Merge branch 'main' of gitlab.ec-lyon.fr:epsa-personnal-git/epsa-pae-ai-self-driving

PAR 152/MCL.py → PAR 152/Yolo V3/TensorFlow-2.x-YOLOv3-master/MCL.py

@@ -3,6 +3,8 @@ from math import cos, sin
 import numpy as np
 import matplotlib.pyplot as plt
 
+from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
+
 ### 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]
@@ -20,6 +22,20 @@ sigma_position = 0.02
 sigma_direction = 0.05
 
 
+dep_x, dep_y, dep_theta = 0,0,0
+nb_particule = 30 
+pos = [[dep_x, dep_y, dep_theta] for i in range(nb_particule)]
+
+F = 1000        #Focale camera
+h_reel = 0.2    #Hauteur d'un plot
+y_0 = 2048/2    #Milieu de l'image
+
+
+# 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)
@@ -28,10 +44,23 @@ def normalisation(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)
+    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 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)]
@@ -47,9 +76,9 @@ def distance_Chamfer(A_x, A_y, B_x, B_y):
 def motion_update(commande, position):
     vitesse, direction = commande
     x,y,theta = position
-    new_x = x + vitesse*cos(direction) + rd.gauss(0, sigma_position)
-    new_y = y + vitesse*sin(direction) + rd.gauss(0, sigma_position)
-    new_theta = theta - direction + rd.gauss(0, sigma_direction)
+    new_x = x + vitesse*cos(direction+theta) + rd.gauss(0, sigma_position)
+    new_y = y + vitesse*sin(direction+theta) + rd.gauss(0, sigma_position)
+    new_theta = theta + direction + rd.gauss(0, sigma_direction)
     return (new_x, new_y, new_theta)
 
 
@@ -123,8 +152,46 @@ def low_variance_resampling(X,W):
         X_t.append(X[i])
     return X_t
 
+
+def get_position(pos, u_t):
+    
+    image_path   = "./IMAGES/IMG_2345.JPEG"
+    video_path   = "./IMAGES/test.mp4"
+    
+    yolo = Load_Yolo_model()
+    boxes = detect_image(yolo, image_path, "./IMAGES/111.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)
+    
+    
+    liste_x =[]
+    liste_y =[]
+    for i in boxes:
+        x,y = boite2coord(i[0],i[1],i[2],i[3])
+        liste_x.append(x)
+        liste_y.append(y)
+        
+    z_t = []
+    for i in range(len(liste_x)):
+        z_t.append((liste_x[i], liste_y[i]))
+    
+    
+    pos, W = particle_filter(pos, u_t, z_t)
+    
+    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]
+    
+    return pos_calc
+    
+
+
 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,
@@ -144,7 +211,7 @@ if __name__ == "__main__":
     for i in range(len(liste_x)):
         observation.append((liste_x[i], liste_y[i]))
     
-    commande = (0.138841, 0.3)
+    commande = (0.138841, -0.)
     
     a,W = particle_filter(pos, commande, observation)
     print(a,W)
@@ -154,9 +221,9 @@ if __name__ == "__main__":
     
     
     plt.figure(1)
-    plt.plot(pos_initiale[0], pos_initiale[1], 'o')
+    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')
+    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,'+')
@@ -164,14 +231,24 @@ if __name__ == "__main__":
     
     pos_x = []
     pos_y = []
-    for k in range(len(a)):
-        i = a[k]
-        pos_x.append(i[0])
-        pos_y.append(i[1])
-        plt.arrow(i[0], i[1], 0.07*cos(i[2]), 0.07*sin(i[2]))
-        plt.annotate(W[k], [i[0], i[1]])
+    #for k in range(len(a)):
+        #i = a[k]
+        #pos_x.append(i[0])
+        #pos_y.append(i[1])
+        #plt.arrow(i[0], i[1], 0.07*cos(i[2]), 0.07*sin(i[2]))
+        #plt.annotate(W[k], [i[0], 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_x,pos_y,'.')
+    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