                              choices=("Masked", "Adjust"), # case sensitive because this is used to load a plug-in.
                              default="Masked",
                              help="Converter to use.")
  
          parser.add_argument('-fr', '--frame-ranges',
                              nargs="+",
                              type=str,
                              help="frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use --frame-ranges 10-50 90-100. \
                              Files must have the frame-number as the last number in the name!"
 
</s> remove         
 </s> add     def add_optional_arguments(self, parser):
        parser.add_argument('-D', '--detector',
                            type=str,
                            choices=("hog", "cnn"), # case sensitive because this is used to load a plugin.
                            default="hog",
                            help="Detector to use. 'cnn' detects much more angles but will be much more resource intensive and may fail on large files.")

        parser.add_argument('-f', '--filter',
                            type=str,
                            dest="filter",
                            default="filter.jpg",
                            help="Reference image for the person you want to process. Should be a front portrait"
                            )

        parser.add_argument('-j', '--processes',
                            type=int,
                            help="Number of processes to use.")
        return parser
 </s> remove         filter_file = "filter.jpg" # TODO Pass as argument
 </s> add         filter_file = self.arguments.filter </s> remove         encodings = face_recognition.face_encodings(detected_face.image)[0] # we could use detected landmarks, but I did not manage to do so
        score = face_recognition.face_distance([self.encoding], encodings)
        print(score)
        return score <= self.threshold

 </s> add         encodings = face_recognition.face_encodings(detected_face.image) # we could use detected landmarks, but I did not manage to do so. TODO The copy/paste below should help
        if encodings is not None and len(encodings) > 0:
            score = face_recognition.face_distance([self.encoding], encodings[0])
            print(score)
            return score <= self.threshold
        else:
            print("No face encodings found")
            return False
 </s> remove         self.encoding = face_recognition.face_encodings(image)[0] # Note: we take only first face, so the reference file should only contain one face. We could also keep all faces found and filter against multiple faces
 </s> add         self.encoding = face_recognition.face_encodings(image)[0] # Note: we take only first face, so the reference file should only contain one face.

        parser.add_argument('-D', '--detector',
                            type=str,
                            choices=("hog", "cnn"), # case sensitive because this is used to load a plugin.
                            default="hog",
                            help="Detector to use. 'cnn' detects much more angles but will be much more resource intensive and may fail on large files.")

                              choices=("Masked", "Adjust"), # case sensitive because this is used to load a plug-in.
                              default="Masked",
                              help="Converter to use.")
  
        parser.add_argument('-D', '--detector',
                            type=str,
                            choices=("hog", "cnn"), # case sensitive because this is used to load a plugin.
                            default="hog",
                            help="Detector to use. 'cnn' detects much more angles but will be much more resource intensive and may fail on large files.")

          parser.add_argument('-fr', '--frame-ranges',
                              nargs="+",
                              type=str,
                              help="frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use --frame-ranges 10-50 90-100. \
                              Files must have the frame-number as the last number in the name!"
keep keep keep add keep keep keep keep keep
 Misc updates on master before GAN. Added multithreading + mmod face detector (#109)

* Preparing GAN plugin

* Adding multithreading for extract

* Adding support for mmod human face detector

* Adding face filter argument

* Added process number argument to multiprocessing extractor.

Fixed progressbar for multiprocessing.

* Added tiff as image type.
compression artefacts hurt my feelings.

* Cleanup 
scripts/convert.py