                                    tileGridSize=(grid_size, grid_size))
                    for grid_size in grid_sizes]
  
          for idx, clahe in zip(indices, clahes):
              batch[idx, :, :, 0] = clahe.apply(batch[idx, :, :, 0])
          return batch
  
      def _random_lab(self, batch):
          """ Perform random color/lightness adjustment in L*a*b* color space on a batch of
          images """
          amount_l = self._config.get("color_lightness", 30) / 100
 
</s> remove     def _random_lab(self, batch):
 </s> add     def _random_lab(self, batch: np.ndarray) -> None: </s> remove         amount_l = self._config.get("color_lightness", 30) / 100
        amount_ab = self._config.get("color_ab", 8) / 100
        adjust = np.array([amount_l, amount_ab, amount_ab], dtype="float32")
        randoms = (
            (np.random.rand(self._batchsize, 1, 1, 3).astype("float32") * (adjust * 2)) - adjust)
        logger.trace("Random LAB adjustments: %s", randoms)

 </s> add         randoms = np.random.uniform(-self._constants.lab_adjust,
                                    self._constants.lab_adjust,
                                    size=(self._batchsize, 1, 1, 3)).astype("float32")
        logger.trace("Random LAB adjustments: %s", randoms)  # type: ignore
        # Iterating through the images and channels is much faster than numpy.where and slightly
        # faster than numexpr.where. </s> remove         return batch
 </s> add  </s> remove     def transform(self, batch):
 </s> add     def transform(self, batch: np.ndarray): </s> remove         if not self.is_display:
            logger.trace("Randomly flipping image")
            randoms = np.random.rand(self._batchsize)
            indices = np.where(randoms > self._config.get("random_flip", 50) / 100)[0]
            batch[indices] = batch[indices, :, ::-1]
            logger.trace("Randomly flipped %s images of %s", len(indices), self._batchsize)
        return batch

    def warp(self, batch, to_landmarks=False, **kwargs):
 </s> add         logger.trace("Randomly flipping image")  # type: ignore
        randoms = np.random.rand(self._batchsize)
        indices = np.where(randoms > int(self._config.get("random_flip", 50)) / 100)[0]
        batch[indices] = batch[indices, :, ::-1]
        logger.trace("Randomly flipped %s images of %s",  # type: ignore
                     len(indices), self._batchsize)

    def warp(self, batch: np.ndarray, to_landmarks: bool = False, **kwargs) -> np.ndarray:
            batch[idx, :, :, 0] = clahe.apply(batch[idx, :, :, 0], )
                                    tileGridSize=(grid_size, grid_size))
                    for grid_size in grid_sizes]
  
          for idx, clahe in zip(indices, clahes):
            batch[idx, :, :, 0] = clahe.apply(batch[idx, :, :, 0], )
            batch[idx, :, :, 0] = clahe.apply(batch[idx, :, :, 0], )
  
      def _random_lab(self, batch):
          """ Perform random color/lightness adjustment in L*a*b* color space on a batch of
          images """
          amount_l = self._config.get("color_lightness", 30) / 100
keep keep keep keep replace replace keep keep keep keep keep
 Data Augmentation update (#1263)

- lib.detected_face
    - Subclass Masks for Landmark based masks
    - Add training mask propery + methods to DetectedFace
  - lib.training_training
    - subclass TrainingDataGenerator for training and preview data
    - Split cache into own module
    - Reduce thread count to 1 to prevent image corruption + data re-use
    - Process on largest model input/output size rather than stored image size
    - Size and crop masks during caching stage
    - Implement ring buffer for data flow
    - Fix preview reload bug
  - augmentation
    - typing
    - switch color aug order
    - better initialization
    - Fix warp + landmark warp to correctly apply at different image scales
    - Slightly improved warp caching
    - Don't store whether image is_preview. Handle all data as training images implicitly
  - plugins.trainer: Typing and fixes to work with trainingdata refactor 
lib/training/augmentation.py