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关于python:机器学习六Python实现进阶人脸识别

jiezi

从人脸图像文件中提取人脸特色存入 CSV

Features extraction from images and save into features_all.csv

return_128d_features() 获取某张图像的 128D 特色

compute_the_mean() 计算 128D 特色均值

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门路

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 特色

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
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进去, 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() 失去 128d 特色
            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 特色的均值
# 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("#####" + 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("特色均值 [PayPal 下载](https://www.gendan5.com/wallet/PayPal.html) / 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")
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