77 lines
2.3 KiB
C++
77 lines
2.3 KiB
C++
#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/core/core.hpp"
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#include <iostream>
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using namespace cv;
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using namespace std;
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// static void help()
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// {
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// cout << "\nThis program demonstrates kmeans clustering.\n"
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// "It generates an image with random points, then assigns a random number of cluster\n"
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// "centers and uses kmeans to move those cluster centers to their representitive location\n"
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// "Call\n"
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// "./kmeans\n" << endl;
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// }
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int main( int /*argc*/, char** /*argv*/ )
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{
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const int MAX_CLUSTERS = 5;
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Scalar colorTab[] =
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{
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Scalar(0, 0, 255),
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Scalar(0,255,0),
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Scalar(255,100,100),
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Scalar(255,0,255),
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Scalar(0,255,255)
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};
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Mat img(500, 500, CV_8UC3);
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RNG rng(12345);
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for(;;)
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{
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int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
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int i, sampleCount = rng.uniform(1, 1001);
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Mat points(sampleCount, 2, CV_32F), labels;
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clusterCount = MIN(clusterCount, sampleCount);
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Mat centers;
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/* generate random sample from multigaussian distribution */
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for( k = 0; k < clusterCount; k++ )
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{
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Point center;
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center.x = rng.uniform(0, img.cols);
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center.y = rng.uniform(0, img.rows);
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Mat pointChunk = points.rowRange(k*sampleCount/clusterCount,
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k == clusterCount - 1 ? sampleCount :
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(k+1)*sampleCount/clusterCount);
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rng.fill(pointChunk, CV_RAND_NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
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}
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randShuffle(points, 1, &rng);
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kmeans(points, clusterCount, labels,
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TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0),
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3, KMEANS_PP_CENTERS, centers);
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img = Scalar::all(0);
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for( i = 0; i < sampleCount; i++ )
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{
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int clusterIdx = labels.at<int>(i);
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Point ipt = points.at<Point2f>(i);
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circle( img, ipt, 2, colorTab[clusterIdx], CV_FILLED, CV_AA );
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}
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imshow("clusters", img);
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char key = (char)waitKey();
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if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'
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break;
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}
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return 0;
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}
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