openCV实战项目--人脸考勤

人脸任务在计算机视觉领域中十分重要，本项目主要使用了两类技术：人脸检测+人脸识别。

代码分为两部分内容：人脸注册 和 人脸识别

• 人脸注册：将人脸特征存储进数据库，这里用feature.csv代替
• 人脸识别：将人脸特征与CSV文件中人脸特征进行比较，如果成功匹配则写入考勤文件attendance.csv

文章前半部分为一步步实现流程介绍，最后会有整理过后的完整项目代码。

一、项目实现

A. 注册：

导入相关包

import cv2
import numpy as np
import dlib
import time
import csv
# from argparse import ArgumentParser
from PIL import Image, ImageDraw, ImageFont

设计注册功能

注册过程我们需要完成的事：

• 打开摄像头获取画面图片
• 在图片中检测并获取人脸位置
• 根据人脸位置获取68个关键点
• 根据68个关键点生成特征描述符
• 保存
• (优化)展示界面，加入注册时成功提示等

1、基本步骤

我们首先进行前三步：

# 检测人脸，获取68个关键点，获取特征描述符
def faceRegister(faceId=1, userName='default', interval=3, faceCount=3, resize_w=700, resize_h=400):
    '''
    faceId:人脸ID
    userName: 人脸姓名
    faceCount: 采集该人脸图片的数量
    interval: 采集间隔
    '''

    cap = cv2.VideoCapture(0)
    # 人脸检测模型
    hog_face_detector = dlib.get_frontal_face_detector()
    # 关键点 检测模型
    shape_detector = dlib.shape_predictor('./weights/shape_predictor_68_face_landmarks.dat')
    # resnet模型
    face_descriptor_extractor = dlib.face_recognition_model_v1('./weights/dlib_face_recognition_resnet_model_v1.dat')
    
    while True:
        ret, frame = cap.read()

        # 镜像
        frame = cv2.flip(frame,1)

        # 转为灰度图
        frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
        
        # 检测人脸
        detections = hog_face_detector(frame,1)

        for face in detections:
            # 人脸框坐标 左上和右下
            l, t, r, b = face.left(), face.top(), face.right(), face.bottom()

            # 获取68个关键点
            points = shape_detector(frame,face)
            
            # 绘制关键点
            for point in points.parts():
                cv2.circle(frame,(point.x,point.y),2,(0,255,0),1)

            # 绘制矩形框
            cv2.rectangle(frame,(l,t),(r,b),(0,255,0),2)
                   
        cv2.imshow("face",frame)
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break
                 
    cap.release()
    cv2.destroyAllWindows
                    
            
faceRegister()      

此时一张帅脸如下：

2、描述符的采集

之后，我们根据参数，即faceCount 和 Interval 进行描述符的生成和采集。

(这里我默认是faceCount=3，Interval=3，即每3秒采集一次，共3次）

def faceRegister(faceId=1, userName='default', interval=3, faceCount=3, resize_w=700, resize_h=400):
    '''
    faceId:人脸ID
    userName: 人脸姓名
    faceCount: 采集该人脸图片的数量
    interval: 采集间隔
    '''

    cap = cv2.VideoCapture(0)
    # 人脸检测模型
    hog_face_detector = dlib.get_frontal_face_detector()
    # 关键点 检测模型
    shape_detector = dlib.shape_predictor('./weights/shape_predictor_68_face_landmarks.dat')
    # resnet模型
    face_descriptor_extractor = dlib.face_recognition_model_v1('./weights/dlib_face_recognition_resnet_model_v1.dat')
    
    
    # 开始时间
    start_time = time.time()
    # 执行次数
    collect_times = 0
    

    while True:
        ret, frame = cap.read()
        
        # 镜像
        frame = cv2.flip(frame,1)

        # 转为灰度图
        frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
        
        # 检测人脸
        detections = hog_face_detector(frame,1)

        for face in detections:
            # 人脸框坐标 左上和右下
            l, t, r, b = face.left(), face.top(), face.right(), face.bottom()

            # 获取68个关键点
            points = shape_detector(frame,face)

            # 绘制人脸关键点
            for point in points.parts():
                cv2.circle(frame, (point.x, point.y), 2, (0, 255, 0), 1)
            # 绘制矩形框
            cv2.rectangle(frame, (l, t), (r, b), (0, 255, 0), 2)
            
            # 采集：
            if collect_times < faceCount:
                # 获取当前时间
                now = time.time()
                # 时间限制
                if now - start_time > interval:
                    # 获取特征描述符
                    face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame,points)
                    # dlib格式转为数组
                    face_descriptor = [f for f in face_descriptor]

                    collect_times += 1
                    start_time = now
                    print("成功采集{}次".format(collect_times))
                else:
                    # 时间间隔不到interval
                    print("等待进行下一次采集")
                    pass
            else:
                # 已经成功采集完3次了
                print("采集完毕")
                cap.release()
                cv2.destroyAllWindows()
                return
              
        cv2.imshow("face",frame)
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break
                  
    cap.release()
    cv2.destroyAllWindows()
                    
            
faceRegister()  

等待进行下一次采集
...
成功采集1次
等待进行下一次采集
...
成功采集2次
等待进行下一次采集
...
成功采集3次
采集完毕

3、完整的注册

最后就是写入csv文件

这里加入了注册成功等的提示，且把一些变量放到了全局，因为后面人脸识别打卡时也会用到。

# 加载人脸检测器
hog_face_detector = dlib.get_frontal_face_detector()
cnn_detector = dlib.cnn_face_detection_model_v1('./weights/mmod_human_face_detector.dat')
haar_face_detector = cv2.CascadeClassifier('./weights/haarcascade_frontalface_default.xml')

# 加载关键点检测器
points_detector = dlib.shape_predictor('./weights/shape_predictor_68_face_landmarks.dat')
# 加载resnet模型
face_descriptor_extractor = dlib.face_recognition_model_v1('./weights/dlib_face_recognition_resnet_model_v1.dat')

# 绘制中文
def cv2AddChineseText(img, text, position, textColor=(0, 255, 0), textSize=30):
    if (isinstance(img, np.ndarray)):  # 判断是否OpenCV图片类型
        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    # 创建一个可以在给定图像上绘图的对象
    draw = ImageDraw.Draw(img)
    # 字体的格式
    fontStyle = ImageFont.truetype(
        "./fonts/songti.ttc", textSize, encoding="utf-8")
    # 绘制文本
    draw.text(position, text, textColor, font=fontStyle)
    # 转换回OpenCV格式
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)

# 绘制左侧信息
def drawLeftInfo(frame, fpsText, mode="Reg", detector='haar', person=1, count=1):
    # 帧率
    cv2.putText(frame, "FPS:  " + str(round(fpsText, 2)), (30, 50), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)

    # 模式：注册、识别
    cv2.putText(frame, "Mode:  " + str(mode), (30, 80), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)

    if mode == 'Recog':
        # 检测器
        cv2.putText(frame, "Detector:  " + detector, (30, 110), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)

        # 人数
        cv2.putText(frame, "Person:  " + str(person), (30, 140), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)

        # 总人数
        cv2.putText(frame, "Count:  " + str(count), (30, 170), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)

# 注册人脸
def faceRegiser(faceId=1, userName='default', interval=3, faceCount=3, resize_w=700, resize_h=400):
    # 计数
    count = 0
    # 开始注册时间
    startTime = time.time()

    # 视频时间
    frameTime = startTime

    # 控制显示打卡成功的时长
    show_time = (startTime - 10)

    # 打开文件
    f = open('./data/feature.csv', 'a', newline='')
    csv_writer = csv.writer(f)

    cap = cv2.VideoCapture(0)

    while True:
        ret, frame = cap.read()
        frame = cv2.resize(frame, (resize_w, resize_h))
        frame = cv2.flip(frame, 1)

        # 检测
        face_detetion = hog_face_detector(frame, 1)

        for face in face_detetion:
            # 识别68个关键点
            points = points_detector(frame, face)
            # 绘制人脸关键点
            for point in points.parts():
                cv2.circle(frame, (point.x, point.y), 2, (0, 255, 0), 1)
            # 绘制框框
            l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
            cv2.rectangle(frame, (l, t), (r, b), (0, 255, 0), 2)

            now = time.time()

            if (now - show_time) < 0.5:
                frame = cv2AddChineseText(frame,
                                          "注册成功 {count}/{faceCount}".format(count=(count + 1), faceCount=faceCount),
                                          (l, b + 30), textColor=(255, 0, 255), textSize=30)

            # 检查次数

            if count < faceCount:

                # 检查时间
                if now - startTime > interval:
                    # 特征描述符
                    face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame, points)

                    face_descriptor = [f for f in face_descriptor]

                    # 描述符增加进data文件
                    line = [faceId, userName, face_descriptor]
                    # 写入
                    csv_writer.writerow(line)

                    # 保存照片样本

                    print('人脸注册成功 {count}/{faceCount}，faceId:{faceId}，userName:{userName}'.format(count=(count + 1),
                                                                                                  faceCount=faceCount,
                                                                                                  faceId=faceId,
                                                                                                  userName=userName))

                    frame = cv2AddChineseText(frame,
                                              "注册成功 {count}/{faceCount}".format(count=(count + 1), faceCount=faceCount),
                                              (l, b + 30), textColor=(255, 0, 255), textSize=30)
                    show_time = time.time()

                    # 时间重置
                    startTime = now
                    # 次数加一
                    count += 1

            else:
                print('人脸注册完毕')
                f.close()
                cap.release()
                cv2.destroyAllWindows()
                return

        now = time.time()
        fpsText = 1 / (now - frameTime)
        frameTime = now
        # 绘制
        drawLeftInfo(frame, fpsText, 'Register')

        cv2.imshow('Face Attendance Demo: Register', frame)
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break
    f.close()
    cap.release()
    cv2.destroyAllWindows()

此时执行：

faceRegiser(3,"用户B")

人脸注册成功 1/3，faceId:3，userName:用户B
人脸注册成功 2/3，faceId:3，userName:用户B
人脸注册成功 3/3，faceId:3，userName:用户B
人脸注册完毕

其features文件：

B. 识别、打卡

识别步骤如下：

• 打开摄像头获取画面
• 根据画面中的图片获取里面的人脸特征描述符
• 根据特征描述符将其与feature.csv文件里特征做距离判断
• 获取ID、NAME
• 考勤记录写入attendance.csv里

这里与上面流程相似，不过是加了一个对比功能，距离小于阈值，则表示匹配成功。就加快速度不一步步来了，代码如下：

# 刷新右侧考勤信息
def updateRightInfo(frame, face_info_list, face_img_list):
    # 重新绘制逻辑：从列表中每隔3个取一批显示，新增人脸放在最前面

    # 如果有更新，重新绘制

    # 如果没有，定时往后移动

    left_x = 30
    left_y = 20
    resize_w = 80

    offset_y = 120
    index = 0

    frame_h = frame.shape[0]
    frame_w = frame.shape[1]

    for face in face_info_list[:3]:
        name = face[0]
        time = face[1]
        face_img = face_img_list[index]

        # print(face_img.shape)

        face_img = cv2.resize(face_img, (resize_w, resize_w))

        offset_y_value = offset_y * index
        frame[(left_y + offset_y_value):(left_y + resize_w + offset_y_value), -(left_x + resize_w):-left_x] = face_img

        cv2.putText(frame, name, ((frame_w - (left_x + resize_w)), (left_y + resize_w) + 15 + offset_y_value),
                    cv2.FONT_ITALIC, 0.5, (0, 255, 0), 1)
        cv2.putText(frame, time, ((frame_w - (left_x + resize_w)), (left_y + resize_w) + 30 + offset_y_value),
                    cv2.FONT_ITALIC, 0.5, (0, 255, 0), 1)

        index += 1

    return frame

# 返回DLIB格式的face
def getDlibRect(detector='hog', face=None):
    l, t, r, b = None, None, None, None

    if detector == 'hog':
        l, t, r, b = face.left(), face.top(), face.right(), face.bottom()

    if detector == 'cnn':
        l = face.rect.left()
        t = face.rect.top()
        r = face.rect.right()
        b = face.rect.bottom()

    if detector == 'haar':
        l = face[0]
        t = face[1]
        r = face[0] + face[2]
        b = face[1] + face[3]

    nonnegative = lambda x: x if x >= 0 else 0
    return map(nonnegative, (l, t, r, b))

# 获取CSV中信息
def getFeatList():
    print('加载注册的人脸特征')

    feature_list = None
    label_list = []
    name_list = []

    # 加载保存的特征样本
    with open('./data/feature.csv', 'r') as f:
        csv_reader = csv.reader(f)
        for line in csv_reader:
            # 重新加载数据
            faceId = line[0]
            userName = line[1]
            face_descriptor = eval(line[2])

            label_list.append(faceId)
            name_list.append(userName)

            # 转为numpy格式
            face_descriptor = np.asarray(face_descriptor, dtype=np.float64)
            # 转为二维矩阵，拼接
            face_descriptor = np.reshape(face_descriptor, (1, -1))
            # 初始化
            if feature_list is None:
                feature_list = face_descriptor
            else:
                # 拼接
                feature_list = np.concatenate((feature_list, face_descriptor), axis=0)
    print("特征加载完毕")
    return feature_list, label_list, name_list

# 人脸识别
def faceRecognize(detector='haar', threshold=0.5, write_video=False, resize_w=700, resize_h=400):
    # 视频时间
    frameTime = time.time()

    # 加载特征
    feature_list, label_list, name_list = getFeatList()

    face_time_dict = {}
    # 保存name,time人脸信息
    face_info_list = []
    # numpy格式人脸图像数据
    face_img_list = []

    # 侦测人数
    person_detect = 0

    # 统计人脸数
    face_count = 0

    # 控制显示打卡成功的时长
    show_time = (frameTime - 10)

    # 考勤记录
    f = open('./data/attendance.csv', 'a')
    csv_writer = csv.writer(f)

    cap = cv2.VideoCapture(0)
    # resize_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))//2
    # resize_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) //2

    videoWriter = cv2.VideoWriter('./record_video/out' + str(time.time()) + '.mp4', cv2.VideoWriter_fourcc(*'MP4V'), 15,
                                  (resize_w, resize_h))

    while True:
        ret, frame = cap.read()
        frame = cv2.resize(frame, (resize_w, resize_h))
        frame = cv2.flip(frame, 1)

        # 切换人脸检测器
        if detector == 'hog':
            face_detetion = hog_face_detector(frame, 1)
        if detector == 'cnn':
            face_detetion = cnn_detector(frame, 1)
        if detector == 'haar':
            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            face_detetion = haar_face_detector.detectMultiScale(frame_gray, minNeighbors=7, minSize=(100, 100))

        person_detect = len(face_detetion)

        for face in face_detetion:

            l, t, r, b = getDlibRect(detector, face)

            face = dlib.rectangle(l, t, r, b)

            # 识别68个关键点
            points = points_detector(frame, face)

            cv2.rectangle(frame, (l, t), (r, b), (0, 255, 0), 2)

            # 人脸区域
            face_crop = frame[t:b, l:r]

            # 特征
            face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame, points)
            face_descriptor = [f for f in face_descriptor]
            face_descriptor = np.asarray(face_descriptor, dtype=np.float64)

            # 计算距离
            distance = np.linalg.norm((face_descriptor - feature_list), axis=1)
            # 最小距离索引
            min_index = np.argmin(distance)
            # 最小距离
            min_distance = distance[min_index]

            predict_name = "Not recog"

            if min_distance < threshold:
                # 距离小于阈值，表示匹配
                predict_id = label_list[min_index]
                predict_name = name_list[min_index]

                # 判断是否新增记录：如果一个人距上次检测时间>3秒，或者换了一个人，将这条记录插入
                need_insert = False
                now = time.time()
                if predict_name in face_time_dict:
                    if (now - face_time_dict[predict_name]) > 3:
                        # 刷新时间
                        face_time_dict[predict_name] = now
                        need_insert = True
                    else:
                        # 还是上次人脸
                        need_insert = False

                else:
                    # 新增数据记录
                    face_time_dict[predict_name] = now
                    need_insert = True

                if (now - show_time) < 1:
                    frame = cv2AddChineseText(frame, "打卡成功", (l, b + 30), textColor=(0, 255, 0), textSize=40)

                if need_insert:
                    # 连续显示打卡成功1s
                    frame = cv2AddChineseText(frame, "打卡成功", (l, b + 30), textColor=(0, 255, 0), textSize=40)
                    show_time = time.time()

                    time_local = time.localtime(face_time_dict[predict_name])
                    # 转换成新的时间格式(2016-05-05 20:28:54)
                    face_time = time.strftime("%H:%M:%S", time_local)
                    face_time_full = time.strftime("%Y-%m-%d %H:%M:%S", time_local)

                    # 开始位置增加

                    face_info_list.insert(0, [predict_name, face_time])
                    face_img_list.insert(0, face_crop)

                    # 写入考勤表
                    line = [predict_id, predict_name, min_distance, face_time_full]
                    csv_writer.writerow(line)

                    face_count += 1

            # 绘制人脸点
            cv2.putText(frame, predict_name + " " + str(round(min_distance, 2)), (l, b + 30), cv2.FONT_ITALIC, 0.8,
                        (0, 255, 0), 2)

            # 处理下一张脸

        now = time.time()
        fpsText = 1 / (now - frameTime)
        frameTime = now
        # 绘制
        drawLeftInfo(frame, fpsText, 'Recog', detector=detector, person=person_detect, count=face_count)

        # 舍弃face_img_list、face_info_list后部分，节约内存
        if len(face_info_list) > 10:
            face_info_list = face_info_list[:9]
            face_img_list = face_img_list[:9]

        frame = updateRightInfo(frame, face_info_list, face_img_list)

        if write_video:
            videoWriter.write(frame)

        cv2.imshow('Face Attendance Demo: Recognition', frame)
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break

    f.close()
    videoWriter.release()
    cap.release()
    cv2.destroyAllWindows()

然后效果就和我们宿舍楼下差不多了~

我年轻的时候，我大概比现在帅个几百倍吧，哎。

二、总代码

上文其实把登录和注册最后一部分代码放在一起就是了，这里就不再复制粘贴了，相关权重文件下载链接：opencv/data at master · opencv/opencv · GitHub

懒得下载或者懒得找也可以私信我发你，看见或有时间回。

当然本项目还有很多需要优化的地方，比如设置用户不能重复、考勤打卡每天只能一次、把csv改为链接成数据库等等，后续代码优化完成后就可以部署然后和室友**了。