          return batch
  
      def process_output(self, batch):
          """ Compile found faces for output """
          generator = zip(batch["feed"], batch["detected_faces"], batch["prediction"])
          for feed, face, prediction in generator:
              face.image = np.concatenate((feed, prediction), axis=-1)
              face.load_feed_face(face.image,
                                  size=self.input_size,
                                  coverage_ratio=self.coverage_ratio)
              face.load_reference_face(face.image,
                                       size=self.output_size,
                                       coverage_ratio=self.coverage_ratio)
          return batch
 
</s> remove         generator = zip(batch["feed"], batch["detected_faces"], batch["prediction"])
        for feed, face, prediction in generator:
            face.image = np.concatenate((feed, prediction), axis=-1)
            face.load_feed_face(face.image,
                                size=self.input_size,
                                coverage_ratio=self.coverage_ratio)
            face.load_reference_face(face.image,
                                     size=self.output_size,
                                     coverage_ratio=self.coverage_ratio)
 </s> add  </s> remove         generator = zip(batch["feed"], batch["detected_faces"], batch["prediction"])
        for feed, face, prediction in generator:
            face.image = np.concatenate((feed, prediction), axis=-1)
            face.load_feed_face(face.image,
                                size=self.input_size,
                                coverage_ratio=self.coverage_ratio)
            face.load_reference_face(face.image,
                                     size=self.output_size,
                                     coverage_ratio=self.coverage_ratio)
 </s> add  </s> remove         for idx, (face, predicts) in enumerate(zip(batch["detected_faces"], batch["prediction"])):
            generator = (cv2.GaussianBlur(mask, (7, 7), 0) for mask in predicts)
            predicted = np.array(tuple(generator))
            predicted[predicted < 10.] = 0.
            predicted[predicted > 245.] = 255.

            face.load_feed_face(face.image,
                                size=self.input_size,
                                coverage_ratio=self.coverage_ratio)
            feed_face = face.feed["face"][..., :3]
            feed_mask = self._resize(predicted, self.input_size).astype('uint8')
            batch["detected_faces"][idx].feed["face"] = np.concatenate((feed_face,
                                                                        feed_mask),
                                                                       axis=-1)
            face.load_reference_face(face.image,
                                     size=self.output_size,
                                     coverage_ratio=self.coverage_ratio)
            ref_face = face.reference["face"][..., :3]
            ref_mask = self._resize(predicted, self.output_size).astype('uint8')
            batch["detected_faces"][idx].reference["face"] = np.concatenate((ref_face,
                                                                             ref_mask),
                                                                            axis=-1)
 </s> add  </s> remove         for idx, (face, predicts) in enumerate(zip(batch["detected_faces"], batch["prediction"])):
            generator = (cv2.GaussianBlur(mask, (7, 7), 0) for mask in predicts)
            predicted = np.array(tuple(generator))
            predicted[predicted < 10.] = 0.
            predicted[predicted > 245.] = 255.

            face.load_feed_face(face.image,
                                size=self.input_size,
                                coverage_ratio=self.coverage_ratio)
            feed_face = face.feed["face"][..., :3]
            feed_mask = self._resize(predicted, self.input_size).astype('uint8')
            batch["detected_faces"][idx].feed["face"] = np.concatenate((feed_face,
                                                                        feed_mask),
                                                                       axis=-1)
            face.load_reference_face(face.image,
                                     size=self.output_size,
                                     coverage_ratio=self.coverage_ratio)
            ref_face = face.reference["face"][..., :3]
            ref_mask = self._resize(predicted, self.output_size).astype('uint8')
            batch["detected_faces"][idx].reference["face"] = np.concatenate((ref_face,
                                                                             ref_mask),
                                                                            axis=-1)
 </s> add  </s> remove         for idx, (face, predicts) in enumerate(zip(batch["detected_faces"], batch["prediction"])):
            generator = (cv2.GaussianBlur(mask, (7, 7), 0) for mask in predicts)
            predicted = np.array(tuple(generator))
            predicted[predicted < 10.] = 0.
            predicted[predicted > 245.] = 255.

            face.load_feed_face(face.image,
                                size=self.input_size,
                                coverage_ratio=self.coverage_ratio)
            feed_face = face.feed["face"][..., :3]
            feed_mask = self._resize(predicted, self.input_size).astype('uint8')
            batch["detected_faces"][idx].feed["face"] = np.concatenate((feed_face,
                                                                        feed_mask),
                                                                       axis=-1)
            face.load_reference_face(face.image,
                                     size=self.output_size,
                                     coverage_ratio=self.coverage_ratio)
            ref_face = face.reference["face"][..., :3]
            ref_mask = self._resize(predicted, self.output_size).astype('uint8')
            batch["detected_faces"][idx].reference["face"] = np.concatenate((ref_face,
                                                                             ref_mask),
                                                                            axis=-1)
 </s> add 
          return batch
  
      def process_output(self, batch):
          """ Compile found faces for output """









          return batch
keep keep keep keep replace replace replace replace replace replace replace replace replace keep
 Mask plugin cleanup

- PEP8 Fixes
- Remove config for non NN Masks
- Tidy up defaults helptext
- cli.py fix typos
- Remove unused imports and functions _base.py
- Standardize input_size param
- Enable and update documentation
- Change references from `aligner` to `masker`
- Change  input_size, output_size and coverage_ratio from kwargs to params
- Move load_aligned to batch input iterator
- Remove unnecessary self.input param
- Add softmax layer append function to KSession
- Remove references to KSession protected objects
- Standardize plugin output into finalize method
- Make masks full frame and add to lib.faces_detect
- Add masks to alignments.json (temporary zipped base64 solution) 
plugins/extract/mask/none.py