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| import tensorflow as tf | |
| from face_recognition import config | |
| import cv2 | |
| from glob import glob | |
| import os | |
| import numpy as np | |
| feature_extractor=tf.keras.models.load_model("face_recognition/feature_extractor.h5",compile=False) | |
| # feature_extractor.summary() | |
| extensions=['.jpg','.jpeg','.png','.svg','.webp'] | |
| db_dir=config.db_dir | |
| _,sub_folders,_=next(os.walk(db_dir)) | |
| print(sub_folders) | |
| for sub_folder in sub_folders: | |
| image_paths=[] | |
| [image_paths.extend(glob(db_dir+"\\"+sub_folder+"\\*"+extension)) for extension in extensions] | |
| all_img_features=[] | |
| for image_path in image_paths: | |
| print(image_path) | |
| img=cv2.resize(cv2.imread(image_path),[config.input_size,config.input_size]) | |
| all_img_features.append(feature_extractor.predict(img[None,:,:,::-1],verbose=0)[0]) | |
| np.savetxt(db_dir+"\\"+sub_folder+"\\features.npy",all_img_features) | |
| # "aligned_all" |