              The metadata for the face image or ``None`` if no metadata is available. If metadata is
              provided the face will be masked by the "components" mask prior to calculating blur.
              Default:``None``
  
          Returns
          -------
          float
              The estimated fft blur score for the face
          """
          if metadata is not None:
              alignments = metadata["alignments"]
              det_face = DetectedFace()
              det_face.from_png_meta(alignments)
 
</s> remove         image: :class:`numpy.ndarray`
            The face image to calculate blur for
        metadata: dict, optional
            The metadata for the face image or ``None`` if no metadata is available. If metadata is
            provided the face will be masked by the "components" mask prior to calculating blur.
            Default:``None``

        Returns
        -------
        float
            The estimated blur score for the face
 </s> add         source: str
            The full path to the source file that is being sorted
        destination: str
            The full path to where the source file should be moved/renamed </s> remove         Parameters
        ----------
        image: :class:`numpy.ndarray`
            Use Fourier Transform to analyze the frequency characteristics of the masked
            face using 2D Discrete Fourier Transform (DFT) filter to find the frequency domain.
            A mean value is assigned to the magnitude spectrum and returns a blur score.
            Adapted from https://www.pyimagesearch.com/2020/06/15/
            opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/
        metadata: dict, optional
            The metadata for the face image or ``None`` if no metadata is available. If metadata is
            provided the face will be masked by the "components" mask prior to calculating blur.
            Default:``None``
 </s> add     def _output_groups(self) -> None:
        """ Move the files to folders. </s> remove         if metadata is not None:
            alignments = metadata["alignments"]
            det_face = DetectedFace()
            det_face.from_png_meta(alignments)
            aln_face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"),
                                   image=image,
                                   centering="legacy",
                                   size=256,
                                   is_aligned=True)
            mask = det_face.mask["components"]
            mask.set_sub_crop(aln_face.pose.offset[mask.stored_centering],
                              aln_face.pose.offset["legacy"],
                              centering="legacy")
            mask = cv2.resize(mask.mask, (256, 256), interpolation=cv2.INTER_CUBIC)[..., None]
            image = np.minimum(aln_face.face, mask)
        if image.ndim == 3:
            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        blur_map = cv2.Laplacian(image, cv2.CV_32F)
        score = np.var(blur_map) / np.sqrt(image.shape[0] * image.shape[1])
        return score
 </s> add         try:
            if self._args.keep_original:
                copyfile(source, destination)
            else:
                os.rename(source, destination)
        except FileNotFoundError as err:
            logger.error("Failed to sort '%s' to '%s'. Original error: %s",
                         source, destination, str(err)) </s> remove         if metadata is not None:
            alignments = metadata["alignments"]
            det_face = DetectedFace()
            det_face.from_png_meta(alignments)
            aln_face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"),
                                   image=image,
                                   centering="legacy",
                                   size=256,
                                   is_aligned=True)
            mask = det_face.mask["components"]
            mask.set_sub_crop(aln_face.pose.offset[mask.stored_centering],
                              aln_face.pose.offset["legacy"],
                              centering="legacy")
            mask = cv2.resize(mask.mask, (256, 256), interpolation=cv2.INTER_CUBIC)[..., None]
            image = np.minimum(aln_face.face, mask)
        if image.ndim == 3:
            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        height, width = image.shape
        c_height, c_width = (int(height / 2.0), int(width / 2.0))
        fft = np.fft.fft2(image)
        fft_shift = np.fft.fftshift(fft)
        fft_shift[c_height - 75:c_height + 75, c_width - 75:c_width + 75] = 0
        ifft_shift = np.fft.ifftshift(fft_shift)
        shift_back = np.fft.ifft2(ifft_shift)
        magnitude = np.log(np.abs(shift_back))
        score = np.mean(magnitude)
        return score

    @staticmethod
    def calc_landmarks_face_pitch(flm):
        """ UNUSED - Calculate the amount of pitch in a face """
        var_t = ((flm[6][1] - flm[8][1]) + (flm[10][1] - flm[8][1])) / 2.0
        var_b = flm[8][1]
        return var_b - var_t
 </s> add         is_rename = self._args.sort_method != "none"

        logger.info("Creating %s group folders in '%s'.",
                    len(self._sorter.binned), self._args.output_dir)
        bin_names = [f"_{b}" for b in self._sorter.bin_names]
        if is_rename:
            bin_names = [f"{name}_by_{self._args.sort_method}" for name in bin_names]
        for name in bin_names:
            folder = os.path.join(self._args.output_dir, name)
            if os.path.exists(folder):
                rmtree(folder)
            os.makedirs(folder)

        description = f"{'Copying' if self._args.keep_original else 'Moving'} into groups"
        description += " and renaming" if is_rename else ""

        pbar = tqdm(range(len(self._sorter.sorted_filelist)),
                    desc=description,
                    file=sys.stdout,
                    leave=False)
        idx = 0
        for bin_id, bin_ in enumerate(self._sorter.binned):
            pbar.set_description(f"{description}: Bin {bin_id + 1} of {len(self._sorter.binned)}")
            output_path = os.path.join(self._args.output_dir, bin_names[bin_id])
            if not bin_:
                logger.debug("Removing empty bin: %s", output_path)
                os.rmdir(output_path)
            for source in bin_:
                basename = os.path.basename(source)
                dst_name = f"{idx:06d}_{basename}" if is_rename else basename
                dest = os.path.join(output_path, dst_name)
                self._sort_file(source, dest)
                idx += 1
                pbar.update(1) </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
        Obtains the bins and original filenames from :attr:`_sorter` and outputs into appropriate
        bins in the output location
              The metadata for the face image or ``None`` if no metadata is available. If metadata is
              provided the face will be masked by the "components" mask prior to calculating blur.
              Default:``None``
  
        Obtains the bins and original filenames from :attr:`_sorter` and outputs into appropriate
        bins in the output location
        Obtains the bins and original filenames from :attr:`_sorter` and outputs into appropriate
        bins in the output location
        Obtains the bins and original filenames from :attr:`_sorter` and outputs into appropriate
        bins in the output location
        Obtains the bins and original filenames from :attr:`_sorter` and outputs into appropriate
        bins in the output location
          """
          if metadata is not None:
              alignments = metadata["alignments"]
              det_face = DetectedFace()
              det_face.from_png_meta(alignments)
keep keep keep keep replace replace 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 
tools/sort/sort.py