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 cv2import numpy as npimport dlibimport timeimport csv# from argparse import ArgumentParserfrom 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facedef 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。