人脸识别+数据采集+人脸特征提取（哈哈哈，绝决子）

本文主要是介绍人脸识别+数据采集+人脸特征提取（哈哈哈，绝决子），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

人脸识别+数据采集+人脸特征提取（哈哈哈，绝绝子）

1.实验效果

根据采集的照片进行特征提取识别出人，效果还不错

在这里插入图片描述

2.基本需要

python 3.6

dlib 19.7.0

pycharm

dlib下载可以去看我的这个python3.8+pycharm下载dlib（搞了好久，终于好了）_Hulk_liu的博客-CSDN博客_python下载dlib

哈哈哈，夸我，now，说完咱就开始把

3.开始实验

3.1人脸采集

import cv2
import dlib
import os
import sys
import random# 存储位置
output_dir = './data/2***'  # 这里填编号+人名(例如1某某某，2某某)
size = 256  # 图片边长if not os.path.exists(output_dir):os.makedirs(output_dir)# 改变图片的亮度与对比度def relight(img, light=1, bias=0):w = img.shape[1]h = img.shape[0]# image = []for i in range(0, w):for j in range(0, h):for c in range(3):tmp = int(img[j, i, c] * light + bias)if tmp > 255:tmp = 255elif tmp < 0:tmp = 0img[j, i, c] = tmpreturn img# 使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
# 打开摄像头 参数为输入流，可以为摄像头或视频文件
camera = cv2.VideoCapture(0)
# camera = cv2.VideoCapture('C:/Users/CUNGU/Videos/Captures/wang.mp4')index = 1
while True:if (index <= 20):  # 存储15张人脸特征图像print('Being processed picture %s' % index)# 从摄像头读取照片success, img = camera.read()# 转为灰度图片gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 使用detector进行人脸检测dets = detector(gray_img, 1)for i, d in enumerate(dets):x1 = d.top() if d.top() > 0 else 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = d.right() if d.right() > 0 else 0face = img[x1:y1, x2:y2]# 调整图片的对比度与亮度， 对比度与亮度值都取随机数，这样能增加样本的多样性face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))face = cv2.resize(face, (size, size))cv2.imshow('image', face)cv2.imwrite(output_dir + '/' + str(index) + '.jpg', face)index += 1key = cv2.waitKey(30) & 0xffif key == 27:breakelse:print('Finished!')# 释放摄像头 release cameracamera.release()# 删除建立的窗口 delete all the windowscv2.destroyAllWindows()break

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-sDrIM8Jw-1649990042671)(人脸识别+数据采集+人脸特征提取（哈哈哈，绝决子）.assets/image-20220415095436712.png)]

这里获取一个人大概20个人脸，我大概采集了20多个人，效果也可以实现

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-zENPzIab-1649990042671)(人脸识别+数据采集+人脸特征提取（哈哈哈，绝决子）.assets/image-20220415095606679.png)]

3.2人脸特征提取

# 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 = "./data/"# Dlib 正向人脸检测器
detector = dlib.get_frontal_face_detector()# Dlib 人脸预测器
predictor = dlib.shape_predictor("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("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 = 0print("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("./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 += 1else: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 128Dif 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("./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 128Dfeatures_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: ./feature/features_all2.csv")

这一步会讲特征提取放到feature里面，然后就会有几个特征的csv文件
、

3.2人脸识别

# 摄像头实时人脸识别
#coding:utf-8
import os
import dlib  # 人脸处理的库 Dlib
import csv  # 存入表格
import time
import sys
import numpy as np  # 数据处理的库 numpy
from cv2 import cv2 as cv2  # 图像处理的库 OpenCv
import pandas as pd  # 数据处理的库 Pandas# 人脸识别模型，提取128D的特征矢量
# face recognition model, the object maps human faces into 128D vectors
# Refer this tutorial: http://dlib.net/python/index.html#dlib.face_recognition_model_v1
facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")# 计算两个128D向量间的欧式距离
# compute the e-distance between two 128D features
def return_euclidean_distance(feature_1, feature_2):feature_1 = np.array(feature_1)feature_2 = np.array(feature_2)dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))return dist# 处理存放所有人脸特征的 csv
path_features_known_csv = "./features_all.csv"
csv_rd = pd.read_csv(path_features_known_csv, header=None)# 用来存放所有录入人脸特征的数组
# the array to save the features of faces in the database
features_known_arr = []# 读取已知人脸数据
# print known faces
for i in range(csv_rd.shape[0]):features_someone_arr = []for j in range(0, len(csv_rd.loc[i, :])):features_someone_arr.append(csv_rd.loc[i, :][j])features_known_arr.append(features_someone_arr)
print("Faces in Database：", len(features_known_arr))# Dlib 检测器和预测器
# The detector and predictor will be used
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')# 创建 cv2 摄像头对象
# cv2.VideoCapture(0) to use the default camera of PC,
# and you can use local video name by use cv2.VideoCapture(filename)
cap = cv2.VideoCapture(0)# cap.set(propId, value)
# 设置视频参数，propId 设置的视频参数，value 设置的参数值
cap.set(3, 400)# cap.isOpened() 返回 true/false 检查初始化是否成功
# when the camera is open
while cap.isOpened():flag, img_rd = cap.read()kk = cv2.waitKey(1)# 取灰度img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)# 人脸数 facesfaces = detector(img_gray, 0)# 待会要写的字体 font to write laterfont = cv2.FONT_HERSHEY_COMPLEX# 存储当前摄像头中捕获到的所有人脸的坐标/名字# the list to save the positions and names of current faces capturedpos_namelist = []name_namelist = []# 按下 q 键退出# press 'q' to exitif kk == ord('q'):breakelse:# 检测到人脸 when face detectedif len(faces) != 0:# 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr# get the features captured and save into features_cap_arrfeatures_cap_arr = []for i in range(len(faces)):shape = predictor(img_rd, faces[i])features_cap_arr.append(facerec.compute_face_descriptor(img_rd, shape))# 遍历捕获到的图像中所有的人脸# traversal all the faces in the databasefor k in range(len(faces)):print("##### camera person", k + 1, "#####")# 让人名跟随在矩形框的下方# 确定人名的位置坐标# 先默认所有人不认识，是 unknown# set the default names of faces with "unknown"name_namelist.append("unknown")# 每个捕获人脸的名字坐标 the positions of faces capturedpos_namelist.append(tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))# 对于某张人脸，遍历所有存储的人脸特征# for every faces detected, compare the faces in the databasee_distance_list = []for i in range(len(features_known_arr)):# 如果 person_X 数据不为空if str(features_known_arr[i][0]) != '0.0':print("with person", str(i + 1), "the e distance: ", end='')e_distance_tmp = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])print(e_distance_tmp)e_distance_list.append(e_distance_tmp)else:# 空数据 person_Xe_distance_list.append(999999999)# 找出最接近的一个人脸数据是第几个# Find the one with minimum e distancesimilar_person_num = e_distance_list.index(min(e_distance_list))print("Minimum e distance with person", int(similar_person_num) + 1)# 计算人脸识别特征与数据集特征的欧氏距离# 距离小于0.4则标出为可识别人物if min(e_distance_list) < 0.4:# 这里可以修改摄像头中标出的人名# Here you can modify the names shown on the camera# 1、遍历文件夹目录folder_name = './data/'# 最接近的人脸sum = similar_person_num + 1key_id = 1  # 从第一个人脸数据文件夹进行对比# 获取文件夹中的文件名:1wang、2zhou、3...file_names = os.listdir(folder_name)for name in file_names:# print(name+'->'+str(key_id))if sum == key_id:# winsound.Beep(300,500)# 响铃：300频率，500持续时间name_namelist[k] = name[1:]  # 人名删去第一个数字（用于视频输出标识）key_id += 1# 播放欢迎光临音效# playsound('D:/myworkspace/JupyterNotebook/People/music/welcome.wav')# print("May be person "+str(int(similar_person_num)+1))# -----------筛选出人脸并保存到visitor文件夹------------for i, d in enumerate(faces):x1 = d.top() if d.top() > 0 else 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = d.right() if d.right() > 0 else 0face = img_rd[x1:y1, x2:y2]size = 64face = cv2.resize(face, (size, size))# 要存储visitor人脸图像文件的路径path_visitors_save_dir = "./data/KnownFacetrainset/"# 存储格式：2019-06-24-14-33-40wang.jpgnow_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())save_name = str(now_time) + str(name_namelist[k]) + '.jpg'# print(save_name)# 本次图片保存的完整urlsave_path = path_visitors_save_dir + '/' + save_name# 遍历visitor文件夹所有文件名visitor_names = os.listdir(path_visitors_save_dir)visitor_name = ''for name in visitor_names:# 名字切片到分钟数：2019-06-26-11-33-00wangyu.jpgvisitor_name = (name[0:16] + '-00' + name[19:])# print(visitor_name)visitor_save = (save_name[0:16] + '-00' + save_name[19:])# print(visitor_save)# 一分钟之内重复的人名不保存if visitor_save != visitor_name:cv2.imwrite(save_path, face)print('新存储：' + path_visitors_save_dir + '/' + str(now_time) + str(name_namelist[k]) + '.jpg')else:print('重复，未保存！')else:# 播放无法识别音效# playsound('D:/myworkspace/JupyterNotebook/People/music/sorry.wav')print("Unknown person")# -----保存图片-------# -----------筛选出人脸并保存到visitor文件夹------------for i, d in enumerate(faces):x1 = d.top() if d.top() > 0 else 0y1 = d.bottom() if d.bottom() > 0 else 0x2 = d.left() if d.left() > 0 else 0y2 = d.right() if d.right() > 0 else 0face = img_rd[x1:y1, x2:y2]size = 64face = cv2.resize(face, (size, size))# 要存储visitor-》unknown人脸图像文件的路径path_visitors_save_dir = "./data/UnKnownFacetrainset"# 存储格式：2019-06-24-14-33-40unknown.jpgnow_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())# print(save_name)# 本次图片保存的完整urlsave_path = path_visitors_save_dir + '/' + str(now_time) + 'unknown.jpg'cv2.imwrite(save_path, face)print('新存储：' + path_visitors_save_dir + '/' + str(now_time) + 'unknown.jpg')# 矩形框# draw rectanglefor kk, d in enumerate(faces):# 绘制矩形框cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)print('\n')# 在人脸框下面写人脸名字# write names under rectanglefor i in range(len(faces)):cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)print("Faces in camera now:", name_namelist, "\n")# cv2.putText(img_rd, "Press 'q': Quit", (20, 450), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)cv2.putText(img_rd, "Face Recognition", (20, 40), font, 1, (0, 0, 255), 1, cv2.LINE_AA)cv2.putText(img_rd, "Visitors: " + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)# 窗口显示 show with opencvcv2.imshow("camera", img_rd)# 释放摄像头 release camera
cap.release()# 删除建立的窗口 delete all the windows
cv2.destroyAllWindows()