diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Calibration_cam/out.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Calibration_cam/out.jpg
index 3b2f09ab135d9b1029dc1fb1a6fdbe3e40c5a5f9..02c35afc965ab6e431b000f0bcc4fbb4df3b0ad7 100644
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Calibration_cam/out.jpg and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/Calibration_cam/out.jpg differ
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
index 6cd07d0cbe0ee518c20595d20a68f5ba584f0093..16b1f4aa1c1649021e14b8b5a8d0b1ef1e9ef754 100644
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/MCL.py
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/MCL.py
@@ -1,12 +1,12 @@
-#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 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
+from mpl_toolkits.mplot3d import axes3d
+from matplotlib import cm
import numpy as np
import random as rd
@@ -17,19 +17,48 @@ from math import cos, sin, tan, atan
### 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]
+# 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_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 = 0.02
-sigma_direction = 1*3.1415/180
+sigma_position = 0.05
+sigma_direction = 8*3.1415/180
seuil_cone = 0.6
@@ -37,9 +66,9 @@ 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 = 156.25 #3957/3 #Focale camera
+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 = 240 #Milieu de l'image
+y_0 = 3264/2 #Milieu de l'image
dist_roue = 0.25 #Distance entre les roues avant et les roues arrieres
@@ -59,13 +88,18 @@ def distance(x_1, y_1, x_2, y_2):
def boite2coord(x1,y1,x2,y2):
- d = F*h_reel/abs(y1-y2)
- #y_r = -((x1+x2)/2 - y_0)*d/F
- ypmax=480/2
- ymax=0.54
- y_r=((x1+x2)/2-ypmax)/ypmax*ymax
- sin_theta= y_r/d
- x_r = d*(1-sin_theta**2)**0.5
+ 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
@@ -98,21 +132,16 @@ def distance_Chamfer(A_x, A_y, B_x, B_y):
return res
-def orientation_voiture(x,y):
- x1, y1=x,y
- #x1,y1 = x-centre_x, y-centre_y
- #print(f"1: x: {x1}, y: {y1}")
-
- if x<0:
- x2,y2 = x1+diametre/2, y1
- #print(f"2: x: {x2}, y: {y2}")
- theta = atan(y2/x2)
- return theta-3.1415/2
- else:
- x2,y2 = x1-diametre/2, y1
- #print(f"2: x: {x2}, y: {y2}")
- theta = atan(y2/x2)
- return theta+3.1415/2
+# 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):
@@ -138,8 +167,7 @@ def motion_update(commande, position):
centre_rotation_y = y + R*cos(theta)
new_x, new_y = rotation((x,y),(centre_rotation_x, centre_rotation_y), angle_rotation)
- new_theta = orientation_voiture(new_x, new_y)
- #print(new_theta)
+ 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)
@@ -167,12 +195,12 @@ def sensor_update(observation, position):
cones_vu_x = []
cones_vu_y = []
for i in range(len(vision_x)):
- if vision_x[i]>0 and abs(vision_y[i])<0.54*vision_x[i]:
+ 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])<0.67*vision_x_ext[i]:
+ 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])
@@ -264,41 +292,25 @@ def get_position(boxes,commande,pos):
if __name__ == "__main__":
- # detection = [[2.18939087e+03, 1.48177417e+03, 2.86449023e+03, 2.54427783e+03, 9.66490030e-01, 0.00000000e+00],
- # [4.22634644e+02, 1.44312561e+03, 1.08822302e+03, 2.31131567e+03, 9.36378121e-01, 0.00000000e+00],
- # [1.33116504e+03, 1.42764783e+03, 1.70783643e+03, 1.98202075e+03, 9.03878212e-01, 0.00000000e+00],
- # [2.11831836e+03, 1.41914294e+03, 2.43602563e+03, 1.87002930e+03, 9.03337121e-01, 0.00000000e+00],
- # [2.24867358e+03, 1.59305701e+03, 2.99900000e+03, 3.76038647e+03, 8.82729828e-01, 0.00000000e+00],
- # [2.64460254e+03, 1.44378406e+03, 2.91649780e+03, 2.14795459e+03, 3.62023830e-01, 0.00000000e+00]]
-
- # detection = [[0.00000000e+00, 1.85167456e+03, 9.15495361e+02, 3.25824146e+03, 9.37295914e-01, 0.00000000e+00],
- # [2.41986987e+03, 1.86332446e+03, 2.86713550e+03, 2.42170972e+03, 9.23870742e-01, 0.00000000e+00],
- # [1.36284387e+03, 1.83517468e+03, 1.92408057e+03, 2.59739648e+03, 9.17667687e-01, 0.00000000e+00]]
-
- detection = [[1.28486133e+03, 1.79538904e+03, 1.78743445e+03, 2.45994067e+03, 9.39055085e-01, 0.00000000e+00],
- [0.00000000e+00, 1.87250220e+03, 7.69682068e+02, 3.12636377e+03, 9.35593545e-01, 0.00000000e+00],
- [2.12719995e+03, 1.72824695e+03, 2.48698218e+03, 2.24433813e+03, 9.07173812e-01, 0.00000000e+00],
- [2.72219897e+03, 2.18795605e+03, 2.99900000e+03, 3.47306348e+03, 3.55658233e-01, 0.00000000e+00]]
-
- # detection = [[1.12150806e+03, 1.82397412e+03, 1.71230017e+03, 2.59400610e+03, 9.47640836e-01, 0.00000000e+00],
- # [2.17284106e+03, 1.79985144e+03, 2.59600366e+03, 2.34515747e+03, 9.44890141e-01, 0.00000000e+00],
- # [2.78233301e+03, 1.75100500e+03, 2.99900000e+03, 2.26862329e+03, 9.10233915e-01, 0.00000000e+00],
- # [0.00000000e+00, 1.92825171e+03, 5.37962524e+02, 3.36087646e+03, 8.53242218e-01, 0.00000000e+00],
- # [2.80115698e+03, 3.22995215e+03, 2.99900000e+03, 3.76810620e+03, 3.35420161e-01, 0.00000000e+00],
- # [6.21354027e+01, 1.80441516e+03, 3.51005554e+02, 2.08294287e+03, 3.16491544e-01, 0.00000000e+00]]
+
+ 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 = [(1.5, 1.180, 3.14/3) for i in range(nb_particle)]
+ #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*2/3)
- liste_y.append(y*2/3)
+ liste_x.append(x)
+ liste_y.append(y)
for i in range(len(liste_x)):
z_t.append((liste_x[i], liste_y[i]))
@@ -310,7 +322,8 @@ if __name__ == "__main__":
# 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] = 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,
@@ -363,7 +376,7 @@ if __name__ == "__main__":
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)]
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
index 143a98a285fc8e618c677c725b1ffde81f4d5358..a67d2f76d8ea432cd871518cde5ab2c70f8b5dd6 100644
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/MCL.cpython-38.pyc and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/__pycache__/MCL.cpython-38.pyc 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
index bb816ef09810d906c95ea7628edc4b0e91ec79b6..b0e1e48caed045b23b0cc06f003b689a600f1220 100644
--- a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/calibration.py
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/calibration.py
@@ -5,6 +5,7 @@ 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
@@ -12,25 +13,42 @@ 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 = "./Calibration_cam/" #".jpg"
+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 = 0
+F = 1/0.0051
h_cone = 0.27
+calib=False
-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))
-
- h_pixel.append(abs(boxes[0][1] - boxes[0][3]))
- F += abs(boxes[0][1] - boxes[0][3])*dist/h_cone
-print(F/len(distances))
\ No newline at end of file
+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/detection_custom.py b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/detection_custom.py
index 50ab1cce38df07693e6682f1752dab7c6057e8f3..c5717f681e4b698ab2ee8efd0ac96744a53dc736 100644
--- 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
@@ -16,12 +16,12 @@ 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 = "./Calibration_cam/0.5m.jpg"
+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))
+_, 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_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/images test/test2_pred.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/images test/test2_pred.jpg
index 52004145ecfc3fd10f5d3c196c1e0a43215ab5db..fd9f1c5105feb106ec3c0ac097c466c1d222e29a 100644
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/images test/test2_pred.jpg and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/images test/test2_pred.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/result.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/result.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1decd32fc417eb98ac3b1635c037f76accbe35e2
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/tel grand angle/0.5m.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/tel grand angle/0.5m.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..38dae11942e3c3b2111c58b2563fd3af569d4a56
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diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/tel grand angle/1.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/tel grand angle/1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fa338fe4759c399442cf44b07e8726da6b363714
Binary files /dev/null and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/tel grand angle/1.jpg differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/tel grand angle/10.jpg b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test/tel grand angle/10.jpg
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--- /dev/null
+++ b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/test_positionnement.py
@@ -0,0 +1,77 @@
+# -*- 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/yolov3/__pycache__/track_2_generator.cpython-38.pyc b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__pycache__/track_2_generator.cpython-38.pyc
index 894fe1bdc11300f1a010521fa34eadf9b6cbd654..6ad871e92aa1ad55e4a0b1b22544fa61978ab86b 100644
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__pycache__/track_2_generator.cpython-38.pyc and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__pycache__/track_2_generator.cpython-38.pyc differ
diff --git a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__pycache__/utils.cpython-38.pyc b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__pycache__/utils.cpython-38.pyc
index 85d639dd599df3056aa50cd8831f9a516beeb23f..041154099828f9eed31c4a67d0d0b060d8297cce 100644
Binary files a/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__pycache__/utils.cpython-38.pyc and b/PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/yolov3/__pycache__/utils.cpython-38.pyc differ
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
index 2b3550bf951fbba3b6d706afd88e40cfd4b14f72..0a67221cc8d2531d1d06c830f031c6ef31cca264 100644
--- 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
@@ -220,14 +220,14 @@ def draw_bbox(image, bboxes,liste_pos_car=liste_pos((0,0,0),1), CLASSES=YOLO_COC
"""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 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()
+ # 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()
diff --git a/PAR 152/carte_cones.py b/PAR 152/carte_cones.py
index 34a4efc75a7e8cdb232dc44c991494a2f77f9bae..be2c6a4cbe56c23d68d21149a534e75b1d3d97ff 100644
--- a/PAR 152/carte_cones.py
+++ b/PAR 152/carte_cones.py
@@ -53,30 +53,55 @@ def motion_update(commande, position):
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_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)]
+# 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 = 3.14/5.5
+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_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))
+# 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)
@@ -84,12 +109,12 @@ 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]
+#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.arrow(x2,y2,0.1*cos(theta2),0.1*sin(theta2), width=0.01)
+#plt.plot(x_r,y_r,'o')
plt.show()