                             int(free_ram), int(linkage_required))
              retval = True
          else:
              raise FaceswapError("Not enough RAM available to sort faces. Try reducing "
                                  "the size of  your dataset. Free RAM: {}MB. "
                                  "Required RAM: {}MB".format(int(free_ram), int(vector_required)))
          logger.debug(retval)
          return retval
  
      def _seriation(self, tree, points, current_index):
          """ Seriation method for sorted similarity.
 
</s> remove     def _seriation(self, tree, points, current_index):
 </s> add     def _do_linkage(self,
                    predictions: np.ndarray,
                    method: Literal["single", "centroid", "median", "ward"]) -> np.ndarray:
        """ Use FastCluster to perform vector or standard linkage

        Parameters
        ----------
        predictions: :class:`numpy.ndarray`
            A stacked matrix of vgg_face2 predictions of the shape (`N`, `D`) where `N` is the
            number of observations and `D` are the number of dimensions.
        method: ['single','centroid','median','ward']
            The clustering method to use.

        Returns
        -------
        :class:`numpy.ndarray`
            The [`num_predictions`, 4] linkage vector
        """
        dims = predictions.shape[-1]
        if self._use_vector_linkage(dims):
            retval = linkage_vector(predictions, method=method)
        else:
            retval = linkage(predictions, method=method, preserve_input=False)
        logger.debug("Linkage shape: %s", retval.shape)
        return retval

    def _process_leaf_node(self,
                           current_index: int,
                           current_bin: int) -> List[Tuple[int, int]]:
        """ Process the output when we have hit a leaf node """
        if not self._should_output_bins:
            return [(current_index, 0)]

        if current_bin not in self._bins:
            next_val = 0 if not self._bins else max(self._bins.values()) + 1
            self._bins[current_bin] = next_val
        return [(current_index, self._bins[current_bin])]

    def _get_bin(self,
                 tree: np.ndarray,
                 points: int,
                 current_index: int,
                 current_bin: int) -> int:
        """ Obtain the bin that we are currently in.

        If we are not currently below the threshold for binning, get a new bin ID from the integer
        iterator.

        Parameters
        ----------
        tree: numpy.ndarray
           A hierarchical tree (dendrogram)
        points: int
            The number of points given to the clustering process
        current_index: int
            The position in the tree for the recursive traversal
        current_bin int, optional
            The ID for the bin we are currently in. Only used when binning is enabled

        Returns
        -------
        int
            The current bin ID for the node
        """
        if tree[current_index - points, 2] >= self._threshold:
            current_bin = next(self._iterator)
            logger.debug("Creating new bin ID: %s", current_bin)
        return current_bin

    def _seriation(self,
                   tree: np.ndarray,
                   points: int,
                   current_index: int,
                   current_bin: int = 0) -> List[Tuple[int, int]]: </s> remove             logger.verbose("Using linkage method")
 </s> add             logger.verbose("Using linkage method")  # type:ignore </s> remove             help=_("Output directory for sorted aligned faces.")))
 </s> add             help=_("Output directory for sorted aligned faces. If not provided and 'keep' is "
                   "selected then a new folder called 'sorted' will be created within the input "
                   "folder to house the output. If not provided and 'keep' is not selected then "
                   "the images will be sorted in-place, overwriting the original contents of the "
                   "'input_dir'"))) </s> remove         if self._args.log_changes:
            self.write_to_log(self.changes)
 </s> add         logger.debug("Final sorter: %s", retval)
        return retval </s> remove         sort_method = self._args.sort_method.lower()
        group_method = self._args.group_method.lower()
        final_method = self._args.final_process.lower()

        img_list = getattr(self, sort_method)()
        if "folders" in final_method:
            # Check if non-dissimilarity sort method and group method are not the same
            if group_method.replace('group_', '') not in sort_method:
                img_list = self.reload_images(group_method, img_list)
                img_list = getattr(self, group_method)(img_list)
            else:
                img_list = getattr(self, group_method)(img_list)

        getattr(self, final_method)(img_list)

        logger.info("Done.")

    # Methods for sorting
    def sort_distance(self):
        """ Sort by comparison of face landmark points to mean face by average distance of core
        landmarks. """
        logger.info("Sorting by average distance of landmarks...")
        filenames = []
        distances = []
        filelist = [os.path.join(self._loader.location, fname)
                    for fname in os.listdir(self._loader.location)
                    if os.path.splitext(fname)[-1] == ".png"]
        for filename, metadata in tqdm(read_image_meta_batch(filelist),
                                       total=len(filelist),
                                       desc="Calculating Distances"):
            if not metadata:
                msg = ("The images to be sorted do not contain alignment data. Images must have "
                       "been generated by Faceswap's Extract process.\nIf you are sorting an "
                       "older faceset, then you should re-extract the faces from your source "
                       "alignments file to generate this data.")
                raise FaceswapError(msg)
            alignments = metadata["itxt"]["alignments"]
            aligned_face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"))
            filenames.append(filename)
            distances.append(aligned_face.average_distance)

        logger.info("Sorting...")
        matched_list = list(zip(filenames, distances))
        img_list = sorted(matched_list, key=operator.itemgetter(1))
        return img_list

    def sort_blur(self):
        """ Sort by blur amount """
        logger.info("Sorting by estimated image blur...")

        blurs = [(filename, self.estimate_blur(image, metadata))
                 for filename, image, metadata in tqdm(self._loader.load(),
                                                       desc="Estimating blur",
                                                       total=self._loader.count,
                                                       leave=False)]
        logger.info("Sorting...")
        return sorted(blurs, key=lambda x: x[1], reverse=True)

    def sort_blur_fft(self):
        """ Sort by fft filtered blur amount with fft"""
        logger.info("Sorting by estimated fft filtered image blur...")

        fft_blurs = [(filename, self.estimate_blur_fft(image, metadata))
                     for filename, image, metadata in tqdm(self._loader.load(),
                                                           desc="Estimating fft blur score",
                                                           total=self._loader.count,
                                                           leave=False)]
        logger.info("Sorting...")
        return sorted(fft_blurs, key=lambda x: x[1], reverse=True)

    def sort_color(self):
        """ Score by channel average intensity """
        logger.info("Sorting by channel average intensity...")
        desired_channel = {'gray': 0, 'luma': 0, 'orange': 1, 'green': 2}
        method = self._args.color_method
        channel_to_sort = next(v for (k, v) in desired_channel.items() if method.endswith(k))
        filename_list, image_list = self._get_images()

        logger.info("Converting to appropriate colorspace...")
        same_size = all(img.size == image_list[0].size for img in image_list)
        images = np.array(image_list, dtype='float32')[None, ...] if same_size else image_list
        converted_images = self._convert_color(images, same_size, method)

        logger.info("Scoring each image...")
        if same_size:
            scores = np.average(converted_images[0], axis=(1, 2))
        else:
            progress_bar = tqdm(converted_images, desc="Scoring", file=sys.stdout)
            scores = np.array([np.average(image, axis=(0, 1)) for image in progress_bar])

        logger.info("Sorting...")
        matched_list = list(zip(filename_list, scores[:, channel_to_sort]))
        sorted_file_img_list = sorted(matched_list, key=operator.itemgetter(1), reverse=True)
        return sorted_file_img_list

    def sort_face(self):
        """ Sort by identity similarity """
        logger.info("Sorting by identity similarity...")
        filenames = []
        preds = []
        for filename, image, metadata in tqdm(self._loader.load(),
                                              desc="Classifying Faces",
                                              total=self._loader.count,
                                              leave=False):
            if not metadata:
                msg = ("The images to be sorted do not contain alignment data. Images must have "
                       "been generated by Faceswap's Extract process.\nIf you are sorting an "
                       "older faceset, then you should re-extract the faces from your source "
                       "alignments file to generate this data.")
                raise FaceswapError(msg)
            alignments = metadata["alignments"]
            face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"),
                               image=image,
                               centering="legacy",
                               size=self._vgg_face.input_size,
                               is_aligned=True).face
            filenames.append(filename)
            preds.append(self._vgg_face.predict(face))

        logger.info("Sorting by ward linkage...")

        indices = self._vgg_face.sorted_similarity(np.array(preds), method="ward")
        img_list = np.array(filenames)[indices]
        return img_list

    def sort_face_cnn(self):
        """ Sort by landmark similarity """
        logger.info("Sorting by landmark similarity...")
        filename_list, _, landmarks = self._get_landmarks()
        img_list = list(zip(filename_list, landmarks))

        logger.info("Comparing landmarks and sorting...")
        img_list_len = len(img_list)
        for i in tqdm(range(0, img_list_len - 1), desc="Comparing", file=sys.stdout):
            min_score = float("inf")
            j_min_score = i + 1
            for j in range(i + 1, img_list_len):
                fl1 = img_list[i][1]
                fl2 = img_list[j][1]
                score = np.sum(np.absolute((fl2 - fl1).flatten()))
                if score < min_score:
                    min_score = score
                    j_min_score = j
            (img_list[i + 1], img_list[j_min_score]) = (img_list[j_min_score], img_list[i + 1])
        return img_list

    def sort_face_cnn_dissim(self):
        """ Sort by landmark dissimilarity """
        logger.info("Sorting by landmark dissimilarity...")
        filename_list, _, landmarks = self._get_landmarks()
        scores = np.zeros(len(filename_list), dtype='float32')
        img_list = list(list(items) for items in zip(filename_list, landmarks, scores))

        logger.info("Comparing landmarks...")
        img_list_len = len(img_list)
        for i in tqdm(range(0, img_list_len - 1), desc="Comparing", file=sys.stdout):
            score_total = 0
            for j in range(i + 1, img_list_len):
                if i == j:
                    continue
                fl1 = img_list[i][1]
                fl2 = img_list[j][1]
                score_total += np.sum(np.absolute((fl2 - fl1).flatten()))
            img_list[i][2] = score_total

        logger.info("Sorting...")
        img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
        return img_list

    def sort_face_yaw(self):
        """ Sort by estimated face yaw angle """
        logger.info("Sorting by estimated face yaw angle..")
        filenames = []
        yaws = []
        filelist = [os.path.join(self._loader.location, fname)
                    for fname in os.listdir(self._loader.location)
                    if os.path.splitext(fname)[-1] == ".png"]
        for filename, metadata in tqdm(read_image_meta_batch(filelist),
                                       total=len(filelist),
                                       desc="Calculating Yaw"):
            if not metadata:
                msg = ("The images to be sorted do not contain alignment data. Images must have "
                       "been generated by Faceswap's Extract process.\nIf you are sorting an "
                       "older faceset, then you should re-extract the faces from your source "
                       "alignments file to generate this data.")
                raise FaceswapError(msg)
            alignments = metadata["itxt"]["alignments"]
            aligned_face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"),
                                       image=None,
                                       centering="legacy",
                                       is_aligned=True)
            filenames.append(filename)
            yaws.append(aligned_face.pose.yaw)

        logger.info("Sorting...")
        matched_list = list(zip(filenames, yaws))
        img_list = sorted(matched_list, key=operator.itemgetter(1), reverse=True)
        return img_list

    def sort_hist(self):
        """ Sort by image histogram similarity """
        logger.info("Sorting by histogram similarity...")

        # TODO We have metadata here, so we can mask the face for hist sorting
        img_list = [(filename, cv2.calcHist([image], [0], None, [256], [0, 256]))
                    for filename, image, _ in tqdm(self._loader.load(),
                                                   desc="Calculating histograms",
                                                   total=self._loader.count,
                                                   leave=False)]

        logger.info("Comparing histograms and sorting...")
        img_list_len = len(img_list)
        for i in tqdm(range(0, img_list_len - 1), desc="Comparing histograms", file=sys.stdout):
            min_score = float("inf")
            j_min_score = i + 1
            for j in range(i + 1, img_list_len):
                score = cv2.compareHist(img_list[i][1], img_list[j][1], cv2.HISTCMP_BHATTACHARYYA)
                if score < min_score:
                    min_score = score
                    j_min_score = j
            (img_list[i + 1], img_list[j_min_score]) = (img_list[j_min_score], img_list[i + 1])
        return img_list

    def sort_hist_dissim(self):
        """ Sort by image histogram dissimilarity """
        logger.info("Sorting by histogram dissimilarity...")

        # TODO We have metadata here, so we can mask the face for hist sorting
        img_list = [[filename, cv2.calcHist([image], [0], None, [256], [0, 256]), 0.0]
                    for filename, image, _ in tqdm(self._loader.load(),
                                                   desc="Calculating histograms",
                                                   total=self._loader.count,
                                                   leave=False)]

        img_list_len = len(img_list)
        for i in tqdm(range(0, img_list_len), desc="Comparing histograms", file=sys.stdout):
            score_total = 0
            for j in range(0, img_list_len):
                if i == j:
                    continue
                score_total += cv2.compareHist(img_list[i][1],
                                               img_list[j][1],
                                               cv2.HISTCMP_BHATTACHARYYA)
            img_list[i][2] = score_total

        logger.info("Sorting...")
        return sorted(img_list, key=lambda x: x[2], reverse=True)

    def sort_size(self):
        """ Sort the faces by largest face (in original frame) to smallest """
        logger.info("Sorting by original face size...")
        img_list = []
        for filename, image, metadata in tqdm(self._loader.load(),
                                              desc="Calculating face sizes",
                                              total=self._loader.count,
                                              leave=False):
            if not metadata:
                msg = ("The images to be sorted do not contain alignment data. Images must have "
                       "been generated by Faceswap's Extract process.\nIf you are sorting an "
                       "older faceset, then you should re-extract the faces from your source "
                       "alignments file to generate this data.")
                raise FaceswapError(msg)
            alignments = metadata["alignments"]
            aligned_face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"),
                                       image=image,
                                       centering="legacy",
                                       is_aligned=True)
            roi = aligned_face.original_roi
            size = ((roi[1][0] - roi[0][0]) ** 2 + (roi[1][1] - roi[0][1]) ** 2) ** 0.5
            img_list.append((filename, size))

        logger.info("Sorting...")
        return sorted(img_list, key=lambda x: x[1], reverse=True)

    def sort_black_pixels(self):
        """ Sort by percentage of black pixels
 </s> add         logger.debug("setting output folder: %s", arguments.output_dir)
        input_dir = arguments.input_dir
        output_dir = arguments.output_dir
        sort_method = arguments.sort_method
        group_method = arguments.group_method

        needs_rename = sort_method != "none" and group_method == "none"

        if needs_rename and arguments.keep_original and (not output_dir or
                                                         output_dir == input_dir):
            output_dir = os.path.join(input_dir, "sorted")
            logger.warning("No output folder selected, but files need renaming. "
                           "Outputting to: '%s'", output_dir)
        elif not output_dir:
            output_dir = input_dir
            logger.warning("No output folder selected, files will be sorted in place in: '%s'",
                           output_dir)

        arguments.output_dir = output_dir
        logger.debug("Set output folder: %s", arguments.output_dir)
        return arguments

    def _parse_arguments(self, arguments):
        """ Parse the arguments and update/format relevant choices

        Parameters:
        ----------
        arguments: :class:`argparse.Namespace`
            The command line arguments passed to the sort process
                                f"the size of  your dataset. Free RAM: {int(free_ram)}MB. "
                                f"Required RAM: {int(vector_required)}MB")
                             int(free_ram), int(linkage_required))
              retval = True
          else:
              raise FaceswapError("Not enough RAM available to sort faces. Try reducing "
                                f"the size of  your dataset. Free RAM: {int(free_ram)}MB. "
                                f"Required RAM: {int(vector_required)}MB")
                                f"the size of  your dataset. Free RAM: {int(free_ram)}MB. "
                                f"Required RAM: {int(vector_required)}MB")
          logger.debug(retval)
          return retval
  
      def _seriation(self, tree, points, current_index):
          """ Seriation method for sorted similarity.
keep keep keep keep replace replace keep keep keep keep keep
 Overhaul sort:
  - Standardize image data reading and writing
  - Optimize loading (just one pass required)
  - Make all sort groups binnable (to greater or lesser results)
  - Add sort by pitch
  - Deprecate multiple options
  - linting, docs + locales 
plugins/extract/recognition/vgg_face2_keras.py