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opencv-flask-web人脸识别

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本文介绍了基于OpenCV 和 Python 、Flask的web人脸检测项目。

  • 1):人脸图片收集
  • 2):人脸数据训练
  • 3):人脸识别
  • 4):集成到Flask 提供web接口

人脸识别的最基础任务是「人脸检测」。你必须首先「捕捉」人脸（第 1 阶段）才能在未来与捕捉到的新人脸对比时（第 3 阶段）识别它。

最常见的人脸检测方式是使用「Haar 级联分类器」。使用基于 Haar 特征的级联分类器的目标检测是 Paul Viola 和 Michael Jones 2001 年在论文《Rapid Object Detection using a Boosted Cascade of Simple Features》中提出的一种高效目标检测方法。这种机器学习方法基于大量正面、负面图像训练级联函数，然后用于检测其他图像中的对象。这里，我们将用它进行人脸识别。最初，该算法需要大量正类图像（人脸图像）和负类图像（不带人脸的图像）来训练分类器。然后我们需要从中提取特征。好消息是 OpenCV 具备训练器和检测器。

web提供3个接口

地址 接口名称
/ 首页
/dataset?share_id 人脸数据收集(参数-人姓名)
/recognition 人脸验证

main.py

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from flask import Flask, render_template, Response, request
from camera import VideoCamera
import cv2
app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')

def gen(camera):
    while True:
        frame = camera.get_frame()
        yield (b'--frame\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')

@app.route('/video_feed')
def video_feed():
    return Response(gen(VideoCamera()),
                    mimetype='multipart/x-mixed-replace; boundary=frame')

@app.route('/dataset', methods = ["GET"])
def dataset():
    face_id = request.args.get("face_id")
    camera  = VideoCamera()
    camera.data_set(face_id)
    msg =  "%s%s" %(face_id, "采集完成")
    content = {"msg": msg}
    return render_template('index.html')

@app.route('/recognition', methods = ["GET"])
def recognition():
    camera  = VideoCamera()
    face_name = camera.recognition()
    msg =  "认证结果为:%s" %(face_name)
    content = {"msg": msg}
    return render_template('index.html', **content)
if __name__ == '__main__':
    app.run(host='0.0.0.0', debug=True)

camera.py
haarcascade_frontalface_default.xml 文件需使用自己本地的,如果使用网上的 需要本地新建文件 然后把内容复制到本地,不然会报错

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import cv2,os
import numpy as np
from PIL import Image
import pymongo,random

class VideoCamera(object):
    def __init__(self):
        # Using OpenCV to capture from device 0. If you have trouble capturing
        # from a webcam, comment the line below out and use a video file
        # instead.
        self.video = cv2.VideoCapture(0)
        self.video.set(3, 640)
        self.video.set(4, 480)
        self.face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
        #width = int(self.video .get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)
        #height = int(self.video .get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)
        #fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        # 第三个参数则是镜头快慢的，10为正常，小于10为慢镜头
        #self.out = cv2.VideoWriter('./output2.avi', fourcc,10,(width,height))
        # If you decide to use video.mp4, you must have this file in the folder
        # as the main.py.
        # self.video = cv2.VideoCapture('video.mp4')
        mongodbs = pymongo.MongoClient('127.0.0.1', 27017)
        self.face = mongodbs.face
        self.path = "dataset"
    def __del__(self):
        self.video.release()

    def get_frame(self):
        success, image = self.video.read()
        #frame = cv2.flip(image, 1)
        #a = self.out.write(frame)
        ret, jpeg = cv2.imencode('.jpg', image)
        return jpeg.tobytes()

    #图片采集
    def data_set(self, face_name):
        count = 0
        face_id = random.randint(0, 100000)
        while(True):
            ret, img = self.video.read()
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            faces = self.face_detector.detectMultiScale(gray, 1.3, 5)
 
            for (x,y,w,h) in faces:
                cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)     
                count += 1
                # Save the captured image into the datasets folder
                cv2.imwrite(self.path+"/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w])
 
            k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting video
            if k == 27:
                break
            elif count >= 30: # Take 30 face sample and stop video
                break
        self.face.face.save({"face_name":face_name,"face_id":face_id})
        self.training()
    #图片训练
    def training(self):
        recognizer = cv2.face.LBPHFaceRecognizer_create()
        imagePaths = [os.path.join(self.path,f) for f in os.listdir(self.path)]     
        faceSamples=[]
        ids = []
        for imagePath in imagePaths:
            PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
            img_numpy = np.array(PIL_img,'uint8')
            id = int(os.path.split(imagePath)[-1].split(".")[1])
            faces = self.face_detector.detectMultiScale(img_numpy)
            for (x,y,w,h) in faces:
                faceSamples.append(img_numpy[y:y+h,x:x+w])
                ids.append(id)
        recognizer.train(faceSamples, np.array(ids))
        recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi
    #认证函数
    def recognition(self):
        recognizer = cv2.face.LBPHFaceRecognizer_create()
        recognizer.read('trainer/trainer.yml')
        font = cv2.FONT_HERSHEY_SIMPLEX
        minW = 0.1*self.video.get(3)
        minH = 0.1*self.video.get(4)
        while True:
            id = 'unknown'
            ret, img = self.video.read()
            gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
     
            faces = self.face_detector.detectMultiScale( 
                gray,
                scaleFactor = 1.2,
                minNeighbors = 5,
                minSize = (int(minW), int(minH)),
            )
 
            for(x,y,w,h) in faces:
                cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
                id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
 
                # Check if confidence is less them 100 ==> "0" is perfect match 
                if (confidence < 100):
                    face = self.face.face.find_one({"face_id":id})
                    id = id
                    confidence = "  {0}%".format(round(100 - confidence))
                else:
                    id = "unknown"
                    confidence = "  {0}%".format(round(100 - confidence))
            if id != "unknown":	
                break
        return face['face_name']

index.html

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<html>
  <head>
    <title>人脸识别</title>
  </head>
  <body>
    <h1>人脸识别</h1>
    <input id="face_id" type="text"><br>
    <span onclick="face_dataset()" style="cursor:pointer">人脸信息采集</span><br>
    <a href="/recognition">人脸信息识别</a><br>
    <span></span>
  </body>
  <script>
    function face_dataset(){
        var face_id = document.getElementById("face_id").value;
        if(!face_id){
            confirm("请输入姓名");
            return;
        }
        location.href = "/dataset?face_id=" + face_id;
    }
  </script>
</html>

参考：
@https://shumeipai.nxez.com/2018/03/09/real-time-face-recognition-an-end-to-end-project-with-raspberry-pi.html
使用 OpenCV 和 Python 从头实现人脸识别 @www.superdatascience.com
理解人脸识别 @www.thecodacus.com