          padding = (full_size - target_size) // 2
          length = target_size // 4
          t_l, b_r = (padding, full_size - padding)
          for img in images:
              cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 3)
              cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 3)
              cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 3)
              cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 3)
              new_images.append(img)
          retval = np.array(new_images)
          logger.debug("Overlayed background. Shape: %s", retval.shape)
          return retval
  
      @classmethod
      def _compile_masked(cls, faces, masks):
          """ Add the mask to the faces for masked preview.
  
 
</s> remove         logger.debug("full_size: %s, target_size: %s, color: %s",
                     images.shape[1], target_size, color)
        new_images = list()
        full_size = images.shape[1]
        padding = (full_size - target_size) // 2
        length = target_size // 4
        t_l, b_r = (padding, full_size - padding)
 </s> add         logger.debug("full_size: %s, prediction_size: %s, color: %s",
                     images.shape[1], prediction_size, color)

        display_size = int(prediction_size * (1 + (1 - self._coverage_ratio)))
        images = self._resize_sample(side, images, display_size)  # Resize targets to display size
        padding = (display_size - prediction_size) // 2
        if padding == 0:
            logger.debug("Resized background. Shape: %s", images.shape)
            return images

        length = display_size // 4
        t_l, b_r = (padding, display_size - padding) </s> remove         new_images = list()
        for idx, img in enumerate(backgrounds):
            img[offset:offset + foregrounds[idx].shape[0],
                offset:offset + foregrounds[idx].shape[1], :3] = foregrounds[idx]
            new_images.append(img)
        retval = np.array(new_images)
        logger.debug("Overlayed foreground. Shape: %s", retval.shape)
        return retval
 </s> add         for foreground, background in zip(foregrounds, backgrounds):
            background[offset:offset + foreground.shape[0],
                       offset:offset + foreground.shape[1], :3] = foreground
        logger.debug("Overlayed foreground. Shape: %s", backgrounds.shape)
        return backgrounds </s> remove     def _get_headers(self, side, width):
 </s> add     @classmethod
    def _get_headers(cls, side, width): </s> remove 
    @staticmethod
    def align_eyes(face, image):
        """ Re-extract a face with the pupils forced to be absolutely horizontally aligned """
        umeyama_landmarks = face.aligned_landmarks
        left_eye_center = umeyama_landmarks[42:48].mean(axis=0)
        right_eye_center = umeyama_landmarks[36:42].mean(axis=0)
        d_y = right_eye_center[1] - left_eye_center[1]
        d_x = right_eye_center[0] - left_eye_center[0]
        theta = np.pi - np.arctan2(d_y, d_x)
        rot_cos = np.cos(theta)
        rot_sin = np.sin(theta)
        rotation_matrix = np.array([[rot_cos, -rot_sin, 0.],
                                    [rot_sin, rot_cos, 0.],
                                    [0., 0., 1.]])

        mat_umeyama = np.concatenate((face.aligned["matrix"], np.array([[0., 0., 1.]])), axis=0)
        corrected_mat = np.dot(rotation_matrix, mat_umeyama)
        face.aligned["matrix"] = corrected_mat[:2]
        face.aligned["face"] = AlignerExtract().transform(image,
                                                          face.aligned["matrix"],
                                                          face.aligned["size"],
                                                          int(face.aligned["size"] * 0.375) // 2)
        logger.trace("Adjusted matrix: %s", face.aligned["matrix"])
        return face
 </s> add  </s> remove     @staticmethod
    def _resize_masks(target_size, masks):
 </s> add     @classmethod
    def _resize_masks(cls, target_size, masks):
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
          padding = (full_size - target_size) // 2
          length = target_size // 4
          t_l, b_r = (padding, full_size - padding)
          for img in images:
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
            cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images
  
      @classmethod
      def _compile_masked(cls, faces, masks):
          """ Add the mask to the faces for masked preview.
  
keep keep keep keep replace replace replace replace replace replace replace replace keep keep keep keep keep
 Extract - Increase area and move centering (#1095)

* Extract
  - Implement aligner re-feeding
  - Add extract type to pipeline.ExtractMedia
  - Add pose annotation to debug
* Convert
  - implement centering
  - remove usage of feed and reference face properties
  - Remove distributed option from convert
  - Force update of alignments file on legacy receive
* Train
  - Resize preview image to model output size
  - Force legacy centering if centering does not exist in model's state file
  - Enable training on legacy face sets

* Alignments Tool
  - Update draw to include head/pose
  - Remove DFL drop + linting
  - Remove remove-frames job
  - remove align-eyes option
  - Update legacy masks to new extract type
  - Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files
  - Re-generate thumbnails on legacy upgrade
* Mask Tool
  - Update for new extract + bugfix full frame
* Manual Tool
  - Update to new extraction method
   - Disable legacy alignments,
   - extract box bugfix
   - extract faces - size to 512 and center on head
* Preview Tool
  - Display based on model centering
* Sort Tool
  - Use alignments for sort by face

* lib.aligner
  - Add Pose Class
  - Add AlignedFace Class
  - center _MEAN_FACE on x
  - Add meta information with versioning to alignments file
  - lib.aligner.get_align_matrix to use landmarks not face
  - Refactor aligned faces in lib.faces_detect
* lib.logger
  - larger file log padding
* lib.config
  - Fix global changeable_items
* lib.face_filter
  - Use new extracted face images
* lib.image
  - bump thumbnail default size to 96px 
plugins/train/trainer/_base.py