CARIA.2.0

Precedent repo CARIA :
Ajout de nouveau modele.
Travail sur multiple ia.
...

server-ia/Camera_Identification_VitesseRoadSign/lire_panneau.py

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+import tensorflow as tf
+from tensorflow.keras import layers, models
+import cv2
+import os
+import numpy as np
+import random
+
+th1=30
+th2=55
+size=42
+video_dir="server-ia/data/videos/"
+dir_images_panneaux="server-ia/data/images/panneaux"
+
+def panneau_model(nbr_classes):
+    model=tf.keras.Sequential()
+
+    model.add(layers.Input(shape=(size, size, 3), dtype='float32'))
+    
+    model.add(layers.Conv2D(128, 3, strides=1))
+    model.add(layers.Dropout(0.2))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Activation('relu'))
+
+    model.add(layers.Conv2D(128, 3, strides=1))
+    model.add(layers.Dropout(0.2))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Activation('relu'))
+
+    model.add(layers.MaxPool2D(pool_size=2, strides=2))
+
+    model.add(layers.Conv2D(256, 3, strides=1))
+    model.add(layers.Dropout(0.3))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Activation('relu'))
+
+    model.add(layers.Conv2D(256, 3, strides=1))
+    model.add(layers.Dropout(0.4))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Activation('relu'))
+
+    model.add(layers.MaxPool2D(pool_size=2, strides=2))
+
+    model.add(layers.Flatten())
+    model.add(layers.Dense(512, activation='relu'))
+    model.add(layers.Dropout(0.5))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Dense(nbr_classes, activation='sigmoid'))
+    
+    return model
+
+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
+
+tab_panneau, tab_image_panneau=lire_images_panneaux(dir_images_panneaux)
+
+model_panneau=panneau_model(len(tab_panneau))
+checkpoint=tf.train.Checkpoint(model_panneau=model_panneau)
+checkpoint.restore(tf.train.latest_checkpoint("server-ia\data\modeles\road_sign_speed_trainers/"))
+
+l=os.listdir(video_dir)
+random.shuffle(l)
+
+for video in l:
+    if not video.endswith("mp4"):
+        continue
+    cap=cv2.VideoCapture(video_dir+"/"+video)
+
+    print("video:", video)
+    id_panneau=-1
+    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]
+
+        # represents the top left corner of rectangle 
+        start_point = (600, 50)
+        # Ending coordinate
+        # represents t
+        # he bottom right corner of rectangle 
+        end_point = (800, 450)
+        # Color in BGR 
+        color = (255, 255, 255)
+        # Line thickness
+        thickness = 1
+
+        cv2.rectangle(frame, start_point, end_point, color, thickness) 
+
+        gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+        circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=th1, param2=th2, 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]], (size, size))/255
+                    cv2.imshow("panneau", panneau)
+                    prediction=model_panneau(np.array([panneau]), training=False)
+                    print("Prediction:", prediction)
+                    if np.any(np.greater(prediction[0], 0.6)):
+                        id_panneau=np.argmax(prediction[0])
+                        print("   -> C'est un panneau:", tab_panneau[id_panneau], "KM/H")
+                        w, h, c=tab_image_panneau[id_panneau].shape
+                    else:
+                        print("   -> Ce n'est pas un panneau")
+        if id_panneau!=-1:
+            frame[0:h, 0:w, :]=tab_image_panneau[id_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()