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()