文章目录

前言
人脸信息录入保存(动图演示)
import cv2 as cv
# 调用摄像头
cap = cv.VideoCapture(0)
num = 1
flag = 1 #判断是否有图像True
# 检测摄像头是否为开启状态
while(cap.isOpened()):
cap_flag,Vshow = cap.read() #得到每帧图像
cv.imshow('testface',Vshow) #显示图像
a = cv.waitKey(0)
if a ==ord('s'): #按下s保存
cv.imwrite('./saveface/number' +str(num)+ '.jpg',Vshow)
print('第'+str(num)+'张保存完毕!!')
num = num +1
elif a == ord(' '): #空格退出
break
# 释放摄像头
cap.release()
# 释放内存
cv.destroyAllWindows()
效果展示:
数据训练
1.导入模块
import os
import cv2
from PIL import Image
import numpy as np
2.定义一个人脸检测函数
def getImageAndLabels(path):
2.1 定义列表来存放图像id和图像数据
facesSamples=[]
ids=[]
imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
2.2 导入联级分类器
#导入分类器
face_detector = cv2.CascadeClassifier('D:/test001/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')
2.3 遍历列表中的图片并对其进行处理
for imagePath in imagePaths:
2.3.1 将图片灰度化,并将其转换为数组
PIL_img=Image.open(imagePath).convert('L')
#将图像转换为数组
img_numpy=np.array(PIL_img,'uint8')
2.3.2 获取人脸特征和id以及姓名
#获取图片人脸特征
faces = face_detector.detectMultiScale(img_numpy)
#获取每张图片的id和姓名
id = int(os.path.split(imagePath)[1].split('.')[0])
2.3.3 预防无效照片
for x,y,w,h in faces:
ids.append(id)
facesSamples.append(img_numpy[y:y+h,x:x+w])
2.3 打印id和图像数组数据
print('id:', id)
print('fs:', facesSamples)
return facesSamples,ids
3.调用函数并对人脸进行训练并将数据保存为yml文件
if __name__ == '__main__':
#图片路径
path='./data/'
#获取图像数组和id标签数组和姓名
faces,ids=getImageAndLabels(path)
#获取训练对象
recognizer=cv2.face.LBPHFaceRecognizer_create()
recognizer.train(faces,np.array(ids))
#保存文件
recognizer.write('./trainer.yml')
4.完整代码及运行结果
import os
import cv2
from PIL import Image
import numpy as np
def getImageAndLabels(path):
facesSamples=[]
ids=[]
imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
#导入分类器
face_detector = cv2.CascadeClassifier('D:/test001/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')
#遍历列表中的图片
for imagePath in imagePaths:
#打开图片,黑白化
PIL_img=Image.open(imagePath).convert('L')
#将图像转换为数组,以黑白深浅
# PIL_img = cv2.resize(PIL_img, dsize=(400, 400))
img_numpy=np.array(PIL_img,'uint8')
#获取图片人脸特征
faces = face_detector.detectMultiScale(img_numpy)
#获取每张图片的id和姓名
id = int(os.path.split(imagePath)[1].split('.')[0])
#预防无面容照片
for x,y,w,h in faces:
ids.append(id)
facesSamples.append(img_numpy[y:y+h,x:x+w])
#打印脸部特征和id
print('id:', id)
print('fs:', facesSamples)
# print('fs:', facesSamples[id])
return facesSamples,ids
if __name__ == '__main__':
#图片路径
path='./data/'
#获取图像数组和id标签数组和姓名
faces,ids=getImageAndLabels(path)
#获取训练对象
recognizer=cv2.face.LBPHFaceRecognizer_create()
recognizer.train(faces,np.array(ids))
#保存文件
recognizer.write('./trainer.yml')
运行结果:
人脸识别小案例
import cv2
import os
import urllib
import urllib.request
#加载训练数据集文件
recogizer=cv2.face.LBPHFaceRecognizer_create()
recogizer.read('./trainer.yml')
names=[]
warningtime = 0
def md5(str):
import hashlib
m = hashlib.md5()
m.update(str.encode("utf8"))
return m.hexdigest()
statusStr = {
'0': '短信发送成功',
'-1': '参数不全',
'-2': '服务器空间不支持,请确认支持curl或者fsocket,联系您的空间商解决或者更换空间',
'30': '密码错误',
'40': '账号不存在',
'41': '余额不足',
'42': '账户已过期',
'43': 'IP地址限制',
'50': '内容含有敏感词'
}
def warning():
smsapi = "http://api.smsbao.com/"
# 短信平台账号
user = '13******10'
# 短信平台密码
password = md5('*******')
# 要发送的短信内容
content = '【报警】\n原因:检测到未知人员\n地点:xxx'
# 要发送短信的手机号码
phone = '*******'
data = urllib.parse.urlencode({'u': user, 'p': password, 'm': phone, 'c': content})
send_url = smsapi + 'sms?' + data
response = urllib.request.urlopen(send_url)
the_page = response.read().decode('utf-8')
print(statusStr[the_page])
#准备识别的图片
def face_detect_demo(img):
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#转换为灰度
face_detector=cv2.CascadeClassifier('D:/test001/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')
face=face_detector.detectMultiScale(gray,1.1,5,cv2.CASCADE_SCALE_IMAGE,(100,100),(300,300))
#face=face_detector.detectMultiScale(gray)
for x,y,w,h in face:
cv2.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1)
# 人脸识别
ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
#print('标签id:',ids,'置信评分:', confidence)
if confidence > 80:
global warningtime
warningtime += 1
if warningtime > 100:
warning()
warningtime = 0
cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
else:
cv2.putText(img,str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
cv2.imshow('result',img)
#print('bug:',ids)
def name():
path = './data/'
#names = []
imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
for imagePath in imagePaths:
name = str(os.path.split(imagePath)[1].split('.',2)[1])
names.append(name)
cap=cv2.VideoCapture('1.mp4')
name()
while True:
flag,frame=cap.read()
if not flag:
break
face_detect_demo(frame)
if ord(' ') == cv2.waitKey(10):
break
cv2.destroyAllWindows()
cap.release()
运行结果(在没有对人脸进行数据训练之前):
在对人脸导入并进行数据训练之后会识别到姓名:
在对没有人脸数据训练的情况下显示密码错误: