def main(args): if sys.version_info < (3, 0): print("Error: Python2 is slow. Use Python3 for max performance.") return cam_index = int(args.webcam) resolutions = [ RESOLUTION_QVGA, RESOLUTION_VGA, RESOLUTION_HD, RESOLUTION_FULLHD ] try: cam_resolution = resolutions[int(args.resolution)] except: cam_resolution = RESOLUTION_QVGA if args.detector and args.encoder and args.speech_synthesizer: try: detector = FaceDetectorModels(int(args.detector)) encoder = FaceEncoderModels(int(args.encoder)) speech_synthesizer = SpeechSynthesizerModels( int(args.speech_synthesizer)) print("Parameters: {} {} {}".format(detector, encoder, speech_synthesizer)) process_facerecognition(detector, encoder, speech_synthesizer, cam_index, cam_resolution) except: print("Invalid parameter") return run(cam_index, cam_resolution)
def main(args): if args.detector and args.encoder: try: detector = FaceDetectorModels(int(args.detector)) encoder = FaceEncoderModels(int(args.encoder)) classifier = FaceClassifierModels(int(args.classifier)) face_embeddings_path = args.face_embeddings_path print("Parameters: {} {} {} {}".format(detector, encoder, classifier, face_embeddings_path)) train_recognition(detector, encoder, classifier, face_embeddings_path, True) print("\nImage dataset training completed!") # generate audio samples for image datasets using text to speech synthesizer if args.set_speech_synthesizer: from libfaceid.speech_synthesizer import SpeechSynthesizerModels # lazy loading speech_synthesizer = SpeechSynthesizerModels( int(args.speech_synthesizer)) #print( "Parameters: {}".format(speech_synthesizer) ) train_audiosets(speech_synthesizer) print("Audio samples created!") except Exception as ex: print(ex) print("Invalid parameter") return run()