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handle single-point sets in kmeans properly
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@ -2459,8 +2459,9 @@ double cv::kmeans( InputArray _data, int K,
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{
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const int SPP_TRIALS = 3;
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Mat data = _data.getMat();
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int N = data.rows > 1 ? data.rows : data.cols;
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int dims = (data.rows > 1 ? data.cols : 1)*data.channels();
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bool isrow = data.rows == 1 && data.channels() > 1;
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int N = !isrow ? data.rows : data.cols;
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int dims = (!isrow ? data.cols : 1)*data.channels();
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int type = data.depth();
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attempts = std::max(attempts, 1);
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@ -2437,42 +2437,48 @@ public:
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protected:
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void run(int)
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{
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int i, iter = 0, N = 0, N0 = 0, K = 0, dims = 0;
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Mat labels;
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try
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{
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RNG& rng = theRNG();
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const int MAX_DIM=5;
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int MAX_POINTS = 100;
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for( int iter = 0; iter < 100; iter++ )
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int MAX_POINTS = 100, maxIter = 100;
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for( iter = 0; iter < maxIter; iter++ )
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{
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ts->update_context(this, iter, true);
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int dims = rng.uniform(1, MAX_DIM+1);
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int N = rng.uniform(1, MAX_POINTS+1);
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int N0 = rng.uniform(1, N/10+1);
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int K = rng.uniform(1, N+1);
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dims = rng.uniform(1, MAX_DIM+1);
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N = rng.uniform(1, MAX_POINTS+1);
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N0 = rng.uniform(1, MAX(N/10, 2));
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K = rng.uniform(1, N+1);
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Mat data0(N0, dims, CV_32F), labels;
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Mat data0(N0, dims, CV_32F);
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rng.fill(data0, RNG::UNIFORM, -1, 1);
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Mat data(N, dims, CV_32F);
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for( int i = 0; i < N; i++ )
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for( i = 0; i < N; i++ )
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data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
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kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
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5, KMEANS_PP_CENTERS);
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Mat hist(K, 1, CV_32S, Scalar(0));
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for( int i = 0; i < N; i++ )
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for( i = 0; i < N; i++ )
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{
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int l = labels.at<int>(i);
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CV_Assert( 0 <= l && l < K );
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CV_Assert(0 <= l && l < K);
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hist.at<int>(l)++;
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}
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for( int i = 0; i < K; i++ )
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for( i = 0; i < K; i++ )
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CV_Assert( hist.at<int>(i) != 0 );
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}
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}
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catch(...)
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{
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ts->printf(cvtest::TS::LOG,
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"context: iteration=%d, N=%d, N0=%d, K=%d\n",
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iter, N, N0, K);
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std::cout << labels << std::endl;
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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}
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}
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