Initial commit

Tensorflow/tutoriel33/train2.py

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+import random
+import tensorflow as tf
+import csv
+import numpy as np
+import cv2
+import model
+
+fichier='ISIC2018_Task3_Training_GroundTruth/ISIC2018_Task3_Training_GroundTruth.csv'
+dir_images='ISIC2018_Task3_Training_Input/'
+
+tab_images=[]
+tab_labels=[]
+
+def rotateImage(image, angle):
+    image_center=tuple(np.array(image.shape[1::-1])/2)
+    rot_mat=cv2.getRotationMatrix2D(image_center, angle, 1.0)
+    result=cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)
+    return result
+
+with open(fichier, newline='') as csvfile:
+    lignes=csv.reader(csvfile, delimiter=',')
+    next(lignes, None)
+    for ligne in lignes:
+        label=np.array(ligne[1:], dtype=np.float32)        
+        img=cv2.imread(dir_images+ligne[0]+'.jpg')
+        if img is None:
+            print("Image absente", dir_images+ligne[0]+'.jpg')
+            quit()
+        img=cv2.resize(img, (100, 75))
+        tab_labels.append(label)
+        tab_images.append(img)
+
+        if label[1]:
+            continue
+
+        flag=0
+        for angle in range(0, 360, 30):
+            img_r=rotateImage(img, angle)
+            
+            if label[2] or label[3] or label[5] or label[6]:
+                tab_labels.append(label)
+                i=cv2.flip(img_r, 0)
+                tab_images.append(i)
+
+            if not flag%3 and (label[0] or label[4]):
+                tab_labels.append(label)
+                i=cv2.flip(img_r, 0)
+                tab_images.append(i)
+            flag+=1
+                
+            if label[2] or label[3] or label[5] or label[6]:
+                tab_labels.append(label)
+                i=cv2.flip(img_r, 1)
+                tab_images.append(i)
+
+            if label[5] or label[6]:
+                tab_labels.append(label)
+                i=cv2.flip(img_r, -1)
+                tab_images.append(i)
+
+tab_labels=np.array(tab_labels, dtype=np.float32)        
+tab_images=np.array(tab_images, dtype=np.float32)/255
+
+indices=np.random.permutation(len(tab_labels))
+tab_labels=tab_labels[indices]
+tab_images=tab_images[indices]
+
+print("SOMME", np.sum(tab_labels, axis=0))
+
+model=model.model(7, 8)
+optimizer=tf.keras.optimizers.RMSprop(learning_rate=1E-4)
+csv_logger=tf.keras.callbacks.CSVLogger('training.log')
+
+class my_callback(tf.keras.callbacks.Callback):    
+    def on_epoch_end(self, epoch, logs=None):
+        if epoch>=30 and not epoch%10:
+            model.save('my_model/{:d}'.format(epoch))
+            
+model.compile(optimizer=optimizer,
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(tab_images,
+          tab_labels,
+          validation_split=0.05,
+          batch_size=64,
+          epochs=300,
+          callbacks=[csv_logger, my_callback()])
+
+