          if mask_type:
              logger.debug("Mask type: '%s'", mask_type)
              mask_func = getattr(masks, mask_type)
          else:
              mask_func = None
          logger.debug("Mask function: %s", mask_func)
          return mask_func
  
      def minibatch_ab(self, images, batchsize, side, do_shuffle=True, is_timelapse=False):
          """ Keep a queue filled to 8x Batch Size """
          logger.debug("Queue batches: (image_count: %s, batchsize: %s, side: '%s', do_shuffle: %s, "
                       "is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
 
</s> remove             mask_func = getattr(masks, mask_type)
 </s> add             mask_class = getattr(masks, mask_type) </s> remove     def set_mask_function(self):
 </s> add     def set_mask_class(self): </s> remove     def __run_ffmpeg(exe="ffmpeg", inputs=None, outputs=None):
 </s> add     def __run_ffmpeg(exe=im_ffm.get_ffmpeg_exe(), inputs=None, outputs=None): </s> remove         logger.trace("Loading aligned face: (size: %s, align_eyes: %s)", size, align_eyes)
        padding = int(size * 0.1875)
 </s> add         logger.trace("Loading aligned face: (size: %s, align_eyes: %s, dtype: %s)",
                     size, align_eyes, dtype)
        padding = int(size * self.extract_ratio) // 2
            mask_class = None
        logger.debug("Mask class: %s", mask_class)
        return mask_class
          if mask_type:
              logger.debug("Mask type: '%s'", mask_type)
              mask_func = getattr(masks, mask_type)
          else:
            mask_class = None
        logger.debug("Mask class: %s", mask_class)
        return mask_class
            mask_class = None
        logger.debug("Mask class: %s", mask_class)
        return mask_class
            mask_class = None
        logger.debug("Mask class: %s", mask_class)
        return mask_class
  
      def minibatch_ab(self, images, batchsize, side, do_shuffle=True, is_timelapse=False):
          """ Keep a queue filled to 8x Batch Size """
          logger.debug("Queue batches: (image_count: %s, batchsize: %s, side: '%s', do_shuffle: %s, "
                       "is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
keep keep keep keep 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 
lib/training_data.py