          face = DetectedFace(x=0, w=width, y=0, h=height)
          return {"image": image,
                  "detected_faces": [face]}
  
      @staticmethod
      def get_landmarks(filename):
          """ Extract the face from a frame (If not alignments file found) """
          image = read_image(filename, raise_error=True)
          feed = Sort.alignment_dict(image)
          feed["filename"] = filename
          queue_manager.get_queue("in").put(feed)
          face = queue_manager.get_queue("out").get()
          landmarks = face["detected_faces"][0].landmarks_xy
          return landmarks
  
      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
 
</s> remove     def alignment_dict(image):
 </s> add     def alignment_dict(filename, image): </s> remove     def estimate_blur(image_file):
 </s> add     def estimate_blur(image): </s> remove         image = read_image(image_file, raise_error=True)
 </s> add  </s> remove         """ Sort by yaw of face """
        self.launch_aligner()
        input_dir = self.args.input_dir
 </s> add         """ Sort by estimated face yaw angle """
        logger.info("Sorting by estimated face yaw angle..")
        filename_list, image_list, landmarks = self._get_landmarks()
    def _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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
          face = DetectedFace(x=0, w=width, y=0, h=height)
          return {"image": image,
                  "detected_faces": [face]}
  
    def _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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 _get_landmarks(self):
        """ Multi-threaded, parallel and sequentially ordered landmark loader """
        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...")
        for feed in tqdm(feed_list, desc="Putting...", file=sys.stdout):
            queue_manager.get_queue("in").put(feed)
        for index, _ in enumerate(tqdm(landmarks, desc="Aligning...", file=sys.stdout)):
            face = queue_manager.get_queue("out").get()
            landmarks[index] = np.array(face["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
keep keep keep keep replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 ENHANCEMENT: Sort image multi-threading and code clarity (#912)

* Use multi-threaded loading

* fast reload

* remove landmark stub

* saves

* correct face sort

* pep8 
tools/sort.py