OpenCV： Kmeans的使用一维和二维点集

OpenCVKmeans算法默认使用了Kmeans++选取种子点

//效果：根据半径聚类，并不一定能得到好的结果。

float CBlotGlint::ClusterByR(  )

{

	//根据半径大小聚类，找出合适的类别个数和每一类的个数

	std::vector<float>   radiuses(this->blobs.size() );

	std::vector<std::pair<float,int> >   radiusesIdx(this->blobs.size() );

	for ( int i=0; i< this->blobs.size(); ++i ){

		radiuses[i] =this->blobs[i].diaGlint;

		radiusesIdx[i].first  = radiuses[i];

		radiusesIdx[i].second = i;

	}

	{

		using namespace cv;

		this->blobs[0].diaGlint;

		const int MAX_CLUSTERS = 5;

		Scalar colorTab[] =

		{

			Scalar(0, 0, 255),

			Scalar(0,255,0),

			Scalar(255,100,100),

			Scalar(255,0,255),

			Scalar(0,255,255)

		};

		Mat img( 500, 500, CV_8UC3 );

		RNG rng( 12345 );

		std::vector<std::vector<float> >  outC;

		std::vector<std::vector<std::pair<float,int> > >  outCidx;

		{

			outC.clear();

			int k;

			int clusterCount = rng.uniform(2, MAX_CLUSTERS+1);

			int i;

			int sampleCount =radiuses.size();// rng.uniform(1, 1001);

			Mat points(sampleCount, 1, CV_32FC1), labels;

			for ( int i=0; i< sampleCount; ++i ){

				points.at<float>(i) = radiuses[i];

			}

			clusterCount = MIN(clusterCount, sampleCount);

			clusterCount = std::max(clusterCount,3);

			clusterCount = 3;

			Mat centers;

			kmeans(points, clusterCount, labels,

				TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0),

				3, KMEANS_PP_CENTERS, centers);

			outC.resize( clusterCount );

			outCidx.resize( clusterCount );

			img = Scalar::all(0);

			for( i = 0; i < sampleCount; i++ )

			{

				int clusterIdx = labels.at<int>( i );

				outC[clusterIdx].push_back( points.at<float>( i  ) );

				outCidx[clusterIdx].push_back(

					std::make_pair ( points.at<float>( i  ) ,radiusesIdx[i].second ) );

				Point ipt = this->blobs[i].centerOfGlint;

				circle( img, ipt, 2, colorTab[clusterIdx], CV_FILLED, CV_AA );

			}

			cv::imshow("clusters", img);

			char key = (char)cv::waitKey(1);

		}

		return 0;

	}

	return 1.0;

}

即使如此，每次聚类的效果仍然不一定相同，显示一定的随机性。

巴特西

OpenCV： Kmeans的使用一维和二维点集

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