          """ Obtains the 68 Point Landmarks for the images in this batch. This is only called if
          config item ``warp_to_landmarks`` is ``True`` or if :attr:`mask_type` is not ``None``. If
          the landmarks for an image cannot be found, then an error is raised. """
          logger.trace("Retrieving landmarks: (filenames: %s, side: '%s')", filenames, side)
          src_points = [self._landmarks[side].get(sha1(face).hexdigest(), None) for face in batch]
  
          # Raise error on missing alignments
          if not all(isinstance(pts, np.ndarray) for pts in src_points):
              indices = [idx for idx, hsh in enumerate(src_points) if hsh is None]
              missing = [filenames[idx] for idx in indices]
              msg = ("Files missing alignments for this batch: {}"
 
</s> remove         config item ``warp_to_landmarks`` is ``True`` or if :attr:`mask_type` is not ``None``. If
        the landmarks for an image cannot be found, then an error is raised. """
 </s> add         config item ``warp_to_landmarks`` is ``True``. If the landmarks for an image cannot be
        found, then an error is raised. """ </s> remove             indices = [idx for idx, hsh in enumerate(src_points) if hsh is None]
            missing = [filenames[idx] for idx in indices]
 </s> add             missing = [filenames[idx] for idx, pts in enumerate(src_points) if pts is None] </s> remove     def _get_landmarks(self, filenames, batch, side):
 </s> add     def _apply_mask(self, filenames, batch, side):
        """ Applies the mask to the 4th channel of the image. If masks are not being used
        applies a dummy all ones mask """
        logger.trace("Input batch shape: %s, side: %s", batch.shape, side)
        if self._masks is None:
            logger.trace("Creating dummy masks. side: %s", side)
            masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
        else:
            logger.trace("Obtaining masks for batch. side: %s", side)
            masks = np.array([self._masks[side][filename].mask
                              for filename, face in zip(filenames, batch)], dtype=batch.dtype)
            masks = self._resize_masks(batch.shape[1], masks)

        logger.trace("masks shape: %s", masks.shape)
        batch = np.concatenate((batch, masks), axis=-1)
        logger.trace("Output batch shape: %s, side: %s", batch.shape, side)
        return batch

    @staticmethod
    def _resize_masks(target_size, masks):
        """ Resize the masks to the target size """
        logger.trace("target size: %s, masks shape: %s", target_size, masks.shape)
        mask_size = masks.shape[1]
        if target_size == mask_size:
            logger.trace("Mask and targets the same size. Not resizing")
            return masks
        interpolator = cv2.INTER_CUBIC if mask_size < target_size else cv2.INTER_AREA
        masks = np.array([cv2.resize(mask,
                                     (target_size, target_size),
                                     interpolation=interpolator)[..., None]
                          for mask in masks])
        logger.trace("Resized masks: %s", masks.shape)
        return masks

    def _get_landmarks(self, filenames, side): </s> remove                    "\nIf you are training with a mask or using 'warp to landmarks' then every "
 </s> add                    "\nIf you are using 'warp to landmarks' then every " </s> remove                 detected_face.load_aligned(None, size=self.size)
                landmarks[detected_face.hash] = detected_face.aligned_landmarks
 </s> add                 detected_faces.append(detected_face)
        logger.debug("Detected Faces count: %s, Skipped faces count: %s",
                     len(detected_faces), skip_count)
        if skip_count != 0:
            logger.warning("%s alignments have been removed as their corresponding faces do not "
                           "exist in the input folder for side %s. Run in verbose mode if you "
                           "wish to see which alignments have been excluded.",
                           skip_count, side.upper())
        return detected_faces

    def _validate_face(self, face, filename, idx, side, side_hashes):
        """ Validate that the currently processing face has a corresponding hash entry and the
        requested mask exists

        Parameters
        ----------
        face: dict
            A face retrieved from an alignments file
        filename: str
            The original frame filename that the given face comes from
        idx: int
            The index of the face in the frame
        side: {'A', 'B'}
            The side that this face belongs to
        side_hashes: set
            A set of hashes that exist in the alignments folder for these faces

        Returns
        -------
        bool
            ``True`` if the face is valid otherwise ``False``

        Raises
        ------
        FaceswapError
            If the current face doesn't pass validation
        """
        mask_type = self._training_opts["mask_type"]
        if mask_type is not None and "mask" not in face:
            msg = ("You have selected a Mask Type in your training configuration options but at "
                   "least one face has no mask stored for it.\nYou should generate the required "
                   "masks with the Mask Tool or set the Mask Type configuration option to `none`."
                   "\nThe face that caused this failure was side: `{}`, frame: `{}`, index: {}. "
                   "However there are probably more faces without masks".format(
                       side.upper(), filename, idx))
            raise FaceswapError(msg)

        if mask_type is not None and mask_type not in face["mask"]:
            msg = ("At least one of your faces does not have the mask `{}` stored for it.\nYou "
                   "should run the Mask Tool to generate this mask for your faceset or "
                   "select a different mask in the training configuration options.\n"
                   "The face that caused this failure was [side: `{}`, frame: `{}`, index: {}]. "
                   "The masks that exist for this face are: {}.\nBe aware that there are probably "
                   "more faces without this Mask Type".format(
                       mask_type, side.upper(), filename, idx, list(face["mask"].keys())))
            raise FaceswapError(msg)

        if face["hash"] not in side_hashes:
            logger.verbose("Skipping alignment for non-existant face in frame '%s' index: %s",
                           filename, idx)
            return False
        return True

    def _check_all_faces(self):
        """ Ensure that all faces in the training folder exist in the alignments file.
        If not, output missing filenames

        Raises
        ------
        FaceswapError
            If there are faces in the training folder which do not exist in the alignments file
        """
        logger.debug("Checking faces exist in alignments")
        missing_alignments = dict()
        for side, train_hashes in self._hashes.items():
            align_hashes = set(face.hash for face in self._detected_faces[side])
            if not align_hashes.issuperset(train_hashes["hashes"]):
                missing_alignments[side] = [
                    os.path.basename(filename)
                    for hsh, filename in zip(train_hashes["hashes"], train_hashes["filenames"])
                    if hsh not in align_hashes]
        if missing_alignments:
            msg = ("There are faces in your training folder(s) which do not exist in your "
                   "alignments file. Training cannot continue. See above for a full list of "
                   "files missing alignments.")
            for side, filelist in missing_alignments.items():
                logger.error("Faces missing alignments for side %s: %s",
                             side.capitalize(), filelist)
            raise FaceswapError(msg)

    # Get landmarks
    def _transform_landmarks(self, side, detected_faces):
        """ Transform frame landmarks to their aligned face variant.

        Parameters
        ----------
        side: {"a" or "b"}
            The side currently being processed
        detected_faces: list
            A list of :class:`lib.faces_detect.DetectedFace` objects

        Returns
        -------
        dict
            The face filenames as keys with the aligned landmarks as value.
        """
        landmarks = dict()
        for face in detected_faces:
            face.load_aligned(None, size=self._training_opts["training_size"])
            for filename in self._hash_to_filenames(side, face.hash):
                landmarks[filename] = face.aligned_landmarks
        src_points = [self._landmarks[side].get(filename, None) for filename in filenames]
          """ Obtains the 68 Point Landmarks for the images in this batch. This is only called if
          config item ``warp_to_landmarks`` is ``True`` or if :attr:`mask_type` is not ``None``. If
          the landmarks for an image cannot be found, then an error is raised. """
          logger.trace("Retrieving landmarks: (filenames: %s, side: '%s')", filenames, side)
        src_points = [self._landmarks[side].get(filename, None) for filename in filenames]
        src_points = [self._landmarks[side].get(filename, None) for filename in filenames]
          # Raise error on missing alignments
          if not all(isinstance(pts, np.ndarray) for pts in src_points):
              indices = [idx for idx, hsh in enumerate(src_points) if hsh is None]
              missing = [filenames[idx] for idx in indices]
              msg = ("Files missing alignments for this batch: {}"
keep keep keep keep replace replace keep keep keep keep keep
 Smart Masks - Training Implementation (#914)

* Smart Masks - Training

- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code

* Update mask helptext in cli.py

* Fix Warp to Landmarks
Remove SHA1 hashing from training data

* Update mask training config

* Capture missing masks at training init

* lib.image.read_image_batch - Return filenames with batch for ordering

* scripts.train - Documentation

* plugins.train.trainer - documentation

* Ensure backward compatibility.
Fix convert for new predicted masks

* Update removed masks to components for legacy models. 
lib/training_data.py