我正在寻找实现人类运动跟踪的方法，也在multiple moving object detection中讨论了使用差分分析和lukas kanade光学方法从多个视频帧中提取的内容。
我找到了科学论文，发现我们必须使用连接的组件filtering connected components进行连续运动跟踪，但是我不知道如何进行此过程。我所需要的只是skeletonization轨迹和人体步态运动的坐标。
我正在使用Opencv和C++，但是在我的情况下，opencv中用于对象检测的文档不足。我来自医学背景，需要这是儿科医生项目的一部分。
我找到了这段代码motion detection并试图执行它(尚不知道它是否检测到并跟踪运动)。但是，它返回了这些错误，我感到困惑，因为这些错误是微不足道的，其他注释提到它们能够运行此代码。但是我无法缓解这些错误，也无法理解它们发生的原因。我正在使用OpenCv2.3，以下是错误
1> c:\ opencv2.3 \ opencv \ build \ include \ opencv2 \ core \ core_c.h(87):请参见'cvReleaseImage'
1> c:\ opencv2.3 \ opencv \ build \ include \ opencv2 \ core \ core_c.h(87):请参见'cvReleaseImage'
错误C2065:“movingAverage”:未声明的标识符
-错误C4430:缺少类型说明符-假定为int。注意:C++不支持default-int
-错误C2365:'cvReleaseImage':重新定义；以前的定义是“功能”
-1> c:\ opencv2.3 \ opencv \ build \ include \ opencv2 \ core \ core_c.h(87):请参见'cvReleaseImage'的声明
-错误C4430:缺少类型说明符-假定为int。注意:C++不支持default-int
-错误C2365:'cvDestroyWindow':重新定义；以前的定义是'function'
-1>没有可以进行此转换的上下文
-错误C2065:“输入”:未声明的标识符
-错误C4430:缺少类型说明符-假定为int。注意:C++不支持default-int
-1> c:\ opencv2.3 \ opencv \ build \ include \ opencv2 \ highgui \ highgui_c.h(311):请参见'cvReleaseCapture'的声明
-错误C2065:“outputMovie”:未声明的标识符
-错误C2365:“cvReleaseVideoWriter”:重新定义；以前的定义是“功能”
-1 c:\ opencv2.3 \ opencv \ build \ include \ opencv2 \ highgui \ highgui_c.h(436):请参见'cvReleaseVideoWriter'的声明
-错误C2059:语法错误:“返回”
========== Build:0成功，1失败，0最新，0跳过==========
CODE
// MotionDetection.cpp : Defines the entry point for the console application.
//
// Contourold.cpp : Defines the entry point for the console application.
//
#include "stdafx.h"
#include "iostream"
#include "stdlib.h"
// OpenCV includes.
#include "cv.h"
#include "highgui.h"
#pragma comment(lib,"cv.lib")
#pragma comment(lib,"cxcore.lib")
#pragma comment(lib,"highgui.lib")
using namespace std;
int main(int argc, char* argv[])
{
//Create a new window.
cvNamedWindow("My Window", CV_WINDOW_AUTOSIZE);
//Create a new movie capture object.
CvCapture *input;
//Assign the movie to capture.
//inputMovie = cvCaptureFromAVI("vinoth.avi");
char *fileName = "E:\\highway.avi";
//char *fileName = "D:\\Profile\\AVI\\cardriving.wmv";
input = cvCaptureFromFile(fileName);
//if (!input)
//cout << "Can't open file" << fileName < ;
//Size of the image.
CvSize imgSize;
IplImage* frame = cvQueryFrame(input);
CvSize imgSize = cvGetSize(frame);
//Images to use in the program.
IplImage* greyImage = cvCreateImage( imgSize, IPL_DEPTH_8U, 1);
IplImage* colourImage;
IplImage* movingAverage = cvCreateImage( imgSize, IPL_DEPTH_32F, 3);
IplImage* difference;
IplImage* temp;
IplImage* motionHistory = cvCreateImage( imgSize, IPL_DEPTH_8U, 3);
//Rectangle to use to put around the people.
CvRect bndRect = cvRect(0,0,0,0);
//Points for the edges of the rectangle.
CvPoint pt1, pt2;
//Create a font object.
CvFont font;
//Create video to output to.
char* outFilename = argc==2 ? argv[1] : "E:\\outputMovie.avi";
CvVideoWriter* outputMovie = cvCreateVideoWriter(outFilename,
CV_FOURCC('F', 'L', 'V', 'I'), 29.97, cvSize(720, 576));
//Capture the movie frame by frame.
int prevX = 0;
int numPeople = 0;
//Buffer to save the number of people when converting the integer
//to a string.
char wow[65];
//The midpoint X position of the rectangle surrounding the moving objects.
int avgX = 0;
//Indicates whether this is the first time in the loop of frames.
bool first = true;
//Indicates the contour which was closest to the left boundary before the object
//entered the region between the buildings.
int closestToLeft = 0;
//Same as above, but for the right.
int closestToRight = 320;
//Keep processing frames...
for(;;)
{
//Get a frame from the input video.
colourImage = cvQueryFrame(input);
//If there are no more frames, jump out of the for.
if( !colourImage )
{
break;
}
//If this is the first time, initialize the images.
if(first)
{
difference = cvCloneImage(colourImage);
temp = cvCloneImage(colourImage);
cvConvertScale(colourImage, movingAverage, 1.0, 0.0);
first = false;
}
//else, make a running average of the motion.
else
{
cvRunningAvg(colourImage, movingAverage, 0.020, NULL);
}
//Convert the scale of the moving average.
cvConvertScale(movingAverage,temp, 1.0, 0.0);
//Minus the current frame from the moving average.
cvAbsDiff(colourImage,temp,difference);
//Convert the image to grayscale.
cvCvtColor(difference,greyImage,CV_RGB2GRAY);
//Convert the image to black and white.
cvThreshold(greyImage, greyImage, 70, 255, CV_THRESH_BINARY);
//Dilate and erode to get people blobs
cvDilate(greyImage, greyImage, 0, 18);
cvErode(greyImage, greyImage, 0, 10);
//Find the contours of the moving images in the frame.
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contour = 0;
cvFindContours( greyImage, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
//Process each moving contour in the current frame...
for( ; contour != 0; contour = contour->h_next )
{
//Get a bounding rectangle around the moving object.
bndRect = cvBoundingRect(contour, 0);
pt1.x = bndRect.x;
pt1.y = bndRect.y;
pt2.x = bndRect.x + bndRect.width;
pt2.y = bndRect.y + bndRect.height;
//Get an average X position of the moving contour.
avgX = (pt1.x + pt2.x) / 2;
//If the contour is within the edges of the building...
if(avgX > 90 && avgX < 250)
{
//If the the previous contour was within 2 of the left boundary...
if(closestToLeft >= 88 && closestToLeft <= 90)
{
//If the current X position is greater than the previous...
if(avgX > prevX)
{
//Increase the number of people.
numPeople++;
//Reset the closest object to the left indicator.
closestToLeft = 0;
}
}
//else if the previous contour was within 2 of the right boundary...
else if(closestToRight >= 250 && closestToRight <= 252)
{
//If the current X position is less than the previous...
if(avgX < prevX)
{
//Increase the number of people.
numPeople++;
//Reset the closest object to the right counter.
closestToRight = 320;
}
}
//Draw the bounding rectangle around the moving object.
cvRectangle(colourImage, pt1, pt2, CV_RGB(255,0,0), 1);
}
//If the current object is closer to the left boundary but still not across
//it, then change the closest to the left counter to this value.
if(avgX > closestToLeft && avgX <= 90)
{
closestToLeft = avgX;
}
//If the current object is closer to the right boundary but still not across
//it, then change the closest to the right counter to this value.
if(avgX < closestToRight && avgX >= 250)
{
closestToRight = avgX;
}
//Save the current X value to use as the previous in the next iteration.
prevX = avgX;
}
//Save the current X value to use as the previous in the next iteration.
prevX = avgX;
}
//Write the number of people counted at the top of the output frame.
cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, 0.8, 0.8, 0, 2);
cvPutText(colourImage, _itoa(numPeople, wow, 10), cvPoint(60, 200), &font, cvScalar(0, 0, 300));
//Show the frame.
cvShowImage("My Window", colourImage);
//Wait for the user to see it.
cvWaitKey(10);
//Write the frame to the output movie.
cvWriteFrame(outputMovie, colourImage);
}
// Destroy the image, movies, and window.
cvReleaseImage(&temp);
cvReleaseImage(&difference);
cvReleaseImage(&greyImage);
cvReleaseImage(&movingAverage);
cvDestroyWindow("My Window");
cvReleaseCapture(&input);
cvReleaseVideoWriter(&outputMovie);
return 0;
}
最佳答案
1.)我想您直接从网站复制了代码(如果我错了，请更正我。)。但是，由于您使用的是OpenCV 2.3，因此大多数API都位于不同的模块中。以下包括您应该拥有的...
#include <opencv/core/core.hpp>
#include <opencv/highgui/highgui.hpp>
#include <opencv/imgproc/imgproc.hpp>
以及相应的库。
2.)要过滤连接的组件，可以使用cvblob library。我认为OpenCV提供的旧Blob库是使用VC 6构建的，因此可能是所需的
stdafx.h3.)缓慢浏览代码以获取某些语法和重新声明错误。
编辑代码
#include <iostream>
#include "stdlib.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "cv.h"
#include "highgui.h"
using namespace std;
using namespace cv;
int main(int argc, char* argv[])
{
cvNamedWindow("My Window", CV_WINDOW_AUTOSIZE);
CvCapture *input;
//char *fileName = "E:\\highway.avi";
input = cvCaptureFromCAM(0);
//input = cvCaptureFromFile(fileName);
CvSize imgSize;
IplImage* frame = cvQueryFrame(input);
imgSize = cvGetSize(frame);
IplImage* greyImage = cvCreateImage( imgSize, IPL_DEPTH_8U, 1);
IplImage* colourImage;
IplImage* movingAverage = cvCreateImage( imgSize, IPL_DEPTH_32F, 3);
IplImage* difference;
IplImage* temp;
IplImage* motionHistory = cvCreateImage( imgSize, IPL_DEPTH_8U, 3);
CvRect bndRect = cvRect(0,0,0,0);
CvPoint pt1, pt2;
CvFont font;
char* outFilename = argc==2 ? argv[1] : "E:\\outputMovie.avi";
CvVideoWriter* outputMovie = cvCreateVideoWriter(outFilename,
CV_FOURCC('F', 'L', 'V', 'I'), 29.97, cvSize(720, 576));
int prevX = 0;
int numPeople = 0;
char wow[65];
int avgX = 0;
bool first = true;
int closestToLeft = 0;
int closestToRight = 320;
for(;;)
{
colourImage = cvQueryFrame(input);
if( !colourImage )
{
break;
}
if(first)
{
difference = cvCloneImage(colourImage);
temp = cvCloneImage(colourImage);
cvConvertScale(colourImage, movingAverage, 1.0, 0.0);
first = false;
}
else
{
cvRunningAvg(colourImage, movingAverage, 0.020, NULL);
}
cvConvertScale(movingAverage,temp, 1.0, 0.0);
cvAbsDiff(colourImage,temp,difference);
cvCvtColor(difference,greyImage,CV_RGB2GRAY);
cvThreshold(greyImage, greyImage, 70, 255, CV_THRESH_BINARY);
cvDilate(greyImage, greyImage, 0, 18);
cvErode(greyImage, greyImage, 0, 10);
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contour = 0;
cvFindContours( greyImage, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
for( ; contour != 0; contour = contour->h_next )
{
bndRect = cvBoundingRect(contour, 0);
pt1.x = bndRect.x;
pt1.y = bndRect.y;
pt2.x = bndRect.x + bndRect.width;
pt2.y = bndRect.y + bndRect.height;
avgX = (pt1.x + pt2.x) / 2;
if(avgX > 90 && avgX < 250)
{
if(closestToLeft >= 88 && closestToLeft <= 90)
{
if(avgX > prevX)
{
numPeople++;
closestToLeft = 0;
}
}
else if(closestToRight >= 250 && closestToRight <= 252)
{
if(avgX < prevX)
{
numPeople++;
closestToRight = 320;
}
}
cvRectangle(colourImage, pt1, pt2, CV_RGB(255,0,0), 1);
}
if(avgX > closestToLeft && avgX <= 90)
{
closestToLeft = avgX;
}
if(avgX < closestToRight && avgX >= 250)
{
closestToRight = avgX;
}
prevX = avgX;
prevX = avgX;
}
cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, 0.8, 0.8, 0, 2);
cvPutText(colourImage, _itoa(numPeople, wow, 10), cvPoint(60, 200), &font, cvScalar(0, 0, 300));
cvShowImage("My Window", colourImage);
cvWaitKey(10);
cvWriteFrame(outputMovie, colourImage);
}
cvReleaseImage(&temp);
cvReleaseImage(&difference);
cvReleaseImage(&greyImage);
cvReleaseImage(&movingAverage);
cvDestroyWindow("My Window");
cvReleaseCapture(&input);
cvReleaseVideoWriter(&outputMovie);
return 0;
}
它至少可以正确编译...它有一些运行时错误...我现在没有调试器来跟踪它...尝试它...我也在尝试..
关于c++ - 用于运动检测的opencv代码中的错误，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/14309111/