              padding = int(size * 0.1875)
              feature_mask = extractor.get_feature_mask(
                  aligned_landmarks / size,
                  size, padding)
              feature_mask = cv2.blur(  # pylint: disable=no-member
                  feature_mask, (10, 10))
              isolated_face = cv2.multiply(  # pylint: disable=no-member
                  feature_mask,
                  resized_face.astype(float)).astype(np.uint8)
              blurry, focus_measure = self.is_blurry(isolated_face)
  
              if blurry:
                  blur_folder = detected_face["file_location"].parts[:-1]
                  blur_folder = get_folder(Path(*blur_folder) / Path("blurry"))
 
</s> remove         gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # pylint: disable=no-member
 </s> add         gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) </s> remove         padding = int((size * (coverage_ratio - 0.625)) / 2)
 </s> add         adjusted_ratio = coverage_ratio - (1 - self._extract_ratio)
        padding = round((size * adjusted_ratio) / 2) </s> remove     @staticmethod
    def _padding_from_coverage(size, coverage_ratio):
 </s> add     def _padding_from_coverage(self, size, coverage_ratio): </s> remove         clahe = cv2.createCLAHE(clipLimit=2.0,  # pylint: disable=no-member
 </s> add         clahe = cv2.createCLAHE(clipLimit=2.0,
            feature_mask = cv2.blur(feature_mask, (10, 10))
            isolated_face = cv2.multiply(feature_mask, resized_face.astype(float)).astype(np.uint8)
              padding = int(size * 0.1875)
              feature_mask = extractor.get_feature_mask(
                  aligned_landmarks / size,
                  size, padding)
            feature_mask = cv2.blur(feature_mask, (10, 10))
            isolated_face = cv2.multiply(feature_mask, resized_face.astype(float)).astype(np.uint8)
            feature_mask = cv2.blur(feature_mask, (10, 10))
            isolated_face = cv2.multiply(feature_mask, resized_face.astype(float)).astype(np.uint8)
            feature_mask = cv2.blur(feature_mask, (10, 10))
            isolated_face = cv2.multiply(feature_mask, resized_face.astype(float)).astype(np.uint8)
            feature_mask = cv2.blur(feature_mask, (10, 10))
            isolated_face = cv2.multiply(feature_mask, resized_face.astype(float)).astype(np.uint8)
            feature_mask = cv2.blur(feature_mask, (10, 10))
            isolated_face = cv2.multiply(feature_mask, resized_face.astype(float)).astype(np.uint8)
              blurry, focus_measure = self.is_blurry(isolated_face)
  
              if blurry:
                  blur_folder = detected_face["file_location"].parts[:-1]
                  blur_folder = get_folder(Path(*blur_folder) / Path("blurry"))
keep keep keep keep replace replace replace replace replace keep keep keep keep keep
 Smart Mask - Extract code review

- Lint simple_tests.py
- Only reformat alignments file if it exists otherwise change filename
- Update legacy alignments to new format at all stages
- faces_detect.Mask.from_dict - logging format fix
- convert.py fix otf for new pipeline
- cli.py - Add note that masks not used. Revert convert masks
- faces_detect.py - Revert non-extract code
- Add .p and .pickle extensions for serializer
- plugins/extract revert some changes
- scripts/fsmedia - Revert code changes
- Pipeline - cleanup
- Consistant alpha channel stripping (fixes single-process)
- Store landmarks as numpy array
- Code attribution
- Normalize feed face and reference face to 0.0 - 1.0 in convert
- Lock in mask VRAM sized
- Add documentation to plugin_loader
- Update alignments tool to work with new format 
scripts/fsmedia.py