          batch_dst_points = np.array([landmarks[choice(hsh)] for hsh in closest_hashes])
          logger.trace("Returning: (batch_dst_points: %s)", batch_dst_points.shape)
          return batch_dst_points
  
      def _cache_closest_hashes(self, filenames, batch_src_points, landmarks):
          """ Cache the nearest landmarks for this batch """
          logger.trace("Caching closest hashes")
          dst_landmarks = list(landmarks.items())
          dst_points = np.array([lm[1] for lm in dst_landmarks])
          batch_closest_hashes = list()
 
</s> remove         logger.trace("Caching closest hashes")
 </s> add         logger.trace("Caching closest matches") </s> remove         batch_dst_points = np.array([landmarks[choice(hsh)] for hsh in closest_hashes])
 </s> add         batch_dst_points = np.array([landmarks[choice(fname)] for fname in closest_matches]) </s> remove         batch_closest_hashes = list()
 </s> add         batch_closest_matches = list() </s> remove             closest_hashes = self._cache_closest_hashes(filenames, batch_src_points, landmarks)
 </s> add             closest_matches = self._cache_closest_matches(filenames, batch_src_points, landmarks) </s> remove             closest_hashes = tuple(dst_landmarks[i][0] for i in closest)
            self._cache["nearest_landmarks"][filename] = closest_hashes
            batch_closest_hashes.append(closest_hashes)
        logger.trace("Cached closest hashes")
        return batch_closest_hashes
 </s> add             closest_matches = tuple(dst_landmarks[i][0] for i in closest)
            self._cache["nearest_landmarks"][filename] = closest_matches
            batch_closest_matches.append(closest_matches)
        logger.trace("Cached closest matches")
        return batch_closest_matches
    def _cache_closest_matches(self, filenames, batch_src_points, landmarks):
          batch_dst_points = np.array([landmarks[choice(hsh)] for hsh in closest_hashes])
          logger.trace("Returning: (batch_dst_points: %s)", batch_dst_points.shape)
          return batch_dst_points
  
    def _cache_closest_matches(self, filenames, batch_src_points, landmarks):
          """ Cache the nearest landmarks for this batch """
          logger.trace("Caching closest hashes")
          dst_landmarks = list(landmarks.items())
          dst_points = np.array([lm[1] for lm in dst_landmarks])
          batch_closest_hashes = list()
keep keep keep keep 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 
lib/training_data.py