                       self.calc_landmarks_face_yaw(np.array(landmarks))])
          elif group_method == 'group_hist':
              temp_list = [
                  [img,
                   cv2.calcHist([cv2.imread(img)], [0], None, [256], [0, 256])]
                  for img in
                  tqdm(self.find_images(input_dir),
                       desc="Reloading",
                       file=sys.stdout)
              ]
 
</s> remove             [img, cv2.calcHist([cv2.imread(img)], [0], None, [256], [0, 256])]
 </s> add             [img, cv2.calcHist([cv2_read_img(img, raise_error=True)], [0], None, [256], [0, 256])] </s> remove              cv2.calcHist([cv2.imread(img)], [0], None, [256], [0, 256]), 0]
 </s> add              cv2.calcHist([cv2_read_img(img, raise_error=True)], [0], None, [256], [0, 256]), 0] </s> remove             temp_list = [[img, self.estimate_blur(cv2.imread(img))]
 </s> add             temp_list = [[img, self.estimate_blur(cv2_read_img(img, raise_error=True))] </s> remove     img = cv2.imread(filename)  # pylint: disable=no-member
 </s> add     img = cv2_read_img(filename, raise_error=True) </s> remove         preds = np.array([self.vgg_face.predict(cv2.imread(img))
 </s> add         preds = np.array([self.vgg_face.predict(cv2_read_img(img, raise_error=True))
                 cv2.calcHist([cv2_read_img(img, raise_error=True)], [0], None, [256], [0, 256])]
                       self.calc_landmarks_face_yaw(np.array(landmarks))])
          elif group_method == 'group_hist':
              temp_list = [
                  [img,
                 cv2.calcHist([cv2_read_img(img, raise_error=True)], [0], None, [256], [0, 256])]
                  for img in
                  tqdm(self.find_images(input_dir),
                       desc="Reloading",
                       file=sys.stdout)
              ]
keep keep keep keep replace keep keep keep keep keep
 Centralize cv2 image reading and handle bad filenames 
tools/sort.py