光流跟踪法的框架图
光流跟踪法的流程图
#include "opencv2/opencv.hpp"
#include <iostream>
#include <stdio.h>
#include <stdarg.h>
#include <opencv/cv.h>
#include <opencv/cvaux.h>
#include <opencv/cxcore.h>
#include <opencv/highgui.h>
const int MAX_CORNERS = 500;
int main(int argc, char** argv) {
// Initialize, load two images from the file system, and
// allocate the images and other structures we will need for
// results.
//
IplImage* imgA = cvLoadImage("OpticalFlow0.jpg",CV_LOAD_IMAGE_GRAYSCALE);
IplImage* imgB = cvLoadImage("OpticalFlow1.jpg",CV_LOAD_IMAGE_GRAYSCALE);
CvSize img_sz = cvGetSize( imgA );
int win_size = 10;
IplImage* imgC = cvLoadImage("OpticalFlow1.jpg",CV_LOAD_IMAGE_UNCHANGED);
// The first thing we need to do is get the features
// we want to track.
//
IplImage* eig_image = cvCreateImage( img_sz, IPL_DEPTH_32F, 1 );
IplImage* tmp_image = cvCreateImage( img_sz, IPL_DEPTH_32F, 1 );
int corner_count = MAX_CORNERS;
CvPoint2D32f* cornersA = new CvPoint2D32f[ MAX_CORNERS ];
/*****************************************************************************************
先计算二阶导数,在计算特征,返回马努跟踪的定义的一系列点
****************************************************************************************/
cvGoodFeaturesToTrack(
imgA,
eig_image,
tmp_image,
cornersA,
&corner_count,
0.01,
5.0,
0,
3,
0,
0.04
);
//发现亚像素精度的角点位置
cvFindCornerSubPix(
imgA,
cornersA,
corner_count,
cvSize(win_size,win_size),
cvSize(-1,-1),
cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,20,0.03)
);
// Call the Lucas Kanade algorithm
//
char features_found[ MAX_CORNERS ];
float feature_errors[ MAX_CORNERS ];
CvSize pyr_sz = cvSize( imgA->width+8, imgB->height/3 );
IplImage* pyrA = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 );
IplImage* pyrB = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 );
CvPoint2D32f* cornersB = new CvPoint2D32f[ MAX_CORNERS ];
/****************************************************************************
CalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr,
CvArr* prev_pyr, CvArr* curr_pyr,
const CvPoint2D32f* prev_features,
CvPoint2D32f* curr_features,
int count,
CvSize win_size,
int level,
char* status,
float* track_error,
CvTermCriteria criteria,
int flags );
参数1:初始图像
参数2:最终图像
函数根据这两幅图像来进行光流跟踪
参数3,4:申请放入两幅图像金字塔的缓存
参数5:存放用于寻找运动的点的数组
参数6:参数5中的点的新位置
参数7:寻找的运动的点的数目
参数8:定义了计算局部连续运动的窗口尺寸
参数9:status中的每个元素被置1(对应点在第二幅图像中被发现),否则置为0
参数10:表示被跟踪的点的原始图像小区域与此点在第二幅图像的小区域间的差的数组,可以删除那些局部外观小区域随点的运动变化剧烈的点
参数11:迭代终止条件 ,是哟欧诺个cvTermCriteria()函数来生成该结构体
参数12:
****************************************************************************/
cvCalcOpticalFlowPyrLK(
imgA,
imgB,
pyrA,
pyrB,
cornersA,
cornersB,
corner_count,
cvSize( win_size,win_size ),
5,
features_found,
feature_errors,
cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, .3 ),
0
);
// Now make some image of what we are looking at:
//
for( int i=0; i<corner_count; i++ ) {
if( features_found[i]==0|| feature_errors[i]>550 ) {
// printf("Error is %f/n",feature_errors[i]);
continue;
}
// printf("Got it/n");
CvPoint p0 = cvPoint(
cvRound( cornersA[i].x ),
cvRound( cornersA[i].y )
);
CvPoint p1 = cvPoint(
cvRound( cornersB[i].x ),
cvRound( cornersB[i].y )
);
cvLine( imgC, p0, p1, CV_RGB(255,0,0),2 );
}
cvNamedWindow("ImageA",0);
cvNamedWindow("ImageB",0);
cvNamedWindow("LKpyr_OpticalFlow",0);
cvShowImage("ImageA",imgA);
cvShowImage("ImageB",imgB);
cvShowImage("LKpyr_OpticalFlow",imgC);
cvWaitKey(0);
return 0;
}#include "o
