  
          logger.trace("Randomly transformed image")
          return batch
  
      def random_flip(self, batch):
          """ Perform random horizontal flipping on the passed in batch.
  
          The probability of flipping an image is set in :file:`config.train.ini`
  
          Parameters
 
</s> remove         logger.trace("Randomly transformed image")
        return batch
 </s> add         logger.trace("Randomly transformed image")  # type: ignore </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: </s> remove     def color_adjust(self, batch):
 </s> add     def color_adjust(self, batch: np.ndarray) -> np.ndarray: </s> remove         batch = np.array([cv2.warpAffine(image,
                                         mat,
                                         (self._training_size, self._training_size),
                                         borderMode=cv2.BORDER_REPLICATE)
                          for image, mat in zip(batch, mats)])
 </s> add         for image, mat in zip(batch, mats):
            cv2.warpAffine(image,
                           mat,
                           (self._processing_size, self._processing_size),
                           dst=image,
                           borderMode=cv2.BORDER_REPLICATE)
    def random_flip(self, batch: np.ndarray):
  
          logger.trace("Randomly transformed image")
          return batch
  
    def random_flip(self, batch: np.ndarray):
          """ Perform random horizontal flipping on the passed in batch.
  
          The probability of flipping an image is set in :file:`config.train.ini`
  
          Parameters
keep keep keep keep 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