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)