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色存入 "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门路 / 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
detector = dlib.get_frontal_face_detector()
Dlib 人脸 landmark 特色点检测器 / 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 的特色矢量 / 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 特色 / Return 128D features for single image
Input: path_img <class 'str'>
Output: face_descriptor <class 'dlib.vector'>
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，检测不进去人脸了, 所以要确保是 检测到人脸的人脸图像拿去算特色# For photos of faces saved, we need to make sure that we can detect faces from the cropped imagesif 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 特色均值 / Return the mean value of 128D face descriptor for person X
Input: path_faces_personX <class 'str'>
Output: features_mean_personX <class 'numpy.ndarray'>
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() 失去 128D 特色 / 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 特色的均值 / Compute the mean# personX 的 N 张图像 x 128D -> 1 x 128Dif 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获取已录入的最初一个人脸序号 / 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("特色均值 / 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")