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Merge pull request #2744 from jet47:kmeans-fix

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This commit is contained in:
Vadim Pisarevsky 2014-05-19 17:33:09 +04:00 committed by OpenCV Buildbot
commit 118b27f5b4
3 changed files with 90 additions and 17 deletions
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14
modules/core/src/matrix.cpp
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@ -2691,16 +2691,18 @@ double cv::kmeans( InputArray _data, int K,
int flags, OutputArray _centers )
{
const int SPP_TRIALS = 3;
Mat data = _data.getMat();
bool isrow = data.rows == 1 && data.channels() > 1;
int N = !isrow ? data.rows : data.cols;
int dims = (!isrow ? data.cols : 1)*data.channels();
int type = data.depth();
Mat data0 = _data.getMat();
bool isrow = data0.rows == 1 && data0.channels() > 1;
int N = !isrow ? data0.rows : data0.cols;
int dims = (!isrow ? data0.cols : 1)*data0.channels();
int type = data0.depth();
attempts = std::max(attempts, 1);
CV_Assert( data.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( N >= K );
Mat data(N, dims, CV_32F, data0.data, isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));
_bestLabels.create(N, 1, CV_32S, -1, true);
Mat _labels, best_labels = _bestLabels.getMat();

91
modules/core/test/test_math.cpp
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@ -2512,6 +2512,15 @@ TEST(Core_SVD, flt)
// TODO: eigenvv, invsqrt, cbrt, fastarctan, (round, floor, ceil(?)),
enum
{
MAT_N_DIM_C1,
MAT_N_1_CDIM,
MAT_1_N_CDIM,
MAT_N_DIM_C1_NONCONT,
MAT_N_1_CDIM_NONCONT,
VECTOR
};
class CV_KMeansSingularTest : public cvtest::BaseTest
{
@ -2519,7 +2528,7 @@ public:
CV_KMeansSingularTest() {}
~CV_KMeansSingularTest() {}
protected:
void run(int)
void run(int inVariant)
{
int i, iter = 0, N = 0, N0 = 0, K = 0, dims = 0;
Mat labels;
@ -2531,20 +2540,70 @@ protected:
for( iter = 0; iter < maxIter; iter++ )
{
ts->update_context(this, iter, true);
dims = rng.uniform(1, MAX_DIM+1);
dims = rng.uniform(inVariant == MAT_1_N_CDIM ? 2 : 1, MAX_DIM+1);
N = rng.uniform(1, MAX_POINTS+1);
N0 = rng.uniform(1, MAX(N/10, 2));
K = rng.uniform(1, N+1);
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
if (inVariant == VECTOR)
{
dims = 2;
Mat data(N, dims, CV_32F);
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
std::vector<cv::Point2f> data0(N0);
rng.fill(data0, RNG::UNIFORM, -1, 1);
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
5, KMEANS_PP_CENTERS);
std::vector<cv::Point2f> data(N);
for( i = 0; i < N; i++ )
data[i] = data0[rng.uniform(0, N0)];
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
5, KMEANS_PP_CENTERS);
}
else
{
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
Mat data;
switch (inVariant)
{
case MAT_N_DIM_C1:
data.create(N, dims, CV_32F);
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
break;
case MAT_N_1_CDIM:
data.create(N, 1, CV_32FC(dims));
for( i = 0; i < N; i++ )
memcpy(data.ptr(i), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
break;
case MAT_1_N_CDIM:
data.create(1, N, CV_32FC(dims));
for( i = 0; i < N; i++ )
memcpy(data.data + i * dims * sizeof(float), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
break;
case MAT_N_DIM_C1_NONCONT:
data.create(N, dims + 5, CV_32F);
data = data(Range(0, N), Range(0, dims));
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
break;
case MAT_N_1_CDIM_NONCONT:
data.create(N, 3, CV_32FC(dims));
data = data.colRange(0, 1);
for( i = 0; i < N; i++ )
memcpy(data.ptr(i), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
break;
}
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
5, KMEANS_PP_CENTERS);
}
Mat hist(K, 1, CV_32S, Scalar(0));
for( i = 0; i < N; i++ )
@ -2568,7 +2627,19 @@ protected:
}
};
TEST(Core_KMeans, singular) { CV_KMeansSingularTest test; test.safe_run(); }
TEST(Core_KMeans, singular) { CV_KMeansSingularTest test; test.safe_run(MAT_N_DIM_C1); }
CV_ENUM(KMeansInputVariant, MAT_N_DIM_C1, MAT_N_1_CDIM, MAT_1_N_CDIM, MAT_N_DIM_C1_NONCONT, MAT_N_1_CDIM_NONCONT, VECTOR)
typedef testing::TestWithParam<KMeansInputVariant> Core_KMeans_InputVariants;
TEST_P(Core_KMeans_InputVariants, singular)
{
CV_KMeansSingularTest test;
test.safe_run(GetParam());
}
INSTANTIATE_TEST_CASE_P(AllVariants, Core_KMeans_InputVariants, KMeansInputVariant::all());
TEST(CovariationMatrixVectorOfMat, accuracy)
{

2
samples/cpp/kmeans.cpp
View File

@ -33,7 +33,7 @@ int main( int /*argc*/, char** /*argv*/ )
{
int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
int i, sampleCount = rng.uniform(1, 1001);
Mat points(sampleCount, 2, CV_32F), labels;
Mat points(sampleCount, 1, CV_32FC2), labels;
clusterCount = MIN(clusterCount, sampleCount);
Mat centers;