meanshift图像分割示例

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#include 

using namespace cv;
using namespace std;

static void help(const char** argv)
{
    cout << "\nDemonstrate mean-shift based color segmentation in spatial pyramid.\n"
    << "Call:\n   " << argv[0] << " image\n"
    << "This program allows you to set the spatial and color radius\n"
    << "of the mean shift window as well as the number of pyramid reduction levels explored\n"
    << endl;
}

//This colors the segmentations
static void floodFillPostprocess( Mat& img, const Scalar& colorDiff=Scalar::all(1) )
{
    CV_Assert( !img.empty() );
    RNG rng = theRNG();
    Mat mask( img.rows+2, img.cols+2, CV_8UC1, Scalar::all(0) );
    for( int y = 0; y < img.rows; y++ )
    {
        for( int x = 0; x < img.cols; x++ )
        {
            if( mask.at(y+1, x+1) == 0 )
            {
                Scalar newVal( rng(256), rng(256), rng(256) );
                floodFill( img, mask, Point(x,y), newVal, 0, colorDiff, colorDiff );
            }
        }
    }
}

static string winName = "meanshift";
static int spatialRad, colorRad, maxPyrLevel;
static Mat img, res;

static void meanShiftSegmentation( int, void* )
{
    cout << "spatialRad=" << spatialRad << "; "
         << "colorRad=" << colorRad << "; "
         << "maxPyrLevel=" << maxPyrLevel << endl;
    pyrMeanShiftFiltering( img, res, spatialRad, colorRad, maxPyrLevel );
    floodFillPostprocess( res, Scalar::all(2) );
    imshow( winName, res );
}

int main(int argc, const char** argv)
{
	const char *fileName = "1.jpg";
    img = imread( fileName );
    if( img.empty() )
        return -1;

    spatialRad = 10;
    colorRad = 10;
    maxPyrLevel = 1;

    namedWindow( winName, WINDOW_AUTOSIZE );

    createTrackbar( "spatialRad", winName, &spatialRad, 80, meanShiftSegmentation );
    createTrackbar( "colorRad", winName, &colorRad, 60, meanShiftSegmentation );
    createTrackbar( "maxPyrLevel", winName, &maxPyrLevel, 5, meanShiftSegmentation );

    meanShiftSegmentation(0, 0);
    waitKey();
    return 0;
}