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/239.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/24.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/240.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/241.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/242.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/243.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/244.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/245.xml
+++ /dev/null
@@ -1,98 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/246.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/247.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/248.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/249.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.jpg
deleted file mode 100644
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.xml
deleted file mode 100644
index ff99b86d07e94aa8ae0bb50c3c99074a2773dbc1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/25.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>25.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\25.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.xml
deleted file mode 100644
index 4db7a2b25d00279a3df16b09a64b8228049699c5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/250.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.xml
deleted file mode 100644
index 7fcc8409c4e90d8ad740eb6ce5fa1cdac781c6a1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/251.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>251.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\251.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.xml
deleted file mode 100644
index dadb13d259b16ff2a085bd66396e33e467727e9b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/252.xml
+++ /dev/null
@@ -1,122 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.xml
deleted file mode 100644
index 16c933e2bf71d555361d697b4d5ce3cdb81222c8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/253.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.xml
deleted file mode 100644
index 42224d1fb8afba80f3b646ea6bc56f12a356caad..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/254.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>254.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\254.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/255.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/26.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>26.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\26.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.xml
deleted file mode 100644
index 269536d45d00144d322bc0772181ce30b54d0ebc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/27.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>27.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\27.jpg</path>
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- <object>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.xml
deleted file mode 100644
index cbd1807eb72ba55ed8fdca85ea4c3ac21ee60edc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/28.xml
+++ /dev/null
@@ -1,86 +0,0 @@
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- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.xml
deleted file mode 100644
index a87ff5cd2153626ebc41d319f3c832a7b08269ee..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/29.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>29.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\29.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.xml
deleted file mode 100644
index 4c93fbceab56b63b9c500003be55dcd5179ce1ce..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/3.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>3.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\3.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.xml
deleted file mode 100644
index 7ad1e12f8c1589093df8009ac398dcbc0c9eb609..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/30.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>30.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\30.jpg</path>
- <source>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.xml
deleted file mode 100644
index 95ea8048e389114c90ad1f35dd058f2bd85358e1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/31.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>31.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\31.jpg</path>
- <source>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.xml
deleted file mode 100644
index 0f671931ef4e20b4ce89001337764f5eedb11c20..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/32.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.xml
deleted file mode 100644
index b18fd0d3c5481f088179262b8fbef9fd89a41aec..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/33.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>33.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\33.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.xml
deleted file mode 100644
index 4b39b925cc17d9c9d98fcc47f013148a7056bd25..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/34.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>34.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\34.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.xml
deleted file mode 100644
index 4b6b98479598bb5468aef6531be82c30aa5f8de2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/35.xml
+++ /dev/null
@@ -1,86 +0,0 @@
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- <filename>35.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.xml
deleted file mode 100644
index 10a88dcb45c48dfcb952ae030b23363ceabe660f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/36.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.xml
deleted file mode 100644
index e9741a2467c31304a3fb39feb5e3a6b029004b29..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/37.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.xml
deleted file mode 100644
index 05e3e59a73a8ed9ab78048e6b9522cc95f2fd82d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/38.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>38.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\38.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/39.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>39.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\39.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/4.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>4.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.xml
deleted file mode 100644
index 54d5cd8d132225e1425f56d52a92b60c73357d51..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/40.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/41.xml
+++ /dev/null
@@ -1,122 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/42.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/43.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.xml
deleted file mode 100644
index 24bc004131fd66c71d6b739a48f1e22b89a604d3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/44.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>44.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\44.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.xml
deleted file mode 100644
index 9fcb50f9ca3c734fa55c734247bb72039fb79423..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/45.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>45.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\45.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.xml
deleted file mode 100644
index 04029dac98ce8d36867c69aa932cc8ef5a6da83c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/46.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>46.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.xml
deleted file mode 100644
index 3e3aa3d1a0ccc16f7695e16d4739e5028c9a5c5e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/47.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>47.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\47.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.xml
deleted file mode 100644
index 68b1fdd321847c9d06d4e7e8c343be44866c4465..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/48.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>48.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.xml
deleted file mode 100644
index 7a3761ffd47ef6872354eae63391148063096287..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/49.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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- <filename>49.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.xml
deleted file mode 100644
index 5b963a42f1c4e195a6b3a367d1566c326ea3f5e6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/5.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>5.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.xml
deleted file mode 100644
index c396b6031b259f2cf9a0af8bdef12cd59eb45594..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/50.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>50.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.xml
deleted file mode 100644
index 16d57f31d918a02bda72df976e45fcce5e519c3a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/51.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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- <filename>51.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.xml
deleted file mode 100644
index 80a8ffbf2fac7cd8c62660bea200651d6512c30c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/52.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/53.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>53.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\53.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.xml
deleted file mode 100644
index 7e059368607fb0a24950911c4330ea9ef7128013..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/54.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>54.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\54.jpg</path>
- <source>
- <database>Unknown</database>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.jpg
deleted file mode 100644
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.xml
deleted file mode 100644
index 870124701ab6dad222cde82a5b397f7ba9df93b3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/55.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>55.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\55.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.xml
deleted file mode 100644
index 256b78685e7e218cb345c084807faa46516a4a09..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/56.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>56.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\56.jpg</path>
- <source>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.xml
deleted file mode 100644
index 70c7754a80ddad37f42d2c093976b8d357f52bb3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/57.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>57.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\57.jpg</path>
- <source>
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- <name>cone</name>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.jpg
deleted file mode 100644
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.xml
deleted file mode 100644
index d7e5b8a5b216526919367bd777880bcca9c78b82..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/58.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>58.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\58.jpg</path>
- <source>
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- <width>369</width>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.xml
deleted file mode 100644
index 57a3eb5e6d8ed886b287f24400929c36df87820a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/59.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>59.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\59.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.xml
deleted file mode 100644
index 30164432ff2f73dbdb5dd172572dc99ea59458e7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/6.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>6.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\6.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.xml
deleted file mode 100644
index fd820d8c78151e52d0ffb4fc0bbc1cac9db90e2e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/60.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>60.jpg</filename>
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- <object>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.xml
deleted file mode 100644
index 234d206c51ad03671c775d091469d1f1a03c1427..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/61.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.xml
deleted file mode 100644
index 6c5e86da09fdff2eda112e4ac2dbefe0b21e3aac..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/62.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.xml
deleted file mode 100644
index 8a22ec396b8b851a79249caf1320e387e82e050f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/63.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.xml
deleted file mode 100644
index 856ed467912123196397451dcd2a4559c0a84b61..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/64.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.xml
deleted file mode 100644
index 802484edee2522128e25d51cbe357c1673f5f584..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/65.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.xml
deleted file mode 100644
index 16f99d0bc13f4cb7a6fae51962fdccf12564eee3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/66.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.xml
deleted file mode 100644
index f0843121c0f2f17800fad45f4e84d9280d95d58a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/67.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.xml
deleted file mode 100644
index 7cb60fc813748d489aa9e7022bc9f1beb14b4cd2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/68.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/69.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/7.xml
+++ /dev/null
@@ -1,98 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.xml
deleted file mode 100644
index 0f9684ad168cc5b2038e22b2b070e9da5e38feee..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/70.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.xml
deleted file mode 100644
index 64c7bf357912453265f66eb997af4b62b6a32e6f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/71.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.xml
deleted file mode 100644
index 0d61d6cbdfd581ccfb81b5b97ceffd1645af1d96..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/72.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.xml
deleted file mode 100644
index 0f8f8107f3f7eb860d2bee386606af66ba915800..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/73.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.xml
deleted file mode 100644
index 6488c5d6052cf3612c807ce9c8a2691c889bedbe..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/74.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/75.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/76.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/77.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/78.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.xml
deleted file mode 100644
index 438ff7841bfccfdf4a44f9c9ffac90bf7f0c3bd8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/79.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.xml
deleted file mode 100644
index f0bfc8c54c84f3fe60fafb5dc905943ff5940370..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/8.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/80.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\80.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.xml
deleted file mode 100644
index 0c4bb3847fb79e9c08f6ecca23529f971d20e66e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/81.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>81.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\81.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.xml
deleted file mode 100644
index 08e829f79ce8bd927ea31c698b72a3efa73b7ffa..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/82.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.xml
deleted file mode 100644
index 1797700a59aab71846ac7cf94a08535dfd379470..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/83.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>83.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.xml
deleted file mode 100644
index b55dd0d3ef3f0e9d7c24aa9be7b8417fb256779d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/84.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.xml
deleted file mode 100644
index 2b14df2b422c4309989d3315e635809905275f8e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/85.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.xml
deleted file mode 100644
index 1c4e504ab35cd39b5812c65a351236812066c7ef..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/86.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.xml
deleted file mode 100644
index 0648a0c8b0f96d2381e2cc2b3fd1904ce05da10f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/87.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.xml
deleted file mode 100644
index f61101ba72552659d6c4480315f3c96bf656c086..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/88.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.xml
deleted file mode 100644
index b953d1ecb6ff011c9f2ade1f3ac49ab87e56a7cd..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/89.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.xml
deleted file mode 100644
index fd785042d0bb01466c4f88da84cf1199d672ef50..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/9.xml
+++ /dev/null
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- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\9.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.xml
deleted file mode 100644
index e40077788f69f2e374503b6d5d0bea7abaec493e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/90.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>90.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\90.jpg</path>
- <source>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.xml
deleted file mode 100644
index 84472c691496745cc5faab9a0176fa1f32bbff79..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/91.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>91.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\91.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.xml
deleted file mode 100644
index f7f18b524196b3a3caea2cbb6998a95ee8cf8ad7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/92.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>92.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\92.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.xml
deleted file mode 100644
index 3805ae278700271f60d861a76293f88457c2e235..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/93.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>93.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\93.jpg</path>
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- <size>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.xml
deleted file mode 100644
index 7337dca0b4b3370976e3e7227bb8778a8b687e5f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/94.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>94.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.jpg
deleted file mode 100644
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.xml
deleted file mode 100644
index c29900471df0f112799cada8017beefbc06bb433..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/95.xml
+++ /dev/null
@@ -1,110 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>95.jpg</filename>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.xml
deleted file mode 100644
index 23a5390244b0c27ab51f5126142e283bd16b3850..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/96.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>96.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\96.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.xml
deleted file mode 100644
index 3c2c10de8201fd621b1d60bd819d4c7c097c8296..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/97.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.xml
deleted file mode 100644
index 8e096381babb4e30abbbb42573548353f5cfa28d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/98.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\98.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.xml
deleted file mode 100644
index fcae8467e04e5d6e324cd8b075ab9e0affe9c2e5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/test/99.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.xml
deleted file mode 100644
index d159c342a9eff91ecfc2b2bff46ab2af8237db2f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/1.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>1.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\1.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/10.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.xml
deleted file mode 100644
index a4f1e281d26fbb19a991c5b4a2aec1439a86652f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/100.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>100.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\100.jpg</path>
- <source>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.xml
deleted file mode 100644
index 958c11046fa5c40a05c221f6776edf732e33ef6a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/101.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>101.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\101.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.xml
deleted file mode 100644
index 0b575db6936b0321e789d26d19ac36ba7a6ff930..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/102.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.xml
deleted file mode 100644
index c717ab0e7d044d5ff6b1ef6360c90f5f50017f4a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/103.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>103.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\103.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.jpg
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.xml
deleted file mode 100644
index 1c2a4ab2cd64de1abb20dbe9000820543b202a39..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/104.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.xml
deleted file mode 100644
index b19e2dba6aef0ea08aa7a2701e7dd202bf9a830b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/105.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/106.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/107.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/108.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/109.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/11.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/110.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/111.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/112.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/113.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/114.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/115.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/116.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/117.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/118.xml
+++ /dev/null
@@ -1,74 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/119.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/12.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/120.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/121.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/131.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/137.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/138.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/138.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/139.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/14.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/14.xml
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/161.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/162.xml
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/163.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/164.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/165.xml
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deleted file mode 100644
index 5494f8e6c259de10b84ac6e6316eb818d701e5f4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/167.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.jpg
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.xml
deleted file mode 100644
index ee3ba82bcba8909d3e316f20a573f9dd3b938c52..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/168.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\168.jpg</path>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/169.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/17.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/170.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/171.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.xml
deleted file mode 100644
index c50e9a089ae8591ba296db14db7561216445147b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/172.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/173.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/174.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/175.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/176.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/177.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/177.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/177.xml
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deleted file mode 100644
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+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/179.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/18.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/180.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/181.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/182.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/185.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/186.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/21.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/246.xml
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/248.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/249.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/25.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/250.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/251.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/252.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/253.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/254.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/255.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/26.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/27.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.xml
deleted file mode 100644
index cbd1807eb72ba55ed8fdca85ea4c3ac21ee60edc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/28.xml
+++ /dev/null
@@ -1,86 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.xml
deleted file mode 100644
index a87ff5cd2153626ebc41d319f3c832a7b08269ee..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/29.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/3.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/30.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.xml
deleted file mode 100644
index 95ea8048e389114c90ad1f35dd058f2bd85358e1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/31.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/32.xml
+++ /dev/null
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/33.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.xml
deleted file mode 100644
index 4b39b925cc17d9c9d98fcc47f013148a7056bd25..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/34.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.xml
deleted file mode 100644
index 4b6b98479598bb5468aef6531be82c30aa5f8de2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/35.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/36.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/37.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/38.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/39.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/4.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/40.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/41.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/42.xml
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deleted file mode 100644
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+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/46.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/47.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/48.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/49.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/5.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/51.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/52.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/53.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/54.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/55.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/55.xml
deleted file mode 100644
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/56.xml
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/57.xml
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Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/58.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/59.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/6.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/60.xml
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/61.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/70.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/71.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/71.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/71.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/73.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/74.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/75.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/75.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/75.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/76.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/77.xml
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/78.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.xml
deleted file mode 100644
index f0bfc8c54c84f3fe60fafb5dc905943ff5940370..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/8.xml
+++ /dev/null
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-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/80.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.xml
deleted file mode 100644
index 0c4bb3847fb79e9c08f6ecca23529f971d20e66e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/81.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/82.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/83.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/84.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/85.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/86.xml
+++ /dev/null
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/87.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.xml
deleted file mode 100644
index f61101ba72552659d6c4480315f3c96bf656c086..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/88.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.xml
deleted file mode 100644
index b953d1ecb6ff011c9f2ade1f3ac49ab87e56a7cd..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/89.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/9.xml
+++ /dev/null
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deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/90.xml
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/91.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/92.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/93.xml
+++ /dev/null
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.xml
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/94.xml
+++ /dev/null
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- <difficult>0</difficult>
- <bndbox>
- <xmin>280</xmin>
- <ymin>92</ymin>
- <xmax>333</xmax>
- <ymax>202</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>121</xmin>
- <ymin>16</ymin>
- <xmax>155</xmax>
- <ymax>65</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>65</xmin>
- <ymin>16</ymin>
- <xmax>106</xmax>
- <ymax>66</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.jpg
deleted file mode 100644
index 92f76386f69f98b22d2f1b83e534a5e0e307dd56..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.xml
deleted file mode 100644
index 23a5390244b0c27ab51f5126142e283bd16b3850..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/96.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>96.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\96.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>505</width>
- <height>342</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>317</xmin>
- <ymin>113</ymin>
- <xmax>373</xmax>
- <ymax>171</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>189</xmin>
- <ymin>94</ymin>
- <xmax>255</xmax>
- <ymax>176</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>218</xmin>
- <ymin>183</ymin>
- <xmax>421</xmax>
- <ymax>320</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>263</xmin>
- <ymin>96</ymin>
- <xmax>289</xmax>
- <ymax>137</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.jpg
deleted file mode 100644
index a8e9cdc28f8f8d7852f781f913b0adfc3fda53ce..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.xml
deleted file mode 100644
index 3c2c10de8201fd621b1d60bd819d4c7c097c8296..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/97.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>97.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\97.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>432</width>
- <height>400</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>97</xmin>
- <ymin>1</ymin>
- <xmax>166</xmax>
- <ymax>108</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.jpg
deleted file mode 100644
index e03c9664414ee18aec9e61db6d64d80e2bb6c3a2..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.xml
deleted file mode 100644
index 8e096381babb4e30abbbb42573548353f5cfa28d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/98.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>98.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\98.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
- <height>339</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>179</xmin>
- <ymin>43</ymin>
- <xmax>255</xmax>
- <ymax>167</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.jpg
deleted file mode 100644
index 7a810e26f4e31ca7dcac781532854e5ca0e3c112..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.jpg and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.xml b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.xml
deleted file mode 100644
index fcae8467e04e5d6e324cd8b075ab9e0affe9c2e5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/custom_dataset/train/99.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>99.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\99.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>476</width>
- <height>362</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>201</xmin>
- <ymin>170</ymin>
- <xmax>294</xmax>
- <ymax>330</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>231</xmin>
- <ymin>156</ymin>
- <xmax>306</xmax>
- <ymax>297</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>266</xmin>
- <ymin>139</ymin>
- <xmax>342</xmax>
- <ymax>265</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>308</xmin>
- <ymin>128</ymin>
- <xmax>370</xmax>
- <ymax>241</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>328</xmin>
- <ymin>117</ymin>
- <xmax>388</xmax>
- <ymax>217</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>372</xmin>
- <ymin>101</ymin>
- <xmax>421</xmax>
- <ymax>186</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/detection.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/detection.py
deleted file mode 100644
index d2c48203fca982f1c27a6c8378ece242cd133f99..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/detection.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-
-
-class Detection(object):
- """
- This class represents a bounding box detection in a single image.
-
- Parameters
- ----------
- tlwh : array_like
- Bounding box in format `(x, y, w, h)`.
- confidence : float
- Detector confidence score.
- feature : array_like
- A feature vector that describes the object contained in this image.
-
- Attributes
- ----------
- tlwh : ndarray
- Bounding box in format `(top left x, top left y, width, height)`.
- confidence : ndarray
- Detector confidence score.
- class_name : ndarray
- Detector class.
- feature : ndarray | NoneType
- A feature vector that describes the object contained in this image.
-
- """
-
- def __init__(self, tlwh, confidence, class_name, feature):
- self.tlwh = np.asarray(tlwh, dtype=np.float)
- self.confidence = float(confidence)
- self.class_name = class_name
- self.feature = np.asarray(feature, dtype=np.float32)
-
- def get_class(self):
- return self.class_name
-
- def to_tlbr(self):
- """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
- `(top left, bottom right)`.
- """
- ret = self.tlwh.copy()
- ret[2:] += ret[:2]
- return ret
-
- def to_xyah(self):
- """Convert bounding box to format `(center x, center y, aspect ratio,
- height)`, where the aspect ratio is `width / height`.
- """
- ret = self.tlwh.copy()
- ret[:2] += ret[2:] / 2
- ret[2] /= ret[3]
- return ret
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/generate_detections.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/generate_detections.py
deleted file mode 100644
index 287a7ad0e64203dc19a8d07c88b18b274c974d3f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/generate_detections.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import os
-import errno
-import argparse
-import numpy as np
-import cv2
-import tensorflow.compat.v1 as tf
-
-physical_devices = tf.config.experimental.list_physical_devices('GPU')
-if len(physical_devices) > 0:
- tf.config.experimental.set_memory_growth(physical_devices[0], True)
-
-def _run_in_batches(f, data_dict, out, batch_size):
- data_len = len(out)
- num_batches = int(data_len / batch_size)
-
- s, e = 0, 0
- for i in range(num_batches):
- s, e = i * batch_size, (i + 1) * batch_size
- batch_data_dict = {k: v[s:e] for k, v in data_dict.items()}
- out[s:e] = f(batch_data_dict)
- if e < len(out):
- batch_data_dict = {k: v[e:] for k, v in data_dict.items()}
- out[e:] = f(batch_data_dict)
-
-
-def extract_image_patch(image, bbox, patch_shape):
- """Extract image patch from bounding box.
-
- Parameters
- ----------
- image : ndarray
- The full image.
- bbox : array_like
- The bounding box in format (x, y, width, height).
- patch_shape : Optional[array_like]
- This parameter can be used to enforce a desired patch shape
- (height, width). First, the `bbox` is adapted to the aspect ratio
- of the patch shape, then it is clipped at the image boundaries.
- If None, the shape is computed from :arg:`bbox`.
-
- Returns
- -------
- ndarray | NoneType
- An image patch showing the :arg:`bbox`, optionally reshaped to
- :arg:`patch_shape`.
- Returns None if the bounding box is empty or fully outside of the image
- boundaries.
-
- """
- bbox = np.array(bbox)
- if patch_shape is not None:
- # correct aspect ratio to patch shape
- target_aspect = float(patch_shape[1]) / patch_shape[0]
- new_width = target_aspect * bbox[3]
- bbox[0] -= (new_width - bbox[2]) / 2
- bbox[2] = new_width
-
- # convert to top left, bottom right
- bbox[2:] += bbox[:2]
- bbox = bbox.astype(np.int)
-
- # clip at image boundaries
- bbox[:2] = np.maximum(0, bbox[:2])
- bbox[2:] = np.minimum(np.asarray(image.shape[:2][::-1]) - 1, bbox[2:])
- if np.any(bbox[:2] >= bbox[2:]):
- return None
- sx, sy, ex, ey = bbox
- image = image[sy:ey, sx:ex]
- image = cv2.resize(image, tuple(patch_shape[::-1]))
- return image
-
-
-class ImageEncoder(object):
-
- def __init__(self, checkpoint_filename, input_name="images", output_name="features"):
- self.session = tf.Session()
- with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle:
- graph_def = tf.GraphDef()
- graph_def.ParseFromString(file_handle.read())
- tf.import_graph_def(graph_def)
- try:
- self.input_var = tf.get_default_graph().get_tensor_by_name(input_name)
- self.output_var = tf.get_default_graph().get_tensor_by_name(output_name)
- except KeyError:
- layers = [i.name for i in tf.get_default_graph().get_operations()]
- self.input_var = tf.get_default_graph().get_tensor_by_name(layers[0]+':0')
- self.output_var = tf.get_default_graph().get_tensor_by_name(layers[-1]+':0')
-
- assert len(self.output_var.get_shape()) == 2
- assert len(self.input_var.get_shape()) == 4
- self.feature_dim = self.output_var.get_shape().as_list()[-1]
- self.image_shape = self.input_var.get_shape().as_list()[1:]
-
- def __call__(self, data_x, batch_size=32):
- out = np.zeros((len(data_x), self.feature_dim), np.float32)
- _run_in_batches(
- lambda x: self.session.run(self.output_var, feed_dict=x),
- {self.input_var: data_x}, out, batch_size)
- return out
-
-
-def create_box_encoder(model_filename, input_name="images:0", output_name="features:0", batch_size=32):
- image_encoder = ImageEncoder(model_filename, input_name, output_name)
- image_shape = image_encoder.image_shape
-
- def encoder(image, boxes):
- image_patches = []
- for box in boxes:
- patch = extract_image_patch(image, box, image_shape[:2])
- if patch is None:
- print("WARNING: Failed to extract image patch: %s." % str(box))
- patch = np.random.uniform(0., 255., image_shape).astype(np.uint8)
- image_patches.append(patch)
- image_patches = np.asarray(image_patches)
- return image_encoder(image_patches, batch_size)
-
- return encoder
-
-
-def generate_detections(encoder, mot_dir, output_dir, detection_dir=None):
- """Generate detections with features.
-
- Parameters
- ----------
- encoder : Callable[image, ndarray] -> ndarray
- The encoder function takes as input a BGR color image and a matrix of
- bounding boxes in format `(x, y, w, h)` and returns a matrix of
- corresponding feature vectors.
- mot_dir : str
- Path to the MOTChallenge directory (can be either train or test).
- output_dir
- Path to the output directory. Will be created if it does not exist.
- detection_dir
- Path to custom detections. The directory structure should be the default
- MOTChallenge structure: `[sequence]/det/det.txt`. If None, uses the
- standard MOTChallenge detections.
-
- """
- if detection_dir is None:
- detection_dir = mot_dir
- try:
- os.makedirs(output_dir)
- except OSError as exception:
- if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
- pass
- else:
- raise ValueError(
- "Failed to created output directory '%s'" % output_dir)
-
- for sequence in os.listdir(mot_dir):
- print("Processing %s" % sequence)
- sequence_dir = os.path.join(mot_dir, sequence)
-
- image_dir = os.path.join(sequence_dir, "img1")
- image_filenames = {
- int(os.path.splitext(f)[0]): os.path.join(image_dir, f)
- for f in os.listdir(image_dir)}
-
- detection_file = os.path.join(
- detection_dir, sequence, "det/det.txt")
- detections_in = np.loadtxt(detection_file, delimiter=',')
- detections_out = []
-
- frame_indices = detections_in[:, 0].astype(np.int)
- min_frame_idx = frame_indices.astype(np.int).min()
- max_frame_idx = frame_indices.astype(np.int).max()
- for frame_idx in range(min_frame_idx, max_frame_idx + 1):
- print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
- mask = frame_indices == frame_idx
- rows = detections_in[mask]
-
- if frame_idx not in image_filenames:
- print("WARNING could not find image for frame %d" % frame_idx)
- continue
- bgr_image = cv2.imread(
- image_filenames[frame_idx], cv2.IMREAD_COLOR)
- features = encoder(bgr_image, rows[:, 2:6].copy())
- detections_out += [np.r_[(row, feature)] for row, feature
- in zip(rows, features)]
-
- output_filename = os.path.join(output_dir, "%s.npy" % sequence)
- np.save(
- output_filename, np.asarray(detections_out), allow_pickle=False)
-
-
-def parse_args():
- """Parse command line arguments.
- """
- parser = argparse.ArgumentParser(description="Re-ID feature extractor")
- parser.add_argument(
- "--model",
- default="resources/networks/mars-small128.pb",
- help="Path to freezed inference graph protobuf.")
- parser.add_argument(
- "--mot_dir", help="Path to MOTChallenge directory (train or test)",
- required=True)
- parser.add_argument(
- "--detection_dir", help="Path to custom detections. Defaults to "
- "standard MOT detections Directory structure should be the default "
- "MOTChallenge structure: [sequence]/det/det.txt", default=None)
- parser.add_argument(
- "--output_dir", help="Output directory. Will be created if it does not"
- " exist.", default="detections")
- return parser.parse_args()
-
-
-def main():
- args = parse_args()
- encoder = create_box_encoder(args.model, batch_size=32)
- generate_detections(encoder, args.mot_dir, args.output_dir,
- args.detection_dir)
-
-
-if __name__ == "__main__":
- main()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/iou_matching.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/iou_matching.py
deleted file mode 100644
index c4dd0b88391d2ac28036c7163d5d4c988d4a9c4c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/iou_matching.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-from __future__ import absolute_import
-import numpy as np
-from . import linear_assignment
-
-
-def iou(bbox, candidates):
- """Computer intersection over union.
-
- Parameters
- ----------
- bbox : ndarray
- A bounding box in format `(top left x, top left y, width, height)`.
- candidates : ndarray
- A matrix of candidate bounding boxes (one per row) in the same format
- as `bbox`.
-
- Returns
- -------
- ndarray
- The intersection over union in [0, 1] between the `bbox` and each
- candidate. A higher score means a larger fraction of the `bbox` is
- occluded by the candidate.
-
- """
- bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
- candidates_tl = candidates[:, :2]
- candidates_br = candidates[:, :2] + candidates[:, 2:]
-
- tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
- np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
- br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
- np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
- wh = np.maximum(0., br - tl)
-
- area_intersection = wh.prod(axis=1)
- area_bbox = bbox[2:].prod()
- area_candidates = candidates[:, 2:].prod(axis=1)
- return area_intersection / (area_bbox + area_candidates - area_intersection)
-
-
-def iou_cost(tracks, detections, track_indices=None,
- detection_indices=None):
- """An intersection over union distance metric.
-
- Parameters
- ----------
- tracks : List[deep_sort.track.Track]
- A list of tracks.
- detections : List[deep_sort.detection.Detection]
- A list of detections.
- track_indices : Optional[List[int]]
- A list of indices to tracks that should be matched. Defaults to
- all `tracks`.
- detection_indices : Optional[List[int]]
- A list of indices to detections that should be matched. Defaults
- to all `detections`.
-
- Returns
- -------
- ndarray
- Returns a cost matrix of shape
- len(track_indices), len(detection_indices) where entry (i, j) is
- `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
-
- """
- if track_indices is None:
- track_indices = np.arange(len(tracks))
- if detection_indices is None:
- detection_indices = np.arange(len(detections))
-
- cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
- for row, track_idx in enumerate(track_indices):
- if tracks[track_idx].time_since_update > 1:
- cost_matrix[row, :] = linear_assignment.INFTY_COST
- continue
-
- bbox = tracks[track_idx].to_tlwh()
- candidates = np.asarray([detections[i].tlwh for i in detection_indices])
- cost_matrix[row, :] = 1. - iou(bbox, candidates)
- return cost_matrix
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/kalman_filter.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/kalman_filter.py
deleted file mode 100644
index 787a76e6a43870a9538647b51fda6a5254ce2d43..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/kalman_filter.py
+++ /dev/null
@@ -1,229 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-import scipy.linalg
-
-
-"""
-Table for the 0.95 quantile of the chi-square distribution with N degrees of
-freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
-function and used as Mahalanobis gating threshold.
-"""
-chi2inv95 = {
- 1: 3.8415,
- 2: 5.9915,
- 3: 7.8147,
- 4: 9.4877,
- 5: 11.070,
- 6: 12.592,
- 7: 14.067,
- 8: 15.507,
- 9: 16.919}
-
-
-class KalmanFilter(object):
- """
- A simple Kalman filter for tracking bounding boxes in image space.
-
- The 8-dimensional state space
-
- x, y, a, h, vx, vy, va, vh
-
- contains the bounding box center position (x, y), aspect ratio a, height h,
- and their respective velocities.
-
- Object motion follows a constant velocity model. The bounding box location
- (x, y, a, h) is taken as direct observation of the state space (linear
- observation model).
-
- """
-
- def __init__(self):
- ndim, dt = 4, 1.
-
- # Create Kalman filter model matrices.
- self._motion_mat = np.eye(2 * ndim, 2 * ndim)
- for i in range(ndim):
- self._motion_mat[i, ndim + i] = dt
- self._update_mat = np.eye(ndim, 2 * ndim)
-
- # Motion and observation uncertainty are chosen relative to the current
- # state estimate. These weights control the amount of uncertainty in
- # the model. This is a bit hacky.
- self._std_weight_position = 1. / 20
- self._std_weight_velocity = 1. / 160
-
- def initiate(self, measurement):
- """Create track from unassociated measurement.
-
- Parameters
- ----------
- measurement : ndarray
- Bounding box coordinates (x, y, a, h) with center position (x, y),
- aspect ratio a, and height h.
-
- Returns
- -------
- (ndarray, ndarray)
- Returns the mean vector (8 dimensional) and covariance matrix (8x8
- dimensional) of the new track. Unobserved velocities are initialized
- to 0 mean.
-
- """
- mean_pos = measurement
- mean_vel = np.zeros_like(mean_pos)
- mean = np.r_[mean_pos, mean_vel]
-
- std = [
- 2 * self._std_weight_position * measurement[3],
- 2 * self._std_weight_position * measurement[3],
- 1e-2,
- 2 * self._std_weight_position * measurement[3],
- 10 * self._std_weight_velocity * measurement[3],
- 10 * self._std_weight_velocity * measurement[3],
- 1e-5,
- 10 * self._std_weight_velocity * measurement[3]]
- covariance = np.diag(np.square(std))
- return mean, covariance
-
- def predict(self, mean, covariance):
- """Run Kalman filter prediction step.
-
- Parameters
- ----------
- mean : ndarray
- The 8 dimensional mean vector of the object state at the previous
- time step.
- covariance : ndarray
- The 8x8 dimensional covariance matrix of the object state at the
- previous time step.
-
- Returns
- -------
- (ndarray, ndarray)
- Returns the mean vector and covariance matrix of the predicted
- state. Unobserved velocities are initialized to 0 mean.
-
- """
- std_pos = [
- self._std_weight_position * mean[3],
- self._std_weight_position * mean[3],
- 1e-2,
- self._std_weight_position * mean[3]]
- std_vel = [
- self._std_weight_velocity * mean[3],
- self._std_weight_velocity * mean[3],
- 1e-5,
- self._std_weight_velocity * mean[3]]
- motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
-
- mean = np.dot(self._motion_mat, mean)
- covariance = np.linalg.multi_dot((
- self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
-
- return mean, covariance
-
- def project(self, mean, covariance):
- """Project state distribution to measurement space.
-
- Parameters
- ----------
- mean : ndarray
- The state's mean vector (8 dimensional array).
- covariance : ndarray
- The state's covariance matrix (8x8 dimensional).
-
- Returns
- -------
- (ndarray, ndarray)
- Returns the projected mean and covariance matrix of the given state
- estimate.
-
- """
- std = [
- self._std_weight_position * mean[3],
- self._std_weight_position * mean[3],
- 1e-1,
- self._std_weight_position * mean[3]]
- innovation_cov = np.diag(np.square(std))
-
- mean = np.dot(self._update_mat, mean)
- covariance = np.linalg.multi_dot((
- self._update_mat, covariance, self._update_mat.T))
- return mean, covariance + innovation_cov
-
- def update(self, mean, covariance, measurement):
- """Run Kalman filter correction step.
-
- Parameters
- ----------
- mean : ndarray
- The predicted state's mean vector (8 dimensional).
- covariance : ndarray
- The state's covariance matrix (8x8 dimensional).
- measurement : ndarray
- The 4 dimensional measurement vector (x, y, a, h), where (x, y)
- is the center position, a the aspect ratio, and h the height of the
- bounding box.
-
- Returns
- -------
- (ndarray, ndarray)
- Returns the measurement-corrected state distribution.
-
- """
- projected_mean, projected_cov = self.project(mean, covariance)
-
- chol_factor, lower = scipy.linalg.cho_factor(
- projected_cov, lower=True, check_finite=False)
- kalman_gain = scipy.linalg.cho_solve(
- (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
- check_finite=False).T
- innovation = measurement - projected_mean
-
- new_mean = mean + np.dot(innovation, kalman_gain.T)
- new_covariance = covariance - np.linalg.multi_dot((
- kalman_gain, projected_cov, kalman_gain.T))
- return new_mean, new_covariance
-
- def gating_distance(self, mean, covariance, measurements,
- only_position=False):
- """Compute gating distance between state distribution and measurements.
-
- A suitable distance threshold can be obtained from `chi2inv95`. If
- `only_position` is False, the chi-square distribution has 4 degrees of
- freedom, otherwise 2.
-
- Parameters
- ----------
- mean : ndarray
- Mean vector over the state distribution (8 dimensional).
- covariance : ndarray
- Covariance of the state distribution (8x8 dimensional).
- measurements : ndarray
- An Nx4 dimensional matrix of N measurements, each in
- format (x, y, a, h) where (x, y) is the bounding box center
- position, a the aspect ratio, and h the height.
- only_position : Optional[bool]
- If True, distance computation is done with respect to the bounding
- box center position only.
-
- Returns
- -------
- ndarray
- Returns an array of length N, where the i-th element contains the
- squared Mahalanobis distance between (mean, covariance) and
- `measurements[i]`.
-
- """
- mean, covariance = self.project(mean, covariance)
- if only_position:
- mean, covariance = mean[:2], covariance[:2, :2]
- measurements = measurements[:, :2]
-
- cholesky_factor = np.linalg.cholesky(covariance)
- d = measurements - mean
- z = scipy.linalg.solve_triangular(
- cholesky_factor, d.T, lower=True, check_finite=False,
- overwrite_b=True)
- squared_maha = np.sum(z * z, axis=0)
- return squared_maha
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/linear_assignment.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/linear_assignment.py
deleted file mode 100644
index 47c8bf065bad27dd276fd6a33efc184f15a59c91..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/linear_assignment.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-from __future__ import absolute_import
-import numpy as np
-from scipy.optimize import linear_sum_assignment
-from . import kalman_filter
-
-
-INFTY_COST = 1e+5
-
-
-def min_cost_matching(
- distance_metric, max_distance, tracks, detections, track_indices=None,
- detection_indices=None):
- """Solve linear assignment problem.
-
- Parameters
- ----------
- distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
- The distance metric is given a list of tracks and detections as well as
- a list of N track indices and M detection indices. The metric should
- return the NxM dimensional cost matrix, where element (i, j) is the
- association cost between the i-th track in the given track indices and
- the j-th detection in the given detection_indices.
- max_distance : float
- Gating threshold. Associations with cost larger than this value are
- disregarded.
- tracks : List[track.Track]
- A list of predicted tracks at the current time step.
- detections : List[detection.Detection]
- A list of detections at the current time step.
- track_indices : List[int]
- List of track indices that maps rows in `cost_matrix` to tracks in
- `tracks` (see description above).
- detection_indices : List[int]
- List of detection indices that maps columns in `cost_matrix` to
- detections in `detections` (see description above).
-
- Returns
- -------
- (List[(int, int)], List[int], List[int])
- Returns a tuple with the following three entries:
- * A list of matched track and detection indices.
- * A list of unmatched track indices.
- * A list of unmatched detection indices.
-
- """
- if track_indices is None:
- track_indices = np.arange(len(tracks))
- if detection_indices is None:
- detection_indices = np.arange(len(detections))
-
- if len(detection_indices) == 0 or len(track_indices) == 0:
- return [], track_indices, detection_indices # Nothing to match.
-
- cost_matrix = distance_metric(
- tracks, detections, track_indices, detection_indices)
- cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
- indices = linear_sum_assignment(cost_matrix)
- indices = np.asarray(indices)
- indices = np.transpose(indices)
- matches, unmatched_tracks, unmatched_detections = [], [], []
- for col, detection_idx in enumerate(detection_indices):
- if col not in indices[:, 1]:
- unmatched_detections.append(detection_idx)
- for row, track_idx in enumerate(track_indices):
- if row not in indices[:, 0]:
- unmatched_tracks.append(track_idx)
- for row, col in indices:
- track_idx = track_indices[row]
- detection_idx = detection_indices[col]
- if cost_matrix[row, col] > max_distance:
- unmatched_tracks.append(track_idx)
- unmatched_detections.append(detection_idx)
- else:
- matches.append((track_idx, detection_idx))
- return matches, unmatched_tracks, unmatched_detections
-
-
-def matching_cascade(
- distance_metric, max_distance, cascade_depth, tracks, detections,
- track_indices=None, detection_indices=None):
- """Run matching cascade.
-
- Parameters
- ----------
- distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
- The distance metric is given a list of tracks and detections as well as
- a list of N track indices and M detection indices. The metric should
- return the NxM dimensional cost matrix, where element (i, j) is the
- association cost between the i-th track in the given track indices and
- the j-th detection in the given detection indices.
- max_distance : float
- Gating threshold. Associations with cost larger than this value are
- disregarded.
- cascade_depth: int
- The cascade depth, should be se to the maximum track age.
- tracks : List[track.Track]
- A list of predicted tracks at the current time step.
- detections : List[detection.Detection]
- A list of detections at the current time step.
- track_indices : Optional[List[int]]
- List of track indices that maps rows in `cost_matrix` to tracks in
- `tracks` (see description above). Defaults to all tracks.
- detection_indices : Optional[List[int]]
- List of detection indices that maps columns in `cost_matrix` to
- detections in `detections` (see description above). Defaults to all
- detections.
-
- Returns
- -------
- (List[(int, int)], List[int], List[int])
- Returns a tuple with the following three entries:
- * A list of matched track and detection indices.
- * A list of unmatched track indices.
- * A list of unmatched detection indices.
-
- """
- if track_indices is None:
- track_indices = list(range(len(tracks)))
- if detection_indices is None:
- detection_indices = list(range(len(detections)))
-
- unmatched_detections = detection_indices
- matches = []
- for level in range(cascade_depth):
- if len(unmatched_detections) == 0: # No detections left
- break
-
- track_indices_l = [
- k for k in track_indices
- if tracks[k].time_since_update == 1 + level
- ]
- if len(track_indices_l) == 0: # Nothing to match at this level
- continue
-
- matches_l, _, unmatched_detections = \
- min_cost_matching(
- distance_metric, max_distance, tracks, detections,
- track_indices_l, unmatched_detections)
- matches += matches_l
- unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
- return matches, unmatched_tracks, unmatched_detections
-
-
-def gate_cost_matrix(
- kf, cost_matrix, tracks, detections, track_indices, detection_indices,
- gated_cost=INFTY_COST, only_position=False):
- """Invalidate infeasible entries in cost matrix based on the state
- distributions obtained by Kalman filtering.
-
- Parameters
- ----------
- kf : The Kalman filter.
- cost_matrix : ndarray
- The NxM dimensional cost matrix, where N is the number of track indices
- and M is the number of detection indices, such that entry (i, j) is the
- association cost between `tracks[track_indices[i]]` and
- `detections[detection_indices[j]]`.
- tracks : List[track.Track]
- A list of predicted tracks at the current time step.
- detections : List[detection.Detection]
- A list of detections at the current time step.
- track_indices : List[int]
- List of track indices that maps rows in `cost_matrix` to tracks in
- `tracks` (see description above).
- detection_indices : List[int]
- List of detection indices that maps columns in `cost_matrix` to
- detections in `detections` (see description above).
- gated_cost : Optional[float]
- Entries in the cost matrix corresponding to infeasible associations are
- set this value. Defaults to a very large value.
- only_position : Optional[bool]
- If True, only the x, y position of the state distribution is considered
- during gating. Defaults to False.
-
- Returns
- -------
- ndarray
- Returns the modified cost matrix.
-
- """
- gating_dim = 2 if only_position else 4
- gating_threshold = kalman_filter.chi2inv95[gating_dim]
- measurements = np.asarray(
- [detections[i].to_xyah() for i in detection_indices])
- for row, track_idx in enumerate(track_indices):
- track = tracks[track_idx]
- gating_distance = kf.gating_distance(
- track.mean, track.covariance, measurements, only_position)
- cost_matrix[row, gating_distance > gating_threshold] = gated_cost
- return cost_matrix
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/nn_matching.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/nn_matching.py
deleted file mode 100644
index 2e7bfea4b87b0c256274937c8323ffc93fa5d61b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/nn_matching.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-
-
-def _pdist(a, b):
- """Compute pair-wise squared distance between points in `a` and `b`.
-
- Parameters
- ----------
- a : array_like
- An NxM matrix of N samples of dimensionality M.
- b : array_like
- An LxM matrix of L samples of dimensionality M.
-
- Returns
- -------
- ndarray
- Returns a matrix of size len(a), len(b) such that eleement (i, j)
- contains the squared distance between `a[i]` and `b[j]`.
-
- """
- a, b = np.asarray(a), np.asarray(b)
- if len(a) == 0 or len(b) == 0:
- return np.zeros((len(a), len(b)))
- a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1)
- r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :]
- r2 = np.clip(r2, 0., float(np.inf))
- return r2
-
-
-def _cosine_distance(a, b, data_is_normalized=False):
- """Compute pair-wise cosine distance between points in `a` and `b`.
-
- Parameters
- ----------
- a : array_like
- An NxM matrix of N samples of dimensionality M.
- b : array_like
- An LxM matrix of L samples of dimensionality M.
- data_is_normalized : Optional[bool]
- If True, assumes rows in a and b are unit length vectors.
- Otherwise, a and b are explicitly normalized to lenght 1.
-
- Returns
- -------
- ndarray
- Returns a matrix of size len(a), len(b) such that eleement (i, j)
- contains the squared distance between `a[i]` and `b[j]`.
-
- """
- if not data_is_normalized:
- a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
- b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
- return 1. - np.dot(a, b.T)
-
-
-def _nn_euclidean_distance(x, y):
- """ Helper function for nearest neighbor distance metric (Euclidean).
-
- Parameters
- ----------
- x : ndarray
- A matrix of N row-vectors (sample points).
- y : ndarray
- A matrix of M row-vectors (query points).
-
- Returns
- -------
- ndarray
- A vector of length M that contains for each entry in `y` the
- smallest Euclidean distance to a sample in `x`.
-
- """
- distances = _pdist(x, y)
- return np.maximum(0.0, distances.min(axis=0))
-
-
-def _nn_cosine_distance(x, y):
- """ Helper function for nearest neighbor distance metric (cosine).
-
- Parameters
- ----------
- x : ndarray
- A matrix of N row-vectors (sample points).
- y : ndarray
- A matrix of M row-vectors (query points).
-
- Returns
- -------
- ndarray
- A vector of length M that contains for each entry in `y` the
- smallest cosine distance to a sample in `x`.
-
- """
- distances = _cosine_distance(x, y)
- return distances.min(axis=0)
-
-
-class NearestNeighborDistanceMetric(object):
- """
- A nearest neighbor distance metric that, for each target, returns
- the closest distance to any sample that has been observed so far.
-
- Parameters
- ----------
- metric : str
- Either "euclidean" or "cosine".
- matching_threshold: float
- The matching threshold. Samples with larger distance are considered an
- invalid match.
- budget : Optional[int]
- If not None, fix samples per class to at most this number. Removes
- the oldest samples when the budget is reached.
-
- Attributes
- ----------
- samples : Dict[int -> List[ndarray]]
- A dictionary that maps from target identities to the list of samples
- that have been observed so far.
-
- """
-
- def __init__(self, metric, matching_threshold, budget=None):
-
-
- if metric == "euclidean":
- self._metric = _nn_euclidean_distance
- elif metric == "cosine":
- self._metric = _nn_cosine_distance
- else:
- raise ValueError(
- "Invalid metric; must be either 'euclidean' or 'cosine'")
- self.matching_threshold = matching_threshold
- self.budget = budget
- self.samples = {}
-
- def partial_fit(self, features, targets, active_targets):
- """Update the distance metric with new data.
-
- Parameters
- ----------
- features : ndarray
- An NxM matrix of N features of dimensionality M.
- targets : ndarray
- An integer array of associated target identities.
- active_targets : List[int]
- A list of targets that are currently present in the scene.
-
- """
- for feature, target in zip(features, targets):
- self.samples.setdefault(target, []).append(feature)
- if self.budget is not None:
- self.samples[target] = self.samples[target][-self.budget:]
- self.samples = {k: self.samples[k] for k in active_targets}
-
- def distance(self, features, targets):
- """Compute distance between features and targets.
-
- Parameters
- ----------
- features : ndarray
- An NxM matrix of N features of dimensionality M.
- targets : List[int]
- A list of targets to match the given `features` against.
-
- Returns
- -------
- ndarray
- Returns a cost matrix of shape len(targets), len(features), where
- element (i, j) contains the closest squared distance between
- `targets[i]` and `features[j]`.
-
- """
- cost_matrix = np.zeros((len(targets), len(features)))
- for i, target in enumerate(targets):
- cost_matrix[i, :] = self._metric(self.samples[target], features)
- return cost_matrix
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/preprocessing.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/preprocessing.py
deleted file mode 100644
index 13bc3269d194cc597f838697f3dd03f46633b41e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/preprocessing.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-import numpy as np
-import cv2
-
-
-def non_max_suppression(boxes, classes, max_bbox_overlap, scores=None):
- """Suppress overlapping detections.
-
- Original code from [1]_ has been adapted to include confidence score.
-
- .. [1] http://www.pyimagesearch.com/2015/02/16/
- faster-non-maximum-suppression-python/
-
- Examples
- --------
-
- >>> boxes = [d.roi for d in detections]
- >>> classes = [d.classes for d in detections]
- >>> scores = [d.confidence for d in detections]
- >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
- >>> detections = [detections[i] for i in indices]
-
- Parameters
- ----------
- boxes : ndarray
- Array of ROIs (x, y, width, height).
- max_bbox_overlap : float
- ROIs that overlap more than this values are suppressed.
- scores : Optional[array_like]
- Detector confidence score.
-
- Returns
- -------
- List[int]
- Returns indices of detections that have survived non-maxima suppression.
-
- """
- if len(boxes) == 0:
- return []
-
- boxes = boxes.astype(np.float)
- pick = []
-
- x1 = boxes[:, 0]
- y1 = boxes[:, 1]
- x2 = boxes[:, 2] + boxes[:, 0]
- y2 = boxes[:, 3] + boxes[:, 1]
-
- area = (x2 - x1 + 1) * (y2 - y1 + 1)
- if scores is not None:
- idxs = np.argsort(scores)
- else:
- idxs = np.argsort(y2)
-
- while len(idxs) > 0:
- last = len(idxs) - 1
- i = idxs[last]
- pick.append(i)
-
- xx1 = np.maximum(x1[i], x1[idxs[:last]])
- yy1 = np.maximum(y1[i], y1[idxs[:last]])
- xx2 = np.minimum(x2[i], x2[idxs[:last]])
- yy2 = np.minimum(y2[i], y2[idxs[:last]])
-
- w = np.maximum(0, xx2 - xx1 + 1)
- h = np.maximum(0, yy2 - yy1 + 1)
-
- overlap = (w * h) / area[idxs[:last]]
-
- idxs = np.delete(
- idxs, np.concatenate(
- ([last], np.where(overlap > max_bbox_overlap)[0])))
-
- return pick
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/test_tracking.gif b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/test_tracking.gif
deleted file mode 100644
index 7fd45e80f06431235a36fb3985e88db95e49de54..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/test_tracking.gif and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/track.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/track.py
deleted file mode 100644
index 68bb88c8990e117d416950eb0912d600c2e3a8c1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/track.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-
-
-class TrackState:
- """
- Enumeration type for the single target track state. Newly created tracks are
- classified as `tentative` until enough evidence has been collected. Then,
- the track state is changed to `confirmed`. Tracks that are no longer alive
- are classified as `deleted` to mark them for removal from the set of active
- tracks.
-
- """
-
- Tentative = 1
- Confirmed = 2
- Deleted = 3
-
-
-class Track:
- """
- A single target track with state space `(x, y, a, h)` and associated
- velocities, where `(x, y)` is the center of the bounding box, `a` is the
- aspect ratio and `h` is the height.
-
- Parameters
- ----------
- mean : ndarray
- Mean vector of the initial state distribution.
- covariance : ndarray
- Covariance matrix of the initial state distribution.
- track_id : int
- A unique track identifier.
- n_init : int
- Number of consecutive detections before the track is confirmed. The
- track state is set to `Deleted` if a miss occurs within the first
- `n_init` frames.
- max_age : int
- The maximum number of consecutive misses before the track state is
- set to `Deleted`.
- feature : Optional[ndarray]
- Feature vector of the detection this track originates from. If not None,
- this feature is added to the `features` cache.
-
- Attributes
- ----------
- mean : ndarray
- Mean vector of the initial state distribution.
- covariance : ndarray
- Covariance matrix of the initial state distribution.
- track_id : int
- A unique track identifier.
- hits : int
- Total number of measurement updates.
- age : int
- Total number of frames since first occurance.
- time_since_update : int
- Total number of frames since last measurement update.
- state : TrackState
- The current track state.
- features : List[ndarray]
- A cache of features. On each measurement update, the associated feature
- vector is added to this list.
-
- """
-
- def __init__(self, mean, covariance, track_id, n_init, max_age,
- feature=None, class_name=None):
- self.mean = mean
- self.covariance = covariance
- self.track_id = track_id
- self.hits = 1
- self.age = 1
- self.time_since_update = 0
-
- self.state = TrackState.Tentative
- self.features = []
- if feature is not None:
- self.features.append(feature)
-
- self._n_init = n_init
- self._max_age = max_age
- self.class_name = class_name
-
- def to_tlwh(self):
- """Get current position in bounding box format `(top left x, top left y,
- width, height)`.
-
- Returns
- -------
- ndarray
- The bounding box.
-
- """
- ret = self.mean[:4].copy()
- ret[2] *= ret[3]
- ret[:2] -= ret[2:] / 2
- return ret
-
- def to_tlbr(self):
- """Get current position in bounding box format `(min x, miny, max x,
- max y)`.
-
- Returns
- -------
- ndarray
- The bounding box.
-
- """
- ret = self.to_tlwh()
- ret[2:] = ret[:2] + ret[2:]
- return ret
-
- def get_class(self):
- return self.class_name
-
- def predict(self, kf):
- """Propagate the state distribution to the current time step using a
- Kalman filter prediction step.
-
- Parameters
- ----------
- kf : kalman_filter.KalmanFilter
- The Kalman filter.
-
- """
- self.mean, self.covariance = kf.predict(self.mean, self.covariance)
- self.age += 1
- self.time_since_update += 1
-
- def update(self, kf, detection):
- """Perform Kalman filter measurement update step and update the feature
- cache.
-
- Parameters
- ----------
- kf : kalman_filter.KalmanFilter
- The Kalman filter.
- detection : Detection
- The associated detection.
-
- """
- self.mean, self.covariance = kf.update(
- self.mean, self.covariance, detection.to_xyah())
- self.features.append(detection.feature)
-
- self.hits += 1
- self.time_since_update = 0
- if self.state == TrackState.Tentative and self.hits >= self._n_init:
- self.state = TrackState.Confirmed
-
- def mark_missed(self):
- """Mark this track as missed (no association at the current time step).
- """
- if self.state == TrackState.Tentative:
- self.state = TrackState.Deleted
- elif self.time_since_update > self._max_age:
- self.state = TrackState.Deleted
-
- def is_tentative(self):
- """Returns True if this track is tentative (unconfirmed).
- """
- return self.state == TrackState.Tentative
-
- def is_confirmed(self):
- """Returns True if this track is confirmed."""
- return self.state == TrackState.Confirmed
-
- def is_deleted(self):
- """Returns True if this track is dead and should be deleted."""
- return self.state == TrackState.Deleted
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/tracker.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/tracker.py
deleted file mode 100644
index 969e1b5447af1bed5bc295a8f6a40a754d305f97..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/deep_sort/tracker.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# vim: expandtab:ts=4:sw=4
-from __future__ import absolute_import
-import numpy as np
-from . import kalman_filter
-from . import linear_assignment
-from . import iou_matching
-from .track import Track
-
-
-class Tracker:
- """
- This is the multi-target tracker.
-
- Parameters
- ----------
- metric : nn_matching.NearestNeighborDistanceMetric
- A distance metric for measurement-to-track association.
- max_age : int
- Maximum number of missed misses before a track is deleted.
- n_init : int
- Number of consecutive detections before the track is confirmed. The
- track state is set to `Deleted` if a miss occurs within the first
- `n_init` frames.
-
- Attributes
- ----------
- metric : nn_matching.NearestNeighborDistanceMetric
- The distance metric used for measurement to track association.
- max_age : int
- Maximum number of missed misses before a track is deleted.
- n_init : int
- Number of frames that a track remains in initialization phase.
- kf : kalman_filter.KalmanFilter
- A Kalman filter to filter target trajectories in image space.
- tracks : List[Track]
- The list of active tracks at the current time step.
-
- """
-
- def __init__(self, metric, max_iou_distance=0.7, max_age=30, n_init=3):
- self.metric = metric
- self.max_iou_distance = max_iou_distance
- self.max_age = max_age
- self.n_init = n_init
-
- self.kf = kalman_filter.KalmanFilter()
- self.tracks = []
- self._next_id = 1
-
- def predict(self):
- """Propagate track state distributions one time step forward.
-
- This function should be called once every time step, before `update`.
- """
- for track in self.tracks:
- track.predict(self.kf)
-
- def update(self, detections):
- """Perform measurement update and track management.
-
- Parameters
- ----------
- detections : List[deep_sort.detection.Detection]
- A list of detections at the current time step.
-
- """
- # Run matching cascade.
- matches, unmatched_tracks, unmatched_detections = \
- self._match(detections)
-
- # Update track set.
- for track_idx, detection_idx in matches:
- self.tracks[track_idx].update(
- self.kf, detections[detection_idx])
- for track_idx in unmatched_tracks:
- self.tracks[track_idx].mark_missed()
- for detection_idx in unmatched_detections:
- self._initiate_track(detections[detection_idx])
- self.tracks = [t for t in self.tracks if not t.is_deleted()]
-
- # Update distance metric.
- active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
- features, targets = [], []
- for track in self.tracks:
- if not track.is_confirmed():
- continue
- features += track.features
- targets += [track.track_id for _ in track.features]
- track.features = []
- self.metric.partial_fit(
- np.asarray(features), np.asarray(targets), active_targets)
-
- def _match(self, detections):
-
- def gated_metric(tracks, dets, track_indices, detection_indices):
- features = np.array([dets[i].feature for i in detection_indices])
- targets = np.array([tracks[i].track_id for i in track_indices])
- cost_matrix = self.metric.distance(features, targets)
- cost_matrix = linear_assignment.gate_cost_matrix(
- self.kf, cost_matrix, tracks, dets, track_indices,
- detection_indices)
-
- return cost_matrix
-
- # Split track set into confirmed and unconfirmed tracks.
- confirmed_tracks = [
- i for i, t in enumerate(self.tracks) if t.is_confirmed()]
- unconfirmed_tracks = [
- i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
-
- # Associate confirmed tracks using appearance features.
- matches_a, unmatched_tracks_a, unmatched_detections = \
- linear_assignment.matching_cascade(
- gated_metric, self.metric.matching_threshold, self.max_age,
- self.tracks, detections, confirmed_tracks)
-
- # Associate remaining tracks together with unconfirmed tracks using IOU.
- iou_track_candidates = unconfirmed_tracks + [
- k for k in unmatched_tracks_a if
- self.tracks[k].time_since_update == 1]
- unmatched_tracks_a = [
- k for k in unmatched_tracks_a if
- self.tracks[k].time_since_update != 1]
- matches_b, unmatched_tracks_b, unmatched_detections = \
- linear_assignment.min_cost_matching(
- iou_matching.iou_cost, self.max_iou_distance, self.tracks,
- detections, iou_track_candidates, unmatched_detections)
-
- matches = matches_a + matches_b
- unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
- return matches, unmatched_tracks, unmatched_detections
-
- def _initiate_track(self, detection):
- mean, covariance = self.kf.initiate(detection.to_xyah())
- class_name = detection.get_class()
- self.tracks.append(Track(
- mean, covariance, self._next_id, self.n_init, self.max_age,
- detection.feature, class_name))
- self._next_id += 1
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detect_mnist.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detect_mnist.py
deleted file mode 100644
index 3deafec505d189afedfdea57576afddaeaf46269..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detect_mnist.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#================================================================
-#
-# File name : detect_mnist.py
-# Author : PyLessons
-# Created date: 2020-08-12
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : mnist object detection example
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import cv2
-import numpy as np
-import random
-import time
-import tensorflow as tf
-from yolov3.yolov4 import Create_Yolo
-from yolov3.utils import detect_image
-from yolov3.configs import *
-
-while True:
- ID = random.randint(0, 200)
- label_txt = "mnist/mnist_test.txt"
- image_info = open(label_txt).readlines()[ID].split()
-
- image_path = image_info[0]
-
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
- yolo.load_weights(f"./checkpoints/{TRAIN_MODEL_NAME}") # use keras weights
-
- detect_image(yolo, image_path, "mnist_test.jpg", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_custom.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_custom.py
deleted file mode 100644
index c5717f681e4b698ab2ee8efd0ac96744a53dc736..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_custom.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#================================================================
-#
-# File name : detection_custom.py
-# Author : PyLessons
-# Created date: 2020-09-17
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : object detection image and video example
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import cv2
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
-from yolov3.configs import *
-
-image_path = "./test/webcam chalon/doehff.jpg"
-video_path = "./IMAGES/test.mp4"
-
-yolo = Load_Yolo_model()
-_, boxes = detect_image(yolo, image_path, "./images test/test2_pred.jpg", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
-#detect_video(yolo, video_path, './IMAGES/detected.mp4', input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
-#detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255, 0, 0))
-
-#detect_video_realtime_mp(video_path, "Output.mp4", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0), realtime=False)
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_demo.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_demo.py
deleted file mode 100644
index ffe17d4d9f22c2ff11f8b17a953c3b045d59cbf2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_demo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#================================================================
-#
-# File name : detection_demo.py
-# Author : PyLessons
-# Created date: 2020-09-27
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : object detection image and video example
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import cv2
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
-from yolov3.configs import *
-
-image_path = "./IMAGES/kite.jpg"
-video_path = "./IMAGES/test.mp4"
-
-yolo = Load_Yolo_model()
-#detect_image(yolo, image_path, "./IMAGES/kite_pred.jpg", input_size=YOLO_INPUT_SIZE, show=True, rectangle_colors=(255,0,0))
-#detect_video(yolo, video_path, "", input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0))
-detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, rectangle_colors=(255, 0, 0))
-
-#detect_video_realtime_mp(video_path, "Output.mp4", input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0), realtime=False)
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/evaluate_mAP.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/evaluate_mAP.py
deleted file mode 100644
index a5643c538db1680cb823ccb24003b27730f1721b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/evaluate_mAP.py
+++ /dev/null
@@ -1,300 +0,0 @@
-#================================================================
-#
-# File name : evaluate_mAP.py
-# Author : PyLessons
-# Created date: 2020-08-17
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : used to evaluate model mAP and FPS
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import cv2
-import numpy as np
-import tensorflow as tf
-from tensorflow.python.saved_model import tag_constants
-from yolov3.dataset import Dataset
-from yolov3.yolov4 import Create_Yolo
-from yolov3.utils import load_yolo_weights, detect_image, image_preprocess, postprocess_boxes, nms, read_class_names
-from yolov3.configs import *
-import shutil
-import json
-import time
-
-gpus = tf.config.experimental.list_physical_devices('GPU')
-if len(gpus) > 0:
- try: tf.config.experimental.set_memory_growth(gpus[0], True)
- except RuntimeError: print("RuntimeError in tf.config.experimental.list_physical_devices('GPU')")
-
-
-def voc_ap(rec, prec):
- """
- --- Official matlab code VOC2012---
- mrec=[0 ; rec ; 1];
- mpre=[0 ; prec ; 0];
- for i=numel(mpre)-1:-1:1
- mpre(i)=max(mpre(i),mpre(i+1));
- end
- i=find(mrec(2:end)~=mrec(1:end-1))+1;
- ap=sum((mrec(i)-mrec(i-1)).*mpre(i));
- """
- rec.insert(0, 0.0) # insert 0.0 at begining of list
- rec.append(1.0) # insert 1.0 at end of list
- mrec = rec[:]
- prec.insert(0, 0.0) # insert 0.0 at begining of list
- prec.append(0.0) # insert 0.0 at end of list
- mpre = prec[:]
- """
- This part makes the precision monotonically decreasing
- (goes from the end to the beginning)
- matlab: for i=numel(mpre)-1:-1:1
- mpre(i)=max(mpre(i),mpre(i+1));
- """
- # matlab indexes start in 1 but python in 0, so I have to do:
- # range(start=(len(mpre) - 2), end=0, step=-1)
- # also the python function range excludes the end, resulting in:
- # range(start=(len(mpre) - 2), end=-1, step=-1)
- for i in range(len(mpre)-2, -1, -1):
- mpre[i] = max(mpre[i], mpre[i+1])
- """
- This part creates a list of indexes where the recall changes
- matlab: i=find(mrec(2:end)~=mrec(1:end-1))+1;
- """
- i_list = []
- for i in range(1, len(mrec)):
- if mrec[i] != mrec[i-1]:
- i_list.append(i) # if it was matlab would be i + 1
- """
- The Average Precision (AP) is the area under the curve
- (numerical integration)
- matlab: ap=sum((mrec(i)-mrec(i-1)).*mpre(i));
- """
- ap = 0.0
- for i in i_list:
- ap += ((mrec[i]-mrec[i-1])*mpre[i])
- return ap, mrec, mpre
-
-
-def get_mAP(Yolo, dataset, score_threshold=0.25, iou_threshold=0.50, TEST_INPUT_SIZE=TEST_INPUT_SIZE):
- MINOVERLAP = 0.5 # default value (defined in the PASCAL VOC2012 challenge)
- NUM_CLASS = read_class_names(TRAIN_CLASSES)
-
- ground_truth_dir_path = 'mAP/ground-truth'
- if os.path.exists(ground_truth_dir_path): shutil.rmtree(ground_truth_dir_path)
-
- if not os.path.exists('mAP'): os.mkdir('mAP')
- os.mkdir(ground_truth_dir_path)
-
- print(f'\ncalculating mAP{int(iou_threshold*100)}...\n')
-
- gt_counter_per_class = {}
- for index in range(dataset.num_samples):
- ann_dataset = dataset.annotations[index]
-
- original_image, bbox_data_gt = dataset.parse_annotation(ann_dataset, True)
-
- if len(bbox_data_gt) == 0:
- bboxes_gt = []
- classes_gt = []
- else:
- bboxes_gt, classes_gt = bbox_data_gt[:, :4], bbox_data_gt[:, 4]
- ground_truth_path = os.path.join(ground_truth_dir_path, str(index) + '.txt')
- num_bbox_gt = len(bboxes_gt)
-
- bounding_boxes = []
- for i in range(num_bbox_gt):
- class_name = NUM_CLASS[classes_gt[i]]
- xmin, ymin, xmax, ymax = list(map(str, bboxes_gt[i]))
- bbox = xmin + " " + ymin + " " + xmax + " " +ymax
- bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False})
-
- # count that object
- if class_name in gt_counter_per_class:
- gt_counter_per_class[class_name] += 1
- else:
- # if class didn't exist yet
- gt_counter_per_class[class_name] = 1
- bbox_mess = ' '.join([class_name, xmin, ymin, xmax, ymax]) + '\n'
- with open(f'{ground_truth_dir_path}/{str(index)}_ground_truth.json', 'w') as outfile:
- json.dump(bounding_boxes, outfile)
-
- gt_classes = list(gt_counter_per_class.keys())
- # sort the classes alphabetically
- gt_classes = sorted(gt_classes)
- n_classes = len(gt_classes)
-
- times = []
- json_pred = [[] for i in range(n_classes)]
- for index in range(dataset.num_samples):
- ann_dataset = dataset.annotations[index]
-
- image_name = ann_dataset[0].split('/')[-1]
- original_image, bbox_data_gt = dataset.parse_annotation(ann_dataset, True)
-
- image = image_preprocess(np.copy(original_image), [TEST_INPUT_SIZE, TEST_INPUT_SIZE])
- image_data = image[np.newaxis, ...].astype(np.float32)
-
- t1 = time.time()
- if YOLO_FRAMEWORK == "tf":
- if tf.__version__ > '2.4.0':
- pred_bbox = Yolo(image_data)
- else:
- pred_bbox = Yolo.predict(image_data)
- elif YOLO_FRAMEWORK == "trt":
- batched_input = tf.constant(image_data)
- result = Yolo(batched_input)
- pred_bbox = []
- for key, value in result.items():
- value = value.numpy()
- pred_bbox.append(value)
-
- t2 = time.time()
-
- times.append(t2-t1)
-
- pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
- pred_bbox = tf.concat(pred_bbox, axis=0)
-
- bboxes = postprocess_boxes(pred_bbox, original_image, TEST_INPUT_SIZE, score_threshold)
- bboxes = nms(bboxes, iou_threshold, method='nms')
-
- for bbox in bboxes:
- coor = np.array(bbox[:4], dtype=np.int32)
- score = bbox[4]
- class_ind = int(bbox[5])
- class_name = NUM_CLASS[class_ind]
- score = '%.4f' % score
- xmin, ymin, xmax, ymax = list(map(str, coor))
- bbox = xmin + " " + ymin + " " + xmax + " " +ymax
- json_pred[gt_classes.index(class_name)].append({"confidence": str(score), "file_id": str(index), "bbox": str(bbox)})
-
- ms = sum(times)/len(times)*1000
- fps = 1000 / ms
-
- for class_name in gt_classes:
- json_pred[gt_classes.index(class_name)].sort(key=lambda x:float(x['confidence']), reverse=True)
- with open(f'{ground_truth_dir_path}/{class_name}_predictions.json', 'w') as outfile:
- json.dump(json_pred[gt_classes.index(class_name)], outfile)
-
- # Calculate the AP for each class
- sum_AP = 0.0
- ap_dictionary = {}
- # open file to store the results
- with open("mAP/results.txt", 'w') as results_file:
- results_file.write("# AP and precision/recall per class\n")
- count_true_positives = {}
- for class_index, class_name in enumerate(gt_classes):
- count_true_positives[class_name] = 0
- # Load predictions of that class
- predictions_file = f'{ground_truth_dir_path}/{class_name}_predictions.json'
- predictions_data = json.load(open(predictions_file))
-
- # Assign predictions to ground truth objects
- nd = len(predictions_data)
- tp = [0] * nd # creates an array of zeros of size nd
- fp = [0] * nd
- for idx, prediction in enumerate(predictions_data):
- file_id = prediction["file_id"]
- # assign prediction to ground truth object if any
- # open ground-truth with that file_id
- gt_file = f'{ground_truth_dir_path}/{str(file_id)}_ground_truth.json'
- ground_truth_data = json.load(open(gt_file))
- ovmax = -1
- gt_match = -1
- # load prediction bounding-box
- bb = [ float(x) for x in prediction["bbox"].split() ] # bounding box of prediction
- for obj in ground_truth_data:
- # look for a class_name match
- if obj["class_name"] == class_name:
- bbgt = [ float(x) for x in obj["bbox"].split() ] # bounding box of ground truth
- bi = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])]
- iw = bi[2] - bi[0] + 1
- ih = bi[3] - bi[1] + 1
- if iw > 0 and ih > 0:
- # compute overlap (IoU) = area of intersection / area of union
- ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]
- + 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
- ov = iw * ih / ua
- if ov > ovmax:
- ovmax = ov
- gt_match = obj
-
- # assign prediction as true positive/don't care/false positive
- if ovmax >= MINOVERLAP:# if ovmax > minimum overlap
- if not bool(gt_match["used"]):
- # true positive
- tp[idx] = 1
- gt_match["used"] = True
- count_true_positives[class_name] += 1
- # update the ".json" file
- with open(gt_file, 'w') as f:
- f.write(json.dumps(ground_truth_data))
- else:
- # false positive (multiple detection)
- fp[idx] = 1
- else:
- # false positive
- fp[idx] = 1
-
- # compute precision/recall
- cumsum = 0
- for idx, val in enumerate(fp):
- fp[idx] += cumsum
- cumsum += val
- cumsum = 0
- for idx, val in enumerate(tp):
- tp[idx] += cumsum
- cumsum += val
- #print(tp)
- rec = tp[:]
- for idx, val in enumerate(tp):
- rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name]
- #print(rec)
- prec = tp[:]
- for idx, val in enumerate(tp):
- prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx])
- #print(prec)
-
- ap, mrec, mprec = voc_ap(rec, prec)
- sum_AP += ap
- text = "{0:.3f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100)
-
- rounded_prec = [ '%.3f' % elem for elem in prec ]
- rounded_rec = [ '%.3f' % elem for elem in rec ]
- # Write to results.txt
- results_file.write(text + "\n Precision: " + str(rounded_prec) + "\n Recall :" + str(rounded_rec) + "\n\n")
-
- print(text)
- ap_dictionary[class_name] = ap
-
- results_file.write("\n# mAP of all classes\n")
- mAP = sum_AP / n_classes
-
- text = "mAP = {:.3f}%, {:.2f} FPS".format(mAP*100, fps)
- results_file.write(text + "\n")
- print(text)
-
- return mAP*100
-
-if __name__ == '__main__':
- if YOLO_FRAMEWORK == "tf": # TensorFlow detection
- if YOLO_TYPE == "yolov4":
- Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
- if YOLO_TYPE == "yolov3":
- Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
-
- if YOLO_CUSTOM_WEIGHTS == False:
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=YOLO_COCO_CLASSES)
- load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
- else:
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
- yolo.load_weights(f"./checkpoints/{TRAIN_MODEL_NAME}") # use custom weights
-
- elif YOLO_FRAMEWORK == "trt": # TensorRT detection
- saved_model_loaded = tf.saved_model.load(f"./checkpoints/{TRAIN_MODEL_NAME}", tags=[tag_constants.SERVING])
- signature_keys = list(saved_model_loaded.signatures.keys())
- yolo = saved_model_loaded.signatures['serving_default']
-
- testset = Dataset('test', TEST_INPUT_SIZE=YOLO_INPUT_SIZE)
- get_mAP(yolo, testset, score_threshold=0.05, iou_threshold=0.50, TEST_INPUT_SIZE=YOLO_INPUT_SIZE)
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+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "17 264 120 460", "used": true}, {"class_name": "cone", "bbox": "4 251 55 385", "used": true}, {"class_name": "cone", "bbox": "208 262 314 460", "used": true}, {"class_name": "cone", "bbox": "269 244 338 379", "used": true}]
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+++ /dev/null
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-[{"class_name": "cone", "bbox": "117 168 303 293", "used": true}, {"class_name": "cone", "bbox": "349 90 492 297", "used": true}, {"class_name": "cone", "bbox": "283 100 365 259", "used": true}]
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-[{"class_name": "cone", "bbox": "209 30 315 226", "used": true}, {"class_name": "cone", "bbox": "25 1 113 125", "used": true}, {"class_name": "cone", "bbox": "83 1 163 81", "used": true}, {"class_name": "cone", "bbox": "262 114 410 338", "used": true}, {"class_name": "cone", "bbox": "270 1 332 124", "used": true}]
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-[{"class_name": "cone", "bbox": "130 259 171 321", "used": true}, {"class_name": "cone", "bbox": "165 232 199 282", "used": true}, {"class_name": "cone", "bbox": "189 213 216 255", "used": true}, {"class_name": "cone", "bbox": "206 200 230 237", "used": true}, {"class_name": "cone", "bbox": "218 190 239 221", "used": true}, {"class_name": "cone", "bbox": "230 183 246 211", "used": true}, {"class_name": "cone", "bbox": "237 178 254 201", "used": true}, {"class_name": "cone", "bbox": "243 172 259 193", "used": true}, {"class_name": "cone", "bbox": "256 162 266 181", "used": true}, {"class_name": "cone", "bbox": "85 306 113 338", "used": true}]
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-[{"class_name": "cone", "bbox": "24 58 318 471", "used": true}]
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+++ /dev/null
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-[{"class_name": "cone", "bbox": "256 264 308 347", "used": true}, {"class_name": "cone", "bbox": "115 249 154 317", "used": true}, {"class_name": "cone", "bbox": "15 240 53 293", "used": true}, {"class_name": "cone", "bbox": "507 283 573 391", "used": true}, {"class_name": "cone", "bbox": "347 192 359 209", "used": true}, {"class_name": "cone", "bbox": "289 190 302 209", "used": true}, {"class_name": "cone", "bbox": "102 194 111 206", "used": true}]
\ No newline at end of file
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-[{"class_name": "cone", "bbox": "6 20 367 434", "used": true}]
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+++ /dev/null
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-[{"class_name": "cone", "bbox": "206 125 321 315", "used": true}, {"class_name": "cone", "bbox": "41 126 139 322", "used": true}, {"class_name": "cone", "bbox": "154 89 233 213", "used": true}, {"class_name": "cone", "bbox": "25 89 74 213", "used": true}, {"class_name": "cone", "bbox": "71 50 119 128", "used": true}, {"class_name": "cone", "bbox": "1 45 53 119", "used": true}, {"class_name": "cone", "bbox": "394 121 508 308", "used": true}, {"class_name": "cone", "bbox": "467 88 508 202", "used": true}, {"class_name": "cone", "bbox": "346 75 413 182", "used": true}, {"class_name": "cone", "bbox": "269 66 320 162", "used": true}, {"class_name": "cone", "bbox": "199 62 252 148", "used": true}, {"class_name": "cone", "bbox": "136 55 191 136", "used": true}, {"class_name": "cone", "bbox": "109 19 136 69", "used": true}, {"class_name": "cone", "bbox": "65 18 91 69", "used": true}, {"class_name": "cone", "bbox": "24 19 52 69", "used": true}, {"class_name": "cone", "bbox": "227 22 256 72", "used": true}, {"class_name": "cone", "bbox": "185 20 213 73", "used": true}, {"class_name": "cone", "bbox": "367 24 396 77", "used": true}, {"class_name": "cone", "bbox": "413 23 448 77", "used": true}, {"class_name": "cone", "bbox": "468 23 501 80", "used": true}]
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-[{"class_name": "cone", "bbox": "220 203 258 250", "used": true}, {"class_name": "cone", "bbox": "121 213 135 241", "used": true}, {"class_name": "cone", "bbox": "70 203 103 244", "used": true}, {"class_name": "cone", "bbox": "6 213 30 243", "used": true}, {"class_name": "cone", "bbox": "308 177 359 261", "used": true}, {"class_name": "cone", "bbox": "396 221 415 241", "used": true}, {"class_name": "cone", "bbox": "458 224 470 241", "used": true}]
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-[{"class_name": "cone", "bbox": "106 88 241 316", "used": true}]
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-[{"class_name": "cone", "bbox": "390 259 632 417", "used": true}, {"class_name": "cone", "bbox": "806 256 895 361", "used": true}, {"class_name": "cone", "bbox": "1 41 261 561", "used": true}, {"class_name": "cone", "bbox": "250 181 393 430", "used": true}, {"class_name": "cone", "bbox": "711 251 788 365", "used": true}]
\ No newline at end of file
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-[{"class_name": "cone", "bbox": "34 65 319 474", "used": true}]
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/111_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/111_ground_truth.json
deleted file mode 100644
index 0926e75bfa19acdf88993ecec81e2d5e0c69c4a5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/111_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "83 170 169 302", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/112_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/112_ground_truth.json
deleted file mode 100644
index a2eb3db1d052821354b5d60df6027788e684e92a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/112_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "74 76 171 260", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/113_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/113_ground_truth.json
deleted file mode 100644
index 9b6f40ff0483c75e71b9f0886e070e3c9d421ec6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/113_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "159 223 287 454", "used": true}, {"class_name": "cone", "bbox": "39 40 77 91", "used": true}, {"class_name": "cone", "bbox": "114 5 143 43", "used": true}, {"class_name": "cone", "bbox": "165 4 178 25", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/114_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/114_ground_truth.json
deleted file mode 100644
index dfed061affd95e558423f54ae468c27cbeb9f1f0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/114_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "148 27 262 161", "used": true}, {"class_name": "cone", "bbox": "257 1 350 137", "used": true}, {"class_name": "cone", "bbox": "1 1 122 88", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/115_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/115_ground_truth.json
deleted file mode 100644
index 38f578fddec63f9e864ea48f68963c963cf2ae70..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/115_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "53 47 259 456", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/116_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/116_ground_truth.json
deleted file mode 100644
index 442d2b479d5504e6a2eb9314aff1980cde4d64ef..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/116_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/117_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/117_ground_truth.json
deleted file mode 100644
index 2cadf691ebaf2dca3fa0b68f2f6666325e52a7ee..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/117_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "183 166 314 388", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/118_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/118_ground_truth.json
deleted file mode 100644
index a5675c50d2b922304d0fa30a1d4c558062e57eeb..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/118_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "193 12 457 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/119_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/119_ground_truth.json
deleted file mode 100644
index 0e1c502a1b58ae7e561072b278fdb474add22a8b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/119_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "52 16 354 393", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/11_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/11_ground_truth.json
deleted file mode 100644
index 72fd4794a30a66872c363fbfdd218ef0fb2aafdb..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/11_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "273 421 471 901", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/120_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/120_ground_truth.json
deleted file mode 100644
index 9717dba7a420edc27e6e9f59d5e78c465e105e18..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/120_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "60 36 291 457", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/121_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/121_ground_truth.json
deleted file mode 100644
index 5c5b7cd249ee2d9ce25b35fc6d02c41034d43c2d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/121_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "268 113 315 171", "used": true}, {"class_name": "cone", "bbox": "185 113 233 170", "used": true}, {"class_name": "cone", "bbox": "103 114 150 170", "used": true}, {"class_name": "cone", "bbox": "15 114 65 170", "used": true}, {"class_name": "cone", "bbox": "351 114 402 171", "used": true}, {"class_name": "cone", "bbox": "440 114 487 171", "used": true}, {"class_name": "cone", "bbox": "528 116 575 171", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/122_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/122_ground_truth.json
deleted file mode 100644
index 6a375b0e2a4230663a976d6b30fc5e2635b55abf..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/122_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "63 36 341 374", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/123_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/123_ground_truth.json
deleted file mode 100644
index 10a6c4e8056aa325a2286a6f5c2b72216a315526..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/123_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "27 44 212 283", "used": true}, {"class_name": "cone", "bbox": "369 9 589 292", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/124_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/124_ground_truth.json
deleted file mode 100644
index ea68948af6b63a56f3770d48f221c5e2da3965fd..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/124_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "50 1 183 188", "used": true}, {"class_name": "cone", "bbox": "253 64 366 268", "used": true}, {"class_name": "cone", "bbox": "339 7 429 179", "used": true}, {"class_name": "cone", "bbox": "127 99 259 335", "used": true}, {"class_name": "cone", "bbox": "425 4 509 171", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/125_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/125_ground_truth.json
deleted file mode 100644
index 95fcc0d564ed28a6cfb999fcde73db54895515db..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/125_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "78 55 260 308", "used": true}, {"class_name": "cone", "bbox": "257 121 356 272", "used": true}, {"class_name": "cone", "bbox": "233 180 278 247", "used": true}, {"class_name": "cone", "bbox": "43 160 123 253", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/126_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/126_ground_truth.json
deleted file mode 100644
index a5b8c69163b16dfffccdbe749eb8dde1b53c58a0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/126_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "8 1 378 441", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/127_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/127_ground_truth.json
deleted file mode 100644
index d26076cb95dde372f250abf1481938652b2e908b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/127_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "12 31 317 477", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/128_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/128_ground_truth.json
deleted file mode 100644
index bd0b0b2d2be6d7d1d019b3313b8221307a4bdfa0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/128_ground_truth.json
+++ /dev/null
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-[{"class_name": "cone", "bbox": "206 140 336 413", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/129_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/129_ground_truth.json
deleted file mode 100644
index b94d00f925c25ec423d23a1d2b0a1e53e95b9a89..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/129_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "197 101 344 293", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/12_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/12_ground_truth.json
deleted file mode 100644
index ccad0fcf34d8ac7a1c6b40822fb1a040c94dbe1b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/12_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "177 125 421 697", "used": true}, {"class_name": "cone", "bbox": "337 269 602 1023", "used": true}, {"class_name": "cone", "bbox": "168 105 316 464", "used": true}, {"class_name": "cone", "bbox": "166 115 247 343", "used": true}, {"class_name": "cone", "bbox": "48 74 124 207", "used": true}, {"class_name": "cone", "bbox": "1 73 45 179", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/130_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/130_ground_truth.json
deleted file mode 100644
index 460f85329bc5bf1d67bfb0156cf3fe69620e844e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/130_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "43 39 257 301", "used": true}, {"class_name": "cone", "bbox": "189 115 384 268", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/131_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/131_ground_truth.json
deleted file mode 100644
index 406c0e528d3356e9704225a57051cc3a06b74f68..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/131_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "132 37 275 302", "used": true}, {"class_name": "cone", "bbox": "2 283 99 508", "used": true}, {"class_name": "cone", "bbox": "107 1 187 95", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/132_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/132_ground_truth.json
deleted file mode 100644
index 29243ef2ed6ad7d1073e0e22c6d156170c096b7a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/132_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 199 111 353", "used": true}, {"class_name": "cone", "bbox": "87 238 158 346", "used": true}, {"class_name": "cone", "bbox": "138 252 184 340", "used": true}, {"class_name": "cone", "bbox": "162 267 204 338", "used": true}, {"class_name": "cone", "bbox": "187 275 211 337", "used": true}, {"class_name": "cone", "bbox": "210 287 225 335", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/133_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/133_ground_truth.json
deleted file mode 100644
index ddc9336c2849b4cd51a43b30d845a9feeb9eed66..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/133_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "306 52 449 189", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/134_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/134_ground_truth.json
deleted file mode 100644
index 9d7b9bdcb15b4df788c7e7c2fb8f5375974cda37..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/134_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "360 41 411 120", "used": true}, {"class_name": "cone", "bbox": "473 32 507 119", "used": true}, {"class_name": "cone", "bbox": "306 37 350 107", "used": true}, {"class_name": "cone", "bbox": "318 2 356 59", "used": true}, {"class_name": "cone", "bbox": "401 1 436 27", "used": true}, {"class_name": "cone", "bbox": "296 22 312 88", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/135_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/135_ground_truth.json
deleted file mode 100644
index f88b7181a0bf00ceafbb88878540ebb008389ddf..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/135_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "133 102 235 257", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/136_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/136_ground_truth.json
deleted file mode 100644
index f6b47756f591ac47491aa4b8f610d17e6bf4bd2d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/136_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "49 45 260 459", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/137_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/137_ground_truth.json
deleted file mode 100644
index 9d1d5f9b74d5c09d3231e749daf27c8286c9da68..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/137_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "273 45 442 337", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/138_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/138_ground_truth.json
deleted file mode 100644
index a8304ac8767f296ed606bbd81bcefe66a390bd7d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/138_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "52 388 93 495", "used": true}, {"class_name": "cone", "bbox": "102 365 181 496", "used": true}, {"class_name": "cone", "bbox": "191 370 248 498", "used": true}, {"class_name": "cone", "bbox": "240 381 307 501", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/139_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/139_ground_truth.json
deleted file mode 100644
index d24c3b74accaa45dfb447af3b13eb6f0363c813e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/139_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/13_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/13_ground_truth.json
deleted file mode 100644
index 71a9a50eb9fb91ceef0ef10aba881e63efb84be4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/13_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "84 150 175 337", "used": true}, {"class_name": "cone", "bbox": "214 117 233 153", "used": true}, {"class_name": "cone", "bbox": "281 121 314 174", "used": true}, {"class_name": "cone", "bbox": "259 117 283 159", "used": true}, {"class_name": "cone", "bbox": "235 118 252 151", "used": true}, {"class_name": "cone", "bbox": "186 117 199 137", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/140_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/140_ground_truth.json
deleted file mode 100644
index a29d52fd41b8d4795b9892f06aeb1940b897837c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/140_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "28 30 240 302", "used": true}, {"class_name": "cone", "bbox": "178 99 452 312", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/141_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/141_ground_truth.json
deleted file mode 100644
index 3a61509bb4c5e9fe88fbda9795fab3dba943dfc1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/141_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "112 35 309 327", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/142_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/142_ground_truth.json
deleted file mode 100644
index c08affe76076d61fbda09e47611a47caee283943..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/142_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "66 11 293 492", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/143_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/143_ground_truth.json
deleted file mode 100644
index 2d0153a79c82f0e37f6a84b1d7da16e9e72b4aff..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/143_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "45 128 240 453", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/144_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/144_ground_truth.json
deleted file mode 100644
index 0e210c8f8335b191e983eb637c154b49936e1e04..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/144_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "69 52 296 462", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/145_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/145_ground_truth.json
deleted file mode 100644
index fc42eaa871d22e2f962865e559bf13d7128a8ca3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/145_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "154 205 284 423", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/146_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/146_ground_truth.json
deleted file mode 100644
index 2ccd72c0cc91875a2e2aefd9c458a58ef485118c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/146_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "175 42 292 277", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/147_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/147_ground_truth.json
deleted file mode 100644
index b06c582e98886f2f857e2e7a4e7415e078d70f6c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/147_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "124 127 228 296", "used": true}, {"class_name": "cone", "bbox": "6 69 93 194", "used": true}, {"class_name": "cone", "bbox": "255 36 321 145", "used": true}, {"class_name": "cone", "bbox": "423 81 519 223", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/148_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/148_ground_truth.json
deleted file mode 100644
index 3f07161e0c0e52178aea04d016739271984887d5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/148_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "184 63 289 256", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/149_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/149_ground_truth.json
deleted file mode 100644
index 799060748724bf8d9c06516ca7a086b4d8bd9a02..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/149_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "314 202 386 318", "used": true}, {"class_name": "cone", "bbox": "93 207 158 313", "used": true}, {"class_name": "cone", "bbox": "403 217 446 285", "used": true}, {"class_name": "cone", "bbox": "276 215 322 291", "used": true}, {"class_name": "cone", "bbox": "17 223 47 273", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/14_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/14_ground_truth.json
deleted file mode 100644
index a2bf6921b453192266b742edb1532bb4d17e2ac1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/14_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "96 64 232 283", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/150_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/150_ground_truth.json
deleted file mode 100644
index 012a54a5626ff76e95ca34e05bedd90b0354aad0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/150_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "273 32 396 243", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/151_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/151_ground_truth.json
deleted file mode 100644
index da098357ca244565a77556fa3ead8cbb28c3e4aa..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/151_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "3 37 333 478", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/152_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/152_ground_truth.json
deleted file mode 100644
index 9e104a71e1c48309ab84cb8ea6fc9935e86f3289..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/152_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "109 68 169 174", "used": true}, {"class_name": "cone", "bbox": "215 181 340 302", "used": true}, {"class_name": "cone", "bbox": "135 141 224 244", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/153_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/153_ground_truth.json
deleted file mode 100644
index 6d65eb5bc3387c8275934f62281d84183cbc6e73..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/153_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "118 113 338 423", "used": true}, {"class_name": "cone", "bbox": "95 175 190 313", "used": true}, {"class_name": "cone", "bbox": "78 196 120 283", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/154_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/154_ground_truth.json
deleted file mode 100644
index be4d1ef7e999457390c41f1d167babbca6fa720b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/154_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "6 39 332 468", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/155_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/155_ground_truth.json
deleted file mode 100644
index 441f82d29c53116f85189f4f99dbf7d7e9e70e53..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/155_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "29 108 423 309", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/156_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/156_ground_truth.json
deleted file mode 100644
index 5d91115139de40f2cf28fa7315ce77aab9b8df14..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/156_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "73 232 155 405", "used": true}, {"class_name": "cone", "bbox": "247 72 323 223", "used": true}, {"class_name": "cone", "bbox": "188 111 251 269", "used": true}, {"class_name": "cone", "bbox": "126 166 203 330", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/157_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/157_ground_truth.json
deleted file mode 100644
index 73813f380bb77719d7bdd374845c715b49408854..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/157_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "242 383 267 436", "used": true}, {"class_name": "cone", "bbox": "171 377 213 437", "used": true}, {"class_name": "cone", "bbox": "107 381 143 434", "used": true}, {"class_name": "cone", "bbox": "32 378 73 436", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/158_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/158_ground_truth.json
deleted file mode 100644
index 10c6afb22b45f189a7da2bbb5f55222e1e8beadf..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/158_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "156 83 255 278", "used": true}, {"class_name": "cone", "bbox": "82 36 170 206", "used": true}, {"class_name": "cone", "bbox": "256 74 359 251", "used": true}, {"class_name": "cone", "bbox": "158 14 241 160", "used": true}, {"class_name": "cone", "bbox": "246 28 338 175", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/159_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/159_ground_truth.json
deleted file mode 100644
index c32dd24706cb1a7e1e9e0a0d7498dd0eac68b876..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/159_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 1 97 103", "used": true}, {"class_name": "cone", "bbox": "35 16 161 104", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/15_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/15_ground_truth.json
deleted file mode 100644
index 98f76c09e9ef82f3cb946be2186911027300eecf..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/15_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 275 68 362", "used": true}, {"class_name": "cone", "bbox": "72 286 124 358", "used": true}, {"class_name": "cone", "bbox": "123 294 164 354", "used": true}, {"class_name": "cone", "bbox": "159 299 193 354", "used": true}, {"class_name": "cone", "bbox": "190 302 215 354", "used": true}, {"class_name": "cone", "bbox": "208 306 232 354", "used": true}, {"class_name": "cone", "bbox": "227 312 249 350", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/160_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/160_ground_truth.json
deleted file mode 100644
index 2b1cf21623917fa36e05c5df13c919479d5f1175..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/160_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "125 259 225 397", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/161_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/161_ground_truth.json
deleted file mode 100644
index 10125b41bfd980b1cc70291e73d5e35a7de1a626..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/161_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "251 152 302 235", "used": true}, {"class_name": "cone", "bbox": "246 149 272 209", "used": true}, {"class_name": "cone", "bbox": "242 145 258 184", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/162_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/162_ground_truth.json
deleted file mode 100644
index f29a22e449d40765a2b7681bd767e777b1d35b35..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/162_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "24 51 223 387", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/163_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/163_ground_truth.json
deleted file mode 100644
index 8deeb2d7d55d6146d9a50e6c713f74dcb4600bbf..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/163_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "138 164 306 457", "used": true}, {"class_name": "cone", "bbox": "53 162 109 252", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/164_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/164_ground_truth.json
deleted file mode 100644
index 589b85aa8499910f9addd48a2c2e4ad6a3e5d5ec..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/164_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "126 267 139 285", "used": true}, {"class_name": "cone", "bbox": "391 261 402 282", "used": true}, {"class_name": "cone", "bbox": "264 260 277 281", "used": true}, {"class_name": "cone", "bbox": "194 261 208 282", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/165_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/165_ground_truth.json
deleted file mode 100644
index fb3008ea3a9b8b8b3cbf2c69ef509e3f17cea2ae..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/165_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "19 32 261 309", "used": true}, {"class_name": "cone", "bbox": "268 76 438 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/166_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/166_ground_truth.json
deleted file mode 100644
index 488d46bbeb60a8afde717f42a8c3e6b4ceeab8f8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/166_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "249 182 346 303", "used": true}, {"class_name": "cone", "bbox": "107 189 223 272", "used": true}, {"class_name": "cone", "bbox": "48 155 128 259", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/167_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/167_ground_truth.json
deleted file mode 100644
index 18f49515499e11f2ce7a7342d7d2816e901aa218..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/167_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "143 48 305 280", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/168_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/168_ground_truth.json
deleted file mode 100644
index 5c21d136655c55d4817219bf5a26823232e3a7bf..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/168_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/169_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/169_ground_truth.json
deleted file mode 100644
index 991c05c2daeec0f6233f3997d8067c0cfabe3323..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/169_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "68 52 299 467", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/16_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/16_ground_truth.json
deleted file mode 100644
index 3ebcffd3ea3d0e900ca8cb60016a4d75cc6d9053..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/16_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "201 144 331 406", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/170_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/170_ground_truth.json
deleted file mode 100644
index 526f7eac418c8e035c1011f20e19aa2f906f1e30..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/170_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 245 124 430", "used": true}, {"class_name": "cone", "bbox": "115 102 210 203", "used": true}, {"class_name": "cone", "bbox": "175 41 232 110", "used": true}, {"class_name": "cone", "bbox": "231 15 278 74", "used": true}, {"class_name": "cone", "bbox": "267 1 309 39", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/171_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/171_ground_truth.json
deleted file mode 100644
index 8897498f56033272230b26c16fa9d402660655b0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/171_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "121 115 340 426", "used": true}, {"class_name": "cone", "bbox": "104 175 192 314", "used": true}, {"class_name": "cone", "bbox": "74 196 118 289", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/172_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/172_ground_truth.json
deleted file mode 100644
index d82f23f22c5e361e870fc79ea155632c602baf47..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/172_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/173_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/173_ground_truth.json
deleted file mode 100644
index 72903394e67fa21c513573f450546d253a6df886..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/173_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "274 57 391 196", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/174_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/174_ground_truth.json
deleted file mode 100644
index 823788a64654592dc90e49a8431752fae611076a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/174_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/175_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/175_ground_truth.json
deleted file mode 100644
index 944671c4da1b369bff1939c8e31cb6bbf37c610d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/175_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "104 218 263 435", "used": true}, {"class_name": "cone", "bbox": "54 30 259 190", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/176_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/176_ground_truth.json
deleted file mode 100644
index 0d823701739b3386fb7c8e2b2bea381405d3fcce..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/176_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "132 162 304 478", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/177_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/177_ground_truth.json
deleted file mode 100644
index 4704c0caf0e09c61a8ebceb63bb71957010ad5af..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/177_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "65 9 343 399", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/178_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/178_ground_truth.json
deleted file mode 100644
index c68908a839c48664a0fb613e061f25dc7f864776..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/178_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "145 377 186 445", "used": true}, {"class_name": "cone", "bbox": "251 357 263 392", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/179_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/179_ground_truth.json
deleted file mode 100644
index 972d232845763b8d438a3222aafeef69ef502f40..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/179_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "49 218 81 262", "used": true}, {"class_name": "cone", "bbox": "146 218 174 260", "used": true}, {"class_name": "cone", "bbox": "182 218 210 259", "used": true}, {"class_name": "cone", "bbox": "210 220 230 257", "used": true}, {"class_name": "cone", "bbox": "230 218 253 256", "used": true}, {"class_name": "cone", "bbox": "301 218 326 255", "used": true}, {"class_name": "cone", "bbox": "443 217 474 250", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/17_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/17_ground_truth.json
deleted file mode 100644
index f27062dc6d95aae7951978f864f5f786a9afce71..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/17_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "99 60 194 234", "used": true}, {"class_name": "cone", "bbox": "279 51 398 303", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/180_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/180_ground_truth.json
deleted file mode 100644
index 2927cf1267a706e3cdc8941a34bce467bd592609..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/180_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/181_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/181_ground_truth.json
deleted file mode 100644
index 547fbe37a352789af10a5a06668b79ad82f1acb2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/181_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "53 57 292 434", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/182_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/182_ground_truth.json
deleted file mode 100644
index 97dfad7a7ab9c49ade803aca9748af7a47657703..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/182_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "40 126 240 450", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/183_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/183_ground_truth.json
deleted file mode 100644
index 53ccfb89cf1302a09c358fdb88236ca4e883e798..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/183_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 57 182 569", "used": true}, {"class_name": "cone", "bbox": "44 53 237 479", "used": true}, {"class_name": "cone", "bbox": "133 66 296 409", "used": true}, {"class_name": "cone", "bbox": "204 62 333 364", "used": true}, {"class_name": "cone", "bbox": "277 43 397 315", "used": true}, {"class_name": "cone", "bbox": "330 33 429 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/184_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/184_ground_truth.json
deleted file mode 100644
index e0e47f20a2df5062bd91ee90699d6c31a7892cd7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/184_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "400 20 508 255", "used": true}, {"class_name": "cone", "bbox": "294 19 405 205", "used": true}, {"class_name": "cone", "bbox": "226 18 312 172", "used": true}, {"class_name": "cone", "bbox": "164 15 243 148", "used": true}, {"class_name": "cone", "bbox": "107 12 172 125", "used": true}, {"class_name": "cone", "bbox": "84 13 135 114", "used": true}, {"class_name": "cone", "bbox": "58 11 99 102", "used": true}, {"class_name": "cone", "bbox": "310 7 341 64", "used": true}, {"class_name": "cone", "bbox": "423 8 464 79", "used": true}, {"class_name": "cone", "bbox": "171 8 194 46", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/185_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/185_ground_truth.json
deleted file mode 100644
index d55b54dac5b375359986737e9d41bd12eebfa863..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/185_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "85 16 159 147", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/186_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/186_ground_truth.json
deleted file mode 100644
index adffb156040fb3d235825b5b07ab564486ebbc94..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/186_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "296 184 408 319", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/187_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/187_ground_truth.json
deleted file mode 100644
index 37edfc6212bad1746bffd7d7e2bfc4d0217c49ae..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/187_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "91 39 178 161", "used": true}, {"class_name": "cone", "bbox": "3 28 76 128", "used": true}, {"class_name": "cone", "bbox": "223 60 327 210", "used": true}, {"class_name": "cone", "bbox": "259 4 290 53", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/188_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/188_ground_truth.json
deleted file mode 100644
index ada63c83ec32ed87fe31481c262334ee6a1d95fc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/188_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "80 35 182 209", "used": true}, {"class_name": "cone", "bbox": "152 14 238 161", "used": true}, {"class_name": "cone", "bbox": "154 84 259 281", "used": true}, {"class_name": "cone", "bbox": "269 72 360 256", "used": true}, {"class_name": "cone", "bbox": "249 26 320 174", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/189_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/189_ground_truth.json
deleted file mode 100644
index 7d63b656cc9882b08788d868041ef7fd60ea986a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/189_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "27 209 154 481", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/18_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/18_ground_truth.json
deleted file mode 100644
index 8c362fabea3afdb90a519744e30f005845b34c2f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/18_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/190_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/190_ground_truth.json
deleted file mode 100644
index 27ece5a442a1940409f18a36b3484cf819a224b1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/190_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "97 1 166 108", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/191_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/191_ground_truth.json
deleted file mode 100644
index 3b44f0cf9c08beb2494a857ce3f139242c65e53a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/191_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "52 243 150 460", "used": true}, {"class_name": "cone", "bbox": "172 184 237 315", "used": true}, {"class_name": "cone", "bbox": "230 152 270 235", "used": true}, {"class_name": "cone", "bbox": "257 139 285 203", "used": true}, {"class_name": "cone", "bbox": "279 128 300 180", "used": true}, {"class_name": "cone", "bbox": "290 126 307 165", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/192_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/192_ground_truth.json
deleted file mode 100644
index 3403e45804897f920e33578f7df9ea5f754b8371..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/192_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/193_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/193_ground_truth.json
deleted file mode 100644
index 7bd82b3ca72ab85f2894724f5dc91291b0ea0178..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/193_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "237 169 296 283", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/194_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/194_ground_truth.json
deleted file mode 100644
index 71076b4d273a736c51a8189f07fa7663043fe643..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/194_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/195_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/195_ground_truth.json
deleted file mode 100644
index ca160e6fb942398897691ffa1cb4112d3ebe8288..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/195_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "49 37 353 587", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/196_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/196_ground_truth.json
deleted file mode 100644
index 510e26028b65e8c2fa5ea9040b86b8ff68972159..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/196_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "83 49 258 314", "used": true}, {"class_name": "cone", "bbox": "181 56 233 191", "used": true}, {"class_name": "cone", "bbox": "206 60 250 147", "used": true}, {"class_name": "cone", "bbox": "232 62 255 116", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/197_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/197_ground_truth.json
deleted file mode 100644
index 8f13ecef8811386d95bbaa45dc8b971b27b0f22a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/197_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "179 43 255 167", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/198_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/198_ground_truth.json
deleted file mode 100644
index e10ac4050be445221d2575473d7fb23c9d8520b2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/198_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "51 44 123 240", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/199_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/199_ground_truth.json
deleted file mode 100644
index dbd8fc2e9498bfde7b09363d80a1b0fe2a64fc09..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/199_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 140 179 497", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/19_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/19_ground_truth.json
deleted file mode 100644
index 6df83cf72389a53829f5f3d52a4bb6023130600a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/19_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "71 40 296 407", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/1_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/1_ground_truth.json
deleted file mode 100644
index 8b2446b24af50d485b4e9e2834917d81f7fb37e3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/1_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "317 113 373 171", "used": true}, {"class_name": "cone", "bbox": "189 94 255 176", "used": true}, {"class_name": "cone", "bbox": "218 183 421 320", "used": true}, {"class_name": "cone", "bbox": "263 96 289 137", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/200_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/200_ground_truth.json
deleted file mode 100644
index 1ffe88d98e027a1e42f4dbd38f397bc765e42d14..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/200_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "200 59 239 156", "used": true}, {"class_name": "cone", "bbox": "90 175 162 337", "used": true}, {"class_name": "cone", "bbox": "245 21 271 76", "used": true}, {"class_name": "cone", "bbox": "456 112 494 234", "used": true}, {"class_name": "cone", "bbox": "392 50 426 131", "used": true}, {"class_name": "cone", "bbox": "356 12 377 61", "used": true}, {"class_name": "cone", "bbox": "344 1 357 32", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/201_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/201_ground_truth.json
deleted file mode 100644
index ad170ae76dc77cddefc7bc87587ecca5c5f07742..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/201_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "110 68 178 179", "used": true}, {"class_name": "cone", "bbox": "217 180 340 299", "used": true}, {"class_name": "cone", "bbox": "135 143 224 244", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/202_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/202_ground_truth.json
deleted file mode 100644
index 9733e8d1b697f9929ccbaa7a0c6e84b5cac0cf85..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/202_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "105 33 191 163", "used": true}, {"class_name": "cone", "bbox": "13 25 83 136", "used": true}, {"class_name": "cone", "bbox": "376 90 506 292", "used": true}, {"class_name": "cone", "bbox": "205 168 363 296", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/203_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/203_ground_truth.json
deleted file mode 100644
index 9a5a36d903938c6d14a62b55186679f461e9ce2a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/203_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "39 101 215 332", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/204_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/204_ground_truth.json
deleted file mode 100644
index 2f37ab463c0362bb7a3a08bae484c39709c0849a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/204_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "172 54 225 147", "used": true}, {"class_name": "cone", "bbox": "340 53 479 326", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/205_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/205_ground_truth.json
deleted file mode 100644
index e64a716a3414993b86d317c80272ff798a65a515..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/205_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "233 132 374 368", "used": true}, {"class_name": "cone", "bbox": "154 95 258 270", "used": true}, {"class_name": "cone", "bbox": "89 79 176 219", "used": true}, {"class_name": "cone", "bbox": "33 65 108 183", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/206_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/206_ground_truth.json
deleted file mode 100644
index 3afe611273b75512d3b470139ba506fc3c603e58..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/206_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "103 222 266 432", "used": true}, {"class_name": "cone", "bbox": "55 27 259 191", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/207_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/207_ground_truth.json
deleted file mode 100644
index 60483957ea18fae85c700644023847443f1d3e76..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/207_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "195 93 309 264", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/208_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/208_ground_truth.json
deleted file mode 100644
index 41f60c850aba31f8c2b6ea52fe7c9ed55c29d107..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/208_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "332 52 460 295", "used": true}, {"class_name": "cone", "bbox": "207 131 242 205", "used": true}, {"class_name": "cone", "bbox": "169 147 192 191", "used": true}, {"class_name": "cone", "bbox": "155 155 169 183", "used": true}, {"class_name": "cone", "bbox": "47 167 52 174", "used": false}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/209_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/209_ground_truth.json
deleted file mode 100644
index f96a5176ce12051d3c886331f6ef88de1b0a2f26..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/209_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "359 94 484 293", "used": true}, {"class_name": "cone", "bbox": "276 104 362 257", "used": true}, {"class_name": "cone", "bbox": "120 168 302 293", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/20_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/20_ground_truth.json
deleted file mode 100644
index 714a6eec7c9affd3e667734ddd8f5bebf50f749c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/20_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "40 66 229 319", "used": true}, {"class_name": "cone", "bbox": "197 73 312 248", "used": true}, {"class_name": "cone", "bbox": "323 76 387 171", "used": true}, {"class_name": "cone", "bbox": "174 80 243 201", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/210_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/210_ground_truth.json
deleted file mode 100644
index efd29908dac0e8f159805e7d08ab5e2e6231bd89..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/210_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "207 93 315 266", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/211_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/211_ground_truth.json
deleted file mode 100644
index c3589f25d8d4a66ec924dc9667e7df64fb791cd0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/211_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "165 28 243 158", "used": true}, {"class_name": "cone", "bbox": "267 150 359 430", "used": true}, {"class_name": "cone", "bbox": "151 1 189 85", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/212_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/212_ground_truth.json
deleted file mode 100644
index 2d28add495d4614e2a903110050406f74ffb8ba6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/212_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "248 39 436 328", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/213_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/213_ground_truth.json
deleted file mode 100644
index 620da8763e6a332eb17c7a971dd0d1e45fa11bb9..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/213_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "5 10 137 162", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/214_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/214_ground_truth.json
deleted file mode 100644
index 3d30ebda8d6e8f68b0cb97953f7b39db1f2624fc..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/214_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "135 182 266 462", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/215_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/215_ground_truth.json
deleted file mode 100644
index 0f8821d1b49275f58663c9707c5f4a0b97c43aac..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/215_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "485 480 568 621", "used": true}, {"class_name": "cone", "bbox": "570 412 639 528", "used": true}, {"class_name": "cone", "bbox": "644 361 704 451", "used": true}, {"class_name": "cone", "bbox": "714 308 764 385", "used": true}, {"class_name": "cone", "bbox": "773 273 817 334", "used": true}, {"class_name": "cone", "bbox": "264 615 390 848", "used": true}, {"class_name": "cone", "bbox": "126 707 284 985", "used": true}, {"class_name": "cone", "bbox": "391 642 536 766", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/216_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/216_ground_truth.json
deleted file mode 100644
index 794cf31cae53a46608476bd7bcb9dc40c1b46b5f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/216_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "103 11 384 322", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/217_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/217_ground_truth.json
deleted file mode 100644
index c5deafc741a845d1c837a21ca562e9496aa5927e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/217_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "201 170 294 330", "used": true}, {"class_name": "cone", "bbox": "231 156 306 297", "used": true}, {"class_name": "cone", "bbox": "266 139 342 265", "used": true}, {"class_name": "cone", "bbox": "308 128 370 241", "used": true}, {"class_name": "cone", "bbox": "328 117 388 217", "used": true}, {"class_name": "cone", "bbox": "372 101 421 186", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/218_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/218_ground_truth.json
deleted file mode 100644
index 0832d77f2027ffd92c99fe21e2ac8f97f2b8bef5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/218_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "47 1 313 265", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/219_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/219_ground_truth.json
deleted file mode 100644
index 5ad1b21e60ab5041ddc6f6ec87541f4b817479d5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/219_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "298 224 333 273", "used": true}, {"class_name": "cone", "bbox": "246 225 275 274", "used": true}, {"class_name": "cone", "bbox": "186 224 219 273", "used": true}, {"class_name": "cone", "bbox": "134 226 168 269", "used": true}, {"class_name": "cone", "bbox": "348 226 377 270", "used": true}, {"class_name": "cone", "bbox": "379 226 407 269", "used": true}, {"class_name": "cone", "bbox": "99 225 131 267", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/21_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/21_ground_truth.json
deleted file mode 100644
index 71d51f1e4c9b6ac0481400866ade984b832c899f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/21_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "259 117 388 338", "used": true}, {"class_name": "cone", "bbox": "365 149 509 339", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/220_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/220_ground_truth.json
deleted file mode 100644
index 267dc74a181b3bf4bd469d09c9d5e1f39d1b5003..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/220_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "62 10 291 492", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/221_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/221_ground_truth.json
deleted file mode 100644
index 939cd5ad1b709daa6b7018d35e29ec3b4752c55e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/221_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "10 16 175 327", "used": true}, {"class_name": "cone", "bbox": "339 18 507 328", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/222_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/222_ground_truth.json
deleted file mode 100644
index 3b587dc1457f67031f81f8a1cce7a992f942d04b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/222_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/223_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/223_ground_truth.json
deleted file mode 100644
index 6f3fdc418fcfc98b8b1eed4668f8ef32c0a3e7e4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/223_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "360 314 649 734", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/224_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/224_ground_truth.json
deleted file mode 100644
index 8a7f87d3f217f88d37e47b2c7f3fa8b3cf6aa4f5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/224_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "91 172 172 325", "used": true}, {"class_name": "cone", "bbox": "319 174 422 333", "used": true}, {"class_name": "cone", "bbox": "422 156 452 207", "used": true}, {"class_name": "cone", "bbox": "339 152 374 207", "used": true}, {"class_name": "cone", "bbox": "487 156 509 204", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/225_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/225_ground_truth.json
deleted file mode 100644
index 23697f81fd0b966a9dbcdb3a9f51aebf0afdaf0c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/225_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "50 154 154 320", "used": true}, {"class_name": "cone", "bbox": "147 125 241 279", "used": true}, {"class_name": "cone", "bbox": "225 104 312 250", "used": true}, {"class_name": "cone", "bbox": "286 87 367 226", "used": true}, {"class_name": "cone", "bbox": "339 73 408 202", "used": true}, {"class_name": "cone", "bbox": "379 59 445 185", "used": true}, {"class_name": "cone", "bbox": "414 46 478 163", "used": true}, {"class_name": "cone", "bbox": "459 31 519 145", "used": true}, {"class_name": "cone", "bbox": "497 21 529 127", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/226_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/226_ground_truth.json
deleted file mode 100644
index 97e12b051e4568e2705d188632267a94e611b312..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/226_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "384 235 439 316", "used": true}, {"class_name": "cone", "bbox": "459 234 507 315", "used": true}, {"class_name": "cone", "bbox": "172 213 197 262", "used": true}, {"class_name": "cone", "bbox": "40 194 61 230", "used": true}, {"class_name": "cone", "bbox": "144 209 173 259", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/227_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/227_ground_truth.json
deleted file mode 100644
index 0b39f26ec29371ae7876cb0d62dea4c9db35709a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/227_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "252 195 299 277", "used": true}, {"class_name": "cone", "bbox": "387 186 433 260", "used": true}, {"class_name": "cone", "bbox": "449 183 492 250", "used": true}, {"class_name": "cone", "bbox": "227 218 247 271", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/228_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/228_ground_truth.json
deleted file mode 100644
index a7cd1464a1a2254638ca9f83dfc15674381747c1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/228_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "237 23 438 313", "used": true}, {"class_name": "cone", "bbox": "152 88 233 214", "used": true}, {"class_name": "cone", "bbox": "110 113 166 189", "used": true}, {"class_name": "cone", "bbox": "85 150 106 186", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/229_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/229_ground_truth.json
deleted file mode 100644
index 1dd8506b1b6750990a5286596c477baa04e44737..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/229_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 18 148 270", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/22_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/22_ground_truth.json
deleted file mode 100644
index 2c7e97fd135ecabcefd4b799bea7577f22b5b7f8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/22_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "264 213 314 308", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/230_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/230_ground_truth.json
deleted file mode 100644
index 5931cb8d568b88b0962ce84b1c8f316747b64bab..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/230_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 52 106 355", "used": true}, {"class_name": "cone", "bbox": "204 125 234 193", "used": true}, {"class_name": "cone", "bbox": "231 139 257 180", "used": true}, {"class_name": "cone", "bbox": "249 145 261 170", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/231_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/231_ground_truth.json
deleted file mode 100644
index e4813169182c07598df92615cf47c9f47afbd004..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/231_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "180 139 236 234", "used": true}, {"class_name": "cone", "bbox": "252 126 304 213", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/232_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/232_ground_truth.json
deleted file mode 100644
index 7e093d0eed16cda74efd5179bbf3b393a0997422..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/232_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "280 45 438 274", "used": true}, {"class_name": "cone", "bbox": "79 150 100 186", "used": true}, {"class_name": "cone", "bbox": "167 154 187 184", "used": true}, {"class_name": "cone", "bbox": "203 152 219 187", "used": true}, {"class_name": "cone", "bbox": "255 114 310 221", "used": true}, {"class_name": "cone", "bbox": "245 137 269 201", "used": true}, {"class_name": "cone", "bbox": "231 145 250 195", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/233_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/233_ground_truth.json
deleted file mode 100644
index 14463f8c453d10833be935505dcdfd172232b463..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/233_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "180 306 244 412", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/234_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/234_ground_truth.json
deleted file mode 100644
index 29674c94dce9dea4e03cb2b2e254b7fbaeeff732..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/234_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "100 223 263 439", "used": true}, {"class_name": "cone", "bbox": "51 26 258 188", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/235_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/235_ground_truth.json
deleted file mode 100644
index 58db3a82bd381a13c514ee282d44d3925d946c5d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/235_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "106 107 640 913", "used": true}, {"class_name": "cone", "bbox": "470 281 936 657", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/236_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/236_ground_truth.json
deleted file mode 100644
index a4a36b0ab7cd468ee1d67e8972243b9eac94afb5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/236_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "60 63 198 300", "used": true}, {"class_name": "cone", "bbox": "177 139 380 300", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/237_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/237_ground_truth.json
deleted file mode 100644
index cfc7116599fb34e69dd3b333e9a65bb7d78ee26e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/237_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "198 204 279 333", "used": true}, {"class_name": "cone", "bbox": "356 161 432 279", "used": true}, {"class_name": "cone", "bbox": "397 109 455 211", "used": true}, {"class_name": "cone", "bbox": "375 48 420 125", "used": true}, {"class_name": "cone", "bbox": "250 8 290 69", "used": true}, {"class_name": "cone", "bbox": "325 58 375 100", "used": true}, {"class_name": "cone", "bbox": "145 29 197 97", "used": true}, {"class_name": "cone", "bbox": "43 83 103 177", "used": true}, {"class_name": "cone", "bbox": "91 202 165 286", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/238_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/238_ground_truth.json
deleted file mode 100644
index c193a9772cd0f64eda947400601152bcfcd6fec6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/238_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "57 168 166 319", "used": true}, {"class_name": "cone", "bbox": "69 125 143 225", "used": true}, {"class_name": "cone", "bbox": "96 73 139 134", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/239_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/239_ground_truth.json
deleted file mode 100644
index 09205831544ac2a124507ca03916b3f74550ba8e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/239_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "150 218 262 464", "used": true}, {"class_name": "cone", "bbox": "273 243 338 507", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/23_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/23_ground_truth.json
deleted file mode 100644
index 3953d53940465dd644cfb5df0f7e8d3c662fd28c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/23_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "671 439 773 587", "used": true}, {"class_name": "cone", "bbox": "537 440 653 588", "used": true}, {"class_name": "cone", "bbox": "346 442 457 572", "used": true}, {"class_name": "cone", "bbox": "95 445 214 565", "used": true}, {"class_name": "cone", "bbox": "1 442 59 553", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/240_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/240_ground_truth.json
deleted file mode 100644
index 34824261910cb32e844e16b40d22bc84bbe15289..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/240_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "378 246 411 286", "used": true}, {"class_name": "cone", "bbox": "22 292 79 368", "used": true}, {"class_name": "cone", "bbox": "178 257 205 309", "used": true}, {"class_name": "cone", "bbox": "113 263 145 321", "used": true}, {"class_name": "cone", "bbox": "79 275 117 339", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/241_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/241_ground_truth.json
deleted file mode 100644
index f0fb351da57e45ac8c09dac1f4cbdc6efb27ab89..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/241_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "10 234 45 272", "used": true}, {"class_name": "cone", "bbox": "63 234 95 271", "used": true}, {"class_name": "cone", "bbox": "114 234 146 269", "used": true}, {"class_name": "cone", "bbox": "165 234 195 268", "used": true}, {"class_name": "cone", "bbox": "216 231 245 267", "used": true}, {"class_name": "cone", "bbox": "264 233 296 268", "used": true}, {"class_name": "cone", "bbox": "316 232 347 268", "used": true}, {"class_name": "cone", "bbox": "367 230 399 268", "used": true}, {"class_name": "cone", "bbox": "419 230 451 265", "used": true}, {"class_name": "cone", "bbox": "469 231 504 265", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/242_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/242_ground_truth.json
deleted file mode 100644
index 7a1d2d4d31e07f561bbcad7481a3bf8f9167c6f7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/242_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "16 29 261 310", "used": true}, {"class_name": "cone", "bbox": "242 76 439 271", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/243_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/243_ground_truth.json
deleted file mode 100644
index c6ae16b08b626252bda7851c149c39bcc3193d03..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/243_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "17 254 57 330", "used": true}, {"class_name": "cone", "bbox": "60 254 102 332", "used": true}, {"class_name": "cone", "bbox": "105 252 145 331", "used": true}, {"class_name": "cone", "bbox": "142 252 186 326", "used": true}, {"class_name": "cone", "bbox": "186 249 227 325", "used": true}, {"class_name": "cone", "bbox": "230 248 268 326", "used": true}, {"class_name": "cone", "bbox": "261 251 305 325", "used": true}, {"class_name": "cone", "bbox": "300 250 338 321", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/244_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/244_ground_truth.json
deleted file mode 100644
index 9daa00950c83ab5798c647ff574049a78aaf05fd..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/244_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "166 210 206 273", "used": true}, {"class_name": "cone", "bbox": "52 212 95 275", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/245_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/245_ground_truth.json
deleted file mode 100644
index 8e0600f2ebb074384626532646d274c26e3cd8d8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/245_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "79 10 306 314", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/246_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/246_ground_truth.json
deleted file mode 100644
index 46b1bfede5c83dc0c0dc9043cd150299b1539f52..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/246_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "129 159 301 476", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/247_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/247_ground_truth.json
deleted file mode 100644
index 024d8325c35071972c81423b2aec08f85e1f561f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/247_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "5 23 102 86", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/248_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/248_ground_truth.json
deleted file mode 100644
index 2fc14909a20dca6aca622181b5678e2c580e0c74..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/248_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "34 171 193 433", "used": true}, {"class_name": "cone", "bbox": "148 174 339 284", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/249_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/249_ground_truth.json
deleted file mode 100644
index d71b136fc3bb9c60570ef2d652fb98bb2f7f293b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/249_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "104 319 141 380", "used": true}, {"class_name": "cone", "bbox": "210 321 246 382", "used": true}, {"class_name": "cone", "bbox": "294 323 329 381", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/24_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/24_ground_truth.json
deleted file mode 100644
index 4b704bce96b637638189af0b8ace07e3c5d37b9f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/24_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "189 224 316 441", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/250_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/250_ground_truth.json
deleted file mode 100644
index d598d2ef9b7e848a722dc0c61f57be0b163b5a51..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/250_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "143 114 234 273", "used": true}, {"class_name": "cone", "bbox": "217 152 269 248", "used": true}, {"class_name": "cone", "bbox": "1 1 114 336", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/251_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/251_ground_truth.json
deleted file mode 100644
index 343dd05d83f4133dc7be97fc916d68af89b05867..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/251_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "69 190 223 389", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/252_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/252_ground_truth.json
deleted file mode 100644
index b7a2f4b106170f9bb8060f37b8fc37047dab7acf..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/252_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "190 275 295 430", "used": true}, {"class_name": "cone", "bbox": "212 260 297 384", "used": false}, {"class_name": "cone", "bbox": "234 234 305 345", "used": true}, {"class_name": "cone", "bbox": "264 226 324 319", "used": false}, {"class_name": "cone", "bbox": "283 214 326 289", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/253_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/253_ground_truth.json
deleted file mode 100644
index 1c15b57bb3708dac77be5e651055acddf1cd06d4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/253_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "252 1 509 336", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/254_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/254_ground_truth.json
deleted file mode 100644
index 35939f9c1b9c04213ffea96c34062a45ffd80317..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/254_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "305 19 480 298", "used": true}, {"class_name": "cone", "bbox": "150 175 212 262", "used": true}, {"class_name": "cone", "bbox": "125 198 157 257", "used": true}, {"class_name": "cone", "bbox": "97 215 120 254", "used": true}, {"class_name": "cone", "bbox": "86 219 101 254", "used": true}, {"class_name": "cone", "bbox": "52 229 65 249", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/25_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/25_ground_truth.json
deleted file mode 100644
index 45beae5048d549cb7d770dcd619a379b9c8c21d6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/25_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "204 343 298 477", "used": true}, {"class_name": "cone", "bbox": "71 2 105 47", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/26_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/26_ground_truth.json
deleted file mode 100644
index 2c746108480904f4aceff8cd81ace762c3292291..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/26_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 82 165 502", "used": true}, {"class_name": "cone", "bbox": "82 1 165 219", "used": true}, {"class_name": "cone", "bbox": "151 1 225 129", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/27_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/27_ground_truth.json
deleted file mode 100644
index 5ddd7983dc7336c98dd504cdf405888b0837097f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/27_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "116 52 210 276", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/28_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/28_ground_truth.json
deleted file mode 100644
index 677f79b4598a096b48ab6344f0954ec7d976dd88..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/28_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "208 25 428 311", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/29_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/29_ground_truth.json
deleted file mode 100644
index 1cba20bc210f9dd2911610b76f28c516e1ce4194..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/29_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "14 25 365 432", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/2_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/2_ground_truth.json
deleted file mode 100644
index f8e6a58f069ec01c106adacf2f639b95e166f51d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/2_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "73 197 127 303", "used": true}, {"class_name": "cone", "bbox": "101 188 151 276", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/30_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/30_ground_truth.json
deleted file mode 100644
index 8d11a1edbf58b9acd28fa534752585d4eef9a898..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/30_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "58 120 329 307", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/31_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/31_ground_truth.json
deleted file mode 100644
index 12baeb6c70f23635add46cb1b2099f2201dc5d4b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/31_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "221 110 259 179", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/32_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/32_ground_truth.json
deleted file mode 100644
index 4aaa3d96ac829314ffa03b4110b4e9d74c177b4d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/32_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "271 42 386 206", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/33_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/33_ground_truth.json
deleted file mode 100644
index ad864521b5e6362a5efde057b239f8b340fa99dd..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/33_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "145 291 192 369", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/34_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/34_ground_truth.json
deleted file mode 100644
index 224c09be767c1034b210337d73b1696c059ab1af..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/34_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "375 1 479 210", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/35_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/35_ground_truth.json
deleted file mode 100644
index f910c84d30a987fbcde6f22022f1537a73a9ac9c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/35_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "292 202 398 293", "used": true}, {"class_name": "cone", "bbox": "130 168 222 270", "used": true}, {"class_name": "cone", "bbox": "52 210 153 335", "used": true}, {"class_name": "cone", "bbox": "183 153 256 218", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/36_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/36_ground_truth.json
deleted file mode 100644
index 48e372d6ffad779151535e325f142695b2143e07..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/36_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "101 88 244 318", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/37_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/37_ground_truth.json
deleted file mode 100644
index 1dec2c10db37c5cd78626d91c713fbe8e87a8c2a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/37_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "308 168 402 327", "used": true}, {"class_name": "cone", "bbox": "297 120 360 250", "used": true}, {"class_name": "cone", "bbox": "279 92 326 207", "used": true}, {"class_name": "cone", "bbox": "369 24 398 77", "used": true}, {"class_name": "cone", "bbox": "432 20 459 75", "used": true}, {"class_name": "cone", "bbox": "313 21 341 76", "used": true}, {"class_name": "cone", "bbox": "152 16 184 64", "used": true}, {"class_name": "cone", "bbox": "121 15 153 61", "used": true}, {"class_name": "cone", "bbox": "103 15 121 65", "used": true}, {"class_name": "cone", "bbox": "72 16 102 62", "used": true}, {"class_name": "cone", "bbox": "39 17 75 67", "used": true}, {"class_name": "cone", "bbox": "15 17 51 69", "used": true}, {"class_name": "cone", "bbox": "260 74 311 173", "used": true}, {"class_name": "cone", "bbox": "252 61 291 148", "used": true}, {"class_name": "cone", "bbox": "223 30 244 87", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/38_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/38_ground_truth.json
deleted file mode 100644
index ca7287031fa0e99a5a640c966768b8dcd2a6d1f4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/38_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "123 257 226 399", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/39_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/39_ground_truth.json
deleted file mode 100644
index d1b29d5de2027e5fccbeb8757e6b7f53ef4b74b2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/39_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "87 6 331 409", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/3_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/3_ground_truth.json
deleted file mode 100644
index b3215d8e0014f3b478946dbb33a9de5923c6a6ef..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/3_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "5 38 336 478", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/40_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/40_ground_truth.json
deleted file mode 100644
index d9333ffdc11ecc7ef3cd67068a319ef2119babb0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/40_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "7 150 95 298", "used": true}, {"class_name": "cone", "bbox": "136 152 205 302", "used": true}, {"class_name": "cone", "bbox": "338 155 416 300", "used": true}, {"class_name": "cone", "bbox": "412 158 496 297", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/41_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/41_ground_truth.json
deleted file mode 100644
index eb534cbfc76d99fc4627e703ce3c957c9de7ef06..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/41_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "158 178 203 247", "used": true}, {"class_name": "cone", "bbox": "165 339 230 416", "used": true}, {"class_name": "cone", "bbox": "158 154 184 196", "used": true}, {"class_name": "cone", "bbox": "186 139 196 153", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/42_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/42_ground_truth.json
deleted file mode 100644
index b676164d5db47c47aee08bbcbc9c1cc9d5572222..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/42_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "214 38 387 363", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/43_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/43_ground_truth.json
deleted file mode 100644
index fd6ebb21da5c755e4ad8e1d35dda7fe84574e52e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/43_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "146 71 228 215", "used": true}, {"class_name": "cone", "bbox": "294 28 407 187", "used": true}, {"class_name": "cone", "bbox": "38 109 114 232", "used": true}, {"class_name": "cone", "bbox": "408 73 497 222", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/44_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/44_ground_truth.json
deleted file mode 100644
index 81f4e82ec824a7b762a40c4f19d434b6d744590e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/44_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "272 163 338 272", "used": true}, {"class_name": "cone", "bbox": "212 175 271 264", "used": true}, {"class_name": "cone", "bbox": "169 181 223 259", "used": true}, {"class_name": "cone", "bbox": "137 189 183 258", "used": true}, {"class_name": "cone", "bbox": "115 194 145 256", "used": true}, {"class_name": "cone", "bbox": "94 198 124 253", "used": true}, {"class_name": "cone", "bbox": "80 200 104 252", "used": true}, {"class_name": "cone", "bbox": "68 203 89 250", "used": true}, {"class_name": "cone", "bbox": "58 206 73 247", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/45_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/45_ground_truth.json
deleted file mode 100644
index 171bd47653ff0372053377126250b645067aa4c5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/45_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "80 119 336 509", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/46_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/46_ground_truth.json
deleted file mode 100644
index 0879ade2bf6ed977cbbb830c234cebe94087aaad..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/46_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "75 44 138 133", "used": true}, {"class_name": "cone", "bbox": "279 162 357 282", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/47_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/47_ground_truth.json
deleted file mode 100644
index 31087e468591c023a5825db87a8f1150f0e3ba84..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/47_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "74 115 177 315", "used": true}, {"class_name": "cone", "bbox": "213 130 256 196", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/48_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/48_ground_truth.json
deleted file mode 100644
index eabb6f0f9d6fd8a674b1cd0bc2ed130fd3f5c036..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/48_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "255 205 314 330", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/49_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/49_ground_truth.json
deleted file mode 100644
index 91a50056361f214d24fe36ba7ff3ded4252f132b..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/49_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "109 61 269 324", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/4_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/4_ground_truth.json
deleted file mode 100644
index f751933f0d6b1b2e26ab7d4cf1e52caea6f9de21..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/4_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "120 67 222 253", "used": true}, {"class_name": "cone", "bbox": "1 59 102 256", "used": true}, {"class_name": "cone", "bbox": "296 77 400 253", "used": true}, {"class_name": "cone", "bbox": "392 74 479 260", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/50_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/50_ground_truth.json
deleted file mode 100644
index fbef0b68732e8d0f308a3df3c0f02c2494502d30..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/50_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "45 155 149 319", "used": true}, {"class_name": "cone", "bbox": "152 128 241 277", "used": true}, {"class_name": "cone", "bbox": "228 106 311 247", "used": true}, {"class_name": "cone", "bbox": "295 90 368 227", "used": true}, {"class_name": "cone", "bbox": "339 72 407 200", "used": true}, {"class_name": "cone", "bbox": "381 61 444 184", "used": true}, {"class_name": "cone", "bbox": "420 47 477 165", "used": true}, {"class_name": "cone", "bbox": "464 35 520 145", "used": true}, {"class_name": "cone", "bbox": "496 22 529 123", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/51_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/51_ground_truth.json
deleted file mode 100644
index 3e614c2bf7110d2fbc293a83db4fc327dd7187a8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/51_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "317 106 435 319", "used": true}, {"class_name": "cone", "bbox": "267 140 340 266", "used": true}, {"class_name": "cone", "bbox": "239 167 272 233", "used": true}, {"class_name": "cone", "bbox": "419 164 470 253", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/52_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/52_ground_truth.json
deleted file mode 100644
index 13a6b0abcdb8834e56987a85d5e9424e301f86ae..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/52_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "23 229 158 394", "used": true}, {"class_name": "cone", "bbox": "171 190 267 307", "used": true}, {"class_name": "cone", "bbox": "257 168 330 260", "used": true}, {"class_name": "cone", "bbox": "311 155 372 229", "used": true}, {"class_name": "cone", "bbox": "355 145 401 208", "used": true}, {"class_name": "cone", "bbox": "388 138 414 191", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/53_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/53_ground_truth.json
deleted file mode 100644
index c0458e8dd6205631d4d3fde51ff3624d72f38b15..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/53_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "56 3 292 445", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/54_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/54_ground_truth.json
deleted file mode 100644
index dc2aefc6b17d21063b97b38c09a585a0ac298bc7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/54_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "207 34 552 668", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/55_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/55_ground_truth.json
deleted file mode 100644
index 3086a7f0f19881a9670217f96a41bf2d832dc41e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/55_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "683 420 965 934", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/56_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/56_ground_truth.json
deleted file mode 100644
index 6cd1e541dbeb3cb3d80d080c25a70200a379a5f1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/56_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "20 157 92 246", "used": true}, {"class_name": "cone", "bbox": "188 97 242 161", "used": true}, {"class_name": "cone", "bbox": "314 56 355 108", "used": true}, {"class_name": "cone", "bbox": "406 29 441 73", "used": true}, {"class_name": "cone", "bbox": "465 8 494 45", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/57_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/57_ground_truth.json
deleted file mode 100644
index e26ab6f71c89ab7578a537c94a1f81e9cb3f4735..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/57_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 1 297 756", "used": false}, {"class_name": "cone", "bbox": "48 1 352 523", "used": true}, {"class_name": "cone", "bbox": "183 48 384 418", "used": true}, {"class_name": "cone", "bbox": "299 125 589 360", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/58_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/58_ground_truth.json
deleted file mode 100644
index b26e4d98bbb103ee8ac772e3b3018f5b4c72d115..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/58_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "112 97 244 345", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/59_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/59_ground_truth.json
deleted file mode 100644
index 2ffbeccf08a9295f0793bd913716f988a03f9f3c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/59_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "1 219 142 500", "used": true}, {"class_name": "cone", "bbox": "238 240 341 439", "used": true}, {"class_name": "cone", "bbox": "304 283 341 391", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/5_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/5_ground_truth.json
deleted file mode 100644
index 235500fa826e05aa80ac403f54f11947e8be4bc9..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/5_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "218 24 397 294", "used": true}, {"class_name": "cone", "bbox": "130 100 183 188", "used": true}, {"class_name": "cone", "bbox": "111 120 138 167", "used": true}, {"class_name": "cone", "bbox": "101 125 122 156", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/60_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/60_ground_truth.json
deleted file mode 100644
index 1dc9bd2cdf4a2d6374b56860679321854fccc705..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/60_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "123 145 573 970", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/61_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/61_ground_truth.json
deleted file mode 100644
index 8634174c38d8092ed808633d87bd521d96b9119a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/61_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "105 338 289 547", "used": true}, {"class_name": "cone", "bbox": "229 393 473 594", "used": true}, {"class_name": "cone", "bbox": "538 395 737 650", "used": true}, {"class_name": "cone", "bbox": "413 347 566 498", "used": true}, {"class_name": "cone", "bbox": "650 341 800 466", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/62_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/62_ground_truth.json
deleted file mode 100644
index de0da706c92321605c921aa58a4b362bf42eebb9..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/62_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "51 125 149 300", "used": true}, {"class_name": "cone", "bbox": "147 108 230 247", "used": true}, {"class_name": "cone", "bbox": "54 12 84 57", "used": true}, {"class_name": "cone", "bbox": "150 10 172 48", "used": true}, {"class_name": "cone", "bbox": "218 8 241 43", "used": true}, {"class_name": "cone", "bbox": "444 43 479 101", "used": true}, {"class_name": "cone", "bbox": "305 156 407 254", "used": true}, {"class_name": "cone", "bbox": "275 10 293 38", "used": true}, {"class_name": "cone", "bbox": "485 35 507 82", "used": true}, {"class_name": "cone", "bbox": "314 9 328 35", "used": true}, {"class_name": "cone", "bbox": "354 9 368 33", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/63_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/63_ground_truth.json
deleted file mode 100644
index c385e12a22b42e47f324386c316804e4f0db3abd..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/63_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "171 26 254 178", "used": true}, {"class_name": "cone", "bbox": "2 179 189 474", "used": true}, {"class_name": "cone", "bbox": "252 1 301 85", "used": true}, {"class_name": "cone", "bbox": "286 1 322 43", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/64_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/64_ground_truth.json
deleted file mode 100644
index 9efd271305db04624e3250dbe64dde6e4cae4db8..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/64_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "190 89 270 217", "used": true}, {"class_name": "cone", "bbox": "236 36 289 123", "used": true}, {"class_name": "cone", "bbox": "73 244 146 359", "used": true}, {"class_name": "cone", "bbox": "249 11 291 77", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/65_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/65_ground_truth.json
deleted file mode 100644
index d458f338bbe8a81bcf968aba2f022b22d3699a97..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/65_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "69 257 329 648", "used": true}, {"class_name": "cone", "bbox": "649 239 922 621", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/66_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/66_ground_truth.json
deleted file mode 100644
index 838b8c142f50fc64e2cbdc1b7cd18071709b0467..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/66_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "120 115 555 963", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/67_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/67_ground_truth.json
deleted file mode 100644
index 676f635b26779dc05b6e02e3247cffb792c9df11..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/67_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "25 80 225 418", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/68_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/68_ground_truth.json
deleted file mode 100644
index 7d5b428ea789b74043e95977c64de50a71a3ba2f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/68_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "172 201 457 594", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/69_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/69_ground_truth.json
deleted file mode 100644
index a3514bc6304a8146b68e78e5a9c2869305527635..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/69_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "113 207 280 461", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/6_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/6_ground_truth.json
deleted file mode 100644
index f0e5dd33741ef63adf91fa308aabb6f95bf6c03f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/6_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "61 52 124 139", "used": true}, {"class_name": "cone", "bbox": "413 45 474 131", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/70_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/70_ground_truth.json
deleted file mode 100644
index 924a7e0deb3bcbb87c4fb1d074dbdc702c82ce7a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/70_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "39 317 122 427", "used": true}, {"class_name": "cone", "bbox": "30 288 72 361", "used": true}, {"class_name": "cone", "bbox": "17 278 46 332", "used": true}, {"class_name": "cone", "bbox": "7 266 26 298", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/71_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/71_ground_truth.json
deleted file mode 100644
index 87a9e598271624dd21ff66aa01604efd3e2ad058..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/71_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "80 49 295 298", "used": true}, {"class_name": "cone", "bbox": "216 81 399 271", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/72_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/72_ground_truth.json
deleted file mode 100644
index 03145d771c30da111959165de840fa0e1a53450a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/72_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "351 214 417 300", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/73_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/73_ground_truth.json
deleted file mode 100644
index 170cd60ae85d5316302390e0080c9b53f59297be..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/73_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "6 155 173 456", "used": true}, {"class_name": "cone", "bbox": "71 23 224 371", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/74_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/74_ground_truth.json
deleted file mode 100644
index f4bf45005883e4dbdf65135cb4b8a34c8e0411c3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/74_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "192 189 333 325", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/75_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/75_ground_truth.json
deleted file mode 100644
index 881730176e61e644ffc904e4a8d7bd7a44405c59..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/75_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "130 151 282 426", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/76_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/76_ground_truth.json
deleted file mode 100644
index e5194e6e0c4ba17666a218d39141a4c52212da2d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/76_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/77_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/77_ground_truth.json
deleted file mode 100644
index dfef808c074322578fb765f5594aacbcc4ccdaa2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/77_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/78_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/78_ground_truth.json
deleted file mode 100644
index 9af412829f99f0d124f0d2a69cf8665e9769b061..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/78_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/79_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/79_ground_truth.json
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/79_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/7_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/7_ground_truth.json
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/7_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/80_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/80_ground_truth.json
deleted file mode 100644
index eebbd6ccb8e1afe51c87ddf59cf2764e5e212468..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/80_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/81_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/81_ground_truth.json
deleted file mode 100644
index 60c7dc5260d6360cb11cdcda16aa06371308b5c5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/81_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "16 18 280 499", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/82_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/82_ground_truth.json
deleted file mode 100644
index b13f488dd0071a77812b3fda5d15deb4142920a0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/82_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "130 155 304 477", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/83_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/83_ground_truth.json
deleted file mode 100644
index 0cea701b0ecd6d2404955c359b5f3b836f068c5f..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/83_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/84_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/84_ground_truth.json
deleted file mode 100644
index 737ab2c0cae23ec1034de6d3ae40bcfc3dd13632..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/84_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/85_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/85_ground_truth.json
deleted file mode 100644
index 86cd66e33b5e092258a9b5d0feff57298e651927..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/85_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "97 87 234 335", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/86_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/86_ground_truth.json
deleted file mode 100644
index 940bf184bf8e39416e3a1b7af97127e450c9a9b5..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/86_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/87_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/87_ground_truth.json
deleted file mode 100644
index 9f9278b00f6ff90fe0343f28ea9ff827cd0d45f1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/87_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/88_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/88_ground_truth.json
deleted file mode 100644
index 15111149f636d2f251b77199f7deefcf1d3b41e6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/88_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/89_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/89_ground_truth.json
deleted file mode 100644
index 34b1f3dd71642e899ad285ceca44b17f24f7d5e3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/89_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/8_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/8_ground_truth.json
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/8_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/90_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/90_ground_truth.json
deleted file mode 100644
index a51f728bb5b8980895ce9d4e39e750f598ab1466..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/90_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "63 14 294 490", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/91_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/91_ground_truth.json
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/91_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/92_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/92_ground_truth.json
deleted file mode 100644
index be97ccae6cc1fde3978650e216769ec65009487c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/92_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "232 235 347 416", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/93_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/93_ground_truth.json
deleted file mode 100644
index 13b8bdbe1c2b44ef424722f5919cf214e29b0604..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/93_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "147 7 258 161", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/94_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/94_ground_truth.json
deleted file mode 100644
index 0de5e1a498bb50b3b53951f68706d042d498eb9d..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/94_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "209 97 319 304", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/95_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/95_ground_truth.json
deleted file mode 100644
index 2b99c21e21684a56ab61929adb069a0b6a8c060a..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/95_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "117 71 187 185", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/96_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/96_ground_truth.json
deleted file mode 100644
index 2466249320befa18487bbbab7d6754435f64cf09..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/96_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/97_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/97_ground_truth.json
deleted file mode 100644
index 2ded5a75c952eff62be20ff9a60e0780f6e8c532..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/97_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "163 64 294 269", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/98_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/98_ground_truth.json
deleted file mode 100644
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--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/98_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/99_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/99_ground_truth.json
deleted file mode 100644
index 93045d35260b6e1ad398bb9f02e7c896611f444e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/99_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/9_ground_truth.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/9_ground_truth.json
deleted file mode 100644
index 2b76ce2b9bfdeb0b7c6c219d755113890980fa76..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/9_ground_truth.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"class_name": "cone", "bbox": "17 19 92 158", "used": true}]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/cone_predictions.json b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/cone_predictions.json
deleted file mode 100644
index 760442cc244db0a31b19fc703775411ae7515659..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/ground-truth/cone_predictions.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/results.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/results.txt
deleted file mode 100644
index f9ecc226a1300ccf38d9f01a65304880809a09e2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mAP/results.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-# AP and precision/recall per class
-99.334% = cone AP
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- Recall :['0.000', '0.001', '0.003', '0.004', '0.005', '0.006', '0.008', '0.009', '0.010', '0.012', '0.013', '0.014', '0.015', '0.017', '0.018', '0.019', '0.021', '0.022', '0.023', '0.024', '0.026', '0.027', '0.028', '0.030', '0.031', '0.032', '0.033', '0.035', '0.036', '0.037', '0.039', '0.040', '0.041', '0.042', '0.044', '0.045', '0.046', '0.047', '0.049', '0.050', '0.051', '0.053', '0.054', '0.055', '0.056', '0.058', '0.059', '0.060', '0.062', '0.063', '0.064', '0.065', '0.067', '0.068', '0.069', '0.071', '0.072', '0.073', '0.074', '0.076', '0.077', '0.078', '0.080', '0.081', '0.082', '0.083', '0.085', '0.086', '0.087', '0.089', '0.090', '0.091', '0.092', '0.094', '0.095', '0.096', '0.098', '0.099', '0.100', '0.101', '0.103', '0.104', '0.105', '0.107', '0.108', '0.109', '0.110', '0.112', '0.113', '0.114', '0.116', '0.117', '0.118', '0.119', '0.121', '0.122', '0.123', '0.125', '0.126', '0.127', '0.128', '0.130', '0.131', '0.132', '0.134', '0.135', '0.136', '0.137', '0.139', '0.140', 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'0.711', '0.712', '0.714', '0.715', '0.716', '0.718', '0.719', '0.720', '0.721', '0.723', '0.724', '0.725', '0.727', '0.728', '0.729', '0.730', '0.732', '0.733', '0.734', '0.736', '0.737', '0.738', '0.739', '0.741', '0.742', '0.743', '0.745', '0.746', '0.747', '0.748', '0.750', '0.751', '0.752', '0.754', '0.755', '0.756', '0.757', '0.759', '0.760', '0.761', '0.763', '0.764', '0.765', '0.766', '0.768', '0.769', '0.770', '0.772', '0.773', '0.774', '0.775', '0.777', '0.778', '0.779', '0.780', '0.782', '0.783', '0.784', '0.786', '0.787', '0.788', '0.789', '0.791', '0.792', '0.793', '0.795', '0.796', '0.797', '0.798', '0.800', '0.801', '0.802', '0.804', '0.805', '0.806', '0.807', '0.809', '0.810', '0.811', '0.813', '0.814', '0.815', '0.816', '0.818', '0.819', '0.820', '0.822', '0.823', '0.824', '0.825', '0.827', '0.828', '0.829', '0.831', '0.832', '0.833', '0.834', '0.836', '0.837', '0.838', '0.840', '0.841', '0.842', '0.843', '0.845', '0.846', '0.847', '0.849', '0.850', '0.851', '0.852', '0.854', '0.855', '0.856', '0.858', '0.859', '0.860', '0.861', '0.863', '0.864', '0.865', '0.866', '0.868', '0.869', '0.870', '0.872', '0.873', '0.874', '0.875', '0.877', '0.878', '0.879', '0.881', '0.882', '0.883', '0.884', '0.886', '0.887', '0.888', '0.890', '0.891', '0.892', '0.893', '0.895', '0.896', '0.897', '0.899', '0.900', '0.901', '0.902', '0.904', '0.905', '0.906', '0.908', '0.909', '0.910', '0.911', '0.913', '0.914', '0.915', '0.917', '0.918', '0.919', '0.920', '0.922', '0.923', '0.924', '0.926', '0.927', '0.928', '0.929', '0.931', '0.932', '0.933', '0.935', '0.936', '0.937', '0.938', '0.940', '0.941', '0.942', '0.944', '0.945', '0.946', '0.947', '0.949', '0.950', '0.951', '0.953', '0.954', '0.955', '0.956', '0.958', '0.959', '0.960', '0.960', '0.961', '0.963', '0.964', '0.965', '0.967', '0.968', '0.969', '0.970', '0.972', '0.973', '0.973', '0.974', '0.976', '0.976', '0.977', '0.977', '0.978', '0.978', '0.979', '0.981', '0.981', '0.982', '0.983', '0.983', '0.985', '0.986', '0.986', '0.986', '0.986', '0.987', '0.987', '0.988', '0.988', '0.988', '0.988', '0.990', '0.990', '0.991', '0.991', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.992', '0.994', '0.994', '0.994', '1.000']
-
-
-# mAP of all classes
-mAP = 99.334%, 12.29 FPS
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/make_data.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/make_data.py
deleted file mode 100644
index 3f5044d5f86b632bedb676144737b450a274ad87..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/make_data.py
+++ /dev/null
@@ -1,138 +0,0 @@
-#================================================================
-#
-# File name : make_data.py
-# Author : PyLessons
-# Created date: 2020-04-20
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : create mnist example dataset to train custom yolov3
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-import cv2
-import numpy as np
-import shutil
-import random
-from zipfile import ZipFile
-
-SIZE = 416
-images_num_train = 1000
-images_num_test = 200
-
-image_sizes = [3, 6, 3] # small, medium, big
-
-# this helps to run script both from terminal and python IDLE
-add_path = "mnist"
-if os.getcwd().split(os.sep)[-1] != "mnist":
- add_path = "mnist"
- os.chdir(add_path)
-else:
- add_path = ""
-
-def compute_iou(box1, box2):
- # xmin, ymin, xmax, ymax
- A1 = (box1[2] - box1[0])*(box1[3] - box1[1])
- A2 = (box2[2] - box2[0])*(box2[3] - box2[1])
-
- xmin = max(box1[0], box2[0])
- ymin = max(box1[1], box2[1])
- xmax = min(box1[2], box2[2])
- ymax = min(box1[3], box2[3])
-
- if ymin >= ymax or xmin >= xmax: return 0
- return ((xmax-xmin) * (ymax - ymin)) / (A1 + A2)
-
-
-def make_image(data, image_path, ratio=1):
- blank = data[0]
- boxes = data[1]
- label = data[2]
-
- ID = image_path.split("/")[-1][0]
- image = cv2.imread(image_path)
- image = cv2.resize(image, (int(28*ratio), int(28*ratio)))
- h, w, c = image.shape
-
- while True:
- xmin = np.random.randint(0, SIZE-w, 1)[0]
- ymin = np.random.randint(0, SIZE-h, 1)[0]
- xmax = xmin + w
- ymax = ymin + h
- box = [xmin, ymin, xmax, ymax]
-
- iou = [compute_iou(box, b) for b in boxes]
- if max(iou) < 0.02:
- boxes.append(box)
- label.append(ID)
- break
-
- for i in range(w):
- for j in range(h):
- x = xmin + i
- y = ymin + j
- blank[y][x] = image[j][i]
-
- # cv2.rectangle(blank, (xmin, ymin), (xmax, ymax), [0, 0, 255], 2)
- return blank
-
-
-for file in ["train", "test"]:
- if not os.path.exists(f"mnist/{file}"):
- with ZipFile(f"mnist/{file}.zip", 'r') as zip:
- # extracting all the files
- print(f'Extracting all {file} files now...')
- zip.extractall()
- shutil.move(file, "mnist")
- print('Done!')
-
-for file in ['train','test']:
- images_path = os.getcwd()+f"/mnist_{file}"
- labels_txt = os.getcwd()+f"/mnist_{file}.txt"
-
- if file == 'train': images_num = images_num_train
- if file == 'test': images_num = images_num_test
-
- if os.path.exists(images_path): shutil.rmtree(images_path)
- os.mkdir(images_path)
-
- image_paths = [os.path.join(os.path.realpath("."), os.getcwd()+f"/mnist/{file}/" + image_name)
- for image_name in os.listdir(os.getcwd()+f"/mnist/{file}")]
-
- with open(labels_txt, "w") as wf:
- image_num = 0
- while image_num < images_num:
- image_path = os.path.realpath(os.path.join(images_path, "%06d.jpg" %(image_num+1)))
- #print(image_path)
- annotation = image_path
- blanks = np.ones(shape=[SIZE, SIZE, 3]) * 255
- bboxes = [[0,0,1,1]]
- labels = [0]
- data = [blanks, bboxes, labels]
- bboxes_num = 0
-
- # ratios small, medium, big objects
- ratios = [[0.5, 0.8], [1., 1.5, 2.], [3., 4.]]
- for i in range(len(ratios)):
- N = random.randint(0, image_sizes[i])
- if N !=0: bboxes_num += 1
- for _ in range(N):
- ratio = random.choice(ratios[i])
- idx = random.randint(0, len(image_paths)-1)
- data[0] = make_image(data, image_paths[idx], ratio)
-
- if bboxes_num == 0: continue
- cv2.imwrite(image_path, data[0])
- for i in range(len(labels)):
- if i == 0: continue
- xmin = str(bboxes[i][0])
- ymin = str(bboxes[i][1])
- xmax = str(bboxes[i][2])
- ymax = str(bboxes[i][3])
- class_ind = str(labels[i])
- annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(class_ind)])
- image_num += 1
- print("=> %s" %annotation)
- wf.write(annotation + "\n")
-
-if add_path != "": os.chdir("..")
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist.names b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist.names
deleted file mode 100644
index 8b1acc12b635c26f3decadeaa251729d3ce512e9..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist.names
+++ /dev/null
@@ -1,10 +0,0 @@
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/test.zip b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/test.zip
deleted file mode 100644
index 94850dc2313a83f2d7c756181070c7e7a8573c15..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/test.zip and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/train.zip b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/train.zip
deleted file mode 100644
index ce8b46a83514818d80bba47380e79f92a7d05c99..0000000000000000000000000000000000000000
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/mnist/train.zip and /dev/null differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/show_image.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/show_image.py
deleted file mode 100644
index 7327d470d8aafa0f1a8a225ef0a48bbf981a0c03..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/mnist/show_image.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#================================================================
-#
-# File name : show_image.py
-# Author : PyLessons
-# Created date: 2020-04-20
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : show random image from created dataset
-#
-#================================================================
-import random
-import cv2
-import numpy as np
-from PIL import Image
-
-ID = random.randint(0, 200)
-label_txt = "./mnist_train.txt"
-image_info = open(label_txt).readlines()[ID].split()
-
-image_path = image_info[0]
-image = cv2.imread(image_path)
-for bbox in image_info[1:]:
- bbox = bbox.split(",")
- image = cv2.rectangle(image,(int(float(bbox[0])),
- int(float(bbox[1]))),
- (int(float(bbox[2])),
- int(float(bbox[3]))), (255,0,0), 2)
-
-image = Image.fromarray(np.uint8(image))
-image.show()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/coco/coco.names b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/coco/coco.names
deleted file mode 100644
index eea3a0b36c2fc9c31f635c0df11de15048c0dfa7..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/coco/coco.names
+++ /dev/null
@@ -1,80 +0,0 @@
-person
-bicycle
-car
-motorbike
-aeroplane
-bus
-train
-truck
-boat
-traffic-light
-fire-hydrant
-stop-sign
-parking-meter
-bench
-bird
-cat
-dog
-horse
-sheep
-cow
-elephant
-bear
-zebra
-giraffe
-backpack
-umbrella
-handbag
-tie
-suitcase
-frisbee
-skis
-snowboard
-sports-ball
-kite
-baseball-bat
-baseball-glove
-skateboard
-surfboard
-tennis-racket
-bottle
-wine-glass
-cup
-fork
-knife
-spoon
-bowl
-banana
-apple
-sandwich
-orange
-broccoli
-carrot
-hot-dog
-pizza
-donut
-cake
-chair
-sofa
-pottedplant
-bed
-diningtable
-toilet
-tvmonitor
-laptop
-mouse
-remote
-keyboard
-cell-phone
-microwave
-oven
-toaster
-sink
-refrigerator
-book
-clock
-vase
-scissors
-teddy-bear
-hair-drier
-toothbrush
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone.txt
deleted file mode 100644
index 8bf9e6dadd2cfd69fe55409129f3cd9e532f63f0..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone.txt
+++ /dev/null
@@ -1 +0,0 @@
-cone
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone_test.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone_test.txt
deleted file mode 100644
index 5e38c8d46b07d1f01b6d1f0939728f2d8aa7a9b1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone_test.txt
+++ /dev/null
@@ -1,255 +0,0 @@
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/1.jpg 112,35,309,327,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/10.jpg 248,39,436,328,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/100.jpg 40,66,229,319,0 197,73,312,248,0 323,76,387,171,0 174,80,243,201,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/101.jpg 242,383,267,436,0 171,377,213,437,0 107,381,143,434,0 32,378,73,436,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/102.jpg 190,275,295,430,0 212,260,297,384,0 234,234,305,345,0 264,226,324,319,0 283,214,326,289,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/103.jpg 124,127,228,296,0 6,69,93,194,0 255,36,321,145,0 423,81,519,223,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/104.jpg 127,71,261,300,0 70,96,168,288,0 281,31,449,338,0 255,87,350,289,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/105.jpg 1,57,182,569,0 44,53,237,479,0 133,66,296,409,0 204,62,333,364,0 277,43,397,315,0 330,33,429,275,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/106.jpg 5,23,102,86,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/107.jpg 189,224,316,441,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/108.jpg 49,37,353,587,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/109.jpg 298,224,333,273,0 246,225,275,274,0 186,224,219,273,0 134,226,168,269,0 348,226,377,270,0 379,226,407,269,0 99,225,131,267,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/11.jpg 400,20,508,255,0 294,19,405,205,0 226,18,312,172,0 164,15,243,148,0 107,12,172,125,0 84,13,135,114,0 58,11,99,102,0 310,7,341,64,0 423,8,464,79,0 171,8,194,46,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/110.jpg 109,61,269,324,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/111.jpg 1,18,148,270,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/112.jpg 233,186,303,304,0 31,194,108,312,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/113.jpg 166,210,206,273,0 52,212,95,275,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/114.jpg 113,207,280,461,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/115.jpg 384,235,439,316,0 459,234,507,315,0 172,213,197,262,0 40,194,61,230,0 144,209,173,259,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/116.jpg 193,216,281,397,0 139,183,217,331,0 245,138,334,182,0 132,117,186,236,0 188,114,241,210,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/117.jpg 156,83,255,278,0 82,36,170,206,0 256,74,359,251,0 158,14,241,160,0 246,28,338,175,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/118.jpg 671,439,773,587,0 537,440,653,588,0 346,442,457,572,0 95,445,214,565,0 1,442,59,553,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/119.jpg 73,197,127,303,0 101,188,151,276,0
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-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/134.jpg 87,6,331,409,0
-C:\Users\Antoine Dufour\Desktop\TensorFlow-2.x-YOLOv3-master/custom_dataset/test/135.jpg 142,133,285,303,0 323,84,443,213,0 40,111,171,245,0
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone_train.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/model_data/cone_train.txt
deleted file mode 100644
index ed6ac99a64b498ce21024ade112b73cbbf8cc2ef..0000000000000000000000000000000000000000
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/object_tracker.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/object_tracker.py
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index c23388ad1e1ecebc34e26bb3564080d9eac0b422..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/object_tracker.py
+++ /dev/null
@@ -1,151 +0,0 @@
-#================================================================
-#
-# File name : object_tracker.py
-# Author : PyLessons
-# Created date: 2020-09-17
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : code to track detected object from video or webcam
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import cv2
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import Load_Yolo_model, image_preprocess, postprocess_boxes, nms, draw_bbox, read_class_names
-from yolov3.configs import *
-import time
-
-from deep_sort import nn_matching
-from deep_sort.detection import Detection
-from deep_sort.tracker import Tracker
-from deep_sort import generate_detections as gdet
-
-video_path = "./IMAGES/test.mp4"
-
-def Object_tracking(Yolo, video_path, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors='', Track_only = []):
- # Definition of the parameters
- max_cosine_distance = 0.7
- nn_budget = None
-
- #initialize deep sort object
- model_filename = 'model_data/mars-small128.pb'
- encoder = gdet.create_box_encoder(model_filename, batch_size=1)
- metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
- tracker = Tracker(metric)
-
- times, times_2 = [], []
-
- if video_path:
- vid = cv2.VideoCapture(video_path) # detect on video
- else:
- vid = cv2.VideoCapture(0) # detect from webcam
-
- # by default VideoCapture returns float instead of int
- width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
- height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
- fps = int(vid.get(cv2.CAP_PROP_FPS))
- codec = cv2.VideoWriter_fourcc(*'XVID')
- out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
-
- NUM_CLASS = read_class_names(CLASSES)
- key_list = list(NUM_CLASS.keys())
- val_list = list(NUM_CLASS.values())
- while True:
- _, frame = vid.read()
-
- try:
- original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
- except:
- break
-
- image_data = image_preprocess(np.copy(original_frame), [input_size, input_size])
- #image_data = tf.expand_dims(image_data, 0)
- image_data = image_data[np.newaxis, ...].astype(np.float32)
-
- t1 = time.time()
- if YOLO_FRAMEWORK == "tf":
- pred_bbox = Yolo.predict(image_data)
- elif YOLO_FRAMEWORK == "trt":
- batched_input = tf.constant(image_data)
- result = Yolo(batched_input)
- pred_bbox = []
- for key, value in result.items():
- value = value.numpy()
- pred_bbox.append(value)
-
- #t1 = time.time()
- #pred_bbox = Yolo.predict(image_data)
- t2 = time.time()
-
- pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
- pred_bbox = tf.concat(pred_bbox, axis=0)
-
- bboxes = postprocess_boxes(pred_bbox, original_frame, input_size, score_threshold)
- bboxes = nms(bboxes, iou_threshold, method='nms')
-
- # extract bboxes to boxes (x, y, width, height), scores and names
- boxes, scores, names = [], [], []
- for bbox in bboxes:
- if len(Track_only) !=0 and NUM_CLASS[int(bbox[5])] in Track_only or len(Track_only) == 0:
- boxes.append([bbox[0].astype(int), bbox[1].astype(int), bbox[2].astype(int)-bbox[0].astype(int), bbox[3].astype(int)-bbox[1].astype(int)])
- scores.append(bbox[4])
- names.append(NUM_CLASS[int(bbox[5])])
-
- # Obtain all the detections for the given frame.
- boxes = np.array(boxes)
- names = np.array(names)
- scores = np.array(scores)
- features = np.array(encoder(original_frame, boxes))
- detections = [Detection(bbox, score, class_name, feature) for bbox, score, class_name, feature in zip(boxes, scores, names, features)]
-
- # Pass detections to the deepsort object and obtain the track information.
- tracker.predict()
- tracker.update(detections)
-
- # Obtain info from the tracks
- tracked_bboxes = []
- for track in tracker.tracks:
- if not track.is_confirmed() or track.time_since_update > 5:
- continue
- bbox = track.to_tlbr() # Get the corrected/predicted bounding box
- class_name = track.get_class() #Get the class name of particular object
- tracking_id = track.track_id # Get the ID for the particular track
- index = key_list[val_list.index(class_name)] # Get predicted object index by object name
- tracked_bboxes.append(bbox.tolist() + [tracking_id, index]) # Structure data, that we could use it with our draw_bbox function
-
- # draw detection on frame
- image = draw_bbox(original_frame, tracked_bboxes, CLASSES=CLASSES, tracking=True)
-
- t3 = time.time()
- times.append(t2-t1)
- times_2.append(t3-t1)
-
- times = times[-20:]
- times_2 = times_2[-20:]
-
- ms = sum(times)/len(times)*1000
- fps = 1000 / ms
- fps2 = 1000 / (sum(times_2)/len(times_2)*1000)
-
- image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
-
- # draw original yolo detection
- #image = draw_bbox(image, bboxes, CLASSES=CLASSES, show_label=False, rectangle_colors=rectangle_colors, tracking=True)
-
- print("Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(ms, fps, fps2))
- if output_path != '': out.write(image)
- if show:
- cv2.imshow('output', image)
-
- if cv2.waitKey(25) & 0xFF == ord("q"):
- cv2.destroyAllWindows()
- break
-
- cv2.destroyAllWindows()
-
-
-yolo = Load_Yolo_model()
-Object_tracking(yolo, video_path, "detection.mp4", input_size=YOLO_INPUT_SIZE, show=True, iou_threshold=0.1, rectangle_colors=(255,0,0), Track_only = ["person"])
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/requirements.txt b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/requirements.txt
deleted file mode 100644
index 96aa428d2e4cca5331b1f4ce23fdf4014174ae7e..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/requirements.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-numpy>=1.18.2
-scipy>=1.4.1
-wget>=3.2
-seaborn>=0.10.0
-tensorflow
-opencv-python==4.4.0.46
-tqdm==4.43.0
-pandas
-awscli
-urllib3
-mss
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test_positionnement.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test_positionnement.py
deleted file mode 100644
index 508e337bd32d8ac5a10d08149efad715096c15f6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test_positionnement.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Mar 14 16:03:28 2023
-
-@author: paull
-"""
-
-import matplotlib.pyplot as plt
-from MCL import get_position
-from yolov3.utils import detect_image, Load_Yolo_model
-from yolov3.configs import *
-from math import sin, cos
-
-image_path = './test/webcam chalon/'
-
-
-# pixel_x_ext, pixel_y_ext = [242, 220, 165, 110, 63, 33, 22, 34, 63, 110, 165, 220, 243, 310, 334, 388, 443, 490, 521, 531, 520, 489, 443, 388, 333, 310], [76, 64, 52, 64, 95, 141, 196, 252, 298, 330, 340, 328, 318, 316, 328, 339, 329, 298, 251, 196, 142, 95, 64, 53, 64, 77]
-# pixel_x_int, pixel_y_int = [245, 238, 222, 196, 166, 134, 108, 91, 85, 90, 109, 134, 165, 196, 222, 239, 308, 314, 332, 358, 388, 419, 445, 462, 468, 462, 445, 419, 388, 359, 332, 314], [201, 167, 140, 123, 116, 123, 140, 165, 195, 228, 253, 270, 277, 270, 253, 227, 200, 226, 253, 270, 277, 270, 253, 228, 197, 166, 140, 122, 117, 123, 140, 166]
-# diametre = 225
-# centre_x, centre_y = 278, 200
-# coord_x_ext, coord_y_ext = [i/diametre for i in pixel_x_ext], [i/diametre for i in pixel_y_ext]
-# coord_x_int, coord_y_int = [i/diametre for i in pixel_x_int], [i/diametre for i in pixel_y_int]
-
-coord_x_int = []
-coord_y_int = []
-
-coord_x_ext = []
-coord_y_ext = []
-
-r_in = 1-0.14/2
-r_ext = r_in + 0.394 + 0.14
-
-for i in range(16):
- theta = 2*3.1415*i/16
- coord_x_int.append(-1.2+r_in*cos(theta))
- coord_y_int.append(r_in*sin(theta))
-
- coord_x_int.append(1.2+r_in*cos(theta))
- coord_y_int.append(r_in*sin(theta))
-
- if 1<i<15:
- coord_x_ext.append(-1.2+r_ext*cos(theta))
- coord_y_ext.append(r_ext*sin(theta))
-
- if not 7<=i<=9:
- coord_x_ext.append(1.2+r_ext*cos(theta))
- coord_y_ext.append(r_ext*sin(theta))
-
-yolo = Load_Yolo_model()
-
-particles = [(1.2,1.19,0) for i in range(50)]
-
-plt.ion()
-fig = plt.figure()
-ax = fig.add_subplot(111)
-exterieur = ax.plot(coord_x_ext, coord_y_ext,'+')
-interieur = ax.plot(coord_x_int, coord_y_int,'+')
-line1, = ax.plot(1.2, 1.19, '.')
-line2, = ax.plot(0,0,'+')
-
-if __name__ == '__main__':
- for i in range(1,20):
- image = image_path + str(i) +'.jpg'
- _, boxes = detect_image(yolo, image, "./test/result.jpg", input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
-
- pos, particles,cone_x,cone_y = get_position(boxes, (0.05, -15*3.14/180, 1), particles)
-
- print("Position : ", pos)
-
- line1.set_xdata(pos[0])
- line1.set_ydata(pos[1])
- line2.set_xdata(cone_x)
- line2.set_ydata(cone_y)
- plt.arrow(pos[0], pos[1], 0.1*cos(pos[2]), 0.1*sin(pos[2]))
- ## Affichage graphique
-
- plt.pause(0.001)
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_TRT.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_TRT.py
deleted file mode 100644
index 46eb988be1c498592d7fb81f0dedde7b746400e9..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_TRT.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#================================================================
-#
-# File name : Convert_to_TRT.py
-# Author : PyLessons
-# Created date: 2020-08-17
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : convert TF frozen graph to TensorRT model
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import sys
-
-foldername = os.path.basename(os.getcwd())
-if foldername == "tools":
- os.chdir("..")
-sys.path.insert(1, os.getcwd())
-
-import tensorflow as tf
-import numpy as np
-physical_devices = tf.config.experimental.list_physical_devices('GPU')
-if len(physical_devices) > 0:
- tf.config.experimental.set_memory_growth(physical_devices[0], True)
-from yolov3.configs import *
-from tensorflow.python.compiler.tensorrt import trt_convert as trt
-
-def calibration_input():
- for i in range(100):
- batched_input = np.random.random((1, YOLO_INPUT_SIZE, YOLO_INPUT_SIZE, 3)).astype(np.float32)
- batched_input = tf.constant(batched_input)
- yield (batched_input,)
-
-conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS
-conversion_params = conversion_params._replace(max_workspace_size_bytes=4000000000)
-conversion_params = conversion_params._replace(precision_mode=YOLO_TRT_QUANTIZE_MODE)
-conversion_params = conversion_params._replace(max_batch_size=1)
-if YOLO_TRT_QUANTIZE_MODE == 'INT8':
- conversion_params = conversion_params._replace(use_calibration=True)
-
-converter = trt.TrtGraphConverterV2(input_saved_model_dir=f'./checkpoints/{YOLO_TYPE}-{YOLO_INPUT_SIZE}', conversion_params=conversion_params)
-if YOLO_TRT_QUANTIZE_MODE == 'INT8':
- converter.convert(calibration_input_fn=calibration_input)
-else:
- converter.convert()
-
-converter.save(output_saved_model_dir=f'./checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}-{YOLO_INPUT_SIZE}')
-print(f'Done Converting to TensorRT, model saved to: /checkpoints/{YOLO_TYPE}-trt-{YOLO_TRT_QUANTIZE_MODE}-{YOLO_INPUT_SIZE}')
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_pb.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_pb.py
deleted file mode 100644
index 1ce98bcf4ac211dff43dd41a5b52cfabad9e408c..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Convert_to_pb.py
+++ /dev/null
@@ -1,40 +0,0 @@
-#================================================================
-#
-# File name : Convert_to_pb.py
-# Author : PyLessons
-# Created date: 2020-08-17
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : used to freeze tf model to .pb model
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-import sys
-
-foldername = os.path.basename(os.getcwd())
-if foldername == "tools":
- os.chdir("..")
-sys.path.insert(1, os.getcwd())
-
-import tensorflow as tf
-from yolov3.yolov4 import Create_Yolo
-from yolov3.utils import load_yolo_weights
-from yolov3.configs import *
-
-if YOLO_TYPE == "yolov4":
- Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
-if YOLO_TYPE == "yolov3":
- Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
-
-if YOLO_CUSTOM_WEIGHTS == False:
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=YOLO_COCO_CLASSES)
- load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
-else:
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
- yolo.load_weights(YOLO_CUSTOM_WEIGHTS) # use custom weights
-
-yolo.summary()
-yolo.save(f'./checkpoints/{YOLO_TYPE}-{YOLO_INPUT_SIZE}')
-
-print(f"model saves to /checkpoints/{YOLO_TYPE}-{YOLO_INPUT_SIZE}")
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Detection_to_XML.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Detection_to_XML.py
deleted file mode 100644
index 5cfcf6dfa29a9f90ae46aaebfcee98a3d2f9d362..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/Detection_to_XML.py
+++ /dev/null
@@ -1,125 +0,0 @@
-#================================================================
-#
-# File name : Detection_to_XML.py
-# Author : PyLessons
-# Created date: 2020-09-27
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : converts YOLO detection to XML file
-#
-#===============================================================
-from textwrap import dedent
-from lxml import etree
-import glob
-import os
-import cv2
-import time
-
-def CreateXMLfile(path, file_name, image, bboxes, NUM_CLASS):
- boxes = []
- for bbox in bboxes:
- boxes.append([bbox[0].astype(int), bbox[1].astype(int), bbox[2].astype(int), bbox[3].astype(int), NUM_CLASS[int(bbox[5])]])#, bbox[4], NUM_CLASS[int(bbox[5])]])
-
- if not os.path.exists(path):
- os.makedirs(path)
- os.chdir(path)
-
- img_name = "XML_"+file_name+".png"
-
- cv2.imwrite(img_name,image)
-
- annotation = etree.Element("annotation")
-
- folder = etree.Element("folder")
- folder.text = os.path.basename(os.getcwd())
- annotation.append(folder)
-
- filename_xml = etree.Element("filename")
- filename_str = img_name.split(".")[0]
- filename_xml.text = img_name
- annotation.append(filename_xml)
-
- path = etree.Element("path")
- path.text = os.path.join(os.getcwd(), filename_str + ".jpg")
- annotation.append(path)
-
- source = etree.Element("source")
- annotation.append(source)
-
- database = etree.Element("database")
- database.text = "Unknown"
- source.append(database)
-
- size = etree.Element("size")
- annotation.append(size)
-
- width = etree.Element("width")
- height = etree.Element("height")
- depth = etree.Element("depth")
-
- img = cv2.imread(filename_xml.text)
-
- width.text = str(img.shape[1])
- height.text = str(img.shape[0])
- depth.text = str(img.shape[2])
-
- size.append(width)
- size.append(height)
- size.append(depth)
-
- segmented = etree.Element("segmented")
- segmented.text = "0"
- annotation.append(segmented)
-
- for Object in boxes:
- class_name = Object[4]
- xmin_l = str(int(float(Object[0])))
- ymin_l = str(int(float(Object[1])))
- xmax_l = str(int(float(Object[2])))
- ymax_l = str(int(float(Object[3])))
-
- obj = etree.Element("object")
- annotation.append(obj)
-
- name = etree.Element("name")
- name.text = class_name
- obj.append(name)
-
- pose = etree.Element("pose")
- pose.text = "Unspecified"
- obj.append(pose)
-
- truncated = etree.Element("truncated")
- truncated.text = "0"
- obj.append(truncated)
-
- difficult = etree.Element("difficult")
- difficult.text = "0"
- obj.append(difficult)
-
- bndbox = etree.Element("bndbox")
- obj.append(bndbox)
-
- xmin = etree.Element("xmin")
- xmin.text = xmin_l
- bndbox.append(xmin)
-
- ymin = etree.Element("ymin")
- ymin.text = ymin_l
- bndbox.append(ymin)
-
- xmax = etree.Element("xmax")
- xmax.text = xmax_l
- bndbox.append(xmax)
-
- ymax = etree.Element("ymax")
- ymax.text = ymax_l
- bndbox.append(ymax)
-
- # write xml to file
- s = etree.tostring(annotation, pretty_print=True)
- with open(filename_str + ".xml", 'wb') as f:
- f.write(s)
- f.close()
-
- os.chdir("..")
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/XML_to_YOLOv3.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/XML_to_YOLOv3.py
deleted file mode 100644
index 7f9d78aacb4d4669c1a4e188a1a54b0f4fa21112..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/XML_to_YOLOv3.py
+++ /dev/null
@@ -1,66 +0,0 @@
-#================================================================
-#
-# File name : XML_to_YOLOv3.py
-# Author : PyLessons
-# Created date: 2020-06-04
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : used to convert XML labels to YOLOv3 training labels
-#
-#================================================================
-import xml.etree.ElementTree as ET
-import os
-import glob
-
-foldername = os.path.basename(os.getcwd())
-if foldername == "tools": os.chdir("..")
-
-
-data_dir = '/custom_dataset/'
-Dataset_names_path = "model_data/cone.txt"
-Dataset_train = "model_data/cone_train.txt"
-Dataset_test = "model_data/cone_test.txt"
-is_subfolder = False
-
-Dataset_names = []
-
-def ParseXML(img_folder, file):
- for xml_file in glob.glob(img_folder+'/*.xml'):
- tree=ET.parse(open(xml_file))
- root = tree.getroot()
- image_name = root.find('filename').text
- img_path = img_folder+'/'+image_name
- for i, obj in enumerate(root.iter('object')):
- difficult = obj.find('difficult').text
- cls = obj.find('name').text
- if cls not in Dataset_names:
- Dataset_names.append(cls)
- cls_id = Dataset_names.index(cls)
- xmlbox = obj.find('bndbox')
- OBJECT = (str(int(float(xmlbox.find('xmin').text)))+','
- +str(int(float(xmlbox.find('ymin').text)))+','
- +str(int(float(xmlbox.find('xmax').text)))+','
- +str(int(float(xmlbox.find('ymax').text)))+','
- +str(cls_id))
- img_path += ' '+OBJECT
- print(img_path)
- file.write(img_path+'\n')
-
-def run_XML_to_YOLOv3():
- for i, folder in enumerate(['train','test']):
- with open([Dataset_train,Dataset_test][i], "w") as file:
- print(os.getcwd()+data_dir+folder)
- img_path = os.path.join(os.getcwd()+data_dir+folder)
- if is_subfolder:
- for directory in os.listdir(img_path):
- xml_path = os.path.join(img_path, directory)
- ParseXML(xml_path, file)
- else:
- ParseXML(img_path, file)
-
- print("Dataset_names:", Dataset_names)
- with open(Dataset_names_path, "w") as file:
- for name in Dataset_names:
- file.write(str(name)+'\n')
-
-run_XML_to_YOLOv3()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/oid_to_pascal_voc_xml.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/oid_to_pascal_voc_xml.py
deleted file mode 100644
index 3accbfcd237a6ca415c3635421d1722765cb04f2..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/tools/oid_to_pascal_voc_xml.py
+++ /dev/null
@@ -1,175 +0,0 @@
-#================================================================
-#
-# File name : oid_to_pascal_vos_xml.py
-# Author : PyLessons
-# Created date: 2020-06-04
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : used to convert oid labels to pascal vos xml
-#
-#================================================================
-import os
-from tqdm import tqdm
-from sys import exit
-import argparse
-import cv2
-from textwrap import dedent
-from lxml import etree
-
-foldername = os.path.basename(os.getcwd())
-if foldername == "tools": os.chdir("..")
-
-Dataset_path = "OIDv4_ToolKit/OID/Dataset"
-
-def convert_to_xml():
- current_path = os.getcwd()
- os.chdir(Dataset_path)
- DIRS = os.listdir(os.getcwd())
-
- for DIR in DIRS:
- if os.path.isdir(DIR):
- os.chdir(DIR)
-
- print("Currently in Subdirectory:", DIR)
- CLASS_DIRS = os.listdir(os.getcwd())
- for CLASS_DIR in CLASS_DIRS:
- if " " in CLASS_DIR:
- os.rename(CLASS_DIR, CLASS_DIR.replace(" ", "_"))
-
- CLASS_DIRS = os.listdir(os.getcwd())
- for CLASS_DIR in CLASS_DIRS:
- if os.path.isdir(CLASS_DIR):
- os.chdir(CLASS_DIR)
-
- print("\n" + "Creating PASCAL VOC XML Files for Class:", CLASS_DIR)
- # Create Directory for annotations if it does not exist yet
-
- #Read Labels from OIDv4 ToolKit
- os.chdir("Label")
-
- #Create PASCAL XML
- for filename in tqdm(os.listdir(os.getcwd())):
- if filename.endswith(".txt"):
- filename_str = str.split(filename, ".")[0]
-
-
- annotation = etree.Element("annotation")
-
- os.chdir("..")
- folder = etree.Element("folder")
- folder.text = os.path.basename(os.getcwd())
- annotation.append(folder)
-
- filename_xml = etree.Element("filename")
- filename_xml.text = filename_str + ".jpg"
- annotation.append(filename_xml)
-
- path = etree.Element("path")
- path.text = os.path.join(os.path.dirname(os.path.abspath(filename)), filename_str + ".jpg")
- annotation.append(path)
-
- source = etree.Element("source")
- annotation.append(source)
-
- database = etree.Element("database")
- database.text = "Unknown"
- source.append(database)
-
- size = etree.Element("size")
- annotation.append(size)
-
- width = etree.Element("width")
- height = etree.Element("height")
- depth = etree.Element("depth")
-
- img = cv2.imread(filename_xml.text)
-
- try:
- width.text = str(img.shape[1])
- except AttributeError:
- os.chdir("Label")
- continue
- height.text = str(img.shape[0])
- depth.text = str(img.shape[2])
-
- size.append(width)
- size.append(height)
- size.append(depth)
-
- segmented = etree.Element("segmented")
- segmented.text = "0"
- annotation.append(segmented)
-
- os.chdir("Label")
- label_original = open(filename, 'r')
-
- # Labels from OIDv4 Toolkit: name_of_class X_min Y_min X_max Y_max
- for line in label_original:
- line = line.strip()
- l = line.split(' ')
-
- class_name_len = len(l) - 4 # 4 coordinates
- class_name = l[0]
- for i in range(1,class_name_len):
- class_name = f"{class_name}_{l[i]}"
-
- addi = class_name_len
-
- xmin_l = str(int(round(float(l[0+addi]))))
- ymin_l = str(int(round(float(l[1+addi]))))
- xmax_l = str(int(round(float(l[2+addi]))))
- ymax_l = str(int(round(float(l[3+addi]))))
-
- obj = etree.Element("object")
- annotation.append(obj)
-
- name = etree.Element("name")
- name.text = class_name
- obj.append(name)
-
- pose = etree.Element("pose")
- pose.text = "Unspecified"
- obj.append(pose)
-
- truncated = etree.Element("truncated")
- truncated.text = "0"
- obj.append(truncated)
-
- difficult = etree.Element("difficult")
- difficult.text = "0"
- obj.append(difficult)
-
- bndbox = etree.Element("bndbox")
- obj.append(bndbox)
-
- xmin = etree.Element("xmin")
- xmin.text = xmin_l
- bndbox.append(xmin)
-
- ymin = etree.Element("ymin")
- ymin.text = ymin_l
- bndbox.append(ymin)
-
- xmax = etree.Element("xmax")
- xmax.text = xmax_l
- bndbox.append(xmax)
-
- ymax = etree.Element("ymax")
- ymax.text = ymax_l
- bndbox.append(ymax)
-
- os.chdir("..")
- # write xml to file
- s = etree.tostring(annotation, pretty_print=True)
- with open(filename_str + ".xml", 'wb') as f:
- f.write(s)
- f.close()
-
- os.chdir("Label")
-
- os.chdir("..")
- os.chdir("..")
-
- os.chdir("..")
-
-convert_to_xml()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/train.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/train.py
deleted file mode 100644
index be0198509ad92f62b36e82121e650d35d0d055af..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/train.py
+++ /dev/null
@@ -1,188 +0,0 @@
-#================================================================
-#
-# File name : train.py
-# Author : PyLessons
-# Created date: 2020-08-06
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : used to train custom object detector
-#
-#================================================================
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
-from tensorflow.python.client import device_lib
-print(device_lib.list_local_devices())
-import shutil
-import numpy as np
-import tensorflow as tf
-#from tensorflow.keras.utils import plot_model
-from yolov3.dataset import Dataset
-from yolov3.yolov4 import Create_Yolo, compute_loss
-from yolov3.utils import load_yolo_weights
-from yolov3.configs import *
-from evaluate_mAP import get_mAP
-
-if YOLO_TYPE == "yolov4":
- Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
-if YOLO_TYPE == "yolov3":
- Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
-if TRAIN_YOLO_TINY: TRAIN_MODEL_NAME += "_Tiny"
-
-def main():
- global TRAIN_FROM_CHECKPOINT
-
- gpus = tf.config.experimental.list_physical_devices('GPU')
- print(f'GPUs {gpus}')
- if len(gpus) > 0:
- try: tf.config.experimental.set_memory_growth(gpus[0], True)
- except RuntimeError: pass
-
- if os.path.exists(TRAIN_LOGDIR): shutil.rmtree(TRAIN_LOGDIR)
- writer = tf.summary.create_file_writer(TRAIN_LOGDIR)
-
- trainset = Dataset('train')
- testset = Dataset('test')
-
- steps_per_epoch = len(trainset)
- global_steps = tf.Variable(1, trainable=False, dtype=tf.int64)
- warmup_steps = TRAIN_WARMUP_EPOCHS * steps_per_epoch
- total_steps = TRAIN_EPOCHS * steps_per_epoch
-
- if TRAIN_TRANSFER:
- Darknet = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=YOLO_COCO_CLASSES)
- load_yolo_weights(Darknet, Darknet_weights) # use darknet weights
-
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, training=True, CLASSES=TRAIN_CLASSES)
- if TRAIN_FROM_CHECKPOINT:
- try:
- yolo.load_weights(f"./checkpoints/{TRAIN_MODEL_NAME}")
- except ValueError:
- print("Shapes are incompatible, transfering Darknet weights")
- TRAIN_FROM_CHECKPOINT = False
-
- if TRAIN_TRANSFER and not TRAIN_FROM_CHECKPOINT:
- for i, l in enumerate(Darknet.layers):
- layer_weights = l.get_weights()
- if layer_weights != []:
- try:
- yolo.layers[i].set_weights(layer_weights)
- except:
- print("skipping", yolo.layers[i].name)
-
- optimizer = tf.keras.optimizers.Adam()
-
-
- def train_step(image_data, target):
- with tf.GradientTape() as tape:
- pred_result = yolo(image_data, training=True)
- giou_loss=conf_loss=prob_loss=0
-
- # optimizing process
- grid = 3 if not TRAIN_YOLO_TINY else 2
- for i in range(grid):
- conv, pred = pred_result[i*2], pred_result[i*2+1]
- loss_items = compute_loss(pred, conv, *target[i], i, CLASSES=TRAIN_CLASSES)
- giou_loss += loss_items[0]
- conf_loss += loss_items[1]
- prob_loss += loss_items[2]
-
- total_loss = giou_loss + conf_loss + prob_loss
-
- gradients = tape.gradient(total_loss, yolo.trainable_variables)
- optimizer.apply_gradients(zip(gradients, yolo.trainable_variables))
-
- # update learning rate
- # about warmup: https://arxiv.org/pdf/1812.01187.pdf&usg=ALkJrhglKOPDjNt6SHGbphTHyMcT0cuMJg
- global_steps.assign_add(1)
- if global_steps < warmup_steps:# and not TRAIN_TRANSFER:
- lr = global_steps / warmup_steps * TRAIN_LR_INIT
- else:
- lr = TRAIN_LR_END + 0.5 * (TRAIN_LR_INIT - TRAIN_LR_END)*(
- (1 + tf.cos((global_steps - warmup_steps) / (total_steps - warmup_steps) * np.pi)))
- optimizer.lr.assign(lr.numpy())
-
- # writing summary data
- with writer.as_default():
- tf.summary.scalar("lr", optimizer.lr, step=global_steps)
- tf.summary.scalar("loss/total_loss", total_loss, step=global_steps)
- tf.summary.scalar("loss/giou_loss", giou_loss, step=global_steps)
- tf.summary.scalar("loss/conf_loss", conf_loss, step=global_steps)
- tf.summary.scalar("loss/prob_loss", prob_loss, step=global_steps)
- writer.flush()
-
- return global_steps.numpy(), optimizer.lr.numpy(), giou_loss.numpy(), conf_loss.numpy(), prob_loss.numpy(), total_loss.numpy()
-
- validate_writer = tf.summary.create_file_writer(TRAIN_LOGDIR)
- def validate_step(image_data, target):
- with tf.GradientTape() as tape:
- pred_result = yolo(image_data, training=False)
- giou_loss=conf_loss=prob_loss=0
-
- # optimizing process
- grid = 3 if not TRAIN_YOLO_TINY else 2
- for i in range(grid):
- conv, pred = pred_result[i*2], pred_result[i*2+1]
- loss_items = compute_loss(pred, conv, *target[i], i, CLASSES=TRAIN_CLASSES)
- giou_loss += loss_items[0]
- conf_loss += loss_items[1]
- prob_loss += loss_items[2]
-
- total_loss = giou_loss + conf_loss + prob_loss
-
- return giou_loss.numpy(), conf_loss.numpy(), prob_loss.numpy(), total_loss.numpy()
-
- mAP_model = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES) # create second model to measure mAP
-
- best_val_loss = 1000 # should be large at start
- for epoch in range(TRAIN_EPOCHS):
- for image_data, target in trainset:
- results = train_step(image_data, target)
- cur_step = results[0]%steps_per_epoch
- print("epoch:{:2.0f} step:{:5.0f}/{}, lr:{:.6f}, giou_loss:{:7.2f}, conf_loss:{:7.2f}, prob_loss:{:7.2f}, total_loss:{:7.2f}"
- .format(epoch, cur_step, steps_per_epoch, results[1], results[2], results[3], results[4], results[5]))
-
- if len(testset) == 0:
- print("configure TEST options to validate model")
- yolo.save_weights(os.path.join(TRAIN_CHECKPOINTS_FOLDER, TRAIN_MODEL_NAME))
- continue
-
- count, giou_val, conf_val, prob_val, total_val = 0., 0, 0, 0, 0
- for image_data, target in testset:
- results = validate_step(image_data, target)
- count += 1
- giou_val += results[0]
- conf_val += results[1]
- prob_val += results[2]
- total_val += results[3]
- # writing validate summary data
- with validate_writer.as_default():
- tf.summary.scalar("validate_loss/total_val", total_val/count, step=epoch)
- tf.summary.scalar("validate_loss/giou_val", giou_val/count, step=epoch)
- tf.summary.scalar("validate_loss/conf_val", conf_val/count, step=epoch)
- tf.summary.scalar("validate_loss/prob_val", prob_val/count, step=epoch)
- validate_writer.flush()
-
- print("\n\ngiou_val_loss:{:7.2f}, conf_val_loss:{:7.2f}, prob_val_loss:{:7.2f}, total_val_loss:{:7.2f}\n\n".
- format(giou_val/count, conf_val/count, prob_val/count, total_val/count))
-
- if TRAIN_SAVE_CHECKPOINT and not TRAIN_SAVE_BEST_ONLY:
- save_directory = os.path.join(TRAIN_CHECKPOINTS_FOLDER, TRAIN_MODEL_NAME+"_val_loss_{:7.2f}".format(total_val/count))
- yolo.save_weights(save_directory)
- if TRAIN_SAVE_BEST_ONLY and best_val_loss>total_val/count:
- save_directory = os.path.join(TRAIN_CHECKPOINTS_FOLDER, TRAIN_MODEL_NAME)
- yolo.save_weights(save_directory)
- best_val_loss = total_val/count
- if not TRAIN_SAVE_BEST_ONLY and not TRAIN_SAVE_CHECKPOINT:
- save_directory = os.path.join(TRAIN_CHECKPOINTS_FOLDER, TRAIN_MODEL_NAME)
- yolo.save_weights(save_directory)
-
- # measure mAP of trained custom model
- try:
- mAP_model.load_weights(save_directory) # use keras weights
- get_mAP(mAP_model, testset, score_threshold=TEST_SCORE_THRESHOLD, iou_threshold=TEST_IOU_THRESHOLD)
- except UnboundLocalError:
- print("You don't have saved model weights to measure mAP, check TRAIN_SAVE_BEST_ONLY and TRAIN_SAVE_CHECKPOINT lines in configs.py")
-
-if __name__ == '__main__':
- main()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__ init __.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__ init __.py
deleted file mode 100644
index 792d6005489ebee62cde02066f19c5521e620451..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__ init __.py
+++ /dev/null
@@ -1 +0,0 @@
-#
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/car_interface.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/car_interface.py
deleted file mode 100644
index d11cbdc5e0357ed20534505786add3d4a522a126..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/car_interface.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import serial #Importation de la bibliothèque « pySerial »
-import time
-
-def get_ser():
- ser = serial.Serial(port="COM3", baudrate=115200, timeout=0.1,write_timeout=0.2) #Création du port lié au COM6 a une vitesse de 115200 bauds et un timout d’une seconde
- #time.sleep(3)
- return ser
-
-def start_ser(ser):
- ser.close() #Cloture du port pour le cas ou il serait déjà ouvert ailleurs
- ser.open() #Ouverture du port
-
-def close_ser(ser):
- ser.close()
-
-def send_infos(ser, a,b,c):
- n = f"['{a} {b} {c}']"
- #print("input: "+str(n))
- ser.write((str(n)+'\n').encode("utf-8"))
- cc=str(ser.readline())
- #print("output: "+cc[2:][:-5])
- #print("Success")
- return cc[2:][:-5]
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/configs.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/configs.py
deleted file mode 100644
index 5dfd1c84ee2af112f511d652d19f532b45fdcc85..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/configs.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#================================================================
-#
-# File name : configs.py
-# Author : PyLessons
-# Created date: 2020-08-18
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : yolov3 configuration file
-#
-#================================================================
-
-# YOLO options
-YOLO_TYPE = "yolov3" # yolov4 or yolov3
-YOLO_FRAMEWORK = "tf" # "tf" or "trt"
-YOLO_V3_WEIGHTS = "model_data/yolov3.weights"
-YOLO_V4_WEIGHTS = "model_data/yolov4.weights"
-YOLO_V3_TINY_WEIGHTS = "model_data/yolov3-tiny.weights"
-YOLO_V4_TINY_WEIGHTS = "model_data/yolov4-tiny.weights"
-YOLO_TRT_QUANTIZE_MODE = "INT8" # INT8, FP16, FP32
-YOLO_CUSTOM_WEIGHTS = True # "checkpoints/yolov3_custom" # used in evaluate_mAP.py and custom model detection, if not using leave False
- # YOLO_CUSTOM_WEIGHTS also used with TensorRT and custom model detection
-YOLO_COCO_CLASSES = "model_data/coco/coco.names"
-YOLO_STRIDES = [8, 16, 32]
-YOLO_IOU_LOSS_THRESH = 0.5
-YOLO_ANCHOR_PER_SCALE = 3
-YOLO_MAX_BBOX_PER_SCALE = 100
-YOLO_INPUT_SIZE = 416
-if YOLO_TYPE == "yolov4":
- YOLO_ANCHORS = [[[12, 16], [19, 36], [40, 28]],
- [[36, 75], [76, 55], [72, 146]],
- [[142,110], [192, 243], [459, 401]]]
-if YOLO_TYPE == "yolov3":
- YOLO_ANCHORS = [[[10, 13], [16, 30], [33, 23]],
- [[30, 61], [62, 45], [59, 119]],
- [[116, 90], [156, 198], [373, 326]]]
-# Train options
-TRAIN_YOLO_TINY = False
-TRAIN_SAVE_BEST_ONLY = False # saves only best model according validation loss (True recommended)
-TRAIN_SAVE_CHECKPOINT = False # saves all best validated checkpoints in training process (may require a lot disk space) (False recommended)
-#TRAIN_CLASSES = "mnist/mnist.names"
-TRAIN_CLASSES = "./model_data/cone.txt"
-#TRAIN_ANNOT_PATH = "mnist/mnist_train.txt"
-TRAIN_ANNOT_PATH = "./model_data/cone_train.txt"
-TRAIN_LOGDIR = "log"
-TRAIN_CHECKPOINTS_FOLDER = "checkpoints"
-TRAIN_MODEL_NAME = f"{YOLO_TYPE}_custom"
-TRAIN_LOAD_IMAGES_TO_RAM = True # With True faster training, but need more RAM
-TRAIN_BATCH_SIZE = 4
-TRAIN_INPUT_SIZE = 416
-TRAIN_DATA_AUG = True
-TRAIN_TRANSFER = True
-TRAIN_FROM_CHECKPOINT = False # "checkpoints/yolov3_custom"
-TRAIN_LR_INIT = 1e-4
-TRAIN_LR_END = 1e-6
-TRAIN_WARMUP_EPOCHS = 2
-TRAIN_EPOCHS = 100
-
-# TEST options
-#TEST_ANNOT_PATH = "mnist/mnist_test.txt"
-TEST_ANNOT_PATH = "./model_data/cone_test.txt"
-TEST_BATCH_SIZE = 4
-TEST_INPUT_SIZE = 416
-TEST_DATA_AUG = False
-TEST_DECTECTED_IMAGE_PATH = ""
-TEST_SCORE_THRESHOLD = 0.3
-TEST_IOU_THRESHOLD = 0.45
-
-if TRAIN_YOLO_TINY:
- YOLO_STRIDES = [16, 32]
- # YOLO_ANCHORS = [[[23, 27], [37, 58], [81, 82]], # this line can be uncommented for default coco weights
- YOLO_ANCHORS = [[[10, 14], [23, 27], [37, 58]],
- [[81, 82], [135, 169], [344, 319]]]
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/dataset.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/dataset.py
deleted file mode 100644
index 3666bc61ed587a8c7469bd795955943a99dad0ef..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/dataset.py
+++ /dev/null
@@ -1,313 +0,0 @@
-#================================================================
-#
-# File name : dataset.py
-# Author : PyLessons
-# Created date: 2020-07-31
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : functions used to prepare dataset for custom training
-#
-#================================================================
-# TODO: transfer numpy to tensorflow operations
-import os
-import cv2
-import random
-import numpy as np
-import tensorflow as tf
-from yolov3.utils import read_class_names, image_preprocess
-from yolov3.yolov3 import bbox_iou
-from yolov3.configs import *
-
-
-class Dataset(object):
- # Dataset preprocess implementation
- def __init__(self, dataset_type, TEST_INPUT_SIZE=TEST_INPUT_SIZE):
- self.annot_path = TRAIN_ANNOT_PATH if dataset_type == 'train' else TEST_ANNOT_PATH
- self.input_sizes = TRAIN_INPUT_SIZE if dataset_type == 'train' else TEST_INPUT_SIZE
- self.batch_size = TRAIN_BATCH_SIZE if dataset_type == 'train' else TEST_BATCH_SIZE
- self.data_aug = TRAIN_DATA_AUG if dataset_type == 'train' else TEST_DATA_AUG
-
- self.train_yolo_tiny = TRAIN_YOLO_TINY
- self.train_input_sizes = TRAIN_INPUT_SIZE
- self.strides = np.array(YOLO_STRIDES)
- self.classes = read_class_names(TRAIN_CLASSES)
- self.num_classes = len(self.classes)
- self.anchors = (np.array(YOLO_ANCHORS).T/self.strides).T
- self.anchor_per_scale = YOLO_ANCHOR_PER_SCALE
- self.max_bbox_per_scale = YOLO_MAX_BBOX_PER_SCALE
-
- self.annotations = self.load_annotations(dataset_type)
- self.num_samples = len(self.annotations)
- self.num_batchs = int(np.ceil(self.num_samples / self.batch_size))
- self.batch_count = 0
-
-
- def load_annotations(self, dataset_type):
- final_annotations = []
- with open(self.annot_path, 'r') as f:
- txt = f.read().splitlines()
- annotations = [line.strip() for line in txt if len(line.strip().split()[1:]) != 0]
- np.random.shuffle(annotations)
-
- # for annotation in annotations:
- # image_extension = '.jpg'
- # extension_index = annotation.find(image_extension)
- # image_path = annotation[:extension_index+len(image_extension)]
- # line = annotation[extension_index+len(image_extension):].split()
- # if not os.path.exists(image_path):
- # raise KeyError("%s does not exist ... " %image_path)
- # if TRAIN_LOAD_IMAGES_TO_RAM:
- # image = cv2.imread(image_path)
- # else:
- # image = ''
- # final_annotations.append([image_path, line, image])
- # return final_annotations
- for annotation in annotations:
- # fully parse annotations
- line = annotation.split()
- image_path, index = "", 1
- for i, one_line in enumerate(line):
- if not one_line.replace(",","").isnumeric():
- if image_path != "": image_path += " "
- image_path += one_line
- else:
- index = i
- break
- if not os.path.exists(image_path):
- raise KeyError("%s does not exist ... " %image_path)
- if TRAIN_LOAD_IMAGES_TO_RAM:
- image = cv2.imread(image_path)
- else:
- image = ''
- final_annotations.append([image_path, line[index:], image])
- return final_annotations
-
- def __iter__(self):
- return self
-
- def Delete_bad_annotation(self, bad_annotation):
- print(f'Deleting {bad_annotation} annotation line')
- bad_image_path = bad_annotation[0]
- bad_image_name = bad_annotation[0].split('/')[-1] # can be used to delete bad image
- bad_xml_path = bad_annotation[0][:-3]+'xml' # can be used to delete bad xml file
-
- # remove bad annotation line from annotation file
- with open(self.annot_path, "r+") as f:
- d = f.readlines()
- f.seek(0)
- for i in d:
- if bad_image_name not in i:
- f.write(i)
- f.truncate()
-
- def __next__(self):
- with tf.device('/cpu:0'):
- self.train_input_size = random.choice([self.train_input_sizes])
- self.train_output_sizes = self.train_input_size // self.strides
-
- batch_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3), dtype=np.float32)
-
- if self.train_yolo_tiny:
- batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
- batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
- else:
- batch_label_sbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
- batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
- batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[2], self.train_output_sizes[2], self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
-
- batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
-
- batch_mbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
- batch_lbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
-
- exceptions = False
- num = 0
- if self.batch_count < self.num_batchs:
- while num < self.batch_size:
- index = self.batch_count * self.batch_size + num
- if index >= self.num_samples: index -= self.num_samples
- annotation = self.annotations[index]
- image, bboxes = self.parse_annotation(annotation)
- try:
- if self.train_yolo_tiny:
- label_mbbox, label_lbbox, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes)
- else:
- label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes)
- except IndexError:
- exceptions = True
- self.Delete_bad_annotation(annotation)
- print("IndexError, something wrong with", annotation[0], "removed this line from annotation file")
-
- batch_image[num, :, :, :] = image
- batch_label_mbbox[num, :, :, :, :] = label_mbbox
- batch_label_lbbox[num, :, :, :, :] = label_lbbox
- batch_mbboxes[num, :, :] = mbboxes
- batch_lbboxes[num, :, :] = lbboxes
- if not self.train_yolo_tiny:
- batch_label_sbbox[num, :, :, :, :] = label_sbbox
- batch_sbboxes[num, :, :] = sbboxes
-
- num += 1
-
- if exceptions:
- print('\n')
- raise Exception("There were problems with dataset, I fixed them, now restart the training process.")
- self.batch_count += 1
- if not self.train_yolo_tiny:
- batch_smaller_target = batch_label_sbbox, batch_sbboxes
- batch_medium_target = batch_label_mbbox, batch_mbboxes
- batch_larger_target = batch_label_lbbox, batch_lbboxes
-
- if self.train_yolo_tiny:
- return batch_image, (batch_medium_target, batch_larger_target)
- return batch_image, (batch_smaller_target, batch_medium_target, batch_larger_target)
- else:
- self.batch_count = 0
- np.random.shuffle(self.annotations)
- raise StopIteration
-
- def random_horizontal_flip(self, image, bboxes):
- if random.random() < 0.5:
- _, w, _ = image.shape
- image = image[:, ::-1, :]
- bboxes[:, [0,2]] = w - bboxes[:, [2,0]]
-
- return image, bboxes
-
- def random_crop(self, image, bboxes):
- if random.random() < 0.5:
- h, w, _ = image.shape
- max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1)
-
- max_l_trans = max_bbox[0]
- max_u_trans = max_bbox[1]
- max_r_trans = w - max_bbox[2]
- max_d_trans = h - max_bbox[3]
-
- crop_xmin = max(0, int(max_bbox[0] - random.uniform(0, max_l_trans)))
- crop_ymin = max(0, int(max_bbox[1] - random.uniform(0, max_u_trans)))
- crop_xmax = max(w, int(max_bbox[2] + random.uniform(0, max_r_trans)))
- crop_ymax = max(h, int(max_bbox[3] + random.uniform(0, max_d_trans)))
-
- image = image[crop_ymin : crop_ymax, crop_xmin : crop_xmax]
-
- bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin
- bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin
-
- return image, bboxes
-
- def random_translate(self, image, bboxes):
- if random.random() < 0.5:
- h, w, _ = image.shape
- max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1)
-
- max_l_trans = max_bbox[0]
- max_u_trans = max_bbox[1]
- max_r_trans = w - max_bbox[2]
- max_d_trans = h - max_bbox[3]
-
- tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1))
- ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1))
-
- M = np.array([[1, 0, tx], [0, 1, ty]])
- image = cv2.warpAffine(image, M, (w, h))
-
- bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx
- bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty
-
- return image, bboxes
-
- def parse_annotation(self, annotation, mAP = 'False'):
- if TRAIN_LOAD_IMAGES_TO_RAM:
- image_path = annotation[0]
- image = annotation[2]
- else:
- image_path = annotation[0]
- image = cv2.imread(image_path)
-
- bboxes = np.array([list(map(int, box.split(','))) for box in annotation[1]])
-
- if self.data_aug:
- image, bboxes = self.random_horizontal_flip(np.copy(image), np.copy(bboxes))
- image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes))
- image, bboxes = self.random_translate(np.copy(image), np.copy(bboxes))
-
- #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- if mAP == True:
- return image, bboxes
-
- image, bboxes = image_preprocess(np.copy(image), [self.input_sizes, self.input_sizes], np.copy(bboxes))
- return image, bboxes
-
- def preprocess_true_boxes(self, bboxes):
- OUTPUT_LEVELS = len(self.strides)
-
- label = [np.zeros((self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale,
- 5 + self.num_classes)) for i in range(OUTPUT_LEVELS)]
- bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(OUTPUT_LEVELS)]
- bbox_count = np.zeros((OUTPUT_LEVELS,))
-
- for bbox in bboxes:
- bbox_coor = bbox[:4]
- bbox_class_ind = bbox[4]
-
- onehot = np.zeros(self.num_classes, dtype=np.float)
- onehot[bbox_class_ind] = 1.0
- uniform_distribution = np.full(self.num_classes, 1.0 / self.num_classes)
- deta = 0.01
- smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution
-
- bbox_xywh = np.concatenate([(bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2]], axis=-1)
- bbox_xywh_scaled = 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis]
-
- iou = []
- exist_positive = False
- for i in range(OUTPUT_LEVELS):#range(3):
- anchors_xywh = np.zeros((self.anchor_per_scale, 4))
- anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5
- anchors_xywh[:, 2:4] = self.anchors[i]
-
- iou_scale = bbox_iou(bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh)
- iou.append(iou_scale)
- iou_mask = iou_scale > 0.3
-
- if np.any(iou_mask):
- xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32)
-
- label[i][yind, xind, iou_mask, :] = 0
- label[i][yind, xind, iou_mask, 0:4] = bbox_xywh
- label[i][yind, xind, iou_mask, 4:5] = 1.0
- label[i][yind, xind, iou_mask, 5:] = smooth_onehot
-
- bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale)
- bboxes_xywh[i][bbox_ind, :4] = bbox_xywh
- bbox_count[i] += 1
-
- exist_positive = True
-
- if not exist_positive:
- best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1)
- best_detect = int(best_anchor_ind / self.anchor_per_scale)
- best_anchor = int(best_anchor_ind % self.anchor_per_scale)
- xind, yind = np.floor(bbox_xywh_scaled[best_detect, 0:2]).astype(np.int32)
-
- label[best_detect][yind, xind, best_anchor, :] = 0
- label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh
- label[best_detect][yind, xind, best_anchor, 4:5] = 1.0
- label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot
-
- bbox_ind = int(bbox_count[best_detect] % self.max_bbox_per_scale)
- bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh
- bbox_count[best_detect] += 1
-
- if self.train_yolo_tiny:
- label_mbbox, label_lbbox = label
- mbboxes, lbboxes = bboxes_xywh
- return label_mbbox, label_lbbox, mbboxes, lbboxes
-
- label_sbbox, label_mbbox, label_lbbox = label
- sbboxes, mbboxes, lbboxes = bboxes_xywh
- return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
-
- def __len__(self):
- return self.num_batchs
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/pid_controller.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/pid_controller.py
deleted file mode 100644
index 74483f308415ef31a1bf51c068dc8cd413b9b2f4..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/pid_controller.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import numpy as np
-
-
-class PidController:
- def __init__(self, p_gain, i_gain, d_gain, set_point=0):
- self.p_gain = p_gain
- self.i_gain = i_gain
- self.d_gain = d_gain
- self.set_point = set_point
- self.integrated_error = 0
- self.previous_error = 0
-
- def get_control(self, process_value):
- error = self.set_point - process_value
- control = self.p_gain * error + self.i_gain * self.integrated_error + self.d_gain * (error - self.previous_error)
- self.previous_error = error
- self.integrated_error += error
- return np.sign(control) * abs(control)
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/pilote.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/pilote.py
deleted file mode 100644
index 47723ca682b02a56482501e0157cda5702011fa1..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/pilote.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import keyboard
-
-def get_command():
- forward = 0
- direction = 30
- if keyboard.is_pressed('q'):
- direction=-50
- elif keyboard.is_pressed('d'):
- direction=110
- if keyboard.is_pressed('z'):
- forward = 10
- if keyboard.is_pressed('s'):
- forward = -10
- return forward, direction, 0
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/track_2_generator.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/track_2_generator.py
deleted file mode 100644
index a1bfd729b5fc507837fc7952644dc5776bcd2fd6..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/track_2_generator.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from math import cos, sin, pi
-def create_track(r, start_point_distance_y, end_point_distance_y, tot_shift):
- laps = 1 #nombre de tours
- resolution_circle = 10 #resolution du cercle en degre
- resolution_line = 10
-
- #contruire les points qui vont former le chemin
- points = [(0, start_point_distance_y)]
- current_x, current_y = 0, start_point_distance_y
- if current_y<0:
- #faire les points de la première ligne droite
- while current_y<0:
- current_y += resolution_line
- points.append((current_x, current_y))
-
- #faire les points des cercles
- current_angle = 0 #angle en degree
- for i in range(laps):
- while current_angle<720:
- rad = current_angle/360*2*pi
- if 0<current_angle<360:
- current_x, current_y = r-r*cos(rad), r*sin(rad)
- else:
- current_x, current_y = -(r-r*cos(rad)), r*sin(rad)
- current_angle+=resolution_circle
- points.append((current_x, current_y))
-
- if current_y<end_point_distance_y:
- current_x=0
- while current_y<end_point_distance_y:
- current_y += resolution_line
- points.append((current_x, current_y))
-
-
- #on decale les points
- shift_x = -min([x[0] for x in points])
- shift_y = -min([x[1] for x in points])
-
- points = [(x+shift_x+tot_shift[0],y+shift_y+tot_shift[1]) for x,y in points]
-
- #print(points)
- return points
-
-def create_double_circles(r, left_center, right_center):
- resolution_circle = 10 #resolution du cercle en degre
- current_angle=0
- l_l = []
- l_r = []
- while current_angle<360:
- rad = current_angle/360*2*pi
- current_x_left, current_y_left = left_center[0]+r*cos(rad), left_center[1]+r*sin(rad)
- current_x_right, current_y_right = right_center[0]+r*cos(rad), right_center[1]+r*sin(rad)
- #if current_x_left<(left_center[0]+right_center[0])/2:
- l_l.append((current_x_left, current_y_left))
- #if current_x_right>(left_center[0]+right_center[0])/2:
- l_r.append((current_x_right, current_y_right))
- current_angle+=resolution_circle
-
- return l_l+l_r
-
-def get_cone_map():
- pixel_x_ext, pixel_y_ext = [242, 220, 165, 110, 63, 33, 22, 34, 63, 110, 165, 220, 243, 310, 334, 388, 443, 490, 521, 531, 520, 489, 443, 388, 333, 310], [76, 64, 52, 64, 95, 141, 196, 252, 298, 330, 340, 328, 318, 316, 328, 339, 329, 298, 251, 196, 142, 95, 64, 53, 64, 77]
- pixel_x_int, pixel_y_int = [245, 238, 222, 196, 166, 134, 108, 91, 85, 90, 109, 134, 165, 196, 222, 239, 308, 314, 332, 358, 388, 419, 445, 462, 468, 462, 445, 419, 388, 359, 332, 314], [201, 167, 140, 123, 116, 123, 140, 165, 195, 228, 253, 270, 277, 270, 253, 227, 200, 226, 253, 270, 277, 270, 253, 228, 197, 166, 140, 122, 117, 123, 140, 166]
- diametre = 225
- centre_x, centre_y = 278,200
-
- coord_ext = [((i-centre_x)/diametre+1 , (j-centre_y)/diametre+0.5) for i,j in zip(pixel_x_ext, pixel_y_ext)]
- coord_int = [((i-centre_x)/diametre+1 , (j-centre_y)/diametre+0.5) for i,j in zip(pixel_x_int, pixel_y_int)]
- return coord_int+coord_ext
-
-
-if __name__ =="__main__":
- points = create_track(1, -20, 20)
- #construire le svg
- mid = ""
- #prendre le code autour du chemin
- f = open("basic_track.svg", "r")
- t = f.read().split('CUT_HERE')
- for i,point in enumerate(points):
- mid += f'<circle \n id="path{i}" \n style="fill:#000000;stroke:none" \n cx="{point[0]}" \n cy="{point[1]}" \n r="0.1" /> \n'
- t = t[0]+mid+t[1]
-
- f = open("track_11.svg", "w")
- f.write(t)
- f.close()
-
\ No newline at end of file
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/utils.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/utils.py
deleted file mode 100644
index 0a67221cc8d2531d1d06c830f031c6ef31cca264..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/utils.py
+++ /dev/null
@@ -1,662 +0,0 @@
-#================================================================
-#
-# File name : utils.py
-# Author : PyLessons
-# Created date: 2020-09-27
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : additional yolov3 and yolov4 functions
-#
-#================================================================
-from multiprocessing import Process, Queue, Pipe
-import cv2
-import time
-import random
-import colorsys
-import numpy as np
-import tensorflow as tf
-from yolov3.configs import *
-from yolov3.yolov4 import *
-from tensorflow.python.saved_model import tag_constants
-
-import matplotlib.pyplot as plt
-
-from yolov3.track_2_generator import create_track, get_cone_map
-from yolov3.pid_controller import PidController
-from shapely.geometry import Polygon, Point, LineString
-from yolov3.car_interface import get_ser, start_ser, close_ser, send_infos
-
-from MCL import get_position
-import yolov3.pilote as pilot
-
-
-class liste_pos:
-
- def __init__(self, pos_depart, nb_particle):
- self.liste = [(pos_depart) for i in range(nb_particle)]
-
- def update_liste(self,new_liste):
- self.liste = new_liste
-
-def load_yolo_weights(model, weights_file):
- tf.keras.backend.clear_session() # used to reset layer names
- # load Darknet original weights to TensorFlow model
- if YOLO_TYPE == "yolov3":
- range1 = 75 if not TRAIN_YOLO_TINY else 13
- range2 = [58, 66, 74] if not TRAIN_YOLO_TINY else [9, 12]
- if YOLO_TYPE == "yolov4":
- range1 = 110 if not TRAIN_YOLO_TINY else 21
- range2 = [93, 101, 109] if not TRAIN_YOLO_TINY else [17, 20]
-
- with open(weights_file, 'rb') as wf:
- major, minor, revision, seen, _ = np.fromfile(wf, dtype=np.int32, count=5)
-
- j = 0
- for i in range(range1):
- if i > 0:
- conv_layer_name = 'conv2d_%d' %i
- else:
- conv_layer_name = 'conv2d'
-
- if j > 0:
- bn_layer_name = 'batch_normalization_%d' %j
- else:
- bn_layer_name = 'batch_normalization'
-
- conv_layer = model.get_layer(conv_layer_name)
- filters = conv_layer.filters
- k_size = conv_layer.kernel_size[0]
- in_dim = conv_layer.input_shape[-1]
-
- if i not in range2:
- # darknet weights: [beta, gamma, mean, variance]
- bn_weights = np.fromfile(wf, dtype=np.float32, count=4 * filters)
- # tf weights: [gamma, beta, mean, variance]
- bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]
- bn_layer = model.get_layer(bn_layer_name)
- j += 1
- else:
- conv_bias = np.fromfile(wf, dtype=np.float32, count=filters)
-
- # darknet shape (out_dim, in_dim, height, width)
- conv_shape = (filters, in_dim, k_size, k_size)
- conv_weights = np.fromfile(wf, dtype=np.float32, count=np.product(conv_shape))
- # tf shape (height, width, in_dim, out_dim)
- conv_weights = conv_weights.reshape(conv_shape).transpose([2, 3, 1, 0])
-
- if i not in range2:
- conv_layer.set_weights([conv_weights])
- bn_layer.set_weights(bn_weights)
- else:
- conv_layer.set_weights([conv_weights, conv_bias])
-
- assert len(wf.read()) == 0, 'failed to read all data'
-
-def Load_Yolo_model():
- gpus = tf.config.experimental.list_physical_devices('GPU')
- if len(gpus) > 0:
- print(f'GPUs {gpus}')
- try: tf.config.experimental.set_memory_growth(gpus[0], True)
- except RuntimeError: pass
-
- if YOLO_FRAMEWORK == "tf": # TensorFlow detection
- if YOLO_TYPE == "yolov4":
- Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
- if YOLO_TYPE == "yolov3":
- Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
-
- if YOLO_CUSTOM_WEIGHTS == False:
- print("Loading Darknet_weights from:", Darknet_weights)
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=YOLO_COCO_CLASSES)
- load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
- else:
- checkpoint = f"./checkpoints/{TRAIN_MODEL_NAME}"
- if TRAIN_YOLO_TINY:
- checkpoint += "_Tiny"
- print("Loading custom weights from:", checkpoint)
- yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)
- yolo.load_weights(checkpoint) # use custom weights
-
- elif YOLO_FRAMEWORK == "trt": # TensorRT detection
- saved_model_loaded = tf.saved_model.load(YOLO_CUSTOM_WEIGHTS, tags=[tag_constants.SERVING])
- signature_keys = list(saved_model_loaded.signatures.keys())
- yolo = saved_model_loaded.signatures['serving_default']
-
- return yolo
-
-def image_preprocess(image, target_size, gt_boxes=None):
- ih, iw = target_size
- h, w, _ = image.shape
-
- scale = min(iw/w, ih/h)
- nw, nh = int(scale * w), int(scale * h)
- image_resized = cv2.resize(image, (nw, nh))
-
- image_paded = np.full(shape=[ih, iw, 3], fill_value=128.0)
- dw, dh = (iw - nw) // 2, (ih-nh) // 2
- image_paded[dh:nh+dh, dw:nw+dw, :] = image_resized
- image_paded = image_paded / 255.
-
- if gt_boxes is None:
- return image_paded
-
- else:
- gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw
- gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh
- return image_paded, gt_boxes
-
-
-def draw_bbox(image, bboxes,liste_pos_car=liste_pos((0,0,0),1), CLASSES=YOLO_COCO_CLASSES, show_label=True, show_confidence = True, Text_colors=(255,255,0), rectangle_colors='', tracking=False, ser=None, previous_command=(0,0,1)):
- NUM_CLASS = read_class_names(CLASSES)
- num_classes = len(NUM_CLASS)
- image_h, image_w, _ = image.shape
- print(image_h, image_w)
- hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
- #print("hsv_tuples", hsv_tuples)
- colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
- colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
-
- random.seed(0)
- random.shuffle(colors)
- random.seed(None)
-
- for i, bbox in enumerate(bboxes):
- coor = np.array(bbox[:4], dtype=np.int32)
- score = bbox[4]
- class_ind = int(bbox[5])
- bbox_color = rectangle_colors if rectangle_colors != '' else colors[class_ind]
- bbox_thick = int(0.6 * (image_h + image_w) / 1000)
- if bbox_thick < 1: bbox_thick = 1
- fontScale = 0.75 * bbox_thick
- (x1, y1), (x2, y2) = (coor[0], coor[1]), (coor[2], coor[3])
-
- # put object rectangle
- cv2.rectangle(image, (x1, y1), (x2, y2), bbox_color, bbox_thick*2)
-
- #print((x1, y1),(x2, y2))
-
- """Determinate position"""
-
-
-
- if show_label:
- # get text label
- score_str = " {:.2f}".format(score) if show_confidence else ""
-
- if tracking: score_str = " "+str(score)
-
- try:
- label = "{}".format(NUM_CLASS[class_ind]) + score_str
- except KeyError:
- print("You received KeyError, this might be that you are trying to use yolo original weights")
- print("while using custom classes, if using custom model in configs.py set YOLO_CUSTOM_WEIGHTS = True")
-
- # get text size
- (text_width, text_height), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_COMPLEX_SMALL,
- fontScale, thickness=bbox_thick)
- # put filled text rectangle
- cv2.rectangle(image, (x1, y1), (x1 + text_width, y1 - text_height - baseline), bbox_color, thickness=cv2.FILLED)
-
- # put text above rectangle
- cv2.putText(image, label, (x1, y1-4), cv2.FONT_HERSHEY_COMPLEX_SMALL,
- fontScale, Text_colors, bbox_thick, lineType=cv2.LINE_AA)
-
-
- #Determination de la position de la voiture
- #speed with 10= 3.8/40
- pos_car, liste_pos_car_temp, cone_x, cone_y = get_position(bboxes, (previous_command[0]*3.7/170, previous_command[1]/200*35/360*2*3.1415, previous_command[2]), liste_pos_car.liste)
- liste_pos_car.update_liste(liste_pos_car_temp)
-
- """Algorithme de conduite"""
- """x_car, y_car"""
- x_car, y_car = pos_car[0], pos_car[1]
- print(f"x: {x_car}, y: {y_car}")
- x_car, y_car = (x_car-278/225)/2.4+1, (y_car-200/225)/2.4+0.5
- print(f"recalé x: {x_car}, y: {y_car}")
-
- """loading trajectory"""#TODO: load it only once
- track_points = create_track(0.5,0,0, (0,0))[:-1]+[(1,0.5)]
- track = Polygon(track_points)
-
- """loading cones"""
- cone_map = get_cone_map()
- # plt.plot([x for x,y in cone_map], [y for x,y in cone_map], 'o')
-
- # plt.plot(x_car, y_car,'+')
- # for x,y in zip(cone_x, cone_y):
- # x, y= (x-278/225)/2.4+1, (y-200/225)/2.4+0.5
- # plt.plot(x,y,'*')
- # plt.plot([i for i,j in track_points], [j for i,j in track_points])
- # plt.show()
-
- speed, commande, a = pilot.get_command()
-
- """calculate distance"""
- #front_center = Point(np.array([x_car,y_car]))
- #error = track.distance(front_center)
-
- """calculate PID response"""
- #pid = PidController(50,0.005,5) #P, I, D
- #commande = pid.get_control(error)
- #commande -= 30
-
- print(f"command: {commande}")
-
- """sending info to car"""
- try:
- a = send_infos(ser,speed,commande,0)
- #print(f"poten: {a}")
- except:
- pass
-
- return image, speed, commande, liste_pos_car
-
-
-def bboxes_iou(boxes1, boxes2):
- boxes1 = np.array(boxes1)
- boxes2 = np.array(boxes2)
-
- boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
- boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])
-
- left_up = np.maximum(boxes1[..., :2], boxes2[..., :2])
- right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:])
-
- inter_section = np.maximum(right_down - left_up, 0.0)
- inter_area = inter_section[..., 0] * inter_section[..., 1]
- union_area = boxes1_area + boxes2_area - inter_area
- ious = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps)
-
- return ious
-
-
-def nms(bboxes, iou_threshold, sigma=0.3, method='nms'):
- """
- :param bboxes: (xmin, ymin, xmax, ymax, score, class)
-
- Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf
- https://github.com/bharatsingh430/soft-nms
- """
- classes_in_img = list(set(bboxes[:, 5]))
- best_bboxes = []
-
- for cls in classes_in_img:
- cls_mask = (bboxes[:, 5] == cls)
- cls_bboxes = bboxes[cls_mask]
- # Process 1: Determine whether the number of bounding boxes is greater than 0
- while len(cls_bboxes) > 0:
- # Process 2: Select the bounding box with the highest score according to socre order A
- max_ind = np.argmax(cls_bboxes[:, 4])
- best_bbox = cls_bboxes[max_ind]
- best_bboxes.append(best_bbox)
- cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]])
- # Process 3: Calculate this bounding box A and
- # Remain all iou of the bounding box and remove those bounding boxes whose iou value is higher than the threshold
- iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4])
- weight = np.ones((len(iou),), dtype=np.float32)
-
- assert method in ['nms', 'soft-nms']
-
- if method == 'nms':
- iou_mask = iou > iou_threshold
- weight[iou_mask] = 0.0
-
- if method == 'soft-nms':
- weight = np.exp(-(1.0 * iou ** 2 / sigma))
-
- cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight
- score_mask = cls_bboxes[:, 4] > 0.
- cls_bboxes = cls_bboxes[score_mask]
-
- return best_bboxes
-
-
-def postprocess_boxes(pred_bbox, original_image, input_size, score_threshold):
- valid_scale=[0, np.inf]
- pred_bbox = np.array(pred_bbox)
-
- pred_xywh = pred_bbox[:, 0:4]
- pred_conf = pred_bbox[:, 4]
- pred_prob = pred_bbox[:, 5:]
-
- # 1. (x, y, w, h) --> (xmin, ymin, xmax, ymax)
- pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5,
- pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1)
- # 2. (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org)
- org_h, org_w = original_image.shape[:2]
- resize_ratio = min(input_size / org_w, input_size / org_h)
-
- dw = (input_size - resize_ratio * org_w) / 2
- dh = (input_size - resize_ratio * org_h) / 2
-
- pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio
- pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio
-
- # 3. clip some boxes those are out of range
- pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]),
- np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1)
- invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3]))
- pred_coor[invalid_mask] = 0
-
- # 4. discard some invalid boxes
- bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1))
- scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1]))
-
- # 5. discard boxes with low scores
- classes = np.argmax(pred_prob, axis=-1)
- scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes]
- score_mask = scores > score_threshold
- mask = np.logical_and(scale_mask, score_mask)
- coors, scores, classes = pred_coor[mask], scores[mask], classes[mask]
-
- return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1)
-
-
-def detect_image(Yolo, image_path, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors=''):
- original_image = cv2.imread(image_path)
- original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
- original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
-
- image_data = image_preprocess(np.copy(original_image), [input_size, input_size])
- image_data = image_data[np.newaxis, ...].astype(np.float32)
-
- if YOLO_FRAMEWORK == "tf":
- pred_bbox = Yolo.predict(image_data)
- elif YOLO_FRAMEWORK == "trt":
- batched_input = tf.constant(image_data)
- result = Yolo(batched_input)
- pred_bbox = []
- for key, value in result.items():
- value = value.numpy()
- pred_bbox.append(value)
-
- pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
- pred_bbox = tf.concat(pred_bbox, axis=0)
-
- bboxes = postprocess_boxes(pred_bbox, original_image, input_size, score_threshold)
- bboxes = nms(bboxes, iou_threshold, method='nms')
-
- image,_,_,_ = draw_bbox(original_image, bboxes, CLASSES=CLASSES, rectangle_colors=rectangle_colors)
- # CreateXMLfile("XML_Detections", str(int(time.time())), original_image, bboxes, read_class_names(CLASSES))
-
- if output_path != '': cv2.imwrite(output_path, image)
- if show:
- # Show the image
- cv2.imshow("predicted image", image)
- # Load and hold the image
- cv2.waitKey(0)
- # To close the window after the required kill value was provided
- cv2.destroyAllWindows()
-
- return image, bboxes
-
-def Predict_bbox_mp(Frames_data, Predicted_data, Processing_times):
- gpus = tf.config.experimental.list_physical_devices('GPU')
- if len(gpus) > 0:
- try: tf.config.experimental.set_memory_growth(gpus[0], True)
- except RuntimeError: print("RuntimeError in tf.config.experimental.list_physical_devices('GPU')")
- Yolo = Load_Yolo_model()
- times = []
- while True:
- if Frames_data.qsize()>0:
- image_data = Frames_data.get()
- t1 = time.time()
- Processing_times.put(time.time())
-
- if YOLO_FRAMEWORK == "tf":
- if tf.__version__ > '2.4.0':
- pred_bbox = Yolo(image_data)
- else:
- pred_bbox = Yolo.predict(image_data)
- elif YOLO_FRAMEWORK == "trt":
- batched_input = tf.constant(image_data)
- result = Yolo(batched_input)
- pred_bbox = []
- for key, value in result.items():
- value = value.numpy()
- pred_bbox.append(value)
-
- pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
- pred_bbox = tf.concat(pred_bbox, axis=0)
-
- Predicted_data.put(pred_bbox)
-
-
-def postprocess_mp(Predicted_data, original_frames, Processed_frames, Processing_times, input_size, CLASSES, score_threshold, iou_threshold, rectangle_colors, realtime):
- times = []
- while True:
- if Predicted_data.qsize()>0:
- pred_bbox = Predicted_data.get()
- if realtime:
- while original_frames.qsize() > 1:
- original_image = original_frames.get()
- else:
- original_image = original_frames.get()
-
- bboxes = postprocess_boxes(pred_bbox, original_image, input_size, score_threshold)
- bboxes = nms(bboxes, iou_threshold, method='nms')
- image = draw_bbox(original_image, bboxes, CLASSES=CLASSES, rectangle_colors=rectangle_colors)
- times.append(time.time()-Processing_times.get())
- times = times[-20:]
-
- ms = sum(times)/len(times)*1000
- fps = 1000 / ms
- image = cv2.putText(image, "Time: {:.1f}FPS".format(fps), (0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
- #print("Time: {:.2f}ms, Final FPS: {:.1f}".format(ms, fps))
-
- Processed_frames.put(image)
-
-def Show_Image_mp(Processed_frames, show, Final_frames):
- while True:
- if Processed_frames.qsize()>0:
- image = Processed_frames.get()
- Final_frames.put(image)
- if show:
- cv2.imshow('output', image)
- if cv2.waitKey(25) & 0xFF == ord("q"):
- cv2.destroyAllWindows()
- break
-
-# detect from webcam
-def detect_video_realtime_mp(video_path, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors='', realtime=False):
- if realtime:
- vid = cv2.VideoCapture(0)
- else:
- vid = cv2.VideoCapture(video_path)
-
- # by default VideoCapture returns float instead of int
- width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
- height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
- fps = int(vid.get(cv2.CAP_PROP_FPS))
- codec = cv2.VideoWriter_fourcc(*'XVID')
- out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
- no_of_frames = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))
-
- original_frames = Queue()
- Frames_data = Queue()
- Predicted_data = Queue()
- Processed_frames = Queue()
- Processing_times = Queue()
- Final_frames = Queue()
-
- p1 = Process(target=Predict_bbox_mp, args=(Frames_data, Predicted_data, Processing_times))
- p2 = Process(target=postprocess_mp, args=(Predicted_data, original_frames, Processed_frames, Processing_times, input_size, CLASSES, score_threshold, iou_threshold, rectangle_colors, realtime))
- p3 = Process(target=Show_Image_mp, args=(Processed_frames, show, Final_frames))
- p1.start()
- p2.start()
- p3.start()
-
- while True:
- ret, img = vid.read()
- if not ret:
- break
-
- original_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
- original_frames.put(original_image)
-
- image_data = image_preprocess(np.copy(original_image), [input_size, input_size])
- image_data = image_data[np.newaxis, ...].astype(np.float32)
- Frames_data.put(image_data)
-
- while True:
- if original_frames.qsize() == 0 and Frames_data.qsize() == 0 and Predicted_data.qsize() == 0 and Processed_frames.qsize() == 0 and Processing_times.qsize() == 0 and Final_frames.qsize() == 0:
- p1.terminate()
- p2.terminate()
- p3.terminate()
- break
- elif Final_frames.qsize()>0:
- image = Final_frames.get()
- if output_path != '': out.write(image)
-
- cv2.destroyAllWindows()
-
-def detect_video(Yolo, video_path, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors=''):
- times, times_2 = [], []
- vid = cv2.VideoCapture(video_path)
-
- # by default VideoCapture returns float instead of int
- width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
- height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
- fps = int(vid.get(cv2.CAP_PROP_FPS))
- codec = cv2.VideoWriter_fourcc(*'XVID')
- out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
-
- while True:
- _, img = vid.read()
-
- try:
- original_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
- except:
- break
-
- image_data = image_preprocess(np.copy(original_image), [input_size, input_size])
- image_data = image_data[np.newaxis, ...].astype(np.float32)
-
- t1 = time.time()
- if YOLO_FRAMEWORK == "tf":
- if tf.__version__ > '2.4.0':
- pred_bbox = Yolo(image_data, training=False)
- else:
- pred_bbox = Yolo.predict(image_data)
- elif YOLO_FRAMEWORK == "trt":
- batched_input = tf.constant(image_data)
- result = Yolo(batched_input)
- pred_bbox = []
- for key, value in result.items():
- value = value.numpy()
- pred_bbox.append(value)
-
- t2 = time.time()
-
- pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
- pred_bbox = tf.concat(pred_bbox, axis=0)
-
- bboxes = postprocess_boxes(pred_bbox, original_image, input_size, score_threshold)
- bboxes = nms(bboxes, iou_threshold, method='nms')
-
- image = draw_bbox(original_image, bboxes, CLASSES=CLASSES, rectangle_colors=rectangle_colors)
-
- t3 = time.time()
- times.append(t2-t1)
- times_2.append(t3-t1)
-
- times = times[-20:]
- times_2 = times_2[-20:]
-
- ms = sum(times)/len(times)*1000
- fps = 1000 / ms
- fps2 = 1000 / (sum(times_2)/len(times_2)*1000)
-
- image = cv2.putText(image, "Time: {:.1f}FPS".format(fps), (0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
- # CreateXMLfile("XML_Detections", str(int(time.time())), original_image, bboxes, read_class_names(CLASSES))
-
- #print("Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(ms, fps, fps2))
- if output_path != '': out.write(image)
- if show:
- cv2.imshow('output', image)
- if cv2.waitKey(25) & 0xFF == ord("q"):
- cv2.destroyAllWindows()
- break
-
- cv2.destroyAllWindows()
-
-# detect from webcam
-def detect_realtime(Yolo, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors=''):
- times = []
- vid = cv2.VideoCapture(1)
-
- if output_path:
- # by default VideoCapture returns float instead of int
- width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
- height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
- fps = int(vid.get(cv2.CAP_PROP_FPS))
- codec = cv2.VideoWriter_fourcc(*'XVID')
- out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
-
- liste_pos_car = liste_pos((278/225, 200/225,3.1415/2), 50)
-
- ser = get_ser()
- start_ser(ser)
- command=0
- speed=0
-
- while True:
- ret, frame = vid.read()
- frame = np.rot90(frame, k=3, axes=(0, 1))
-
- try:
- original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
- except:
- break
- image_data = image_preprocess(np.copy(original_frame), [input_size, input_size])
- image_data = image_data[np.newaxis, ...].astype(np.float32)
-
- t1 = time.time()
- if YOLO_FRAMEWORK == "tf":
- if tf.__version__ > '2.4.0':
- pred_bbox = Yolo(image_data, training=False)
- else:
- pred_bbox = Yolo.predict(image_data)
- # if True:
- # pred_bbox = Yolo.predict(image_data)
- elif YOLO_FRAMEWORK == "trt":
- batched_input = tf.constant(image_data)
- result = Yolo(batched_input)
- pred_bbox = []
- for key, value in result.items():
- value = value.numpy()
- pred_bbox.append(value)
-
- t2 = time.time()
-
- pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
- pred_bbox = tf.concat(pred_bbox, axis=0)
-
- bboxes = postprocess_boxes(pred_bbox, original_frame, input_size, score_threshold)
- bboxes = nms(bboxes, iou_threshold, method='nms')
-
- times.append(t2-t1)
- times = times[-20:]
-
- ms = sum(times)/len(times)*1000
- fps = 1000 / ms
-
- print("Time: {:.2f}ms, {:.1f} FPS".format(ms, fps))
-
- frame, speed, command, liste_pos_car = draw_bbox(original_frame, bboxes,liste_pos_car, CLASSES=CLASSES, rectangle_colors=rectangle_colors, ser=ser, previous_command=(speed, command, fps))
- # CreateXMLfile("XML_Detections", str(int(time.time())), original_frame, bboxes, read_class_names(CLASSES))
- image = cv2.putText(frame, "Time: {:.1f}FPS".format(fps), (0, 30),
- cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
-
- if output_path != '': out.write(frame)
- if show:
- cv2.imshow('output', frame)
- if cv2.waitKey(25) & 0xFF == ord("q"):
- pass
- #cv2.destroyAllWindows()
- #break
-
- cv2.destroyAllWindows()
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov3.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov3.py
deleted file mode 100644
index af98d2f732431802e4a50d5e17fe08104898adc3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov3.py
+++ /dev/null
@@ -1,366 +0,0 @@
-#================================================================
-#
-# File name : yolov3.py
-# Author : PyLessons
-# Created date: 2020-06-04
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : main yolov3 functions
-#
-#================================================================
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, ZeroPadding2D, BatchNormalization, MaxPool2D
-from tensorflow.keras.regularizers import l2
-from yolov3.utils import read_class_names
-from yolov3.configs import *
-
-STRIDES = np.array(YOLO_STRIDES)
-ANCHORS = (np.array(YOLO_ANCHORS).T/STRIDES).T
-
-class BatchNormalization(BatchNormalization):
- # "Frozen state" and "inference mode" are two separate concepts.
- # `layer.trainable = False` is to freeze the layer, so the layer will use
- # stored moving `var` and `mean` in the "inference mode", and both `gama`
- # and `beta` will not be updated !
- def call(self, x, training=False):
- if not training:
- training = tf.constant(False)
- training = tf.logical_and(training, self.trainable)
- return super().call(x, training)
-
-def convolutional(input_layer, filters_shape, downsample=False, activate=True, bn=True):
- if downsample:
- input_layer = ZeroPadding2D(((1, 0), (1, 0)))(input_layer)
- padding = 'valid'
- strides = 2
- else:
- strides = 1
- padding = 'same'
-
- conv = Conv2D(filters=filters_shape[-1], kernel_size = filters_shape[0], strides=strides,
- padding=padding, use_bias=not bn, kernel_regularizer=l2(0.0005),
- kernel_initializer=tf.random_normal_initializer(stddev=0.01),
- bias_initializer=tf.constant_initializer(0.))(input_layer)
- if bn:
- conv = BatchNormalization()(conv)
- if activate == True:
- conv = LeakyReLU(alpha=0.1)(conv)
-
- return conv
-
-def residual_block(input_layer, input_channel, filter_num1, filter_num2):
- short_cut = input_layer
- conv = convolutional(input_layer, filters_shape=(1, 1, input_channel, filter_num1))
- conv = convolutional(conv , filters_shape=(3, 3, filter_num1, filter_num2))
-
- residual_output = short_cut + conv
- return residual_output
-
-def upsample(input_layer):
- return tf.image.resize(input_layer, (input_layer.shape[1] * 2, input_layer.shape[2] * 2), method='nearest')
-
-
-def darknet53(input_data):
- input_data = convolutional(input_data, (3, 3, 3, 32))
- input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True)
-
- for i in range(1):
- input_data = residual_block(input_data, 64, 32, 64)
-
- input_data = convolutional(input_data, (3, 3, 64, 128), downsample=True)
-
- for i in range(2):
- input_data = residual_block(input_data, 128, 64, 128)
-
- input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True)
-
- for i in range(8):
- input_data = residual_block(input_data, 256, 128, 256)
-
- route_1 = input_data
- input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True)
-
- for i in range(8):
- input_data = residual_block(input_data, 512, 256, 512)
-
- route_2 = input_data
- input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True)
-
- for i in range(4):
- input_data = residual_block(input_data, 1024, 512, 1024)
-
- return route_1, route_2, input_data
-
-def darknet19_tiny(input_data):
- input_data = convolutional(input_data, (3, 3, 3, 16))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 16, 32))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 32, 64))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 64, 128))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 128, 256))
- route_1 = input_data
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 256, 512))
- input_data = MaxPool2D(2, 1, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 512, 1024))
-
- return route_1, input_data
-
-def YOLOv3(input_layer, NUM_CLASS):
- # After the input layer enters the Darknet-53 network, we get three branches
- route_1, route_2, conv = darknet53(input_layer)
- # See the orange module (DBL) in the figure above, a total of 5 Subconvolution operation
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv = convolutional(conv, (3, 3, 512, 1024))
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv = convolutional(conv, (3, 3, 512, 1024))
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv_lobj_branch = convolutional(conv, (3, 3, 512, 1024))
-
- # conv_lbbox is used to predict large-sized objects , Shape = [None, 13, 13, 255]
- conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 1024, 3*(NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(conv, (1, 1, 512, 256))
- # upsample here uses the nearest neighbor interpolation method, which has the advantage that the
- # upsampling process does not need to learn, thereby reducing the network parameter
- conv = upsample(conv)
-
- conv = tf.concat([conv, route_2], axis=-1)
- conv = convolutional(conv, (1, 1, 768, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
- conv_mobj_branch = convolutional(conv, (3, 3, 256, 512))
-
- # conv_mbbox is used to predict medium-sized objects, shape = [None, 26, 26, 255]
- conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 512, 3*(NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = upsample(conv)
-
- conv = tf.concat([conv, route_1], axis=-1)
- conv = convolutional(conv, (1, 1, 384, 128))
- conv = convolutional(conv, (3, 3, 128, 256))
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = convolutional(conv, (3, 3, 128, 256))
- conv = convolutional(conv, (1, 1, 256, 128))
- conv_sobj_branch = convolutional(conv, (3, 3, 128, 256))
-
- # conv_sbbox is used to predict small size objects, shape = [None, 52, 52, 255]
- conv_sbbox = convolutional(conv_sobj_branch, (1, 1, 256, 3*(NUM_CLASS +5)), activate=False, bn=False)
-
- return [conv_sbbox, conv_mbbox, conv_lbbox]
-
-def YOLOv3_tiny(input_layer, NUM_CLASS):
- # After the input layer enters the Darknet-53 network, we get three branches
- route_1, conv = darknet19_tiny(input_layer)
-
- conv = convolutional(conv, (1, 1, 1024, 256))
- conv_lobj_branch = convolutional(conv, (3, 3, 256, 512))
-
- # conv_lbbox is used to predict large-sized objects , Shape = [None, 26, 26, 255]
- conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 512, 3*(NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(conv, (1, 1, 256, 128))
- # upsample here uses the nearest neighbor interpolation method, which has the advantage that the
- # upsampling process does not need to learn, thereby reducing the network parameter
- conv = upsample(conv)
-
- conv = tf.concat([conv, route_1], axis=-1)
- conv_mobj_branch = convolutional(conv, (3, 3, 128, 256))
- # conv_mbbox is used to predict medium size objects, shape = [None, 13, 13, 255]
- conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
-
- return [conv_mbbox, conv_lbbox]
-
-def Create_Yolov3(input_size=416, channels=3, training=False, CLASSES=YOLO_COCO_CLASSES):
- NUM_CLASS = len(read_class_names(CLASSES))
- input_layer = Input([input_size, input_size, channels])
-
- if TRAIN_YOLO_TINY:
- conv_tensors = YOLOv3_tiny(input_layer, NUM_CLASS)
- else:
- conv_tensors = YOLOv3(input_layer, NUM_CLASS)
-
- output_tensors = []
- for i, conv_tensor in enumerate(conv_tensors):
- pred_tensor = decode(conv_tensor, NUM_CLASS, i)
- if training: output_tensors.append(conv_tensor)
- output_tensors.append(pred_tensor)
-
- YoloV3 = tf.keras.Model(input_layer, output_tensors)
- return YoloV3
-
-def decode(conv_output, NUM_CLASS, i=0):
- # where i = 0, 1 or 2 to correspond to the three grid scales
- conv_shape = tf.shape(conv_output)
- batch_size = conv_shape[0]
- output_size = conv_shape[1]
-
- conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
-
- conv_raw_dxdy = conv_output[:, :, :, :, 0:2] # offset of center position
- conv_raw_dwdh = conv_output[:, :, :, :, 2:4] # Prediction box length and width offset
- conv_raw_conf = conv_output[:, :, :, :, 4:5] # confidence of the prediction box
- conv_raw_prob = conv_output[:, :, :, :, 5: ] # category probability of the prediction box
-
- # next need Draw the grid. Where output_size is equal to 13, 26 or 52
- y = tf.range(output_size, dtype=tf.int32)
- y = tf.expand_dims(y, -1)
- y = tf.tile(y, [1, output_size])
- x = tf.range(output_size,dtype=tf.int32)
- x = tf.expand_dims(x, 0)
- x = tf.tile(x, [output_size, 1])
-
- xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)
- xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, 3, 1])
- xy_grid = tf.cast(xy_grid, tf.float32)
-
- # Calculate the center position of the prediction box:
- pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * STRIDES[i]
- # Calculate the length and width of the prediction box:
- pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS[i]) * STRIDES[i]
-
- pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
- pred_conf = tf.sigmoid(conv_raw_conf) # object box calculates the predicted confidence
- pred_prob = tf.sigmoid(conv_raw_prob) # calculating the predicted probability category box object
-
- # calculating the predicted probability category box object
- return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)
-
-def bbox_iou(boxes1, boxes2):
- boxes1_area = boxes1[..., 2] * boxes1[..., 3]
- boxes2_area = boxes2[..., 2] * boxes2[..., 3]
-
- boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
- boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
- boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
- boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
-
- left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])
- right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
-
- inter_section = tf.maximum(right_down - left_up, 0.0)
- inter_area = inter_section[..., 0] * inter_section[..., 1]
- union_area = boxes1_area + boxes2_area - inter_area
-
- return 1.0 * inter_area / union_area
-
-def bbox_giou(boxes1, boxes2):
- boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
- boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
- boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
- boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
-
- boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]),
- tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis=-1)
- boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]),
- tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis=-1)
-
- boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
- boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])
-
- left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])
- right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
-
- inter_section = tf.maximum(right_down - left_up, 0.0)
- inter_area = inter_section[..., 0] * inter_section[..., 1]
- union_area = boxes1_area + boxes2_area - inter_area
-
- # Calculate the iou value between the two bounding boxes
- iou = inter_area / union_area
-
- # Calculate the coordinates of the upper left corner and the lower right corner of the smallest closed convex surface
- enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2])
- enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:])
- enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0)
-
- # Calculate the area of the smallest closed convex surface C
- enclose_area = enclose[..., 0] * enclose[..., 1]
-
- # Calculate the GIoU value according to the GioU formula
- giou = iou - 1.0 * (enclose_area - union_area) / enclose_area
-
- return giou
-
-# testing (should be better than giou)
-def bbox_ciou(boxes1, boxes2):
- boxes1_coor = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
- boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
- boxes2_coor = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
- boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
-
- left = tf.maximum(boxes1_coor[..., 0], boxes2_coor[..., 0])
- up = tf.maximum(boxes1_coor[..., 1], boxes2_coor[..., 1])
- right = tf.maximum(boxes1_coor[..., 2], boxes2_coor[..., 2])
- down = tf.maximum(boxes1_coor[..., 3], boxes2_coor[..., 3])
-
- c = (right - left) * (right - left) + (up - down) * (up - down)
- iou = bbox_iou(boxes1, boxes2)
-
- u = (boxes1[..., 0] - boxes2[..., 0]) * (boxes1[..., 0] - boxes2[..., 0]) + (boxes1[..., 1] - boxes2[..., 1]) * (boxes1[..., 1] - boxes2[..., 1])
- d = u / c
-
- ar_gt = boxes2[..., 2] / boxes2[..., 3]
- ar_pred = boxes1[..., 2] / boxes1[..., 3]
-
- ar_loss = 4 / (np.pi * np.pi) * (tf.atan(ar_gt) - tf.atan(ar_pred)) * (tf.atan(ar_gt) - tf.atan(ar_pred))
- alpha = ar_loss / (1 - iou + ar_loss + 0.000001)
- ciou_term = d + alpha * ar_loss
-
- return iou - ciou_term
-
-
-def compute_loss(pred, conv, label, bboxes, i=0, CLASSES=YOLO_COCO_CLASSES):
- NUM_CLASS = len(read_class_names(CLASSES))
- conv_shape = tf.shape(conv)
- batch_size = conv_shape[0]
- output_size = conv_shape[1]
- input_size = STRIDES[i] * output_size
- conv = tf.reshape(conv, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
-
- conv_raw_conf = conv[:, :, :, :, 4:5]
- conv_raw_prob = conv[:, :, :, :, 5:]
-
- pred_xywh = pred[:, :, :, :, 0:4]
- pred_conf = pred[:, :, :, :, 4:5]
-
- label_xywh = label[:, :, :, :, 0:4]
- respond_bbox = label[:, :, :, :, 4:5]
- label_prob = label[:, :, :, :, 5:]
-
- giou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis=-1)
- input_size = tf.cast(input_size, tf.float32)
-
- bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)
- giou_loss = respond_bbox * bbox_loss_scale * (1 - giou)
-
- iou = bbox_iou(pred_xywh[:, :, :, :, np.newaxis, :], bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :])
- # Find the value of IoU with the real box The largest prediction box
- max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1)
-
- # If the largest iou is less than the threshold, it is considered that the prediction box contains no objects, then the background box
- respond_bgd = (1.0 - respond_bbox) * tf.cast( max_iou < YOLO_IOU_LOSS_THRESH, tf.float32 )
-
- conf_focal = tf.pow(respond_bbox - pred_conf, 2)
-
- # Calculate the loss of confidence
- # we hope that if the grid contains objects, then the network output prediction box has a confidence of 1 and 0 when there is no object.
- conf_loss = conf_focal * (
- respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
- +
- respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
- )
-
- prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob)
-
- giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis=[1,2,3,4]))
- conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1,2,3,4]))
- prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1,2,3,4]))
-
- return giou_loss, conf_loss, prob_loss
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov4.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov4.py
deleted file mode 100644
index 6e67fc4f3c9d7bb01f04456414cf207d0f2d53b3..0000000000000000000000000000000000000000
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/yolov4.py
+++ /dev/null
@@ -1,582 +0,0 @@
-#================================================================
-#
-# File name : yolov4.py
-# Author : PyLessons
-# Created date: 2020-09-31
-# Website : https://pylessons.com/
-# GitHub : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
-# Description : main yolov3 & yolov4 functions
-#
-#================================================================
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, ZeroPadding2D, BatchNormalization, MaxPool2D
-from tensorflow.keras.regularizers import l2
-from yolov3.configs import *
-
-STRIDES = np.array(YOLO_STRIDES)
-ANCHORS = (np.array(YOLO_ANCHORS).T/STRIDES).T
-
-def read_class_names(class_file_name):
- # loads class name from a file
- names = {}
- with open(class_file_name, 'r') as data:
- for ID, name in enumerate(data):
- names[ID] = name.strip('\n')
- return names
-
-class BatchNormalization(BatchNormalization):
- # "Frozen state" and "inference mode" are two separate concepts.
- # `layer.trainable = False` is to freeze the layer, so the layer will use
- # stored moving `var` and `mean` in the "inference mode", and both `gama`
- # and `beta` will not be updated !
- def call(self, x, training=False):
- if not training:
- training = tf.constant(False)
- training = tf.logical_and(training, self.trainable)
- return super().call(x, training)
-
-def convolutional(input_layer, filters_shape, downsample=False, activate=True, bn=True, activate_type='leaky'):
- if downsample:
- input_layer = ZeroPadding2D(((1, 0), (1, 0)))(input_layer)
- padding = 'valid'
- strides = 2
- else:
- strides = 1
- padding = 'same'
-
- conv = Conv2D(filters=filters_shape[-1], kernel_size = filters_shape[0], strides=strides,
- padding=padding, use_bias=not bn, kernel_regularizer=l2(0.0005),
- kernel_initializer=tf.random_normal_initializer(stddev=0.01),
- bias_initializer=tf.constant_initializer(0.))(input_layer)
- if bn:
- conv = BatchNormalization()(conv)
- if activate == True:
- if activate_type == "leaky":
- conv = LeakyReLU(alpha=0.1)(conv)
- elif activate_type == "mish":
- conv = mish(conv)
-
- return conv
-
-def mish(x):
- return x * tf.math.tanh(tf.math.softplus(x))
-
-def residual_block(input_layer, input_channel, filter_num1, filter_num2, activate_type='leaky'):
- short_cut = input_layer
- conv = convolutional(input_layer, filters_shape=(1, 1, input_channel, filter_num1), activate_type=activate_type)
- conv = convolutional(conv , filters_shape=(3, 3, filter_num1, filter_num2), activate_type=activate_type)
-
- residual_output = short_cut + conv
- return residual_output
-
-def upsample(input_layer):
- return tf.image.resize(input_layer, (input_layer.shape[1] * 2, input_layer.shape[2] * 2), method='nearest')
-
-def route_group(input_layer, groups, group_id):
- convs = tf.split(input_layer, num_or_size_splits=groups, axis=-1)
- return convs[group_id]
-
-def darknet53(input_data):
- input_data = convolutional(input_data, (3, 3, 3, 32))
- input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True)
-
- for i in range(1):
- input_data = residual_block(input_data, 64, 32, 64)
-
- input_data = convolutional(input_data, (3, 3, 64, 128), downsample=True)
-
- for i in range(2):
- input_data = residual_block(input_data, 128, 64, 128)
-
- input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True)
-
- for i in range(8):
- input_data = residual_block(input_data, 256, 128, 256)
-
- route_1 = input_data
- input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True)
-
- for i in range(8):
- input_data = residual_block(input_data, 512, 256, 512)
-
- route_2 = input_data
- input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True)
-
- for i in range(4):
- input_data = residual_block(input_data, 1024, 512, 1024)
-
- return route_1, route_2, input_data
-
-def cspdarknet53(input_data):
- input_data = convolutional(input_data, (3, 3, 3, 32), activate_type="mish")
- input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True, activate_type="mish")
-
- route = input_data
- route = convolutional(route, (1, 1, 64, 64), activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
- for i in range(1):
- input_data = residual_block(input_data, 64, 32, 64, activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
-
- input_data = tf.concat([input_data, route], axis=-1)
- input_data = convolutional(input_data, (1, 1, 128, 64), activate_type="mish")
- input_data = convolutional(input_data, (3, 3, 64, 128), downsample=True, activate_type="mish")
- route = input_data
- route = convolutional(route, (1, 1, 128, 64), activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 128, 64), activate_type="mish")
- for i in range(2):
- input_data = residual_block(input_data, 64, 64, 64, activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
- input_data = tf.concat([input_data, route], axis=-1)
-
- input_data = convolutional(input_data, (1, 1, 128, 128), activate_type="mish")
- input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True, activate_type="mish")
- route = input_data
- route = convolutional(route, (1, 1, 256, 128), activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 256, 128), activate_type="mish")
- for i in range(8):
- input_data = residual_block(input_data, 128, 128, 128, activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 128, 128), activate_type="mish")
- input_data = tf.concat([input_data, route], axis=-1)
-
- input_data = convolutional(input_data, (1, 1, 256, 256), activate_type="mish")
- route_1 = input_data
- input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True, activate_type="mish")
- route = input_data
- route = convolutional(route, (1, 1, 512, 256), activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 512, 256), activate_type="mish")
- for i in range(8):
- input_data = residual_block(input_data, 256, 256, 256, activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 256, 256), activate_type="mish")
- input_data = tf.concat([input_data, route], axis=-1)
-
- input_data = convolutional(input_data, (1, 1, 512, 512), activate_type="mish")
- route_2 = input_data
- input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type="mish")
- route = input_data
- route = convolutional(route, (1, 1, 1024, 512), activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 1024, 512), activate_type="mish")
- for i in range(4):
- input_data = residual_block(input_data, 512, 512, 512, activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 512, 512), activate_type="mish")
- input_data = tf.concat([input_data, route], axis=-1)
-
- input_data = convolutional(input_data, (1, 1, 1024, 1024), activate_type="mish")
- input_data = convolutional(input_data, (1, 1, 1024, 512))
- input_data = convolutional(input_data, (3, 3, 512, 1024))
- input_data = convolutional(input_data, (1, 1, 1024, 512))
-
- max_pooling_1 = tf.keras.layers.MaxPool2D(pool_size=13, padding='SAME', strides=1)(input_data)
- max_pooling_2 = tf.keras.layers.MaxPool2D(pool_size=9, padding='SAME', strides=1)(input_data)
- max_pooling_3 = tf.keras.layers.MaxPool2D(pool_size=5, padding='SAME', strides=1)(input_data)
- input_data = tf.concat([max_pooling_1, max_pooling_2, max_pooling_3, input_data], axis=-1)
-
- input_data = convolutional(input_data, (1, 1, 2048, 512))
- input_data = convolutional(input_data, (3, 3, 512, 1024))
- input_data = convolutional(input_data, (1, 1, 1024, 512))
-
- return route_1, route_2, input_data
-
-def darknet19_tiny(input_data):
- input_data = convolutional(input_data, (3, 3, 3, 16))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 16, 32))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 32, 64))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 64, 128))
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 128, 256))
- route_1 = input_data
- input_data = MaxPool2D(2, 2, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 256, 512))
- input_data = MaxPool2D(2, 1, 'same')(input_data)
- input_data = convolutional(input_data, (3, 3, 512, 1024))
-
- return route_1, input_data
-
-def cspdarknet53_tiny(input_data): # not sure how this should be called
- input_data = convolutional(input_data, (3, 3, 3, 32), downsample=True)
- input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True)
- input_data = convolutional(input_data, (3, 3, 64, 64))
-
- route = input_data
- input_data = route_group(input_data, 2, 1)
- input_data = convolutional(input_data, (3, 3, 32, 32))
- route_1 = input_data
- input_data = convolutional(input_data, (3, 3, 32, 32))
- input_data = tf.concat([input_data, route_1], axis=-1)
- input_data = convolutional(input_data, (1, 1, 32, 64))
- input_data = tf.concat([route, input_data], axis=-1)
- input_data = MaxPool2D(2, 2, 'same')(input_data)
-
- input_data = convolutional(input_data, (3, 3, 64, 128))
- route = input_data
- input_data = route_group(input_data, 2, 1)
- input_data = convolutional(input_data, (3, 3, 64, 64))
- route_1 = input_data
- input_data = convolutional(input_data, (3, 3, 64, 64))
- input_data = tf.concat([input_data, route_1], axis=-1)
- input_data = convolutional(input_data, (1, 1, 64, 128))
- input_data = tf.concat([route, input_data], axis=-1)
- input_data = MaxPool2D(2, 2, 'same')(input_data)
-
- input_data = convolutional(input_data, (3, 3, 128, 256))
- route = input_data
- input_data = route_group(input_data, 2, 1)
- input_data = convolutional(input_data, (3, 3, 128, 128))
- route_1 = input_data
- input_data = convolutional(input_data, (3, 3, 128, 128))
- input_data = tf.concat([input_data, route_1], axis=-1)
- input_data = convolutional(input_data, (1, 1, 128, 256))
- route_1 = input_data
- input_data = tf.concat([route, input_data], axis=-1)
- input_data = MaxPool2D(2, 2, 'same')(input_data)
-
- input_data = convolutional(input_data, (3, 3, 512, 512))
-
- return route_1, input_data
-
-def YOLOv3(input_layer, NUM_CLASS):
- # After the input layer enters the Darknet-53 network, we get three branches
- route_1, route_2, conv = darknet53(input_layer)
- # See the orange module (DBL) in the figure above, a total of 5 Subconvolution operation
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv = convolutional(conv, (3, 3, 512, 1024))
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv = convolutional(conv, (3, 3, 512, 1024))
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv_lobj_branch = convolutional(conv, (3, 3, 512, 1024))
-
- # conv_lbbox is used to predict large-sized objects , Shape = [None, 13, 13, 255]
- conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 1024, 3*(NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(conv, (1, 1, 512, 256))
- # upsample here uses the nearest neighbor interpolation method, which has the advantage that the
- # upsampling process does not need to learn, thereby reducing the network parameter
- conv = upsample(conv)
-
- conv = tf.concat([conv, route_2], axis=-1)
- conv = convolutional(conv, (1, 1, 768, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
- conv_mobj_branch = convolutional(conv, (3, 3, 256, 512))
-
- # conv_mbbox is used to predict medium-sized objects, shape = [None, 26, 26, 255]
- conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 512, 3*(NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = upsample(conv)
-
- conv = tf.concat([conv, route_1], axis=-1)
- conv = convolutional(conv, (1, 1, 384, 128))
- conv = convolutional(conv, (3, 3, 128, 256))
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = convolutional(conv, (3, 3, 128, 256))
- conv = convolutional(conv, (1, 1, 256, 128))
- conv_sobj_branch = convolutional(conv, (3, 3, 128, 256))
-
- # conv_sbbox is used to predict small size objects, shape = [None, 52, 52, 255]
- conv_sbbox = convolutional(conv_sobj_branch, (1, 1, 256, 3*(NUM_CLASS +5)), activate=False, bn=False)
-
- return [conv_sbbox, conv_mbbox, conv_lbbox]
-
-def YOLOv4(input_layer, NUM_CLASS):
- route_1, route_2, conv = cspdarknet53(input_layer)
-
- route = conv
- conv = convolutional(conv, (1, 1, 512, 256))
- conv = upsample(conv)
- route_2 = convolutional(route_2, (1, 1, 512, 256))
- conv = tf.concat([route_2, conv], axis=-1)
-
- conv = convolutional(conv, (1, 1, 512, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
-
- route_2 = conv
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = upsample(conv)
- route_1 = convolutional(route_1, (1, 1, 256, 128))
- conv = tf.concat([route_1, conv], axis=-1)
-
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = convolutional(conv, (3, 3, 128, 256))
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = convolutional(conv, (3, 3, 128, 256))
- conv = convolutional(conv, (1, 1, 256, 128))
-
- route_1 = conv
- conv = convolutional(conv, (3, 3, 128, 256))
- conv_sbbox = convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(route_1, (3, 3, 128, 256), downsample=True)
- conv = tf.concat([conv, route_2], axis=-1)
-
- conv = convolutional(conv, (1, 1, 512, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
- conv = convolutional(conv, (3, 3, 256, 512))
- conv = convolutional(conv, (1, 1, 512, 256))
-
- route_2 = conv
- conv = convolutional(conv, (3, 3, 256, 512))
- conv_mbbox = convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(route_2, (3, 3, 256, 512), downsample=True)
- conv = tf.concat([conv, route], axis=-1)
-
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv = convolutional(conv, (3, 3, 512, 1024))
- conv = convolutional(conv, (1, 1, 1024, 512))
- conv = convolutional(conv, (3, 3, 512, 1024))
- conv = convolutional(conv, (1, 1, 1024, 512))
-
- conv = convolutional(conv, (3, 3, 512, 1024))
- conv_lbbox = convolutional(conv, (1, 1, 1024, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
-
- return [conv_sbbox, conv_mbbox, conv_lbbox]
-
-def YOLOv3_tiny(input_layer, NUM_CLASS):
- # After the input layer enters the Darknet-53 network, we get three branches
- route_1, conv = darknet19_tiny(input_layer)
-
- conv = convolutional(conv, (1, 1, 1024, 256))
- conv_lobj_branch = convolutional(conv, (3, 3, 256, 512))
-
- # conv_lbbox is used to predict large-sized objects , Shape = [None, 26, 26, 255]
- conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 512, 3*(NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(conv, (1, 1, 256, 128))
- # upsample here uses the nearest neighbor interpolation method, which has the advantage that the
- # upsampling process does not need to learn, thereby reducing the network parameter
- conv = upsample(conv)
-
- conv = tf.concat([conv, route_1], axis=-1)
- conv_mobj_branch = convolutional(conv, (3, 3, 128, 256))
- # conv_mbbox is used to predict medium size objects, shape = [None, 13, 13, 255]
- conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
-
- return [conv_mbbox, conv_lbbox]
-
-def YOLOv4_tiny(input_layer, NUM_CLASS):
- route_1, conv = cspdarknet53_tiny(input_layer)
-
- conv = convolutional(conv, (1, 1, 512, 256))
-
- conv_lobj_branch = convolutional(conv, (3, 3, 256, 512))
- conv_lbbox = convolutional(conv_lobj_branch, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
-
- conv = convolutional(conv, (1, 1, 256, 128))
- conv = upsample(conv)
- conv = tf.concat([conv, route_1], axis=-1)
-
- conv_mobj_branch = convolutional(conv, (3, 3, 128, 256))
- conv_mbbox = convolutional(conv_mobj_branch, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
-
- return [conv_mbbox, conv_lbbox]
-
-def Create_Yolo(input_size=416, channels=3, training=False, CLASSES=YOLO_COCO_CLASSES):
- NUM_CLASS = len(read_class_names(CLASSES))
- input_layer = Input([input_size, input_size, channels])
-
- if TRAIN_YOLO_TINY:
- if YOLO_TYPE == "yolov4":
- conv_tensors = YOLOv4_tiny(input_layer, NUM_CLASS)
- if YOLO_TYPE == "yolov3":
- conv_tensors = YOLOv3_tiny(input_layer, NUM_CLASS)
- else:
- if YOLO_TYPE == "yolov4":
- conv_tensors = YOLOv4(input_layer, NUM_CLASS)
- if YOLO_TYPE == "yolov3":
- conv_tensors = YOLOv3(input_layer, NUM_CLASS)
-
- output_tensors = []
- for i, conv_tensor in enumerate(conv_tensors):
- pred_tensor = decode(conv_tensor, NUM_CLASS, i)
- if training: output_tensors.append(conv_tensor)
- output_tensors.append(pred_tensor)
-
- Yolo = tf.keras.Model(input_layer, output_tensors)
- return Yolo
-
-
-def decode(conv_output, NUM_CLASS, i=0):
- # where i = 0, 1 or 2 to correspond to the three grid scales
- conv_shape = tf.shape(conv_output)
- batch_size = conv_shape[0]
- output_size = conv_shape[1]
-
- conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
-
- #conv_raw_dxdy = conv_output[:, :, :, :, 0:2] # offset of center position
- #conv_raw_dwdh = conv_output[:, :, :, :, 2:4] # Prediction box length and width offset
- #conv_raw_conf = conv_output[:, :, :, :, 4:5] # confidence of the prediction box
- #conv_raw_prob = conv_output[:, :, :, :, 5: ] # category probability of the prediction box
- conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS), axis=-1)
-
- # next need Draw the grid. Where output_size is equal to 13, 26 or 52
- #y = tf.range(output_size, dtype=tf.int32)
- #y = tf.expand_dims(y, -1)
- #y = tf.tile(y, [1, output_size])
- #x = tf.range(output_size,dtype=tf.int32)
- #x = tf.expand_dims(x, 0)
- #x = tf.tile(x, [output_size, 1])
- xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))
- xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2) # [gx, gy, 1, 2]
- xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [batch_size, 1, 1, 3, 1])
- xy_grid = tf.cast(xy_grid, tf.float32)
-
- #xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)
- #xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, 3, 1])
- #y_grid = tf.cast(xy_grid, tf.float32)
-
- # Calculate the center position of the prediction box:
- pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * STRIDES[i]
- # Calculate the length and width of the prediction box:
- pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS[i]) * STRIDES[i]
-
- pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
- pred_conf = tf.sigmoid(conv_raw_conf) # object box calculates the predicted confidence
- pred_prob = tf.sigmoid(conv_raw_prob) # calculating the predicted probability category box object
-
- # calculating the predicted probability category box object
- return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)
-
-
-def bbox_iou(boxes1, boxes2):
- boxes1_area = boxes1[..., 2] * boxes1[..., 3]
- boxes2_area = boxes2[..., 2] * boxes2[..., 3]
-
- boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
- boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
- boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
- boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
-
- left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])
- right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
-
- inter_section = tf.maximum(right_down - left_up, 0.0)
- inter_area = inter_section[..., 0] * inter_section[..., 1]
- union_area = boxes1_area + boxes2_area - inter_area
-
- return 1.0 * inter_area / union_area
-
-def bbox_giou(boxes1, boxes2):
- boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
- boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
- boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
- boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
-
- boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]),
- tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis=-1)
- boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]),
- tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis=-1)
-
- boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
- boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])
-
- left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])
- right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])
-
- inter_section = tf.maximum(right_down - left_up, 0.0)
- inter_area = inter_section[..., 0] * inter_section[..., 1]
- union_area = boxes1_area + boxes2_area - inter_area
-
- # Calculate the iou value between the two bounding boxes
- iou = inter_area / union_area
-
- # Calculate the coordinates of the upper left corner and the lower right corner of the smallest closed convex surface
- enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2])
- enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:])
- enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0)
-
- # Calculate the area of the smallest closed convex surface C
- enclose_area = enclose[..., 0] * enclose[..., 1]
-
- # Calculate the GIoU value according to the GioU formula
- giou = iou - 1.0 * (enclose_area - union_area) / enclose_area
-
- return giou
-
-# testing (should be better than giou)
-def bbox_ciou(boxes1, boxes2):
- boxes1_coor = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,
- boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)
- boxes2_coor = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,
- boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)
-
- left = tf.maximum(boxes1_coor[..., 0], boxes2_coor[..., 0])
- up = tf.maximum(boxes1_coor[..., 1], boxes2_coor[..., 1])
- right = tf.maximum(boxes1_coor[..., 2], boxes2_coor[..., 2])
- down = tf.maximum(boxes1_coor[..., 3], boxes2_coor[..., 3])
-
- c = (right - left) * (right - left) + (up - down) * (up - down)
- iou = bbox_iou(boxes1, boxes2)
-
- u = (boxes1[..., 0] - boxes2[..., 0]) * (boxes1[..., 0] - boxes2[..., 0]) + (boxes1[..., 1] - boxes2[..., 1]) * (boxes1[..., 1] - boxes2[..., 1])
- d = u / c
-
- ar_gt = boxes2[..., 2] / boxes2[..., 3]
- ar_pred = boxes1[..., 2] / boxes1[..., 3]
-
- ar_loss = 4 / (np.pi * np.pi) * (tf.atan(ar_gt) - tf.atan(ar_pred)) * (tf.atan(ar_gt) - tf.atan(ar_pred))
- alpha = ar_loss / (1 - iou + ar_loss + 0.000001)
- ciou_term = d + alpha * ar_loss
-
- return iou - ciou_term
-
-
-def compute_loss(pred, conv, label, bboxes, i=0, CLASSES=YOLO_COCO_CLASSES):
- NUM_CLASS = len(read_class_names(CLASSES))
- conv_shape = tf.shape(conv)
- batch_size = conv_shape[0]
- output_size = conv_shape[1]
- input_size = STRIDES[i] * output_size
- conv = tf.reshape(conv, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
-
- conv_raw_conf = conv[:, :, :, :, 4:5]
- conv_raw_prob = conv[:, :, :, :, 5:]
-
- pred_xywh = pred[:, :, :, :, 0:4]
- pred_conf = pred[:, :, :, :, 4:5]
-
- label_xywh = label[:, :, :, :, 0:4]
- respond_bbox = label[:, :, :, :, 4:5]
- label_prob = label[:, :, :, :, 5:]
-
- giou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis=-1)
- input_size = tf.cast(input_size, tf.float32)
-
- bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)
- giou_loss = respond_bbox * bbox_loss_scale * (1 - giou)
-
- iou = bbox_iou(pred_xywh[:, :, :, :, np.newaxis, :], bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :])
- # Find the value of IoU with the real box The largest prediction box
- max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1)
-
- # If the largest iou is less than the threshold, it is considered that the prediction box contains no objects, then the background box
- respond_bgd = (1.0 - respond_bbox) * tf.cast( max_iou < YOLO_IOU_LOSS_THRESH, tf.float32 )
-
- conf_focal = tf.pow(respond_bbox - pred_conf, 2)
-
- # Calculate the loss of confidence
- # we hope that if the grid contains objects, then the network output prediction box has a confidence of 1 and 0 when there is no object.
- conf_loss = conf_focal * (
- respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
- +
- respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)
- )
-
- prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob)
-
- giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis=[1,2,3,4]))
- conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1,2,3,4]))
- prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1,2,3,4]))
-
- return giou_loss, conf_loss, prob_loss
diff --git a/PAR 152/carte_cones.py b/PAR 152/carte_cones.py
deleted file mode 100644
index be2c6a4cbe56c23d68d21149a534e75b1d3d97ff..0000000000000000000000000000000000000000
--- a/PAR 152/carte_cones.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jan 27 11:53:00 2023
-
-@author: paull
-"""
-
-import matplotlib.pyplot as plt
-from math import cos,sin,tan
-import numpy as np
-
-dist_roue = 0.2 #Distance entre les roues avant et les roues arrieres
-dt = 1 #Frequence de calcul
-
-
-def rotation(point, centre, angle):
-
- x1,y1 = point
- x2,y2 = centre
-
- new_x = (x1-x2)*cos(angle) - (y1-y2)*sin(angle) + x2
- new_y = (x1-x2)*sin(angle) + (y1-y2)*cos(angle) + y2
-
- return new_x, new_y
-
-def motion_update(commande, position):
- vitesse, direction = commande
- x,y,theta = position
-
- if direction ==0:
- new_x = x + dt*vitesse*cos(theta)
- new_y = y + dt*vitesse*sin(theta)
- new_theta = theta
-
- else:
- R = dist_roue/tan(direction)
- angle_rotation = dt*vitesse/R
-
- if direction>0:
- centre_rotation_x = x + R*sin(theta)
- centre_rotation_y = y - R*cos(theta)
- else:
- centre_rotation_x = x - R*sin(theta)
- centre_rotation_y = y + R*cos(theta)
-
- circle = np.linspace(0,2*np.pi, 150)
- a = R*np.cos(circle) + centre_rotation_x
- b = R*np.sin(circle) + centre_rotation_y
- plt.plot(a,b)
- new_x, new_y = rotation((x,y),(centre_rotation_x, centre_rotation_y), angle_rotation)
- new_theta = theta + angle_rotation
-
- return [(new_x, new_y, new_theta), (centre_rotation_x, centre_rotation_y)]
-
-
-# pixel_x_ext, pixel_y_ext = [242, 220, 165, 110, 63, 33, 22, 34, 63, 110, 165, 220, 243, 310, 334, 388, 443, 490, 521, 531, 520, 489, 443, 388, 333, 310], [76, 64, 52, 64, 95, 141, 196, 252, 298, 330, 340, 328, 318, 316, 328, 339, 329, 298, 251, 196, 142, 95, 64, 53, 64, 77]
-# pixel_x_int, pixel_y_int = [245, 238, 222, 196, 166, 134, 108, 91, 85, 90, 109, 134, 165, 196, 222, 239, 308, 314, 332, 358, 388, 419, 445, 462, 468, 462, 445, 419, 388, 359, 332, 314], [201, 167, 140, 123, 116, 123, 140, 165, 195, 228, 253, 270, 277, 270, 253, 227, 200, 226, 253, 270, 277, 270, 253, 228, 197, 166, 140, 122, 117, 123, 140, 166]
-# diametre = 225
-# centre_x, centre_y = 278, 200
-# coord_x_ext, coord_y_ext = [i/diametre for i in pixel_x_ext], [i/diametre for i in pixel_y_ext]
-# coord_x_int, coord_y_int = [i/diametre for i in pixel_x_int], [i/diametre for i in pixel_y_int]
-# print(coord_x_int, coord_x_int)
-
-coord_x_int = []
-coord_y_int = []
-
-coord_x_ext = []
-coord_y_ext = []
-
-r_in = 1-0.14/2
-r_ext = r_in + 0.394 + 0.14
-
-for i in range(16):
- theta = 2*3.1415*i/16
- coord_x_int.append(-1.2+r_in*cos(theta))
- coord_y_int.append(r_in*sin(theta))
-
- coord_x_int.append(1.2+r_in*cos(theta))
- coord_y_int.append(r_in*sin(theta))
-
- if 1<i<15:
- coord_x_ext.append(-1.2+r_ext*cos(theta))
- coord_y_ext.append(r_ext*sin(theta))
-
- if not 7<=i<=9:
- coord_x_ext.append(1.2+r_ext*cos(theta))
- coord_y_ext.append(r_ext*sin(theta))
-
-#coord_int = [(i/diametre , j/diametre) for i,j in zip(pixel_x_int, pixel_y_int)]
-#coord_ext = [(i/diametre , j/diametre) for i,j in zip(pixel_x_ext, pixel_y_ext)]
-x = 1.4
-y = 1.271
-theta = 0
-vision_x = []
-vision_y = []
-vision_x_ext = []
-vision_y_ext = []
-# for pt in coord_int:
-# vision_x.append((pt[0]-x)*cos(theta) + (pt[1]-y)*sin(theta))
-# vision_y.append(-(pt[0]-x)*sin(theta) + (pt[1]-y)*cos(theta))
-
-# for pt in coord_ext:
-# vision_x_ext.append((pt[0]-x)*cos(theta) + (pt[1]-y)*sin(theta))
-# vision_y_ext.append(-(pt[0]-x)*sin(theta) + (pt[1]-y)*cos(theta))
-
-
-plt.figure(1)
-plt.plot(coord_x_ext, coord_y_ext,'+')
-plt.plot(coord_x_int, coord_y_int,'+')
-#plt.arrow(x,y,0.1*cos(theta),0.1*sin(theta), width=0.01)
-
-#tab = motion_update((0.1,-3.14/6), (x,y,theta))
-#x2,y2,theta2 = tab[0]
-#x_r, y_r = tab[1]
-
-#plt.arrow(x2,y2,0.1*cos(theta2),0.1*sin(theta2), width=0.01)
-#plt.plot(x_r,y_r,'o')
-
-plt.show()
-
-# =============================================================================
-# cones_vu_x = []
-# cones_vu_y = []
-# for i in range(len(vision_x)):
-# if vision_x[i]>0 and abs(vision_y[i])<vision_x[i]:
-# cones_vu_x.append(vision_x[i])
-# cones_vu_y.append(vision_y[i])
-#
-# for i in range(len(vision_x_ext)):
-# if vision_x_ext[i]>0 and abs(vision_y_ext[i])<vision_x_ext[i]:
-# cones_vu_x.append(vision_x_ext[i])
-# cones_vu_y.append(vision_y_ext[i])
-#
-# plt.figure(2)
-# plt.plot(cones_vu_x, cones_vu_y, '+')
-# plt.arrow(0,0,0.1,0,width=0.01)
-# plt.show()
-# =============================================================================
diff --git a/PAR 152/cone_dataset/1.jpg b/PAR 152/cone_dataset/1.jpg
deleted file mode 100644
index 01ebe69cad036a034263da538170207c0e9c6ce4..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/1.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/1.xml b/PAR 152/cone_dataset/1.xml
deleted file mode 100644
index d159c342a9eff91ecfc2b2bff46ab2af8237db2f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/1.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>1.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\1.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>414</width>
- <height>414</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>112</xmin>
- <ymin>35</ymin>
- <xmax>309</xmax>
- <ymax>327</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/10.jpg b/PAR 152/cone_dataset/10.jpg
deleted file mode 100644
index a02e00027b0ae41f3f9ba675604cd3ad057d2ace..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/10.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/10.xml b/PAR 152/cone_dataset/10.xml
deleted file mode 100644
index 97cce271f9014d863bade26e13d8ffe88d572943..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/10.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>10.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\10.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>508</width>
- <height>337</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>248</xmin>
- <ymin>39</ymin>
- <xmax>436</xmax>
- <ymax>328</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/100.jpg b/PAR 152/cone_dataset/100.jpg
deleted file mode 100644
index 80c8338e7769becad871547d903d316ff4626a90..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/100.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/100.xml b/PAR 152/cone_dataset/100.xml
deleted file mode 100644
index a4f1e281d26fbb19a991c5b4a2aec1439a86652f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/100.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>100.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\100.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
- <height>339</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>40</xmin>
- <ymin>66</ymin>
- <xmax>229</xmax>
- <ymax>319</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>197</xmin>
- <ymin>73</ymin>
- <xmax>312</xmax>
- <ymax>248</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>323</xmin>
- <ymin>76</ymin>
- <xmax>387</xmax>
- <ymax>171</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>174</xmin>
- <ymin>80</ymin>
- <xmax>243</xmax>
- <ymax>201</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/101.jpg b/PAR 152/cone_dataset/101.jpg
deleted file mode 100644
index 59bed827780f4c68a9e708dacacdf257cb0a129f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/101.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/101.xml b/PAR 152/cone_dataset/101.xml
deleted file mode 100644
index 958c11046fa5c40a05c221f6776edf732e33ef6a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/101.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>101.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\101.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>336</width>
- <height>509</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>242</xmin>
- <ymin>383</ymin>
- <xmax>267</xmax>
- <ymax>436</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>171</xmin>
- <ymin>377</ymin>
- <xmax>213</xmax>
- <ymax>437</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>107</xmin>
- <ymin>381</ymin>
- <xmax>143</xmax>
- <ymax>434</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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diff --git a/PAR 152/cone_dataset/102.jpg b/PAR 152/cone_dataset/102.jpg
deleted file mode 100644
index eecc32834e0367d99fbee310b49f4a50dfe5505f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/102.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/102.xml b/PAR 152/cone_dataset/102.xml
deleted file mode 100644
index 0b575db6936b0321e789d26d19ac36ba7a6ff930..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/102.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>102.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\102.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>359</width>
- <height>479</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>190</xmin>
- <ymin>275</ymin>
- <xmax>295</xmax>
- <ymax>430</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>212</xmin>
- <ymin>260</ymin>
- <xmax>297</xmax>
- <ymax>384</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>234</xmin>
- <ymin>234</ymin>
- <xmax>305</xmax>
- <ymax>345</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>264</xmin>
- <ymin>226</ymin>
- <xmax>324</xmax>
- <ymax>319</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <ymax>289</ymax>
- </bndbox>
- </object>
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diff --git a/PAR 152/cone_dataset/103.jpg b/PAR 152/cone_dataset/103.jpg
deleted file mode 100644
index ee5237156bfe91c2ca8d3b9befc3cc21ac68fcef..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/103.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/103.xml b/PAR 152/cone_dataset/103.xml
deleted file mode 100644
index c717ab0e7d044d5ff6b1ef6360c90f5f50017f4a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/103.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>103.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\103.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>525</width>
- <height>329</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
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- <ymin>127</ymin>
- <xmax>228</xmax>
- <ymax>296</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>6</xmin>
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- <xmax>93</xmax>
- <ymax>194</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>255</xmin>
- <ymin>36</ymin>
- <xmax>321</xmax>
- <ymax>145</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmax>519</xmax>
- <ymax>223</ymax>
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- </object>
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diff --git a/PAR 152/cone_dataset/104.jpg b/PAR 152/cone_dataset/104.jpg
deleted file mode 100644
index 3852ad3e3fea4f14901a042cf27b8735d996fb66..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/104.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/104.xml b/PAR 152/cone_dataset/104.xml
deleted file mode 100644
index 1c2a4ab2cd64de1abb20dbe9000820543b202a39..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/104.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>104.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\104.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>449</width>
- <height>382</height>
- <depth>3</depth>
- </size>
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- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>127</xmin>
- <ymin>71</ymin>
- <xmax>261</xmax>
- <ymax>300</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>70</xmin>
- <ymin>96</ymin>
- <xmax>168</xmax>
- <ymax>288</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>281</xmin>
- <ymin>31</ymin>
- <xmax>449</xmax>
- <ymax>338</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmax>350</xmax>
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- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/105.jpg b/PAR 152/cone_dataset/105.jpg
deleted file mode 100644
index b4f6c262b920a13fb34a72919827d7a23a7d931f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/105.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/105.xml b/PAR 152/cone_dataset/105.xml
deleted file mode 100644
index b19e2dba6aef0ea08aa7a2701e7dd202bf9a830b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/105.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>105.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\105.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>445</width>
- <height>594</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
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- <bndbox>
- <xmin>1</xmin>
- <ymin>57</ymin>
- <xmax>182</xmax>
- <ymax>569</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>44</xmin>
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- <xmax>237</xmax>
- <ymax>479</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>133</xmin>
- <ymin>66</ymin>
- <xmax>296</xmax>
- <ymax>409</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>204</xmin>
- <ymin>62</ymin>
- <xmax>333</xmax>
- <ymax>364</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>277</xmin>
- <ymin>43</ymin>
- <xmax>397</xmax>
- <ymax>315</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>330</xmin>
- <ymin>33</ymin>
- <xmax>429</xmax>
- <ymax>275</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/106.jpg b/PAR 152/cone_dataset/106.jpg
deleted file mode 100644
index 90f1137c3fa01883e90e2e3a1aced2834b8a3621..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/106.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/106.xml b/PAR 152/cone_dataset/106.xml
deleted file mode 100644
index e0d0731395371c618affdf81c108809c4e1ad1fb..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/106.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>106.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\106.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>170</width>
- <height>108</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <xmax>102</xmax>
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- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/107.jpg b/PAR 152/cone_dataset/107.jpg
deleted file mode 100644
index cd6dc6f32e806e34f485db5b2874f1b6997c1635..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/107.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/107.xml b/PAR 152/cone_dataset/107.xml
deleted file mode 100644
index 7df09c28143339caef9663afa46044eacf8aec12..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/107.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>107.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\107.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>493</width>
- <height>594</height>
- <depth>3</depth>
- </size>
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- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>189</xmin>
- <ymin>224</ymin>
- <xmax>316</xmax>
- <ymax>441</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/108.jpg b/PAR 152/cone_dataset/108.jpg
deleted file mode 100644
index d42fdba9fc565cb19b93e68e3b9a7e249b384edd..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/108.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/108.xml b/PAR 152/cone_dataset/108.xml
deleted file mode 100644
index fd46a3df4dea17af6e9cc0341e47b4caa4151dfe..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/108.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>108.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\108.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>395</width>
- <height>594</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
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- <xmax>353</xmax>
- <ymax>587</ymax>
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- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/109.jpg b/PAR 152/cone_dataset/109.jpg
deleted file mode 100644
index 2f0d11ebd8ff59aad8619347acf3b1193a388f45..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/109.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/109.xml b/PAR 152/cone_dataset/109.xml
deleted file mode 100644
index 7c8b5113f509fd1910898a775309dbd7e37b5a93..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/109.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>109.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\109.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>298</xmin>
- <ymin>224</ymin>
- <xmax>333</xmax>
- <ymax>273</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>246</xmin>
- <ymin>225</ymin>
- <xmax>275</xmax>
- <ymax>274</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmin>186</xmin>
- <ymin>224</ymin>
- <xmax>219</xmax>
- <ymax>273</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>134</xmin>
- <ymin>226</ymin>
- <xmax>168</xmax>
- <ymax>269</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>348</xmin>
- <ymin>226</ymin>
- <xmax>377</xmax>
- <ymax>270</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>379</xmin>
- <ymin>226</ymin>
- <xmax>407</xmax>
- <ymax>269</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>99</xmin>
- <ymin>225</ymin>
- <xmax>131</xmax>
- <ymax>267</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/11.jpg b/PAR 152/cone_dataset/11.jpg
deleted file mode 100644
index d4587717f7889bca35ef1967bd6a750dcff60a58..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/11.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/11.xml b/PAR 152/cone_dataset/11.xml
deleted file mode 100644
index 4f2ab8385285dd4e1750c7607e44353d17e93df8..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/11.xml
+++ /dev/null
@@ -1,134 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>11.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\11.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>508</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
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- <bndbox>
- <xmin>400</xmin>
- <ymin>20</ymin>
- <xmax>508</xmax>
- <ymax>255</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>294</xmin>
- <ymin>19</ymin>
- <xmax>405</xmax>
- <ymax>205</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>226</xmin>
- <ymin>18</ymin>
- <xmax>312</xmax>
- <ymax>172</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>164</xmin>
- <ymin>15</ymin>
- <xmax>243</xmax>
- <ymax>148</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>107</xmin>
- <ymin>12</ymin>
- <xmax>172</xmax>
- <ymax>125</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>84</xmin>
- <ymin>13</ymin>
- <xmax>135</xmax>
- <ymax>114</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>58</xmin>
- <ymin>11</ymin>
- <xmax>99</xmax>
- <ymax>102</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>310</xmin>
- <ymin>7</ymin>
- <xmax>341</xmax>
- <ymax>64</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>423</xmin>
- <ymin>8</ymin>
- <xmax>464</xmax>
- <ymax>79</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>171</xmin>
- <ymin>8</ymin>
- <xmax>194</xmax>
- <ymax>46</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/110.jpg b/PAR 152/cone_dataset/110.jpg
deleted file mode 100644
index 4b646a06ec36ecfa82aa3e6f5b1edebe9af803c7..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/110.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/110.xml b/PAR 152/cone_dataset/110.xml
deleted file mode 100644
index ca3674f25b2a0174db035fe033d5cf1191c0bf64..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/110.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>110.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\110.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>413</width>
- <height>416</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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diff --git a/PAR 152/cone_dataset/111.jpg b/PAR 152/cone_dataset/111.jpg
deleted file mode 100644
index a4396847afc3922ec74260655a5147bd036332bc..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/111.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/111.xml b/PAR 152/cone_dataset/111.xml
deleted file mode 100644
index c193e113c287d37b2e3e40ae61d3e357e2c7b69e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/111.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>111.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\111.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>359</width>
- <height>478</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
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- <xmax>148</xmax>
- <ymax>270</ymax>
- </bndbox>
- </object>
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diff --git a/PAR 152/cone_dataset/112.jpg b/PAR 152/cone_dataset/112.jpg
deleted file mode 100644
index aea7e0c07f1233fbd8234f66cb8eec86e463b745..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/112.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/112.xml b/PAR 152/cone_dataset/112.xml
deleted file mode 100644
index 6b5ae328e4255250e9da0f97637d182e5bdaa2dd..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/112.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>112.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\112.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>337</height>
- <depth>3</depth>
- </size>
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- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>233</xmin>
- <ymin>186</ymin>
- <xmax>303</xmax>
- <ymax>304</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
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- <ymax>312</ymax>
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- </object>
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diff --git a/PAR 152/cone_dataset/113.jpg b/PAR 152/cone_dataset/113.jpg
deleted file mode 100644
index 8a620706850e254cbba0f18b660ce4cf018d6ecd..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/113.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/113.xml b/PAR 152/cone_dataset/113.xml
deleted file mode 100644
index f215a8926395304615b57553496ff2916527559e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/113.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>113.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\113.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>337</height>
- <depth>3</depth>
- </size>
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- <object>
- <name>cone</name>
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- <ymin>210</ymin>
- <xmax>206</xmax>
- <ymax>273</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
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- <xmax>95</xmax>
- <ymax>275</ymax>
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- </object>
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diff --git a/PAR 152/cone_dataset/114.jpg b/PAR 152/cone_dataset/114.jpg
deleted file mode 100644
index 7876fa36c5549296476537ae24151b473365dde1..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/114.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/114.xml b/PAR 152/cone_dataset/114.xml
deleted file mode 100644
index 63c7af1c30678797a8487a0db6c79f503546bbf2..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/114.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>114.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\114.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>359</width>
- <height>478</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
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- <xmax>280</xmax>
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- </bndbox>
- </object>
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diff --git a/PAR 152/cone_dataset/115.jpg b/PAR 152/cone_dataset/115.jpg
deleted file mode 100644
index f30c3a3e24cc899948e634351742c76762aef97f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/115.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/115.xml b/PAR 152/cone_dataset/115.xml
deleted file mode 100644
index bf71ccf9a473f81f5cb5cd4938f212e8ddfbd312..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/115.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>115.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\115.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>384</xmin>
- <ymin>235</ymin>
- <xmax>439</xmax>
- <ymax>316</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>459</xmin>
- <ymin>234</ymin>
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- <ymax>315</ymax>
- </bndbox>
- </object>
- <object>
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- <ymax>262</ymax>
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- </object>
- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>40</xmin>
- <ymin>194</ymin>
- <xmax>61</xmax>
- <ymax>230</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>144</xmin>
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- <xmax>173</xmax>
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- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/116.jpg b/PAR 152/cone_dataset/116.jpg
deleted file mode 100644
index 0b1a61b2f03768f49e4f1662b6d490d579dfd756..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/116.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/116.xml b/PAR 152/cone_dataset/116.xml
deleted file mode 100644
index ccb5344abb84b37663afcb63de06581f18f280e3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/116.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>116.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\116.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>416</width>
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- <depth>3</depth>
- </size>
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- <object>
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- <xmax>281</xmax>
- <ymax>397</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>139</xmin>
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- <xmax>217</xmax>
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- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>132</xmin>
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- <xmax>186</xmax>
- <ymax>236</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>188</xmin>
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- <ymax>210</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/117.jpg b/PAR 152/cone_dataset/117.jpg
deleted file mode 100644
index 929b1e3835c003981a2c85723d6c10655584dc11..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/117.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/117.xml b/PAR 152/cone_dataset/117.xml
deleted file mode 100644
index 2f72f24c9191dcead939676baa69cd3f7f328c9b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/117.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>117.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\117.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
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- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>156</xmin>
- <ymin>83</ymin>
- <xmax>255</xmax>
- <ymax>278</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>82</xmin>
- <ymin>36</ymin>
- <xmax>170</xmax>
- <ymax>206</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>256</xmin>
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- <xmax>359</xmax>
- <ymax>251</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>158</xmin>
- <ymin>14</ymin>
- <xmax>241</xmax>
- <ymax>160</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>246</xmin>
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- <xmax>338</xmax>
- <ymax>175</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/118.jpg b/PAR 152/cone_dataset/118.jpg
deleted file mode 100644
index af3dfcff6cdb9b7e9583209cea8216e4938f65ed..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/118.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/118.xml b/PAR 152/cone_dataset/118.xml
deleted file mode 100644
index 68e0cefa006ddf1881ea87bedb7a289fe153eeb3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/118.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>118.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\118.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>1024</width>
- <height>665</height>
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- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>671</xmin>
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- <xmax>773</xmax>
- <ymax>587</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>537</xmin>
- <ymin>440</ymin>
- <xmax>653</xmax>
- <ymax>588</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>346</xmin>
- <ymin>442</ymin>
- <xmax>457</xmax>
- <ymax>572</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>95</xmin>
- <ymin>445</ymin>
- <xmax>214</xmax>
- <ymax>565</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
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- <bndbox>
- <xmin>1</xmin>
- <ymin>442</ymin>
- <xmax>59</xmax>
- <ymax>553</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/119.jpg b/PAR 152/cone_dataset/119.jpg
deleted file mode 100644
index a0495e3be0bd4ff0e0f07906c3a099da8797fed2..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/119.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/119.xml b/PAR 152/cone_dataset/119.xml
deleted file mode 100644
index 4f383288670b2a8db0cd41847b974c8dfc09fc78..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/119.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>119.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\119.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
- <height>339</height>
- <depth>3</depth>
- </size>
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- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>73</xmin>
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- <xmax>127</xmax>
- <ymax>303</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>101</xmin>
- <ymin>188</ymin>
- <xmax>151</xmax>
- <ymax>276</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/12.jpg b/PAR 152/cone_dataset/12.jpg
deleted file mode 100644
index 82412f5b9cf30baed42b8bf5095aa5e5c3f15381..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/12.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/12.xml b/PAR 152/cone_dataset/12.xml
deleted file mode 100644
index 9f58bcc29b2a73e0c6219b1587ee49c1d6b9bb4c..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/12.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>12.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\12.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>359</width>
- <height>479</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>121</xmin>
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- <xmax>340</xmax>
- <ymax>426</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>104</xmin>
- <ymin>175</ymin>
- <xmax>192</xmax>
- <ymax>314</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>74</xmin>
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- <xmax>118</xmax>
- <ymax>289</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/120.jpg b/PAR 152/cone_dataset/120.jpg
deleted file mode 100644
index dbd1b526509aadc7ed5f947cc5ebe6f084aedc1b..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/120.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/120.xml b/PAR 152/cone_dataset/120.xml
deleted file mode 100644
index 1c0cab18d7ae5a65a8e117f40bedd15886b4bf49..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/120.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>120.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\120.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>506</width>
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- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
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- <xmax>314</xmax>
- <ymax>308</ymax>
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- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/121.jpg b/PAR 152/cone_dataset/121.jpg
deleted file mode 100644
index 865719aaa22dada261f0bcd2258246e9710535e3..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/121.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/121.xml b/PAR 152/cone_dataset/121.xml
deleted file mode 100644
index 944761234557b7ea96673f2349f6a8b12108ca93..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/121.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>121.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\121.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>337</height>
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- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>84</xmin>
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- </bndbox>
- </object>
- <object>
- <name>cone</name>
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deleted file mode 100644
index 89e170917e0d48164f90581bd1a9aabf691af34e..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/122.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/122.xml b/PAR 152/cone_dataset/122.xml
deleted file mode 100644
index b9d947a59c2202afa2f9fb04dd505affde1fb881..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/122.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>122.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\122.jpg</path>
- <source>
- <database>Unknown</database>
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- </size>
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- </object>
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deleted file mode 100644
index 08bc6f4fc4af17feb909db1b6dd7f2e093d7bdcf..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/123.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/123.xml b/PAR 152/cone_dataset/123.xml
deleted file mode 100644
index 0eb8bd7ef2d788a9d84a48dac0cb01ec3e2a1c28..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/123.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\123.jpg</path>
- <source>
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deleted file mode 100644
index 1b3ce2f1217c628809e00db20e5bce24983091e6..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/124.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/124.xml b/PAR 152/cone_dataset/124.xml
deleted file mode 100644
index b5fe822327a216ca72025717584bca76c40d97b1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/124.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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deleted file mode 100644
index a361e399665eb15e12d8b0f6ffebcfa8e78836cd..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/125.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/125.xml b/PAR 152/cone_dataset/125.xml
deleted file mode 100644
index 245b581fc7f24b23d4a358f830bea9315307d9fc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/125.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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- <source>
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diff --git a/PAR 152/cone_dataset/126.jpg b/PAR 152/cone_dataset/126.jpg
deleted file mode 100644
index ae566fa972545bad4b0cafcf8b6eb478c96a63fe..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/126.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/126.xml b/PAR 152/cone_dataset/126.xml
deleted file mode 100644
index 1d15e09d77114e104f70ae17f1e1ccabdc3f98d1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/126.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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deleted file mode 100644
index 3c384b8ef14a07741777d8467d0f3f01ea296d92..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/127.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/127.xml b/PAR 152/cone_dataset/127.xml
deleted file mode 100644
index 53f02dd33dc696122921082e84b4b2bc94b896b5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/127.xml
+++ /dev/null
@@ -1,122 +0,0 @@
-<annotation>
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diff --git a/PAR 152/cone_dataset/128.jpg b/PAR 152/cone_dataset/128.jpg
deleted file mode 100644
index 9f8ee6d4c11cb00f8801d6fecf48e58e470e7c86..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/128.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/128.xml b/PAR 152/cone_dataset/128.xml
deleted file mode 100644
index fab7b12143c8c8b859dd49a6354cee9bab868c80..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/128.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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diff --git a/PAR 152/cone_dataset/129.jpg b/PAR 152/cone_dataset/129.jpg
deleted file mode 100644
index 0d6c00d96e51c81f7ee267f53b9eb89720532d1f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/129.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/129.xml b/PAR 152/cone_dataset/129.xml
deleted file mode 100644
index ad637f0742b7c7f9f8b3d9d7b89c664781a70572..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/129.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\129.jpg</path>
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- <object>
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diff --git a/PAR 152/cone_dataset/13.jpg b/PAR 152/cone_dataset/13.jpg
deleted file mode 100644
index dd1bb05d815945a7fffeadabdcf251cf41b577ff..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/13.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/13.xml b/PAR 152/cone_dataset/13.xml
deleted file mode 100644
index fab6df7a28fc5d5b6c3a24e81fe756167fa26c62..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/13.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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diff --git a/PAR 152/cone_dataset/130.jpg b/PAR 152/cone_dataset/130.jpg
deleted file mode 100644
index 479508971ee955463799ad75b5feeb321b66e63b..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/130.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/130.xml b/PAR 152/cone_dataset/130.xml
deleted file mode 100644
index 9949338f42a422895792c5c8759efe6b463f8c8a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/130.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/cone_dataset/131.jpg b/PAR 152/cone_dataset/131.jpg
deleted file mode 100644
index 64d169561db4552fbd7ce3af3e8c89ea91a249c0..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/131.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/131.xml b/PAR 152/cone_dataset/131.xml
deleted file mode 100644
index 57720ed77f73671baeeda38413d6433a2e4a450c..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/131.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/cone_dataset/132.jpg b/PAR 152/cone_dataset/132.jpg
deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/132.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/132.xml b/PAR 152/cone_dataset/132.xml
deleted file mode 100644
index 94d0d12a648cdf7994145766520f81a43f8f7d9b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/132.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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deleted file mode 100644
index 3e20f026c2ce8d90c06d48db7e4e1d616e3e2da4..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/133.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/133.xml b/PAR 152/cone_dataset/133.xml
deleted file mode 100644
index 286952000d06ca9843d44ac6927b25023ccbbd99..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/133.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\133.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
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deleted file mode 100644
index f88e78bd50d67eacdae68a139e2ebc590614a378..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/134.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/134.xml b/PAR 152/cone_dataset/134.xml
deleted file mode 100644
index 6e85e037a6cbddd243198021b6c5613493aba001..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/134.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\134.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
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- </size>
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deleted file mode 100644
index 377db54a5ff54c1a336f0d681539cf3b72d3e477..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/135.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/135.xml b/PAR 152/cone_dataset/135.xml
deleted file mode 100644
index 47ff1be42fc3e7096f279a7e5b6c0be952c624b1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/135.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>135.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\135.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
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- </size>
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- </object>
- <object>
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deleted file mode 100644
index 9450c66428b75490742f64aa49e9336e647c0cd2..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/136.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/136.xml b/PAR 152/cone_dataset/136.xml
deleted file mode 100644
index deea19282e6f408d6d097797be297c58181f5578..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/136.xml
+++ /dev/null
@@ -1,110 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>136.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\136.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>837</width>
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- </size>
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- </object>
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- <object>
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- <object>
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- </object>
- <object>
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- <xmax>284</xmax>
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- </bndbox>
- </object>
- <object>
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diff --git a/PAR 152/cone_dataset/137.jpg b/PAR 152/cone_dataset/137.jpg
deleted file mode 100644
index 5ea3555212980a9a5e49999f6e94c863ba876f05..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/137.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/137.xml b/PAR 152/cone_dataset/137.xml
deleted file mode 100644
index 5bc04c6a295eeb36113a32e575defe7db9ea83dc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/137.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>137.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\137.jpg</path>
- <source>
- <database>Unknown</database>
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deleted file mode 100644
index 5387d06aecc6a705827ea8a41f516adc2a8c0f82..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/138.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/138.xml b/PAR 152/cone_dataset/138.xml
deleted file mode 100644
index 161be36dfd6b0e9ec41456dd28229e5ae5a9aa3f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/138.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>138.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\138.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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diff --git a/PAR 152/cone_dataset/139.jpg b/PAR 152/cone_dataset/139.jpg
deleted file mode 100644
index e229ee1c6fb5049866625ded90073c26df5fbecf..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/139.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/139.xml b/PAR 152/cone_dataset/139.xml
deleted file mode 100644
index 3a9b7b1544a11328c9dfb89ea523ad1c8c7cf6de..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/139.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>139.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\139.jpg</path>
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- <object>
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- <ymax>460</ymax>
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- </object>
- <object>
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- </object>
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diff --git a/PAR 152/cone_dataset/14.jpg b/PAR 152/cone_dataset/14.jpg
deleted file mode 100644
index 82412f5b9cf30baed42b8bf5095aa5e5c3f15381..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/14.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/14.xml b/PAR 152/cone_dataset/14.xml
deleted file mode 100644
index ba95eccc852d8f09b42e811e6cfa8410276402e8..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/14.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>14.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\14.jpg</path>
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- <database>Unknown</database>
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- <object>
- <name>cone</name>
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diff --git a/PAR 152/cone_dataset/140.jpg b/PAR 152/cone_dataset/140.jpg
deleted file mode 100644
index 24c8addfdc85c4cdcad71731689bcdfe00942d12..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/140.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/140.xml b/PAR 152/cone_dataset/140.xml
deleted file mode 100644
index 80e5d6637db504bc0d76836d7136b1569ee4ac43..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/140.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>140.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\140.jpg</path>
- <source>
- <database>Unknown</database>
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diff --git a/PAR 152/cone_dataset/141.jpg b/PAR 152/cone_dataset/141.jpg
deleted file mode 100644
index 5de0b10d1627f61e7528d6b5325e3e4733e7b497..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/141.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/141.xml b/PAR 152/cone_dataset/141.xml
deleted file mode 100644
index a5a1a653ae50d0e4f05c31bd97a2d3d6a43228ed..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/141.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\141.jpg</path>
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diff --git a/PAR 152/cone_dataset/142.jpg b/PAR 152/cone_dataset/142.jpg
deleted file mode 100644
index 735e21e65353c8cd3b610714239739b08441ccca..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/142.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/142.xml b/PAR 152/cone_dataset/142.xml
deleted file mode 100644
index 362b7d5e41b8420a34ddfd59cd4f31fb6a4a67f7..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/142.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <folder>cone_dataset</folder>
- <filename>142.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\142.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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diff --git a/PAR 152/cone_dataset/143.jpg b/PAR 152/cone_dataset/143.jpg
deleted file mode 100644
index 7ca6552e5e7c57a6c699da7236addae19293c80a..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/143.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/143.xml b/PAR 152/cone_dataset/143.xml
deleted file mode 100644
index cad7584590ab5dc9fb94d7a6a2496ca9f5745b95..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/143.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\143.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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- <ymax>477</ymax>
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diff --git a/PAR 152/cone_dataset/144.jpg b/PAR 152/cone_dataset/144.jpg
deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/144.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/144.xml b/PAR 152/cone_dataset/144.xml
deleted file mode 100644
index 457d404573f9d702d11de873691e974b98440eae..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/144.xml
+++ /dev/null
@@ -1,62 +0,0 @@
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- <folder>cone_dataset</folder>
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- <xmin>165</xmin>
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- <object>
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deleted file mode 100644
index 02fb790b1876b308d8e7f3d1ea94f2868543a1be..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/145.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/145.xml b/PAR 152/cone_dataset/145.xml
deleted file mode 100644
index 134a8ba3917ac98619e1e797b1a2cee2ff22b332..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/145.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>145.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\145.jpg</path>
- <source>
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- <size>
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deleted file mode 100644
index 78159f2e458b771b5beb4869ffbedc81385533db..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/146.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/146.xml b/PAR 152/cone_dataset/146.xml
deleted file mode 100644
index 74b85a02752cbdb8feec841ecf1019d5f1e19910..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/146.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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deleted file mode 100644
index 7e10982a0c453f90bb81366ff7aa5e1397e2644c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/147.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/147.xml b/PAR 152/cone_dataset/147.xml
deleted file mode 100644
index 482aa8a09094d5941bb6a34274f4fb355a08528b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/147.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\147.jpg</path>
- <source>
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deleted file mode 100644
index 48cbd9952e14d2b7c7b69e9cf78ec4e1ff8d4a1f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/148.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/148.xml b/PAR 152/cone_dataset/148.xml
deleted file mode 100644
index bd5eab860bb705b27b17467cb17edaec9c9abcfe..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/148.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\148.jpg</path>
- <source>
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deleted file mode 100644
index 3314ed2b67ded8ec1ea2b8ed287e4bac72e75186..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/149.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/149.xml b/PAR 152/cone_dataset/149.xml
deleted file mode 100644
index cca92371bac194de8c02bf0f9096e1d625a62a31..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/149.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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deleted file mode 100644
index 90ef67dca44063a2eaada849f5ecb271bf67f229..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/15.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/15.xml b/PAR 152/cone_dataset/15.xml
deleted file mode 100644
index 019d4deb362b19a63e57c1c0f8449949b51aea10..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/15.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>15.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\15.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
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- </size>
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deleted file mode 100644
index 93b977b1f8b516f4df2a60653e0324ae17ed0788..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/150.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/150.xml b/PAR 152/cone_dataset/150.xml
deleted file mode 100644
index 2995b80b8d861da6272db85aa08ee34553c693f5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/150.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>150.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\150.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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deleted file mode 100644
index d4c00af736330984eaf97ef3f4f868e2e4372562..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/151.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/151.xml b/PAR 152/cone_dataset/151.xml
deleted file mode 100644
index 2034df9012fc38b48bb7dfa3f2bb51240c62716b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/151.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\151.jpg</path>
- <source>
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- <ymax>123</ymax>
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- </object>
- <object>
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- <object>
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deleted file mode 100644
index 06c9fd5df48060c8b4fa6f32683295f5db1eab24..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/152.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/152.xml b/PAR 152/cone_dataset/152.xml
deleted file mode 100644
index d0171f2627166e579829b798383327e9d0931963..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/152.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>152.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\152.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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- <ymax>196</ymax>
- </bndbox>
- </object>
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diff --git a/PAR 152/cone_dataset/153.jpg b/PAR 152/cone_dataset/153.jpg
deleted file mode 100644
index d54a287026558bd6139804e35315c442c1bbf3bf..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/153.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/153.xml b/PAR 152/cone_dataset/153.xml
deleted file mode 100644
index 396945bb8fd7f1780c6c73acb7155eaa7710ac4e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/153.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>153.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\153.jpg</path>
- <source>
- <database>Unknown</database>
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- <object>
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- <xmin>339</xmin>
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- <xmax>374</xmax>
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- </object>
- <object>
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diff --git a/PAR 152/cone_dataset/154.jpg b/PAR 152/cone_dataset/154.jpg
deleted file mode 100644
index ad6353f5b23ce13a29e1cf876bf9a745408fde78..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/154.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/154.xml b/PAR 152/cone_dataset/154.xml
deleted file mode 100644
index 2756ff8e2a1174a065188a6337e012b39a2d7889..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/154.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>154.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\154.jpg</path>
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- <size>
- <width>359</width>
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- </size>
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- <xmin>110</xmin>
- <ymin>68</ymin>
- <xmax>178</xmax>
- <ymax>179</ymax>
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- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>217</xmin>
- <ymin>180</ymin>
- <xmax>340</xmax>
- <ymax>299</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmax>224</xmax>
- <ymax>244</ymax>
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diff --git a/PAR 152/cone_dataset/155.jpg b/PAR 152/cone_dataset/155.jpg
deleted file mode 100644
index aef3841892159501ee808847ea22c4e2ad7e09c9..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/155.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/155.xml b/PAR 152/cone_dataset/155.xml
deleted file mode 100644
index 82bc0d357a80d7177d2a19235eddea2f11f8b47b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/155.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>155.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\155.jpg</path>
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- <size>
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- <object>
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diff --git a/PAR 152/cone_dataset/156.jpg b/PAR 152/cone_dataset/156.jpg
deleted file mode 100644
index 4d66846e14441b98c2bdc9786b527335a3493e56..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/156.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/156.xml b/PAR 152/cone_dataset/156.xml
deleted file mode 100644
index 80b4b0b170dcf0594ab1c5fb073c9121da2887fc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/156.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>156.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\156.jpg</path>
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- <database>Unknown</database>
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- <size>
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Binary files a/PAR 152/cone_dataset/157.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/157.xml b/PAR 152/cone_dataset/157.xml
deleted file mode 100644
index c523b4e61c43637dd169408366a01f17565cc314..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/157.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/cone_dataset/158.xml b/PAR 152/cone_dataset/158.xml
deleted file mode 100644
index dcf256b50a50c21ed6e789b7297dc1884986fca5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/158.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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deleted file mode 100644
index c72ca86fe18915da04d2312730fdf0d89cf6ba6a..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,86 +0,0 @@
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deleted file mode 100644
index 703d882532a61c920092776e8fd47f3fd6c6c6e4..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/16.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/16.xml b/PAR 152/cone_dataset/16.xml
deleted file mode 100644
index 1d7ea701eadfbe5c49172c2da5e89e8bfb6d2b93..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/16.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\16.jpg</path>
- <source>
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Binary files a/PAR 152/cone_dataset/160.jpg and /dev/null differ
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deleted file mode 100644
index 088a0165cf948d1d1f5e8cab48e0295fdcb36244..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
index b5e38a85bfe2c0b588a1a41e5f5b41f4442481b3..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/161.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/161.xml b/PAR 152/cone_dataset/161.xml
deleted file mode 100644
index 4cf83e77f85bfbfbd18a977104831a97931cb6ff..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/161.xml
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@@ -1,254 +0,0 @@
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diff --git a/PAR 152/cone_dataset/162.jpg b/PAR 152/cone_dataset/162.jpg
deleted file mode 100644
index 505960f9097d1559129a0e12896096a37e93b035..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/162.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/162.xml b/PAR 152/cone_dataset/162.xml
deleted file mode 100644
index a9a71716e4d3ae0c327b3a79fb905601a9d949ed..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/162.xml
+++ /dev/null
@@ -1,110 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- </object>
- <object>
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diff --git a/PAR 152/cone_dataset/163.jpg b/PAR 152/cone_dataset/163.jpg
deleted file mode 100644
index a3829a87450a952900eec7354185c64654c0a08b..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/163.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/163.xml b/PAR 152/cone_dataset/163.xml
deleted file mode 100644
index bbb8815c44081a3f5f46509800216c10ce99cff7..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/163.xml
+++ /dev/null
@@ -1,86 +0,0 @@
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- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\163.jpg</path>
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diff --git a/PAR 152/cone_dataset/164.jpg b/PAR 152/cone_dataset/164.jpg
deleted file mode 100644
index 025c84855bc4779cafa1f4ea6a1c31c65953f76c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/164.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/164.xml b/PAR 152/cone_dataset/164.xml
deleted file mode 100644
index 5a62d5b830c0c9e5e7ee205893ed390ad94b3ea3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/164.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <folder>cone_dataset</folder>
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deleted file mode 100644
index 0ac5ff0e69abdbba1b955797d943e014ad90465c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/165.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/165.xml b/PAR 152/cone_dataset/165.xml
deleted file mode 100644
index a21a1fc6de49057e86ca36ef62260800b66aafd6..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/165.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>165.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\165.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
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- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmin>90</xmin>
- <ymin>175</ymin>
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- <object>
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- <object>
- <name>cone</name>
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- <xmax>377</xmax>
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- <object>
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diff --git a/PAR 152/cone_dataset/166.jpg b/PAR 152/cone_dataset/166.jpg
deleted file mode 100644
index 77e5432e7bbb421d9ae0e6da2606cf508d14c3b2..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/166.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/166.xml b/PAR 152/cone_dataset/166.xml
deleted file mode 100644
index c6fe5e9e0ef2e6be4efeb145aadc3a7c25437837..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/166.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\166.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
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- </size>
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diff --git a/PAR 152/cone_dataset/167.jpg b/PAR 152/cone_dataset/167.jpg
deleted file mode 100644
index 8a9814e9c9df5572ecf31ef5f206b5753d61253c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/167.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/167.xml b/PAR 152/cone_dataset/167.xml
deleted file mode 100644
index 5494f8e6c259de10b84ac6e6316eb818d701e5f4..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/167.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>167.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\167.jpg</path>
- <source>
- <database>Unknown</database>
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deleted file mode 100644
index a899503782fe6140e055e4ee5dbfedb0f6569759..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/168.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/168.xml b/PAR 152/cone_dataset/168.xml
deleted file mode 100644
index ee3ba82bcba8909d3e316f20a573f9dd3b938c52..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/168.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>168.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\168.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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diff --git a/PAR 152/cone_dataset/169.jpg b/PAR 152/cone_dataset/169.jpg
deleted file mode 100644
index b0d61bf2708201480161ba1fa9dc68f62ffb22c7..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/169.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/169.xml b/PAR 152/cone_dataset/169.xml
deleted file mode 100644
index a746a6ab81a0f3dc3a075134069c968eb5fda4fc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/169.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>169.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\169.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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diff --git a/PAR 152/cone_dataset/17.jpg b/PAR 152/cone_dataset/17.jpg
deleted file mode 100644
index f4ac7318edb7e8b07d4cc4055d21b9d8040ce91b..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/17.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/17.xml b/PAR 152/cone_dataset/17.xml
deleted file mode 100644
index 0917b58198ec496f543687e542bc21fee5f174e0..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/17.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>17.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\17.jpg</path>
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- <database>Unknown</database>
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- <size>
- <width>338</width>
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- </size>
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diff --git a/PAR 152/cone_dataset/170.jpg b/PAR 152/cone_dataset/170.jpg
deleted file mode 100644
index 5f40f3a9b42b69d3de097a0156287f1f7e54ab6d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/170.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/170.xml b/PAR 152/cone_dataset/170.xml
deleted file mode 100644
index 93e0899c02040dfe35e4c9bbb47cf8290127bc8a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/170.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\170.jpg</path>
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diff --git a/PAR 152/cone_dataset/171.jpg b/PAR 152/cone_dataset/171.jpg
deleted file mode 100644
index 7226ae6c9ee9f01a6358fb55dffbc1bffdb387f5..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/171.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/171.xml b/PAR 152/cone_dataset/171.xml
deleted file mode 100644
index 9fe9e59f7b1d484e8db47f98978f1a4c6dca804c..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/171.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/cone_dataset/172.jpg b/PAR 152/cone_dataset/172.jpg
deleted file mode 100644
index 36364509b598ce31a9a137c4d8f9493577372452..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/172.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/172.xml b/PAR 152/cone_dataset/172.xml
deleted file mode 100644
index c50e9a089ae8591ba296db14db7561216445147b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/172.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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- </bndbox>
- </object>
- <object>
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diff --git a/PAR 152/cone_dataset/173.jpg b/PAR 152/cone_dataset/173.jpg
deleted file mode 100644
index 5a98cdbcded6afe7b6bd5ae46962512403335213..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/173.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/173.xml b/PAR 152/cone_dataset/173.xml
deleted file mode 100644
index ee6b1c23b4ce4d5606f0fd6fcd31d829b23c22ec..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/173.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\173.jpg</path>
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- <xmin>61</xmin>
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- </bndbox>
- </object>
- <object>
- <name>cone</name>
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diff --git a/PAR 152/cone_dataset/174.jpg b/PAR 152/cone_dataset/174.jpg
deleted file mode 100644
index 7e260787f9b2ebe0802eddd07d30e0c0d3195dfa..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/174.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/174.xml b/PAR 152/cone_dataset/174.xml
deleted file mode 100644
index c48ea16e82e6e32945184b4a124fd199ffbdcc9e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/174.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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- <xmin>232</xmin>
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- <object>
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diff --git a/PAR 152/cone_dataset/175.jpg b/PAR 152/cone_dataset/175.jpg
deleted file mode 100644
index 9066101cf307fc53ade4cdb318329c85841b8e87..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/175.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/175.xml b/PAR 152/cone_dataset/175.xml
deleted file mode 100644
index 300f112bd22eee858f11f2ce4cd8f4483ae9941d..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/175.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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diff --git a/PAR 152/cone_dataset/176.jpg b/PAR 152/cone_dataset/176.jpg
deleted file mode 100644
index 4ba75b112680b148634fd2b60ee2994ab231f0fb..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/176.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/176.xml b/PAR 152/cone_dataset/176.xml
deleted file mode 100644
index b5ff0834e0b3e5b442d87eae4106a92641ede84b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/176.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/cone_dataset/177.jpg b/PAR 152/cone_dataset/177.jpg
deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/177.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/177.xml b/PAR 152/cone_dataset/177.xml
deleted file mode 100644
index 2db0a0aa4944d025381e9c230f64877116ff7e77..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/177.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\177.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
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index 53dbe6ca95812684a7b209e840c9b9d698ae703c..0000000000000000000000000000000000000000
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diff --git a/PAR 152/cone_dataset/178.xml b/PAR 152/cone_dataset/178.xml
deleted file mode 100644
index 5d23130a2887603ed8eb5caaee9298b92a7dd6f1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/178.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\178.jpg</path>
- <source>
- <database>Unknown</database>
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diff --git a/PAR 152/cone_dataset/179.jpg b/PAR 152/cone_dataset/179.jpg
deleted file mode 100644
index a8cc559a6c1356a74f5d294f108eee2366467529..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/179.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/179.xml b/PAR 152/cone_dataset/179.xml
deleted file mode 100644
index bd6fa60952d69400e9a9eabbfd9a21411bd15951..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/179.xml
+++ /dev/null
@@ -1,122 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\179.jpg</path>
- <source>
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diff --git a/PAR 152/cone_dataset/18.jpg b/PAR 152/cone_dataset/18.jpg
deleted file mode 100644
index 137d4ed358a82804098b3e63ce8f1fcf85124ded..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/18.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/18.xml b/PAR 152/cone_dataset/18.xml
deleted file mode 100644
index e68fd03950d2b0ce96d1a6c738dfa1b43c73d52e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/18.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>18.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\18.jpg</path>
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- <database>Unknown</database>
- </source>
- <size>
- <width>338</width>
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deleted file mode 100644
index 7b5658c234354a15c87f98ee8dd60c36e877fb46..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/180.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/180.xml b/PAR 152/cone_dataset/180.xml
deleted file mode 100644
index 345f2e21caf998cfc9fe9f7767720238915be188..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/180.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\180.jpg</path>
- <source>
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deleted file mode 100644
index 11e3bb0cdae4e089078f7df2dc97461d72633e76..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/181.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/181.xml b/PAR 152/cone_dataset/181.xml
deleted file mode 100644
index 1cb0ae43293c25570965af86ba919c3e2aaa893b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/181.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\181.jpg</path>
- <source>
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- <object>
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diff --git a/PAR 152/cone_dataset/182.jpg b/PAR 152/cone_dataset/182.jpg
deleted file mode 100644
index cd5978f179400ccb4696ed50c07536c509b9508d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/182.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/182.xml b/PAR 152/cone_dataset/182.xml
deleted file mode 100644
index 3da8b19d8b5d939ef76cabf3acf8c98c3611427d..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/182.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\182.jpg</path>
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diff --git a/PAR 152/cone_dataset/183.jpg b/PAR 152/cone_dataset/183.jpg
deleted file mode 100644
index 68aaefaeff0afc8f324d69e56a69cd78986a1d7a..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/183.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/183.xml b/PAR 152/cone_dataset/183.xml
deleted file mode 100644
index d2e7a0d193db3e42bcc0f507bccac9e3d3c25f5c..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/183.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/cone_dataset/184.jpg b/PAR 152/cone_dataset/184.jpg
deleted file mode 100644
index 137d4ed358a82804098b3e63ce8f1fcf85124ded..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/184.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/184.xml b/PAR 152/cone_dataset/184.xml
deleted file mode 100644
index 12c9406e42884d12edac7ee08ae37054a8f4d8d5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/184.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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- <object>
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diff --git a/PAR 152/cone_dataset/185.jpg b/PAR 152/cone_dataset/185.jpg
deleted file mode 100644
index f825fc5cf9f69c10b9595ad810d20f10ee637680..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/185.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/185.xml b/PAR 152/cone_dataset/185.xml
deleted file mode 100644
index 2ae38611e918f26c577d6d5a55ec633ea7e49f96..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/185.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\185.jpg</path>
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- <object>
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deleted file mode 100644
index 5654cf886f0d44f5f43ee40f9a15683c8a5c0706..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/186.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/186.xml b/PAR 152/cone_dataset/186.xml
deleted file mode 100644
index 1b9caedcfd4b05dd79f03205add860bcbbbb4794..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/186.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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diff --git a/PAR 152/cone_dataset/187.jpg b/PAR 152/cone_dataset/187.jpg
deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/187.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/187.xml b/PAR 152/cone_dataset/187.xml
deleted file mode 100644
index 41fac624c56a6863c634e82e4b65467988bad2fb..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/187.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/cone_dataset/188.jpg b/PAR 152/cone_dataset/188.jpg
deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/188.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/188.xml b/PAR 152/cone_dataset/188.xml
deleted file mode 100644
index 86fe7763385afe643821471b39f8de442412e285..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/188.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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Binary files a/PAR 152/cone_dataset/189.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/189.xml b/PAR 152/cone_dataset/189.xml
deleted file mode 100644
index 331abb6107d0602e667d296443c3b6607dcd758e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/189.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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diff --git a/PAR 152/cone_dataset/19.jpg b/PAR 152/cone_dataset/19.jpg
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index 9a03aee7b077abf3840bfca95d273e625ff36863..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/19.xml
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@@ -1,62 +0,0 @@
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index cfb3760d0a0fce8a8be2782a0a593eef288e78f0..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/190.xml
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@@ -1,26 +0,0 @@
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deleted file mode 100644
index 67436a77eb4983aa4da37b27feebada147c9e6e6..0000000000000000000000000000000000000000
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@@ -1,26 +0,0 @@
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deleted file mode 100644
index 929c2f4d3fa28ab750eb31485ae95a3aed8e05f0..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/192.xml
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@@ -1,26 +0,0 @@
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index e6d4abad87e78d4e9b771f883fec3f22ce068989..0000000000000000000000000000000000000000
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deleted file mode 100644
index 5634147d3a799331d03b94a21cfbabbdf869ee5c..0000000000000000000000000000000000000000
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deleted file mode 100644
index 07914ca283422ac22138fe03f02c0644b962d834..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/195.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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deleted file mode 100644
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deleted file mode 100644
index dce0d9a6d7da98e8ecbd92b5af297b3c8dead642..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/196.xml
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@@ -1,38 +0,0 @@
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index 88b9f33b48426890a95bccf97d48eab8f04303a9..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/197.xml
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@@ -1,26 +0,0 @@
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Binary files a/PAR 152/cone_dataset/198.jpg and /dev/null differ
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index 78e9cbbf303b8ac941773990a7fcece877cce0d1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/198.xml
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@@ -1,26 +0,0 @@
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deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/199.jpg and /dev/null differ
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index 8803d31f786dd0ac7956a86029d9ae971d57930d..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/199.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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Binary files a/PAR 152/cone_dataset/2.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/2.xml b/PAR 152/cone_dataset/2.xml
deleted file mode 100644
index 4f130d65a364588d87b526e4cb544e5206e50ec3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/2.xml
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@@ -1,26 +0,0 @@
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deleted file mode 100644
index 9cf2d551759cd629cfcc0fafd64c93a3cc67cfaa..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/20.xml
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@@ -1,38 +0,0 @@
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deleted file mode 100644
index f03057c7db1ca714d0a170a4003286424a041eef..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/200.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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Binary files a/PAR 152/cone_dataset/201.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/201.xml b/PAR 152/cone_dataset/201.xml
deleted file mode 100644
index 097e67470ec816cc3265961bd71ba480116908bc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/201.xml
+++ /dev/null
@@ -1,86 +0,0 @@
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index c15fcad3f347e27c50fe0512002737810a7a2904..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/202.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/202.xml b/PAR 152/cone_dataset/202.xml
deleted file mode 100644
index 6c71d6d8fb6a8e6e330cfe051f5e1708c7517edc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/202.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>202.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\202.jpg</path>
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diff --git a/PAR 152/cone_dataset/203.xml b/PAR 152/cone_dataset/203.xml
deleted file mode 100644
index d9d68e8000dfdc5f0dce5970ebd87dc32ebf2a2f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/203.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\203.jpg</path>
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deleted file mode 100644
index 65159409ae6dd3ca8ff930366f80ba79842571b7..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/204.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/204.xml b/PAR 152/cone_dataset/204.xml
deleted file mode 100644
index 08873bbff8f5021aa7e738b86e1e1f1427ef8752..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/204.xml
+++ /dev/null
@@ -1,266 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\204.jpg</path>
- <source>
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- <size>
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- <ymax>321</ymax>
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- <ymax>288</ymax>
- </bndbox>
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- <object>
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- <object>
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diff --git a/PAR 152/cone_dataset/205.jpg b/PAR 152/cone_dataset/205.jpg
deleted file mode 100644
index a9d316bac6ac8a3b51cd631e812fc160f62b1615..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/205.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/205.xml b/PAR 152/cone_dataset/205.xml
deleted file mode 100644
index 606bf836a24adb8e2af4f8a15eb3b48f3be35bcd..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/205.xml
+++ /dev/null
@@ -1,134 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>205.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\205.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
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- </size>
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- <xmin>10</xmin>
- <ymin>234</ymin>
- <xmax>45</xmax>
- <ymax>272</ymax>
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- </object>
- <object>
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- </object>
- <object>
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- <xmin>114</xmin>
- <ymin>234</ymin>
- <xmax>146</xmax>
- <ymax>269</ymax>
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- </object>
- <object>
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- <ymax>268</ymax>
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- <object>
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- <ymax>267</ymax>
- </bndbox>
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- <object>
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- <bndbox>
- <xmin>264</xmin>
- <ymin>233</ymin>
- <xmax>296</xmax>
- <ymax>268</ymax>
- </bndbox>
- </object>
- <object>
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- <xmin>316</xmin>
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- <xmax>347</xmax>
- <ymax>268</ymax>
- </bndbox>
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- <object>
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- <ymax>268</ymax>
- </bndbox>
- </object>
- <object>
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- <xmin>419</xmin>
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- <ymax>265</ymax>
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diff --git a/PAR 152/cone_dataset/206.jpg b/PAR 152/cone_dataset/206.jpg
deleted file mode 100644
index 9066101cf307fc53ade4cdb318329c85841b8e87..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/206.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/206.xml b/PAR 152/cone_dataset/206.xml
deleted file mode 100644
index 427a09ee1338b4a3516c8b2adf7ce255f9cdc190..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/206.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>206.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\206.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>338</width>
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diff --git a/PAR 152/cone_dataset/207.jpg b/PAR 152/cone_dataset/207.jpg
deleted file mode 100644
index d33b3283369fb734acd8372ab42dcd7a9aa492b4..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/207.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/207.xml b/PAR 152/cone_dataset/207.xml
deleted file mode 100644
index 31b466c59f6b9d6c1d0591cf48f97cb58687d2ab..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/207.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>207.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\207.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>510</width>
- <height>339</height>
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- </size>
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- <ymax>277</ymax>
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diff --git a/PAR 152/cone_dataset/208.jpg b/PAR 152/cone_dataset/208.jpg
deleted file mode 100644
index 5790ebf75ef8081af78ba50c26f48ef9de33b270..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/208.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/208.xml b/PAR 152/cone_dataset/208.xml
deleted file mode 100644
index eaea94d9dfeb48df25755c1f913666ae6693c2f4..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/208.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>208.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\208.jpg</path>
- <source>
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- <size>
- <width>507</width>
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diff --git a/PAR 152/cone_dataset/209.jpg b/PAR 152/cone_dataset/209.jpg
deleted file mode 100644
index 21c63b5a634945a9dc5b2cef65316396cbebd0e5..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/209.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/209.xml b/PAR 152/cone_dataset/209.xml
deleted file mode 100644
index 7ec9a42458eb7b3406ae89335df6d0cad6c4a601..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/209.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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deleted file mode 100644
index c722cf295782b862d6b6483643313b64db95385c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/21.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/21.xml b/PAR 152/cone_dataset/21.xml
deleted file mode 100644
index ecca5bab9afbe365f9f650d7884c5fcee8ec5f4e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/21.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>21.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\21.jpg</path>
- <source>
- <database>Unknown</database>
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deleted file mode 100644
index a6d22867024397988245f14b95407f297588d192..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/210.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/210.xml b/PAR 152/cone_dataset/210.xml
deleted file mode 100644
index 43c7ce58b63137a2404f720ee833d879d0e14690..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/210.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
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deleted file mode 100644
index 6544e6dfd053b4d476edb01a89462d67f0397b46..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/211.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/211.xml b/PAR 152/cone_dataset/211.xml
deleted file mode 100644
index a68e768a75174393828432202b8f90095f5ef2e1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/211.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>211.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\211.jpg</path>
- <source>
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deleted file mode 100644
index d105162a0cc8a16ab51ab74b455dd30e9730dabd..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/212.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/212.xml b/PAR 152/cone_dataset/212.xml
deleted file mode 100644
index 0fd6509eec8077a0567167eab9795c40e310e932..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/212.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <ymax>256</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <ymax>255</ymax>
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deleted file mode 100644
index 017a02aecbf05187a1eccfe5f5a7e83e087e39a6..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/213.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/213.xml b/PAR 152/cone_dataset/213.xml
deleted file mode 100644
index f8f6777cbe07ce75ecc9d15f7ba2e8d3956e1d14..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/213.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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- <size>
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diff --git a/PAR 152/cone_dataset/214.jpg b/PAR 152/cone_dataset/214.jpg
deleted file mode 100644
index da8d5ba91807901c58b58a5204880cd97b3ddec1..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/214.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/214.xml b/PAR 152/cone_dataset/214.xml
deleted file mode 100644
index b866d7032fc7e4b6c4caba3501ad1c6a49a0552f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/214.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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- <object>
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- <object>
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diff --git a/PAR 152/cone_dataset/215.jpg b/PAR 152/cone_dataset/215.jpg
deleted file mode 100644
index e346361484bab2c896ee157a04add6c205d7f420..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/215.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/215.xml b/PAR 152/cone_dataset/215.xml
deleted file mode 100644
index 913b736b02989a9b28e2b72ed9a7f4ca7db9841a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/215.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\215.jpg</path>
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diff --git a/PAR 152/cone_dataset/216.jpg b/PAR 152/cone_dataset/216.jpg
deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/216.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/216.xml b/PAR 152/cone_dataset/216.xml
deleted file mode 100644
index 68cf18f7ed0afd5dee47d91b1741bc995d7af7f1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/216.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/PAR 152/cone_dataset/217.jpg b/PAR 152/cone_dataset/217.jpg
deleted file mode 100644
index c04ddf104a1cee4b33ade230159fec0827b8e79c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/217.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/217.xml b/PAR 152/cone_dataset/217.xml
deleted file mode 100644
index ee07ea4820f118bb83dc62952249cdc8493c8504..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/217.xml
+++ /dev/null
@@ -1,50 +0,0 @@
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deleted file mode 100644
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Binary files a/PAR 152/cone_dataset/218.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/218.xml b/PAR 152/cone_dataset/218.xml
deleted file mode 100644
index 0797498d60e4d2aa78900300a939aa3391aa7907..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/218.xml
+++ /dev/null
@@ -1,86 +0,0 @@
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deleted file mode 100644
index eed8aac79f979ff1b531ece250df243e668d6cc6..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/219.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/219.xml b/PAR 152/cone_dataset/219.xml
deleted file mode 100644
index 9dccd54a75bf67c6e31c2adac5c7fe30f836c03b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/219.xml
+++ /dev/null
@@ -1,194 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>219.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\219.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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deleted file mode 100644
index 391a91991b7b15f907cbe9e240943914afc28de9..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/22.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/22.xml b/PAR 152/cone_dataset/22.xml
deleted file mode 100644
index 88bd6c6ca793f1ee76450359ecef7778a8253a82..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/22.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>22.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\22.jpg</path>
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- <database>Unknown</database>
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diff --git a/PAR 152/cone_dataset/220.jpg b/PAR 152/cone_dataset/220.jpg
deleted file mode 100644
index 92f76386f69f98b22d2f1b83e534a5e0e307dd56..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/220.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/220.xml b/PAR 152/cone_dataset/220.xml
deleted file mode 100644
index 31f460ffd7ee58c79ee058a83f5a724d53a0c34f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/220.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>220.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\220.jpg</path>
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- <database>Unknown</database>
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diff --git a/PAR 152/cone_dataset/221.jpg b/PAR 152/cone_dataset/221.jpg
deleted file mode 100644
index 175b18e445fee83730a8359ea7b74f87f065d70d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/221.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/221.xml b/PAR 152/cone_dataset/221.xml
deleted file mode 100644
index 30214aa533d6a9481bcc6f7df2d2e6921b844579..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/221.xml
+++ /dev/null
@@ -1,170 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\221.jpg</path>
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- <object>
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- <xmax>391</xmax>
- <ymax>246</ymax>
- </bndbox>
- </object>
- <object>
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- <xmin>392</xmin>
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diff --git a/PAR 152/cone_dataset/222.jpg b/PAR 152/cone_dataset/222.jpg
deleted file mode 100644
index 0f7204ae92e89a5d2a0eedbf3722ff950d4dc382..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/222.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/222.xml b/PAR 152/cone_dataset/222.xml
deleted file mode 100644
index 0d82b5c3ed1365cd03f0ee7e759f9710b873d871..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/222.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>222.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\222.jpg</path>
- <source>
- <database>Unknown</database>
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- </size>
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diff --git a/PAR 152/cone_dataset/223.jpg b/PAR 152/cone_dataset/223.jpg
deleted file mode 100644
index 6d0eb5c7ccf6bd5c27b68b9bc84c341dfbc3f791..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/223.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/223.xml b/PAR 152/cone_dataset/223.xml
deleted file mode 100644
index 25a28926da320788a8e1e7645fa5080192ec6276..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/223.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\223.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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- </size>
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- <object>
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- <xmax>194</xmax>
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diff --git a/PAR 152/cone_dataset/224.jpg b/PAR 152/cone_dataset/224.jpg
deleted file mode 100644
index b5dc95b4bbff987d767bcf1ea17aa0d6f2b9de73..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/224.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/224.xml b/PAR 152/cone_dataset/224.xml
deleted file mode 100644
index 2b117f04131ab5d1bb6a790b3a85f1977c305ef4..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/224.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <folder>cone_dataset</folder>
- <filename>224.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\224.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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diff --git a/PAR 152/cone_dataset/225.jpg b/PAR 152/cone_dataset/225.jpg
deleted file mode 100644
index edf9363b9df8bd614dbe3af016fa2a1404fd7d88..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/225.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/225.xml b/PAR 152/cone_dataset/225.xml
deleted file mode 100644
index 57289b095fc96c0af8251229bd3fdaa9355cb484..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/225.xml
+++ /dev/null
@@ -1,38 +0,0 @@
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diff --git a/PAR 152/cone_dataset/226.jpg b/PAR 152/cone_dataset/226.jpg
deleted file mode 100644
index 87717e2c0d874eaf9d0d8bebbd3fb497a5238f09..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/226.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/226.xml b/PAR 152/cone_dataset/226.xml
deleted file mode 100644
index 7b7abaa075930e80a3de28d2ee554890feeabf1a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/226.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\226.jpg</path>
- <source>
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- </source>
- <size>
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deleted file mode 100644
index f181fcbdf0cef0201aaa506be1a38355c0659867..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/227.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/227.xml b/PAR 152/cone_dataset/227.xml
deleted file mode 100644
index 49b939d640ee4152d439b8620ce3deefac6bbd0a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/227.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\227.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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deleted file mode 100644
index d9128e6eada1f688a5b95cc055d340dbae521dc2..0000000000000000000000000000000000000000
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diff --git a/PAR 152/cone_dataset/228.xml b/PAR 152/cone_dataset/228.xml
deleted file mode 100644
index 9adab7e03408dc312f79055fca43f9a67b25115b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/228.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\228.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
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deleted file mode 100644
index 7a1a8dac1ed0e2cf3ba26e6f814a196a2a6a848d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/229.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/229.xml b/PAR 152/cone_dataset/229.xml
deleted file mode 100644
index 944b54f47c74890477224450f1aff7127ca3b421..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/229.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\229.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>416</width>
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deleted file mode 100644
index 1c6090a4c68c0c9980f44f746d4476e0067b0419..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/23.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/23.xml b/PAR 152/cone_dataset/23.xml
deleted file mode 100644
index 85ba55bbaf08d5df33644dc84a711c8e5b1b2a07..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/23.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\23.jpg</path>
- <source>
- <database>Unknown</database>
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- </size>
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- </bndbox>
- </object>
- <object>
- <name>cone</name>
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deleted file mode 100644
index c94a5d54e91d8d3c16656a67e8ca24f76118ab45..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/230.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/230.xml b/PAR 152/cone_dataset/230.xml
deleted file mode 100644
index d056eb59e926bbd8f90485ee74a71c63d09617d8..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/230.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\230.jpg</path>
- <source>
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- <size>
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- </size>
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deleted file mode 100644
index 258d884e424fe6334c7a7921ada3b3f20d8899f6..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/231.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/231.xml b/PAR 152/cone_dataset/231.xml
deleted file mode 100644
index d3a69a02992ba4901e78e22665506209ddaad6bc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/231.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\231.jpg</path>
- <source>
- <database>Unknown</database>
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- </size>
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deleted file mode 100644
index bca15da6737f9d93e710a83704acdf6a6e3d54c7..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/232.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/232.xml b/PAR 152/cone_dataset/232.xml
deleted file mode 100644
index 06d42a1305233062b9f57e54278c63873a1e555b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/232.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\232.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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- </size>
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deleted file mode 100644
index 2b2f24aa2c7f3101f2dd22723f898354aaae5036..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/233.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/233.xml b/PAR 152/cone_dataset/233.xml
deleted file mode 100644
index d6c3bc6fee80becebfe589cf671dd066b673aa4f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/233.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\233.jpg</path>
- <source>
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- <size>
- <width>482</width>
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- </size>
- <segmented>0</segmented>
- <object>
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- </object>
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diff --git a/PAR 152/cone_dataset/234.jpg b/PAR 152/cone_dataset/234.jpg
deleted file mode 100644
index fbb18efe4f01563d610d8c6b2dfcacfcb37bc59a..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/234.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/234.xml b/PAR 152/cone_dataset/234.xml
deleted file mode 100644
index a6a4e2ddc7924bbeacf6ab90ac661a1dbb83e61d..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/234.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\234.jpg</path>
- <source>
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- <size>
- <width>371</width>
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- </size>
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- <name>cone</name>
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- <bndbox>
- <xmin>6</xmin>
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- <ymax>456</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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diff --git a/PAR 152/cone_dataset/235.jpg b/PAR 152/cone_dataset/235.jpg
deleted file mode 100644
index 1cb3cea7568f7b51ee3a66e7b02109ef25f69ad4..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/235.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/235.xml b/PAR 152/cone_dataset/235.xml
deleted file mode 100644
index 39f8da132e732cd8868bfb01c3a222fafeb16cbf..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/235.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\235.jpg</path>
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- <size>
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diff --git a/PAR 152/cone_dataset/236.jpg b/PAR 152/cone_dataset/236.jpg
deleted file mode 100644
index e9bcf0e2841f4b0ccb804ff8a9b9f9cc473eaa34..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/236.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/236.xml b/PAR 152/cone_dataset/236.xml
deleted file mode 100644
index 26ba8bc91a84826611b19c7018710a94c2127497..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/236.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\236.jpg</path>
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diff --git a/PAR 152/cone_dataset/237.jpg b/PAR 152/cone_dataset/237.jpg
deleted file mode 100644
index be727126b76d4890224030906be199e9af38e592..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/237.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/237.xml b/PAR 152/cone_dataset/237.xml
deleted file mode 100644
index add17552a010f76d44ca23368e5bf3e25e92b78a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/237.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
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- </object>
- <object>
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- <object>
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- <object>
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diff --git a/PAR 152/cone_dataset/238.jpg b/PAR 152/cone_dataset/238.jpg
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Binary files a/PAR 152/cone_dataset/238.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/238.xml b/PAR 152/cone_dataset/238.xml
deleted file mode 100644
index 093e95bfbe254312e2fdbda602fd8b7fbdea7c92..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/238.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\238.jpg</path>
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- <object>
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- <object>
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diff --git a/PAR 152/cone_dataset/239.jpg b/PAR 152/cone_dataset/239.jpg
deleted file mode 100644
index a3dc1058cc157923dd114cbb6b3fb37f31fab923..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/239.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/239.xml b/PAR 152/cone_dataset/239.xml
deleted file mode 100644
index c05abbfabfad744e3b77906448753800812b1f86..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/239.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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- <object>
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diff --git a/PAR 152/cone_dataset/24.jpg b/PAR 152/cone_dataset/24.jpg
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Binary files a/PAR 152/cone_dataset/24.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/24.xml b/PAR 152/cone_dataset/24.xml
deleted file mode 100644
index ebbeef64420c6fc0164f424155052c4bf7820636..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/24.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <source>
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index ac4d431210e725dd960957978cd7692581c29574..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/240.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/240.xml b/PAR 152/cone_dataset/240.xml
deleted file mode 100644
index 3b707f184b432f9c9f8fbb8030ed52ded558b84c..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/240.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>240.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\240.jpg</path>
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deleted file mode 100644
index 4e037b74457a8fb282a17d57a6d1a899174037fd..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/241.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/241.xml b/PAR 152/cone_dataset/241.xml
deleted file mode 100644
index 3b3992d4cb4d1bbd644f4999d8f51037c63ba0c5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/241.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\241.jpg</path>
- <source>
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- <size>
- <width>509</width>
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index b040bf209944dcc3185df1fbf1103dc3783b20ca..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/242.jpg and /dev/null differ
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deleted file mode 100644
index 7c184931388544e153ce3c688a22bdc2ad1a65c1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/242.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <size>
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deleted file mode 100644
index f09beff05baf9c3e207e621bd747b66a8481727b..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/243.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/243.xml b/PAR 152/cone_dataset/243.xml
deleted file mode 100644
index 004ad9ae2dab2731f3f5fae99b13b907f109fe05..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/243.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
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- <object>
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deleted file mode 100644
index dbdbb15c4be11c3ed9d44495e2c18d667f94a986..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/244.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/244.xml b/PAR 152/cone_dataset/244.xml
deleted file mode 100644
index cc9009d4d839ea3e4a11cd9f476f2560285cd103..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/244.xml
+++ /dev/null
@@ -1,122 +0,0 @@
-<annotation>
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\244.jpg</path>
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- <size>
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- <object>
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- <object>
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- <object>
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- <object>
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- <ymax>250</ymax>
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diff --git a/PAR 152/cone_dataset/245.jpg b/PAR 152/cone_dataset/245.jpg
deleted file mode 100644
index 6c4cd50a01627e39b03b0607352f62c5c75b3903..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/245.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/245.xml b/PAR 152/cone_dataset/245.xml
deleted file mode 100644
index 1ea01ddec8debf2d735a3f18ea90008c9750d161..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/245.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>245.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\245.jpg</path>
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- <size>
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- </size>
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- </object>
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- <object>
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- <xmax>215</xmax>
- <ymax>354</ymax>
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- <object>
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diff --git a/PAR 152/cone_dataset/246.jpg b/PAR 152/cone_dataset/246.jpg
deleted file mode 100644
index 001070d978da9587d07f2dc68a80968adc611569..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/246.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/246.xml b/PAR 152/cone_dataset/246.xml
deleted file mode 100644
index f9cca2da0d6f09bbe1e46a9fdea5d086b8d00fa9..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/246.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>246.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\246.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
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- <height>507</height>
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- </size>
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- <object>
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- <xmax>111</xmax>
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- <object>
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diff --git a/PAR 152/cone_dataset/247.jpg b/PAR 152/cone_dataset/247.jpg
deleted file mode 100644
index 0c940cbe7c5cd469f3fbb73142fdd486b47526db..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/247.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/247.xml b/PAR 152/cone_dataset/247.xml
deleted file mode 100644
index 151d36129f29ad732edc32b3a72394f32b0d8a39..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/247.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>247.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\247.jpg</path>
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- <size>
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diff --git a/PAR 152/cone_dataset/248.jpg b/PAR 152/cone_dataset/248.jpg
deleted file mode 100644
index 95e4cec4f42e35180f89a51ccdc4c6a75e37db54..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/248.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/248.xml b/PAR 152/cone_dataset/248.xml
deleted file mode 100644
index 8a568d3149339cd72a913968fbd12a3c19f04568..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/248.xml
+++ /dev/null
@@ -1,26 +0,0 @@
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- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\248.jpg</path>
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- <database>Unknown</database>
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- <size>
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diff --git a/PAR 152/cone_dataset/249.jpg b/PAR 152/cone_dataset/249.jpg
deleted file mode 100644
index 6768d8dc27aca8260bdfc85b2e48c4e4d238df21..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/249.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/249.xml b/PAR 152/cone_dataset/249.xml
deleted file mode 100644
index a48aa2ca13482356050272461e63b309381ed7a8..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/249.xml
+++ /dev/null
@@ -1,134 +0,0 @@
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- <size>
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- <object>
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- <bndbox>
- <xmin>230</xmin>
- <ymin>183</ymin>
- <xmax>246</xmax>
- <ymax>211</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>237</xmin>
- <ymin>178</ymin>
- <xmax>254</xmax>
- <ymax>201</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>243</xmin>
- <ymin>172</ymin>
- <xmax>259</xmax>
- <ymax>193</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>256</xmin>
- <ymin>162</ymin>
- <xmax>266</xmax>
- <ymax>181</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>85</xmin>
- <ymin>306</ymin>
- <xmax>113</xmax>
- <ymax>338</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/25.jpg b/PAR 152/cone_dataset/25.jpg
deleted file mode 100644
index 5b1541eb462eeb33b26a741d5097004500d5f277..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/25.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/25.xml b/PAR 152/cone_dataset/25.xml
deleted file mode 100644
index ff99b86d07e94aa8ae0bb50c3c99074a2773dbc1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/25.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>25.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\25.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>1024</width>
- <height>989</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>106</xmin>
- <ymin>107</ymin>
- <xmax>640</xmax>
- <ymax>913</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>470</xmin>
- <ymin>281</ymin>
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- <ymax>657</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/250.jpg b/PAR 152/cone_dataset/250.jpg
deleted file mode 100644
index 3fc522811442ecdd3fc27f9c956738ce60c76cdf..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/250.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/250.xml b/PAR 152/cone_dataset/250.xml
deleted file mode 100644
index 4db7a2b25d00279a3df16b09a64b8228049699c5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/250.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>250.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\250.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>533</width>
- <height>651</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>172</xmin>
- <ymin>201</ymin>
- <xmax>457</xmax>
- <ymax>594</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/251.jpg b/PAR 152/cone_dataset/251.jpg
deleted file mode 100644
index 9363b12557d4359ace4b4d1a43b18e53bb117846..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/251.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/251.xml b/PAR 152/cone_dataset/251.xml
deleted file mode 100644
index 7fcc8409c4e90d8ad740eb6ce5fa1cdac781c6a1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/251.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>251.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\251.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>353</width>
- <height>485</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>56</xmin>
- <ymin>3</ymin>
- <xmax>292</xmax>
- <ymax>445</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/252.jpg b/PAR 152/cone_dataset/252.jpg
deleted file mode 100644
index b8c048bdf1ea5c25b21ab731284d1c2be20f62d6..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/252.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/252.xml b/PAR 152/cone_dataset/252.xml
deleted file mode 100644
index dadb13d259b16ff2a085bd66396e33e467727e9b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/252.xml
+++ /dev/null
@@ -1,122 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>252.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\252.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>375</width>
- <height>458</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>181</xmin>
- <ymin>298</ymin>
- <xmax>231</xmax>
- <ymax>389</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>205</xmin>
- <ymin>242</ymin>
- <xmax>243</xmax>
- <ymax>316</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>247</xmin>
- <ymin>205</ymin>
- <xmax>279</xmax>
- <ymax>266</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>258</xmin>
- <ymin>172</ymin>
- <xmax>286</xmax>
- <ymax>219</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>244</xmin>
- <ymin>147</ymin>
- <xmax>265</xmax>
- <ymax>182</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>198</xmin>
- <ymin>128</ymin>
- <xmax>222</xmax>
- <ymax>158</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>145</xmin>
- <ymin>129</ymin>
- <xmax>162</xmax>
- <ymax>162</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>100</xmin>
- <ymin>152</ymin>
- <xmax>123</xmax>
- <ymax>194</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>84</xmin>
- <ymin>180</ymin>
- <xmax>112</xmax>
- <ymax>234</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/253.jpg b/PAR 152/cone_dataset/253.jpg
deleted file mode 100644
index 3a349575a3217e03605fd0c6c71916210011cc43..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/253.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/253.xml b/PAR 152/cone_dataset/253.xml
deleted file mode 100644
index 16c933e2bf71d555361d697b4d5ce3cdb81222c8..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/253.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>253.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\253.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>338</width>
- <height>507</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>150</xmin>
- <ymin>218</ymin>
- <xmax>262</xmax>
- <ymax>464</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>273</xmin>
- <ymin>243</ymin>
- <xmax>338</xmax>
- <ymax>507</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/254.jpg b/PAR 152/cone_dataset/254.jpg
deleted file mode 100644
index 54b18ae28cde2e3d092d00eca0c2d982eb2bac11..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/254.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/254.xml b/PAR 152/cone_dataset/254.xml
deleted file mode 100644
index 42224d1fb8afba80f3b646ea6bc56f12a356caad..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/254.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>254.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\254.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>358</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>29</xmin>
- <ymin>108</ymin>
- <xmax>423</xmax>
- <ymax>309</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/255.jpg b/PAR 152/cone_dataset/255.jpg
deleted file mode 100644
index 9f8ee6d4c11cb00f8801d6fecf48e58e470e7c86..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/255.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/255.xml b/PAR 152/cone_dataset/255.xml
deleted file mode 100644
index e39b53d456ba8157efca896ce47c63537b17c836..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/255.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>255.jpg</filename>
- <path>C:\Users\Antoine Dufour\Desktop\cours-1a\PAr\cone_dataset\255.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>339</width>
- <height>506</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>68</xmin>
- <ymin>52</ymin>
- <xmax>299</xmax>
- <ymax>467</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/26.jpg b/PAR 152/cone_dataset/26.jpg
deleted file mode 100644
index e542081c3d472297955a5e3a74513409c2494ded..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/26.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/26.xml b/PAR 152/cone_dataset/26.xml
deleted file mode 100644
index 21e4685b0b0c520c8a9b3c399189003e0b003557..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/26.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>26.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\26.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>686</width>
- <height>1024</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>120</xmin>
- <ymin>115</ymin>
- <xmax>555</xmax>
- <ymax>963</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/27.jpg b/PAR 152/cone_dataset/27.jpg
deleted file mode 100644
index f9b98b3c5bfa0f5bbee41a12a04b8ffa08892dbc..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/27.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/27.xml b/PAR 152/cone_dataset/27.xml
deleted file mode 100644
index 269536d45d00144d322bc0772181ce30b54d0ebc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/27.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>27.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\27.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>602</width>
- <height>1024</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>177</xmin>
- <ymin>125</ymin>
- <xmax>421</xmax>
- <ymax>697</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>337</xmin>
- <ymin>269</ymin>
- <xmax>602</xmax>
- <ymax>1023</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>168</xmin>
- <ymin>105</ymin>
- <xmax>316</xmax>
- <ymax>464</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>166</xmin>
- <ymin>115</ymin>
- <xmax>247</xmax>
- <ymax>343</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>48</xmin>
- <ymin>74</ymin>
- <xmax>124</xmax>
- <ymax>207</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>1</xmin>
- <ymin>73</ymin>
- <xmax>45</xmax>
- <ymax>179</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/28.jpg b/PAR 152/cone_dataset/28.jpg
deleted file mode 100644
index fa2c3f401141fd670eada8faf5d31b33e5c77e8c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/28.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/28.xml b/PAR 152/cone_dataset/28.xml
deleted file mode 100644
index cbd1807eb72ba55ed8fdca85ea4c3ac21ee60edc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/28.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>28.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\28.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>1024</width>
- <height>658</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>1</xmin>
- <ymin>46</ymin>
- <xmax>246</xmax>
- <ymax>655</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>1</xmin>
- <ymin>42</ymin>
- <xmax>265</xmax>
- <ymax>562</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>431</xmin>
- <ymin>225</ymin>
- <xmax>537</xmax>
- <ymax>393</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>652</xmin>
- <ymin>244</ymin>
- <xmax>737</xmax>
- <ymax>371</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>813</xmin>
- <ymin>260</ymin>
- <xmax>894</xmax>
- <ymax>362</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>316</xmin>
- <ymin>197</ymin>
- <xmax>389</xmax>
- <ymax>432</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/29.jpg b/PAR 152/cone_dataset/29.jpg
deleted file mode 100644
index 7afafddad9203c7f2cd1acd4777edb8161939207..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/29.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/29.xml b/PAR 152/cone_dataset/29.xml
deleted file mode 100644
index a87ff5cd2153626ebc41d319f3c832a7b08269ee..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/29.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>29.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\29.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>1024</width>
- <height>658</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>390</xmin>
- <ymin>259</ymin>
- <xmax>632</xmax>
- <ymax>417</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>806</xmin>
- <ymin>256</ymin>
- <xmax>895</xmax>
- <ymax>361</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>1</xmin>
- <ymin>41</ymin>
- <xmax>261</xmax>
- <ymax>561</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>250</xmin>
- <ymin>181</ymin>
- <xmax>393</xmax>
- <ymax>430</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>711</xmin>
- <ymin>251</ymin>
- <xmax>788</xmax>
- <ymax>365</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/3.jpg b/PAR 152/cone_dataset/3.jpg
deleted file mode 100644
index 899a0b2d4fe10b46d0bba0fbdf7ee9743199e52c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/3.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/3.xml b/PAR 152/cone_dataset/3.xml
deleted file mode 100644
index 4c93fbceab56b63b9c500003be55dcd5179ce1ce..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/3.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>3.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\3.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>142</width>
- <height>170</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>5</xmin>
- <ymin>10</ymin>
- <xmax>137</xmax>
- <ymax>162</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/30.jpg b/PAR 152/cone_dataset/30.jpg
deleted file mode 100644
index 2e9246ea00f85d535b512bfa2d24f048754a3144..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/30.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/30.xml b/PAR 152/cone_dataset/30.xml
deleted file mode 100644
index 7ad1e12f8c1589093df8009ac398dcbc0c9eb609..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/30.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>30.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\30.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>1024</width>
- <height>768</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>1</xmin>
- <ymin>1</ymin>
- <xmax>297</xmax>
- <ymax>756</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>48</xmin>
- <ymin>1</ymin>
- <xmax>352</xmax>
- <ymax>523</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>183</xmin>
- <ymin>48</ymin>
- <xmax>384</xmax>
- <ymax>418</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>299</xmin>
- <ymin>125</ymin>
- <xmax>589</xmax>
- <ymax>360</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/31.jpg b/PAR 152/cone_dataset/31.jpg
deleted file mode 100644
index f20e43d431fa9743325fec2bdd151f3eab1ddaf4..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/31.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/31.xml b/PAR 152/cone_dataset/31.xml
deleted file mode 100644
index 95ea8048e389114c90ad1f35dd058f2bd85358e1..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/31.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>31.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\31.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>19</xmin>
- <ymin>32</ymin>
- <xmax>261</xmax>
- <ymax>309</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>268</xmin>
- <ymin>76</ymin>
- <xmax>438</xmax>
- <ymax>275</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/32.jpg b/PAR 152/cone_dataset/32.jpg
deleted file mode 100644
index 1a88a042be91f82216ead3d534222983b5b07fe5..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/32.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/32.xml b/PAR 152/cone_dataset/32.xml
deleted file mode 100644
index 0f671931ef4e20b4ce89001337764f5eedb11c20..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/32.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>32.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\32.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>1024</width>
- <height>768</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>105</xmin>
- <ymin>338</ymin>
- <xmax>289</xmax>
- <ymax>547</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>229</xmin>
- <ymin>393</ymin>
- <xmax>473</xmax>
- <ymax>594</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>538</xmin>
- <ymin>395</ymin>
- <xmax>737</xmax>
- <ymax>650</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>413</xmin>
- <ymin>347</ymin>
- <xmax>566</xmax>
- <ymax>498</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>650</xmin>
- <ymin>341</ymin>
- <xmax>800</xmax>
- <ymax>466</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/33.jpg b/PAR 152/cone_dataset/33.jpg
deleted file mode 100644
index 1b9fbe5d2024e13bcecd606c29c13e89403c123d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/33.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/33.xml b/PAR 152/cone_dataset/33.xml
deleted file mode 100644
index b18fd0d3c5481f088179262b8fbef9fd89a41aec..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/33.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>33.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\33.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>83</xmin>
- <ymin>49</ymin>
- <xmax>258</xmax>
- <ymax>314</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>181</xmin>
- <ymin>56</ymin>
- <xmax>233</xmax>
- <ymax>191</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>206</xmin>
- <ymin>60</ymin>
- <xmax>250</xmax>
- <ymax>147</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>232</xmin>
- <ymin>62</ymin>
- <xmax>255</xmax>
- <ymax>116</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/34.jpg b/PAR 152/cone_dataset/34.jpg
deleted file mode 100644
index 19d43722c14bd38d0c451ad9cb4f23fc08a772d2..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/34.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/34.xml b/PAR 152/cone_dataset/34.xml
deleted file mode 100644
index 4b39b925cc17d9c9d98fcc47f013148a7056bd25..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/34.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>34.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\34.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>237</xmin>
- <ymin>23</ymin>
- <xmax>438</xmax>
- <ymax>313</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>152</xmin>
- <ymin>88</ymin>
- <xmax>233</xmax>
- <ymax>214</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>110</xmin>
- <ymin>113</ymin>
- <xmax>166</xmax>
- <ymax>189</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>85</xmin>
- <ymin>150</ymin>
- <xmax>106</xmax>
- <ymax>186</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/35.jpg b/PAR 152/cone_dataset/35.jpg
deleted file mode 100644
index 1956703b306b7c5937d6a8467a8c88e0cebbabf7..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/35.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/35.xml b/PAR 152/cone_dataset/35.xml
deleted file mode 100644
index 4b6b98479598bb5468aef6531be82c30aa5f8de2..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/35.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>35.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\35.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>305</xmin>
- <ymin>19</ymin>
- <xmax>480</xmax>
- <ymax>298</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>150</xmin>
- <ymin>175</ymin>
- <xmax>212</xmax>
- <ymax>262</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>125</xmin>
- <ymin>198</ymin>
- <xmax>157</xmax>
- <ymax>257</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>97</xmin>
- <ymin>215</ymin>
- <xmax>120</xmax>
- <ymax>254</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>86</xmin>
- <ymin>219</ymin>
- <xmax>101</xmax>
- <ymax>254</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>52</xmin>
- <ymin>229</ymin>
- <xmax>65</xmax>
- <ymax>249</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/36.jpg b/PAR 152/cone_dataset/36.jpg
deleted file mode 100644
index a73acd285fd86c8caebfb75425e174519d45cf14..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/36.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/36.xml b/PAR 152/cone_dataset/36.xml
deleted file mode 100644
index 10a88dcb45c48dfcb952ae030b23363ceabe660f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/36.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>36.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\36.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>323</width>
- <height>529</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>1</xmin>
- <ymin>17</ymin>
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- <ymax>504</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/37.jpg b/PAR 152/cone_dataset/37.jpg
deleted file mode 100644
index 51d6c1901fbca1942b4f6b6c6094c3ece6d1fc31..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/37.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/37.xml b/PAR 152/cone_dataset/37.xml
deleted file mode 100644
index e9741a2467c31304a3fb39feb5e3a6b029004b29..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/37.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>37.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\37.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>421</width>
- <height>407</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>43</xmin>
- <ymin>39</ymin>
- <xmax>257</xmax>
- <ymax>301</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>189</xmin>
- <ymin>115</ymin>
- <xmax>384</xmax>
- <ymax>268</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/38.jpg b/PAR 152/cone_dataset/38.jpg
deleted file mode 100644
index 962eab6eb3fbf4fc309c16e02c0266f1c0e9345d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/38.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/38.xml b/PAR 152/cone_dataset/38.xml
deleted file mode 100644
index 05e3e59a73a8ed9ab78048e6b9522cc95f2fd82d..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/38.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>38.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\38.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>492</width>
- <height>348</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>208</xmin>
- <ymin>25</ymin>
- <xmax>428</xmax>
- <ymax>311</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/39.jpg b/PAR 152/cone_dataset/39.jpg
deleted file mode 100644
index 27aa64ebee438e321853a38b800d450ba3a3071c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/39.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/39.xml b/PAR 152/cone_dataset/39.xml
deleted file mode 100644
index a66d6d4be73f0712cce547d699e0e885348eeae5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/39.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>39.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\39.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>469</width>
- <height>368</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>58</xmin>
- <ymin>120</ymin>
- <xmax>329</xmax>
- <ymax>307</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/4.jpg b/PAR 152/cone_dataset/4.jpg
deleted file mode 100644
index 8aa37c4a7a12c29419d95a348fc3ea4bdbc33270..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/4.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/4.xml b/PAR 152/cone_dataset/4.xml
deleted file mode 100644
index 6ae7150fa5243bdb61f61c2fc0d7cd3bb73770a2..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/4.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>4.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\4.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>381</width>
- <height>454</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <xmax>365</xmax>
- <ymax>432</ymax>
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- </object>
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diff --git a/PAR 152/cone_dataset/40.jpg b/PAR 152/cone_dataset/40.jpg
deleted file mode 100644
index 963bd3acaec85a2c7e7ee23cca4459fba5e84dcf..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/40.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/40.xml b/PAR 152/cone_dataset/40.xml
deleted file mode 100644
index 54d5cd8d132225e1425f56d52a92b60c73357d51..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/40.xml
+++ /dev/null
@@ -1,146 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>40.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\40.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>51</xmin>
- <ymin>125</ymin>
- <xmax>149</xmax>
- <ymax>300</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>147</xmin>
- <ymin>108</ymin>
- <xmax>230</xmax>
- <ymax>247</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>54</xmin>
- <ymin>12</ymin>
- <xmax>84</xmax>
- <ymax>57</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>150</xmin>
- <ymin>10</ymin>
- <xmax>172</xmax>
- <ymax>48</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>218</xmin>
- <ymin>8</ymin>
- <xmax>241</xmax>
- <ymax>43</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>444</xmin>
- <ymin>43</ymin>
- <xmax>479</xmax>
- <ymax>101</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>305</xmin>
- <ymin>156</ymin>
- <xmax>407</xmax>
- <ymax>254</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>275</xmin>
- <ymin>10</ymin>
- <xmax>293</xmax>
- <ymax>38</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>485</xmin>
- <ymin>35</ymin>
- <xmax>507</xmax>
- <ymax>82</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>314</xmin>
- <ymin>9</ymin>
- <xmax>328</xmax>
- <ymax>35</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>354</xmin>
- <ymin>9</ymin>
- <xmax>368</xmax>
- <ymax>33</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/41.jpg b/PAR 152/cone_dataset/41.jpg
deleted file mode 100644
index 3c384b8ef14a07741777d8467d0f3f01ea296d92..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/41.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/41.xml b/PAR 152/cone_dataset/41.xml
deleted file mode 100644
index c49262c9497ef61c6eb8ce92a5ed051d82039973..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/41.xml
+++ /dev/null
@@ -1,122 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>41.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\41.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>529</width>
- <height>327</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>45</xmin>
- <ymin>155</ymin>
- <xmax>149</xmax>
- <ymax>319</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>152</xmin>
- <ymin>128</ymin>
- <xmax>241</xmax>
- <ymax>277</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>228</xmin>
- <ymin>106</ymin>
- <xmax>311</xmax>
- <ymax>247</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>295</xmin>
- <ymin>90</ymin>
- <xmax>368</xmax>
- <ymax>227</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>339</xmin>
- <ymin>72</ymin>
- <xmax>407</xmax>
- <ymax>200</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>381</xmin>
- <ymin>61</ymin>
- <xmax>444</xmax>
- <ymax>184</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>420</xmin>
- <ymin>47</ymin>
- <xmax>477</xmax>
- <ymax>165</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>464</xmin>
- <ymin>35</ymin>
- <xmax>520</xmax>
- <ymax>145</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>496</xmin>
- <ymin>22</ymin>
- <xmax>529</xmax>
- <ymax>123</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/42.jpg b/PAR 152/cone_dataset/42.jpg
deleted file mode 100644
index f4ac7318edb7e8b07d4cc4055d21b9d8040ce91b..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/42.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/42.xml b/PAR 152/cone_dataset/42.xml
deleted file mode 100644
index 695e55e16778cb35ea2b99b4b5be45e154091f3e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/42.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>42.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\42.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>338</width>
- <height>507</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>66</xmin>
- <ymin>11</ymin>
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- <ymax>492</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/43.jpg b/PAR 152/cone_dataset/43.jpg
deleted file mode 100644
index d06cf0f3ea474fc47341b89f6b18790ce3b2d689..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/43.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/43.xml b/PAR 152/cone_dataset/43.xml
deleted file mode 100644
index 0feb64e1c1383bfefcce73cc193b471ca31805ff..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/43.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>43.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\43.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>126</xmin>
- <ymin>267</ymin>
- <xmax>139</xmax>
- <ymax>285</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>391</xmin>
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- <xmax>402</xmax>
- <ymax>282</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>264</xmin>
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- <xmax>277</xmax>
- <ymax>281</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>194</xmin>
- <ymin>261</ymin>
- <xmax>208</xmax>
- <ymax>282</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/44.jpg b/PAR 152/cone_dataset/44.jpg
deleted file mode 100644
index 7b307cc905f9a83e7a709326c0a8806977c764f1..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/44.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/44.xml b/PAR 152/cone_dataset/44.xml
deleted file mode 100644
index 24bc004131fd66c71d6b739a48f1e22b89a604d3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/44.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>44.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\44.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>337</width>
- <height>508</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>132</xmin>
- <ymin>37</ymin>
- <xmax>275</xmax>
- <ymax>302</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>2</xmin>
- <ymin>283</ymin>
- <xmax>99</xmax>
- <ymax>508</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
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- <bndbox>
- <xmin>107</xmin>
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- <xmax>187</xmax>
- <ymax>95</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/45.jpg b/PAR 152/cone_dataset/45.jpg
deleted file mode 100644
index 4c4f5024c87a38fe492c1c3d982da849011adea3..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/45.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/45.xml b/PAR 152/cone_dataset/45.xml
deleted file mode 100644
index 9fcb50f9ca3c734fa55c734247bb72039fb79423..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/45.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>45.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\45.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>594</width>
- <height>396</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>256</xmin>
- <ymin>264</ymin>
- <xmax>308</xmax>
- <ymax>347</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>115</xmin>
- <ymin>249</ymin>
- <xmax>154</xmax>
- <ymax>317</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>15</xmin>
- <ymin>240</ymin>
- <xmax>53</xmax>
- <ymax>293</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>507</xmin>
- <ymin>283</ymin>
- <xmax>573</xmax>
- <ymax>391</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>347</xmin>
- <ymin>192</ymin>
- <xmax>359</xmax>
- <ymax>209</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>289</xmin>
- <ymin>190</ymin>
- <xmax>302</xmax>
- <ymax>209</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>102</xmin>
- <ymin>194</ymin>
- <xmax>111</xmax>
- <ymax>206</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/46.jpg b/PAR 152/cone_dataset/46.jpg
deleted file mode 100644
index 7ca6552e5e7c57a6c699da7236addae19293c80a..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/46.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/46.xml b/PAR 152/cone_dataset/46.xml
deleted file mode 100644
index 04029dac98ce8d36867c69aa932cc8ef5a6da83c..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/46.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>46.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\46.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>338</width>
- <height>507</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>132</xmin>
- <ymin>162</ymin>
- <xmax>304</xmax>
- <ymax>478</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/47.jpg b/PAR 152/cone_dataset/47.jpg
deleted file mode 100644
index 2cf705c85041f072f9000e3fef20f3f7989e6bf6..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/47.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/47.xml b/PAR 152/cone_dataset/47.xml
deleted file mode 100644
index 3e3aa3d1a0ccc16f7695e16d4739e5028c9a5c5e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/47.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>47.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\47.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>103</xmin>
- <ymin>11</ymin>
- <xmax>384</xmax>
- <ymax>322</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/48.jpg b/PAR 152/cone_dataset/48.jpg
deleted file mode 100644
index eb62e66ad07dd40e16e197f2888339f684f1c72d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/48.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/48.xml b/PAR 152/cone_dataset/48.xml
deleted file mode 100644
index 68b1fdd321847c9d06d4e7e8c343be44866c4465..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/48.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>48.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\48.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>514</width>
- <height>333</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>16</xmin>
- <ymin>23</ymin>
- <xmax>80</xmax>
- <ymax>135</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>113</xmin>
- <ymin>34</ymin>
- <xmax>190</xmax>
- <ymax>164</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>375</xmin>
- <ymin>90</ymin>
- <xmax>506</xmax>
- <ymax>291</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>205</xmin>
- <ymin>165</ymin>
- <xmax>363</xmax>
- <ymax>301</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/49.jpg b/PAR 152/cone_dataset/49.jpg
deleted file mode 100644
index 7673a3353051954e73afef7ab2a4692fd625a7c8..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/49.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/49.xml b/PAR 152/cone_dataset/49.xml
deleted file mode 100644
index 7a3761ffd47ef6872354eae63391148063096287..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/49.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>49.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\49.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>594</width>
- <height>334</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>27</xmin>
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- <xmax>212</xmax>
- <ymax>283</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
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- <ymax>292</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/5.jpg b/PAR 152/cone_dataset/5.jpg
deleted file mode 100644
index 391a91991b7b15f907cbe9e240943914afc28de9..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/5.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/5.xml b/PAR 152/cone_dataset/5.xml
deleted file mode 100644
index 5b963a42f1c4e195a6b3a367d1566c326ea3f5e6..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/5.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>5.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\5.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>337</width>
- <height>508</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
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- <ymin>38</ymin>
- <xmax>336</xmax>
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- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/50.jpg b/PAR 152/cone_dataset/50.jpg
deleted file mode 100644
index 5de0b10d1627f61e7528d6b5325e3e4733e7b497..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/50.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/50.xml b/PAR 152/cone_dataset/50.xml
deleted file mode 100644
index c396b6031b259f2cf9a0af8bdef12cd59eb45594..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/50.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>50.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\50.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>337</width>
- <height>508</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>53</xmin>
- <ymin>47</ymin>
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- <ymax>456</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/51.jpg b/PAR 152/cone_dataset/51.jpg
deleted file mode 100644
index 5727012fa105123957c9e42e4b6ab53a5b9cbf7a..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/51.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/51.xml b/PAR 152/cone_dataset/51.xml
deleted file mode 100644
index 16d57f31d918a02bda72df976e45fcce5e519c3a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/51.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>51.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\51.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>594</width>
- <height>401</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <xmin>378</xmin>
- <ymin>246</ymin>
- <xmax>411</xmax>
- <ymax>286</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>22</xmin>
- <ymin>292</ymin>
- <xmax>79</xmax>
- <ymax>368</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>178</xmin>
- <ymin>257</ymin>
- <xmax>205</xmax>
- <ymax>309</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>113</xmin>
- <ymin>263</ymin>
- <xmax>145</xmax>
- <ymax>321</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>79</xmin>
- <ymin>275</ymin>
- <xmax>117</xmax>
- <ymax>339</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/52.jpg b/PAR 152/cone_dataset/52.jpg
deleted file mode 100644
index 5c79c02e9b15f8fabc13d74dc185f0d202bf32bf..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/52.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/52.xml b/PAR 152/cone_dataset/52.xml
deleted file mode 100644
index 80a8ffbf2fac7cd8c62660bea200651d6512c30c..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/52.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>52.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\52.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>170</width>
- <height>106</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>1</truncated>
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- <bndbox>
- <xmin>1</xmin>
- <ymin>1</ymin>
- <xmax>97</xmax>
- <ymax>103</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>35</xmin>
- <ymin>16</ymin>
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- <ymax>104</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/53.jpg b/PAR 152/cone_dataset/53.jpg
deleted file mode 100644
index 09d260081ed1d3f2288b6e689fb7c0752f4b279f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/53.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/53.xml b/PAR 152/cone_dataset/53.xml
deleted file mode 100644
index a86fb83d76693f31bba0053ec6e6486359612f54..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/53.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>53.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\53.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>114</width>
- <height>170</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>17</xmin>
- <ymin>19</ymin>
- <xmax>92</xmax>
- <ymax>158</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/54.jpg b/PAR 152/cone_dataset/54.jpg
deleted file mode 100644
index 6ee6c1d9ca20d6de8cc7a63e66400baebdce72d1..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/54.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/54.xml b/PAR 152/cone_dataset/54.xml
deleted file mode 100644
index 7e059368607fb0a24950911c4330ea9ef7128013..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/54.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>54.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\54.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>416</width>
- <height>416</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>309</xmin>
- <ymin>158</ymin>
- <xmax>383</xmax>
- <ymax>264</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>13</xmin>
- <ymin>161</ymin>
- <xmax>85</xmax>
- <ymax>264</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>34</xmin>
- <ymin>90</ymin>
- <xmax>95</xmax>
- <ymax>166</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>276</xmin>
- <ymin>82</ymin>
- <xmax>324</xmax>
- <ymax>156</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/55.jpg b/PAR 152/cone_dataset/55.jpg
deleted file mode 100644
index 90ef67dca44063a2eaada849f5ecb271bf67f229..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/55.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/55.xml b/PAR 152/cone_dataset/55.xml
deleted file mode 100644
index 870124701ab6dad222cde82a5b397f7ba9df93b3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/55.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>55.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\55.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>177</xmin>
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- <xmax>355</xmax>
- <ymax>286</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmax>448</xmax>
- <ymax>261</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/56.jpg b/PAR 152/cone_dataset/56.jpg
deleted file mode 100644
index 3de4d3adc6560de9f1e4a9253330b48ee09d92b1..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/56.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/56.xml b/PAR 152/cone_dataset/56.xml
deleted file mode 100644
index 256b78685e7e218cb345c084807faa46516a4a09..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/56.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>56.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\56.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>422</width>
- <height>407</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>79</xmin>
- <ymin>10</ymin>
- <xmax>306</xmax>
- <ymax>314</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/57.jpg b/PAR 152/cone_dataset/57.jpg
deleted file mode 100644
index 1159b708018d81ac10869de4849a54afe43e0c63..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/57.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/57.xml b/PAR 152/cone_dataset/57.xml
deleted file mode 100644
index 70c7754a80ddad37f42d2c093976b8d357f52bb3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/57.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>57.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\57.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>414</width>
- <height>414</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>23</xmin>
- <ymin>229</ymin>
- <xmax>158</xmax>
- <ymax>394</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>171</xmin>
- <ymin>190</ymin>
- <xmax>267</xmax>
- <ymax>307</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>257</xmin>
- <ymin>168</ymin>
- <xmax>330</xmax>
- <ymax>260</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>311</xmin>
- <ymin>155</ymin>
- <xmax>372</xmax>
- <ymax>229</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>355</xmin>
- <ymin>145</ymin>
- <xmax>401</xmax>
- <ymax>208</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>388</xmin>
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- <ymax>191</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/58.jpg b/PAR 152/cone_dataset/58.jpg
deleted file mode 100644
index 9afdc7e3357fb32fb9c75b44399dbea68bbb9dd1..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/58.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/58.xml b/PAR 152/cone_dataset/58.xml
deleted file mode 100644
index d7e5b8a5b216526919367bd777880bcca9c78b82..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/58.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>58.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\58.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>369</width>
- <height>464</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>34</xmin>
- <ymin>171</ymin>
- <xmax>193</xmax>
- <ymax>433</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>148</xmin>
- <ymin>174</ymin>
- <xmax>339</xmax>
- <ymax>284</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/59.jpg b/PAR 152/cone_dataset/59.jpg
deleted file mode 100644
index 960c455c0a11635108fccdb1a77a675dbc623be5..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/59.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/59.xml b/PAR 152/cone_dataset/59.xml
deleted file mode 100644
index 57a3eb5e6d8ed886b287f24400929c36df87820a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/59.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>59.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\59.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>359</width>
- <height>478</height>
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- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>53</xmin>
- <ymin>57</ymin>
- <xmax>292</xmax>
- <ymax>434</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/6.jpg b/PAR 152/cone_dataset/6.jpg
deleted file mode 100644
index a39ba1e368e2a384d4a8a83fcc3eb50bae5005a5..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/6.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/6.xml b/PAR 152/cone_dataset/6.xml
deleted file mode 100644
index 30164432ff2f73dbdb5dd172572dc99ea59458e7..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/6.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>6.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\6.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>337</width>
- <height>508</height>
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- </size>
- <segmented>0</segmented>
- <object>
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- <difficult>0</difficult>
- <bndbox>
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- <xmax>332</xmax>
- <ymax>468</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/60.jpg b/PAR 152/cone_dataset/60.jpg
deleted file mode 100644
index 87327bce4c2eedb637ace032229ab8a3517e81aa..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/60.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/60.xml b/PAR 152/cone_dataset/60.xml
deleted file mode 100644
index fd820d8c78151e52d0ffb4fc0bbc1cac9db90e2e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/60.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>60.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\60.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
- <width>482</width>
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- </size>
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- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>50</xmin>
- <ymin>148</ymin>
- <xmax>195</xmax>
- <ymax>337</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>212</xmin>
- <ymin>109</ymin>
- <xmax>317</xmax>
- <ymax>235</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>306</xmin>
- <ymin>81</ymin>
- <xmax>389</xmax>
- <ymax>182</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>368</xmin>
- <ymin>67</ymin>
- <xmax>434</xmax>
- <ymax>149</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>422</xmin>
- <ymin>54</ymin>
- <xmax>468</xmax>
- <ymax>125</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
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- <xmin>454</xmin>
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- <ymax>105</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/61.jpg b/PAR 152/cone_dataset/61.jpg
deleted file mode 100644
index 86b02e0acffe271cb984dafd06f6137b8a9726bc..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/61.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/61.xml b/PAR 152/cone_dataset/61.xml
deleted file mode 100644
index 234d206c51ad03671c775d091469d1f1a03c1427..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/61.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>61.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\61.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>57</xmin>
- <ymin>168</ymin>
- <xmax>166</xmax>
- <ymax>319</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>69</xmin>
- <ymin>125</ymin>
- <xmax>143</xmax>
- <ymax>225</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>96</xmin>
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- <ymax>134</ymax>
- </bndbox>
- </object>
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diff --git a/PAR 152/cone_dataset/62.jpg b/PAR 152/cone_dataset/62.jpg
deleted file mode 100644
index db7b81dc3917af4558b09d11029e5ffbd0497828..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/62.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/62.xml b/PAR 152/cone_dataset/62.xml
deleted file mode 100644
index 6c5e86da09fdff2eda112e4ac2dbefe0b21e3aac..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/62.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>62.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\62.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>249</xmin>
- <ymin>182</ymin>
- <xmax>346</xmax>
- <ymax>303</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>107</xmin>
- <ymin>189</ymin>
- <xmax>223</xmax>
- <ymax>272</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>48</xmin>
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- <xmax>128</xmax>
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- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/63.jpg b/PAR 152/cone_dataset/63.jpg
deleted file mode 100644
index 60a029dcd62bd5126266adae7ab4d9405daa6ed2..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/63.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/63.xml b/PAR 152/cone_dataset/63.xml
deleted file mode 100644
index 8a22ec396b8b851a79249caf1320e387e82e050f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/63.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>63.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\63.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
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- <object>
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- <xmin>292</xmin>
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- <xmax>398</xmax>
- <ymax>293</ymax>
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- <object>
- <name>cone</name>
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- <xmin>130</xmin>
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- <xmax>222</xmax>
- <ymax>270</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
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- <bndbox>
- <xmin>52</xmin>
- <ymin>210</ymin>
- <xmax>153</xmax>
- <ymax>335</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <xmin>183</xmin>
- <ymin>153</ymin>
- <xmax>256</xmax>
- <ymax>218</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/64.jpg b/PAR 152/cone_dataset/64.jpg
deleted file mode 100644
index 86f5399311c51cc329fb7d8fd103c7a80992930f..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/64.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/64.xml b/PAR 152/cone_dataset/64.xml
deleted file mode 100644
index 856ed467912123196397451dcd2a4559c0a84b61..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/64.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>64.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\64.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>359</width>
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- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>171</xmin>
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- <xmax>254</xmax>
- <ymax>178</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>2</xmin>
- <ymin>179</ymin>
- <xmax>189</xmax>
- <ymax>474</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>252</xmin>
- <ymin>1</ymin>
- <xmax>301</xmax>
- <ymax>85</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>286</xmin>
- <ymin>1</ymin>
- <xmax>322</xmax>
- <ymax>43</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/65.jpg b/PAR 152/cone_dataset/65.jpg
deleted file mode 100644
index 3ff2198fe242735d5e87fa5b778563d65db8f116..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/65.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/65.xml b/PAR 152/cone_dataset/65.xml
deleted file mode 100644
index 802484edee2522128e25d51cbe357c1673f5f584..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/65.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>65.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\65.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>339</width>
- <height>509</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>24</xmin>
- <ymin>58</ymin>
- <xmax>318</xmax>
- <ymax>471</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/66.jpg b/PAR 152/cone_dataset/66.jpg
deleted file mode 100644
index d0cb30d523cc494bb94692e434850f3b15013005..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/66.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/66.xml b/PAR 152/cone_dataset/66.xml
deleted file mode 100644
index 16f99d0bc13f4cb7a6fae51962fdccf12564eee3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/66.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>66.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\66.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>1024</width>
- <height>680</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>69</xmin>
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- <xmax>329</xmax>
- <ymax>648</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>649</xmin>
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- <xmax>922</xmax>
- <ymax>621</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/67.jpg b/PAR 152/cone_dataset/67.jpg
deleted file mode 100644
index c94a5d54e91d8d3c16656a67e8ca24f76118ab45..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/67.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/67.xml b/PAR 152/cone_dataset/67.xml
deleted file mode 100644
index f0843121c0f2f17800fad45f4e84d9280d95d58a..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/67.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>67.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\67.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>356</width>
- <height>481</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>106</xmin>
- <ymin>88</ymin>
- <xmax>241</xmax>
- <ymax>316</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/68.jpg b/PAR 152/cone_dataset/68.jpg
deleted file mode 100644
index c0cc64b7acfc6533991db4630656471405630760..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/68.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/68.xml b/PAR 152/cone_dataset/68.xml
deleted file mode 100644
index 7cb60fc813748d489aa9e7022bc9f1beb14b4cd2..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/68.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>68.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\68.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>221</xmin>
- <ymin>110</ymin>
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- <ymax>179</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/69.jpg b/PAR 152/cone_dataset/69.jpg
deleted file mode 100644
index 3fc33a9fc2e82752469d34c91782f850f0eff22a..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/69.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/69.xml b/PAR 152/cone_dataset/69.xml
deleted file mode 100644
index b13e86158c90b864f77db1ff8af3b28c750e8dfc..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/69.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>69.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\69.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
- <height>359</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>28</xmin>
- <ymin>30</ymin>
- <xmax>240</xmax>
- <ymax>302</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>178</xmin>
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- <ymax>312</ymax>
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- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/7.jpg b/PAR 152/cone_dataset/7.jpg
deleted file mode 100644
index 55c95a5f157d99f295fd8c7110455a7764e3a1f3..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/7.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/7.xml b/PAR 152/cone_dataset/7.xml
deleted file mode 100644
index 24cdb64b5e9aeeeab9b3272251a371bd210d5955..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/7.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>7.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\7.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
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- <bndbox>
- <xmin>280</xmin>
- <ymin>45</ymin>
- <xmax>438</xmax>
- <ymax>274</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>79</xmin>
- <ymin>150</ymin>
- <xmax>100</xmax>
- <ymax>186</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>167</xmin>
- <ymin>154</ymin>
- <xmax>187</xmax>
- <ymax>184</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>203</xmin>
- <ymin>152</ymin>
- <xmax>219</xmax>
- <ymax>187</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>255</xmin>
- <ymin>114</ymin>
- <xmax>310</xmax>
- <ymax>221</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>245</xmin>
- <ymin>137</ymin>
- <xmax>269</xmax>
- <ymax>201</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmin>231</xmin>
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- <xmax>250</xmax>
- <ymax>195</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/70.jpg b/PAR 152/cone_dataset/70.jpg
deleted file mode 100644
index dab53d9cf82b01a0387f327cf5657aee5c6cfd57..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/70.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/70.xml b/PAR 152/cone_dataset/70.xml
deleted file mode 100644
index 0f9684ad168cc5b2038e22b2b070e9da5e38feee..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/70.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>70.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\70.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
- <width>363</width>
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- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>73</xmin>
- <ymin>232</ymin>
- <xmax>155</xmax>
- <ymax>405</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmin>247</xmin>
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- <xmax>323</xmax>
- <ymax>223</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmin>188</xmin>
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- <ymax>269</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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diff --git a/PAR 152/cone_dataset/71.jpg b/PAR 152/cone_dataset/71.jpg
deleted file mode 100644
index 17c71595c09e52093feda39ded6af86e24358b41..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/71.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/71.xml b/PAR 152/cone_dataset/71.xml
deleted file mode 100644
index 64c7bf357912453265f66eb997af4b62b6a32e6f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/71.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>71.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\71.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>412</width>
- <height>415</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>177</xmin>
- <ymin>69</ymin>
- <xmax>373</xmax>
- <ymax>393</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>65</xmin>
- <ymin>166</ymin>
- <xmax>125</xmax>
- <ymax>255</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
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- <bndbox>
- <xmin>342</xmin>
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- <ymax>265</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/72.jpg b/PAR 152/cone_dataset/72.jpg
deleted file mode 100644
index 7ca6552e5e7c57a6c699da7236addae19293c80a..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/72.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/72.xml b/PAR 152/cone_dataset/72.xml
deleted file mode 100644
index 0d61d6cbdfd581ccfb81b5b97ceffd1645af1d96..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/72.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>72.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\72.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>338</width>
- <height>507</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
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- <xmax>301</xmax>
- <ymax>476</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/73.jpg b/PAR 152/cone_dataset/73.jpg
deleted file mode 100644
index f96929d640efb84c3cce5e2bc250072aea5158be..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/73.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/73.xml b/PAR 152/cone_dataset/73.xml
deleted file mode 100644
index 0f8f8107f3f7eb860d2bee386606af66ba915800..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/73.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>73.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\73.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>604</width>
- <height>283</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>268</xmin>
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- <xmax>315</xmax>
- <ymax>171</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <xmin>185</xmin>
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- <xmax>233</xmax>
- <ymax>170</ymax>
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- </object>
- <object>
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- <xmax>150</xmax>
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- </object>
- <object>
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- <bndbox>
- <xmin>15</xmin>
- <ymin>114</ymin>
- <xmax>65</xmax>
- <ymax>170</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>351</xmin>
- <ymin>114</ymin>
- <xmax>402</xmax>
- <ymax>171</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>440</xmin>
- <ymin>114</ymin>
- <xmax>487</xmax>
- <ymax>171</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>528</xmin>
- <ymin>116</ymin>
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- <ymax>171</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/74.jpg b/PAR 152/cone_dataset/74.jpg
deleted file mode 100644
index d42eb9dcfaf5bac9e6e639137ee778a466f3987c..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/74.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/74.xml b/PAR 152/cone_dataset/74.xml
deleted file mode 100644
index 6488c5d6052cf3612c807ce9c8a2691c889bedbe..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/74.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>74.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\74.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
- <height>339</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
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- <bndbox>
- <xmin>50</xmin>
- <ymin>1</ymin>
- <xmax>183</xmax>
- <ymax>188</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>253</xmin>
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- <xmax>366</xmax>
- <ymax>268</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>339</xmin>
- <ymin>7</ymin>
- <xmax>429</xmax>
- <ymax>179</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>127</xmin>
- <ymin>99</ymin>
- <xmax>259</xmax>
- <ymax>335</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
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- <bndbox>
- <xmin>425</xmin>
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- <ymax>171</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/75.jpg b/PAR 152/cone_dataset/75.jpg
deleted file mode 100644
index 57c98cd0ec0b3552619e7ba263564025a7179dda..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/75.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/75.xml b/PAR 152/cone_dataset/75.xml
deleted file mode 100644
index efa46fc84bf45338d5f506073e56a6f3b19b6ed7..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/75.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>75.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\75.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>416</width>
- <height>416</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>52</xmin>
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- <ymax>393</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/76.jpg b/PAR 152/cone_dataset/76.jpg
deleted file mode 100644
index e7cce22d3a2901dff01f473601f3538a4047d5bd..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/76.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/76.xml b/PAR 152/cone_dataset/76.xml
deleted file mode 100644
index 63c748a0e852e2fa76d55778d2cd946a5781f3b3..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/76.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>76.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\76.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>492</width>
- <height>348</height>
- <depth>3</depth>
- </size>
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- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>80</xmin>
- <ymin>49</ymin>
- <xmax>295</xmax>
- <ymax>298</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
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- <bndbox>
- <xmin>216</xmin>
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- <xmax>399</xmax>
- <ymax>271</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/77.jpg b/PAR 152/cone_dataset/77.jpg
deleted file mode 100644
index 2cadc057f4c36a8585f5d7b90dee4d5cf2945ab1..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/77.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/77.xml b/PAR 152/cone_dataset/77.xml
deleted file mode 100644
index 0a2ab193a08b57922c41fff5876dc24c38cfed0b..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/77.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>77.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\77.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>387</width>
- <height>443</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <truncated>1</truncated>
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- <bndbox>
- <xmin>8</xmin>
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- <xmax>378</xmax>
- <ymax>441</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/78.jpg b/PAR 152/cone_dataset/78.jpg
deleted file mode 100644
index f5610066fdc2e0127805545d4ead5edf2bc41c93..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/78.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/78.xml b/PAR 152/cone_dataset/78.xml
deleted file mode 100644
index ca56d792b10f822085cf171a72295f8a66256072..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/78.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>78.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\78.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>337</width>
- <height>506</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>138</xmin>
- <ymin>164</ymin>
- <xmax>306</xmax>
- <ymax>457</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>53</xmin>
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- <xmax>109</xmax>
- <ymax>252</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/79.jpg b/PAR 152/cone_dataset/79.jpg
deleted file mode 100644
index ece385097ff446a46bc08c20c02cb28eca2ad2fc..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/79.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/79.xml b/PAR 152/cone_dataset/79.xml
deleted file mode 100644
index 438ff7841bfccfdf4a44f9c9ffac90bf7f0c3bd8..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/79.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>79.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\79.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>505</width>
- <height>339</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>317</xmin>
- <ymin>106</ymin>
- <xmax>435</xmax>
- <ymax>319</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>267</xmin>
- <ymin>140</ymin>
- <xmax>340</xmax>
- <ymax>266</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>239</xmin>
- <ymin>167</ymin>
- <xmax>272</xmax>
- <ymax>233</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <difficult>0</difficult>
- <bndbox>
- <xmin>419</xmin>
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- <xmax>470</xmax>
- <ymax>253</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/8.jpg b/PAR 152/cone_dataset/8.jpg
deleted file mode 100644
index ddbb595eb4a93591f923c97270bbfbbc9f2a9a35..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/8.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/8.xml b/PAR 152/cone_dataset/8.xml
deleted file mode 100644
index f0bfc8c54c84f3fe60fafb5dc905943ff5940370..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/8.xml
+++ /dev/null
@@ -1,74 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>8.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\8.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
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- <bndbox>
- <xmin>332</xmin>
- <ymin>52</ymin>
- <xmax>460</xmax>
- <ymax>295</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>207</xmin>
- <ymin>131</ymin>
- <xmax>242</xmax>
- <ymax>205</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>169</xmin>
- <ymin>147</ymin>
- <xmax>192</xmax>
- <ymax>191</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>155</xmin>
- <ymin>155</ymin>
- <xmax>169</xmax>
- <ymax>183</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>47</xmin>
- <ymin>167</ymin>
- <xmax>52</xmax>
- <ymax>174</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/80.jpg b/PAR 152/cone_dataset/80.jpg
deleted file mode 100644
index 169efd966d71d1069cba2818faff14482d8acdf4..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/80.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/80.xml b/PAR 152/cone_dataset/80.xml
deleted file mode 100644
index 6fda51997bc8d2cea21b50c137b24bca933c66f8..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/80.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>80.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\80.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>366</width>
- <height>467</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>98</xmin>
- <ymin>211</ymin>
- <xmax>182</xmax>
- <ymax>357</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>99</xmin>
- <ymin>202</ymin>
- <xmax>148</xmax>
- <ymax>299</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>134</xmin>
- <ymin>241</ymin>
- <xmax>228</xmax>
- <ymax>465</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>108</xmin>
- <ymin>195</ymin>
- <xmax>133</xmax>
- <ymax>249</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/81.jpg b/PAR 152/cone_dataset/81.jpg
deleted file mode 100644
index a4a3efbeec838e054a4bbf4a041fdbd90cb9f417..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/81.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/81.xml b/PAR 152/cone_dataset/81.xml
deleted file mode 100644
index 0c4bb3847fb79e9c08f6ecca23529f971d20e66e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/81.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>81.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\81.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
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- <object>
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diff --git a/PAR 152/cone_dataset/82.jpg b/PAR 152/cone_dataset/82.jpg
deleted file mode 100644
index eb62e66ad07dd40e16e197f2888339f684f1c72d..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/82.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/82.xml b/PAR 152/cone_dataset/82.xml
deleted file mode 100644
index 08e829f79ce8bd927ea31c698b72a3efa73b7ffa..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/82.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>82.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\82.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>514</width>
- <height>333</height>
- <depth>3</depth>
- </size>
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- <object>
- <name>cone</name>
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- <xmax>191</xmax>
- <ymax>163</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- </object>
- <object>
- <name>cone</name>
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- </bndbox>
- </object>
- <object>
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diff --git a/PAR 152/cone_dataset/83.jpg b/PAR 152/cone_dataset/83.jpg
deleted file mode 100644
index 7a255f822981028fbc5df1becf05a10c2df95d60..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/83.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/83.xml b/PAR 152/cone_dataset/83.xml
deleted file mode 100644
index 1797700a59aab71846ac7cf94a08535dfd379470..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/83.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>83.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\83.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
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- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>252</xmin>
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- <ymax>336</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/84.jpg b/PAR 152/cone_dataset/84.jpg
deleted file mode 100644
index d466eac9ac903144d8515cfa3958e2727d9853ac..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/84.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/84.xml b/PAR 152/cone_dataset/84.xml
deleted file mode 100644
index b55dd0d3ef3f0e9d7c24aa9be7b8417fb256779d..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/84.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>84.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\84.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
- <height>339</height>
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- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <ymax>325</ymax>
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- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/85.jpg b/PAR 152/cone_dataset/85.jpg
deleted file mode 100644
index 710f6b99d04e7999f40c2604335368d870ca9fed..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/85.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/85.xml b/PAR 152/cone_dataset/85.xml
deleted file mode 100644
index 2b14df2b422c4309989d3315e635809905275f8e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/85.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>85.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\85.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
- <height>339</height>
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- </size>
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- <object>
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- <ymax>215</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
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- <ymin>28</ymin>
- <xmax>407</xmax>
- <ymax>187</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
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- </bndbox>
- </object>
- <object>
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- </bndbox>
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diff --git a/PAR 152/cone_dataset/86.jpg b/PAR 152/cone_dataset/86.jpg
deleted file mode 100644
index 9556b6df8396bb1c8197bca623854c38893bd437..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/86.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/86.xml b/PAR 152/cone_dataset/86.xml
deleted file mode 100644
index 1c4e504ab35cd39b5812c65a351236812066c7ef..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/86.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>86.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\86.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
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- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
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- <ymax>275</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/87.jpg b/PAR 152/cone_dataset/87.jpg
deleted file mode 100644
index fba51bade931d2fdff72e4ae76544e8ebf4f89f5..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/87.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/87.xml b/PAR 152/cone_dataset/87.xml
deleted file mode 100644
index 0648a0c8b0f96d2381e2cc2b3fd1904ce05da10f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/87.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>87.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\87.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>339</width>
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- </size>
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- <object>
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- <xmin>159</xmin>
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- <xmax>287</xmax>
- <ymax>454</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>39</xmin>
- <ymin>40</ymin>
- <xmax>77</xmax>
- <ymax>91</ymax>
- </bndbox>
- </object>
- <object>
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- </object>
- <object>
- <name>cone</name>
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- <xmax>178</xmax>
- <ymax>25</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/88.jpg b/PAR 152/cone_dataset/88.jpg
deleted file mode 100644
index 1c6bbd2fde51c2b43fcc08de769a93ed3d632152..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/88.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/88.xml b/PAR 152/cone_dataset/88.xml
deleted file mode 100644
index f61101ba72552659d6c4480315f3c96bf656c086..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/88.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>88.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\88.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>478</width>
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- </size>
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- <ymax>355</ymax>
- </bndbox>
- </object>
- <object>
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- </bndbox>
- </object>
- <object>
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- <ymax>180</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <ymax>170</ymax>
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- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/89.jpg b/PAR 152/cone_dataset/89.jpg
deleted file mode 100644
index f4ac7318edb7e8b07d4cc4055d21b9d8040ce91b..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/89.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/89.xml b/PAR 152/cone_dataset/89.xml
deleted file mode 100644
index b953d1ecb6ff011c9f2ade1f3ac49ab87e56a7cd..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/89.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>89.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\89.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>338</width>
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- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>62</xmin>
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- <xmax>291</xmax>
- <ymax>492</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/9.jpg b/PAR 152/cone_dataset/9.jpg
deleted file mode 100644
index 5ea3555212980a9a5e49999f6e94c863ba876f05..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/9.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/9.xml b/PAR 152/cone_dataset/9.xml
deleted file mode 100644
index fd785042d0bb01466c4f88da84cf1199d672ef50..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/9.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>9.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\9.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>338</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <xmax>484</xmax>
- <ymax>293</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>276</xmin>
- <ymin>104</ymin>
- <xmax>362</xmax>
- <ymax>257</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/90.jpg b/PAR 152/cone_dataset/90.jpg
deleted file mode 100644
index 18b17914904fc0e94e8f8109496f700d92503a56..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/90.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/90.xml b/PAR 152/cone_dataset/90.xml
deleted file mode 100644
index e40077788f69f2e374503b6d5d0bea7abaec493e..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/90.xml
+++ /dev/null
@@ -1,98 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>90.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\90.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>535</width>
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- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>220</xmin>
- <ymin>203</ymin>
- <xmax>258</xmax>
- <ymax>250</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>121</xmin>
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- </bndbox>
- </object>
- <object>
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- <object>
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- </object>
- <object>
- <name>cone</name>
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- <xmin>308</xmin>
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- <xmax>359</xmax>
- <ymax>261</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>396</xmin>
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- </object>
- <object>
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diff --git a/PAR 152/cone_dataset/91.jpg b/PAR 152/cone_dataset/91.jpg
deleted file mode 100644
index b8cfdaf0c1eeccad0a85f8e139357f77e1e044ee..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/91.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/91.xml b/PAR 152/cone_dataset/91.xml
deleted file mode 100644
index 84472c691496745cc5faab9a0176fa1f32bbff79..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/91.xml
+++ /dev/null
@@ -1,50 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>91.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\91.jpg</path>
- <source>
- <database>Unknown</database>
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- <size>
- <width>553</width>
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- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>148</xmin>
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- <xmax>262</xmax>
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- </bndbox>
- </object>
- <object>
- <name>cone</name>
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- <bndbox>
- <xmin>257</xmin>
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- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>1</xmin>
- <ymin>1</ymin>
- <xmax>122</xmax>
- <ymax>88</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/92.jpg b/PAR 152/cone_dataset/92.jpg
deleted file mode 100644
index aa8b6063eb577fa9456e9252b8d5bd66ce588ad0..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/92.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/92.xml b/PAR 152/cone_dataset/92.xml
deleted file mode 100644
index f7f18b524196b3a3caea2cbb6998a95ee8cf8ad7..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/92.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>92.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\92.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>359</width>
- <height>479</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>69</xmin>
- <ymin>190</ymin>
- <xmax>223</xmax>
- <ymax>389</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/93.jpg b/PAR 152/cone_dataset/93.jpg
deleted file mode 100644
index b9e54e70e62c21919d4fed814a885f47e4f0b4d5..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/93.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/93.xml b/PAR 152/cone_dataset/93.xml
deleted file mode 100644
index 3805ae278700271f60d861a76293f88457c2e235..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/93.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>93.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\93.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>337</width>
- <height>507</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>91</xmin>
- <ymin>39</ymin>
- <xmax>178</xmax>
- <ymax>161</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>3</xmin>
- <ymin>28</ymin>
- <xmax>76</xmax>
- <ymax>128</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>223</xmin>
- <ymin>60</ymin>
- <xmax>327</xmax>
- <ymax>210</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>259</xmin>
- <ymin>4</ymin>
- <xmax>290</xmax>
- <ymax>53</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/94.jpg b/PAR 152/cone_dataset/94.jpg
deleted file mode 100644
index 98a8d510dd4ddd4de3aa82558e83072c74b1dfed..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/94.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/94.xml b/PAR 152/cone_dataset/94.xml
deleted file mode 100644
index 7337dca0b4b3370976e3e7227bb8778a8b687e5f..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/94.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>94.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\94.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>507</width>
- <height>339</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>218</xmin>
- <ymin>24</ymin>
- <xmax>397</xmax>
- <ymax>294</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>130</xmin>
- <ymin>100</ymin>
- <xmax>183</xmax>
- <ymax>188</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>111</xmin>
- <ymin>120</ymin>
- <xmax>138</xmax>
- <ymax>167</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>101</xmin>
- <ymin>125</ymin>
- <xmax>122</xmax>
- <ymax>156</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/95.jpg b/PAR 152/cone_dataset/95.jpg
deleted file mode 100644
index eed8aac79f979ff1b531ece250df243e668d6cc6..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/95.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/95.xml b/PAR 152/cone_dataset/95.xml
deleted file mode 100644
index c29900471df0f112799cada8017beefbc06bb433..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/95.xml
+++ /dev/null
@@ -1,110 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>95.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\95.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>510</width>
- <height>337</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>312</xmin>
- <ymin>168</ymin>
- <xmax>402</xmax>
- <ymax>325</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>430</xmin>
- <ymin>23</ymin>
- <xmax>462</xmax>
- <ymax>75</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>366</xmin>
- <ymin>22</ymin>
- <xmax>399</xmax>
- <ymax>77</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>311</xmin>
- <ymin>23</ymin>
- <xmax>341</xmax>
- <ymax>75</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>292</xmin>
- <ymin>117</ymin>
- <xmax>366</xmax>
- <ymax>248</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>280</xmin>
- <ymin>92</ymin>
- <xmax>333</xmax>
- <ymax>202</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>121</xmin>
- <ymin>16</ymin>
- <xmax>155</xmax>
- <ymax>65</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>65</xmin>
- <ymin>16</ymin>
- <xmax>106</xmax>
- <ymax>66</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/96.jpg b/PAR 152/cone_dataset/96.jpg
deleted file mode 100644
index 92f76386f69f98b22d2f1b83e534a5e0e307dd56..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/96.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/96.xml b/PAR 152/cone_dataset/96.xml
deleted file mode 100644
index 23a5390244b0c27ab51f5126142e283bd16b3850..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/96.xml
+++ /dev/null
@@ -1,62 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>96.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\96.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>505</width>
- <height>342</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>317</xmin>
- <ymin>113</ymin>
- <xmax>373</xmax>
- <ymax>171</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>189</xmin>
- <ymin>94</ymin>
- <xmax>255</xmax>
- <ymax>176</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>218</xmin>
- <ymin>183</ymin>
- <xmax>421</xmax>
- <ymax>320</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>263</xmin>
- <ymin>96</ymin>
- <xmax>289</xmax>
- <ymax>137</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/97.jpg b/PAR 152/cone_dataset/97.jpg
deleted file mode 100644
index a8e9cdc28f8f8d7852f781f913b0adfc3fda53ce..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/97.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/97.xml b/PAR 152/cone_dataset/97.xml
deleted file mode 100644
index 3c2c10de8201fd621b1d60bd819d4c7c097c8296..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/97.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>97.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\97.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>432</width>
- <height>400</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>1</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>97</xmin>
- <ymin>1</ymin>
- <xmax>166</xmax>
- <ymax>108</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/98.jpg b/PAR 152/cone_dataset/98.jpg
deleted file mode 100644
index e03c9664414ee18aec9e61db6d64d80e2bb6c3a2..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/98.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/98.xml b/PAR 152/cone_dataset/98.xml
deleted file mode 100644
index 8e096381babb4e30abbbb42573548353f5cfa28d..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/98.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>98.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\98.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>509</width>
- <height>339</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>179</xmin>
- <ymin>43</ymin>
- <xmax>255</xmax>
- <ymax>167</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/cone_dataset/99.jpg b/PAR 152/cone_dataset/99.jpg
deleted file mode 100644
index 7a810e26f4e31ca7dcac781532854e5ca0e3c112..0000000000000000000000000000000000000000
Binary files a/PAR 152/cone_dataset/99.jpg and /dev/null differ
diff --git a/PAR 152/cone_dataset/99.xml b/PAR 152/cone_dataset/99.xml
deleted file mode 100644
index fcae8467e04e5d6e324cd8b075ab9e0affe9c2e5..0000000000000000000000000000000000000000
--- a/PAR 152/cone_dataset/99.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<annotation>
- <folder>cone_dataset</folder>
- <filename>99.jpg</filename>
- <path>C:\Users\dufan\Desktop\cours-1a\PAr\cone_dataset\99.jpg</path>
- <source>
- <database>Unknown</database>
- </source>
- <size>
- <width>476</width>
- <height>362</height>
- <depth>3</depth>
- </size>
- <segmented>0</segmented>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>201</xmin>
- <ymin>170</ymin>
- <xmax>294</xmax>
- <ymax>330</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>231</xmin>
- <ymin>156</ymin>
- <xmax>306</xmax>
- <ymax>297</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>266</xmin>
- <ymin>139</ymin>
- <xmax>342</xmax>
- <ymax>265</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>308</xmin>
- <ymin>128</ymin>
- <xmax>370</xmax>
- <ymax>241</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>328</xmin>
- <ymin>117</ymin>
- <xmax>388</xmax>
- <ymax>217</ymax>
- </bndbox>
- </object>
- <object>
- <name>cone</name>
- <pose>Unspecified</pose>
- <truncated>0</truncated>
- <difficult>0</difficult>
- <bndbox>
- <xmin>372</xmin>
- <ymin>101</ymin>
- <xmax>421</xmax>
- <ymax>186</ymax>
- </bndbox>
- </object>
-</annotation>
diff --git a/PAR 152/positionnement.py b/PAR 152/positionnement.py
deleted file mode 100644
index 7843c1f22530a29fc55ae02e38b4d9e580b6a142..0000000000000000000000000000000000000000
--- a/PAR 152/positionnement.py
+++ /dev/null
@@ -1,158 +0,0 @@
-from scipy.cluster.hierarchy import linkage, fcluster, dendrogram
-from numpy import array
-import numpy as np
-from math import sqrt
-import matplotlib.pyplot as plt
-
-carte = [] #carte contenant les coordonnées (x,y) de chaque plot, l'indince i correspond au plot i
-carte = np.array(carte)
-
-
-
-boxes = [array([ 71.55448151, 48.70854568, 117.95120239, 128.82731628,
- 0.96357524, 0. ]), array([265.82519531, 63.02380371, 317.72790527, 161.43673706,
- 0.95850408, 0. ]), array([204.24989319, 124.79711151, 320.40344238, 312.65106201,
- 0.95731342, 0. ]), array([392.64465332, 120.61515808, 507. , 307.8104248 ,
- 0.95594758, 0. ]), array([467.19827271, 83.70771027, 507. , 204.51119995,
- 0.95114303, 0. ]), array([183.88371277, 18.13962936, 212.38748169, 72.65458679,
- 0.94486743, 0. ]), array([467.60531616, 21.23407555, 501.08724976, 79.89296722,
- 0.93922979, 0. ]), array([ 39.37908936, 126.7710495 , 137.76670837, 320.09881592,
- 0.93714899, 0. ]), array([346.33688354, 74.00056458, 411.95550537, 180.61778259,
- 0.9355219 , 0. ]), array([198.56393433, 60.01593781, 251.088974 , 148.07209778,
- 0.93453765, 0. ]), array([107.80426025, 17.43326759, 135.90390015, 67.50372314,
- 0.92372549, 0. ]), array([226.01693726, 20.78704262, 255.77336121, 71.31395721,
- 0.92241275, 0. ]), array([ 0.73022616, 43.94894028, 50.41823196, 118.34261322,
- 0.92166394, 0. ]), array([413.37960815, 21.73137093, 447.26065063, 77.86586761,
- 0.91958499, 0. ]), array([135.58837891, 52.80698395, 189.51263428, 134.50894165,
- 0.91850674, 0. ]), array([366.38388062, 22.89640999, 395.97808838, 77.14238739,
- 0.91489226, 0. ]), array([23.01332664, 17.67047882, 51.43401337, 67.86820221, 0.91442037,
- 0. ]), array([154.2374115 , 88.37498474, 231.43321228, 213.48825073,
- 0.9137339 , 0. ]), array([ 22.97482491, 88.09642029, 71.89588165, 214.76116943,
- 0.90880054, 0. ]), array([63.18217087, 15.96899509, 91.19147491, 69.62237549, 0.90012252,
- 0. ]), array([12.51589012, 22.42526627, 51.88586807, 94.58940887, 0.38236487,
- 0. ])]
-milieu = 2
-F = 500
-h_reel = 0.7
-y0 = (boxes[milieu][0] + boxes[milieu][2])/2
-
-seuil = 0.5
-
-def distance(pt1,pt2):
- return sqrt((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)
-
-
-def boite2coord(x1,y1,x2,y2):
-
- d = F*h_reel/abs(y1-y2)
-
- y_r = ((x1+x2)/2 - y0)*d/F
- sin_theta= y_r/d
- x_r = d*(1-sin_theta**2)**0.5
-
- return x_r,y_r
-
-def changement_referentiel(coord_voiture, coord_plot):
- theta = coord_voiture[2]
- x_v = coord_plot[0] - coord_voiture[0]
- y_v = coord_plot[1] - coord_voiture[1]
- x = x_v*np.cos(theta) + y_v*np.sin(theta)
- y = x_v*np.sin(theta) + y_v*np.cos(theta)
- return [x,y]
-
-
-
-
-def get_groupe_max(cluster):
- occ = [0 for i in range(max(cluster)+1)]
- print(occ)
- for i in cluster:
- occ[i] += 1
- return np.argmax(occ)
-
-def association(cone, old_pos_cone):
- dist = []
- for pt in old_pos_cone:
- dist.append(distance(cone,pt))
- res = np.argmin(dist)
- if dist[res]<seuil:
- return res
- else:
- return None
-
-
-
-def identification_cones(old_pos, old_pos_cone, new_pos_cone, ind_cone):
- #Regrouper les cones par deux si possible
-
- correspondance = [-1 for i in range(len(new_pos_cone))]
-
- for i in range(len(new_pos_cone)):
- cone = association(new_pos_cone[i], old_pos_cone)
- if cone != None:
- correspondance[i] = cone
-
- liste_ind_cone = [-1]*len(new_pos_cone)
- for i in range(len(new_pos_cone)):
- if correspondance[i] != -1:
- liste_ind_cone[i] = ind_cone[correspondance[i]]
-
- new_pos = positionnement(new_pos_cone, liste_ind_cone) #permet de renvoyer ls cones
-
- for i in range(len(liste_ind_cone)):
- if liste_ind_cone[i] == -1:
- liste_ind_cone[i] = identification_new(new_pos, i)
-
- new_pos = positionnement(new_pos_cone, liste_ind_cone)
-
- return new_pos, liste_ind_cone
-
-
-
-
-def identification_new(pos, pos_cone):
-
- new_pos_cone = changement_referentiel(pos, pos_cone)
-
- dist = []
- for cone in carte:
- dist.append(distance(cone,new_pos_cone))
- res = np.argmin(dist)
-
- return res
-
-
-
-def positionnement(new_pos_cone, liste_ind_cone):
- pass
-
-
-
-
-
-
-if __name__ == '__main__':
-
- #initialisation
- car_pos = (0,0)
- car_angle = 0
- pos = [0,0,0]
- old_pos_cone = []
- ind_cone = []
-
- #boucle
- pos_cones = []
- for i in boxes:
- pos_cones.append(boite2coord(i[0],i[1],i[2],i[3]))
-
- pos2=[]
- for c in pos_cones:
- pos2.append((car_pos[0]+np.cos(car_angle)*c[0]+np.sin(car_angle)*c[1], car_pos[1]+np.cos(car_angle)*c[1]-np.sin(car_angle)*c[0]))
- print(pos2)
-
-
- #pos, liste_ind_cone = identification_cones(pos, old_pos_cone, pos_cone, ind_cone)
- #old_pos_cone = pos_cone
-
-
-
\ No newline at end of file
diff --git a/PAR 153/__pycache__/car_interface.cpython-310.pyc b/PAR 153/__pycache__/car_interface.cpython-310.pyc
deleted file mode 100644
index 61d0b4e01106754cc8dd164fcb30f7ff123cae81..0000000000000000000000000000000000000000
Binary files a/PAR 153/__pycache__/car_interface.cpython-310.pyc and /dev/null differ
diff --git a/PAR 153/__pycache__/mesure_discret_to_adim.cpython-310.pyc b/PAR 153/__pycache__/mesure_discret_to_adim.cpython-310.pyc
deleted file mode 100644
index aafdd84c24f60a389bac58474c15aac493406f0d..0000000000000000000000000000000000000000
Binary files a/PAR 153/__pycache__/mesure_discret_to_adim.cpython-310.pyc and /dev/null differ
diff --git a/PAR 153/__pycache__/pid_controller.cpython-310.pyc b/PAR 153/__pycache__/pid_controller.cpython-310.pyc
deleted file mode 100644
index 51aaff3169579afb8e6a251cf9b1efe462d350db..0000000000000000000000000000000000000000
Binary files a/PAR 153/__pycache__/pid_controller.cpython-310.pyc and /dev/null differ
diff --git a/PAR 153/__pycache__/track_2_generator.cpython-310.pyc b/PAR 153/__pycache__/track_2_generator.cpython-310.pyc
deleted file mode 100644
index 5ba7b0ff9a9a2e5c33d6a5e0d0106a8ca6473d87..0000000000000000000000000000000000000000
Binary files a/PAR 153/__pycache__/track_2_generator.cpython-310.pyc and /dev/null differ
diff --git a/PAR 153/car_interface.py b/PAR 153/car_interface.py
deleted file mode 100644
index d11cbdc5e0357ed20534505786add3d4a522a126..0000000000000000000000000000000000000000
--- a/PAR 153/car_interface.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import serial #Importation de la bibliothèque « pySerial »
-import time
-
-def get_ser():
- ser = serial.Serial(port="COM3", baudrate=115200, timeout=0.1,write_timeout=0.2) #Création du port lié au COM6 a une vitesse de 115200 bauds et un timout d’une seconde
- #time.sleep(3)
- return ser
-
-def start_ser(ser):
- ser.close() #Cloture du port pour le cas ou il serait déjà ouvert ailleurs
- ser.open() #Ouverture du port
-
-def close_ser(ser):
- ser.close()
-
-def send_infos(ser, a,b,c):
- n = f"['{a} {b} {c}']"
- #print("input: "+str(n))
- ser.write((str(n)+'\n').encode("utf-8"))
- cc=str(ser.readline())
- #print("output: "+cc[2:][:-5])
- #print("Success")
- return cc[2:][:-5]
\ No newline at end of file
diff --git a/PAR 153/discretisation.py b/PAR 153/discretisation.py
deleted file mode 100644
index cbc2f71ea99f2836c559de08c7d1b6c73976f3d5..0000000000000000000000000000000000000000
--- a/PAR 153/discretisation.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import matplotlib.pyplot as plt
-from shapely.geometry import Point, LineString
-from time import sleep, time
-
-from car_interface import get_ser, start_ser, close_ser, send_infos
-import track_2_generator as tg
-from pid_controller import PidController
-from mesure_discret_to_adim import pol2xy
-import json
-from math import cos, sin
-
-def update_pos(x,y,cp):
- m = tg.create_track(0.5,0,0, (0,0))[:-1]+[(1,0.5)]
- c = tg.get_cone_map()
- p = tg.checkpoints(cp)
- plt.plot([x for x,y in m], [y for x,y in m], '--g')
- plt.plot(x,y, "r+")
- plt.plot([x for x,y in c], [y for x,y in c], 'ob')
- plt.plot([x for x,y in p], [y for x,y in p], '--r')
- plt.axis("equal")
- #plt.show()
- return
-
-
-def simulation(n, pid=(100,0.1,10), plot=True):
- essai_number = n
-
- max_lap = 6
- current_lap = -1
-
- p,i,d = pid
- """initilize PID"""
- pid = PidController(p,i,d)
-
- """initialize CP"""
- cp=0
-
- data = {"essai": essai_number, "PID": {"P":p,"I":i,"D":d}, 'run': []}
-
- """loop"""
- t = True
- while t:
- step_data = {}
-
- """set new pos"""
- l = input('cercle, r, theta, pas: ')
- try:
- c, r, theta, p = l.split(' ')
- except:
- c = "s"
- #c = "d"
- #c, r, theta = "d", "1.2", "0"
- if c=="s":
- break
-
- """loading communication"""
- ser = get_ser()
- start_ser(ser)
-
- r, theta,p = float(r), float(theta), float(p)
-
- step_data["input"] = {"cercle":c, "r":r, "theta":theta}
-
- #x,y = input("x,y: ").split(' ')
- x,y = pol2xy(c, r, theta)
-
- step_data["pos"] = {"x":x, "y":y}
-
-
- """get distance"""
- current_cp = LineString(tg.checkpoints(cp))
- n_cp=cp+1
- if cp==5:
- n_cp=0
-
- next_cp = LineString(tg.checkpoints(n_cp))
- pos = Point(x,y)
- d1 = current_cp.distance(pos)
- d2 = next_cp.distance(pos)
- d=d1
-
- forward = False
- if d1>d2:
- cp=n_cp
- d = d2
- if cp==0:
- current_lap+=1
- if max_lap<=current_lap:
- t = False
- if cp==3 or cp==0:
- pid.previous_error *= -1
- pid.integrated_error *= -1
-
- #forward = True
-
-
- d = r-1.2
-
- step_data["dist"] = d
- step_data["cp"] = cp
- step_data["lap"] = current_lap
-
- if plot:
- """display"""
- update_pos(x,y,cp)
-
- """get new order"""
- speed = 10
- direction = pid.get_control(d)
- g = tg.is_in_track(x, y)
- if cp in [0,1,2]:
- direction*=-1
- #if not g:
- #if 1.2-r>0:
- #direction*=-1
-
- direction = int(direction)
-
- if direction>255:
- direction = 255
- elif direction<-255:
- direction = -255
- #print(f"PID direction: {direction}")
- #print(f"PID direction integrated: {pid.integrated_error}")
- #print(f"PID direction previous: {pid.previous_error}")
-
- if forward:
- direction_ideal = 0
- elif cp in [0,1,2]:
- direction_ideal = 100
- elif cp in [3,4,5]:
- direction_ideal = -20
- direction_correction = direction
-
- direction = direction_ideal+direction_correction
-
- if direction>127:
- direction = 127
- elif direction<-127:
- direction = -127
-
- """passage au centre: dead zone"""
- x0,y0 = 1.0,0.4
- r=0.07
- if plot:
- xs,ys = [x0+r*cos(theta/50*2*3.1415) for theta in range(50)], [y0+r*sin(theta/50*2*3.1415) for theta in range(50)]
- plt.plot(xs,ys, 'r-')
- plt.show()
-
- #if (x-x0)**2+(y-y0)**2<r**2:
- # direction = (-30 + 100)/2
-
- step_data["order"] = {"speed":speed, "direction":direction}
-
- #direction = 100
- print(f"direction: {direction}")
-
- """set new order"""
- t_end = time() + 4
- while time() < t_end:
- a = send_infos(ser, 0, direction, 0)
-
- try:
- direct = a.split(',')[0]
- except:
- direct = ''
- print(f"capteurs1: {direct}->{direction}")
- pass
-
- t_end = time() + p
- previous_print = time()
- while time() < t_end:
- a = send_infos(ser, speed, direction, 0)
-
- try:
- direct = a.split(',')[0]
- except:
- direct = ''
- print(f"capteurs2: {direct}->{direction}")
- pass
-
- """stop"""
- t_end = time() + 2
- while time() < t_end:
- send_infos(ser, 0, direction, 0)
- pass
-
- close_ser(ser)
-
- print(f"cp: {cp}")
-
- data["run"].append(step_data)
-
- return data
-
-if __name__=="__main__":
- essai_number = input("Numero de l'essai : ")
- #x = [1.12219182e+02, 4.92516610e-01, 7.51131752e+03]
- x = [ 100, 0.2, 257]
- data = simulation(essai_number, pid=x, plot=True)
-
- with open(f'essai_{essai_number}.json', 'w') as outfile:
- json.dump(data, outfile, indent=4)
-
- '''
- essai_number = input("Numero de l'essai")
-
- p,i,d = 1000,100,1000
- """initilize PID"""
- pid = PidController(p,i,d)
-
- """initialize CP"""
- cp=0
-
- data = {"PID": {"P":p,"I":i,"D":d}, 'run': []}
-
- """loop"""
- t = True
- while t:
- step_data = {}
- """set new pos"""
- r, theta = input('cercle, r, theta: d ').split(' ')
- c = "d"
- #c, r, theta = "d", "1.2", "0"
- if c=="s":
- break
-
- """loading communication"""
- #ser = get_ser()
- #start_ser(ser)
-
- r, theta = float(r), float(theta)
-
- step_data["input"] = {"cercle":c, "r":r, "theta":theta}
-
- #x,y = input("x,y: ").split(' ')
- x,y = pol2xy(c, r, theta)
-
- step_data["pos"] = {"x":x, "y":y}
-
- """get distance"""
- current_cp = LineString(tg.checkpoints(cp))
- n_cp=cp+1
- if cp==4:
- n_cp=1
-
- next_cp = LineString(tg.checkpoints(n_cp))
- pos = Point(x,y)
- d1 = current_cp.distance(pos)
- d2 = next_cp.distance(pos)
- d=d1
- if d1>d2:
- cp=n_cp
- d = d2
-
- step_data["dist"] = d
- step_data["cp"] = cp
-
- print(f"d: {d}")
-
- """display"""
- update_pos(x,y,cp)
-
- """get new order"""
- speed = 10
- direction = pid.get_control(d)
- g = tg.is_in_track(x, y)
- if x<1:
- g = not g
- if not g:
- direction*=-1
-
- dec = 60
- braquage = 150
- direction = direction+dec#decallage
- if abs(direction-dec)>braquage:
- direction = direction/abs(direction)*braquage+dec
- print("Max turn")
-
- direction = int(direction)
-
- step_data["order"] = {"speed":speed, "direction":direction}
-
- #direction = 100
- print(f"direction: {direction}")
-
- """set new order"""
- t_end = time() + 3
- while time() < t_end:
- #a = send_infos(ser, speed, direction, 0)
- #print(f"capteurs: {a}")
- pass
-
- """stop"""
- t_end = time() + 2
- while time() < t_end:
- #send_infos(ser, 0, direction, 0)
- pass
-
- data["run"].append(step_data)
-
- #close_ser(ser)
- '''
\ No newline at end of file
diff --git a/PAR 153/essai_1.json b/PAR 153/essai_1.json
deleted file mode 100644
index d76ed134a8ec42811dcaa7e20a13043ac7da81dc..0000000000000000000000000000000000000000
--- a/PAR 153/essai_1.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "1", "PID": {"P": 54.11317513, "I": 0.522774415, "D": 257.9071004}, "run": [{"input": {"cercle": "d", "r": 1.18, "theta": 70.0}, "pos": {"x": 1.3318400961982129, "y": 0.03798446144692841}, "dist": -0.020000000000000018, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 94}}, {"input": {"cercle": "d", "r": 1.13, "theta": 50.0}, "pos": {"x": 1.1973541671059211, "y": 0.13932074136481454}, "dist": -0.07000000000000006, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 84}}, {"input": {"cercle": "d", "r": 1.1, "theta": 25.0}, "pos": {"x": 1.0846089309415354, "y": 0.3062999633688461}, "dist": -0.09999999999999987, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 87}}, {"input": {"cercle": "d", "r": 1.1, "theta": 10.0}, "pos": {"x": 1.0486297798694046, "y": 0.42041125190265694}, "dist": -0.09999999999999987, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 95}}, {"input": {"cercle": "g", "r": 1.32, "theta": 10.0}, "pos": {"x": 1.0416442641567145, "y": 0.5955064977168116}, "dist": 0.1200000000000001, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -63}}, {"input": {"cercle": "g", "r": 1.15, "theta": 30.0}, "pos": {"x": 0.9149705059800435, "y": 0.7395833333333333}, "dist": -0.050000000000000044, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -54}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_11.json b/PAR 153/essai_11.json
deleted file mode 100644
index 485cda07608bbe11376162c974c985b93ec88012..0000000000000000000000000000000000000000
--- a/PAR 153/essai_11.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "11", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 1.2, "theta": 0.0}, "pos": {"x": 1.0, "y": 0.5}, "dist": 0.0, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 90}}, {"input": {"cercle": "d", "r": 1.16, "theta": -20.0}, "pos": {"x": 1.045815233286811, "y": 0.6653097359407398}, "dist": -0.040000000000000036, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 76}}, {"input": {"cercle": "d", "r": 1.16, "theta": -35.0}, "pos": {"x": 1.1040765119269873, "y": 0.7772286109030055}, "dist": -0.040000000000000036, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 86}}, {"input": {"cercle": "d", "r": 1.19, "theta": -55.0}, "pos": {"x": 1.2156016836426065, "y": 0.9061628886266251}, "dist": -0.010000000000000009, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 96}}, {"input": {"cercle": "d", "r": 1.23, "theta": -70.0}, "pos": {"x": 1.3247146765455948, "y": 0.9815924681527781}, "dist": 0.030000000000000027, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 103}}, {"input": {"cercle": "d", "r": 1.25, "theta": -85.0}, "pos": {"x": 1.4546063839855947, "y": 1.0188514052561175}, "dist": 0.050000000000000044, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 100}}, {"input": {"cercle": "d", "r": 1.28, "theta": -100.0}, "pos": {"x": 1.5926123614223628, "y": 1.025230801606511}, "dist": 0.08000000000000007, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 105}}, {"input": {"cercle": "d", "r": 1.3, "theta": -115.0}, "pos": {"x": 1.7289182251095454, "y": 0.9909167179781855}, "dist": 0.10000000000000009, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 105}}, {"input": {"cercle": "d", "r": 1.31, "theta": -130.0}, "pos": {"x": 1.8508549036205695, "y": 0.9181325918691089}, "dist": 0.1100000000000001, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 103}}, {"input": {"cercle": "d", "r": 1.33, "theta": -140.0}, "pos": {"x": 1.924516295561767, "y": 0.8562114670346239}, "dist": 0.13000000000000012, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 108}}, {"input": {"cercle": "d", "r": 1.35, "theta": -150.0}, "pos": {"x": 1.9871392896287468, "y": 0.78125}, "dist": 0.15000000000000013, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 110}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_18.json b/PAR 153/essai_18.json
deleted file mode 100644
index 68f2125eeda45d27a616f834aee258534fae6631..0000000000000000000000000000000000000000
--- a/PAR 153/essai_18.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "18", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": []}
\ No newline at end of file
diff --git a/PAR 153/essai_19.json b/PAR 153/essai_19.json
deleted file mode 100644
index 8a5647c881c075c067998927affd169d38b43ba2..0000000000000000000000000000000000000000
--- a/PAR 153/essai_19.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "19", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 1.2, "theta": 65.0}, "pos": {"x": 1.2886908691296504, "y": 0.04684610648167503}, "dist": 0.0, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 90}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_2.json b/PAR 153/essai_2.json
deleted file mode 100644
index 0dcc4255a93cc7715877d91ac3bb9b130a74e0ab..0000000000000000000000000000000000000000
--- a/PAR 153/essai_2.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 153/essai_20.json b/PAR 153/essai_20.json
deleted file mode 100644
index b88b0efabae930fc5b11a983c9f175e2f284ba13..0000000000000000000000000000000000000000
--- a/PAR 153/essai_20.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "20", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 1.19, "theta": 60.0}, "pos": {"x": 1.2520833333333332, "y": 0.07059573729021584}, "dist": -0.010000000000000009, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 87}}, {"input": {"cercle": "d", "r": 1.18, "theta": 40.0}, "pos": {"x": 1.1233614821331692, "y": 0.18396275857078487}, "dist": -0.020000000000000018, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 86}}, {"input": {"cercle": "d", "r": 1.23, "theta": 25.0}, "pos": {"x": 1.0355172591437167, "y": 0.2834081408578915}, "dist": 0.030000000000000027, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 105}}, {"input": {"cercle": "d", "r": 1.27, "theta": 5.0}, "pos": {"x": 0.9728469722597847, "y": 0.4538800861293642}, "dist": 0.07000000000000006, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 15}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_21.json b/PAR 153/essai_21.json
deleted file mode 100644
index 3175eb5b759cc09155466561d10e6507127c0605..0000000000000000000000000000000000000000
--- a/PAR 153/essai_21.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "21", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 1.18, "theta": 95.0}, "pos": {"x": 1.5428515735175987, "y": 0.010204273438225109}, "dist": -0.020000000000000018, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 83}}, {"input": {"cercle": "d", "r": 1.18, "theta": 80.0}, "pos": {"x": 1.4146229793137592, "y": 0.01580285476899773}, "dist": -0.020000000000000018, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 88}}, {"input": {"cercle": "d", "r": 1.21, "theta": 60.0}, "pos": {"x": 1.2479166666666666, "y": 0.06337885892534556}, "dist": 0.010000000000000009, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 98}}, {"input": {"cercle": "d", "r": 1.24, "theta": 40.0}, "pos": {"x": 1.1042103710551947, "y": 0.16789306832862133}, "dist": 0.040000000000000036, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 101}}, {"input": {"cercle": "d", "r": 1.27, "theta": 20.0}, "pos": {"x": 1.0027459881674567, "y": 0.31901434082350033}, "dist": 0.07000000000000006, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 104}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_22.json b/PAR 153/essai_22.json
deleted file mode 100644
index 15d539e4b2568067dfb409d84e14d64016ed9f7f..0000000000000000000000000000000000000000
--- a/PAR 153/essai_22.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "22", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 1.18, "theta": 95.0}, "pos": {"x": 1.5428515735175987, "y": 0.010204273438225109}, "dist": -0.020000000000000018, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 93}}, {"input": {"cercle": "d", "r": 1.17, "theta": 80.0}, "pos": {"x": 1.4153465133873715, "y": 0.019906220406548603}, "dist": -0.030000000000000027, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 95}}, {"input": {"cercle": "d", "r": 1.17, "theta": 60.0}, "pos": {"x": 1.2562499999999999, "y": 0.07781261565508618}, "dist": -0.030000000000000027, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 97}}, {"input": {"cercle": "d", "r": 1.19, "theta": 35.0}, "pos": {"x": 1.093837111373375, "y": 0.21560168364260635}, "dist": -0.010000000000000009, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 104}}, {"input": {"cercle": "d", "r": 1.19, "theta": 15.0}, "pos": {"x": 1.0210617777983368, "y": 0.3716688901366668}, "dist": -0.010000000000000009, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 15}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_23.json b/PAR 153/essai_23.json
deleted file mode 100644
index 8836f86afd1482145f98a42ee68c03a2c9ee51e5..0000000000000000000000000000000000000000
--- a/PAR 153/essai_23.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "23", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 1.18, "theta": 95.0}, "pos": {"x": 1.5428515735175987, "y": 0.010204273438225109}, "dist": -0.020000000000000018, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 93}}, {"input": {"cercle": "d", "r": 1.14, "theta": 75.0}, "pos": {"x": 1.3770609535763025, "y": 0.04118523251269257}, "dist": -0.06000000000000005, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 84}}, {"input": {"cercle": "d", "r": 1.18, "theta": 55.0}, "pos": {"x": 1.2179915854607357, "y": 0.09725024489124573}, "dist": -0.020000000000000018, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 108}}, {"input": {"cercle": "d", "r": 1.22, "theta": 40.0}, "pos": {"x": 1.1105940747478529, "y": 0.17324963174267588}, "dist": 0.020000000000000018, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 112}}, {"input": {"cercle": "d", "r": 1.22, "theta": 20.0}, "pos": {"x": 1.022322917767163, "y": 0.3261397604761184}, "dist": 0.020000000000000018, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 101}}, {"input": {"cercle": "d", "r": 1.21, "theta": 0.0}, "pos": {"x": 0.9958333333333333, "y": 0.5}, "dist": 0.010000000000000009, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": 35.0}}, {"input": {"cercle": "g", "r": 1.2, "theta": 15.0}, "pos": {"x": 0.9829629131445341, "y": 0.6294095225512604}, "dist": 0.0, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -28}}, {"input": {"cercle": "g", "r": 1.18, "theta": 40.0}, "pos": {"x": 0.8766385178668308, "y": 0.8160372414292152}, "dist": -0.020000000000000018, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -23}}, {"input": {"cercle": "g", "r": 1.18, "theta": 60.0}, "pos": {"x": 0.7458333333333333, "y": 0.9257958235273489}, "dist": -0.020000000000000018, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -28}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_24.json b/PAR 153/essai_24.json
deleted file mode 100644
index fc3861d42f3739bd3fd23635420c754abdbae90e..0000000000000000000000000000000000000000
--- a/PAR 153/essai_24.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "24", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "g", "r": 1.2, "theta": -100.0}, "pos": {"x": 0.41317591116653485, "y": 0.00759612349389599}, "dist": 0.0, "cp": 4, "lap": -1, "order": {"speed": 10, "direction": -30}}, {"input": {"cercle": "g", "r": 1.1, "theta": -80.0}, "pos": {"x": 0.5795887480973431, "y": 0.048629779869404643}, "dist": -0.09999999999999987, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": 5}}, {"input": {"cercle": "g", "r": 1.08, "theta": -20.0}, "pos": {"x": 0.9228616793536588, "y": 0.34609093550344905}, "dist": -0.11999999999999988, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -13}}, {"input": {"cercle": "g", "r": 1.17, "theta": -40.0}, "pos": {"x": 0.8734466660205018, "y": 0.18664104027781214}, "dist": -0.030000000000000027, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -50}}, {"input": {"cercle": "g", "r": 1.19, "theta": -10.0}, "pos": {"x": 0.9883005108685532, "y": 0.41389944524014705}, "dist": -0.010000000000000009, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": 35.0}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_25.json b/PAR 153/essai_25.json
deleted file mode 100644
index e1a13cc0e889fce326bc2673b41f6729f437d3e5..0000000000000000000000000000000000000000
--- a/PAR 153/essai_25.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "25", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "g", "r": 1.2, "theta": -100.0}, "pos": {"x": 0.41317591116653485, "y": 0.00759612349389599}, "dist": 0.0, "cp": 4, "lap": -1, "order": {"speed": 10, "direction": -30}}, {"input": {"cercle": "g", "r": 1.13, "theta": -85.0}, "pos": {"x": 0.5410358288770224, "y": 0.03095832964846984}, "dist": -0.07000000000000006, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -6}}, {"input": {"cercle": "g", "r": 1.13, "theta": -65.0}, "pos": {"x": 0.6989827649029127, "y": 0.07328008360357735}, "dist": -0.07000000000000006, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -23}}, {"input": {"cercle": "g", "r": 1.18, "theta": -45.0}, "pos": {"x": 0.8476608340833859, "y": 0.15233916591661412}, "dist": -0.020000000000000018, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -40}}, {"input": {"cercle": "g", "r": 1.15, "theta": -20.0}, "pos": {"x": 0.9502693807932477, "y": 0.33611534798978376}, "dist": -0.050000000000000044, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -18}}, {"input": {"cercle": "g", "r": 1.16, "theta": 0.0}, "pos": {"x": 0.9833333333333334, "y": 0.5}, "dist": -0.040000000000000036, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": 35.0}}, {"input": {"cercle": "d", "r": 1.26, "theta": -20.0}, "pos": {"x": 1.006661374087398, "y": 0.6795605752459761}, "dist": 0.06000000000000005, "cp": 0, "lap": 0, "order": {"speed": 10, "direction": 127}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_26.json b/PAR 153/essai_26.json
deleted file mode 100644
index d3be12c0b83136cba012bd729db1ebc38fea979f..0000000000000000000000000000000000000000
--- a/PAR 153/essai_26.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "26", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "g", "r": 1.2, "theta": -100.0}, "pos": {"x": 0.41317591116653485, "y": 0.00759612349389599}, "dist": 0.0, "cp": 4, "lap": -1, "order": {"speed": 10, "direction": -30}}, {"input": {"cercle": "g", "r": 1.2, "theta": -80.0}, "pos": {"x": 0.5868240888334653, "y": 0.00759612349389599}, "dist": 0.0, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -30}}, {"input": {"cercle": "g", "r": 1.17, "theta": -60.0}, "pos": {"x": 0.74375, "y": 0.07781261565508618}, "dist": -0.030000000000000027, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -20}}, {"input": {"cercle": "g", "r": 1.21, "theta": -45.0}, "pos": {"x": 0.8564996688482177, "y": 0.1435003311517823}, "dist": 0.010000000000000009, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -41}}, {"input": {"cercle": "g", "r": 1.21, "theta": -20.0}, "pos": {"x": 0.9737616963128954, "y": 0.327564844406642}, "dist": 0.010000000000000009, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -30}}, {"input": {"cercle": "g", "r": 1.2, "theta": 0.0}, "pos": {"x": 1.0, "y": 0.5}, "dist": 0.0, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": 35.0}}, {"input": {"cercle": "d", "r": 1.36, "theta": -30.0}, "pos": {"x": 1.009252271188818, "y": 0.7833333333333333}, "dist": 0.16000000000000014, "cp": 0, "lap": 0, "order": {"speed": 10, "direction": 127}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_27.json b/PAR 153/essai_27.json
deleted file mode 100644
index a4e50a76c5a14318281997deaa7304bfc0ebf9ca..0000000000000000000000000000000000000000
--- a/PAR 153/essai_27.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "27", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 1.1, "theta": -35.0}, "pos": {"x": 1.124555313034212, "y": 0.7628891999942294}, "dist": -0.09999999999999987, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 65}}, {"input": {"cercle": "d", "r": 1.16, "theta": -50.0}, "pos": {"x": 1.1893193219848393, "y": 0.8702548141741727}, "dist": -0.040000000000000036, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 111}}, {"input": {"cercle": "d", "r": 1.24, "theta": -80.0}, "pos": {"x": 1.410281774872086, "y": 1.0088173390563075}, "dist": 0.040000000000000036, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 124}}, {"input": {"cercle": "d", "r": 1.28, "theta": -95.0}, "pos": {"x": 1.5464830627987511, "y": 1.0313038389822644}, "dist": 0.08000000000000007, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 118}}, {"input": {"cercle": "d", "r": 1.25, "theta": -110.0}, "pos": {"x": 1.6781354913154525, "y": 0.9894232399926607}, "dist": 0.050000000000000044, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 98}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_28.json b/PAR 153/essai_28.json
deleted file mode 100644
index e8dd8aa06f30dbd26dc78c2ca05624185403c013..0000000000000000000000000000000000000000
--- a/PAR 153/essai_28.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "28", "PID": {"P": 100, "I": 0.2, "D": 257}, "run": [{"input": {"cercle": "d", "r": 120.0, "theta": 0.0}, "pos": {"x": -48.5, "y": 0.5}, "dist": 118.8, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 127}}, {"input": {"cercle": "d", "r": 110.0, "theta": -16.0}, "pos": {"x": -42.55782773050628, "y": 13.13337880827913}, "dist": 108.8, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 127}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_29.json b/PAR 153/essai_29.json
deleted file mode 100644
index f7ee4df20b4275b3709b8985f48a89a525b1b341..0000000000000000000000000000000000000000
--- a/PAR 153/essai_29.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 153/essai_3.html b/PAR 153/essai_3.html
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+++ /dev/null
@@ -1,224 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0//EN" "http://www.w3.org/TR/REC-html40/strict.dtd">
-<html><head>
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="qrichtext" content="1" /><style type="text/css">
-p, li { white-space: pre-wrap; }
-</style></head><body style=" font-family:'DejaVu Sans Mono'; font-size:9pt; font-weight:400; font-style:normal;">
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Python </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Type "copyright", "credits" or "license" for more information.</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">IPython -- An enhanced Interactive Python.</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" color:#00ff00;">In [</span><span style=" font-weight:600; color:#00ff00;">1</span><span style=" color:#00ff00;">]:</span> runfile('C:/Users/MARTELET Tom Port/Documents/GitHub/epsa-pae-ai-self-driving/PAR 153/discretisation.py', wdir='C:/Users/MARTELET Tom Port/Documents/GitHub/epsa-pae-ai-self-driving/PAR 153')</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Numero de l'essai : 3</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">cercle, r, theta: d 1.2 0</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">0.5</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /> </p>
-<table border="0.5" style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px;" align="center" width="90%" cellspacing="0" cellpadding="8">
-<tr>
-<td bgcolor="#ce4b01">
-<p align="center" style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-weight:600; background-color:#ce4b01;">Warning</span></p></td></tr>
-<tr>
-<td>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Figures now render in the Plots pane by default. To make them also appear inline in the Console, uncheck "Mute Inline Plotting" under the Plots pane options menu.</p></td></tr></table>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> error: 0.0</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">PID direction: 0</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">direction: 90</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: angle commande output</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 117.52,90.00,-152.85</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 114.87,90.00,149.88</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 122.07,90.00,151.05</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 135.34,90.00,-158.48</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 113.35,90.00,-163.90</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 79.61,90.00,89.25</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 76.20,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 97.43,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 99.33,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 98.95,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 99.33,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 98.95,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 99.33,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 98.95,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 99.33,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 98.95,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 99.33,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 98.95,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 99.33,90.00,62.63</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">cercle, r, theta: Traceback <span style=" color:#00ffff;">(most recent call last)</span>:</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> File <span style=" color:#006400;">~\AppData\Local\Programs\Python\Python310\lib\site-packages\spyder_kernels\py3compat.py:356</span> in <span style=" color:#9400d3;">compat_exec</span></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> exec(code, globals, locals)</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> File <span style=" color:#006400;">c:\users\martelet tom port\documents\github\epsa-pae-ai-self-driving\par 153\discretisation.py:171</span></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> data = simulation(essai_number, pid=x, plot=True)</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> File <span style=" color:#006400;">c:\users\martelet tom port\documents\github\epsa-pae-ai-self-driving\par 153\discretisation.py:45</span> in <span style=" color:#9400d3;">simulation</span></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> c, r, theta = input('cercle, r, theta: ').split(' ')</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> File <span style=" color:#006400;">~\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\kernelbase.py:1187</span> in <span style=" color:#9400d3;">raw_input</span></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> return self._input_request(</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" color:#00ffff;"> File </span><span style=" color:#00ff00;">~\AppData\Local\Programs\Python\Python310\lib\site-packages\ipykernel\kernelbase.py:1230</span><span style=" color:#00ffff;"> in </span><span style=" color:#ff00ff;">_input_request</span></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" color:#ffff00;"> raise KeyboardInterrupt(msg) from None</span></p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; color:#00ffff;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" color:#ff0000;">KeyboardInterrupt:</span> Interrupted by user</p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" color:#00ff00;">In [</span><span style=" font-weight:600; color:#00ff00;">2</span><span style=" color:#00ff00;">]:</span> runfile('C:/Users/MARTELET Tom Port/Documents/GitHub/epsa-pae-ai-self-driving/PAR 153/discretisation.py', wdir='C:/Users/MARTELET Tom Port/Documents/GitHub/epsa-pae-ai-self-driving/PAR 153')</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" text-decoration: underline; color:#a52a2a;">Reloaded modules</span><span style=" color:#a52a2a;">: car_interface, track_2_generator, pid_controller, mesure_discret_to_adim</span></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Numero de l'essai : 3</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">cercle, r, theta: d 1.2 0</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">0.5</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">error: 0.0</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">PID direction: 0</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">direction: 90</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
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-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
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-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: -18.20,90.00,139.56</p>
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-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 43.22,90.00,106.59</p>
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-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 93.64,90.00,166.05</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 111.08,90.00,-166.33</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.99,90.00,-102.75</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 77.34,90.00,144.86</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 94.40,90.00,76.20</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 114.87,90.00,-114.16</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 110.70,90.00,-107.94</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 81.13,90.00,-131.01</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 76.20,90.00,178.56</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 100.46,90.00,101.11</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 109.56,90.00,-153.52</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 89.09,90.00,-105.71</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 76.96,90.00,157.36</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 95.53,90.00,69.74</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 106.91,90.00,-112.75</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">cercle, r, theta: d 1.17 50</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">0.4875</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">C:\Users\MARTELET Tom Port\AppData\Local\Programs\Python\Python310\lib\site-packages\shapely\measurement.py:74: RuntimeWarning: invalid value encountered in distance</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"> return lib.distance(a, b, **kwargs)</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">error: 0.030000000000000027</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">PID direction: -9</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">direction: 81</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: angle commande output</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 94.40,90.00,64.38</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 83.02,0.00,-124.82</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 32.98,81.00,104.68</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 17.06,81.00,105.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 51.94,81.00,43.07</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 95.53,81.00,-83.08</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 99.33,81.00,-58.52</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 70.89,81.00,73.10</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 68.24,81.00,57.57</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 89.47,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.23,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.23,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.99,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.23,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 89.47,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 89.47,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 89.85,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 89.85,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.23,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.23,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.61,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 90.61,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: 89.85,81.00,88.54</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">capteurs: </p>
-<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" color:#00ff00;">In [</span><span style=" font-weight:600; color:#00ff00;">3</span><span style=" color:#00ff00;">]:</span> runfile('C:/Users/MARTELET Tom Port/Documents/GitHub/epsa-pae-ai-self-driving/PAR 153/discretisation.py', wdir='C:/Users/MARTELET Tom Port/Documents/GitHub/epsa-pae-ai-self-driving/PAR 153')</p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" text-decoration: underline; color:#a52a2a;">Reloaded modules</span><span style=" color:#a52a2a;">: car_interface, track_2_generator, pid_controller, mesure_discret_to_adim</span></p>
-<p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Numero de l'essai : 4</p></body></html>
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diff --git a/PAR 153/essai_3.json b/PAR 153/essai_3.json
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index 069e61ec252011b14750235f15f86b687e6421d3..0000000000000000000000000000000000000000
--- a/PAR 153/essai_3.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "3", "PID": {"P": 54.11317513, "I": 0.522774415, "D": 257.9071004}, "run": [{"input": {"cercle": "d", "r": 1.2, "theta": 0.0}, "pos": {"x": 1.0, "y": 0.5}, "dist": 0.0, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 90}}, {"input": {"cercle": "d", "r": 1.17, "theta": 50.0}, "pos": {"x": 1.186641040277812, "y": 0.12655333397949825}, "dist": -0.030000000000000027, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 81}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_30.json b/PAR 153/essai_30.json
deleted file mode 100644
index 4e8c1902c8099c5585a68a429429fba39ab67e69..0000000000000000000000000000000000000000
--- a/PAR 153/essai_30.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/PAR 153/essai_31.json b/PAR 153/essai_31.json
deleted file mode 100644
index 765976eedfa64562b75f082f7cdc3bdce7c8fc12..0000000000000000000000000000000000000000
--- a/PAR 153/essai_31.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
- "essai": "31",
- "PID": {
- "P": 100,
- "I": 0.2,
- "D": 257
- },
- "run": [
- {
- "input": {
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- "r": 1.2,
- "theta": 0.0
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- "pos": {
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- "y": 0.5
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- "dist": 0.0,
- "cp": 2,
- "lap": -1,
- "order": {
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- "direction": 35.0
- }
- }
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-}
\ No newline at end of file
diff --git a/PAR 153/essai_32.json b/PAR 153/essai_32.json
deleted file mode 100644
index 8e8d87810014d404411dd704d72ba8ed44debe9b..0000000000000000000000000000000000000000
--- a/PAR 153/essai_32.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
- "essai": "32",
- "PID": {
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- "I": 0.2,
- "D": 257
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- "run": [
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\ No newline at end of file
diff --git a/PAR 153/essai_33.json b/PAR 153/essai_33.json
deleted file mode 100644
index 47cb3f95ede24be8f3e3650af2e24452327d5249..0000000000000000000000000000000000000000
--- a/PAR 153/essai_33.json
+++ /dev/null
@@ -1,46 +0,0 @@
-{
- "essai": "33",
- "PID": {
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- "I": 0.2,
- "D": 257
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- "run": [
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\ No newline at end of file
diff --git a/PAR 153/essai_34.json b/PAR 153/essai_34.json
deleted file mode 100644
index b17c1bd98acf86e30afb625988e370d0146426b5..0000000000000000000000000000000000000000
--- a/PAR 153/essai_34.json
+++ /dev/null
@@ -1,118 +0,0 @@
-{
- "essai": "34",
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- "I": 0.2,
- "D": 257
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- "run": [
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\ No newline at end of file
diff --git a/PAR 153/essai_35.json b/PAR 153/essai_35.json
deleted file mode 100644
index c45123d758fcc2f28d8840c452ab64a23183536e..0000000000000000000000000000000000000000
--- a/PAR 153/essai_35.json
+++ /dev/null
@@ -1,442 +0,0 @@
-{
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- "D": 257
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diff --git a/PAR 153/essai_36.json b/PAR 153/essai_36.json
deleted file mode 100644
index 099ad27cb36f238f1e70444068bb58a7c17a3da9..0000000000000000000000000000000000000000
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diff --git a/PAR 153/essai_37.json b/PAR 153/essai_37.json
deleted file mode 100644
index 11246fcec0db4567b457f1c2a8d7970a6f9a0fbf..0000000000000000000000000000000000000000
--- a/PAR 153/essai_37.json
+++ /dev/null
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diff --git a/PAR 153/essai_38.json b/PAR 153/essai_38.json
deleted file mode 100644
index 0f3e181e0ae4d7eb09c7c2f5b5f8667d6595322a..0000000000000000000000000000000000000000
--- a/PAR 153/essai_38.json
+++ /dev/null
@@ -1,154 +0,0 @@
-{
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\ No newline at end of file
diff --git a/PAR 153/essai_39.json b/PAR 153/essai_39.json
deleted file mode 100644
index 11e8976f54ed37be03ec57afcb60679f3e125f0e..0000000000000000000000000000000000000000
--- a/PAR 153/essai_39.json
+++ /dev/null
@@ -1,64 +0,0 @@
-{
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diff --git a/PAR 153/essai_4.json b/PAR 153/essai_4.json
deleted file mode 100644
index e35b6629e5c02c4c142ca55c46b7a85e2fd41bef..0000000000000000000000000000000000000000
--- a/PAR 153/essai_4.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/PAR 153/essai_40.json b/PAR 153/essai_40.json
deleted file mode 100644
index a113b053139a816bc582c568d36711a376b0a19d..0000000000000000000000000000000000000000
--- a/PAR 153/essai_40.json
+++ /dev/null
@@ -1,856 +0,0 @@
-{
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diff --git a/PAR 153/essai_41.json b/PAR 153/essai_41.json
deleted file mode 100644
index fe19462b84f153285390348a57f0b85fe361af43..0000000000000000000000000000000000000000
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-{
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index a91dfdfef6de441f7fb2c1c85157826c46e2aee6..0000000000000000000000000000000000000000
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index 26304f2652cafefe180b8933617a92ca2a9f5ea9..0000000000000000000000000000000000000000
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diff --git a/PAR 153/essai_45.json b/PAR 153/essai_45.json
deleted file mode 100644
index 00c0ee9521f194ae96a614fbd8ab95a0b8ca69aa..0000000000000000000000000000000000000000
--- a/PAR 153/essai_45.json
+++ /dev/null
@@ -1,46 +0,0 @@
-{
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diff --git a/PAR 153/essai_46.json b/PAR 153/essai_46.json
deleted file mode 100644
index 7543d3526b977a2ce560ab4e8e0d3d1247988f1c..0000000000000000000000000000000000000000
--- a/PAR 153/essai_46.json
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-{
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diff --git a/PAR 153/essai_47.json b/PAR 153/essai_47.json
deleted file mode 100644
index 4409af162ef3f7cf737d0bf1a4db276cd3fbd42d..0000000000000000000000000000000000000000
--- a/PAR 153/essai_47.json
+++ /dev/null
@@ -1,154 +0,0 @@
-{
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\ No newline at end of file
diff --git a/PAR 153/essai_49.json b/PAR 153/essai_49.json
deleted file mode 100644
index 14dcf13b2b355901e97583934b6d9f4d112310bc..0000000000000000000000000000000000000000
--- a/PAR 153/essai_49.json
+++ /dev/null
@@ -1,730 +0,0 @@
-{
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- "x": 0.41317591116653485,
- "y": 0.9924038765061041
- },
- "dist": 0.0,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -20
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.19,
- "theta": 115.0
- },
- "pos": {
- "x": 0.2904517785535699,
- "y": 0.949377611072339
- },
- "dist": -0.010000000000000009,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -17
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.19,
- "theta": 135.0
- },
- "pos": {
- "x": 0.1493928876616702,
- "y": 0.8506071123383299
- },
- "dist": -0.010000000000000009,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -20
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.19,
- "theta": 155.0
- },
- "pos": {
- "x": 0.05062238892766108,
- "y": 0.7095482214464301
- },
- "dist": -0.010000000000000009,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -20
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.23,
- "theta": 175.0
- },
- "pos": {
- "x": -0.010549782772019678,
- "y": 0.544667318158175
- },
- "dist": 0.030000000000000027,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -33
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.27,
- "theta": -155.0
- },
- "pos": {
- "x": 0.020412129359772713,
- "y": 0.27636450316221317
- },
- "dist": 0.07000000000000006,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -37
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.23,
- "theta": -135.0
- },
- "pos": {
- "x": 0.1376077746418944,
- "y": 0.13760777464189433
- },
- "dist": 0.030000000000000027,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -13
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.19,
- "theta": -110.0
- },
- "pos": {
- "x": 0.33041501226768927,
- "y": 0.03406907552698707
- },
- "dist": -0.010000000000000009,
- "cp": 4,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -9
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.22,
- "theta": -85.0
- },
- "pos": {
- "x": 0.5443041692300595,
- "y": -0.0063989715299705985
- },
- "dist": 0.020000000000000018,
- "cp": 5,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -29
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.23,
- "theta": -65.0
- },
- "pos": {
- "x": 0.7165918591421085,
- "y": 0.03551725914371684
- },
- "dist": 0.030000000000000027,
- "cp": 5,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -25
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.2,
- "theta": -45.0
- },
- "pos": {
- "x": 0.8535533905932737,
- "y": 0.1464466094067262
- },
- "dist": 0.0,
- "cp": 5,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -13
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.21,
- "theta": -25.0
- },
- "pos": {
- "x": 0.956930175964311,
- "y": 0.2869299597057307
- },
- "dist": 0.010000000000000009,
- "cp": 5,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -23
- }
- },
- {
- "input": {
- "cercle": "g",
- "r": 1.21,
- "theta": -10.0
- },
- "pos": {
- "x": 0.9965072421436549,
- "y": 0.4124523770929226
- },
- "dist": 0.010000000000000009,
- "cp": 5,
- "lap": -1,
- "order": {
- "speed": 10,
- "direction": -21
- }
- },
- {
- "input": {
- "cercle": "d",
- "r": 1.13,
- "theta": -5.0
- },
- "pos": {
- "x": 1.03095832964847,
- "y": 0.5410358288770224
- },
- "dist": -0.07000000000000006,
- "cp": 0,
- "lap": 0,
- "order": {
- "speed": 10,
- "direction": 78
- }
- },
- {
- "input": {
- "cercle": "d",
- "r": 1.12,
- "theta": -15.0
- },
- "pos": {
- "x": 1.0492346143984348,
- "y": 0.6207822210478431
- },
- "dist": -0.07999999999999985,
- "cp": 0,
- "lap": 0,
- "order": {
- "speed": 10,
- "direction": 90
- }
- },
- {
- "input": {
- "cercle": "d",
- "r": 1.1,
- "theta": -40.0
- },
- "pos": {
- "x": 1.1488962969038017,
- "y": 0.7946109877729972
- },
- "dist": -0.09999999999999987,
- "cp": 0,
- "lap": 0,
- "order": {
- "speed": 10,
- "direction": 85
- }
- },
- {
- "input": {
- "cercle": "d",
- "r": 1.13,
- "theta": -55.0
- },
- "pos": {
- "x": 1.2299410945513825,
- "y": 0.8856840875194003
- },
- "dist": -0.07000000000000006,
- "cp": 0,
- "lap": 0,
- "order": {
- "speed": 10,
- "direction": 100
- }
- },
- {
- "input": {
- "cercle": "d",
- "r": 1.16,
- "theta": -80.0
- },
- "pos": {
- "x": 1.4160700474609835,
- "y": 0.9759904139559006
- },
- "dist": -0.040000000000000036,
- "cp": 0,
- "lap": 0,
- "order": {
- "speed": 10,
- "direction": 103
- }
- },
- {
- "input": {
- "cercle": "d",
- "r": 1.16,
- "theta": -110.0
- },
- "pos": {
- "x": 1.6653097359407398,
- "y": 0.9541847667131891
- },
- "dist": -0.040000000000000036,
- "cp": 1,
- "lap": 0,
- "order": {
- "speed": 10,
- "direction": 96
- }
- },
- {
- "input": {
- "cercle": "d",
- "r": 1.15,
- "theta": -140.0
- },
- "pos": {
- "x": 1.8670629623278434,
- "y": 0.8080023963081335
- },
- "dist": -0.050000000000000044,
- "cp": 1,
- "lap": 0,
- "order": {
- "speed": 10,
- "direction": 93
- }
- }
- ]
-}
\ No newline at end of file
diff --git a/PAR 153/essai_5.json b/PAR 153/essai_5.json
deleted file mode 100644
index 7f654010a8505a5bedc0fcfccddfaa1bf3aab525..0000000000000000000000000000000000000000
--- a/PAR 153/essai_5.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "5", "PID": {"P": 54.11317513, "I": 0.522774415, "D": 257.9071004}, "run": [{"input": {"cercle": "d", "r": 1.19, "theta": 80.0}, "pos": {"x": 1.413899445240147, "y": 0.01169948913144686}, "dist": -0.010000000000000009, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 87}}, {"input": {"cercle": "d", "r": 1.18, "theta": 65.0}, "pos": {"x": 1.2922126879774893, "y": 0.05439867137364712}, "dist": -0.020000000000000018, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 87}}, {"input": {"cercle": "d", "r": 1.21, "theta": 50.0}, "pos": {"x": 1.1759279134497032, "y": 0.11378592659418191}, "dist": 0.010000000000000009, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 98}}, {"input": {"cercle": "d", "r": 1.23, "theta": 30.0}, "pos": {"x": 1.0561619805604752, "y": 0.24375000000000002}, "dist": 0.030000000000000027, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 96}}, {"input": {"cercle": "d", "r": 1.24, "theta": 20.0}, "pos": {"x": 1.0144921459272807, "y": 0.3232895926150712}, "dist": 0.040000000000000036, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 94}}, {"input": {"cercle": "d", "r": 1.27, "theta": 5.0}, "pos": {"x": 0.9728469722597847, "y": 0.4538800861293642}, "dist": 0.07000000000000006, "cp": 2, "lap": -1, "order": {"speed": 10, "direction": 101}}, {"input": {"cercle": "g", "r": 1.17, "theta": 10.0}, "pos": {"x": 0.9800937795934515, "y": 0.5846534866126285}, "dist": -0.030000000000000027, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": 27}}, {"input": {"cercle": "d", "r": 1.27, "theta": 20.0}, "pos": {"x": 1.0027459881674567, "y": 0.31901434082350033}, "dist": 0.07000000000000006, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -49}}, {"input": {"cercle": "g", "r": 1.32, "theta": 45.0}, "pos": {"x": 0.8889087296526013, "y": 0.8889087296526013}, "dist": 0.1200000000000001, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -39}}, {"input": {"cercle": "g", "r": 1.29, "theta": 60.0}, "pos": {"x": 0.76875, "y": 0.9654886545341359}, "dist": 0.09000000000000008, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -18}}, {"input": {"cercle": "g", "r": 1.23, "theta": 80.0}, "pos": {"x": 0.5889946910543018, "y": 1.0047139734187567}, "dist": 0.030000000000000027, "cp": 3, "lap": -1, "order": {"speed": 10, "direction": -7}}, {"input": {"cercle": "g", "r": 1.2, "theta": 100.0}, "pos": {"x": 0.41317591116653485, "y": 0.9924038765061041}, "dist": 0.0, "cp": 4, "lap": -1, "order": {"speed": 10, "direction": -13}}, {"input": {"cercle": "g", "r": 1.17, "theta": 115.0}, "pos": {"x": 0.29397359740140905, "y": 0.9418250461803669}, "dist": -0.030000000000000027, "cp": 4, "lap": -1, "order": {"speed": 10, "direction": -11}}, {"input": {"cercle": "g", "r": 1.14, "theta": 1.25}, "pos": {"x": 0.9748869628629568, "y": 0.5103620703914166}, "dist": -0.06000000000000005, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -10}}, {"input": {"cercle": "g", "r": 1.14, "theta": 140.0}, "pos": {"x": 0.1361288895184855, "y": 0.8053241146011063}, "dist": -0.06000000000000005, "cp": 5, "lap": -1, "order": {"speed": 10, "direction": -17}}]}
\ No newline at end of file
diff --git a/PAR 153/essai_9.json b/PAR 153/essai_9.json
deleted file mode 100644
index 1e30c494ee08a92a3678981d097c59ca7fd951e5..0000000000000000000000000000000000000000
--- a/PAR 153/essai_9.json
+++ /dev/null
@@ -1 +0,0 @@
-{"essai": "9", "PID": {"P": 54.11317513, "I": 0.222774415, "D": 257.9071004}, "run": [{"input": {"cercle": "d", "r": 1.19, "theta": 0.0}, "pos": {"x": 1.0041666666666667, "y": 0.5}, "dist": -0.010000000000000009, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 87}}, {"input": {"cercle": "d", "r": 1.18, "theta": -20.0}, "pos": {"x": 1.0379844614469285, "y": 0.6681599038017871}, "dist": -0.020000000000000018, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 87}}, {"input": {"cercle": "d", "r": 1.18, "theta": -35.0}, "pos": {"x": 1.0972502448912458, "y": 0.7820084145392643}, "dist": -0.020000000000000018, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 89}}, {"input": {"cercle": "d", "r": 1.2, "theta": -60.0}, "pos": {"x": 1.25, "y": 0.9330127018922193}, "dist": 0.0, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 95}}, {"input": {"cercle": "d", "r": 1.25, "theta": -75.0}, "pos": {"x": 1.3651984140091038, "y": 1.0030863678588897}, "dist": 0.050000000000000044, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 105}}, {"input": {"cercle": "d", "r": 1.3, "theta": -90.0}, "pos": {"x": 1.5, "y": 1.0416666666666667}, "dist": 0.10000000000000009, "cp": 0, "lap": -1, "order": {"speed": 10, "direction": 108}}, {"input": {"cercle": "d", "r": 1.3, "theta": -105.0}, "pos": {"x": 1.640193649430532, "y": 1.0232098225732456}, "dist": 0.10000000000000009, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 95}}, {"input": {"cercle": "d", "r": 1.32, "theta": -115.0}, "pos": {"x": 1.7324400439573846, "y": 0.9984692828701576}, "dist": 0.1200000000000001, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 101}}, {"input": {"cercle": "d", "r": 1.32, "theta": -125.0}, "pos": {"x": 1.8154670399930752, "y": 0.9505336243589457}, "dist": 0.1200000000000001, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 96}}, {"input": {"cercle": "d", "r": 1.32, "theta": -130.0}, "pos": {"x": 1.8535331853275967, "y": 0.921324443715438}, "dist": 0.1200000000000001, "cp": 1, "lap": -1, "order": {"speed": 10, "direction": 96}}]}
\ No newline at end of file
diff --git a/PAR 153/essais.zip b/PAR 153/essais.zip
deleted file mode 100644
index 5d79b83a6e9298c3185e1953cc9c47c9218649a7..0000000000000000000000000000000000000000
Binary files a/PAR 153/essais.zip and /dev/null differ
diff --git a/PAR 153/mesure_discret_to_adim.py b/PAR 153/mesure_discret_to_adim.py
deleted file mode 100644
index c17ec246d6a8eb77fa004089940f0837c4f476bb..0000000000000000000000000000000000000000
--- a/PAR 153/mesure_discret_to_adim.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import math
-
-def pol2xy(cercle,rayon,angle):
- distance_norm=rayon/2.4 #18.25 distance entre les deux cercles, égale à 1 ici
- print(distance_norm)
- # Angle mesuré depuis le centre du cercle choisi vers le point (1,1) dans le sens Trigo
- angle_rad=math.pi*angle/180
- if cercle=="g":
- x=0.5+distance_norm*math.cos(angle_rad)
- y=0.5+distance_norm*math.sin(angle_rad)
- else:
- x=1.5-distance_norm*math.cos(angle_rad)
- y=0.5-distance_norm*math.sin(angle_rad)
- return(x,y)
-
-if __name__=="__main__":
- print(pol2xy("droite",1.2,-90))
\ No newline at end of file
diff --git a/PAR 153/pid_controller.py b/PAR 153/pid_controller.py
deleted file mode 100644
index 051538994b5b0e5f3456729356947b4e000e73e5..0000000000000000000000000000000000000000
--- a/PAR 153/pid_controller.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-
-
-class PidController:
- def __init__(self, p_gain, i_gain, d_gain, set_point=0):
- self.p_gain = p_gain
- self.i_gain = i_gain
- self.d_gain = d_gain
- self.set_point = set_point
- self.integrated_error = 0
- self.previous_error = 0
-
- def get_control(self, process_value):
- error = self.set_point - process_value
- print(f"error: {error}")
- control = self.p_gain * error + self.i_gain * self.integrated_error + self.d_gain * (error - self.previous_error)
- self.previous_error = error
- self.integrated_error += error
- return np.sign(control) * abs(control)
diff --git a/PAR 153/pilote.py b/PAR 153/pilote.py
deleted file mode 100644
index 47723ca682b02a56482501e0157cda5702011fa1..0000000000000000000000000000000000000000
--- a/PAR 153/pilote.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import keyboard
-
-def get_command():
- forward = 0
- direction = 30
- if keyboard.is_pressed('q'):
- direction=-50
- elif keyboard.is_pressed('d'):
- direction=110
- if keyboard.is_pressed('z'):
- forward = 10
- if keyboard.is_pressed('s'):
- forward = -10
- return forward, direction, 0
\ No newline at end of file
diff --git a/PAR 153/test_communication/ecrire_port_serie.py b/PAR 153/test_communication/ecrire_port_serie.py
deleted file mode 100644
index 4fb2c36f5c15941b5bf0e143817cdd13339d0bc5..0000000000000000000000000000000000000000
--- a/PAR 153/test_communication/ecrire_port_serie.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import serial #Importation de la bibliothèque « pySerial »
-import time
-import json
-import keyboard
-
-ser = serial.Serial(port="COM3", baudrate=115200, timeout=0.05,write_timeout=0.1) #Création du port lié au COM6 a une vitesse de 115200 bauds et un timout d’une seconde
-ser.close() #Cloture du port pour le cas ou il serait déjà ouvert ailleurs
-ser.open() #Ouverture du port
-
-if ser.isOpen():
- print(ser.name + " is open…")
- print(ser.get_settings()) #Grace a ces 3 lignes lorsque le Port est ouvert c’est indiqué dans le LOG
-
-#ser.write(b‘hello’) #Ecriture de « Hello » sur le port
-
-
-time.sleep(3)
-"""script inputs"""
-#"""
-while True:
- with open('input.txt') as f:
- n = f.readlines()
- n = f"{n}"
- print("input: "+n)
- ser.write((n+'\n').encode("utf-8"))
- cc=str(ser.readline())
- print("output: "+cc[2:][:-5])
- print("Success")
-#"""
-
-
-"""kb inputs"""
-
-"""
-while True:
- forward = 0
- direction = 30
- if keyboard.is_pressed('q'):
- direction=-70
- if keyboard.is_pressed('x'):
- direction=direction/2
- elif keyboard.is_pressed('d'):
- direction=130
- if keyboard.is_pressed('x'):
- direction=direction/2
- if keyboard.is_pressed('z'):
- forward = 20
- if keyboard.is_pressed('s'):
- forward = -10
- n = f"['{str(forward)} {str(direction)} 0']"
- print("input: "+str(n))
- ser.write((str(n)+'\n').encode("utf-8"))
- cc=str(ser.readline())
- print("output: "+cc[2:][:-5])
- print("Success")
-#"""
-
-ser.close()
\ No newline at end of file
diff --git a/PAR 153/test_communication/get_ports.py b/PAR 153/test_communication/get_ports.py
deleted file mode 100644
index 2740212d92a652cce7e29080a60710ae86bd77f5..0000000000000000000000000000000000000000
--- a/PAR 153/test_communication/get_ports.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import sys
-import glob
-import serial
-
-
-def serial_ports():
- """ Lists serial port names
-
- :raises EnvironmentError:
- On unsupported or unknown platforms
- :returns:
- A list of the serial ports available on the system
- """
- if sys.platform.startswith('win'):
- ports = ['COM%s' % (i + 1) for i in range(256)]
- elif sys.platform.startswith('linux') or sys.platform.startswith('cygwin'):
- # this excludes your current terminal "/dev/tty"
- ports = glob.glob('/dev/tty[A-Za-z]*')
- elif sys.platform.startswith('darwin'):
- ports = glob.glob('/dev/tty.*')
- else:
- raise EnvironmentError('Unsupported platform')
-
- result = []
- for port in ports:
- try:
- s = serial.Serial(port)
- s.close()
- result.append(port)
- except (OSError, serial.SerialException):
- pass
- return result
-
-
-if __name__ == '__main__':
- print(serial_ports())
\ No newline at end of file
diff --git a/PAR 153/test_communication/input.txt b/PAR 153/test_communication/input.txt
deleted file mode 100644
index 0f16ef5782a90112ae37411d6e2ce9961400b1b9..0000000000000000000000000000000000000000
--- a/PAR 153/test_communication/input.txt
+++ /dev/null
@@ -1 +0,0 @@
-10 0 0
\ No newline at end of file
diff --git a/PAR 153/track_2_generator.py b/PAR 153/track_2_generator.py
deleted file mode 100644
index f16019d79ff8c0438e70c2f82e5e4d5d723c0880..0000000000000000000000000000000000000000
--- a/PAR 153/track_2_generator.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from math import cos, sin, pi
-import matplotlib.pyplot as plt
-from shapely.geometry import Point, Polygon
-
-def create_track(r, start_point_distance_y, end_point_distance_y, tot_shift):
- laps = 1 #nombre de tours
- resolution_circle = 10 #resolution du cercle en degre
- resolution_line = 10
-
- #contruire les points qui vont former le chemin
- points = [(0, start_point_distance_y)]
- current_x, current_y = 0, start_point_distance_y
- if current_y<0:
- #faire les points de la première ligne droite
- while current_y<0:
- current_y += resolution_line
- points.append((current_x, current_y))
-
- #faire les points des cercles
- current_angle = 0 #angle en degree
- for i in range(laps):
- while current_angle<720:
- rad = current_angle/360*2*pi
- if 0<current_angle<360:
- current_x, current_y = r-r*cos(rad), r*sin(rad)
- else:
- current_x, current_y = -(r-r*cos(rad)), r*sin(rad)
- current_angle+=resolution_circle
- points.append((current_x, current_y))
-
- if current_y<end_point_distance_y:
- current_x=0
- while current_y<end_point_distance_y:
- current_y += resolution_line
- points.append((current_x, current_y))
-
-
- #on decale les points
- shift_x = -min([x[0] for x in points])
- shift_y = -min([x[1] for x in points])
-
- points = [(x+shift_x+tot_shift[0],y+shift_y+tot_shift[1]) for x,y in points]
-
- #print(points)
- return points
-
-def create_double_circles(r, left_center, right_center):
- resolution_circle = 10 #resolution du cercle en degre
- current_angle=0
- l_l = []
- l_r = []
- while current_angle<360:
- rad = current_angle/360*2*pi
- current_x_left, current_y_left = left_center[0]+r*cos(rad), left_center[1]+r*sin(rad)
- current_x_right, current_y_right = right_center[0]+r*cos(rad), right_center[1]+r*sin(rad)
- #if current_x_left<(left_center[0]+right_center[0])/2:
- l_l.append((current_x_left, current_y_left))
- #if current_x_right>(left_center[0]+right_center[0])/2:
- l_r.append((current_x_right, current_y_right))
- current_angle+=resolution_circle
-
- return l_l+l_r
-
-def get_cone_map():
- pixel_x_ext, pixel_y_ext = [242, 220, 165, 110, 63, 33, 22, 34, 63, 110, 165, 220, 243, 310, 334, 388, 443, 490, 521, 531, 520, 489, 443, 388, 333, 310], [76, 64, 52, 64, 95, 141, 196, 252, 298, 330, 340, 328, 318, 316, 328, 339, 329, 298, 251, 196, 142, 95, 64, 53, 64, 77]
- pixel_x_int, pixel_y_int = [245, 238, 222, 196, 166, 134, 108, 91, 85, 90, 109, 134, 165, 196, 222, 239, 308, 314, 332, 358, 388, 419, 445, 462, 468, 462, 445, 419, 388, 359, 332, 314], [201, 167, 140, 123, 116, 123, 140, 165, 195, 228, 253, 270, 277, 270, 253, 227, 200, 226, 253, 270, 277, 270, 253, 228, 197, 166, 140, 122, 117, 123, 140, 166]
- diametre = 225
- centre_x, centre_y = 278,200
-
- coord_ext = [((i-centre_x)/diametre+1 , (j-centre_y)/diametre+0.5) for i,j in zip(pixel_x_ext, pixel_y_ext)]
- coord_int = [((i-centre_x)/diametre+1 , (j-centre_y)/diametre+0.5) for i,j in zip(pixel_x_int, pixel_y_int)]
- return coord_int+coord_ext
-
-def checkpoints(n):
- points = create_track(0.5,0,0, (0,0))[:-1]+[(1,0.5)]
- if n==0:
- return points[:11]
- elif n==1:
- return points[10:29]
- elif n==2:
- return points[28:38]
- elif n==3:
- return points[37:47]
- elif n==4:
- return points[46:65]
- elif n==5:
- return points[64:]
- return points
-
-def is_in_track(x,y):
- t = Polygon(create_track(0.5,0,0, (0,0))[:-1]+[(1,0.5)])
- p = Point(x,y)
- return t.contains(p)
-
-if __name__ =="__main__":
- points = create_track(0.5,0,0, (0,0))[:-1]+[(1,0.5)]
- cp_points = checkpoints(5)
- plt.plot([x for x,y in cp_points], [y for x,y in cp_points], 'or')
- plt.plot([x for x,y in points], [y for x,y in points])
- """
- #construire le svg
- mid = ""
- #prendre le code autour du chemin
- f = open("basic_track.svg", "r")
- t = f.read().split('CUT_HERE')
- for i,point in enumerate(points):
- mid += f'<circle \n id="path{i}" \n style="fill:#000000;stroke:none" \n cx="{point[0]}" \n cy="{point[1]}" \n r="0.1" /> \n'
- t = t[0]+mid+t[1]
-
- f = open("track_11.svg", "w")
- f.write(t)
- f.close()
- """
-
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