Refactored

Author: imnotkk

load_images.py

@@ -24,7 +24,7 @@ def imgload(self):
                 # print(file+" "+str(i))
                 img = cv2.imread(path+'/'+file, -1)
 
-                img = cv2.resize(img, (self.width,self.height))
+                img = cv2.resize(img, (self.width, self.height))
                 img = img_to_array(img)
                 self.data.append(img)
                 self.labels.append(i)

model_trainer.py

@@ -27,8 +27,10 @@
 trainX, testX, trainY, testY = train_test_split(data, labels, test_size=0.1, random_state=42)
 trainY = to_categorical(trainY, num_classes=3)
 testY = to_categorical(testY, num_classes=3)
-aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, fill_mode="nearest")
+aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2,
+                         zoom_range=0.2, fill_mode="nearest")
 opt = Adam(lr=learning_rate, decay=learning_rate/epochs)
 model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
-h = model.fit_generator(aug.flow(trainX, trainY, batch_size=bs), validation_data=(testX, testY), steps_per_epoch=len(trainX)//bs, epochs=epochs, verbose=1)
+h = model.fit_generator(aug.flow(trainX, trainY, batch_size=bs), validation_data=(testX, testY),
+                        steps_per_epoch=len(trainX)//bs, epochs=epochs, verbose=1)
 model.save("blind_with_regularization.model")

video2image(data Generator).py

@@ -8,7 +8,7 @@
     ret, frame = cap.read()
     frame = cv2.resize(frame, (640, 480))
     cv2.imshow('s', frame)
-    if i%5 == 0:
+    if i % 5 == 0:
         cv2.imwrite('C:/Users/satinder/Desktop/center/'+str(j)+'.jpg', frame)
         j = j+1
     i = i+1