95 lines
4.2 KiB
Python
Executable File
95 lines
4.2 KiB
Python
Executable File
# Copyright (C) 2020 coneypo
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# SPDX-License-Identifier: MIT
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# Author: coneypo
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# Blog: http://www.cnblogs.com/AdaminXie
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# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
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# Mail: coneypo@foxmail.com
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# 从人脸图像文件中提取人脸特征存入 "features_all.csv" / Extract features from images and save into "features_all.csv"
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import os
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import dlib
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from skimage import io
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import csv
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import numpy as np
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# 要读取人脸图像文件的路径 / Path of cropped faces
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path_images_from_camera = "data/data_faces_from_camera/"
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# Dlib 正向人脸检测器 / Use frontal face detector of Dlib
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detector = dlib.get_frontal_face_detector()
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# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
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predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
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# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
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face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
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# 返回单张图像的 128D 特征 / Return 128D features for single image
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# Input: path_img <class 'str'>
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# Output: face_descriptor <class 'dlib.vector'>
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def return_128d_features(path_img):
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img_rd = io.imread(path_img)
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faces = detector(img_rd, 1)
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print("%-40s %-20s" % ("检测到人脸的图像 / Image with faces detected:", path_img), '\n')
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# 因为有可能截下来的人脸再去检测，检测不出来人脸了, 所以要确保是 检测到人脸的人脸图像拿去算特征
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# For photos of faces saved, we need to make sure that we can detect faces from the cropped images
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if len(faces) != 0:
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shape = predictor(img_rd, faces[0])
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face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
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else:
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face_descriptor = 0
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print("no face")
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return face_descriptor
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# 返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X
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# Input: path_faces_personX <class 'str'>
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# Output: features_mean_personX <class 'numpy.ndarray'>
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def return_features_mean_personX(path_faces_personX):
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features_list_personX = []
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photos_list = os.listdir(path_faces_personX)
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if photos_list:
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for i in range(len(photos_list)):
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# 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX
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print("%-40s %-20s" % ("正在读的人脸图像 / Reading image:", path_faces_personX + "/" + photos_list[i]))
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features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])
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# 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image
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if features_128d == 0:
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i += 1
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else:
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features_list_personX.append(features_128d)
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else:
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print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n')
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# 计算 128D 特征的均值 / Compute the mean
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# personX 的 N 张图像 x 128D -> 1 x 128D
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if features_list_personX:
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features_mean_personX = np.array(features_list_personX).mean(axis=0)
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else:
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features_mean_personX = np.zeros(128, dtype=int, order='C')
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print(type(features_mean_personX))
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return features_mean_personX
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# 获取已录入的最后一个人脸序号 / Get the order of latest person
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person_list = os.listdir("data/data_faces_from_camera/")
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person_num_list = []
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for person in person_list:
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person_num_list.append(int(person.split('_')[-1]))
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person_cnt = max(person_num_list)
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with open("data/features_all.csv", "w", newline="") as csvfile:
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writer = csv.writer(csvfile)
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for person in range(person_cnt):
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# Get the mean/average features of face/personX, it will be a list with a length of 128D
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print(path_images_from_camera + "person_" + str(person + 1))
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features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
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writer.writerow(features_mean_personX)
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print("特征均值 / The mean of features:", list(features_mean_personX))
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print('\n')
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print("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv") |