学习笔记：web页面的人脸识别注册及登录
前端代码：
index.html:
client video
人脸识别
请输入您的名称：
index.js：调用摄像头截图发送
使用定时器来间断获取截图进行识别，直到识别成功或达到一定时间
视频识别电脑风扇哗哗转 顶不住
function getUserMedia(constraints, success, error) {
if (navigator.mediaDevices.getUserMedia) {
//最新的标准API
navigator.mediaDevices.getUserMedia(constraints).then(success).catch(error);
} else if (navigator.webkitGetUserMedia) {
//webkit核心浏览器
navigator.webkitGetUserMedia(constraints, success, error)
} else if (navigator.mozGetUserMedia) {
//firfox浏览器
navigator.mozGetUserMedia(constraints, success, error);
} else if (navigator.getUserMedia) {
//旧版API
navigator.getUserMedia(constraints, success, error);
}
}
let video = document.getElementById('video');
video.controls = false;
let canvas = document.getElementById('canvas');
//返回一个用于在画布上绘图的环境
let context = canvas.getContext('2d');
context.arc(250, 250, 200, 0, 0.75 * Math.PI, false);
function success(stream) {
//兼容webkit核心浏览器
let CompatibleURL = window.URL || window.webkitURL;
//将视频流设置为video元素的源
//video.src = CompatibleURL.createObjectURL(stream);
video.srcObject = stream;
video.play();
}
function error(error) {
console.log(`访问用户媒体设备失败${error.name}, ${error.message}`);
alert("访问用户媒体设备失败")
}
function open_video() { // 打开摄像头并显示
if (navigator.mediaDevices.getUserMedia || navigator.getUserMedia || navigator.webkitGetUserMedia || navigator.mozGetUserMedia) {
//调用用户媒体设备, 访问摄像头
getUserMedia({video: {width: 640, height: 480}}, success, error);
} else {
alert('不支持访问用户媒体');
}
}
function closeVideo() { // 关闭摄像头
video.srcObject.getTracks()[0].stop();
video.srcObject.getTracks()[0].stop();
}
function endRecognize(timer, error) {
clearInterval(timer); // 检测到人脸，停止检测
clearInterval(error); // 检测到人脸，取消提示
closeVideo();
}
function face_recognize(timer, error) { // 验证脸库是否存在该人脸
let imgData
context.drawImage(video, 0, 0, 500, 380);
//获得截图base64编码
imgData = canvas.toDataURL("image/png", 0.8);
//ajax发送校验
$.ajax({
url: "/check_face",
type: "POST",
dataType: 'json',
data: {"imgData": imgData},
success: function (data) {
if (data.result == "fail") {
$("#result_tag").text("检测不出人脸");
} else {
endRecognize(timer, error);
let file_rand = data.file_rand;
$.ajax({
url: "/recognize",
type: "POST",
data: {"file_rand": file_rand},
success: function (data) {
$("#result_tag").text("识别结果：" + data);
}
})
}
}
})
}
// 验证用户人脸进行登录
document.getElementById("check").addEventListener('click', function () {
let timer;
let error;
open_video();
timer = setInterval(function () { // 每秒检测一次
face_recognize(timer, error);
}, 1000);
error = setInterval(function () { // 检测不到人脸进行提醒
alert('检测不到人脸，请重试！！');
}, 4900);
setTimeout(function () {
endRecognize(timer, error)// 五秒后还未检测到人脸则停止检测
}, 5000);
})
// 验证用户进行注册
function register(timer, error) {
context.drawImage(video, 0, 0, 640, 480);
//获得截图base64编码
let imgData = canvas.toDataURL("image/png", 0.8);
let uname = document.getElementById("uname").value
//ajax发送校验
$.ajax({
url: "/register",
type: "POST",
dataType: 'json',
data: {"imgData": imgData, "uname": uname},
success: function (data) {
if (data.result == "fail") {
$("#result_tag").text("检测不出人脸");
} else {
endRecognize(timer, error);
if (data.result == "repetition") {
$("#result_tag").text("请勿重复注册！！");
} else {
$("#result_tag").text("注册成功," + uname + "欢迎你！");
}
}
}
})
}
document.getElementById("register").addEventListener("click", function () {
let timer;
let error;
open_video();
timer = setInterval(function () { // 每秒检测一次
register(timer, error);
}, 1000);
error = setInterval(function () { // 检测不到人脸进行提醒
alert('检测不到人脸，请重试！！')
}, 4900);
setTimeout(function () {
endRecognize(timer, error)
}, 5000);
});app.py
from flask import Flask, render_template, jsonify
from flask.globals import request
import base64
import random
import os
import face_recognize
app = Flask(__name__)
@app.route("/")
def hello():
return render_template("index.html")
@app.route("/check_face", methods=["GET", "POST"]) # 进行识别
def check_face():
if request.method == "POST":
img_base64 = request.form["imgData"]
file_rand = random.randint(1, 9999)
img_data = base64.b64decode(img_base64.split(",")[1])
file_paths = os.path.join(app.static_folder, "img", "{}.png".format(file_rand))
with open(file_paths, "wb+") as f:
f.write(img_data)
result = face_recognize.check_face(file_paths)
if result == "fail":
os.remove(file_paths)
return jsonify({"result": result, "file_rand": str(file_rand)})
@app.route("/register", methods=["GET", "POST"]) # 进行注册
def register():
if request.method == "POST":
img_base64 = request.form["imgData"]
uname = request.form["uname"]
rand = random.randint(1, 9999)
file_name = f'{uname}_{rand}'
img_data = base64.b64decode(img_base64.split(",")[1])
file_paths = os.path.join(app.static_folder, "init_face", "{}.png".format(file_name))
with open(file_paths, "wb+") as f:
f.write(img_data)
result = face_recognize.check_face(file_paths)
if result == "fail":
os.remove(file_paths) # 识别不到人脸
elif face_recognize.face_recognize_image(file_paths):
os.remove(file_paths)
result = "repetition" # 重复注册
return jsonify({"result": result})
@app.route('/recognize', methods=["GET", "POST"])
def recognize():
if request.method == "POST":
file_rand = request.get_data().decode("utf-8").split("=")[1]
name = face_recognize.face_recognize_image(os.path.join(app.static_folder, "img", "{}.png".format(file_rand)))
if name:
return name
else:
return "未知人员"
if __name__ == '__main__':
app.run(debug=True)
face_recognize.py 进行人脸识别和验证
import cv2
import face_recognition
import os
# 获取脸库的所有人脸特征
def get_features():
basefacefilespath = "static\\init_face"
baseface_titles = [] # 名称
baseface_face_encodings = [] # 人脸数据
# 读取人脸资源
for fn in os.listdir(basefacefilespath):
baseface_face_encodings.append(
face_recognition.face_encodings(face_recognition.load_image_file(basefacefilespath + "\\" + fn))[0])
fn = fn[:(len(fn) - 4)]
baseface_titles.append(fn)
return baseface_face_encodings, baseface_titles
# 人脸识别
def face_recognize_image(filename):
frame = face_recognition.load_image_file(filename)
face_encoding = face_recognition.face_encodings(frame)[0]
baseface_face_encodings, baseface_titles = get_features()
for i, v in enumerate(baseface_face_encodings):
match = face_recognition.compare_faces([v], face_encoding, tolerance=0.5)
print(match)
name = None
if match[0]:
name = baseface_titles[i]
break
return name
def face_recognize_video(video): # 使用视频进行识别
input_movie = cv2.VideoCapture(video)
baseface_face_encodings, baseface_titles = get_features()
while True:
# 抓取每一帧
ret, frame = input_movie.read()
# Quit when the input video file ends
if not ret:
break
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
for face_encoding in face_encodings:
for i, v in enumerate(baseface_face_encodings):
match = face_recognition.compare_faces([v], face_encoding, tolerance=0.5)
print(match)
name = "未知人员"
if match[0]:
name = baseface_titles[i]
break
if (name):
print(name)
return name
cv2.destroyAllWindows()
# 检测照片是否存在人脸
def check_face(filename):
frame = face_recognition.load_image_file(filename)
face_location = face_recognition.face_locations(frame)
if len(face_location) == 0:
return "fail"
else:
return "success"
if __name__ == "__main__":
pass
部分代码参考：基于face_recognition & flask 构建的人脸识别web服务_阿坨的博客-CSDN博客_flask实现人脸识别前言此demo采用face_recognition和flask搭建一个人脸识别的网页版demo，网页端可上传注册人脸图并调用摄像头捕获人脸进行识别。face_recognition是世界上最简洁的人脸识别库，你可以使用Python和命令行工具提取、识别、操作人脸。face_recognition的人脸识别是基于业内领先的C++开源库 dlib中的深度学习模型，用Labeled Faces in the Wild人脸数据集进行测试，有高达99.38%的准确率。但对小孩和亚洲人脸的识别准确率尚待提升。安装https://blog.csdn.net/atuo200/article/details/117992251