cvGoodFeaturesToTrack的解析与应用

原文地址：/article/7723394.html

[cpp]
copy

void cvGoodFeaturesToTrack(
const CvArr* image //(8,1) or (32,1) (8-bit ,single-channel) (floating-point 32-bit,single-channel)
CvArr* eigImage,//(32,1)
CvArr* tempImage //(32,1)
CvPoint2D32f* corners
int* cornerCount
double qualityLevel
double minDistance
const CvArr* mask=NULL
int blockSize=3
int useHarris=0
double k=0.04 );

[cpp]
copy

corners //是数组,检测到的角点的位置坐标就存在这里

[cpp]
copy

int* cornerCount //表示最多可以检测到的角点（如果有那么多的话），实际上检测到的点没有那么多，所以调用函数以后，<pre name="code" class="cpp">cornerCount的值就变成实际检测到的角点的数目。因此，在对cornerCount赋初始值的时候，可以给大一点，本文赋初始值为1000。</pre>

[cpp]
copy

double qualityLevel //理解还不是很好，只知道一般取值是0.10 或者0.01 ，why？谁可以告诉我答案

[cpp]
copy

minDistance Limit, specifying the minimum possible distance between the returned corners;
Euclidian distance is used //角点与角点之间的距离不小于minDistance个像素。

[cpp]
copy

mask Region of interest. The function selects points either in the specified region or in the whole
image if the mask is NULL //如果mask不为空，则在mask指定的区域内寻找角点；mask为NULL则在整张图像中寻找角点。

[cpp]
copy

blockSize Size of the averaging block, passed to the underlying cvCornerMinEigenVal or cvCornerHarris used by the function //默认就行了

[cpp]
copy

useHarris If nonzero, Harris operator ( cvCornerHarris) is used instead of default cvCorner-
MinEigenVal //非零，就用cvCornerHarris而不是使用默认的cvCornerMinEigenVal。这是函数内部的处理过程。

[cpp]copy

k Free parameter of Harris detector; used only if (useHarris! = 0)

demo代码如下，不解析，很简单

[cpp]copy

//http://blog.csdn.net/moc062066
//chinamochen@gmail.com
//2011年7月26日10:41:54
//

#include <cv.h>
#include <highgui.h>
#include <stdio.h>

#pragma comment(lib, "opencv_core220d.lib")
#pragma comment(lib, "opencv_highgui220d.lib")
#pragma comment(lib, "opencv_imgproc220d.lib")

int main(int argc, char *argv[])
{

// Load a color image, and convert it into grayscale
const char* filename = "D:\\mochen_WIN32\\opencv\\CH10\\good_fearture_to_track_demo\\one_way_train_0001.jpg" ;
const char* windowname = "http://blog.csdn.net/moc062066" ;

IplImage* img = cvLoadImage(filename,CV_LOAD_IMAGE_COLOR);
assert( NULL != img ) ;
IplImage* img_gray = cvCreateImage(cvGetSize(img), 8, 1);
cvCvtColor(img, img_gray, CV_BGR2GRAY);

// Create temporary images required by cvGoodFeaturesToTrack
IplImage* img_temp = cvCreateImage(cvGetSize(img), 32, 1);
IplImage* img_eigen = cvCreateImage(cvGetSize(img), 32, 1);

// Create the array to store the points detected( <= 1000 )
int count = 1000;
CvPoint2D32f* corners = new CvPoint2D32f[count];

// Find corners
cvGoodFeaturesToTrack(img_gray, img_eigen, img_temp, corners, &count, 0.1, 10);

// Mark these corners on the original image
for(int i=0;i<count;i++)
{
cvLine(img, cvPoint(corners[i].x, corners[i].y), cvPoint(corners[i].x, corners[i].y), CV_RGB(255,0,0), 5);
}

// Display it
cvNamedWindow(windowname);
cvShowImage(windowname, img);

// Print the number of corners
printf("Detected Points : %d\n", count);

cvWaitKey(0);

return 0 ;
}

结果：



实验所用图像：