学习笔记：python人脸识别3-通过web客户端摄像头识别人脸
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2022-07-12 20:53:38
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在web登录人脸认证中，需要通过浏览器调用客户端电脑或手机的摄像头，再把获取的图像发送到服务端识别认证，这是通过人脸登录的基础。
本次测试采用html5调用客户端浏览器摄像头，获取图像发送到服务端认证
html5获取客户端摄像头画面，点击验证后，把客户端截屏数据进行base64编码，并模拟form提交数据，服务端获取post的form数据后，base64解码并存储在服务器临时图片文件，再把这个文件作为输入进行人脸识别。
1. checkface.html 发起认证
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Document</title>
<style>
video {
border: 1px solid #ccc;
display: block;
margin: 0 0 20px 0;
float: left;
}
#canvas {
margin-top: 20px;
border: 1px solid #ccc;
display: block;
}
</style>
</head>
<body>
<video id="video" width="500" height="400" autoplay></video>
<canvas id="canvas" width="500" height="400"></canvas>
<button id="snap" style="width:500px;height:30px;font-size:24px;">拍照并人脸识别</button>
<script type="text/javascript">
var context = canvas.getContext("2d");
window.addEventListener("DOMContentLoaded", function() {
var canvas = document.getElementById("canvas");
var video = document.getElementById("video");
var videoObj = {"video" : true};
var errBack = function(error) {
console.log("Video capture error: ", error.code);
};
if (navigator.getUserMedia) {
navigator.getUserMedia(videoObj, function(stream) {
video.srcObject = stream;
video.play();
}, errBack);
} else if (navigator.webkitGetUserMedia) {
navigator.webkitGetUserMedia(videoObj, function(stream) {
video.srcObject = window.webkitURL.createObjectURL(stream);
video.play();
}, errBack);
} else if (navigator.mozGetUserMedia) {
navigator.mozGetUserMedia(videoObj, function (stream) {
video.srcObject = window.URL.createObjectURL(stream);
video.play();
}, errBack);
}
}, false);
document.getElementById("snap").addEventListener("click", function () {
context.drawImage(video, 0, 0, 500, 400);
var dataURL = canvas.toDataURL();
var temp = document.createElement("form");
temp.action = "http://127.0.0.1:5002";
temp.method = "post";
temp.style.display = "none";
var opt = document.createElement("textarea");
opt.name ="imgdata";
if (dataURL !== null) { opt.value = dataURL; }
temp.appendChild(opt);
document.body.appendChild(temp);
temp.submit();
});
</script>
</body>
</html>2. 服务端checkface.py 接收认证图片并识别
import face_recognition
from flask import Flask, jsonify, request, redirect
import base64
import cv2
import time
import os
app = Flask(__name__)
@app.route('/', methods=['GET', 'POST'])
def upload_image():
if request.method == 'POST':
imgData = base64.b64decode(request.form['imgdata'].replace('data:image/png;base64,', ''))
finename=str(int(round(time.time() * 1000))) + '.jpg'
file = open(finename, 'wb')
file.write(imgData)
file.close()
return detect_faces_in_image(finename)
else:
return redirect('checkface.html')
def detect_faces_in_image(file_stream):
# 用face_recognition.face_encodings(img)接口提前把奥巴马人脸的编码录入
known_face_encoding = [-0.09634063, 0.12095481, -0.00436332, -0.07643753, 0.0080383,
0.01902981, -0.07184699, -0.09383309, 0.18518871, -0.09588896,
0.23951106, 0.0986533 , -0.22114635, -0.1363683 , 0.04405268,
0.11574756, -0.19899382, -0.09597053, -0.11969153, -0.12277931,
0.03416885, -0.00267565, 0.09203379, 0.04713435, -0.12731361,
-0.35371891, -0.0503444 , -0.17841317, -0.00310897, -0.09844551,
-0.06910533, -0.00503746, -0.18466514, -0.09851682, 0.02903969,
-0.02174894, 0.02261871, 0.0032102 , 0.20312519, 0.02999607,
-0.11646006, 0.09432904, 0.02774341, 0.22102901, 0.26725179,
0.06896867, -0.00490024, -0.09441824, 0.11115381, -0.22592428,
0.06230862, 0.16559327, 0.06232892, 0.03458837, 0.09459756,
-0.18777156, 0.00654241, 0.08582542, -0.13578284, 0.0150229 ,
0.00670836, -0.08195844, -0.04346499, 0.03347827, 0.20310158,
0.09987706, -0.12370517, -0.06683611, 0.12704916, -0.02160804,
0.00984683, 0.00766284, -0.18980607, -0.19641446, -0.22800779,
0.09010898, 0.39178532, 0.18818057, -0.20875394, 0.03097027,
-0.21300618, 0.02532415, 0.07938635, 0.01000703, -0.07719778,
-0.12651891, -0.04318593, 0.06219772, 0.09163868, 0.05039065,
-0.04922386, 0.21839413, -0.02394437, 0.06173781, 0.0292527 ,
0.06160797, -0.15553983, -0.02440624, -0.17509389, -0.0630486 ,
0.01428208, -0.03637431, 0.03971229, 0.13983178, -0.23006812,
0.04999552, 0.0108454 , -0.03970895, 0.02501768, 0.08157793,
-0.03224047, -0.04502571, 0.0556995 , -0.24374914, 0.25514284,
0.24795187, 0.04060191, 0.17597422, 0.07966681, 0.01920104,
-0.01194376, -0.02300822, -0.17204897, -0.0596558 , 0.05307484,
0.07417042, 0.07126575, 0.00209804]
# 载入用户上传的图片
img = face_recognition.load_image_file(file_stream)
# 为用户上传的图片中的人脸编码
unknown_face_encodings = face_recognition.face_encodings(img)
os.remove(file_stream)
face_found = False
is_obama = False
if len(unknown_face_encodings) > 0:
face_found = True
# 看看图片中的第一张脸是不是奥巴马
match_results = face_recognition.compare_faces([known_face_encoding], unknown_face_encodings[0])
if match_results[0]:
is_obama = True
# 讲识别结果以json键值对的数据结构输出
result = {
"face_found_in_image": face_found,
"is_picture_of_obama": is_obama
}
return jsonify(result)
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5002, debug=True)3. 测试运行。
（1）先启动服务端 checkface.py 会在5002端口提供认证服务
（2）浏览器 打开checkface.html
(3) 点击“拍照并人脸识别”会拷贝截屏并发送认证，服务器返回认证结果
可见，识别到人脸，并比对识别确实是obama,如果拍照自己的脸比对则失败
4. 改造为登录认证实际使用，有几点要做
（1） 人脸特征库从数据库读取，预先从各员工照片中抽取并保存到数据库
（2） 不是判别是否是某个人，而是从一个人脸库数组中比对找出某个人
（3） 很明显，需要防作弊，比如活体检测，才能保障安全，要能识别出照片或视频代替本人出镜。
（4）本次为简单测试，是把上传的图片保存为临时文件，再套用常见的照片比对程序，为提高性能，可以不保存，直接对上传的图片数据操作内存比对。
本文人脸识别部分参考网址：https://github.com/ageitgey/face_recognition
下一篇： opencv3 人脸检测和识别