人脸检测项目（opencv+python）
调用opencv训练好的分类器（cv2.CascadeClassifier）实现人脸检测，
调用detectMultiScale()函数检测，调整函数的参数可以使检测结果更加精确，
把检测到的人脸等用矩形（或者圆形等其他图形）画出来。
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
4. minNeighbors表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大小都可以检测到人脸),
5.minSize为目标的最小尺寸
6.minSize为目标的最大尺寸
##图片检测
import cv2
def face_detected(im):
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
face_detect = cv2.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
face = face_detect.detectMultiScale(im_gray, 1.01, 5)
for x, y, w, h in face:
cv2.rectangle(im, (x, y), (x+w, y+h), color=(0, 0, 255))
cv2.imshow('result', im)
img = cv2.imread('E:/face_project/dzw.jpg')
img = cv2.resize(img, (500, 400))
face_detected(img)
while True:
if ord('q') == cv2.waitKey(0):
break
cv2.destroyWindow()##视频检测
import cv2
def face_detected(im):
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
face_detect = cv2.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
face = face_detect.detectMultiScale(im_gray, 1.1)
for x, y, w, h in face:
cv2.rectangle(im, (x, y), (x+w, y+h), color=(0, 0, 255))
cv2.imshow('result', im)
cap = cv2.VideoCapture(0)
while True:
flag, frame = cap.read()
if not flag:
break
face_detected(frame)
cv2.waitKey(1)
# if ord('q') == cv2.waitKey(1):
# break
cv2.destroyWindow('result')
cap.release()## 人脸识别的数据训练
import os
import cv2
import numpy as np
from PIL import Image
def getImageAndLabels(path):
faceSamples = []
ids = []
imagesPath = [os.path.join(path, f) for f in os.listdir(path)]
faceDetector = cv2.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
for imPath in imagesPath:
#用Image打开图片，模式”L“
print(imPath)
img = Image.open(imPath).convert('L')
img = img.resize((500, 400))
print(img.size)
#将图像转换成数组
imgNumpy = np.array(img, 'uint8')
#获取人脸特征
faces = faceDetector.detectMultiScale(imgNumpy)
id = int(os.path.split(imPath)[1].split('.')[0])
for x, y, w, h in faces:
ids.append(id)
faceSamples.append(imgNumpy[y:y+h, x:x+w])
print(ids)
return faceSamples, ids
if __name__ == '__main__':
path = "./data"
# 获取训练数据
faces, ids = getImageAndLabels(path)
# 加载识别器
# recognizer = cv2.face.LBPHFaceRecognizer_create()
#训练
recognizer = cv2.face.LBPHFaceRecognizer_create()
# recognizer.train(faces,names)#np.array(ids)
recognizer.train(faces, np.array(ids))
# 保存文件
recognizer.write('trainer/trainer.yml')## 人脸识别
# -*- coding: utf-8 -*-
# @Author : 董张伟
# @Time : 2021/10/4 20:17
# @Function:
import cv2
import numpy as np
import os
# coding=utf-8
import urllib
import urllib.request
import hashlib
#加载训练数据集文件
recogizer=cv2.face.LBPHFaceRecognizer_create()
recogizer.read('trainer/trainer.yml')
names=[]
warningtime = 0
def md5(str):
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:/opencv/opencv/sources/data/haarcascades/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)
cv2.putText(img, 'id:'+str(ids)+"confidence:"+str(confidence), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 0, 255), 1)
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(0)
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()
