          self.batchsize = 0
          self.model_input_size = model_input_size
          self.model_output_size = model_output_size
          self.training_opts = training_opts
          self.mask_function = self.set_mask_function()
          self.landmarks = self.training_opts.get("landmarks", None)
          self._nearest_landmarks = None
          self.processing = ImageManipulation(model_input_size,
                                              model_output_size,
                                              training_opts.get("coverage_ratio", 0.625))
 
</s> remove     def set_mask_function(self):
 </s> add     def set_mask_class(self): </s> remove             mask_func = getattr(masks, mask_type)
 </s> add             mask_class = getattr(masks, mask_type) </s> remove         self.output_dir = get_folder(self.args.output_dir)
        self.extractor = None
        self.faces_count = 0
 </s> add         Utils.set_verbosity(self.args.loglevel) </s> remove         if self.mask_function:
            image = self.mask_function(src_pts, image, channels=4)
 </s> add         if self.mask_class:
            image = self.mask_class(src_pts, image, channels=4).mask </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__)
        self.mask_class = self.set_mask_class()
          self.batchsize = 0
          self.model_input_size = model_input_size
          self.model_output_size = model_output_size
          self.training_opts = training_opts
        self.mask_class = self.set_mask_class()
          self.landmarks = self.training_opts.get("landmarks", None)
          self._nearest_landmarks = None
          self.processing = ImageManipulation(model_input_size,
                                              model_output_size,
                                              training_opts.get("coverage_ratio", 0.625))
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/training_data.py