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merged all the latest changes from 2.4 to trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-13 21:50:59 +00:00
parent 020f9a6047
commit 2fd1e2ea57
416 changed files with 12852 additions and 6070 deletions
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171
modules/core/test/test_math.cpp
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@@ -2347,6 +2347,41 @@ void Core_SolvePolyTest::run( int )
}
}
class Core_CheckRange_Empty : public cvtest::BaseTest
{
public:
Core_CheckRange_Empty(){}
~Core_CheckRange_Empty(){}
protected:
virtual void run( int start_from );
};
void Core_CheckRange_Empty::run( int )
{
cv::Mat m;
ASSERT_TRUE( cv::checkRange(m) );
}
TEST(Core_CheckRange_Empty, accuracy) { Core_CheckRange_Empty test; test.safe_run(); }
class Core_CheckRange_INT_MAX : public cvtest::BaseTest
{
public:
Core_CheckRange_INT_MAX(){}
~Core_CheckRange_INT_MAX(){}
protected:
virtual void run( int start_from );
};
void Core_CheckRange_INT_MAX::run( int )
{
cv::Mat m(3, 3, CV_32SC1, cv::Scalar(INT_MAX));
ASSERT_FALSE( cv::checkRange(m, true, 0, 0, INT_MAX) );
ASSERT_TRUE( cv::checkRange(m) );
}
TEST(Core_CheckRange_INT_MAX, accuracy) { Core_CheckRange_INT_MAX test; test.safe_run(); }
template <typename T> class Core_CheckRange : public testing::Test {};
TYPED_TEST_CASE_P(Core_CheckRange);
@@ -2402,7 +2437,17 @@ TYPED_TEST_P(Core_CheckRange, Bounds)
delete bad_pt;
}
REGISTER_TYPED_TEST_CASE_P(Core_CheckRange, Negative, Positive, Bounds);
TYPED_TEST_P(Core_CheckRange, Zero)
{
double min_bound = 0.0;
double max_bound = 0.1;
cv::Mat src = cv::Mat::zeros(3,3, cv::DataDepth<TypeParam>::value);
ASSERT_TRUE( checkRange(src, true, NULL, min_bound, max_bound) );
}
REGISTER_TYPED_TEST_CASE_P(Core_CheckRange, Negative, Positive, Bounds, Zero);
typedef ::testing::Types<signed char,unsigned char, signed short, unsigned short, signed int> mat_data_types;
INSTANTIATE_TYPED_TEST_CASE_P(Negative_Test, Core_CheckRange, mat_data_types);
@@ -2428,5 +2473,129 @@ TEST(Core_SolvePoly, accuracy) { Core_SolvePolyTest test; test.safe_run(); }
// TODO: eigenvv, invsqrt, cbrt, fastarctan, (round, floor, ceil(?)),
class CV_KMeansSingularTest : public cvtest::BaseTest
{
public:
CV_KMeansSingularTest() {}
~CV_KMeansSingularTest() {}
protected:
void run(int)
{
int i, iter = 0, N = 0, N0 = 0, K = 0, dims = 0;
Mat labels;
try
{
RNG& rng = theRNG();
const int MAX_DIM=5;
int MAX_POINTS = 100, maxIter = 100;
for( iter = 0; iter < maxIter; iter++ )
{
ts->update_context(this, iter, true);
dims = rng.uniform(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);
Mat data(N, dims, CV_32F);
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
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++ )
{
int l = labels.at<int>(i);
CV_Assert(0 <= l && l < K);
hist.at<int>(l)++;
}
for( i = 0; i < K; i++ )
CV_Assert( hist.at<int>(i) != 0 );
}
}
catch(...)
{
ts->printf(cvtest::TS::LOG,
"context: iteration=%d, N=%d, N0=%d, K=%d\n",
iter, N, N0, K);
std::cout << labels << std::endl;
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
}
}
};
TEST(Core_KMeans, singular) { CV_KMeansSingularTest test; test.safe_run(); }
TEST(CovariationMatrixVectorOfMat, accuracy)
{
unsigned int col_problem_size = 8, row_problem_size = 8, vector_size = 16;
cv::Mat src(vector_size, col_problem_size * row_problem_size, CV_32F);
int singleMatFlags = CV_COVAR_ROWS;
cv::Mat gold;
cv::Mat goldMean;
cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F);
std::vector<cv::Mat> srcVec;
for(size_t i = 0; i < vector_size; i++)
{
srcVec.push_back(src.row(static_cast<int>(i)).reshape(0,col_problem_size));
}
cv::Mat actual;
cv::Mat actualMean;
cv::calcCovarMatrix(srcVec, actual, actualMean,singleMatFlags,CV_32F);
cv::Mat diff;
cv::absdiff(gold, actual, diff);
cv::Scalar s = cv::sum(diff);
ASSERT_EQ(s.dot(s), 0.0);
cv::Mat meanDiff;
cv::absdiff(goldMean, actualMean.reshape(0,1), meanDiff);
cv::Scalar sDiff = cv::sum(meanDiff);
ASSERT_EQ(sDiff.dot(sDiff), 0.0);
}
TEST(CovariationMatrixVectorOfMatWithMean, accuracy)
{
unsigned int col_problem_size = 8, row_problem_size = 8, vector_size = 16;
cv::Mat src(vector_size, col_problem_size * row_problem_size, CV_32F);
int singleMatFlags = CV_COVAR_ROWS | CV_COVAR_USE_AVG;
cv::Mat gold;
cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
cv::Mat goldMean;
cv::reduce(src,goldMean,0 ,CV_REDUCE_AVG, CV_32F);
cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F);
std::vector<cv::Mat> srcVec;
for(size_t i = 0; i < vector_size; i++)
{
srcVec.push_back(src.row(static_cast<int>(i)).reshape(0,col_problem_size));
}
cv::Mat actual;
cv::Mat actualMean = goldMean.reshape(0, row_problem_size);
cv::calcCovarMatrix(srcVec, actual, actualMean,singleMatFlags,CV_32F);
cv::Mat diff;
cv::absdiff(gold, actual, diff);
cv::Scalar s = cv::sum(diff);
ASSERT_EQ(s.dot(s), 0.0);
cv::Mat meanDiff;
cv::absdiff(goldMean, actualMean.reshape(0,1), meanDiff);
cv::Scalar sDiff = cv::sum(meanDiff);
ASSERT_EQ(sDiff.dot(sDiff), 0.0);
}
/* End of file. */