以前的

1.检测多个人脸

#导入cv模块
import cv2 as cv
#检测函数
def face_detect_demo():
    gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier('D:/Junior second/shixun/OPENCV(WIN)/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gary)
    for x,y,w,h in face:
        cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
    cv.imshow('result',img)

#读取图像
img = cv.imread('face2.jpg')
#检测函数
face_detect_demo()
#等待
while True:
    if ord('q') == cv.waitKey(0):
        break
#释放内存
cv.destroyAllWindows()

效果：识别图片中的所有人脸

2.视频检测

#导入cv模块
import cv2 as cv
#检测函数
def face_detect_demo(img):
    gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier('D:/Junior second/shixun/OPENCV(WIN)/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gary)
    for x,y,w,h in face:
        cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
    cv.imshow('result',img)

#读取摄像头
#cap = cv2.VideoCapture(0)
#读取视频
cap = cv.VideoCapture('3.mp4')
#循环
while cap.isOpened():
    ret, frame = cap.read()
    # 调整窗口大小
    cv.namedWindow("result", 0)  # 0可调大小，注意：窗口名必须imshow里面的一窗口名一直
    cv.resizeWindow("result", 800, 450)  # 设置长和宽
    cv.imshow('result', frame)
    face_detect_demo(frame)
    if cv.waitKey(1) & 0xFF == ord('q'):
        break
#释放内存
cv.destroyAllWindows()
#释放摄像头
cap.release()

效果：播放视频时人脸识别

3. 人脸录入&数据训练

import os
import cv2
import sys
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)]
    #检测人脸
    face_detector = cv2.CascadeClassifier('D:/Junior second/shixun/OPENCV(WIN)/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
    #打印数组imagePaths
    print('数据排列：',imagePaths)
    #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))
        #人脸数组列表
        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('fs:', facesSamples)
        print('id:', id)
        #print('fs:', facesSamples[id])
    print('fs:', facesSamples)
    #print('脸部例子：',facesSamples[0])
    #print('身份信息：',ids[0])
    return facesSamples,ids

if __name__ == '__main__':
    #图片路径
    path='./data/jm/'
    #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,names)#np.array(ids)
    recognizer.train(faces,np.array(ids))
    #保存文件
    recognizer.write('trainer/trainer.yml')
    #save_to_file('names.txt',names)

获取人脸的灰度值（颜色越浅，值越小）

得到trainer.yml训练结果，用于后续识别人和id的对应关系。

注意：卸载原来的opencv-python，安装opencv-contrib-python

4.视频人脸识别

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):
    import hashlib
    m = hashlib.md5()
    m.update(str.encode("utf8"))
    return m.hexdigest()


    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:/Junior second/shixun/OPENCV(WIN)//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)
        if confidence > 80:  #值越大，越不可信
            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/jm/'
    #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()
#print(names)

效果：根据人脸识别出人物名字id

对于trainer.yml没有存储的人脸会显示unknown

5.网页视频&RTMP协议

RTMP(Real-Time Messaging Protocol实时消息传送协议)的缩写，它是Adobe Systems公司为Flash播放器和服务器之间音频、视频和数据传输开发的协议。这是一个标准的，未加密的实时消息传递协议，默认端口是1935，如果未指定连接端口，那么flash客户端会尝试连接其他端口，其尝试连接顺序按照下列顺序依次连接：1935、443、80(RTMP), 80(RTMPT)。

电视节目rtmp推流地址

import cv2

class CaptureVideo(object):
	def net_video(self):
		# 获取网络视频流
		cam = cv2.VideoCapture("rtmp://58.200.131.2:1935/livetv/cctv5")
		while cam.isOpened():
			sucess, frame = cam.read()
			cv2.imshow("Network", frame)
			cv2.waitKey(1)
if __name__ == "__main__":
	capture_video = CaptureVideo()
	capture_video.net_video()

可用于后续工程项目。