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Tensorflow/tutoriel30/README.md

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+# Tutoriel Keras
+## Réseau binaire avec Keras
+
+La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=YLhDlHhthIM
+
+
+

Tensorflow/tutoriel30/config.py

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+
+size=100
+
+dir_neg=".\\images_negatives\\"
+dir_pos=".\\images_positives\\"

Tensorflow/tutoriel30/enregistrement.py

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+import cv2
+import numpy as np
+import os
+import config
+
+saut=10
+
+dir=config.dir_neg
+#dir=config.dir_pos
+os.makedirs(dir, exist_ok=True)
+
+id=0
+while os.path.isfile(dir+"image-{:d}.png".format(id)):
+    id+=1
+id*=saut
+
+cap=cv2.VideoCapture(0)
+width=int(cap.get(3))
+enregistre=0
+while True:
+    ret, frame=cap.read()
+
+    if enregistre:
+        if not id%saut:
+            id_=int(id/saut)
+            fichier=dir+"image-{:d}.png".format(id_)
+            print("Création du fichier", fichier)
+            cv2.imwrite(fichier, frame)
+        id+=1
+
+    cv2.rectangle(frame, (0, 0), (width, 30), (100, 100, 100), cv2.FILLED)
+    cv2.putText(frame, "[e] enregistrement  repertoire: {}  [q] quitter".format(dir), (10, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 0)
+    if enregistre:
+        cv2.circle(frame, (width-20, 15), 5, (0, 0, 255), 8)
+
+    cv2.imshow('Camera', frame)
+    key=cv2.waitKey(1)&0xFF
+    if key==ord('e'):
+        enregistre=not enregistre
+    if key==ord('q'):
+        quit()

Tensorflow/tutoriel30/inference.py

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+import tensorflow as tf
+import cv2
+import numpy as np
+import config
+
+my_model=tf.keras.models.load_model('saved_model\\my_model')
+
+cap=cv2.VideoCapture(0)
+width=cap.get(3)
+height=cap.get(4)
+
+while True:
+    ret, frame=cap.read()
+    img=cv2.resize(frame, (config.size, config.size))/255
+    img=np.array([img], dtype=np.float32)
+    prediction=my_model.predict(img)
+    if prediction[0][0]>0.3:
+        color=(0, 255, 0)
+    else:
+        color=(0, 0, 255)
+    cv2.rectangle(frame, (0, int(height)-30), (int(width*prediction[0][0]), int(height)), color, cv2.FILLED)
+    cv2.imshow('Camera', frame)
+    if cv2.waitKey(1)&0xFF==ord('q'):
+        quit()

Tensorflow/tutoriel30/model.py

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+from tensorflow.keras import layers, models
+
+# Fonction d'activation à tester: sigmoid, tanh, relu, 
+
+def model(size, nbr_cc):
+    entree=layers.Input(shape=(size, size, 3), dtype='float32')
+
+    result=layers.Conv2D(nbr_cc, 3, activation='relu', padding='same')(entree)
+    result=layers.Conv2D(nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.MaxPool2D()(result)
+
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.MaxPool2D()(result)
+
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.MaxPool2D()(result)
+
+    result=layers.Flatten()(result)
+    result=layers.Dense(1024, activation='relu')(result)
+    sortie=layers.Dense(1, activation='sigmoid')(result)
+
+    model=models.Model(inputs=entree, outputs=sortie)
+    return model

Tensorflow/tutoriel30/photo.py

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+import cv2
+import numpy as np
+import os
+import config
+
+os.makedirs(config.dir_pos, exist_ok=True)
+id_pos=0
+while os.path.isfile(config.dir_pos+"image-{:d}.png".format(id_pos)):
+    id_pos+=1
+
+os.makedirs(config.dir_neg, exist_ok=True)
+id_neg=0
+while os.path.isfile(config.dir_neg+"image-{:d}.png".format(id_neg)):
+    id_neg+=1
+
+cap=cv2.VideoCapture(0)
+width=int(cap.get(3))
+
+while True:
+    ret, frame=cap.read()
+
+    cv2.rectangle(frame, (0, 0), (width, 30), (100, 100, 100), cv2.FILLED)
+    cv2.putText(frame, "[p] photo positive    [n] photo negative    [q] quitter", (10, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)
+
+    cv2.imshow('Camera', frame)
+    key=cv2.waitKey(1)&0xFF
+    if key==ord('p'):
+        fichier=config.dir_pos+"image-{:d}.png".format(id_pos)
+        print("Création du fichier", fichier)
+        cv2.imwrite(fichier, frame)
+        id_pos+=1
+    if key==ord('n'):
+        fichier=config.dir_neg+"image-{:d}.png".format(id_neg)
+        print("Création du fichier", fichier)
+        cv2.imwrite(fichier, frame)
+        id_neg+=1
+    if key==ord('q'):
+        quit()

Tensorflow/tutoriel30/train.py

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+import tensorflow as tf
+import numpy as np
+import glob
+import cv2
+import model
+import config
+
+tab_images=[]
+tab_labels=[]
+
+def complete_dataset(files, value):
+    for image in glob.glob(files):
+        img=cv2.imread(image)
+        img=cv2.resize(img, (config.size, config.size))
+        tab_images.append(img)
+        tab_labels.append([value])
+        img=cv2.flip(img, 1)
+        tab_images.append(img)
+        tab_labels.append([value])
+        img=cv2.flip(img, 0)
+        tab_images.append(img)
+        tab_labels.append([value])
+
+complete_dataset(config.dir_pos+'\\*.png', 1.)
+complete_dataset(config.dir_neg+'\\*.png', 0.)
+
+tab_images=np.array(tab_images, dtype=np.float32)/255
+tab_labels=np.array(tab_labels, dtype=np.float32)
+
+index=np.random.permutation(len(tab_images))
+tab_images=tab_images[index]
+tab_labels=tab_labels[index]
+
+#for i in range(len(tab_images)):
+#    cv2.imshow('Camera', tab_images[i])
+#    print("Label", tab_labels[i])
+#    if cv2.waitKey()&0xFF==ord('q'):
+#           quit()
+
+model=model.model(config.size, 8)
+
+model.compile(optimizer='adam',
+              loss='binary_crossentropy',
+              metrics=['accuracy'])
+model.fit(tab_images,
+          tab_labels,
+          validation_split=0.05,
+          batch_size=64,
+          epochs=30)
+model.save('saved_model\\my_model')