                                 "an image! '%s'", frame)
  
              yield filename, image, detected_faces
  
      def detect_faces(self, load_queue, filename, image):
          """ Extract the face from a frame (If alignments file not found) """
          inp = {"filename": filename,
                 "image": image}
          load_queue.put(inp)
          faces = next(self.extractor.detect_faces())
  
          landmarks = faces["landmarks"]
          detected_faces = faces["detected_faces"]
          final_faces = list()
  
 
</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) </s> remove         for idx, face in enumerate(detected_faces):
            detected_face = DetectedFace()
            detected_face.from_dlib_rect(face)
            detected_face.landmarksXY = landmarks[idx]
            final_faces.append(detected_face)
        return final_faces
 </s> add     def get_detected_faces(self, filename, image, extract_queue):
        """ Return detected faces from alignments or detector """
        logger.trace("Getting faces for: '%s'", filename)
        if not self.extractor:
            detected_faces = self.alignments_faces(os.path.basename(filename), image)
        else:
            detected_faces = self.detect_faces(extract_queue, filename, image)
        logger.trace("Got %s faces for: '%s'", len(detected_faces), filename)
        return detected_faces </s> remove     def convert(self, converter, item):
        """ Apply the conversion transferring faces onto frames """
        try:
            filename, image, faces = item
            skip = self.opts.check_skipframe(filename)
 </s> add     def detect_faces(self, load_queue, filename, image):
        """ Extract the face from a frame (If alignments file not found) """
        inp = {"filename": filename,
               "image": image}
        load_queue.put(inp)
        faces = next(self.extractor.detect_faces())

        landmarks = faces["landmarks"]
        detected_faces = faces["detected_faces"]
        final_faces = list()

        for idx, face in enumerate(detected_faces):
            detected_face = DetectedFace()
            detected_face.from_dlib_rect(face)
            detected_face.landmarksXY = landmarks[idx]
            final_faces.append(detected_face)
        return final_faces

    # Saving tasks
    def save(self, completion_event):
        """ Save the converted images """
        logger.debug("Save Images: Start")
        for _ in tqdm(range(self.total_count), desc="Converting", file=sys.stdout):
            if self.save_queue.shutdown.is_set():
                logger.debug("Save Queue: Stop signal received. Terminating")
                break
            item = self.save_queue.get()
            if item == "EOF":
                break
            filename, image = item
            self.writer.write(filename, image)
        self.writer.close()
        completion_event.set()
        logger.debug("Save Faces: Complete")


class Predict():
    """ Predict faces from incoming queue """
    def __init__(self, in_queue, queue_size, arguments):
        logger.debug("Initializing %s: (args: %s, queue_size: %s, in_queue: %s)",
                     self.__class__.__name__, arguments, queue_size, in_queue)
        self.batchsize = min(queue_size, 16)
        self.args = arguments
        self.in_queue = in_queue
        self.out_queue = queue_manager.get_queue("patch")
        self.serializer = Serializer.get_serializer("json")
        self.faces_count = 0
        self.verify_output = False
        self.pre_process = PostProcess(arguments)
        self.model = self.load_model()
        self.predictor = self.model.converter(self.args.swap_model)
        self.queues = dict()

        self.thread = MultiThread(self.predict_faces, thread_count=1)
        self.thread.start()
        logger.debug("Initialized %s: (out_queue: %s)", self.__class__.__name__, self.out_queue)

    @property
    def coverage_ratio(self):
        """ Return coverage ratio from training options """
        return self.model.training_opts["coverage_ratio"]

    @property
    def input_size(self):
        """ Return the model input size """
        return self.model.input_shape[0]

    @property
    def output_size(self):
        """ Return the model output size """
        return self.model.output_shape[0]

    @property
    def input_mask(self):
        """ Return the input mask """
        mask = np.zeros(self.model.state.mask_shapes[0], dtype="float32")
        retval = np.expand_dims(mask, 0)
        return retval

    @property
    def has_predicted_mask(self):
        """ Return whether this model has a predicted mask """
        return bool(self.model.state.mask_shapes) </s> remove         landmarks = faces["landmarks"]
        detected_faces = faces["detected_faces"]
        final_faces = list()
 </s> add     def check_skipframe(self, filename):
        """ Check whether frame is to be skipped """
        if not self.frame_ranges:
            return None
        indices = self.imageidxre.findall(filename)
        if not indices:
            logger.warning("Could not determine frame number. Frame will be converted: '%s'",
                           filename)
            return False
        idx = int(indices[0]) if indices else None
        skipframe = not any(map(lambda b: b[0] <= idx <= b[1], self.frame_ranges))
        return skipframe </s> remove                 if self.args.draw_transparent:
                    filename = "{}.png".format(os.path.splitext(filename)[0])
                    logger.trace("Set extension to png: `%s`", filename)
 </s> add     def get_trainer(self, model_dir):
        """ Return the trainer name if provided, or read from state file """
        if self.args.trainer:
            logger.debug("Trainer name provided: '%s'", self.args.trainer)
            return self.args.trainer

        statefile = [fname for fname in os.listdir(str(model_dir))
                     if fname.endswith("_state.json")]
        if len(statefile) != 1:
            logger.error("There should be 1 state file in your model folder. %s were found. "
                         "Specify a trainer with the '-t', '--trainer' option.")
            exit(1)
        statefile = os.path.join(str(model_dir), statefile[0])

        with open(statefile, "rb") as inp:
            state = self.serializer.unmarshal(inp.read().decode("utf-8"))
            trainer = state.get("name", None)

        if not trainer:
            logger.error("Trainer name could not be read from state file. "
                         "Specify a trainer with the '-t', '--trainer' option.")
            exit(1)
        logger.debug("Trainer from state file: '%s'", trainer)
        return trainer

    def predict_faces(self):
        """ Get detected faces from images """
        faces_seen = 0
        batch = list()
        while True:
            item = self.in_queue.get()
            if item != "EOF":
                logger.trace("Got from queue: '%s'", item["filename"])
                faces_count = len(item["detected_faces"])
                if faces_count != 0:
                    self.pre_process.do_actions(item)
                    self.faces_count += faces_count
                if faces_count > 1:
                    self.verify_output = True
                    logger.verbose("Found more than one face in an image! '%s'",
                                   os.path.basename(item["filename"]))

                self.load_aligned(item)

                faces_seen += faces_count
                batch.append(item)

            if faces_seen < self.batchsize and item != "EOF":
                logger.trace("Continuing. Current batchsize: %s", faces_seen)
                continue
        self.load_queue.put("EOF")
        logger.debug("Load Images: Complete")
                                 "an image! '%s'", frame)
  
              yield filename, image, detected_faces
  
        self.load_queue.put("EOF")
        logger.debug("Load Images: Complete")
        self.load_queue.put("EOF")
        logger.debug("Load Images: Complete")
        self.load_queue.put("EOF")
        logger.debug("Load Images: Complete")
        self.load_queue.put("EOF")
        logger.debug("Load Images: Complete")
        self.load_queue.put("EOF")
        logger.debug("Load Images: Complete")
        self.load_queue.put("EOF")
        logger.debug("Load Images: Complete")
  
          landmarks = faces["landmarks"]
          detected_faces = faces["detected_faces"]
          final_faces = list()
  
keep keep keep keep replace replace replace replace 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