          return helptext
  
      def load_config(self):
          """ Load values from config """
          logger.info("Loading config: '%s'", self.configfile)
          self.config.read(self.configfile)
  
      def save_config(self):
          """ Save a config file """
          logger.info("Updating config at: '%s'", self.configfile)
 
</s> remove     @property
    def changeable_items(self):
        """ Training only.
            Return a dict of config items with their set values for items
            that can be altered after the model has been created """
        retval = dict()
        for sect in ("global", self.section):
            for key, val in self.defaults[sect].items():
                if key == "helptext" or val["fixed"]:
                    continue
                retval[key] = self.get(sect, key)
        logger.debug("Alterable for existing models: %s", retval)
        return retval

 </s> add  </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 load_model(self):
        """ Load the model requested for conversion """
        logger.debug("Loading Model")
        model_dir = get_folder(self.args.model_dir)
        model = PluginLoader.get_model(self.args.trainer)(model_dir, self.args.gpus, predict=True)
        logger.debug("Loaded Model")
        return model

    def load_converter(self, model):
        """ Load the requested converter for conversion """
        conv = self.args.converter
        converter = PluginLoader.get_converter(conv)(
            model.converter(self.args.swap_model),
            model=model,
            arguments=self.args)
        return converter

    def prepare_images(self):
        """ Prepare the images for conversion """
        filename = ""
        if self.extractor:
            load_queue = queue_manager.get_queue("load")
        for filename, image in tqdm(self.images.load(),
                                    total=self.images.images_found,
                                    file=sys.stdout):

            if (self.args.discard_frames and
                    self.opts.check_skipframe(filename) == "discard"):
 </s> add         logger.debug("Loaded extractor")

    def init_threads(self):
        """ Initialize queues and threads """
        logger.debug("Initializing DiskIO Threads")
        for task in ("load", "save"):
            self.add_queue(task)
            self.start_thread(task)
        logger.debug("Initialized DiskIO Threads")

    def add_queue(self, task):
        """ Add the queue to queue_manager and set queue attribute """
        logger.debug("Adding queue for task: '%s'", task)
        q_name = "convert_in" if task == "load" else task
        setattr(self, "{}_queue".format(task), queue_manager.get_queue(q_name))
        logger.debug("Added queue for task: '%s'", task)

    def start_thread(self, task):
        """ Start the DiskIO thread """
        logger.debug("Starting thread: '%s'", task)
        args = self.completion_event if task == "save" else None
        func = getattr(self, task)
        io_thread = MultiThread(func, args, thread_count=1)
        io_thread.start()
        setattr(self, "{}_thread".format(task), io_thread)
        logger.debug("Started thread: '%s'", task)

    # Loading tasks
    def load(self, *args):  # pylint: disable=unused-argument
        """ Load the images with detected_faces"""
        logger.debug("Load Images: Start")
        extract_queue = queue_manager.get_queue("extract_in") if self.extractor else None
        idx = 0
        for filename, image in self.images.load():
            idx += 1
            if self.load_queue.shutdown.is_set():
                logger.debug("Load Queue: Stop signal received. Terminating")
                break
            if image is None or not image.any():
                logger.warning("Unable to open image. Skipping: '%s'", filename)
                continue
            if self.check_skipframe(filename):
                if self.args.keep_unchanged:
                    logger.trace("Saving unchanged frame: %s", filename)
                    out_file = os.path.join(self.args.output_dir, os.path.basename(filename))
                    self.save_queue.put((out_file, image))
                else:
                    logger.trace("Discarding frame: '%s'", filename) </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
        logger.verbose("Loading config: '%s'", self.configfile)
          return helptext
  
      def load_config(self):
          """ Load values from config """
        logger.verbose("Loading config: '%s'", self.configfile)
          self.config.read(self.configfile)
  
      def save_config(self):
          """ Save a config file """
          logger.info("Updating config at: '%s'", self.configfile)
keep keep keep keep 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 
lib/config.py