              "type": str.lower,
              "choices": PluginLoader.get_available_extractors("mask", add_none=True),
              "default": "extended",
              "group": "Plugins",
              "help": "R|Masker to use. NB: Masker is not currently used by the rest of the process "
                      "but this will store a mask in the alignments file for use when it has been "
                      "implemented."
                      "\nL|none: Don't use a mask."
                      "\nL|components: Mask designed to provide facial segmentation based on the "
                      "positioning of landmark locations. A convex hull is constructed around the "
                      "exterior of the landmarks to create a mask."
                      "\nL|extended: Mask designed to provide facial segmentation based on the "
 
</s> remove             choices=get_available_masks(), group="mask",
            info="The mask to be used for training:"
                 "\n\t none: Doesn't use any mask."
                 "\n\t components: An improved face hull mask using a facehull of 8 facial parts"
                 "\n\t dfl_full: An improved face hull mask using a facehull of 3 facial parts"
                 "\n\t extended: Based on components mask. Extends the eyebrow points to further "
                 "up the forehead. May perform badly on difficult angles."
                 "\n\t facehull: Face cutout based on landmarks")
 </s> add             choices=PluginLoader.get_available_extractors("mask", add_none=True), group="mask",
            gui_radio=True,
            info="The mask to be used for training. If you have selected 'Learn Mask' or "
                 "'Penalized Mask Loss' you must select a value other than 'none'. The required "
                 "mask should have been selected as part of the Extract process. If it does not "
                 "exist in the alignments file then it will be generated prior to training "
                 "commencing."
                 "\n\tnone: Don't use a mask."
                 "\n\tcomponents: Mask designed to provide facial segmentation based on the "
                 "positioning of landmark locations. A convex hull is constructed around the "
                 "exterior of the landmarks to create a mask."
                 "\n\textended: Mask designed to provide facial segmentation based on the "
                 "positioning of landmark locations. A convex hull is constructed around the "
                 "exterior of the landmarks and the mask is extended upwards onto the forehead."
                 "\n\tvgg-clear: Mask designed to provide smart segmentation of mostly frontal "
                 "faces clear of obstructions. Profile faces and obstructions may result in "
                 "sub-par performance."
                 "\n\tvgg-obstructed: Mask designed to provide smart segmentation of mostly "
                 "frontal faces. The mask model has been specifically trained to recognize "
                 "some facial obstructions (hands and eyeglasses). Profile faces may result in "
                 "sub-par performance."
                 "\n\tunet-dfl: Mask designed to provide smart segmentation of mostly frontal "
                 "faces. The mask model has been trained by community members and will need "
                 "testing for further description. Profile faces may result in sub-par "
                 "performance.") </s> remove                  "edges of the mask, which can help with poorly calculated masks, and give less "
                 "of a hard edge to the predicted mask.")
 </s> add                  "edges of the mask, which can help with poorly calculated masks and give less "
                 "of a hard edge to the predicted mask. The size is in pixels (calculated from "
                 "a 128px mask). Set to 0 to not apply gaussian blur. This value should be odd, "
                 "if an even number is passed in then it will be rounded to the next odd number.")
        self.add_item(
            section=section, title="mask_threshold", datatype=int, default=4,
            min_max=(0, 50), rounding=1, group="mask",
            info="Sets pixels that are near white to white and near black to black. Set to 0 for "
                 "off.")
        self.add_item(
            section=section, title="learn_mask", datatype=bool, default=False, group="mask",
            info="Dedicate a portion of the model to learning how to duplicate the input "
                 "mask. Increases VRAM usage in exchange for learning a quick ability to try "
                 "to replicate more complex mask models.") </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 </s> remove             section=section, title="mask_blur", datatype=bool, default=False, group="mask",
 </s> add             section=section, title="mask_blur_kernel", datatype=int, min_max=(0, 9),
            rounding=1, default=3, group="mask",
            "help": "R|Masker to use. NB - masks generated here can be used for training, and "
                    "converting with the 'predicted' mask. Availability of all masks specified "
                    "here for convert is coming soon."
              "type": str.lower,
              "choices": PluginLoader.get_available_extractors("mask", add_none=True),
              "default": "extended",
              "group": "Plugins",
            "help": "R|Masker to use. NB - masks generated here can be used for training, and "
                    "converting with the 'predicted' mask. Availability of all masks specified "
                    "here for convert is coming soon."
            "help": "R|Masker to use. NB - masks generated here can be used for training, and "
                    "converting with the 'predicted' mask. Availability of all masks specified "
                    "here for convert is coming soon."
            "help": "R|Masker to use. NB - masks generated here can be used for training, and "
                    "converting with the 'predicted' mask. Availability of all masks specified "
                    "here for convert is coming soon."
                      "\nL|none: Don't use a mask."
                      "\nL|components: Mask designed to provide facial segmentation based on the "
                      "positioning of landmark locations. A convex hull is constructed around the "
                      "exterior of the landmarks to create a mask."
                      "\nL|extended: Mask designed to provide facial segmentation based on the "
keep keep keep keep replace 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/cli.py