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Divers/tutoriel25/README.md

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+# Tutoriel 25
+## Lecture des panneaux de limitation de vitesse
+
+Les vidéos de ce tutoriel sont disponibles aux adresses suivantes:<br>
+partie 1: https://www.youtube.com/watch?v=PvD5POjXw8Q <br>
+partie 2: https://www.youtube.com/watch?v=TYDi0SNCUr0 <br>
+partie 3: https://www.youtube.com/watch?v=fBysd-Y17Tw
+

Divers/tutoriel25/common.py

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+import tensorflow as tf
+from tensorflow.keras import layers, models
+import os
+import cv2
+
+size=42
+dir_images_panneaux="images_panneaux"
+dir_images_autres_panneaux="images_autres_panneaux"
+dir_images_sans_panneaux="images_sans_panneaux"
+
+def lire_images_panneaux(dir_images_panneaux, size=None):
+    tab_panneau=[]
+    tab_image_panneau=[]
+
+    if not os.path.exists(dir_images_panneaux):
+        quit("Le repertoire d'image n'existe pas: {}".format(dir_images_panneaux))
+
+    files=os.listdir(dir_images_panneaux)
+    if files is None:
+        quit("Le repertoire d'image est vide: {}".format(dir_images_panneaux))
+
+    for file in sorted(files):
+        if file.endswith("png"):
+            tab_panneau.append(file.split(".")[0])
+            image=cv2.imread(dir_images_panneaux+"/"+file)
+            if size is not None:
+                image=cv2.resize(image, (size, size), cv2.INTER_LANCZOS4)
+            tab_image_panneau.append(image)
+            
+    return tab_panneau, tab_image_panneau

Divers/tutoriel25/dataset.py

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+import numpy as np
+import cv2
+from multiprocessing import Pool
+import multiprocessing
+import random
+
+def bruit(image_orig):
+    h, w, c=image_orig.shape
+    n=np.random.randn(h, w, c)*random.randint(5, 30)
+    return np.clip(image_orig+n, 0, 255).astype(np.uint8)
+
+def change_gamma(image, alpha=1.0, beta=0.0):
+    return np.clip(alpha*image+beta, 0, 255).astype(np.uint8)
+    
+def modif_img(img):
+    h, w, c=img.shape
+
+    r_color=[np.random.randint(255), np.random.randint(255), np.random.randint(255)]
+    img=np.where(img==[142, 142, 142], r_color, img).astype(np.uint8)
+
+    if np.random.randint(3):
+        k_max=3
+        kernel_blur=np.random.randint(k_max)*2+1
+        img=cv2.GaussianBlur(img, (kernel_blur, kernel_blur), 0)
+
+    M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), random.randint(-10, 10), 1)
+    img=cv2.warpAffine(img, M, (w, h))
+        
+    if np.random.randint(2):
+        a=int(max(w, h)/5)+1
+        pts1=np.float32([[0, 0], [w, 0], [0, h], [w, h]])
+        pts2=np.float32([[0+random.randint(-a, a), 0+random.randint(-a, a)], [w-random.randint(-a, a), 0+random.randint(-a, a)], [0+random.randint(-a, a), h-random.randint(-a, a)], [w-random.randint(-a, a), h-random.randint(-a, a)]])        
+        M=cv2.getPerspectiveTransform(pts1,pts2)
+        img=cv2.warpPerspective(img, M, (w, h))
+        
+    if np.random.randint(2):
+        r=random.randint(0, 5)
+        h2=int(h*0.9)
+        w2=int(w*0.9)
+        if r==0:
+            img=img[0:w2, 0:h2]
+        elif r==1:
+            img=img[w-w2:w, 0:h2]
+        elif r==2:
+            img=img[0:w2, h-h2:h]
+        elif r==3:
+            img=img[w-w2:w, h-h2:h]
+        img=cv2.resize(img, (h, w))
+
+    if np.random.randint(2):
+        r=random.randint(1, int(max(w, h)*0.15))
+        img=img[r:w-r, r:h-r]
+        img=cv2.resize(img, (h, w))
+
+    if not np.random.randint(4):
+        t=np.empty((h, w, c) , dtype=np.float32)
+        for i in range(h):
+            for j in range(w):
+                for k in range(c):
+                    t[i][j][k]=(i/h)
+        M=cv2.getRotationMatrix2D((int(w/2), int(h/2)), np.random.randint(4)*90, 1)
+        t=cv2.warpAffine(t, M, (w, h))
+        img=(cv2.multiply((img/255).astype(np.float32), t)*255).astype(np.uint8)
+
+    img=change_gamma(img, random.uniform(0.6, 1.0), -np.random.randint(50))
+
+    if not np.random.randint(4):
+        p=(15+np.random.randint(10))/100
+        img=(img*p+50*(1-p)).astype(np.uint8)+np.random.randint(100)
+
+    img=bruit(img)
+        
+    return img
+
+def create_lot_img(image, nbr, nbr_thread=None):
+    if nbr_thread is None:
+        nbr_thread=multiprocessing.cpu_count()
+    lot_original=np.repeat([image], nbr, axis=0)
+    with Pool(nbr_thread) as p:
+        lot_result=p.map(modif_img, lot_original)
+        p.close()
+    return lot_result

Divers/tutoriel25/extract_panneau.py

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+import cv2
+import os
+import numpy as np
+import random
+import common
+
+video_dir="D:\dashcam Cedric"
+
+l=os.listdir(video_dir)
+
+for video in l:
+    if not video.endswith("mp4"):
+        continue
+    cap=cv2.VideoCapture(video_dir+"/"+video)
+
+    print("video:", video)
+    while True:
+        ret, frame=cap.read()
+        if ret is False:
+            break
+        f_w, f_h, f_c=frame.shape
+        frame=cv2.resize(frame, (int(f_h/1.5), int(f_w/1.5)))
+
+        image=frame[200:400, 700:1000]
+        cv2.rectangle(frame, (700, 200), (1000, 400), (255, 255, 255), 1)
+
+        gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=30, param2=60, minRadius=5, maxRadius=45)
+        if circles is not None:
+            circles=np.int16(np.around(circles))
+            for i in circles[0,:]:
+                if i[2]!=0:
+                    panneau=cv2.resize(image[max(0, i[1]-i[2]):i[1]+i[2], max(0, i[0]-i[2]):i[0]+i[2]], (common.size, common.size))/255
+                    cv2.imshow("panneau", panneau)
+        cv2.putText(frame, "fichier:"+video, (30, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA)
+        cv2.imshow("Video", frame)
+        key=cv2.waitKey(1)&0xFF
+        if key==ord('q'):
+            quit()
+        if key==ord('a'):
+            for cpt in range(100):
+                ret, frame=cap.read()
+        if key==ord('f'):
+            break
+
+cv2.destroyAllWindows()

Divers/tutoriel25/genere_panneaux.py

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+import numpy as np
+from sklearn.utils import shuffle
+import cv2
+import common
+import dataset
+
+tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size)
+
+tab_images=np.array([]).reshape(0, common.size, common.size, 3)
+tab_labels=[]
+
+id=0
+for image in tab_image_panneau:
+    lot=dataset.create_lot_img(image, 1000)
+    tab_images=np.concatenate([tab_images, lot])
+    tab_labels=np.concatenate([tab_labels, np.full(len(lot), id)])
+    id+=1
+
+tab_panneau=np.array(tab_panneau)
+tab_images=np.array(tab_images, dtype=np.float32)/255
+tab_labels=np.array(tab_labels).reshape([-1, 1])
+
+tab_images, tab_labels=shuffle(tab_images, tab_labels)
+
+for i in range(len(tab_images)):
+    cv2.imshow("panneau", tab_images[i])
+    print("label", tab_labels[i], "panneau", tab_panneau[int(tab_labels[i])])
+    if cv2.waitKey()&0xFF==ord('q'):
+        quit()

Divers/tutoriel25/houghcircles.py

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+import cv2
+import numpy as np
+
+param1=30
+param2=55
+dp=1.0
+
+cap=cv2.VideoCapture(0)
+
+while True:
+    ret, frame=cap.read()
+    gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp, 20, param1=param1, param2=param2, minRadius=10, maxRadius=50)
+    if circles is not None:
+        circles=np.around(circles).astype(np.int32)
+        for i in circles[0, :]:
+            if i[2]!=0:
+                cv2.circle(frame, (i[0], i[1]), i[2], (0, 255, 0), 4)
+    cv2.putText(frame, "[i|k]dp: {:4.2f}  [o|l]param1: {:d}  [p|m]param2: {:d}".format(dp, param1, param2), (10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 0), 1)
+    cv2.imshow("Video", frame)
+    key=cv2.waitKey(1)&0xFF
+    if key==ord('q'):
+        quit()
+    if key==ord('i'):
+        dp=min(10, dp+0.1)
+    if key==ord('k'):
+        dp=max(0.1, dp-0.1)
+    if key==ord('o'):
+        param1=min(255, param1+1)
+    if key==ord('l'):
+        param1=max(1, param1-1)
+    if key==ord('p'):
+        param2=min(255, param2+1)
+    if key==ord('m'):
+        param2=max(1, param2-1)
+cv2.destroyAllWindows()