本教程我将分享几个简单步骤解释如何使用OpenCV进行Python对象计数。

需要安装一些软件：

• Python 3
• OpennCV

1.了解Opencv从摄像头获得视频的Python脚本

import cv2, time
#1. Create an object.Zero for external camera
video=cv2. VideoCapture(0)
#1. a variable
a=0
while True:
\ta=a+1
\t#3. Create frame object
\tcheck, frame = video.read()
\tprint(check)
\tprint(frame) # Reprsenting image
\t#6. converting to grascale
\tgray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
\t#4. shadow the frame
\tcv2.imshow("Capturing", gray)
\t#5. for press any key to out (milisecond)
\t#cv2.waitKey(0)
\t#7. for playing
\tkey=cv2.waitKey(1)
\t
\tif key==ord('q'):
\t\tbreak
\t
print (a)
#2. Shutdown the camera
video.release() 
cv2.destroyAllWindows

2.加载视频

现在我们将开始逐步学习这个车辆计数教程。第一步是打开我们将在本教程中使用的视频录制。Python示例代码如下：

import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
while(cap.isOpened()):
 ret, frame = cap.read() #read a frame
 try:
 cv2.imshow('Frame',frame)
 except:
 #if there are no more frames to show...
 print('EOF')
 break
 #Abort and exit with 'Q' or ESC
 k = cv2.waitKey(30) & 0xff
 if k == 27:
 break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows

3. 在视频窗口中绘图

这部分非常简单，因为我们只在视频上显示文字或画线。

使用Python代码在视频文件中显示文本如下：

import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
w = cap.get(3) #get width
h = cap.get(4) #get height
mx = int(w/2)
my = int(h/2)
count = 0
while(cap.isOpened()):
 ret, frame = cap.read() #read a frame
 try:
 count = count + 1
 text = "Statistika UII " + str(count)
 cv2.putText(frame, text ,(mx,my),cv2.FONT_HERSHEY_SIMPLEX
 ,1,(255,255,255),1,cv2.LINE_AA)
 cv2.imshow('Frame',frame)
 except:
 #if there are no more frames to show...
 print('EOF')
 break
 #Abort and exit with 'Q' or ESC
 k = cv2.waitKey(30) & 0xff
 if k == 27:
 break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows

除了显示文字，我们还可以绘制线条，圆圈等。OpenCV有许多绘制几何形状的方法

import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
while(cap.isOpened()):
 ret, frame = cap.read() #read a frame
 try: 
 cv2.imshow('Frame',frame)
 frame2 = frame
 except:
 #if there are no more frames to show...
 print('EOF')
 break 

 line1 = np.array([[100,100],[300,100],[350,200]], np.int32).reshape((-1,1,2))
 line2 = np.array([[400,50],[450,300]], np.int32).reshape((-1,1,2))
 frame2 = cv2.polylines(frame2,[line1],False,(255,0,0),thickness=2)
 frame2 = cv2.polylines(frame2,[line2],False,(0,0,255),thickness=1)
 
 cv2.imshow('Frame 2',frame2)
 
 #Abort and exit with 'Q' or ESC
 k = cv2.waitKey(30) & 0xff
 if k == 27:
 break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows

4.背景分离

此方法通过区分背景和对象（前景）的移动来分离对象。该方法非常广泛地用于进入或离房间计数，交通信息系统中车辆统计，访客数量等。

import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
while(cap.isOpened()):
 ret, frame = cap.read() #read a frame
 
 fgmask = fgbg.apply(frame) #Use the substractor
 
 try: 
 cv2.imshow('Frame',frame)
 cv2.imshow('Background Substraction',fgmask)
 except:
 #if there are no more frames to show...
 print('EOF')
 break
 
 #Abort and exit with 'Q' or ESC
 k = cv2.waitKey(30) & 0xff
 if k == 27:
 break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows

在图像中，黑色的图像为背景，而白色的图像是检测的对象。

5.形态转换

图像处理中的形态学，即数学形态学(mathematical Morphology），是图像处理中应用最为广泛的技术之一，主要用于从图像中提取对表达和描绘区域形状有意义的图像分量，使后续的识别工作能够抓住目标对象最为本质〈最具区分能力－most discriminative）的形状特征，如边界和连通区域等。同时像细化、像素化和修剪毛刺等技术也常应用于图像的预处理和后处理中，成为图像增强技术的有力补充。

经常使用的形态学操作：包括腐蚀、膨胀， 以及开、闭运算。

膨胀： 输出像素的值是所有输入像素值中的最大值。在二值图像中，如果领域中有一个像素值为1，则输出像素值为1。如下图

腐蚀：输出像素的值是所有输入像素值中的最小值，在二值图像中，若果领域中有一个像素值为0，则输出像素值为0，看下图：

膨胀和腐蚀的Python实现如下：

import cv2
import numpy as np
img = cv2.imread("carcount.png")
ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
kernel = np.ones((3,3),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1)
dilation = cv2.dilate(img,kernel,iterations = 1)
cv2.imwrite("erode.png",erosion)
cv2.imwrite("dilate.png",dilation)

查看侵蚀和扩张的结果如下图：

开运算：先腐蚀再膨胀，可以去掉目标外的孤立点。目标外的孤立点是和目标像素值一样的点，而非背景像素点，即为1而非0（0表示选取的空洞或背景像素值）。使用腐蚀，背景扩展，该孤立点被腐蚀掉，但是腐蚀会导致目标区域缩小一圈，因此需要再进行膨胀操作，将目标区域扩展回原来大小。所以，要使用开运算去除目标外的孤立点。

闭运算：先膨胀再腐蚀，可以去掉目标内的孔。目标内的孔，属于周围都是值为1，内部空洞值为0.目的是去除周围都是1的像素中间的0值。闭运算首先进行膨胀操作，目标区域扩张一圈，将目标区域的0去除，但是目标区域同时也会向外扩张一圈，因此需要使用腐蚀操作，使得图像中的目标区域恢复到之前的大小。

代码实现如下：

import cv2
import numpy as np
img = cv2.imread("carcount.png")
ret,thresh1 = cv2.threshold(img,200,255,cv2.THRESH_BINARY)
kernel = np.ones((5,5),np.uint8)
opening = cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(thresh1, cv2.MORPH_CLOSE, kernel)
cv2.imwrite("carcount_closing.png",closing)
cv2.imwrite("carcount_opening.png",opening)

6.寻找轮廓

到目前为止，我们已经过滤了视频流文件，然后我们将检测移动对象上的轮廓。

import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
kernelOp = np.ones((3,3),np.uint8)
kernelCl = np.ones((11,11),np.uint8)
while(cap.isOpened()):
 ret, frame = cap.read() #read a frame
 fgmask = fgbg.apply(frame) #Use the substractor
 try:
 ret,imBin= cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY)
 #Opening (erode->dilate) 
 mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernelOp)
 #Closing (dilate -> erode) 
 mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kernelCl)
 except:
 #if there are no more frames to show...
 print('EOF')
 break
 _, contours0, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
 for cnt in contours0:
 cv2.drawContours(frame, cnt, -1, (0,255,0), 3, 8)
 cv2.imshow('Frame',frame)
 #Abort and exit with 'Q' or ESC
 k = cv2.waitKey(30) & 0xff
 if k == 27:
 break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows

7.定义对象

这是一个非常有趣的部分，我们将轮廓分类为车辆对象。此定义以小红点标记。Python实现如下：

import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
kernelOp = np.ones((3,3),np.uint8)
kernelCl = np.ones((11,11),np.uint8)
areaTH = 500
while(cap.isOpened()):
 ret, frame = cap.read() #read a frame 

 
 fgmask = fgbg.apply(frame) #Use the substractor
 try:
 ret,imBin= cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY)
 #Opening (erode->dilate) 
 mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernelOp)
 #Closing (dilate -> erode) 
 mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kernelCl)
 except:
 #if there are no more frames to show...
 print('EOF')
 break
 _, contours0, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
 for cnt in contours0:
 cv2.drawContours(frame, cnt, -1, (0,255,0), 3, 8)
 area = cv2.contourArea(cnt)
 print (area)
 if area > areaTH:
 #################
 # TRACKING #
 ################# 
 M = cv2.moments(cnt)
 cx = int(M['m10']/M['m00'])
 cy = int(M['m01']/M['m00'])
 x,y,w,h = cv2.boundingRect(cnt)
 cv2.circle(frame,(cx,cy), 5, (0,0,255), -1) 
 img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
 
 cv2.imshow('Frame',frame)
 
 #Abort and exit with 'Q' or ESC
 k = cv2.waitKey(30) & 0xff
 if k == 27:
 break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows

8.移动方向

您已经知道我们的视频上有什么对象，现在您想知道它们往哪里移动（如：向上/向下）。在第一帧中，您需要将检测到的ID对象保存初始位置。然后，在下一帧中，要继续跟踪对象，必须将帧中对象的轮廓与首次出现时的ID匹配，并保存该对象的坐标。然后，在对象跨越视频的边界（或一定量的限制）之后，您可以使用存储的位置来评估它是向上或是向下移动。

import numpy as np
import cv2
import Car
import time
cap = cv2.VideoCapture('peopleCounter.avi') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
kernelOp = np.ones((3,3),np.uint8)
kernelCl = np.ones((11,11),np.uint8)
#Variables
font = cv2.FONT_HERSHEY_SIMPLEX
cars = []
max_p_age = 5
pid = 1
areaTH = 500
while(cap.isOpened()):
 ret, frame = cap.read() #read a frame
 
 fgmask = fgbg.apply(frame) #Use the substractor
 try:
 ret,imBin= cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY)
 #Opening (erode->dilate) 
 mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernelOp)
 #Closing (dilate -> erode) 
 mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kernelCl)
 except:
 #if there are no more frames to show...
 print('EOF')
 break
 _, contours0, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
 for cnt in contours0:
 cv2.drawContours(frame, cnt, -1, (0,255,0), 3, 8)
 area = cv2.contourArea(cnt)
 if area > areaTH:
 #################
 # TRACKING #
 ################# 
 M = cv2.moments(cnt)
 cx = int(M['m10']/M['m00'])
 cy = int(M['m01']/M['m00'])
 x,y,w,h = cv2.boundingRect(cnt)
 
 new = True
 for i in cars:
 if abs(x-i.getX()) <= w and abs(y-i.getY()) <= h:
 # the object is close to one that was already detected before
 new = False
 i.updateCoords(cx,cy) #Update coordinates on the object and resets age
 break
 if new == True: 

 p = Car.MyCar(pid,cx,cy, max_p_age)
 cars.append(p)
 pid += 1 
 cv2.circle(frame,(cx,cy), 5, (0,0,255), -1)
 img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2) 
 cv2.drawContours(frame, cnt, -1, (0,255,0), 3)
 for i in cars:
 if len(i.getTracks()) >= 2:
 pts = np.array(i.getTracks(), np.int32)
 pts = pts.reshape((-1,1,2))
 frame = cv2.polylines(frame,[pts],False,i.getRGB())
 if i.getId() == 9:
 print (str(i.getX()), ',', str(i.getY()))
 cv2.putText(frame, str(i.getId()),(i.getX(),i.getY()),font,0.3,i.getRGB(),1,cv2.LINE_AA)
 
 
 cv2.imshow('Frame',frame)
 
 #Abort and exit with 'Q' or ESC
 k = cv2.waitKey(30) & 0xff
 if k == 27:
 break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows

9.计数

你之前的部分已经知道如何检测对象运动的方法。现在，我们必须看到这个列表并确定对象是否在我们的视频中向上或下降。要做到这一点，首先将创造两条线，这将显示什么时候来评估对象的方向(line_up，line_down)。而且还会有两行界限，告诉我们什么时候停止跟踪物体(up_limit，down_limit)。