def train_recognition(model_detector, model_encoder, model_classifier,
face_embeddings_path, verify):
ensure_directory(config.INPUT_DIR_DATASET)
print("")
names = get_dataset_names(config.INPUT_DIR_DATASET)
if names is not None:
print("Names " + str(names))
for name in names:
for (_d, _n,
files) in os.walk(config.INPUT_DIR_DATASET + "/" + name):
print(name + ": " + str(files))
print("")
ensure_directory(config.INPUT_DIR_MODEL_TRAINING)
face_detector = FaceDetector(model=model_detector,
path=config.INPUT_DIR_MODEL_DETECTION)
face_encoder = FaceEncoder(model=model_encoder,
path=config.INPUT_DIR_MODEL_ENCODING,
path_training=config.INPUT_DIR_MODEL_TRAINING,
training=True)
face_encoder.train(face_detector,
path_dataset=face_embeddings_path,
verify=verify,
classifier=model_classifier)
def train_recognition(model_detector, model_encoder, model_classifier, verify):
ensure_directory(INPUT_DIR_DATASET)
ensure_directory(INPUT_DIR_MODEL_TRAINING)
face_detector = FaceDetector(model=model_detector,
path=INPUT_DIR_MODEL_DETECTION)
face_encoder = FaceEncoder(model=model_encoder,
path=INPUT_DIR_MODEL_ENCODING,
path_training=INPUT_DIR_MODEL_TRAINING,
training=True)
face_encoder.train(face_detector,
path_dataset=INPUT_DIR_DATASET,
verify=verify,
classifier=model_classifier)
