Python 3 利用 Dlib 实现摄像头实时人脸识别 python

Copyright (C) 2020 coneypo SPDX-License-Identifier: MIT Author: coneypo Blog: http://www.cnblogs.com/AdaminXie GitHub: https://github.com/coneypo/Dl... Mail: coneypo@foxmail.com 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" import os
import dlib
from skimage import io
import csv
import numpy as np
要读取人脸图像文件的路径 / Path of cropped faces path_images_from_camera = "data/data_faces_from_camera/"
Dlib 正向人脸检测器 / Use frontal face detector of Dlib 【Python 3 利用 Dlib 实现摄像头实时人脸识别】detector = dlib.get_frontal_face_detector()
Dlib 人脸 landmark 特征点检测器 / Get face landmarks predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
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 face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
返回单张图像的 128D 特征 / Return 128D features for single image Input: path_img Output: face_descriptor def return_128d_features(path_img):

img_rd = io.imread(path_img) faces = detector(img_rd, 1) print("%-40s %-20s" % ("检测到人脸的图像 / Image with faces detected:", path_img), '\n') # 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 # For photos of faces saved, we need to make sure that we can detect faces from the cropped images if len(faces) != 0: shape = [PM](https://www.gendan5.com/wallet/PerfectMoney.html)predictor(img_rd, faces[0]) face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape) else: face_descriptor = 0 print("no face") return face_descriptor

返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X Input: path_faces_personX Output: features_mean_personX def return_features_mean_personX(path_faces_personX):

features_list_personX = [] photos_list = os.listdir(path_faces_personX) if photos_list: for i in range(len(photos_list)): # 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX print("%-40s %-20s" % ("正在读的人脸图像 / Reading image:", path_faces_personX + "/" + photos_list[i])) features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i]) # 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image if features_128d == 0: i += 1 else: features_list_personX.append(features_128d) else: print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n') # 计算 128D 特征的均值 / Compute the mean # personX 的 N 张图像 x 128D -> 1 x 128D if features_list_personX: features_mean_personX = np.array(features_list_personX).mean(axis=0) else: features_mean_personX = np.zeros(128, dtype=int, order='C') print(type(features_mean_personX)) return features_mean_personX

获取已录入的最后一个人脸序号 / Get the order of latest person person_list = os.listdir("data/data_faces_from_camera/")
person_num_list = []
for person in person_list:

person_num_list.append(int(person.split('_')[-1]))

person_cnt = max(person_num_list)
with open("data/features_all.csv", "w", newline="") as csvfile:

writer = csv.writer(csvfile) for person in range(person_cnt): # Get the mean/average features of face/personX, it will be a list with a length of 128D print(path_images_from_camera + "person_" + str(person + 1)) features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1)) writer.writerow(features_mean_personX) print("特征均值 / The mean of features:", list(features_mean_personX)) print('\n') print("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")