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()