          saver = ImagesSaver(str(get_folder(output_folder)), as_bytes=True)
          loader = ImagesLoader(self._input_location, count=self._alignments.frames_count)
          for frame_idx, (filename, image) in enumerate(loader.load()):
              logger.trace("Outputting frame: %s: %s", frame_idx, filename)
              basename = os.path.basename(filename)
              frame_name = os.path.splitext(basename)[0]
              extension = ".png"
              final_faces = []
              progress_queue.put(1)
              for face_idx, face in enumerate(self._frame_faces[frame_idx]):
                  output = "{}_{}{}".format(frame_name, str(face_idx), extension)
                  aligned = AlignedFace(face.landmarks_xy,
                                        image=image,
 
</s> remove         extension = ".png"
 </s> add  </s> remove             face.hash, image = encode_image_with_hash(face.aligned.face, extension)
 </s> add             meta = dict(alignments=face.to_png_meta(),
                        source=dict(alignments_version=self._alignments.version,
                                    original_filename=output_filename,
                                    face_index=idx,
                                    source_filename=extract_media.filename,
                                    source_is_video=self._images.is_video))
            image = encode_image(face.aligned.face, extension, metadata=meta) </s> remove                 face.hash, b_image = encode_image_with_hash(aligned.face, extension)
 </s> add                 meta = dict(alignments=face.to_png_meta(),
                            source=dict(alignments_version=self._alignments.version,
                                        original_filename=output,
                                        face_index=face_idx,
                                        source_filename=src_filename,
                                        source_is_video=self._globals.is_video))

                b_image = encode_image(aligned.face, extension, metadata=meta) </s> remove                                      input_sizes[side])
            for fhash, face in detected_faces.items():
                face.aligned = AlignedFace(face.landmarks_xy,
                                           centering=self._config["centering"],
                                           size=size,
                                           is_aligned=True)
                for filename in self._hash_to_filenames(side, fhash):
                    ret_side[filename] = face.aligned
            retval[side] = ret_side
 </s> add                                      self._image_sizes[side])
            for filename, face in detected_faces.items():
                retval[side][filename] = AlignedFace(face.landmarks_xy,
                                                     centering=self._config["centering"],
                                                     size=size,
                                                     is_aligned=True)
        logger.debug("Loaded aligned faces: %s", {k: len(v) for k, v in retval.items()})
            src_filename = os.path.basename(filename)
            frame_name = os.path.splitext(src_filename)[0]
          saver = ImagesSaver(str(get_folder(output_folder)), as_bytes=True)
          loader = ImagesLoader(self._input_location, count=self._alignments.frames_count)
          for frame_idx, (filename, image) in enumerate(loader.load()):
              logger.trace("Outputting frame: %s: %s", frame_idx, filename)
            src_filename = os.path.basename(filename)
            frame_name = os.path.splitext(src_filename)[0]
            src_filename = os.path.basename(filename)
            frame_name = os.path.splitext(src_filename)[0]
            src_filename = os.path.basename(filename)
            frame_name = os.path.splitext(src_filename)[0]
            src_filename = os.path.basename(filename)
            frame_name = os.path.splitext(src_filename)[0]
              progress_queue.put(1)
              for face_idx, face in enumerate(self._frame_faces[frame_idx]):
                  output = "{}_{}{}".format(frame_name, str(face_idx), extension)
                  aligned = AlignedFace(face.landmarks_xy,
                                        image=image,
keep keep keep keep replace replace replace replace keep keep keep keep keep
 Update extracted faces to use PNG EXIF data (#1123)

Documentation
  - Update Usage.md, align.rst and image.rst
lib.image.py
  - read_image - Remove hash return, add metadata return
  - Remove read_image_hash functions
  - Add read_image_meta functios
  - Replace encode_image_with_hash with encode_image (to store metadata)
  - Add png meta reading and writing functions
  - Update Image Loaders/Savers to handle metadata rather than hashes
lib.training_data
  - Naming updates to remove references to hashes
lib.align.Alignments
  - Add versioning notes
  - Increment alignments version to 2.1
  - Deprecate hashing lookup functions
  - Replace filter_hashes with filter_faces
lib.align.detected_face
  - DetectedFace
    - Remove hash property
    - Add png header data serializing/deserializing functions
  - Mask
    - Add png header data serializing/deserializing functions
  - add update_legacy_png_header function to update png meta data
lib.cli.args - Deprecate alignments files for training
- plugins.train.trainer
  - Update alignments/mask code to read png header data
- scripts.convert
  - Aligned images folder - read data from png headers
- scripts.extract
  - Write png header information and no longer store hash of face
- tools.alignments
  - remove leftover-faces, merge and update-hashes jobs
  - Update jobs to use png meta data rather than hashes
- tools.manual
  - Update extract code to output png meta data and don't store hashes
  - Perform check on launch that tool is not pointing at a faces folder
tools.mask
  - Update to use png meta data
tools.sort
  - Update to use png meta data 
tools/manual/detected_faces.py