  
          for item in batch.iterator():
              self.convert(converter, item)
  
          if self.extractor:
              queue_manager.terminate_queues()
  
          Utils.finalize(self.images.images_found,
                         self.faces_count,
                         self.verify_output)
  
 
</s> remove         model = self.load_model()
        converter = self.load_converter(model)

        batch = BackgroundGenerator(self.prepare_images(), 1)
 </s> add         self.load_queue = None
        self.save_queue = None
        self.load_thread = None
        self.save_thread = None
        self.init_threads()
        logger.debug("Initialized %s", self.__class__.__name__) </s> remove         Utils.finalize(self.images.images_found,
                       self.faces_count,
                       self.verify_output)
 </s> add         minmax = {"min": 0,  # never any frames less than 0
                  "max": float("inf")}
        retval = [tuple(map(lambda q: minmax[q] if q in minmax.keys() else int(q), v.split("-")))
                  for v in self.args.frame_ranges]
        logger.debug("frame ranges: %s", retval)
        return retval </s> remove         for item in batch.iterator():
            self.convert(converter, item)
 </s> add     @property
    def draw_transparent(self):
        """ Draw transparent is an image writer only parameter.
            Return the value here for easy access for predictor """
        return self.writer.config.get("draw_transparent", False)

    @property
    def pre_encode(self):
        """ Return the writer's pre-encoder """
        dummy = np.zeros((20, 20, 3)).astype("uint8")
        test = self.writer.pre_encode(dummy)
        retval = None if test is None else self.writer.pre_encode
        logger.debug("Writer pre_encode function: %s", retval)
        return retval

    @property
    def total_count(self):
        """ Return the total number of frames to be converted """
        if self.frame_ranges and not self.args.keep_unchanged:
            retval = sum([fr[1] - fr[0] for fr in self.frame_ranges])
        else:
            retval = self.images.images_found
        logger.debug(retval)
        return retval

    # Initalization
    def get_writer(self):
        """ Return the writer plugin """
        args = [self.args.output_dir]
        if self.args.writer in ("ffmpeg", "gif"):
            args.append(self.total_count)
        if self.args.writer == "ffmpeg":
            if self.images.is_video:
                args.append(self.args.input_dir)
            else:
                args.append(self.args.reference_video)
        logger.debug("Writer args: %s", args)
        return PluginLoader.get_converter("writer", self.args.writer)(*args) </s> remove             Note: GAN prediction outputs a mask + an image, while other
            predicts only an image. """
        Utils.set_verbosity(self.args.loglevel)
 </s> add         Utils.finalize(self.images.images_found,
                       self.predictor.faces_count,
                       self.predictor.verify_output)
        logger.debug("Completed Conversion")

    def convert_images(self):
        """ Convert the images """
        logger.debug("Converting images")
        save_queue = queue_manager.get_queue("save")
        patch_queue = queue_manager.get_queue("patch")
        pool = PoolProcess(self.converter.process, patch_queue, save_queue,
                           processes=self.pool_processes)
        pool.start()
        while True:
            self.check_thread_error()
            if self.disk_io.completion_event.is_set():
                break
            sleep(1)
        pool.join()

        save_queue.put("EOF")
        logger.debug("Converted images")

    def check_thread_error(self):
        """ Check and raise thread errors """
        for thread in (self.predictor.thread, self.disk_io.load_thread, self.disk_io.save_thread):
            thread.check_and_raise_error()

    def patch_iterator(self, processes):
        """ Prepare the images for conversion """
        out_queue = queue_manager.get_queue("out")
        completed = 0

        while True:
            try:
                item = out_queue.get(True, 1)
            except QueueEmpty:
                self.check_thread_error()
                continue
            self.check_thread_error() </s> remove             frame = os.path.basename(filename)
            if self.extractor:
                detected_faces = self.detect_faces(load_queue, filename, image)
            else:
                detected_faces = self.alignments_faces(frame, image)

            faces_count = len(detected_faces)
            if faces_count != 0:
                # Post processing requires a dict with "detected_faces" key
                self.post_process.do_actions(
                    {"detected_faces": detected_faces})
                self.faces_count += faces_count

            if faces_count > 1:
                self.verify_output = True
                logger.verbose("Found more than one face in "
                               "an image! '%s'", frame)

            yield filename, image, detected_faces
 </s> add             detected_faces = self.get_detected_faces(filename, image, extract_queue)
            item = dict(filename=filename, image=image, detected_faces=detected_faces)
            self.load_queue.put(item)
    def get_frame_ranges(self):
        """ split out the frame ranges and parse out 'min' and 'max' values """
        if not self.args.frame_ranges:
            logger.debug("No frame range set")
            return None
  
          for item in batch.iterator():
              self.convert(converter, item)
  
    def get_frame_ranges(self):
        """ split out the frame ranges and parse out 'min' and 'max' values """
        if not self.args.frame_ranges:
            logger.debug("No frame range set")
            return None
    def get_frame_ranges(self):
        """ split out the frame ranges and parse out 'min' and 'max' values """
        if not self.args.frame_ranges:
            logger.debug("No frame range set")
            return None
  
          Utils.finalize(self.images.images_found,
                         self.faces_count,
                         self.verify_output)
  
keep keep keep keep replace replace 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 
scripts/convert.py