              out_side = max(out_h, out_w)
              cls_prob = np.swapaxes(cls_prob, 1, 2)
              roi = np.swapaxes(roi, 1, 3)
              for idx in range(batch_items):
                  # first index 0 = cls score, 1 = one hot repr
                  rectangle = detect_face_12net(cls_prob[idx, ...],
                                                roi[idx, ...],
                                                out_side,
                                                1 / scale,
                                                width,
 
</s> remove                 batch.setdefault("filename", []).append(item["filename"])
                batch.setdefault("image", []).append(item["image"])
 </s> add                 batch.setdefault("filename", []).append(item.filename) </s> remove                     frame_faces = len(item["detected_faces"])
 </s> add                     frame_faces = len(item.detected_faces) </s> remove                     frame_faces = len(item["detected_faces"])
 </s> add                     frame_faces = len(item.detected_faces) </s> remove             for f_idx, face in enumerate(item["detected_faces"]):
                face.image = self._convert_color(item["image"])
 </s> add             converted_image = item.get_image_copy(self.colorformat)
            for f_idx, face in enumerate(item.detected_faces):
                batch.setdefault("image", []).append(converted_image) </s> remove                         self._rollover = {k: v[f_idx + 1:] if k == "detected_faces" else v
                                          for k, v in item.items()}
 </s> add                         self._rollover = ExtractMedia(item.filename, item.image)
                        self._rollover.add_detected_faces(item.detected_faces[f_idx + 1:])
                # first index 0 = class score, 1 = one hot repr
              out_side = max(out_h, out_w)
              cls_prob = np.swapaxes(cls_prob, 1, 2)
              roi = np.swapaxes(roi, 1, 3)
              for idx in range(batch_items):
                # first index 0 = class score, 1 = one hot repr
                  rectangle = detect_face_12net(cls_prob[idx, ...],
                                                roi[idx, ...],
                                                out_side,
                                                1 / scale,
                                                width,
keep keep keep keep replace keep keep keep keep keep
 plugins.extract - Create ExtractMedia class for pipeline flow
Bugfix - Fix memory leak in extract 
plugins/extract/detect/mtcnn.py