                                              cv2.INTER_AREA)
                                   for image in warped_batch])
          logger.trace("Warped batch shape: %s", warped_batch.shape)
          return warped_batch
  
      def skip_warp(self, batch):
          """ Returns the images resized and cropped for feeding the model, if warping has been
          disabled.
  
          Parameters
          ----------
          batch: :class:`numpy.ndarray`
              The batch should be a 4-dimensional array of shape (`batchsize`, `height`, `width`,
              `3`) and in `BGR` format.
  
          Returns
          -------
          :class:`numpy.ndarray`
              The given batch cropped and resized for feeding the model
          """
          logger.trace("Compiling skip warp images: batch shape: %s", batch.shape)
          slices = self._constants["tgt_slices"]
          retval = np.array([cv2.resize(image[slices, slices, :],
                                        (self._input_size, self._input_size),
                                        cv2.INTER_AREA)
                             for image in batch], dtype='float32') / 255.
          logger.trace("feed batch shape: %s", retval.shape)
          return retval
 
</s> remove         warped_batch = np.array([cv2.resize(image[slices, slices, :],
                                            (self._input_size, self._input_size),
                                            cv2.INTER_AREA)
                                 for image in warped_batch])
        logger.trace("Warped batch shape: %s", warped_batch.shape)
 </s> add         logger.trace("Warped batch shape: %s", warped_batch.shape)  # type: ignore </s> remove     def _random_warp_landmarks(self, batch, batch_src_points, batch_dst_points):
        """ From dfaker. Warp the image to a similar set of landmarks from the opposite side """
        logger.trace("Randomly warping landmarks")
        edge_anchors = self._constants["warp_lm_edge_anchors"]
        grids = self._constants["warp_lm_grids"]
        slices = self._constants["tgt_slices"]
 </s> add     def _random_warp_landmarks(self,
                               batch: np.ndarray,
                               batch_src_points: np.ndarray,
                               batch_dst_points: np.ndarray) -> np.ndarray:
        """ From dfaker. Warp the image to a similar set of landmarks from the opposite side

        batch: :class:`numpy.ndarray`
            The batch should be a 4-dimensional array of shape (`batchsize`, `height`, `width`,
            `3`) and in `BGR` format.
        batch_src_points :class:`numpy.ndarray`
            A batch of 68 point landmarks for the source faces. This is a 3-dimensional array in
            the shape (`batchsize`, `68`, `2`).
        batch_dst_points :class:`numpy.ndarray`
            A batch of randomly chosen closest match destination faces landmarks. This is a
            3-dimensional array in the shape (`batchsize`, `68`, `2`).

        Returns
        ----------
        :class:`numpy.ndarray`
            A 4-dimensional array of the same shape as :attr:`batch` with warping applied.
        """
        logger.trace("Randomly warping landmarks")  # type: ignore
        edge_anchors = self._constants.warp_lm_edge_anchors
        grids = self._constants.warp_lm_grids </s> remove         grids = np.mgrid[0:p_mx:complex(self._training_size), 0:p_mx:complex(self._training_size)]

        self._constants = dict(clahe_base_contrast=clahe_base_contrast,
                               tgt_slices=tgt_slices,
                               warp_mapx=warp_mapx,
                               warp_mapy=warp_mapy,
                               warp_pad=warp_pad,
                               warp_slices=warp_slices,
                               warp_lm_edge_anchors=edge_anchors,
                               warp_lm_grids=grids)
        self.initialized = True
        logger.debug("Initialized constants: %s", {k: str(v) if isinstance(v, np.ndarray) else v
                                                   for k, v in self._constants.items()})

    # <<< TARGET IMAGES >>> #
    def get_targets(self, batch):
        """ Returns the target images, and masks, if required.

        Parameters
        ----------
        batch: :class:`numpy.ndarray`
            This should be a 4+-dimensional array of training images in the format (`batchsize`,
            `height`, `width`, `channels`). Targets should be requested after performing image
            transformations but prior to performing warps.

            The 4th channel should be the mask. Any channels above the 4th should be any additional
            masks that are requested.

        Returns
        -------
        dict
            The following keys will be within the returned dictionary:

            * **targets** (`list`) - A list of 4-dimensional :class:`numpy.ndarray` s in the \
            order and size of each output of the model as defined in :attr:`output_shapes`. The \
            format of these arrays will be (`batchsize`, `height`, `width`, `3`). **NB:** \
            masks are not included in the `targets` list. If masks are to be included in the \
            output they will be returned as their own item from the `masks` key.

            * **masks** (:class:`numpy.ndarray`) - A 4-dimensional array containing the target \
            masks in the format (`batchsize`, `height`, `width`, `1`).
        """
        logger.trace("Compiling targets: batch shape: %s", batch.shape)
        slices = self._constants["tgt_slices"]
        target_batch = [np.array([cv2.resize(image[slices, slices, :],
                                             (size, size),
                                             cv2.INTER_AREA)
                                  for image in batch], dtype='float32') / 255.
                        for size in self._output_sizes]
        logger.trace("Target image shapes: %s",
                     [tgt_images.shape for tgt_images in target_batch])

        retval = self._separate_target_mask(target_batch)
        logger.trace("Final targets: %s",
                     {k: v.shape if isinstance(v, np.ndarray) else [img.shape for img in v]
                      for k, v in retval.items()})
        return retval

    @staticmethod
    def _separate_target_mask(target_batch):
        """ Return the batch and the batch of final masks

        Parameters
        ----------
        target_batch: list
            List of 4 dimension :class:`numpy.ndarray` objects resized the model outputs.
            The 4th channel of the array contains the face mask, any additional channels after
            this are additional masks (e.g. eye mask and mouth mask)

        Returns
        -------
        dict:
            The targets and the masks separated into their own items. The targets are a list of
            3 channel, 4 dimensional :class:`numpy.ndarray` objects sized for each output from the
            model. The masks are a :class:`numpy.ndarray` of the final output size. Any additional
            masks(e.g. eye and mouth masks) will be collated together into a :class:`numpy.ndarray`
            of the final output size. The number of channels will be the number of additional
            masks available
        """
        logger.trace("target_batch shapes: %s", [tgt.shape for tgt in target_batch])
        retval = dict(targets=[batch[..., :3] for batch in target_batch],
                      masks=target_batch[-1][..., 3][..., None])
        if target_batch[-1].shape[-1] > 4:
            retval["additional_masks"] = target_batch[-1][..., 4:]
        logger.trace("returning: %s", {k: v.shape if isinstance(v, np.ndarray) else [tgt.shape
                                                                                     for tgt in v]
                                       for k, v in retval.items()})
 </s> add         grids = np.mgrid[0: p_mx: complex(self._processing_size),  # type: ignore
                         0: p_mx: complex(self._processing_size)]  # type: ignore
        retval = AugConstants(clahe_base_contrast=max(2, self._processing_size // 128),
                              clahe_chance=int(self._config.get("color_clahe_chance", 50)) / 100,
                              clahe_max_size=int(self._config.get("color_clahe_max_size", 4)),
                              lab_adjust=lab_adjust,
                              transform_rotation=int(self._config.get("rotation_range", 10)),
                              transform_zoom=int(self._config.get("zoom_amount", 5)) / 100,
                              transform_shift=tform_shift,
                              warp_maps=np.stack((warp_mapx, warp_mapy), axis=1),
                              warp_pad=(warp_pad, warp_pad),
                              warp_slices=slice(warp_pad // 10, -warp_pad // 10),
                              warp_lm_edge_anchors=edge_anchors,
                              warp_lm_grids=grids)
        logger.debug("Initialized constants: %s", retval) </s> remove 
        Returns
        ----------
        :class:`numpy.ndarray`
            A 4-dimensional array of the same shape as :attr:`batch` with transformation applied.
 </s> add  </s> remove 
        Returns
        ----------
        :class:`numpy.ndarray`
            A 4-dimensional array of the same shape as :attr:`batch` with transformation applied.
 </s> add 
                                              cv2.INTER_AREA)
                                   for image in warped_batch])
          logger.trace("Warped batch shape: %s", warped_batch.shape)
          return warped_batch
























keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace
 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