  
          For align (2nd pass operations):
  
          >>> {'filename': <path to the source image that is to be extracted from>,
          >>>  'image': <the source image as a numpy.array in BGR color format>,
          >>>  'detected_faces: [<list of DetectedFace objects as generated from detect>]}
  
          """
          qname_dict = dict(detect="extract_detect_in",
                            align="extract_align_in",
 
</s> remove         >>>  'image': <the source image as a numpy.array in BGR color format>}
 </s> add         >>>  'image': <the source image as a numpy.ndarray in BGR color format>} </s> remove         qname_dict = dict(detect="extract_detect_in",
                          align="extract_align_in",
                          mask="extract_mask_in")
        qname = "extract_detect_in" if self._is_parallel else qname_dict[self.phase]
 </s> add         qname = "extract_{}_in".format(self.phase) </s> remove         >>>  'scaled_image': <np.array of images standardized for prediction>,
 </s> add         >>>  'scaled_image': <numpy.ndarray of images standardized for prediction>, </s> remove             regardless of phase, the returned dictinary will contain, exclusively, ``filename``:
 </s> add             regardless of phase, the returned dictionary will contain, exclusively, ``filename``: </s> remove     image: np.ndarray, optional
        This is a generic image placeholder that should not be relied on to be holding a particular
        image. It may hold the source frame that holds the face, a cropped face or a scaled image
        depending on the method using this object.
 </s> add     image: numpy.ndarray, optional
        Original frame that holds this face. Optional (not required if just storing coordinates)
        >>>  'image': <the source image as a numpy.ndarray in BGR color format>,
  
          For align (2nd pass operations):
  
          >>> {'filename': <path to the source image that is to be extracted from>,
        >>>  'image': <the source image as a numpy.ndarray in BGR color format>,
          >>>  'detected_faces: [<list of DetectedFace objects as generated from detect>]}
  
          """
          qname_dict = dict(detect="extract_detect_in",
                            align="extract_align_in",
keep keep keep keep replace keep keep keep keep keep
 Smart Mask - Extract code review

- Lint simple_tests.py
- Only reformat alignments file if it exists otherwise change filename
- Update legacy alignments to new format at all stages
- faces_detect.Mask.from_dict - logging format fix
- convert.py fix otf for new pipeline
- cli.py - Add note that masks not used. Revert convert masks
- faces_detect.py - Revert non-extract code
- Add .p and .pickle extensions for serializer
- plugins/extract revert some changes
- scripts/fsmedia - Revert code changes
- Pipeline - cleanup
- Consistant alpha channel stripping (fixes single-process)
- Store landmarks as numpy array
- Code attribution
- Normalize feed face and reference face to 0.0 - 1.0 in convert
- Lock in mask VRAM sized
- Add documentation to plugin_loader
- Update alignments tool to work with new format 
plugins/extract/pipeline.py