用树莓派实现人脸识别打卡门禁系统

背景

源于实习公司的人脸识别打卡系统，完成之前的项目后正好没有事情干，于是想到了这个，公司的这个打卡系统操作流程是这样的，首先用手机把你的人脸录进去，要求绕头半圈，也就是右脸，正脸，左脸，然后你再去摄像头那里，识别到你后就会帮你把门打开，顺便帮你在钉钉上打卡。

功能

我做的是简易版，实现了这个打卡系统的主要功能，能完成：信息录入，正脸识别，开门关门，名字与时间的保存。

硬件

树莓派一个，摄像头一个，显示屏一个

效果

并没有打马赛克，文件大小还被限制了，大家将就着看，
这是在电脑上的效果：

在树莓派上效果：

打卡记录：

看起来效果还是不错的

源码

源码使用的电脑上的源码，其实是差不多的，路径不一样而已
如果树莓派打不开摄像头，参考这个：
https://www.jianshu.com/p/5653b2b7248c
代码注释很全，不详细解释

摄像头测试代码

import cv2

capCamera = cv2.VideoCapture(0)
if(not capCamera.isOpened()):
    print("can't open this camera")
    exit(0)

capCamera.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
capCamera.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)

while(True):
    # handle for the camera
    ret, frame = capCamera.read()
    if ret == True:
        cv2.imshow('camera',frame)
    else:
        break

    # handle for the video

    # handle for exit
    if (cv2.waitKey(1)) == ord('q'):
        break

capCamera.release()
cv2.destroyAllWindows()

录入信息

import cv2
import os

#config
add_name = 'xiaoming'#要录入的人名


target_dir = './pic_dir/{}/'.format(add_name)
if os.path.exists(target_dir) is False:
    os.makedirs(target_dir)

def generate():
    face_cascade = cv2.CascadeClassifier('.\cascades\haarcascade_frontalface_default.xml')
    #打开摄像头
    camera = cv2.VideoCapture(0)
    forword_count = 0
    #正脸采集，一共20张图片
    while (forword_count <= 20):
        ret, frame = camera.read()
        #转化为灰度图像，用来检测人脸
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        faces = face_cascade.detectMultiScale(gray, 1.3, 5)

        for (x, y, w, h) in faces:
            #画出预测框
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)

            f = cv2.resize(gray[y:y + h, x:x + w], (200, 200))
            #保存录入的图片
            cv2.imwrite('./pic_dir/{0}/{1}.png'.format(add_name, forword_count), f)
            print(forword_count)
            forword_count += 1
        #展示图片
        cv2.imshow("camera", frame)
        #一秒钟24帧
        if cv2.waitKey(1000 // 24) & 0xff == ord("q"):
            break

    camera.release()
    cv2.destroyAllWindows()


if __name__ == "__main__":
    generate()

人脸识别

import os
import sys
import cv2
import numpy as np
import time

def change_door(open_later_time,isOpen,new_face_position):
    if len(new_face_position) > 0 and isOpen == False:
        print('打开')
        isOpen = True
    if len(new_face_position) == 0 and isOpen == True:
        open_later_time += 1
    else:
        open_later_time = 0
    if open_later_time == 100:
        open_later_time = 0
        print('关闭')
        isOpen = False
    return open_later_time,isOpen,new_face_position

def read_images(path, sz=None):#给一个地址，返回训练集
    c = 0
    X, Y = [], []
    names = []
    for dirname, dirnames, filenames in os.walk(path):#目录，子目录，子文件（只限一层目录）
        for subdirname in dirnames:
            names.append(subdirname)
            subject_path = os.path.join(dirname, subdirname)
            for filename in os.listdir(subject_path):#遍历每个名字
                try:
                    if (filename == ".directory"):
                        continue
                    filepath = os.path.join(subject_path, filename)
                    im = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
                    if (im is None):
                        print("image " + filepath + " is none")
                    else:
                        print(filepath)
                    if (sz is not None):
                        im = cv2.resize(im, (200, 200))

                    X.append(np.asarray(im, dtype=np.uint8))
                    Y.append(c)
                except IOError:
                    print("I/O error({0}): {1}".format(IOError.errno, IOError.strerror))

                except:
                    print("Unexpected error:", sys.exc_info()[0])

                    raise
            print(c)
            c = c + 1

    print(Y)
    print(names)

    return [X, Y], names


def face_rec():
    image_dir = './pic_dir_1'
    isOpen = False
    open_later_time = 0

    [X, Y] , names = read_images(image_dir)
    Y = np.asarray(Y, dtype=np.int32)

    model = cv2.face.LBPHFaceRecognizer_create()

    model.train(np.asarray(X), np.asarray(Y))
    camera = cv2.VideoCapture(0)
    camera.set(cv2.CAP_PROP_FRAME_WIDTH, 400)
    camera.set(cv2.CAP_PROP_FRAME_HEIGHT, 350)
    face_cascade = cv2.CascadeClassifier('./cascades/haarcascade_frontalface_default.xml')
    re_count = 0
    old_face_position = {}#用来绘制预测框
    new_face_position = {}#用来收集新数据
    while (True):
        #print(old_face_position)
        #print(new_face_position)
        re_count += 1
        read, img = camera.read()
        faces = face_cascade.detectMultiScale(img, scaleFactor =1.3, minNeighbors=5)
        #print('{}的类型{}'.format(faces, type(faces)))
        for (x, y, w, h) in faces:
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            roi = gray[x:x + w, y:y + h]
            try:
                roi = cv2.resize(roi, (200, 200), interpolation=cv2.INTER_LINEAR)
                #print(roi.shape)
                params = model.predict(roi)
                #print("Label: %s, Confidence: %.2f" % (params[0], params[1]))
                new_face_position[names[params[0]]] = (x, y, w, h)
            except:
                continue

        #优化用户体验
        #采集三帧的人脸识别信息，将预测框画出，预测框三帧一刷新，防止预测框频繁抖动的现象
        if re_count == 3:
            re_count = 0
            #print(new_face_position)
            if len(new_face_position) > 0:
                for key in new_face_position.keys():
                    (x, y, w, h) = new_face_position[key]
                    if old_face_position.__contains__(key) is False:
                        img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
                        cv2.putText(img, key, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)
                        old_face_position[key] = (x, y, w, h)
                    else:
                        (o_x, o_y, o_w, o_h) = new_face_position[key]
                        if abs((o_x-x)) <= 5 and abs((o_y-y)) <= 5:
                            img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
                            cv2.putText(img, key, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)
                            old_face_position[key] = (x, y, w, h)
            else:
                old_face_position = {}
            new_face_position = {}
        else:
            for key in old_face_position.keys():
                (o_x, o_y, o_w, o_h) = old_face_position[key]
                img = cv2.rectangle(img, (o_x, o_y), (o_x + o_w, o_y + o_h), (255, 0, 0), 2)
                cv2.putText(img, key, (o_x, o_y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)

        #开关门模拟和保存打卡信息
        #如果检测到人，并且门没有开，则打开门，并且录入信息
        if len(new_face_position) > 0 and isOpen == False:
            print('开门')
            #保存打卡信息
            t = time.strftime('%Y.%m.%d %H:%M:%S', time.localtime(time.time()))
            with open('jilu.txt', 'a') as file:
                file.writelines('{},{}\n'.format(new_face_position.keys(),t))
            isOpen = True
        #如果没有检测到人，并且门开了，计数器+1，否则计数器为0
        if len(new_face_position) == 0 and isOpen == True:
            open_later_time += 1
        else:
            open_later_time = 0
        #当计数器为100的时候关门
        if open_later_time == 100:
            print('关门')
            isOpen = False
            open_later_time = 0

        cv2.imshow("camera", img)
        #树莓派最好将帧数设为最大，不然看起来不舒服
        if cv2.waitKey(1000 // 25) & 0xff == ord("q"):
        #if cv2.waitKey(1000 // 25) & 0xff == ord("q"):
            break
    cv2.destroyAllWindows()


if __name__ == "__main__":
    face_rec()

结论

在电脑上运行的很流畅，在树莓派上运行的话因树莓派而异，能明显感觉到帧数下降，不过基本功能还是能完成的