def tta(image,model):
datagen=ImageDataGenerator()
all_images=np.expand_dims(image,0)
hori_image=np.expand_dims(datagen.apply_transform(x=image,transform_parameters={"flip_horizontal":True}),axis=0)
vert_image=np.expand_dims(datagen.apply_transform(x=image,transform_parameters={"flip_vertical":True}),axis=0)
rotated_image=np.expand_dims(datagen.apply_transform(x=image,transform_parameters={"theta":15}),axis=0)
all_images=np.append(all_images,hori_image,axis=0)
all_images=np.append(all_images,vert_image,axis=0)
all_images=np.append(all_images,rotated_image,axis=0)
prediction=model.predict(all_images)
prediction=np.sum(prediction,axis=0)
# print(prediction)
return np.argmax(prediction)
def tta(image, model, model_output='regression'):
datagen = ImageDataGenerator()
all_images = np.expand_dims(image, 0)
hori_image = np.expand_dims(datagen.apply_transform(x=image, transform_parameters={"flip_horizontal": True}),
axis=0)
vert_image = np.expand_dims(datagen.apply_transform(x=image, transform_parameters={"flip_vertical": True}), axis=0)
rotated_image = np.expand_dims(datagen.apply_transform(x=image, transform_parameters={"theta": 15}), axis=0)
all_images = np.append(all_images, hori_image, axis=0)
all_images = np.append(all_images, vert_image, axis=0)
all_images = np.append(all_images, rotated_image, axis=0)
prediction = model.predict(all_images)
if model_output is 'regression':
return np.mean(prediction)
else:
prediction = np.sum(prediction, axis=0)
return np.argmax(prediction)
def __getitem__(self, idx):
batch_x = []
batch_y = []
video_idx = 0
_iter = 0
while _iter < self.v:
_iter += 1
if self.frame_counter >= self.dataset_size:
self.on_epoch_end()
print('*', end=",")
_iter -= 1
continue
frames2read = self.x[str(
self.video_iterator)][self.frame_iterator:self.frame_iterator +
self.fpv]
temp_frames = []
if self.data_augmentation == True:
img_gen = ImageDataGenerator(
rotation_range=30,
samplewise_center=True,
samplewise_std_normalization=True,
# width_shift_range=0.2,
# height_shift_range=0.2,
# rescale=1./255,
brightness_range=[0.7, 1.0],
channel_shift_range=50.0,
# shear_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
# vertical_flip = True,
fill_mode='nearest')
transform_param = img_gen.get_random_transform(
img_shape=(self.image_size, self.image_size, 3), seed=None)
# print(transform_param)
if len(frames2read) >= self.fpv:
for frame in frames2read:
_image = cv2.imread(frame)
_image = cv2.cvtColor(_image,
cv2.COLOR_BGR2RGB).astype('float64')
_image = cv2.resize(_image,
(self.image_size, self.image_size))
if self.data_augmentation == True:
_image = img_gen.apply_transform(
_image, transform_param)
_image /= 255
temp_frames.append(np.asarray(_image))
batch_x.append(temp_frames)
batch_y.append(self.y[self.video_iterator])
self.pre_classes.append(self.y[self.video_iterator])
else:
_iter -= 1
self.video_iterator += 1
self.frame_counter += len(frames2read)
if self.video_iterator % len(self.x) == 0:
self.video_iterator = 0
self.frame_iterator += len(frames2read)
video_idx += 1
if self.video_iterator % len(self.x) == 0:
self.video_iterator = 0
if len(batch_y) > 0:
batch_y = to_categorical(batch_y, 2)
batch_x = np.array(batch_x)
if self.training == True:
indices = np.arange(batch_x.shape[0])
np.random.shuffle(indices)
batch_x = batch_x[indices]
batch_y = batch_y[indices]
return (batch_x, batch_y)
