苏州外贸网站建设制作方案网站改版业务

文章内容如下：

1）项目简介

2）开发环境和使用的技术知识

3）功能实现

4）项目源码

一。项目简介

       此项目是基于HOG和Dlib开发的一套实时无感考勤系统。首先是待考勤人员的个人信息录入。然后在过道或者入口处装置人脸考勤机，当有人经过时，摄像头抓取人脸并上传服务器，对抓取到的人脸进行人脸识别，判断此人身份，若此人在信息比对库中且第一次考勤，则考勤成功，并显示考勤人员姓名与比对距离，如非第一次考勤，并在一定时间隔外则更新考勤 。

二。开发环境和使用的技术知识

开发环境：Python，Opencv

技术知识：HOG+Dlib技术、Opencv视频读取-缩放-翻转-写入技术

三。功能实现

1. 人脸注册：将人脸特征存进feaure.csv

2. 人脸识别：将检测的人脸特征与feaure.csv人脸特征作比较，如果比中把考勤记录写入attendance.csv

1. 人脸注册：将人脸特征存进feaure.csv

①调取视频流，能显示人脸画面

cap = cv2.VideoCapture(0)"""调取摄像头"""while True:ret,frame = cap.read()frame =  cv2.flip(frame,1)"""y轴镜像"""cv2.imshow('Face attendance',frame)"""展示每一帧图片"""if cv2.waitKey(10) & 0xFF == ord('q'):break
cap.release()
cv2.destroyAllWindows()

  ② 步骤如下：

第1步：启动HOG人脸检测器，读取人脸上下左右坐标，绘制人脸方框；

hog_face_detector = dlib.get_frontal_face_detector()"""启动hog人脸检测器"""
detections = hog_face_detector(frame,1)"""获取人脸参数,缩放因子为1"""
for face in detections:l,t,r,b = face.left(),face.top(),face.right(),face.bottom()"""获取人脸左，上，右，下的坐标"""
cv2.rectangle(frame,(l,t),(r,b),(0,255,0),2)"""绘制人脸框"""

第2步： 启动人脸关键点检测器获取人脸68个关键点，再转为特征描述符；

shape_detector = dlib.shape_predictor('./1/shape_predictor_68_face_landmarks.dat')"""启动人脸关键点检测器"""
detections = hog_face_detector(frame,1)"""获取人脸参数,缩放因子为1""" 
for face in detections:points = shape_detector(frame,face)"""获取人脸68关键点"""
for point in points.parts():cv2.circle(frame,(point.x,point.y),2,(0,255,0),-10)"""绘制68关键点"""
face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame,points)"""获取(转为)特征描述符"""
face_descriptor = [f for f in face_descriptor]"""为方便转换成列表存进feaure.csv"""

  ①②组合在一起复合代码如下：

def faceRegister(label_id=1,name='Jie Wang',count=3,interval=3):
#1)-检测人脸#准备工作hog_face_detector = dlib.get_frontal_face_detector()#①启动hog人脸检测器shape_detector = dlib.shape_predictor('./1/shape_predictor_68_face_landmarks.dat')#①启动人脸关键点检测器face_descriptor_extractor = dlib.face_recognition_model_v1('./1/dlib_face_recognition_resnet_model_v1.dat')#①启动特征描述符检测器cap = cv2.VideoCapture(0)#②调取摄像头start_time = time.time()#开始时间，是为了interval设置的collect_count = 0#初始采集次数f = open('./2/feature.csv','a',newline="")#采集到的特征描述符放在这个文件里csv_writer = csv.writer(f)#采集3次特征  while True:ret,frame = cap.read()frame =  cv2.flip(frame,1)#y轴镜像detections = hog_face_detector(frame,1)#获取人脸参数,缩放因子为1for face in detections:l,t,r,b = face.left(),face.top(),face.right(),face.bottom()#获取人脸左，上，右，下的坐标points = shape_detector(frame,face)#获取人脸68关键点for point in points.parts():cv2.circle(frame,(point.x,point.y),2,(0,255,0),-10)#绘制68关键点cv2.rectangle(frame,(l,t),(r,b),(0,255,0),2)#绘制人脸框if collect_count < count:now = time.time()if now - start_time > interval:face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame,points)#获取特征描述符face_descriptor = [f for f in face_descriptor]#转换成列表方便存进feaure.csvline = [label_id,name,face_descriptor]csv_writer.writerow(line)#写入csv_writercollect_count +=1start_time = nowprint("采集次数：{collect_count}".format(collect_count = collect_count))copy_collect_count = collect_count
#                       print(face_descriptor)else:if 3 == copy_collect_count:print("采集完毕")copy_collect_count = 0break#展示   cv2.imshow('Face attendance',frame)#展示每一帧图片if cv2.waitKey(10) & 0xFF == ord('q'):breakf.close()cap.release()cv2.destroyAllWindows()faceRegister()

2. 人脸识别：将检测的人脸特征与feaure.csv人脸特征作比较，如果比中把考勤记录写入attendance.csv

①从磁盘中调取注册时的人脸特征描述符，解析它

def getFeatureList():label_list = []name_list = []featuree_list = None"""后面用的np.concatenate()方法决定了此处不能是空列表，否则无法拼接"""with open('./2/feature.csv','r') as f:csv_reader = csv.reader(f)for line in csv_reader:label_id = line[0]name = line[1]face_descriptor = eval(line[2])"""从csv文件拿出来的列表是字符串，需要eval处理一下转为list"""face_descriptor = np.asarray(face_descriptor,dtype=np.float64)face_descriptor = np.reshape(face_descriptor,(1,-1))"""转成1xn格式，期望就是这样"""label_list.append(label_id)"""label_list拼接过程"""name_list.append(name)"""name_list拼接过程"""if featuree_list is None:"""featuree_list拼接过程"""featuree_list = face_descriptorelse:featuree_list = np.concatenate((featuree_list,face_descriptor),axis=0)
#             print(name)return  label_list,name_list,featuree_listlabel_list,name_list,featuree_list = getFeatureList()"""读取人脸注册时的特征"""

②特征描述符比对，计算与库的距离

distance = np.linalg.norm((face_descriptor-featuree_list),axis=1)"""范数求差"""
min_index = np.argmin(distance)"""获取最短距离索引"""
min_distance = np.argmin(distance)"""获取最短距离"""
if min_distance < threshold:"""识别到打印信息"""predict_id = label_list[min_distance]predict_name = name_list[min_distance]

③依据条件确认是否记录考勤

need_insert = False"""用于后面判断是否变化了考勤，是则写入到attendance.csv文件"""
if predict_name in recog_record:"""第一次检测到考勤则记录人脸信息，记录的是名字与考勤时间组成的键值对，如这次检测与上次记录差3s，则再考勤一次。上述都不是则跳过"""if time.time()-recog_record[predict_name]>3:need_insert = Truerecog_record[predict_name] = time.time()print(predict_name)else:print("无需重复签到!")need_insert = Falsepass
else:need_insert = Truerecog_record[predict_name] = time.time()print(predict_name)

④将考勤记录写入文件

if need_insert:"""考勤较上次变化了则记录"""time_local = time.localtime(recog_record[predict_name])"""时间戳"""time_str = time.strftime("%Y-%m-%d %H:%M:%S",time_local)"""化时间戳为年月日"""line = [predict_id,predict_name,min_distance,time_str]csv_writer.writerow(line)print("{name} {time}考勤记录已成功更新入文件！".format(name=predict_name,time=time_str))

①-④组合在一起复合代码如下：

def faceRecognizer(threshold = 0.5):#1)实时获取视频流中人脸特征描述符；#1准备工作label_list,name_list,featuree_list = getFeatureList()#读取人脸注册时的特征hog_face_detector = dlib.get_frontal_face_detector()#①启动hog人脸检测器shape_detector = dlib.shape_predictor('./1/shape_predictor_68_face_landmarks.dat')#①启动人脸关键点检测器face_descriptor_extractor = dlib.face_recognition_model_v1('./1/dlib_face_recognition_resnet_model_v1.dat')#①启动特征描述符检测器cap = cv2.VideoCapture(0)#②调取摄像头recog_record = {}#为存考勤信息做准备f = open('./2/attendance.csv','a',newline="")#为文件性质记录考勤数据做准备csv_writer = csv.writer(f)fps_time = time.time()#画面显示fps#2获取人脸识别中采集到的特征while True:ret,frame = cap.read()frame =  cv2.flip(frame,1)#y轴镜像detections = hog_face_detector(frame,1)#获取人脸参数,缩放因子为1for face in detections:l,t,r,b = face.left(),face.top(),face.right(),face.bottom()#获取人脸左，上，右，下的坐标points = shape_detector(frame,face)#获取人脸68关键点cv2.rectangle(frame,(l,t),(r,b),(0,255,0),2)#绘制人脸框face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame,points)#获取特征描述符face_descriptor = [f for f in face_descriptor]#转换成列表方便存进feaure.csv  #3计算与库的距离distance = np.linalg.norm((face_descriptor-featuree_list),axis=1)#范数求差min_index = np.argmin(distance)#获取最短距离索引min_distance = np.argmin(distance)#获取最短距离if min_distance < threshold:#识别到打印信息predict_id = label_list[min_distance]predict_name = name_list[min_distance]#6给人脸框添加内容cv2.putText(frame,predict_name+" "+str(round(min_distance,2)),(l,b+30),cv2.FONT_HERSHEY_COMPLEX_SMALL,1,(0,255,0),1)#4是否记录考勤  need_insert = False#用于后面判断是否变化了考勤，是则写入到attendance.csv文件if predict_name in recog_record:#第一次检测到考勤则记录人脸信息，记录的是名字与考勤时间组成的键值对，如这次检测与上次记录差3s，则再考勤一次。上述都不是则跳过if time.time()-recog_record[predict_name]>3:need_insert = Truerecog_record[predict_name] = time.time()print(predict_name)else:print("无需重复签到!")need_insert = Falsepasselse:need_insert = Truerecog_record[predict_name] = time.time()print(predict_name)if need_insert:#考勤较上次变化了则记录time_local = time.localtime(recog_record[predict_name])#时间戳time_str = time.strftime("%Y-%m-%d %H:%M:%S",time_local)#化时间戳为年月日line = [predict_id,predict_name,min_distance,time_str]csv_writer.writerow(line)print("{name} {time}考勤记录已成功更新入文件！".format(name=predict_name,time=time_str))else:print("未识别到") #5增加FPSnow = time.time()fps = 1/(now - fps_time)fps_time = nowcv2.putText(frame,"FPS:"+str(round(fps,2)),(20,40),cv2.FONT_HERSHEY_COMPLEX_SMALL,1,(0,255,0),1)cv2.imshow('Face attendance',frame)#展示每一帧图片if cv2.waitKey(10) & 0xFF == ord('q'):breakf.close()cap.release()cv2.destroyAllWindows()   #读取人脸注册时的特征
def getFeatureList():label_list = []name_list = []featuree_list = None#后面用的np.concatenate()方法决定了此处不能是空列表，否则无法拼接with open('./2/feature.csv','r') as f:csv_reader = csv.reader(f)for line in csv_reader:label_id = line[0]name = line[1]face_descriptor = eval(line[2])#从csv文件拿出来的列表是字符串，需要eval处理一下转为listface_descriptor = np.asarray(face_descriptor,dtype=np.float64)face_descriptor = np.reshape(face_descriptor,(1,-1))#转成1xn格式，期望就是这样label_list.append(label_id)#label_list拼接过程name_list.append(name)#name_list拼接过程if featuree_list is None:#featuree_list拼接过程featuree_list = face_descriptorelse:featuree_list = np.concatenate((featuree_list,face_descriptor),axis=0)
#             print(name)return  label_list,name_list,featuree_listlabel_list,name_list,featuree_list = getFeatureList()
faceRecognizer(threshold = 10)

四。项目源码

        源码链接：点击这里