              :class:`~lib.faces_detect.Detected_Face` objects.
  
          Example
          -------
          >>> for face in extractor.detected_faces():
          >>>     filename = face["filename"]
          >>>     image = face["image"]
          >>>     detected_faces = face["detected_faces"]
          """
          logger.debug("Running Detection. Phase: '%s'", self.phase)
          # If not multiprocessing, intercept the align in queue for
          # detection phase
          out_queue = self._output_queue
 
</s> remove         faces: dict
            regardless of phase, the returned dictionary will contain, exclusively, ``filename``:
            the filename of the source image, ``image``: the ``numpy.array`` of the source image
            in BGR color format, ``detected_faces``: a list of
            :class:`~lib.faces_detect.Detected_Face` objects.
 </s> add         faces: :class:`ExtractMedia`
            The populated extracted media object. </s> remove         >>>         extractor.input_queue.put({"filename": "path/to/image/file",
        >>>                                    "image": numpy.array(image)})
 </s> add         >>>         extract_media = ExtractMedia("path/to/image/file", image)
        >>>         extractor.input_queue.put(extract_media) </s> remove     def _compile_detection_image(self, input_image):
        """ Compile the detection image for feeding into the model"""
        image = self._convert_color(input_image)
 </s> add     def _compile_detection_image(self, item):
        """ Compile the detection image for feeding into the model </s> remove         >>> {'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>]}
 </s> add         For detect/single phase operations the :attr:`ExtractMedia.filename` and
        :attr:`~ExtractMedia.image` attributes should be populated. </s> remove         self._rollover = []  # Items that are rolled over from the previous batch in get_batch
 </s> add         self._rollover = None  # Items that are rolled over from the previous batch in get_batch
        >>> for extract_media in extractor.detected_faces():
        >>>     filename = extract_media.filename
        >>>     image = extract_media.image
        >>>     detected_faces = extract_media.detected_faces
              :class:`~lib.faces_detect.Detected_Face` objects.
  
          Example
          -------
        >>> for extract_media in extractor.detected_faces():
        >>>     filename = extract_media.filename
        >>>     image = extract_media.image
        >>>     detected_faces = extract_media.detected_faces
        >>> for extract_media in extractor.detected_faces():
        >>>     filename = extract_media.filename
        >>>     image = extract_media.image
        >>>     detected_faces = extract_media.detected_faces
        >>> for extract_media in extractor.detected_faces():
        >>>     filename = extract_media.filename
        >>>     image = extract_media.image
        >>>     detected_faces = extract_media.detected_faces
        >>> for extract_media in extractor.detected_faces():
        >>>     filename = extract_media.filename
        >>>     image = extract_media.image
        >>>     detected_faces = extract_media.detected_faces
          """
          logger.debug("Running Detection. Phase: '%s'", self.phase)
          # If not multiprocessing, intercept the align in queue for
          # detection phase
          out_queue = self._output_queue
keep keep keep keep replace replace replace replace keep keep keep keep keep
 plugins.extract - Create ExtractMedia class for pipeline flow
Bugfix - Fix memory leak in extract 
plugins/extract/pipeline.py