  
  class Mask():
      """ Parent class for masks
  
          channels: 1, 3 or 4:
                      1 - Returns a single channel mask
                      3 - Returns a 3 channel mask
                      4 - Returns the original image with the mask in the alpha channel """
  
 
</s> remove def components(landmarks, face, channels=4):
    """ Component model mask
 </s> add class Mask():
    """ Parent class for masks </s> remove def dfaker(landmarks, face, channels=4):
    """ Dfaker model mask
        Embeds the mask into the face alpha channel

        channels: 1, 3 or 4:
                1 - Return a single channel mask
                3 - Return a 3 channel mask
                4 - Return the original image with the mask in the alpha channel
        """
    padding = int(face.shape[0] * 0.1875)
    coverage = face.shape[0] - (padding * 2)
    logger.trace("face_shape: %s, coverage: %s, landmarks: %s", face.shape, coverage, landmarks)

    mat = umeyama(landmarks[17:], True)[0:2]
    mat = mat.reshape(-1).reshape(2, 3)
    mat = mat * coverage
    mat[:, 2] += padding

    mask = np.zeros(face.shape[0:2] + (1, ), dtype=np.float32)
    hull = cv2.convexHull(landmarks).reshape(1, -1, 2)  # pylint: disable=no-member
    hull = cv2.transform(hull, mat).reshape(-1, 2)  # pylint: disable=no-member
    cv2.fillConvexPoly(mask, hull, 255.)  # pylint: disable=no-member

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))  # pylint: disable=no-member
    mask = cv2.dilate(mask, kernel, borderType=cv2.BORDER_REFLECT)  # pylint: disable=no-member
    mask = np.expand_dims(mask, axis=-1)

    return merge_mask(face, mask, channels)


def dfl_full(landmarks, face, channels=4):
    """ DFL facial mask

        channels: 1, 3 or 4:
            1 - Return a single channel mask
            3 - Return a 3 channel mask
            4 - Return the original image with the mask in the alpha channel
        """
    logger.trace("face_shape: %s, landmarks: %s", face.shape, landmarks)
    mask = np.zeros(face.shape[0:2] + (1, ), dtype=np.float32)

    nose_ridge = (landmarks[27:31], landmarks[33:34])
    jaw = (landmarks[0:17], landmarks[48:68], landmarks[0:1],
           landmarks[8:9], landmarks[16:17])
    eyes = (landmarks[17:27], landmarks[0:1], landmarks[27:28],
            landmarks[16:17], landmarks[33:34])
    parts = [jaw, nose_ridge, eyes]
 </s> add def get_available_masks():
    """ Return a list of the available masks for cli """
    masks = sorted([name for name, obj in inspect.getmembers(sys.modules[__name__])
                    if inspect.isclass(obj) and name != "Mask"])
    masks.append("none")
    logger.debug(masks)
    return masks </s> remove             1 - Return a single channel mask
            3 - Return a 3 channel mask
            4 - Return the original image with the mask in the alpha channel
        """
    logger.trace("face_shape: %s, landmarks: %s", face.shape, landmarks)
    mask = np.zeros(face.shape[0:2] + (1, ), dtype=np.float32)

    r_jaw = (landmarks[0:9], landmarks[17:18])
    l_jaw = (landmarks[8:17], landmarks[26:27])
    r_cheek = (landmarks[17:20], landmarks[8:9])
    l_cheek = (landmarks[24:27], landmarks[8:9])
    nose_ridge = (landmarks[19:25], landmarks[8:9],)
    r_eye = (landmarks[17:22], landmarks[27:28],
             landmarks[31:36], landmarks[8:9])
    l_eye = (landmarks[22:27], landmarks[27:28],
             landmarks[31:36], landmarks[8:9])
    nose = (landmarks[27:31], landmarks[31:36])
    parts = [r_jaw, l_jaw, r_cheek, l_cheek, nose_ridge, r_eye, l_eye, nose]

    for item in parts:
        merged = np.concatenate(item)
        cv2.fillConvexPoly(mask, cv2.convexHull(merged), 255.)  # pylint: disable=no-member

    return merge_mask(face, mask, channels)


def merge_mask(image, mask, channels):
    """ Return the mask in requested shape """
    logger.trace("image_shape: %s, mask_shape: %s, channels: %s",
                 image.shape, mask.shape, channels)
    assert channels in (1, 3, 4), "Channels should be 1, 3 or 4"
    assert mask.shape[2] == 1 and mask.ndim == 3, "Input mask be 3 dimensions with 1 channel"

    if channels == 3:
        retval = np.tile(mask, 3)
    elif channels == 4:
        retval = np.concatenate((image, mask), -1)
    else:
        retval = mask

    logger.trace("Final mask shape: %s", retval.shape)
    return retval
 </s> add                     1 - Returns a single channel mask
                    3 - Returns a 3 channel mask
                    4 - Returns the original image with the mask in the alpha channel """

    def __init__(self, landmarks, face, channels=4):
        logger.trace("Initializing %s: (face_shape: %s, channels: %s, landmarks: %s)",
                     self.__class__.__name__, face.shape, channels, landmarks)
        self.landmarks = landmarks
        self.face = face
        self.channels = channels

        mask = self.build_mask()
        self.mask = self.merge_mask(mask)
        logger.trace("Initialized %s", self.__class__.__name__)

    def build_mask(self):
        """ Override to build the mask """
        raise NotImplementedError

    def merge_mask(self, mask):
        """ Return the mask in requested shape """
        logger.trace("mask_shape: %s", mask.shape)
        assert self.channels in (1, 3, 4), "Channels should be 1, 3 or 4"
        assert mask.shape[2] == 1 and mask.ndim == 3, "Input mask be 3 dimensions with 1 channel"

        if self.channels == 3:
            retval = np.tile(mask, 3)
        elif self.channels == 4:
            retval = np.concatenate((self.face, mask), -1)
        else:
            retval = mask

        logger.trace("Final mask shape: %s", retval.shape)
        return retval


class dfl_full(Mask):  # pylint: disable=invalid-name
    """ DFL facial mask """
    def build_mask(self):
        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=np.float32)

        nose_ridge = (self.landmarks[27:31], self.landmarks[33:34])
        jaw = (self.landmarks[0:17],
               self.landmarks[48:68],
               self.landmarks[0:1],
               self.landmarks[8:9],
               self.landmarks[16:17])
        eyes = (self.landmarks[17:27],
                self.landmarks[0:1],
                self.landmarks[27:28],
                self.landmarks[16:17],
                self.landmarks[33:34])
        parts = [jaw, nose_ridge, eyes]

        for item in parts:
            merged = np.concatenate(item)
            cv2.fillConvexPoly(mask, cv2.convexHull(merged), 255.)  # pylint: disable=no-member
        return mask


class components(Mask):  # pylint: disable=invalid-name
    """ Component model mask """
    def build_mask(self):
        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=np.float32)

        r_jaw = (self.landmarks[0:9], self.landmarks[17:18])
        l_jaw = (self.landmarks[8:17], self.landmarks[26:27])
        r_cheek = (self.landmarks[17:20], self.landmarks[8:9])
        l_cheek = (self.landmarks[24:27], self.landmarks[8:9])
        nose_ridge = (self.landmarks[19:25], self.landmarks[8:9],)
        r_eye = (self.landmarks[17:22],
                 self.landmarks[27:28],
                 self.landmarks[31:36],
                 self.landmarks[8:9])
        l_eye = (self.landmarks[22:27],
                 self.landmarks[27:28],
                 self.landmarks[31:36],
                 self.landmarks[8:9])
        nose = (self.landmarks[27:31], self.landmarks[31:36])
        parts = [r_jaw, l_jaw, r_cheek, l_cheek, nose_ridge, r_eye, l_eye, nose]

        for item in parts:
            merged = np.concatenate(item)
            cv2.fillConvexPoly(mask, cv2.convexHull(merged), 255.)  # pylint: disable=no-member
        return mask


class facehull(Mask):  # pylint: disable=invalid-name
    """ Basic face hull mask """
    def build_mask(self):
        mask = np.zeros(self.face.shape[0:2] + (1, ), dtype=np.float32)
        hull = cv2.convexHull(  # pylint: disable=no-member
            np.array(self.landmarks).reshape((-1, 2)))
        cv2.fillConvexPoly(mask, hull, 1.0, lineType=cv2.LINE_AA)  # pylint: disable=no-member
        return mask </s> remove              "\n\tnone: Doesn't use any mask."
             "\n\tdfaker: A basic face hull mask using a facehull of all 68 landmarks."
             "\n\tdfl_full: An improved face hull mask using a facehull of 3 facial parts"
             "\n\tcomponents: An improved face hull mask using a facehull of 8 facial parts")
 </s> add              "\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 facehull: Face cutout based on landmarks") </s> remove             "choices": ["ellipse",
                        "facehull",
                        "dfl",
                        #  "cnn",  Removed until implemented
                        "none"],
            "default": "facehull",
            "help": "R|Mask to use to replace faces."
                    "\nellipse: Oval around face."
                    "\nfacehull: Face cutout based on landmarks."
                    "\ndfl: A Face Hull mask from DeepFaceLabs."
                    #  "\ncnn: Not yet implemented"  Removed until implemented
                    "\nnone: No mask. Can still use blur and erode on the edges of the swap box."})
        argument_list.append({"opts": ("-b", "--blur-size"),
                              "type": float,
                              "action": Slider,
                              "min_max": (0.0, 100.0),
                              "rounding": 2,
                              "default": 5.0,
                              "help": "Blur kernel size as a percentage of the swap area. Smooths "
                                      "the transition between the swapped face and the background "
                                      "image."})
        argument_list.append({"opts": ("-e", "--erosion-size"),
                              "dest": "erosion_size",
                              "type": float,
 </s> add             "choices": get_available_masks() + ["predicted"],
            "default": "predicted",
            "help": "R|Mask to use to replace faces. Blending of the masks can be adjusted in "
                    "'/config/convert.ini' or 'Edit > Configure Convert Plugins':"
                    "\nL|components: An improved face hull mask using a facehull of 8 facial "
                    "parts."
                    "\nL|dfl_full: An improved face hull mask using a facehull of 3 facial parts."
                    "\nL|facehull: Face cutout based on landmarks."
                    "\nL|predicted: The predicted mask generated from the model. If the model was "
                    "not trained with a mask then this will fallback to "
                    "'{}'".format(get_default_mask()) +
                    "\nL|none: Don't use a mask."})
        argument_list.append({"opts": ("-w", "--writer"),
                              "action": Radio,
                              "type": str,
                              "choices": PluginLoader.get_available_convert_plugins("writer",
                                                                                    False),
                              "default": "opencv",
                              "help": "R|The plugin to use to output the converted images. The "
                                      "writers are configurable in '/config/convert.ini' or `Edit "
                                      "> Configure Convert Plugins:'"
                                      "\nL|ffmpeg: [video] Writes out the convert straight to "
                                      "video. When the input is a series of images then the "
                                      "'-ref' (--reference-video) parameter must be set."
                                      "\nL|gif: [animated image] Create an animated gif."
                                      "\nL|opencv: [images] The fastest image writer, but less "
                                      "options and formats than other plugins."
                                      "\nL|pillow: [images] Slower than opencv, but has more "
                                      "options and supports more formats."})
        argument_list.append({"opts": ("-osc", "--output-scale"),
                              "dest": "output_scale",
        the output mask will be <mask_type>.mask
  
  class Mask():
      """ Parent class for masks
  
        the output mask will be <mask_type>.mask
          channels: 1, 3 or 4:
                      1 - Returns a single channel mask
                      3 - Returns a 3 channel mask
                      4 - Returns the original image with the mask in the alpha channel """
  
keep keep keep add keep keep keep keep keep
 Convert refactor (#703)

* Separate predict and implement pool

* Add check and raise error to multithreading

Box functions to config. Add crop box option.

* All masks to mask module. Refactor convert masks

Predicted mask passed from model. Cli update

* Intesect box with mask and fixes

* Use raw NN output for convert

Use raw mask for face adjustments. Split adjustments to pre and post warp

* Separate out adjustments. Add unmask sharpen

* Set sensible defaults. Pre PR Testing

* Fix queue sizes. Move masked.py to lib

* Fix Skip Frames. Fix GUI Config popup

* Sensible queue limits. Add a less resource intensive single processing mode

* Fix predicted mask. Amend smooth box defaults

* Deterministic ordering for video output

* Video to Video convert implemented

* Fixups

- Remove defaults from folders across all stages
- Move match-hist and aca into color adjustments selectable
- Handle crashes properly for pooled processes
- Fix output directory does not exist error when creating a new output folder
- Force output to frames if input is not a video

* Add Color Transfer adjustment method

Wrap info text in GUI plugin configure popup

* Refactor image adjustments. Easier to create plugins

Start implementing config options for video encoding

* Add video encoding config options

Allow video encoding for frames input (must pass in a reference video)
Move video and image output writers to plugins

* Image writers config options

Move scaling to cli
Move draw_transparent to images config
Add config options for cv2 writer
Add Pillow image writer

* Add gif filetype to Pillow. Fix draw transparent for Pillow

* Add Animated GIF writer

standardize opencv/pillow defaults

* [speedup] Pre-encode supported writers in the convert pool (opencv, pillow)

Move scaling to convert pool
Remove dfaker mask

* Fix default writer

* Bugfixes

* Better custom argparse formatting 
lib/model/masks.py