opencv人脸识别考勤 python_基于python+opencv的简易人脸识别打卡系统

importcv2importosimportnumpy as npfrom PIL importImageimportdatetimeimportcsvfrom time importsleep#调用笔记本内置摄像头,所以参数为0,如果有其他的摄像头可以调整参数为1,2

Path = r"C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml"face_detector=cv2.CascadeClassifier(Path)

names=[]

zh_name=[]

with open("maxmember.csv","r",encoding='UTF-8') as csv_file:

reader=csv.reader(csv_file)for item inreader:#print(item)

names.append(item[2])

zh_name.append(item[1])#print (zh_name)

defdata_collection():

cap= cv2.VideoCapture(0,cv2.CAP_DSHOW)#cv2.CAP_DSHOW是作为open调用的一部分传递标志,还有许多其它的参数,而这个CAP_DSHOW是微软特有的。

face_id = input('\n 请输入你的ID:')print('\n 数据初始化中,请直视摄像机录入数据....')

count=0whileTrue:#从摄像头读取图片

sucess, img =cap.read()#转为灰度图片

gray =cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#检测人脸

faces = face_detector.detectMultiScale(gray, 1.3, 5)#1.image表示的是要检测的输入图像# 2.objects表示检测到的人脸目标序列# 3.scaleFactor表示每次图像尺寸减小的比例

for (x, y, w, h) infaces:#画矩形

cv2.rectangle(img, (x, y), (x + w, y + w), (255, 0, 0))

count+= 1

#保存图像

cv2.imwrite("facedata/Member." + str(face_id) + '.' + str(count) + '.jpg', gray[y: y + h, x: x +w])

cv2.imshow('data collection', img)#保持画面的持续。

k = cv2.waitKey(1)if k == 27: #通过esc键退出摄像

break

elif count >= 200: #得到n个样本后退出摄像

breakcap.release()

cv2.destroyAllWindows()defface_training():#人脸数据路径

path = './facedata'recognizer=cv2.face.LBPHFaceRecognizer_create()#LBP是一种特征提取方式,能提取出图像的局部的纹理特征

defget_images_and_labels(path):

imagePaths= [os.path.join(path, f) for f in os.listdir(path)] #join函数将多个路径组合后返回

faceSamples =[]

ids=[]#遍历图片路径,导入图片和id,添加到list

for imagePath inimagePaths:

PIL_img= Image.open(imagePath).convert('L') #通过图片路径并将其转换为灰度图片。

img_numpy= np.array(PIL_img, 'uint8')

id= int(os.path.split(imagePath)[-1].split(".")[1])

faces=face_detector.detectMultiScale(img_numpy)for (x, y, w, h) infaces:

faceSamples.append(img_numpy[y:y+ h, x: x +w])

ids.append(id)returnfaceSamples, idsprint('数据训练中')

faces, ids=get_images_and_labels(path)

recognizer.train(faces, np.array(ids))

recognizer.write(r'.\trainer.yml')defface_ientification():

cap=cv2.VideoCapture(0)

recognizer=cv2.face.LBPHFaceRecognizer_create()

recognizer.read('./trainer.yml')

faceCascade=cv2.CascadeClassifier(Path)

font=cv2.FONT_HERSHEY_SIMPLEX

idnum=0globalnamess

cam=cv2.VideoCapture(0)#设置大小

minW = 0.1 * cam.get(3)

minH= 0.1 * cam.get(4)whileTrue:

ret, img=cam.read()#图像灰度处理

gray =cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#将人脸用vector保存各个人脸的坐标、大小(用矩形表示)

faces =faceCascade.detectMultiScale(

gray,

scaleFactor=1.2,#表示在前后两次相继的扫描中,搜索窗口的比例系数

minNeighbors=5,#表示构成检测目标的相邻矩形的最小个数(默认为3个)

minSize=(int(minW), int(minH))#minSize和maxSize用来限制得到的目标区域的范围

)for (x, y, w, h) infaces:

cv2.rectangle(img, (x, y), (x+ w, y + h), (0, 255, 0), 2)#返回侦测到的人脸的id和近似度conf(数字越大和训练数据越不像)

idnum, confidence = recognizer.predict(gray[y:y + h, x:x +w])if confidence < 100:

namess=names[idnum]

confidence= "{0}%".format(round(100 -confidence))else:

namess= "unknown"confidence= "{0}%".format(round(100 -confidence))

cv2.putText(img, str(namess), (x+ 5, y - 5), font, 1, (0, 0, 255), 1)

cv2.putText(img, str(confidence), (x+ 5, y + h - 5), font, 1, (0, 0, 0), 1)#输出置信度

cv2.imshow(u'Identification punch', img)

k= cv2.waitKey(5)if k == 13:

theTime=datetime.datetime.now()#print(zh_name[idnum])

strings =[str(zh_name[idnum]),str(theTime)]print(strings)

with open("log.csv", "a",newline="") as csvFile:

writer=csv.writer(csvFile)

writer.writerow([str(zh_name[idnum]),str(theTime)])elif k==27:

cap.release()

cv2.destroyAllWindows()break

whileTrue:

a= int(input("输入1,录入脸部,输入2进行识别打卡:"))if a==1:

data_collection()elif a==2:

face_ientification()elif a==3:

face_training()

你可能感兴趣的:(opencv人脸识别考勤,python)