机器学习（六）Python实现进阶人脸识别

# 从人脸图像文件中提取人脸特征存入 CSV

# Features extraction from images and save into features_all.csv

# return_128d_features()          获取某张图像的128D特征

# compute_the_mean()              计算128D特征均值

from cv2 import cv2 as cv2

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_images_from_camera = "D:/No1WorkSpace/JupyterNotebook/Facetrainset/"

# Dlib 正向人脸检测器

detector = dlib.get_frontal_face_detector()

# Dlib 人脸预测器

predictor = dlib.shape_predictor("D:/No1WorkSpace/JupyterNotebook/model/shape_predictor_68_face_landmarks.dat")

# Dlib 人脸识别模型

# Face recognition model, the object maps human faces into 128D vectors

face_rec = dlib.face_recognition_model_v1("D:/No1WorkSpace/JupyterNotebook/model/dlib_face_recognition_resnet_model_v1.dat")

# 返回单张图像的 128D 特征

def return_128d_features(path_img):

    img_rd = io.imread(path_img)

    img_gray = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)

    faces = detector(img_gray, 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

    if len(faces) != 0:

        shape = predictor(img_gray, faces[0])

        face_descriptor = face_rec.compute_face_descriptor(img_gray, shape)

    else:

        face_descriptor = 0

        print("no face")

    return face_descriptor

# 将文件夹中照片特征提取出来, 写入 CSV

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)):

            with open("D:/No1WorkSpace/JupyterNotebook/feature/featuresGiao"+str(i)+".csv", "w", newline="") as csvfile:

                writer = csv.writer(csvfile)

            # 调用return_128d_features()得到128d特征

                print("%-40s %-20s" % ("正在读的人脸图像 / image to read:", path_faces_personX + "/" + photos_list[i]))

                features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])

                print(features_128d)

                writer.writerow(features_128d)

            # 遇到没有检测出人脸的图片跳过

                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

    # N x 128D -> 1 x 128D

    if features_list_personX:

        features_mean_personX = np.array(features_list_personX).mean(axis=0)

    else:

        features_mean_personX = '0'

    return features_mean_personX

# 读取某人所有的人脸图像的数据

people = os.listdir(path_images_from_camera)

people.sort()

with open("D:/No1WorkSpace/JupyterNotebook/feature/features_all.csv", "w", newline="") as csvfile:

    writer = csv.writer(csvfile)

    for person in people:

        print("##### "外汇跟单gendan5.com + person + " #####")

        # Get the mean/average features of face/personX, it will be a list with a length of 128D

        features_mean_personX = return_features_mean_personX(path_images_from_camera + person)

        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: D:/myworkspace/JupyterNotebook/People/feature/features_all2.csv")