def data_generation(self, list_files_temp):
data_x = []
data_y = []
for file_name in list_files_temp:
path_to_image = "{}/{}".format(self.path_to_data, file_name)
try:
im_frame = Image.open(path_to_image)
img = np.asarray(im_frame, dtype="float32")
img /= 255
if self.augment_zoom_only:
img = random_zoom(img, zoom_range=(0.85, 1.15), channel_axis=2, row_axis=0, col_axis=1,
fill_mode='constant', cval=0.0)
elif self.augment:
img = random_zoom(img, zoom_range=(0.85, 1.15), channel_axis=2, row_axis=0, col_axis=1,
fill_mode='constant', cval=0.0)
img = ab.HorizontalFlip()(image=img)["image"]
img = ab.VerticalFlip()(image=img)["image"]
data_x.append(img)
data_y.append(0)
except:
print("Error while loading image {}.".format(path_to_image))
continue
data_x = np.stack(data_x)
data_y = np.stack(data_y)
return data_x, data_y
def test_img_transforms(self):
x = np.random.random((3, 200, 200))
_ = preprocessing_image.random_rotation(x, 20)
_ = preprocessing_image.random_shift(x, 0.2, 0.2)
_ = preprocessing_image.random_shear(x, 2.)
_ = preprocessing_image.random_zoom(x, (0.5, 0.5))
_ = preprocessing_image.apply_channel_shift(x, 2, 2)
_ = preprocessing_image.apply_affine_transform(x, 2)
with self.assertRaises(ValueError):
preprocessing_image.random_zoom(x, (0, 0, 0))
_ = preprocessing_image.random_channel_shift(x, 2.)
def rand_zoom(x):
return random_zoom(x,
zoom_range=(0.85, 1.15),
channel_axis=2,
row_axis=0,
col_axis=1,
fill_mode='constant',
cval=0.0)
def data_generation(self, list_files_temp):
data_x = []
data_y = []
for c in list_files_temp:
image = self.load_image(c)
label = self.dataset.iloc[c][1]
if self.augment:
image = random_zoom(image,
zoom_range=(0.85, 1.15),
channel_axis=2,
row_axis=0,
col_axis=1,
fill_mode='constant',
cval=0.0)
image = ab.HorizontalFlip()(image=image)["image"]
image = ab.VerticalFlip()(image=image)["image"]
if self.multilabel:
if label == 0:
label = [1, 0, 0, 0, 0]
elif label == 1:
label = [1, 1, 0, 0, 0]
elif label == 2:
label = [1, 1, 1, 0, 0]
elif label == 3:
label = [1, 1, 1, 1, 0]
elif label == 4:
label = [1, 1, 1, 1, 1]
label = np.array(label)
data_x.append(image)
data_y.append(label)
data_x = np.stack(data_x)
data_y = np.stack(data_y)
return data_x, data_y
