def create_face_embedding(model_path,dataset_path,out_emb_path,out_filename):
# 建立npy文件
files_list,names_list=file_processing.gen_files_labels(dataset_path,postfix=['*.jpg'])
embeddings,label_list=get_face_embedding(model_path,files_list, names_list)
print("label_list:{}".format(label_list))
print("have {} label".format(len(label_list)))
embeddings=np.asarray(embeddings)
np.save(out_emb_path, embeddings)
file_processing.write_list_data(out_filename, label_list, mode='w')
def save_id(train_id_path, train_id, val_id_path, val_id):
if not os.path.exists(os.path.dirname(train_id_path)):
os.makedirs(os.path.dirname(train_id_path))
if not os.path.exists(os.path.dirname(val_id_path)):
os.makedirs(os.path.dirname(val_id_path))
# 保存图片id数据
file_processing.write_list_data(train_id_path, train_id, mode="w")
file_processing.write_list_data(val_id_path, val_id, mode="w")
print("train num:{},save path:{}".format(len(train_id), train_id_path))
print("val num:{},save path:{}".format(len(val_id), val_id_path))
def create_face_embedding(model_path, dataset_path, out_emb_path,
out_filename):
'''
:param model_path: faceNet模型路径
:param dataset_path: 人脸数据库路径，每一类单独一个文件夹
:param out_emb_path: 输出embeddings的路径
:param out_filename: 输出与embeddings一一对应的标签
:return: None
'''
files_list, names_list = file_processing.gen_files_labels(
dataset_path, postfix=['*.jpg', '*jpeg'])
embeddings, label_list = get_face_embedding(model_path, files_list,
names_list)
print("label_list:{}".format(label_list))
print("have {} label".format(len(label_list)))
embeddings = np.asarray(embeddings)
np.save(out_emb_path, embeddings)
file_processing.write_list_data(out_filename, label_list, mode='w')