def _reconstruct(self, predictions, ids):
counter = 0
for (prediction, id) in predictions:
# prediction = 255- (prediction*255)
# prediction = prediction * 255
img_ref = next((x for x in self.test_img_refs if x.probe_file_id == id), None)
try:
if self._is_keras_img_model():
pred = 255 - np.array(MinMaxScaler((0, 255)).fit_transform(prediction[0].reshape(-1, 1))).flatten()
img = pred.reshape(self.patch_shape, self.patch_shape)
else:
pred = 255 - np.array(MinMaxScaler((0, 255)).fit_transform(prediction.reshape(-1, 1))).flatten()
if self.config['data_type']=="image":
img = pred.reshape(
img_ref.img_height//self.image_downscale_factor,
img_ref.img_width//self.image_downscale_factor)
elif self.config['data_type'] =="csv":
img = pred.reshape(
img_ref.img_height,
img_ref.img_width)
img_original_size = cv2.resize(
img, (img_ref.img_orig_width, img_ref.img_orig_height))
except:
counter +=1
img_original_size = np.zeros((img_ref.img_orig_width, img_ref.img_orig_height))
file_name = f'{img_ref.probe_file_id}.png'
file_path = self.output_dir + file_name
ImageUtils.save_image(img_original_size, file_path)
print(f'Number of errors in reconstruction {counter}')
def _reconstruct(self, predictions, ids):
for (prediction, id), patch_img_ref in zip(predictions,
self.test_img_refs):
prediction = 255 - (prediction * 255)
# prediction = prediction * 255
img = self._reconstruct_image_from_patches(prediction,
patch_img_ref)
img_original_size = cv2.resize(
img,
(patch_img_ref.img_orig_width, patch_img_ref.img_orig_height))
file_name = f'{patch_img_ref.probe_file_id}.png'
file_path = self.output_dir + file_name
ImageUtils.save_image(img_original_size, file_path)
def _create_img_patch(self, img_ref, targets_path):
target_image_path = os.path.join(targets_path, "manipulation", "mask",
img_ref.probe_mask_file_name) + ".png"
border_value = [255, 255, 255]
if self.tuning['black_border_y'] is True or self.tuning[
'dilate_y_black_border_y'] is True:
border_value = [0, 0, 0]
bordered_img, border_top, border_left, original_img_shape = ImageUtils.get_image_with_border(
target_image_path,
self.patch_shape,
self.img_downscale_factor,
border_value=border_value)
if self.tuning["dilate_y"] is True:
diluted_target = ImageUtils.dilate(bordered_img)
target_image_out_dir = self.output_dir + 'target_image/'
bordered_image_patches, patch_window_shape = PatchUtils.get_patches(
bordered_img, self.patch_shape)
for i, patch in enumerate(bordered_image_patches):
path = f'{target_image_out_dir}{img_ref.probe_file_id}_{i}.png'
ImageUtils.save_image(patch, path)
patch_img_ref = PatchImageRefFactory.create_img_ref(
img_ref.probe_file_id, bordered_img.shape, patch_window_shape,
img_ref.probe_mask_file_name, original_img_shape, border_top,
border_left)
indicators = None
for indicator_dir in self.indicator_directories:
indicator_out_path = self.output_dir + indicator_dir + '/'
img_path = os.path.join(self.indicators_path, indicator_dir,
"mask", img_ref.probe_file_id) + ".png"
try:
img, _, _, _ = ImageUtils.get_image_with_border(
img_path,
self.patch_shape,
self.img_downscale_factor,
top=border_top,
left=border_left)
except FileNotFoundError as err:
img = self._handle_missing_indicator_image(bordered_img.shape)
finally:
img_patches, _ = PatchUtils.get_patches(img, self.patch_shape)
for i, patch in enumerate(img_patches):
path = f'{indicator_out_path}{img_ref.probe_file_id}_{i}.png'
ImageUtils.save_image(patch, path)
return patch_img_ref
def _save_image(self,img,base_path, file_name):
path = f'{base_path}{file_name}.png'
ImageUtils.save_image(img,path)