faceswap/tools/sort/sort.py

#!/usr/bin/env python3
"""
A tool that allows for sorting and grouping images in different ways.
"""
import logging
import os
import sys
import operator
from concurrent import futures
from shutil import copyfile

import numpy as np
import cv2
from tqdm import tqdm

# faceswap imports
from lib.serializer import get_serializer_from_filename
from lib.faces_detect import DetectedFace
from lib.image import ImagesLoader, read_image
from lib.utils import get_backend
from lib.vgg_face2_keras import VGGFace2 as VGGFace
from plugins.extract.pipeline import Extractor, ExtractMedia
from plugins.extract._config import Config

logger = logging.getLogger(__name__)  # pylint: disable=invalid-name


class Sort():
    """ Sorts folders of faces based on input criteria """
    # pylint: disable=no-member
    def __init__(self, arguments):
        self.args = arguments
        self.changes = None
        self.serializer = None
        self.vgg_face = None
        # TODO set this as ImagesLoader in init. Need to move all processes to use it
        self._loader = None

    def process(self):
        """ Main processing function of the sort tool """

        # Setting default argument values that cannot be set by argparse

        # Set output dir to the same value as input dir
        # if the user didn't specify it.
        if self.args.output_dir is None:
            logger.verbose("No output directory provided. Using input dir as output dir.")
            self.args.output_dir = self.args.input_dir

        # Assigning default threshold values based on grouping method
        if (self.args.final_process == "folders"
                and self.args.min_threshold < 0.0):
            method = self.args.group_method.lower()
            if method == 'face-cnn':
                self.args.min_threshold = 7.2
            elif method == 'hist':
                self.args.min_threshold = 0.3

        # Load VGG Face if sorting by face
        if self.args.sort_method.lower() == "face":
            conf = Config("global", configfile=self.args.configfile)
            allow_growth = (conf.config_dict["allow_growth"] and
                            self.args.backend.lower() == "gpu" and
                            get_backend() == "nvidia")
            self.vgg_face = VGGFace(backend=self.args.backend,
                                    allow_growth=allow_growth,
                                    loglevel=self.args.loglevel)

        # If logging is enabled, prepare container
        if self.args.log_changes:
            self.changes = dict()

            # Assign default sort_log.json value if user didn't specify one
            if self.args.log_file_path == 'sort_log.json':
                self.args.log_file_path = os.path.join(self.args.input_dir,
                                                       'sort_log.json')

            # Set serializer based on logfile extension
            self.serializer = get_serializer_from_filename(self.args.log_file_path)

        # Prepare sort, group and final process method names
        _sort = "sort_" + self.args.sort_method.lower()
        _group = "group_" + self.args.group_method.lower()
        _final = "final_process_" + self.args.final_process.lower()
        if _sort.startswith('sort_color-'):
            self.args.color_method = _sort.replace('sort_color-', '')
            _sort = _sort[:10]
        self.args.sort_method = _sort.replace('-', '_')
        self.args.group_method = _group.replace('-', '_')
        self.args.final_process = _final.replace('-', '_')

        self.sort_process()

    @staticmethod
    def launch_aligner():
        """ Load the aligner plugin to retrieve landmarks """
        extractor = Extractor(None, "fan", None, normalize_method="hist")
        extractor.set_batchsize("align", 1)
        extractor.launch()
        return extractor

    @staticmethod
    def alignment_dict(filename, image):
        """ Set the image to an ExtractMedia object for alignment """
        height, width = image.shape[:2]
        face = DetectedFace(x=0, w=width, y=0, h=height)
        return ExtractMedia(filename, image, detected_faces=[face])

    def _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        extractor = self.launch_aligner()
        filename_list, image_list = self._get_images()
        feed_list = list(map(Sort.alignment_dict, filename_list, image_list))
        landmarks = np.zeros((len(feed_list), 68, 2), dtype='float32')

        logger.info("Finding landmarks in images...")
        # TODO thread the put to queue so we don't have to put and get at the same time
        # Or even better, set up a proper background loader from disk (i.e. use lib.image.ImageIO)
        for idx, feed in enumerate(tqdm(feed_list, desc="Aligning...", file=sys.stdout)):
            extractor.input_queue.put(feed)
            landmarks[idx] = next(extractor.detected_faces()).detected_faces[0].landmarks_xy

        return filename_list, image_list, landmarks

    def _get_images(self):
        """ Multi-threaded, parallel and sequentially ordered image loader """
        logger.info("Loading images...")
        filename_list = self.find_images(self.args.input_dir)
        with futures.ThreadPoolExecutor() as executor:
            image_list = list(tqdm(executor.map(read_image, filename_list),
                                   desc="Loading Images...",
                                   file=sys.stdout,
                                   total=len(filename_list)))

        return filename_list, image_list

    def sort_process(self):
        """
        This method dynamically assigns the functions that will be used to run
        the core process of sorting, optionally grouping, renaming/moving into
        folders. After the functions are assigned they are executed.
        """
        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-dissim 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_blur(self):
        """ Sort by blur amount """
        logger.info("Sorting by estimated image blur...")
        filename_list, image_list = self._get_images()

        logger.info("Estimating blur...")
        blurs = [self.estimate_blur(img) for img in image_list]

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

    def sort_face(self):
        """ Sort by identity similarity """
        logger.info("Sorting by identity similarity...")

        # TODO This should be set in init
        self._loader = ImagesLoader(self.args.input_dir)

        filenames = []
        preds = np.empty((self._loader.count, 512), dtype="float32")
        for idx, (filename, image) in enumerate(tqdm(self._loader.load(),
                                                     desc="Classifying Faces...",
                                                     total=self._loader.count)):
            filenames.append(filename)
            preds[idx] = self.vgg_face.predict(image)

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

        indices = self.vgg_face.sorted_similarity(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..")
        filename_list, _, landmarks = self._get_landmarks()

        logger.info("Estimating yaw...")
        yaws = [self.calc_landmarks_face_yaw(mark) for mark in landmarks]

        logger.info("Sorting...")
        matched_list = list(zip(filename_list, 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...")
        filename_list, image_list = self._get_images()
        distance = cv2.HISTCMP_BHATTACHARYYA

        logger.info("Calculating histograms...")
        histograms = [cv2.calcHist([img], [0], None, [256], [0, 256]) for img in image_list]
        img_list = list(zip(filename_list, histograms))

        logger.info("Comparing histograms 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):
                score = cv2.compareHist(img_list[i][1], img_list[j][1], distance)
                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...")
        filename_list, image_list = self._get_images()
        scores = np.zeros(len(filename_list), dtype='float32')
        distance = cv2.HISTCMP_BHATTACHARYYA

        logger.info("Calculating histograms...")
        histograms = [cv2.calcHist([img], [0], None, [256], [0, 256]) for img in image_list]
        img_list = list(list(items) for items in zip(filename_list, histograms, scores))

        logger.info("Comparing histograms...")
        img_list_len = len(img_list)
        for i in tqdm(range(0, img_list_len), desc="Comparing", 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], distance)
            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_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

    # Methods for grouping
    def group_blur(self, img_list):
        """ Group into bins by blur """
        # Starting the binning process
        num_bins = self.args.num_bins

        # The last bin will get all extra images if it's
        # not possible to distribute them evenly
        num_per_bin = len(img_list) // num_bins
        remainder = len(img_list) % num_bins

        logger.info("Grouping by blur...")
        bins = [[] for _ in range(num_bins)]
        idx = 0
        for i in range(num_bins):
            for _ in range(num_per_bin):
                bins[i].append(img_list[idx][0])
                idx += 1

        # If remainder is 0, nothing gets added to the last bin.
        for i in range(1, remainder + 1):
            bins[-1].append(img_list[-i][0])

        return bins

    def group_face_cnn(self, img_list):
        """ Group into bins by CNN face similarity """
        logger.info("Grouping by face-cnn similarity...")

        # Groups are of the form: group_num -> reference faces
        reference_groups = dict()

        # Bins array, where index is the group number and value is
        # an array containing the file paths to the images in that group.
        bins = []

        # Comparison threshold used to decide how similar
        # faces have to be to be grouped together.
        # It is multiplied by 1000 here to allow the cli option to use smaller
        # numbers.
        min_threshold = self.args.min_threshold * 1000

        img_list_len = len(img_list)

        for i in tqdm(range(0, img_list_len - 1),
                      desc="Grouping",
                      file=sys.stdout):
            fl1 = img_list[i][1]

            current_best = [-1, float("inf")]

            for key, references in reference_groups.items():
                try:
                    score = self.get_avg_score_faces_cnn(fl1, references)
                except TypeError:
                    score = float("inf")
                except ZeroDivisionError:
                    score = float("inf")
                if score < current_best[1]:
                    current_best[0], current_best[1] = key, score

            if current_best[1] < min_threshold:
                reference_groups[current_best[0]].append(fl1[0])
                bins[current_best[0]].append(img_list[i][0])
            else:
                reference_groups[len(reference_groups)] = [img_list[i][1]]
                bins.append([img_list[i][0]])

        return bins

    def group_face_yaw(self, img_list):
        """ Group into bins by yaw of face """
        # Starting the binning process
        num_bins = self.args.num_bins

        # The last bin will get all extra images if it's
        # not possible to distribute them evenly
        num_per_bin = len(img_list) // num_bins
        remainder = len(img_list) % num_bins

        logger.info("Grouping by face-yaw...")
        bins = [[] for _ in range(num_bins)]
        idx = 0
        for i in range(num_bins):
            for _ in range(num_per_bin):
                bins[i].append(img_list[idx][0])
                idx += 1

        # If remainder is 0, nothing gets added to the last bin.
        for i in range(1, remainder + 1):
            bins[-1].append(img_list[-i][0])

        return bins

    def group_hist(self, img_list):
        """ Group into bins by histogram """
        logger.info("Grouping by histogram...")

        # Groups are of the form: group_num -> reference histogram
        reference_groups = dict()

        # Bins array, where index is the group number and value is
        # an array containing the file paths to the images in that group
        bins = []

        min_threshold = self.args.min_threshold

        img_list_len = len(img_list)
        reference_groups[0] = [img_list[0][1]]
        bins.append([img_list[0][0]])

        for i in tqdm(range(1, img_list_len),
                      desc="Grouping",
                      file=sys.stdout):
            current_best = [-1, float("inf")]
            for key, value in reference_groups.items():
                score = self.get_avg_score_hist(img_list[i][1], value)
                if score < current_best[1]:
                    current_best[0], current_best[1] = key, score

            if current_best[1] < min_threshold:
                reference_groups[current_best[0]].append(img_list[i][1])
                bins[current_best[0]].append(img_list[i][0])
            else:
                reference_groups[len(reference_groups)] = [img_list[i][1]]
                bins.append([img_list[i][0]])

        return bins

    # Final process methods
    def final_process_rename(self, img_list):
        """ Rename the files """
        output_dir = self.args.output_dir

        process_file = self.set_process_file_method(self.args.log_changes,
                                                    self.args.keep_original)

        # Make sure output directory exists
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)

        description = (
            "Copying and Renaming" if self.args.keep_original
            else "Moving and Renaming"
        )

        for i in tqdm(range(0, len(img_list)),
                      desc=description,
                      leave=False,
                      file=sys.stdout):
            src = img_list[i] if isinstance(img_list[i], str) else img_list[i][0]
            src_basename = os.path.basename(src)

            dst = os.path.join(output_dir, '{:05d}_{}'.format(i, src_basename))
            try:
                process_file(src, dst, self.changes)
            except FileNotFoundError as err:
                logger.error(err)
                logger.error('fail to rename %s', src)

        for i in tqdm(range(0, len(img_list)),
                      desc=description,
                      file=sys.stdout):
            renaming = self.set_renaming_method(self.args.log_changes)
            fname = img_list[i] if isinstance(img_list[i], str) else img_list[i][0]
            src, dst = renaming(fname, output_dir, i, self.changes)

            try:
                os.rename(src, dst)
            except FileNotFoundError as err:
                logger.error(err)
                logger.error('fail to rename %s', format(src))

        if self.args.log_changes:
            self.write_to_log(self.changes)

    def final_process_folders(self, bins):
        """ Move the files to folders """
        output_dir = self.args.output_dir

        process_file = self.set_process_file_method(self.args.log_changes,
                                                    self.args.keep_original)

        # First create new directories to avoid checking
        # for directory existence in the moving loop
        logger.info("Creating group directories.")
        for i in range(len(bins)):
            directory = os.path.join(output_dir, str(i))
            if not os.path.exists(directory):
                os.makedirs(directory)

        description = (
            "Copying into Groups" if self.args.keep_original
            else "Moving into Groups"
        )

        logger.info("Total groups found: %s", len(bins))
        for i in tqdm(range(len(bins)), desc=description, file=sys.stdout):
            for j in range(len(bins[i])):
                src = bins[i][j]
                src_basename = os.path.basename(src)

                dst = os.path.join(output_dir, str(i), src_basename)
                try:
                    process_file(src, dst, self.changes)
                except FileNotFoundError as err:
                    logger.error(err)
                    logger.error("Failed to move '%s' to '%s'", src, dst)

        if self.args.log_changes:
            self.write_to_log(self.changes)

    # Various helper methods
    def write_to_log(self, changes):
        """ Write the changes to log file """
        logger.info("Writing sort log to: '%s'", self.args.log_file_path)
        self.serializer.save(self.args.log_file_path, changes)

    def reload_images(self, group_method, img_list):
        """
        Reloads the image list by replacing the comparative values with those
        that the chosen grouping method expects.
        :param group_method: str name of the grouping method that will be used.
        :param img_list: image list that has been sorted by one of the sort
        methods.
        :return: img_list but with the comparative values that the chosen
        grouping method expects.
        """
        logger.info("Preparing to group...")
        if group_method == 'group_blur':
            filename_list, image_list = self._get_images()
            blurs = [self.estimate_blur(img) for img in image_list]
            temp_list = list(zip(filename_list, blurs))
        elif group_method == 'group_face_cnn':
            filename_list, image_list, landmarks = self._get_landmarks()
            temp_list = list(zip(filename_list, landmarks))
        elif group_method == 'group_face_yaw':
            filename_list, image_list, landmarks = self._get_landmarks()
            yaws = [self.calc_landmarks_face_yaw(mark) for mark in landmarks]
            temp_list = list(zip(filename_list, yaws))
        elif group_method == 'group_hist':
            filename_list, image_list = self._get_images()
            histograms = [cv2.calcHist([img], [0], None, [256], [0, 256]) for img in image_list]
            temp_list = list(zip(filename_list, histograms))
        else:
            raise ValueError("{} group_method not found.".format(group_method))

        return self.splice_lists(img_list, temp_list)

    @staticmethod
    def _convert_color(imgs, same_size, method):
        """ Helper function to convert colorspaces """

        if method.endswith('gray'):
            conversion = np.array([[0.0722], [0.7152], [0.2126]])
        else:
            conversion = np.array([[0.25, 0.5, 0.25], [-0.5, 0.0, 0.5], [-0.25, 0.5, -0.25]])

        if same_size:
            path = 'greedy'
            operation = 'bijk, kl -> bijl' if method.endswith('gray') else 'bijl, kl -> bijk'
        else:
            operation = 'ijk, kl -> ijl' if method.endswith('gray') else 'ijl, kl -> ijk'
            path = np.einsum_path(operation, imgs[0][..., :3], conversion, optimize='optimal')[0]

        progress_bar = tqdm(imgs, desc="Converting", file=sys.stdout)
        images = [np.einsum(operation, img[..., :3], conversion, optimize=path).astype('float32')
                  for img in progress_bar]
        return images

    @staticmethod
    def splice_lists(sorted_list, new_vals_list):
        """
        This method replaces the value at index 1 in each sub-list in the
        sorted_list with the value that is calculated for the same img_path,
        but found in new_vals_list.

        Format of lists: [[img_path, value], [img_path2, value2], ...]

        :param sorted_list: list that has been sorted by one of the sort
        methods.
        :param new_vals_list: list that has been loaded by a different method
        than the sorted_list.
        :return: list that is sorted in the same way as the input sorted list
        but the values corresponding to each image are from new_vals_list.
        """
        new_list = []
        # Make new list of just image paths to serve as an index
        val_index_list = [i[0] for i in new_vals_list]
        for i in tqdm(range(len(sorted_list)), desc="Splicing", file=sys.stdout):
            current_img = sorted_list[i] if isinstance(sorted_list[i], str) else sorted_list[i][0]
            new_val_index = val_index_list.index(current_img)
            new_list.append([current_img, new_vals_list[new_val_index][1]])

        return new_list

    @staticmethod
    def find_images(input_dir):
        """ Return list of images at specified location """
        result = []
        extensions = [".jpg", ".png", ".jpeg"]
        for root, _, files in os.walk(input_dir):
            for file in files:
                if os.path.splitext(file)[1].lower() in extensions:
                    result.append(os.path.join(root, file))
            break
        return result

    @staticmethod
    def estimate_blur(image):
        """
        Estimate the amount of blur an image has with the variance of the Laplacian.
        Normalize by pixel number to offset the effect of image size on pixel gradients & variance
        """
        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

    @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

    @staticmethod
    def calc_landmarks_face_yaw(flm):
        """ Calculate the amount of yaw in a face """
        var_l = ((flm[27][0] - flm[0][0])
                 + (flm[28][0] - flm[1][0])
                 + (flm[29][0] - flm[2][0])) / 3.0
        var_r = ((flm[16][0] - flm[27][0])
                 + (flm[15][0] - flm[28][0])
                 + (flm[14][0] - flm[29][0])) / 3.0
        return var_r - var_l

    @staticmethod
    def set_process_file_method(log_changes, keep_original):
        """
        Assigns the final file processing method based on whether changes are
        being logged and whether the original files are being kept in the
        input directory.
        Relevant cli arguments: -k, -l
        :return: function reference
        """
        if log_changes:
            if keep_original:
                def process_file(src, dst, changes):
                    """ Process file method if logging changes
                        and keeping original """
                    copyfile(src, dst)
                    changes[src] = dst

            else:
                def process_file(src, dst, changes):
                    """ Process file method if logging changes
                        and not keeping original """
                    os.rename(src, dst)
                    changes[src] = dst

        else:
            if keep_original:
                def process_file(src, dst, changes):  # pylint: disable=unused-argument
                    """ Process file method if not logging changes
                        and keeping original """
                    copyfile(src, dst)

            else:
                def process_file(src, dst, changes):  # pylint: disable=unused-argument
                    """ Process file method if not logging changes
                        and not keeping original """
                    os.rename(src, dst)
        return process_file

    @staticmethod
    def set_renaming_method(log_changes):
        """ Set the method for renaming files """
        if log_changes:
            def renaming(src, output_dir, i, changes):
                """ Rename files  method if logging changes """
                src_basename = os.path.basename(src)

                __src = os.path.join(output_dir,
                                     '{:05d}_{}'.format(i, src_basename))
                dst = os.path.join(
                    output_dir,
                    '{:05d}{}'.format(i, os.path.splitext(src_basename)[1]))
                changes[src] = dst
                return __src, dst
        else:
            def renaming(src, output_dir, i, changes):  # pylint: disable=unused-argument
                """ Rename files method if not logging changes """
                src_basename = os.path.basename(src)

                src = os.path.join(output_dir,
                                   '{:05d}_{}'.format(i, src_basename))
                dst = os.path.join(
                    output_dir,
                    '{:05d}{}'.format(i, os.path.splitext(src_basename)[1]))
                return src, dst
        return renaming

    @staticmethod
    def get_avg_score_hist(img1, references):
        """ Return the average histogram score between a face and
            reference image """
        scores = []
        for img2 in references:
            score = cv2.compareHist(img1, img2, cv2.HISTCMP_BHATTACHARYYA)
            scores.append(score)
        return sum(scores) / len(scores)

    @staticmethod
    def get_avg_score_faces_cnn(fl1, references):
        """ Return the average CNN similarity score
            between a face and reference image """
        scores = []
        for fl2 in references:
            score = np.sum(np.absolute((fl2 - fl1).flatten()))
            scores.append(score)
        return sum(scores) / len(scores)