程序示例精选
Python OpenCV识别行人入口进出人数统计
如需安装运行环境或远程调试，见文章底部微信名片，由专业技术人员远程协助！
前言
这篇博客针对《Python OpenCV识别行人入口进出人数统计》编写代码，功能包括了入口行人识别，人数统计。代码整洁，规则，易读。应用推荐首选。
文章目录
一、所需工具软件
二、使用步骤
1. 引入库
2.识别特征图像
3. 运行结果
三、在线协助
一、所需工具软件
1. Python3.6以上
2. Pycharm代码编辑器
3. OpenCV, Numpy库
二、使用步骤
1.引入库
代码如下（示例）：
#导入需要的包import numpy as npimport cv2import Personimport time2.识别特征图像
代码如下（示例）：
video=cv2.VideoCapture("counting_test.avi")#输出视频fourcc = cv2.VideoWriter_fourcc(*'XVID')#输出视频制编码out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))w = video.get(3)h = video.get(4)print("视频的原宽度为:")print(int(w))print("视频的原高度为:")area = h*wprint(int(h))areaTHreshold = area/500print('Area Threshold', areaTHreshold)#计算画线的位置line_up = int(1*(h/4))line_down = int(2.7*(h/4))up_limit = int(.5*(h/4))down_limit = int(3.2*(h/4))print ("Red line y:",str(line_down))print ("Green line y:", str(line_up))pt5 = [0, up_limit]pt6 = [w, up_limit]pts_L3 = np.array([pt5,pt6], np.int32)pts_L3 = pts_L3.reshape((-1,1,2))pt7 = [0, down_limit]pt8 = [w, down_limit]pts_L4 = np.array([pt7,pt8], np.int32)pts_L4 = pts_L4.reshape((-1,1,2))#背景剔除# fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True)fgbg = cv2.createBackgroundSubtractorKNN()#用于后面形态学处理的核kernel = np.ones((3,3),np.uint8)kerne2 = np.ones((5,5),np.uint8)kerne3 = np.ones((11,11),np.uint8)while(video.isOpened()): ret,frame=video.read() if frame is None: break #应用背景剔除 gray = cv2.GaussianBlur(frame, (31, 31), 0) #cv2.imshow('GaussianBlur', frame) #cv2.imshow('GaussianBlur', gray) fgmask = fgbg.apply(gray) fgmask2 = fgbg.apply(gray) try: #*************************************************************** #二值化 ret,imBin= cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY) ret,imBin2 = cv2.threshold(fgmask2,200,255,cv2.THRESH_BINARY) #cv2.imshow('imBin', imBin2) #开操作(腐蚀->膨胀)消除噪声 mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kerne3) mask2 = cv2.morphologyEx(imBin2, cv2.MORPH_OPEN, kerne3) #闭操作(膨胀->腐蚀)将区域连接起来 mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kerne3) mask2 = cv2.morphologyEx(mask2, cv2.MORPH_CLOSE, kerne3) #cv2.imshow('closing_mask', mask2) #************************************************************* except: print('EOF') print ('IN:',cnt_in+count_in) print ('OUT:',cnt_in+count_in) break #找到边界 _mask2,contours0, hierarchy = cv2.findContours(mask2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours0: rect = cv2.boundingRect(cnt)#矩形边框 area=cv2.contourArea(cnt)#每个矩形框的面积 if area>areaTHreshold: #************************************************ #moments里包含了许多有用的信息 M=cv2.moments(cnt) cx=int(M['m10']/M['m00'])#计算重心 cy=int(M['m01']/M['m00']) x, y, w, h = cv2.boundingRect(cnt)#x,y为矩形框左上方点的坐标，w为宽，h为高 new=True if cy in range(up_limit,down_limit): for i in persons: if abs(cx-i.getX())<=w and abs(cy-i.getY())80: count_in=w/40 print("In:执行了/60") time.strftime("%c")) elif i.going_DOWN(line_down,line_up)==True: # cv2.circle(frame, (cx, cy), 5, (0, 0, 255), -1) # img = cv2.rectangle(frame, (x, y), (x + w, y + h), line_down_color, 2)time.strftime("%c")) break #状态为1表明 if i.getState() == '1': if i.getDir() == 'down' and i.getY() > down_limit: i.setDone() elif i.getDir() == 'up' and i.getY() < up_limit: i.setDone() if i.timedOut(): # 已经记过数且超出边界将其移出persons队列 index = persons.index(i) persons.pop(index) del i # 清楚内存中的第i个人 if new == True: p = Person.MyPerson(pid, cx, cy, max_p_age) persons.append(p) pid += 1print("进入的总人数为:")print(cnt_in)print("出去的总人数为:")print(cnt_out)video.release();cv2.destroyAllWindows()3.运行结果如下：
三、在线协助：
如需安装运行环境或远程调试，见文章底部微信名片，由专业技术人员远程协助！