          return retval
  
      def load_extractor(self):
          """ Set on the fly extraction """
          logger.debug("Loading extractor")
          logger.warning("No Alignments file found. Extracting on the fly.")
          logger.warning("NB: This will use the inferior cv2-dnn for extraction "
                         "and  landmarks. It is recommended to perfom Extract first for "
                         "superior results")
 
</s> remove         extract_args = {"detector": "cv2_dnn",
                        "aligner": "cv2_dnn",
                        "loglevel": self.args.loglevel}
        self.extractor = Extractor(None, converter_args=extract_args)
        self.extractor.launch_detector()
        self.extractor.launch_aligner()
 </s> add         extractor = Extractor(detector="cv2-dnn",
                              aligner="cv2-dnn",
                              loglevel=self.args.loglevel,
                              multiprocess=False,
                              rotate_images=None,
                              min_size=20)
        extractor.launch() </s> remove     """ Face filter for extraction """
    def __init__(self, reference_file_paths, nreference_file_paths, threshold=0.6):
 </s> add     """ Face filter for extraction
        NB: we take only first face, so the reference file should only contain one face. """

    def __init__(self, reference_file_paths, nreference_file_paths, detector, aligner, loglevel,
                 multiprocess=False, threshold=0.4):
 </s> remove                               "help": "Reference images for the person you "
                                      "want to process. Should be a front "
                                      "portrait. Multiple images can be added "
                                      "space separated"})
 </s> add                               "help": "Reference images for the person you want to process. "
                                      "Should be a front portrait with a single person in the "
                                      "image. Multiple images can be added space separated. NB: "
                                      "Using face filter will significantly decrease extraction "
                                      "speed."}) </s> remove     def detect_faces(self, load_queue, filename, image):
 </s> add     def detect_faces(self, filename, image): </s> remove                               "help": "Reference image for the persons you do "
                                      "not want to process. Should be a front "
                                      "portrait. Multiple images can be added "
                                      "space separated"})
 </s> add                               "help": "Reference image for the persons you do not want to "
                                      "process. Should be a front portrait with a single person "
                                      "in the image. Multiple images can be added space "
                                      "separated. NB: Using face filter will significantly "
                                      "decrease extraction speed."})
        if self.alignments.have_alignments_file:
            return None

          return retval
  
      def load_extractor(self):
          """ Set on the fly extraction """
        if self.alignments.have_alignments_file:
            return None

          logger.debug("Loading extractor")
          logger.warning("No Alignments file found. Extracting on the fly.")
          logger.warning("NB: This will use the inferior cv2-dnn for extraction "
                         "and  landmarks. It is recommended to perfom Extract first for "
                         "superior results")
keep keep keep add keep keep keep keep keep
 Face filter vgg face (#724)

* Implement extraction pipeline

* Face filter to vgg_face. Resume partial model downloads

* On-the-fly conversion to extraction pipeline

* Move git model ids from get_model to model definition 
scripts/convert.py