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2771 lines
84 KiB
C++
2771 lines
84 KiB
C++
//////////////////////////////////////////////////////////////////////////////////////////
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/////////////////// tests for matrix operations and math functions ///////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
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#include <float.h>
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#include <math.h>
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using namespace cv;
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using namespace std;
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/// !!! NOTE !!! These tests happily avoid overflow cases & out-of-range arguments
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/// so that output arrays contain neigher Inf's nor Nan's.
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/// Handling such cases would require special modification of check function
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/// (validate_test_results) => TBD.
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/// Also, need some logarithmic-scale generation of input data. Right now it is done (in some tests)
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/// by generating min/max boundaries for random data in logarimithic scale, but
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/// within the same test case all the input array elements are of the same order.
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class Core_MathTest : public cvtest::ArrayTest
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{
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public:
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typedef cvtest::ArrayTest Base;
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Core_MathTest();
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protected:
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes,
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vector<vector<int> >& types);
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double get_success_error_level( int /*test_case_idx*/, int i, int j );
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bool test_nd;
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};
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Core_MathTest::Core_MathTest()
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{
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optional_mask = false;
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test_array[INPUT].push_back(NULL);
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test_array[OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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test_nd = false;
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}
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double Core_MathTest::get_success_error_level( int /*test_case_idx*/, int i, int j )
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{
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return test_mat[i][j].depth() == CV_32F ? FLT_EPSILON*128 : DBL_EPSILON*1024;
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}
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void Core_MathTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes,
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vector<vector<int> >& types)
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{
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RNG& rng = ts->get_rng();
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int depth = cvtest::randInt(rng)%2 + CV_32F;
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int cn = cvtest::randInt(rng) % 4 + 1, type = CV_MAKETYPE(depth, cn);
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size_t i, j;
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Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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for( i = 0; i < test_array.size(); i++ )
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{
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size_t count = test_array[i].size();
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for( j = 0; j < count; j++ )
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types[i][j] = type;
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}
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test_nd = cvtest::randInt(rng)%3 == 0;
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}
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////////// pow /////////////
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class Core_PowTest : public Core_MathTest
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{
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public:
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typedef Core_MathTest Base;
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Core_PowTest();
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protected:
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void get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes,
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vector<vector<int> >& types );
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void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
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void run_func();
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void prepare_to_validation( int test_case_idx );
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double get_success_error_level( int test_case_idx, int i, int j );
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double power;
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};
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Core_PowTest::Core_PowTest()
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{
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power = 0;
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}
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void Core_PowTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes,
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vector<vector<int> >& types )
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{
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RNG& rng = ts->get_rng();
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int depth = cvtest::randInt(rng) % (CV_64F+1);
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int cn = cvtest::randInt(rng) % 4 + 1;
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size_t i, j;
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Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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depth += depth == CV_8S;
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if( depth < CV_32F || cvtest::randInt(rng)%8 == 0 )
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// integer power
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power = (int)(cvtest::randInt(rng)%21 - 10);
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else
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{
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i = cvtest::randInt(rng)%17;
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power = i == 16 ? 1./3 : i == 15 ? 0.5 : i == 14 ? -0.5 : cvtest::randReal(rng)*10 - 5;
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}
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for( i = 0; i < test_array.size(); i++ )
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{
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size_t count = test_array[i].size();
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int type = CV_MAKETYPE(depth, cn);
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for( j = 0; j < count; j++ )
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types[i][j] = type;
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}
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test_nd = cvtest::randInt(rng)%3 == 0;
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}
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double Core_PowTest::get_success_error_level( int test_case_idx, int i, int j )
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{
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int depth = test_mat[i][j].depth();
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if( depth < CV_32F )
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return power == cvRound(power) && power >= 0 ? 0 : 1;
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else
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return Base::get_success_error_level( test_case_idx, i, j );
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}
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void Core_PowTest::get_minmax_bounds( int /*i*/, int /*j*/, int type, Scalar& low, Scalar& high )
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{
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double l, u = cvtest::randInt(ts->get_rng())%1000 + 1;
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if( power > 0 )
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{
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double mval = cvtest::getMaxVal(type);
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double u1 = pow(mval,1./power)*2;
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u = MIN(u,u1);
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}
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l = power == cvRound(power) ? -u : FLT_EPSILON;
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low = Scalar::all(l);
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high = Scalar::all(u);
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}
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void Core_PowTest::run_func()
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{
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if(!test_nd)
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{
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if( fabs(power-1./3) <= DBL_EPSILON && test_mat[INPUT][0].depth() == CV_32F )
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{
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Mat a = test_mat[INPUT][0], b = test_mat[OUTPUT][0];
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a = a.reshape(1);
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b = b.reshape(1);
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for( int i = 0; i < a.rows; i++ )
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{
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b.at<float>(i,0) = (float)fabs(cvCbrt(a.at<float>(i,0)));
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for( int j = 1; j < a.cols; j++ )
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b.at<float>(i,j) = (float)fabs(cv::cubeRoot(a.at<float>(i,j)));
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}
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}
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else
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cvPow( test_array[INPUT][0], test_array[OUTPUT][0], power );
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}
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else
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{
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Mat& a = test_mat[INPUT][0];
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Mat& b = test_mat[OUTPUT][0];
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if(power == 0.5)
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cv::sqrt(a, b);
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else
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cv::pow(a, power, b);
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}
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}
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inline static int ipow( int a, int power )
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{
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int b = 1;
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while( power > 0 )
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{
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if( power&1 )
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b *= a, power--;
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else
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a *= a, power >>= 1;
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}
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return b;
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}
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inline static double ipow( double a, int power )
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{
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double b = 1.;
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while( power > 0 )
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{
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if( power&1 )
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b *= a, power--;
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else
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a *= a, power >>= 1;
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}
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return b;
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}
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void Core_PowTest::prepare_to_validation( int /*test_case_idx*/ )
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{
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const Mat& a = test_mat[INPUT][0];
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Mat& b = test_mat[REF_OUTPUT][0];
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int depth = a.depth();
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int ncols = a.cols*a.channels();
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int ipower = cvRound(power), apower = abs(ipower);
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int i, j;
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for( i = 0; i < a.rows; i++ )
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{
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const uchar* a_data = a.ptr(i);
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uchar* b_data = b.ptr(i);
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switch( depth )
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{
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case CV_8U:
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if( ipower < 0 )
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for( j = 0; j < ncols; j++ )
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{
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int val = ((uchar*)a_data)[j];
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((uchar*)b_data)[j] = (uchar)(val <= 1 ? val :
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val == 2 && ipower == -1 ? 1 : 0);
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}
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else
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for( j = 0; j < ncols; j++ )
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{
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int val = ((uchar*)a_data)[j];
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val = ipow( val, ipower );
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((uchar*)b_data)[j] = saturate_cast<uchar>(val);
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}
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break;
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case CV_8S:
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if( ipower < 0 )
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for( j = 0; j < ncols; j++ )
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{
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int val = ((char*)a_data)[j];
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((char*)b_data)[j] = (char)((val&~1)==0 ? val :
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val ==-1 ? 1-2*(ipower&1) :
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val == 2 && ipower == -1 ? 1 : 0);
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}
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else
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for( j = 0; j < ncols; j++ )
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{
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int val = ((char*)a_data)[j];
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val = ipow( val, ipower );
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((char*)b_data)[j] = saturate_cast<schar>(val);
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}
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break;
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case CV_16U:
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if( ipower < 0 )
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for( j = 0; j < ncols; j++ )
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{
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int val = ((ushort*)a_data)[j];
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((ushort*)b_data)[j] = (ushort)((val&~1)==0 ? val :
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val ==-1 ? 1-2*(ipower&1) :
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val == 2 && ipower == -1 ? 1 : 0);
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}
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else
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for( j = 0; j < ncols; j++ )
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{
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int val = ((ushort*)a_data)[j];
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val = ipow( val, ipower );
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((ushort*)b_data)[j] = saturate_cast<ushort>(val);
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}
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break;
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case CV_16S:
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if( ipower < 0 )
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for( j = 0; j < ncols; j++ )
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{
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int val = ((short*)a_data)[j];
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((short*)b_data)[j] = (short)((val&~1)==0 ? val :
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val ==-1 ? 1-2*(ipower&1) :
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val == 2 && ipower == -1 ? 1 : 0);
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}
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else
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for( j = 0; j < ncols; j++ )
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{
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int val = ((short*)a_data)[j];
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val = ipow( val, ipower );
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((short*)b_data)[j] = saturate_cast<short>(val);
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}
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break;
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case CV_32S:
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if( ipower < 0 )
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for( j = 0; j < ncols; j++ )
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{
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int val = ((int*)a_data)[j];
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((int*)b_data)[j] = (val&~1)==0 ? val :
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val ==-1 ? 1-2*(ipower&1) :
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val == 2 && ipower == -1 ? 1 : 0;
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}
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else
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for( j = 0; j < ncols; j++ )
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{
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int val = ((int*)a_data)[j];
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val = ipow( val, ipower );
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((int*)b_data)[j] = val;
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}
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break;
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case CV_32F:
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if( power != ipower )
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for( j = 0; j < ncols; j++ )
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{
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double val = ((float*)a_data)[j];
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val = pow( fabs(val), power );
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((float*)b_data)[j] = (float)val;
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}
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else
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for( j = 0; j < ncols; j++ )
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{
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double val = ((float*)a_data)[j];
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if( ipower < 0 )
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val = 1./val;
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val = ipow( val, apower );
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((float*)b_data)[j] = (float)val;
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}
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break;
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case CV_64F:
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if( power != ipower )
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for( j = 0; j < ncols; j++ )
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{
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double val = ((double*)a_data)[j];
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val = pow( fabs(val), power );
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((double*)b_data)[j] = (double)val;
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}
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else
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for( j = 0; j < ncols; j++ )
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{
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double val = ((double*)a_data)[j];
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if( ipower < 0 )
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val = 1./val;
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val = ipow( val, apower );
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((double*)b_data)[j] = (double)val;
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}
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break;
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}
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}
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}
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///////////////////////////////////////// matrix tests ////////////////////////////////////////////
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class Core_MatrixTest : public cvtest::ArrayTest
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{
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public:
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typedef cvtest::ArrayTest Base;
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Core_MatrixTest( int in_count, int out_count,
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bool allow_int, bool scalar_output, int max_cn );
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protected:
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void get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes,
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vector<vector<int> >& types );
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double get_success_error_level( int test_case_idx, int i, int j );
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bool allow_int;
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bool scalar_output;
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int max_cn;
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};
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Core_MatrixTest::Core_MatrixTest( int in_count, int out_count,
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bool _allow_int, bool _scalar_output, int _max_cn )
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: allow_int(_allow_int), scalar_output(_scalar_output), max_cn(_max_cn)
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{
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int i;
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for( i = 0; i < in_count; i++ )
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test_array[INPUT].push_back(NULL);
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for( i = 0; i < out_count; i++ )
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{
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test_array[OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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}
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element_wise_relative_error = false;
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}
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void Core_MatrixTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes,
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vector<vector<int> >& types )
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{
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RNG& rng = ts->get_rng();
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int depth = cvtest::randInt(rng) % (allow_int ? CV_64F+1 : 2);
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int cn = cvtest::randInt(rng) % max_cn + 1;
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size_t i, j;
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if( allow_int )
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depth += depth == CV_8S;
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else
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depth += CV_32F;
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Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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for( i = 0; i < test_array.size(); i++ )
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{
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size_t count = test_array[i].size();
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int flag = (i == OUTPUT || i == REF_OUTPUT) && scalar_output;
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int type = !flag ? CV_MAKETYPE(depth, cn) : CV_64FC1;
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for( j = 0; j < count; j++ )
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{
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types[i][j] = type;
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if( flag )
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sizes[i][j] = Size( 4, 1 );
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}
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}
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}
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double Core_MatrixTest::get_success_error_level( int test_case_idx, int i, int j )
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{
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int input_depth = test_mat[INPUT][0].depth();
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double input_precision = input_depth < CV_32F ? 0 : input_depth == CV_32F ? 5e-5 : 5e-10;
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double output_precision = Base::get_success_error_level( test_case_idx, i, j );
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return MAX(input_precision, output_precision);
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}
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///////////////// Trace /////////////////////
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class Core_TraceTest : public Core_MatrixTest
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{
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public:
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Core_TraceTest();
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protected:
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void run_func();
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void prepare_to_validation( int test_case_idx );
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};
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Core_TraceTest::Core_TraceTest() : Core_MatrixTest( 1, 1, true, true, 4 )
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{
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}
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void Core_TraceTest::run_func()
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{
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test_mat[OUTPUT][0].at<Scalar>(0,0) = cvTrace(test_array[INPUT][0]);
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}
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void Core_TraceTest::prepare_to_validation( int )
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{
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Mat& mat = test_mat[INPUT][0];
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int count = MIN( mat.rows, mat.cols );
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Mat diag(count, 1, mat.type(), mat.data, mat.step + mat.elemSize());
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Scalar r = cvtest::mean(diag);
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r *= (double)count;
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test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = r;
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}
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///////// dotproduct //////////
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class Core_DotProductTest : public Core_MatrixTest
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{
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public:
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Core_DotProductTest();
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protected:
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void run_func();
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void prepare_to_validation( int test_case_idx );
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};
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Core_DotProductTest::Core_DotProductTest() : Core_MatrixTest( 2, 1, true, true, 4 )
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{
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}
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void Core_DotProductTest::run_func()
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{
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test_mat[OUTPUT][0].at<Scalar>(0,0) = Scalar(cvDotProduct( test_array[INPUT][0], test_array[INPUT][1] ));
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}
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void Core_DotProductTest::prepare_to_validation( int )
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{
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test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = Scalar(cvtest::crossCorr( test_mat[INPUT][0], test_mat[INPUT][1] ));
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}
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///////// crossproduct //////////
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class Core_CrossProductTest : public Core_MatrixTest
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{
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public:
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Core_CrossProductTest();
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protected:
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void get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes,
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vector<vector<int> >& types );
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void run_func();
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void prepare_to_validation( int test_case_idx );
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};
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Core_CrossProductTest::Core_CrossProductTest() : Core_MatrixTest( 2, 1, false, false, 1 )
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{
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}
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void Core_CrossProductTest::get_test_array_types_and_sizes( int,
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|
vector<vector<Size> >& sizes,
|
|
vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int depth = cvtest::randInt(rng) % 2 + CV_32F;
|
|
int cn = cvtest::randInt(rng) & 1 ? 3 : 1, type = CV_MAKETYPE(depth, cn);
|
|
CvSize sz;
|
|
|
|
types[INPUT][0] = types[INPUT][1] = types[OUTPUT][0] = types[REF_OUTPUT][0] = type;
|
|
|
|
if( cn == 3 )
|
|
sz = Size(1,1);
|
|
else if( cvtest::randInt(rng) & 1 )
|
|
sz = Size(3,1);
|
|
else
|
|
sz = Size(1,3);
|
|
|
|
sizes[INPUT][0] = sizes[INPUT][1] = sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = sz;
|
|
}
|
|
|
|
|
|
void Core_CrossProductTest::run_func()
|
|
{
|
|
cvCrossProduct( test_array[INPUT][0], test_array[INPUT][1], test_array[OUTPUT][0] );
|
|
}
|
|
|
|
|
|
void Core_CrossProductTest::prepare_to_validation( int )
|
|
{
|
|
CvScalar a = {{0,0,0,0}}, b = {{0,0,0,0}}, c = {{0,0,0,0}};
|
|
|
|
if( test_mat[INPUT][0].rows > 1 )
|
|
{
|
|
a.val[0] = cvGetReal2D( test_array[INPUT][0], 0, 0 );
|
|
a.val[1] = cvGetReal2D( test_array[INPUT][0], 1, 0 );
|
|
a.val[2] = cvGetReal2D( test_array[INPUT][0], 2, 0 );
|
|
|
|
b.val[0] = cvGetReal2D( test_array[INPUT][1], 0, 0 );
|
|
b.val[1] = cvGetReal2D( test_array[INPUT][1], 1, 0 );
|
|
b.val[2] = cvGetReal2D( test_array[INPUT][1], 2, 0 );
|
|
}
|
|
else if( test_mat[INPUT][0].cols > 1 )
|
|
{
|
|
a.val[0] = cvGetReal1D( test_array[INPUT][0], 0 );
|
|
a.val[1] = cvGetReal1D( test_array[INPUT][0], 1 );
|
|
a.val[2] = cvGetReal1D( test_array[INPUT][0], 2 );
|
|
|
|
b.val[0] = cvGetReal1D( test_array[INPUT][1], 0 );
|
|
b.val[1] = cvGetReal1D( test_array[INPUT][1], 1 );
|
|
b.val[2] = cvGetReal1D( test_array[INPUT][1], 2 );
|
|
}
|
|
else
|
|
{
|
|
a = cvGet1D( test_array[INPUT][0], 0 );
|
|
b = cvGet1D( test_array[INPUT][1], 0 );
|
|
}
|
|
|
|
c.val[2] = a.val[0]*b.val[1] - a.val[1]*b.val[0];
|
|
c.val[1] = -a.val[0]*b.val[2] + a.val[2]*b.val[0];
|
|
c.val[0] = a.val[1]*b.val[2] - a.val[2]*b.val[1];
|
|
|
|
if( test_mat[REF_OUTPUT][0].rows > 1 )
|
|
{
|
|
cvSetReal2D( test_array[REF_OUTPUT][0], 0, 0, c.val[0] );
|
|
cvSetReal2D( test_array[REF_OUTPUT][0], 1, 0, c.val[1] );
|
|
cvSetReal2D( test_array[REF_OUTPUT][0], 2, 0, c.val[2] );
|
|
}
|
|
else if( test_mat[REF_OUTPUT][0].cols > 1 )
|
|
{
|
|
cvSetReal1D( test_array[REF_OUTPUT][0], 0, c.val[0] );
|
|
cvSetReal1D( test_array[REF_OUTPUT][0], 1, c.val[1] );
|
|
cvSetReal1D( test_array[REF_OUTPUT][0], 2, c.val[2] );
|
|
}
|
|
else
|
|
{
|
|
cvSet1D( test_array[REF_OUTPUT][0], 0, c );
|
|
}
|
|
}
|
|
|
|
|
|
///////////////// gemm /////////////////////
|
|
|
|
class Core_GEMMTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_GEMMTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
int tabc_flag;
|
|
double alpha, beta;
|
|
};
|
|
|
|
Core_GEMMTest::Core_GEMMTest() : Core_MatrixTest( 5, 1, false, false, 2 )
|
|
{
|
|
test_case_count = 100;
|
|
max_log_array_size = 10;
|
|
alpha = beta = 0;
|
|
}
|
|
|
|
|
|
void Core_GEMMTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
Size sizeA;
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
sizeA = sizes[INPUT][0];
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
sizes[INPUT][0] = sizeA;
|
|
sizes[INPUT][2] = sizes[INPUT][3] = Size(1,1);
|
|
types[INPUT][2] = types[INPUT][3] &= ~CV_MAT_CN_MASK;
|
|
|
|
tabc_flag = cvtest::randInt(rng) & 7;
|
|
|
|
switch( tabc_flag & (CV_GEMM_A_T|CV_GEMM_B_T) )
|
|
{
|
|
case 0:
|
|
sizes[INPUT][1].height = sizes[INPUT][0].width;
|
|
sizes[OUTPUT][0].height = sizes[INPUT][0].height;
|
|
sizes[OUTPUT][0].width = sizes[INPUT][1].width;
|
|
break;
|
|
case CV_GEMM_B_T:
|
|
sizes[INPUT][1].width = sizes[INPUT][0].width;
|
|
sizes[OUTPUT][0].height = sizes[INPUT][0].height;
|
|
sizes[OUTPUT][0].width = sizes[INPUT][1].height;
|
|
break;
|
|
case CV_GEMM_A_T:
|
|
sizes[INPUT][1].height = sizes[INPUT][0].height;
|
|
sizes[OUTPUT][0].height = sizes[INPUT][0].width;
|
|
sizes[OUTPUT][0].width = sizes[INPUT][1].width;
|
|
break;
|
|
case CV_GEMM_A_T | CV_GEMM_B_T:
|
|
sizes[INPUT][1].width = sizes[INPUT][0].height;
|
|
sizes[OUTPUT][0].height = sizes[INPUT][0].width;
|
|
sizes[OUTPUT][0].width = sizes[INPUT][1].height;
|
|
break;
|
|
}
|
|
|
|
sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
|
|
|
|
if( cvtest::randInt(rng) & 1 )
|
|
sizes[INPUT][4] = Size(0,0);
|
|
else if( !(tabc_flag & CV_GEMM_C_T) )
|
|
sizes[INPUT][4] = sizes[OUTPUT][0];
|
|
else
|
|
{
|
|
sizes[INPUT][4].width = sizes[OUTPUT][0].height;
|
|
sizes[INPUT][4].height = sizes[OUTPUT][0].width;
|
|
}
|
|
}
|
|
|
|
|
|
int Core_GEMMTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Base::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
{
|
|
alpha = cvGetReal2D( test_array[INPUT][2], 0, 0 );
|
|
beta = cvGetReal2D( test_array[INPUT][3], 0, 0 );
|
|
}
|
|
return code;
|
|
}
|
|
|
|
|
|
void Core_GEMMTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
|
|
{
|
|
low = Scalar::all(-10.);
|
|
high = Scalar::all(10.);
|
|
}
|
|
|
|
|
|
void Core_GEMMTest::run_func()
|
|
{
|
|
cvGEMM( test_array[INPUT][0], test_array[INPUT][1], alpha,
|
|
test_array[INPUT][4], beta, test_array[OUTPUT][0], tabc_flag );
|
|
}
|
|
|
|
|
|
void Core_GEMMTest::prepare_to_validation( int )
|
|
{
|
|
cvtest::gemm( test_mat[INPUT][0], test_mat[INPUT][1], alpha,
|
|
test_array[INPUT][4] ? test_mat[INPUT][4] : Mat(),
|
|
beta, test_mat[REF_OUTPUT][0], tabc_flag );
|
|
}
|
|
|
|
|
|
///////////////// multransposed /////////////////////
|
|
|
|
class Core_MulTransposedTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
Core_MulTransposedTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
int order;
|
|
};
|
|
|
|
|
|
Core_MulTransposedTest::Core_MulTransposedTest() : Core_MatrixTest( 2, 1, false, false, 1 )
|
|
{
|
|
test_case_count = 100;
|
|
order = 0;
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_MulTransposedTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
int src_type = cvtest::randInt(rng) % 5;
|
|
int dst_type = cvtest::randInt(rng) % 2;
|
|
|
|
src_type = src_type == 0 ? CV_8U : src_type == 1 ? CV_16U : src_type == 2 ? CV_16S :
|
|
src_type == 3 ? CV_32F : CV_64F;
|
|
dst_type = dst_type == 0 ? CV_32F : CV_64F;
|
|
dst_type = MAX( dst_type, src_type );
|
|
|
|
Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
|
|
if( bits & 1 )
|
|
sizes[INPUT][1] = Size(0,0);
|
|
else
|
|
{
|
|
sizes[INPUT][1] = sizes[INPUT][0];
|
|
if( bits & 2 )
|
|
sizes[INPUT][1].height = 1;
|
|
if( bits & 4 )
|
|
sizes[INPUT][1].width = 1;
|
|
}
|
|
|
|
sizes[TEMP][0] = sizes[INPUT][0];
|
|
types[INPUT][0] = src_type;
|
|
types[OUTPUT][0] = types[REF_OUTPUT][0] = types[INPUT][1] = types[TEMP][0] = dst_type;
|
|
|
|
order = (bits & 8) != 0;
|
|
sizes[OUTPUT][0].width = sizes[OUTPUT][0].height = order == 0 ?
|
|
sizes[INPUT][0].height : sizes[INPUT][0].width;
|
|
sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
|
|
}
|
|
|
|
|
|
void Core_MulTransposedTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
|
|
{
|
|
low = cvScalarAll(-10.);
|
|
high = cvScalarAll(10.);
|
|
}
|
|
|
|
|
|
void Core_MulTransposedTest::run_func()
|
|
{
|
|
cvMulTransposed( test_array[INPUT][0], test_array[OUTPUT][0],
|
|
order, test_array[INPUT][1] );
|
|
}
|
|
|
|
|
|
void Core_MulTransposedTest::prepare_to_validation( int )
|
|
{
|
|
const Mat& src = test_mat[INPUT][0];
|
|
Mat delta = test_mat[INPUT][1];
|
|
Mat& temp = test_mat[TEMP][0];
|
|
if( !delta.empty() )
|
|
{
|
|
if( delta.rows < src.rows || delta.cols < src.cols )
|
|
{
|
|
cv::repeat( delta, src.rows/delta.rows, src.cols/delta.cols, temp);
|
|
delta = temp;
|
|
}
|
|
cvtest::add( src, 1, delta, -1, Scalar::all(0), temp, temp.type());
|
|
}
|
|
else
|
|
src.convertTo(temp, temp.type());
|
|
|
|
cvtest::gemm( temp, temp, 1., Mat(), 0, test_mat[REF_OUTPUT][0], order == 0 ? GEMM_2_T : GEMM_1_T );
|
|
}
|
|
|
|
|
|
///////////////// Transform /////////////////////
|
|
|
|
class Core_TransformTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_TransformTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
|
|
double scale;
|
|
bool diagMtx;
|
|
};
|
|
|
|
|
|
Core_TransformTest::Core_TransformTest() : Core_MatrixTest( 3, 1, true, false, 4 )
|
|
{
|
|
}
|
|
|
|
|
|
void Core_TransformTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
int depth, dst_cn, mat_cols, mattype;
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
|
|
mat_cols = CV_MAT_CN(types[INPUT][0]);
|
|
depth = CV_MAT_DEPTH(types[INPUT][0]);
|
|
dst_cn = cvtest::randInt(rng) % 4 + 1;
|
|
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth, dst_cn);
|
|
|
|
mattype = depth < CV_32S ? CV_32F : depth == CV_64F ? CV_64F : bits & 1 ? CV_32F : CV_64F;
|
|
types[INPUT][1] = mattype;
|
|
types[INPUT][2] = CV_MAKETYPE(mattype, dst_cn);
|
|
|
|
scale = 1./((cvtest::randInt(rng)%4)*50+1);
|
|
|
|
if( bits & 2 )
|
|
{
|
|
sizes[INPUT][2] = Size(0,0);
|
|
mat_cols += (bits & 4) != 0;
|
|
}
|
|
else if( bits & 4 )
|
|
sizes[INPUT][2] = Size(1,1);
|
|
else
|
|
{
|
|
if( bits & 8 )
|
|
sizes[INPUT][2] = Size(dst_cn,1);
|
|
else
|
|
sizes[INPUT][2] = Size(1,dst_cn);
|
|
types[INPUT][2] &= ~CV_MAT_CN_MASK;
|
|
}
|
|
diagMtx = (bits & 16) != 0;
|
|
|
|
sizes[INPUT][1] = Size(mat_cols,dst_cn);
|
|
}
|
|
|
|
|
|
int Core_TransformTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Base::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
{
|
|
Mat& m = test_mat[INPUT][1];
|
|
cvtest::add(m, scale, m, 0, Scalar::all(0), m, m.type() );
|
|
if(diagMtx)
|
|
{
|
|
Mat mask = Mat::eye(m.rows, m.cols, CV_8U)*255;
|
|
mask = ~mask;
|
|
m.setTo(Scalar::all(0), mask);
|
|
}
|
|
}
|
|
return code;
|
|
}
|
|
|
|
|
|
double Core_TransformTest::get_success_error_level( int test_case_idx, int i, int j )
|
|
{
|
|
int depth = test_mat[INPUT][0].depth();
|
|
return depth <= CV_8S ? 1 : depth <= CV_32S ? 9 : Base::get_success_error_level( test_case_idx, i, j );
|
|
}
|
|
|
|
void Core_TransformTest::run_func()
|
|
{
|
|
CvMat _m = test_mat[INPUT][1], _shift = test_mat[INPUT][2];
|
|
cvTransform( test_array[INPUT][0], test_array[OUTPUT][0], &_m, _shift.data.ptr ? &_shift : 0);
|
|
}
|
|
|
|
|
|
void Core_TransformTest::prepare_to_validation( int )
|
|
{
|
|
Mat transmat = test_mat[INPUT][1];
|
|
Mat shift = test_mat[INPUT][2];
|
|
|
|
cvtest::transform( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], transmat, shift );
|
|
}
|
|
|
|
|
|
///////////////// PerspectiveTransform /////////////////////
|
|
|
|
class Core_PerspectiveTransformTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
Core_PerspectiveTransformTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
};
|
|
|
|
|
|
Core_PerspectiveTransformTest::Core_PerspectiveTransformTest() : Core_MatrixTest( 2, 1, false, false, 2 )
|
|
{
|
|
}
|
|
|
|
|
|
void Core_PerspectiveTransformTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
int depth, cn, mattype;
|
|
Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
|
|
cn = CV_MAT_CN(types[INPUT][0]) + 1;
|
|
depth = CV_MAT_DEPTH(types[INPUT][0]);
|
|
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth, cn);
|
|
|
|
mattype = depth == CV_64F ? CV_64F : bits & 1 ? CV_32F : CV_64F;
|
|
types[INPUT][1] = mattype;
|
|
sizes[INPUT][1] = Size(cn + 1, cn + 1);
|
|
}
|
|
|
|
|
|
double Core_PerspectiveTransformTest::get_success_error_level( int test_case_idx, int i, int j )
|
|
{
|
|
int depth = test_mat[INPUT][0].depth();
|
|
return depth == CV_32F ? 1e-4 : depth == CV_64F ? 1e-8 :
|
|
Core_MatrixTest::get_success_error_level(test_case_idx, i, j);
|
|
}
|
|
|
|
|
|
void Core_PerspectiveTransformTest::run_func()
|
|
{
|
|
CvMat _m = test_mat[INPUT][1];
|
|
cvPerspectiveTransform( test_array[INPUT][0], test_array[OUTPUT][0], &_m );
|
|
}
|
|
|
|
|
|
static void cvTsPerspectiveTransform( const CvArr* _src, CvArr* _dst, const CvMat* transmat )
|
|
{
|
|
int i, j, cols;
|
|
int cn, depth, mat_depth;
|
|
CvMat astub, bstub, *a, *b;
|
|
double mat[16];
|
|
|
|
a = cvGetMat( _src, &astub, 0, 0 );
|
|
b = cvGetMat( _dst, &bstub, 0, 0 );
|
|
|
|
cn = CV_MAT_CN(a->type);
|
|
depth = CV_MAT_DEPTH(a->type);
|
|
mat_depth = CV_MAT_DEPTH(transmat->type);
|
|
cols = transmat->cols;
|
|
|
|
// prepare cn x (cn + 1) transform matrix
|
|
if( mat_depth == CV_32F )
|
|
{
|
|
for( i = 0; i < transmat->rows; i++ )
|
|
for( j = 0; j < cols; j++ )
|
|
mat[i*cols + j] = ((float*)(transmat->data.ptr + transmat->step*i))[j];
|
|
}
|
|
else
|
|
{
|
|
assert( mat_depth == CV_64F );
|
|
for( i = 0; i < transmat->rows; i++ )
|
|
for( j = 0; j < cols; j++ )
|
|
mat[i*cols + j] = ((double*)(transmat->data.ptr + transmat->step*i))[j];
|
|
}
|
|
|
|
// transform data
|
|
cols = a->cols * cn;
|
|
vector<double> buf(cols);
|
|
|
|
for( i = 0; i < a->rows; i++ )
|
|
{
|
|
uchar* src = a->data.ptr + i*a->step;
|
|
uchar* dst = b->data.ptr + i*b->step;
|
|
|
|
switch( depth )
|
|
{
|
|
case CV_32F:
|
|
for( j = 0; j < cols; j++ )
|
|
buf[j] = ((float*)src)[j];
|
|
break;
|
|
case CV_64F:
|
|
for( j = 0; j < cols; j++ )
|
|
buf[j] = ((double*)src)[j];
|
|
break;
|
|
default:
|
|
assert(0);
|
|
}
|
|
|
|
switch( cn )
|
|
{
|
|
case 2:
|
|
for( j = 0; j < cols; j += 2 )
|
|
{
|
|
double t0 = buf[j]*mat[0] + buf[j+1]*mat[1] + mat[2];
|
|
double t1 = buf[j]*mat[3] + buf[j+1]*mat[4] + mat[5];
|
|
double w = buf[j]*mat[6] + buf[j+1]*mat[7] + mat[8];
|
|
w = w ? 1./w : 0;
|
|
buf[j] = t0*w;
|
|
buf[j+1] = t1*w;
|
|
}
|
|
break;
|
|
case 3:
|
|
for( j = 0; j < cols; j += 3 )
|
|
{
|
|
double t0 = buf[j]*mat[0] + buf[j+1]*mat[1] + buf[j+2]*mat[2] + mat[3];
|
|
double t1 = buf[j]*mat[4] + buf[j+1]*mat[5] + buf[j+2]*mat[6] + mat[7];
|
|
double t2 = buf[j]*mat[8] + buf[j+1]*mat[9] + buf[j+2]*mat[10] + mat[11];
|
|
double w = buf[j]*mat[12] + buf[j+1]*mat[13] + buf[j+2]*mat[14] + mat[15];
|
|
w = w ? 1./w : 0;
|
|
buf[j] = t0*w;
|
|
buf[j+1] = t1*w;
|
|
buf[j+2] = t2*w;
|
|
}
|
|
break;
|
|
default:
|
|
assert(0);
|
|
}
|
|
|
|
switch( depth )
|
|
{
|
|
case CV_32F:
|
|
for( j = 0; j < cols; j++ )
|
|
((float*)dst)[j] = (float)buf[j];
|
|
break;
|
|
case CV_64F:
|
|
for( j = 0; j < cols; j++ )
|
|
((double*)dst)[j] = buf[j];
|
|
break;
|
|
default:
|
|
assert(0);
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
void Core_PerspectiveTransformTest::prepare_to_validation( int )
|
|
{
|
|
CvMat transmat = test_mat[INPUT][1];
|
|
cvTsPerspectiveTransform( test_array[INPUT][0], test_array[REF_OUTPUT][0], &transmat );
|
|
}
|
|
|
|
///////////////// Mahalanobis /////////////////////
|
|
|
|
class Core_MahalanobisTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_MahalanobisTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
};
|
|
|
|
|
|
Core_MahalanobisTest::Core_MahalanobisTest() : Core_MatrixTest( 3, 1, false, true, 1 )
|
|
{
|
|
test_case_count = 100;
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_MahalanobisTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
|
|
if( cvtest::randInt(rng) & 1 )
|
|
sizes[INPUT][0].width = sizes[INPUT][1].width = 1;
|
|
else
|
|
sizes[INPUT][0].height = sizes[INPUT][1].height = 1;
|
|
|
|
sizes[TEMP][0] = sizes[TEMP][1] = sizes[INPUT][0];
|
|
sizes[INPUT][2].width = sizes[INPUT][2].height = sizes[INPUT][0].width + sizes[INPUT][0].height - 1;
|
|
sizes[TEMP][2] = sizes[INPUT][2];
|
|
types[TEMP][0] = types[TEMP][1] = types[TEMP][2] = types[INPUT][0];
|
|
}
|
|
|
|
int Core_MahalanobisTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Base::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
{
|
|
// make sure that the inverted "covariation" matrix is symmetrix and positively defined.
|
|
cvtest::gemm( test_mat[INPUT][2], test_mat[INPUT][2], 1., Mat(), 0., test_mat[TEMP][2], GEMM_2_T );
|
|
cvtest::copy( test_mat[TEMP][2], test_mat[INPUT][2] );
|
|
}
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void Core_MahalanobisTest::run_func()
|
|
{
|
|
test_mat[OUTPUT][0].at<Scalar>(0,0) =
|
|
cvRealScalar(cvMahalanobis(test_array[INPUT][0], test_array[INPUT][1], test_array[INPUT][2]));
|
|
}
|
|
|
|
void Core_MahalanobisTest::prepare_to_validation( int )
|
|
{
|
|
cvtest::add( test_mat[INPUT][0], 1., test_mat[INPUT][1], -1.,
|
|
Scalar::all(0), test_mat[TEMP][0], test_mat[TEMP][0].type() );
|
|
if( test_mat[INPUT][0].rows == 1 )
|
|
cvtest::gemm( test_mat[TEMP][0], test_mat[INPUT][2], 1.,
|
|
Mat(), 0., test_mat[TEMP][1], 0 );
|
|
else
|
|
cvtest::gemm( test_mat[INPUT][2], test_mat[TEMP][0], 1.,
|
|
Mat(), 0., test_mat[TEMP][1], 0 );
|
|
|
|
test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = cvRealScalar(sqrt(cvtest::crossCorr(test_mat[TEMP][0], test_mat[TEMP][1])));
|
|
}
|
|
|
|
|
|
///////////////// covarmatrix /////////////////////
|
|
|
|
class Core_CovarMatrixTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
Core_CovarMatrixTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
vector<void*> temp_hdrs;
|
|
vector<uchar> hdr_data;
|
|
int flags, t_flag, len, count;
|
|
bool are_images;
|
|
};
|
|
|
|
|
|
Core_CovarMatrixTest::Core_CovarMatrixTest() : Core_MatrixTest( 1, 1, true, false, 1 ),
|
|
flags(0), t_flag(0), are_images(false)
|
|
{
|
|
test_case_count = 100;
|
|
test_array[INPUT_OUTPUT].push_back(NULL);
|
|
test_array[REF_INPUT_OUTPUT].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_CovarMatrixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
int i, single_matrix;
|
|
Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
|
|
flags = bits & (CV_COVAR_NORMAL | CV_COVAR_USE_AVG | CV_COVAR_SCALE | CV_COVAR_ROWS );
|
|
single_matrix = flags & CV_COVAR_ROWS;
|
|
t_flag = (bits & 256) != 0;
|
|
|
|
const int min_count = 2;
|
|
|
|
if( !t_flag )
|
|
{
|
|
len = sizes[INPUT][0].width;
|
|
count = sizes[INPUT][0].height;
|
|
count = MAX(count, min_count);
|
|
sizes[INPUT][0] = Size(len, count);
|
|
}
|
|
else
|
|
{
|
|
len = sizes[INPUT][0].height;
|
|
count = sizes[INPUT][0].width;
|
|
count = MAX(count, min_count);
|
|
sizes[INPUT][0] = Size(count, len);
|
|
}
|
|
|
|
if( single_matrix && t_flag )
|
|
flags = (flags & ~CV_COVAR_ROWS) | CV_COVAR_COLS;
|
|
|
|
if( CV_MAT_DEPTH(types[INPUT][0]) == CV_32S )
|
|
types[INPUT][0] = (types[INPUT][0] & ~CV_MAT_DEPTH_MASK) | CV_32F;
|
|
|
|
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = flags & CV_COVAR_NORMAL ? Size(len,len) : Size(count,count);
|
|
sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = !t_flag ? Size(len,1) : Size(1,len);
|
|
sizes[TEMP][0] = sizes[INPUT][0];
|
|
|
|
types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] =
|
|
types[OUTPUT][0] = types[REF_OUTPUT][0] = types[TEMP][0] =
|
|
CV_MAT_DEPTH(types[INPUT][0]) == CV_64F || (bits & 512) ? CV_64F : CV_32F;
|
|
|
|
are_images = (bits & 1024) != 0;
|
|
for( i = 0; i < (single_matrix ? 1 : count); i++ )
|
|
temp_hdrs.push_back(NULL);
|
|
}
|
|
|
|
|
|
int Core_CovarMatrixTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Core_MatrixTest::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
{
|
|
int i;
|
|
int single_matrix = flags & (CV_COVAR_ROWS|CV_COVAR_COLS);
|
|
int hdr_size = are_images ? sizeof(IplImage) : sizeof(CvMat);
|
|
|
|
hdr_data.resize(count*hdr_size);
|
|
uchar* _hdr_data = &hdr_data[0];
|
|
if( single_matrix )
|
|
{
|
|
if( !are_images )
|
|
*((CvMat*)_hdr_data) = test_mat[INPUT][0];
|
|
else
|
|
*((IplImage*)_hdr_data) = test_mat[INPUT][0];
|
|
temp_hdrs[0] = _hdr_data;
|
|
}
|
|
else
|
|
for( i = 0; i < count; i++ )
|
|
{
|
|
Mat part;
|
|
void* ptr = _hdr_data + i*hdr_size;
|
|
|
|
if( !t_flag )
|
|
part = test_mat[INPUT][0].row(i);
|
|
else
|
|
part = test_mat[INPUT][0].col(i);
|
|
|
|
if( !are_images )
|
|
*((CvMat*)ptr) = part;
|
|
else
|
|
*((IplImage*)ptr) = part;
|
|
|
|
temp_hdrs[i] = ptr;
|
|
}
|
|
}
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void Core_CovarMatrixTest::run_func()
|
|
{
|
|
cvCalcCovarMatrix( (const void**)&temp_hdrs[0], count,
|
|
test_array[OUTPUT][0], test_array[INPUT_OUTPUT][0], flags );
|
|
}
|
|
|
|
|
|
void Core_CovarMatrixTest::prepare_to_validation( int )
|
|
{
|
|
Mat& avg = test_mat[REF_INPUT_OUTPUT][0];
|
|
double scale = 1.;
|
|
|
|
if( !(flags & CV_COVAR_USE_AVG) )
|
|
{
|
|
Mat hdrs0 = cvarrToMat(temp_hdrs[0]);
|
|
|
|
int i;
|
|
avg = Scalar::all(0);
|
|
|
|
for( i = 0; i < count; i++ )
|
|
{
|
|
Mat vec;
|
|
if( flags & CV_COVAR_ROWS )
|
|
vec = hdrs0.row(i);
|
|
else if( flags & CV_COVAR_COLS )
|
|
vec = hdrs0.col(i);
|
|
else
|
|
vec = cvarrToMat(temp_hdrs[i]);
|
|
|
|
cvtest::add(avg, 1, vec, 1, Scalar::all(0), avg, avg.type());
|
|
}
|
|
|
|
cvtest::add(avg, 1./count, avg, 0., Scalar::all(0), avg, avg.type());
|
|
}
|
|
|
|
if( flags & CV_COVAR_SCALE )
|
|
{
|
|
scale = 1./count;
|
|
}
|
|
|
|
Mat& temp0 = test_mat[TEMP][0];
|
|
cv::repeat( avg, temp0.rows/avg.rows, temp0.cols/avg.cols, temp0 );
|
|
cvtest::add( test_mat[INPUT][0], 1, temp0, -1, Scalar::all(0), temp0, temp0.type());
|
|
|
|
cvtest::gemm( temp0, temp0, scale, Mat(), 0., test_mat[REF_OUTPUT][0],
|
|
t_flag ^ ((flags & CV_COVAR_NORMAL) != 0) ? CV_GEMM_A_T : CV_GEMM_B_T );
|
|
temp_hdrs.clear();
|
|
}
|
|
|
|
|
|
static void cvTsFloodWithZeros( Mat& mat, RNG& rng )
|
|
{
|
|
int k, total = mat.rows*mat.cols, type = mat.type();
|
|
int zero_total = cvtest::randInt(rng) % total;
|
|
CV_Assert( type == CV_32FC1 || type == CV_64FC1 );
|
|
|
|
for( k = 0; k < zero_total; k++ )
|
|
{
|
|
int i = cvtest::randInt(rng) % mat.rows;
|
|
int j = cvtest::randInt(rng) % mat.cols;
|
|
|
|
if( type == CV_32FC1 )
|
|
mat.at<float>(i,j) = 0.f;
|
|
else
|
|
mat.at<double>(i,j) = 0.;
|
|
}
|
|
}
|
|
|
|
|
|
///////////////// determinant /////////////////////
|
|
|
|
class Core_DetTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_DetTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
};
|
|
|
|
|
|
Core_DetTest::Core_DetTest() : Core_MatrixTest( 1, 1, false, true, 1 )
|
|
{
|
|
test_case_count = 100;
|
|
max_log_array_size = 7;
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_DetTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
|
|
sizes[INPUT][0].width = sizes[INPUT][0].height;
|
|
sizes[TEMP][0] = sizes[INPUT][0];
|
|
types[TEMP][0] = CV_64FC1;
|
|
}
|
|
|
|
|
|
void Core_DetTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
|
|
{
|
|
low = cvScalarAll(-2.);
|
|
high = cvScalarAll(2.);
|
|
}
|
|
|
|
|
|
double Core_DetTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
|
{
|
|
return CV_MAT_DEPTH(cvGetElemType(test_array[INPUT][0])) == CV_32F ? 1e-2 : 1e-5;
|
|
}
|
|
|
|
|
|
int Core_DetTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Core_MatrixTest::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
cvTsFloodWithZeros( test_mat[INPUT][0], ts->get_rng() );
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void Core_DetTest::run_func()
|
|
{
|
|
test_mat[OUTPUT][0].at<Scalar>(0,0) = cvRealScalar(cvDet(test_array[INPUT][0]));
|
|
}
|
|
|
|
|
|
// LU method that chooses the optimal in a column pivot element
|
|
static double cvTsLU( CvMat* a, CvMat* b=NULL, CvMat* x=NULL, int* rank=0 )
|
|
{
|
|
int i, j, k, N = a->rows, N1 = a->cols, Nm = MIN(N, N1), step = a->step/sizeof(double);
|
|
int M = b ? b->cols : 0, b_step = b ? b->step/sizeof(double) : 0;
|
|
int x_step = x ? x->step/sizeof(double) : 0;
|
|
double *a0 = a->data.db, *b0 = b ? b->data.db : 0;
|
|
double *x0 = x ? x->data.db : 0;
|
|
double t, det = 1.;
|
|
assert( CV_MAT_TYPE(a->type) == CV_64FC1 &&
|
|
(!b || CV_ARE_TYPES_EQ(a,b)) && (!x || CV_ARE_TYPES_EQ(a,x)));
|
|
|
|
for( i = 0; i < Nm; i++ )
|
|
{
|
|
double max_val = fabs(a0[i*step + i]);
|
|
double *a1, *a2, *b1 = 0, *b2 = 0;
|
|
k = i;
|
|
|
|
for( j = i+1; j < N; j++ )
|
|
{
|
|
t = fabs(a0[j*step + i]);
|
|
if( max_val < t )
|
|
{
|
|
max_val = t;
|
|
k = j;
|
|
}
|
|
}
|
|
|
|
if( k != i )
|
|
{
|
|
for( j = i; j < N1; j++ )
|
|
CV_SWAP( a0[i*step + j], a0[k*step + j], t );
|
|
|
|
for( j = 0; j < M; j++ )
|
|
CV_SWAP( b0[i*b_step + j], b0[k*b_step + j], t );
|
|
det = -det;
|
|
}
|
|
|
|
if( max_val == 0 )
|
|
{
|
|
if( rank )
|
|
*rank = i;
|
|
return 0.;
|
|
}
|
|
|
|
a1 = a0 + i*step;
|
|
a2 = a1 + step;
|
|
b1 = b0 + i*b_step;
|
|
b2 = b1 + b_step;
|
|
|
|
for( j = i+1; j < N; j++, a2 += step, b2 += b_step )
|
|
{
|
|
t = a2[i]/a1[i];
|
|
for( k = i+1; k < N1; k++ )
|
|
a2[k] -= t*a1[k];
|
|
|
|
for( k = 0; k < M; k++ )
|
|
b2[k] -= t*b1[k];
|
|
}
|
|
|
|
det *= a1[i];
|
|
}
|
|
|
|
if( x )
|
|
{
|
|
assert( b );
|
|
|
|
for( i = N-1; i >= 0; i-- )
|
|
{
|
|
double* a1 = a0 + i*step;
|
|
double* b1 = b0 + i*b_step;
|
|
for( j = 0; j < M; j++ )
|
|
{
|
|
t = b1[j];
|
|
for( k = i+1; k < N1; k++ )
|
|
t -= a1[k]*x0[k*x_step + j];
|
|
x0[i*x_step + j] = t/a1[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
if( rank )
|
|
*rank = i;
|
|
return det;
|
|
}
|
|
|
|
|
|
void Core_DetTest::prepare_to_validation( int )
|
|
{
|
|
test_mat[INPUT][0].convertTo(test_mat[TEMP][0], test_mat[TEMP][0].type());
|
|
CvMat temp0 = test_mat[TEMP][0];
|
|
test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = cvRealScalar(cvTsLU(&temp0, 0, 0));
|
|
}
|
|
|
|
|
|
///////////////// invert /////////////////////
|
|
|
|
class Core_InvertTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_InvertTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
int method, rank;
|
|
double result;
|
|
};
|
|
|
|
|
|
Core_InvertTest::Core_InvertTest()
|
|
: Core_MatrixTest( 1, 1, false, false, 1 ), method(0), rank(0), result(0.)
|
|
{
|
|
test_case_count = 100;
|
|
max_log_array_size = 7;
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_InvertTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
int min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );
|
|
|
|
if( (bits & 3) == 0 )
|
|
{
|
|
method = CV_SVD;
|
|
if( bits & 4 )
|
|
{
|
|
sizes[INPUT][0] = Size(min_size, min_size);
|
|
if( bits & 16 )
|
|
method = CV_CHOLESKY;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
method = CV_LU;
|
|
sizes[INPUT][0] = Size(min_size, min_size);
|
|
}
|
|
|
|
sizes[TEMP][0].width = sizes[INPUT][0].height;
|
|
sizes[TEMP][0].height = sizes[INPUT][0].width;
|
|
sizes[TEMP][1] = sizes[INPUT][0];
|
|
types[TEMP][0] = types[INPUT][0];
|
|
types[TEMP][1] = CV_64FC1;
|
|
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = Size(min_size, min_size);
|
|
}
|
|
|
|
|
|
double Core_InvertTest::get_success_error_level( int /*test_case_idx*/, int, int )
|
|
{
|
|
return CV_MAT_DEPTH(cvGetElemType(test_array[OUTPUT][0])) == CV_32F ? 1e-2 : 1e-6;
|
|
}
|
|
|
|
int Core_InvertTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Core_MatrixTest::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
{
|
|
cvTsFloodWithZeros( test_mat[INPUT][0], ts->get_rng() );
|
|
|
|
if( method == CV_CHOLESKY )
|
|
{
|
|
cvtest::gemm( test_mat[INPUT][0], test_mat[INPUT][0], 1.,
|
|
Mat(), 0., test_mat[TEMP][0], CV_GEMM_B_T );
|
|
cvtest::copy( test_mat[TEMP][0], test_mat[INPUT][0] );
|
|
}
|
|
}
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
|
|
void Core_InvertTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
|
|
{
|
|
low = cvScalarAll(-1.);
|
|
high = cvScalarAll(1.);
|
|
}
|
|
|
|
|
|
void Core_InvertTest::run_func()
|
|
{
|
|
result = cvInvert(test_array[INPUT][0], test_array[TEMP][0], method);
|
|
}
|
|
|
|
|
|
static double cvTsSVDet( CvMat* mat, double* ratio )
|
|
{
|
|
int type = CV_MAT_TYPE(mat->type);
|
|
int i, nm = MIN( mat->rows, mat->cols );
|
|
CvMat* w = cvCreateMat( nm, 1, type );
|
|
double det = 1.;
|
|
|
|
cvSVD( mat, w, 0, 0, 0 );
|
|
|
|
if( type == CV_32FC1 )
|
|
{
|
|
for( i = 0; i < nm; i++ )
|
|
det *= w->data.fl[i];
|
|
*ratio = w->data.fl[nm-1] < FLT_EPSILON ? 0 : w->data.fl[nm-1]/w->data.fl[0];
|
|
}
|
|
else
|
|
{
|
|
for( i = 0; i < nm; i++ )
|
|
det *= w->data.db[i];
|
|
*ratio = w->data.db[nm-1] < FLT_EPSILON ? 0 : w->data.db[nm-1]/w->data.db[0];
|
|
}
|
|
|
|
cvReleaseMat( &w );
|
|
return det;
|
|
}
|
|
|
|
void Core_InvertTest::prepare_to_validation( int )
|
|
{
|
|
Mat& input = test_mat[INPUT][0];
|
|
Mat& temp0 = test_mat[TEMP][0];
|
|
Mat& temp1 = test_mat[TEMP][1];
|
|
Mat& dst0 = test_mat[REF_OUTPUT][0];
|
|
Mat& dst = test_mat[OUTPUT][0];
|
|
CvMat _input = input;
|
|
double ratio = 0, det = cvTsSVDet( &_input, &ratio );
|
|
double threshold = (input.depth() == CV_32F ? FLT_EPSILON : DBL_EPSILON)*1000;
|
|
|
|
cvtest::convert( input, temp1, temp1.type() );
|
|
|
|
if( det < threshold ||
|
|
((method == CV_LU || method == CV_CHOLESKY) && (result == 0 || ratio < threshold)) ||
|
|
((method == CV_SVD || method == CV_SVD_SYM) && result < threshold) )
|
|
{
|
|
dst = Scalar::all(0);
|
|
dst0 = Scalar::all(0);
|
|
return;
|
|
}
|
|
|
|
if( input.rows >= input.cols )
|
|
cvtest::gemm( temp0, input, 1., Mat(), 0., dst, 0 );
|
|
else
|
|
cvtest::gemm( input, temp0, 1., Mat(), 0., dst, 0 );
|
|
|
|
cv::setIdentity( dst0, Scalar::all(1) );
|
|
}
|
|
|
|
|
|
///////////////// solve /////////////////////
|
|
|
|
class Core_SolveTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_SolveTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
int method, rank;
|
|
double result;
|
|
};
|
|
|
|
|
|
Core_SolveTest::Core_SolveTest() : Core_MatrixTest( 2, 1, false, false, 1 ), method(0), rank(0), result(0.)
|
|
{
|
|
test_case_count = 100;
|
|
max_log_array_size = 7;
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_SolveTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
CvSize in_sz = sizes[INPUT][0];
|
|
if( in_sz.width > in_sz.height )
|
|
in_sz = cvSize(in_sz.height, in_sz.width);
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
sizes[INPUT][0] = in_sz;
|
|
int min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );
|
|
|
|
if( (bits & 3) == 0 )
|
|
{
|
|
method = CV_SVD;
|
|
if( bits & 4 )
|
|
{
|
|
sizes[INPUT][0] = Size(min_size, min_size);
|
|
/*if( bits & 8 )
|
|
method = CV_SVD_SYM;*/
|
|
}
|
|
}
|
|
else
|
|
{
|
|
method = CV_LU;
|
|
sizes[INPUT][0] = Size(min_size, min_size);
|
|
}
|
|
|
|
sizes[INPUT][1].height = sizes[INPUT][0].height;
|
|
sizes[TEMP][0].width = sizes[INPUT][1].width;
|
|
sizes[TEMP][0].height = sizes[INPUT][0].width;
|
|
sizes[TEMP][1] = sizes[INPUT][0];
|
|
types[TEMP][0] = types[INPUT][0];
|
|
types[TEMP][1] = CV_64FC1;
|
|
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = Size(sizes[INPUT][1].width, min_size);
|
|
}
|
|
|
|
|
|
int Core_SolveTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Core_MatrixTest::prepare_test_case( test_case_idx );
|
|
|
|
/*if( method == CV_SVD_SYM )
|
|
{
|
|
cvTsGEMM( test_array[INPUT][0], test_array[INPUT][0], 1.,
|
|
0, 0., test_array[TEMP][0], CV_GEMM_B_T );
|
|
cvTsCopy( test_array[TEMP][0], test_array[INPUT][0] );
|
|
}*/
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void Core_SolveTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
|
|
{
|
|
low = cvScalarAll(-1.);
|
|
high = cvScalarAll(1.);
|
|
}
|
|
|
|
|
|
double Core_SolveTest::get_success_error_level( int /*test_case_idx*/, int, int )
|
|
{
|
|
return CV_MAT_DEPTH(cvGetElemType(test_array[OUTPUT][0])) == CV_32F ? 5e-2 : 1e-8;
|
|
}
|
|
|
|
|
|
void Core_SolveTest::run_func()
|
|
{
|
|
result = cvSolve(test_array[INPUT][0], test_array[INPUT][1], test_array[TEMP][0], method);
|
|
}
|
|
|
|
void Core_SolveTest::prepare_to_validation( int )
|
|
{
|
|
//int rank = test_mat[REF_OUTPUT][0].rows;
|
|
Mat& input = test_mat[INPUT][0];
|
|
Mat& dst = test_mat[OUTPUT][0];
|
|
Mat& dst0 = test_mat[REF_OUTPUT][0];
|
|
|
|
if( method == CV_LU )
|
|
{
|
|
if( result == 0 )
|
|
{
|
|
Mat& temp1 = test_mat[TEMP][1];
|
|
cvtest::convert(input, temp1, temp1.type());
|
|
dst = Scalar::all(0);
|
|
CvMat _temp1 = temp1;
|
|
double det = cvTsLU( &_temp1, 0, 0 );
|
|
dst0 = Scalar::all(det != 0);
|
|
return;
|
|
}
|
|
|
|
double threshold = (input.type() == CV_32F ? FLT_EPSILON : DBL_EPSILON)*1000;
|
|
CvMat _input = input;
|
|
double ratio = 0, det = cvTsSVDet( &_input, &ratio );
|
|
if( det < threshold || ratio < threshold )
|
|
{
|
|
dst = Scalar::all(0);
|
|
dst0 = Scalar::all(0);
|
|
return;
|
|
}
|
|
}
|
|
|
|
Mat* pdst = input.rows <= input.cols ? &test_mat[OUTPUT][0] : &test_mat[INPUT][1];
|
|
|
|
cvtest::gemm( input, test_mat[TEMP][0], 1., test_mat[INPUT][1], -1., *pdst, 0 );
|
|
if( pdst != &dst )
|
|
cvtest::gemm( input, *pdst, 1., Mat(), 0., dst, CV_GEMM_A_T );
|
|
dst0 = Scalar::all(0);
|
|
}
|
|
|
|
|
|
///////////////// SVD /////////////////////
|
|
|
|
class Core_SVDTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_SVDTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
int flags;
|
|
bool have_u, have_v, symmetric, compact, vector_w;
|
|
};
|
|
|
|
|
|
Core_SVDTest::Core_SVDTest() :
|
|
Core_MatrixTest( 1, 4, false, false, 1 ),
|
|
flags(0), have_u(false), have_v(false), symmetric(false), compact(false), vector_w(false)
|
|
{
|
|
test_case_count = 100;
|
|
max_log_array_size = 8;
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_SVDTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
int min_size, i, m, n;
|
|
|
|
min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );
|
|
|
|
flags = bits & (CV_SVD_MODIFY_A+CV_SVD_U_T+CV_SVD_V_T);
|
|
have_u = (bits & 8) != 0;
|
|
have_v = (bits & 16) != 0;
|
|
symmetric = (bits & 32) != 0;
|
|
compact = (bits & 64) != 0;
|
|
vector_w = (bits & 128) != 0;
|
|
|
|
if( symmetric )
|
|
sizes[INPUT][0] = Size(min_size, min_size);
|
|
|
|
m = sizes[INPUT][0].height;
|
|
n = sizes[INPUT][0].width;
|
|
|
|
if( compact )
|
|
sizes[TEMP][0] = Size(min_size, min_size);
|
|
else
|
|
sizes[TEMP][0] = sizes[INPUT][0];
|
|
sizes[TEMP][3] = Size(0,0);
|
|
|
|
if( vector_w )
|
|
{
|
|
sizes[TEMP][3] = sizes[TEMP][0];
|
|
if( bits & 256 )
|
|
sizes[TEMP][0] = Size(1, min_size);
|
|
else
|
|
sizes[TEMP][0] = Size(min_size, 1);
|
|
}
|
|
|
|
if( have_u )
|
|
{
|
|
sizes[TEMP][1] = compact ? Size(min_size, m) : Size(m, m);
|
|
|
|
if( flags & CV_SVD_U_T )
|
|
CV_SWAP( sizes[TEMP][1].width, sizes[TEMP][1].height, i );
|
|
}
|
|
else
|
|
sizes[TEMP][1] = Size(0,0);
|
|
|
|
if( have_v )
|
|
{
|
|
sizes[TEMP][2] = compact ? Size(n, min_size) : Size(n, n);
|
|
|
|
if( !(flags & CV_SVD_V_T) )
|
|
CV_SWAP( sizes[TEMP][2].width, sizes[TEMP][2].height, i );
|
|
}
|
|
else
|
|
sizes[TEMP][2] = Size(0,0);
|
|
|
|
types[TEMP][0] = types[TEMP][1] = types[TEMP][2] = types[TEMP][3] = types[INPUT][0];
|
|
types[OUTPUT][0] = types[OUTPUT][1] = types[OUTPUT][2] = types[INPUT][0];
|
|
types[OUTPUT][3] = CV_8UC1;
|
|
sizes[OUTPUT][0] = !have_u || !have_v ? Size(0,0) : sizes[INPUT][0];
|
|
sizes[OUTPUT][1] = !have_u ? Size(0,0) : compact ? Size(min_size,min_size) : Size(m,m);
|
|
sizes[OUTPUT][2] = !have_v ? Size(0,0) : compact ? Size(min_size,min_size) : Size(n,n);
|
|
sizes[OUTPUT][3] = Size(min_size,1);
|
|
|
|
for( i = 0; i < 4; i++ )
|
|
{
|
|
sizes[REF_OUTPUT][i] = sizes[OUTPUT][i];
|
|
types[REF_OUTPUT][i] = types[OUTPUT][i];
|
|
}
|
|
}
|
|
|
|
|
|
int Core_SVDTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Core_MatrixTest::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
{
|
|
Mat& input = test_mat[INPUT][0];
|
|
cvTsFloodWithZeros( input, ts->get_rng() );
|
|
|
|
if( symmetric && (have_u || have_v) )
|
|
{
|
|
Mat& temp = test_mat[TEMP][have_u ? 1 : 2];
|
|
cvtest::gemm( input, input, 1., Mat(), 0., temp, CV_GEMM_B_T );
|
|
cvtest::copy( temp, input );
|
|
}
|
|
|
|
if( (flags & CV_SVD_MODIFY_A) && test_array[OUTPUT][0] )
|
|
cvtest::copy( input, test_mat[OUTPUT][0] );
|
|
}
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void Core_SVDTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
|
|
{
|
|
low = cvScalarAll(-2.);
|
|
high = cvScalarAll(2.);
|
|
}
|
|
|
|
double Core_SVDTest::get_success_error_level( int test_case_idx, int i, int j )
|
|
{
|
|
int input_depth = CV_MAT_DEPTH(cvGetElemType( test_array[INPUT][0] ));
|
|
double input_precision = input_depth < CV_32F ? 0 : input_depth == CV_32F ? 1e-5 : 5e-11;
|
|
double output_precision = Base::get_success_error_level( test_case_idx, i, j );
|
|
return MAX(input_precision, output_precision);
|
|
}
|
|
|
|
void Core_SVDTest::run_func()
|
|
{
|
|
CvArr* src = test_array[!(flags & CV_SVD_MODIFY_A) ? INPUT : OUTPUT][0];
|
|
if( !src )
|
|
src = test_array[INPUT][0];
|
|
cvSVD( src, test_array[TEMP][0], test_array[TEMP][1], test_array[TEMP][2], flags );
|
|
}
|
|
|
|
|
|
void Core_SVDTest::prepare_to_validation( int /*test_case_idx*/ )
|
|
{
|
|
Mat& input = test_mat[INPUT][0];
|
|
int depth = input.depth();
|
|
int i, m = input.rows, n = input.cols, min_size = MIN(m, n);
|
|
Mat *src, *dst, *w;
|
|
double prev = 0, threshold = depth == CV_32F ? FLT_EPSILON : DBL_EPSILON;
|
|
|
|
if( have_u )
|
|
{
|
|
src = &test_mat[TEMP][1];
|
|
dst = &test_mat[OUTPUT][1];
|
|
cvtest::gemm( *src, *src, 1., Mat(), 0., *dst, src->rows == dst->rows ? CV_GEMM_B_T : CV_GEMM_A_T );
|
|
cv::setIdentity( test_mat[REF_OUTPUT][1], Scalar::all(1.) );
|
|
}
|
|
|
|
if( have_v )
|
|
{
|
|
src = &test_mat[TEMP][2];
|
|
dst = &test_mat[OUTPUT][2];
|
|
cvtest::gemm( *src, *src, 1., Mat(), 0., *dst, src->rows == dst->rows ? CV_GEMM_B_T : CV_GEMM_A_T );
|
|
cv::setIdentity( test_mat[REF_OUTPUT][2], Scalar::all(1.) );
|
|
}
|
|
|
|
w = &test_mat[TEMP][0];
|
|
for( i = 0; i < min_size; i++ )
|
|
{
|
|
double normval = 0, aii;
|
|
if( w->rows > 1 && w->cols > 1 )
|
|
{
|
|
normval = cvtest::norm( w->row(i), NORM_L1 );
|
|
aii = depth == CV_32F ? w->at<float>(i,i) : w->at<double>(i,i);
|
|
}
|
|
else
|
|
{
|
|
normval = aii = depth == CV_32F ? w->at<float>(i) : w->at<double>(i);
|
|
}
|
|
|
|
normval = fabs(normval - aii);
|
|
test_mat[OUTPUT][3].at<uchar>(i) = aii >= 0 && normval < threshold && (i == 0 || aii <= prev);
|
|
prev = aii;
|
|
}
|
|
|
|
test_mat[REF_OUTPUT][3] = Scalar::all(1);
|
|
|
|
if( have_u && have_v )
|
|
{
|
|
if( vector_w )
|
|
{
|
|
test_mat[TEMP][3] = Scalar::all(0);
|
|
for( i = 0; i < min_size; i++ )
|
|
{
|
|
double val = depth == CV_32F ? w->at<float>(i) : w->at<double>(i);
|
|
cvSetReal2D( test_array[TEMP][3], i, i, val );
|
|
}
|
|
w = &test_mat[TEMP][3];
|
|
}
|
|
|
|
if( m >= n )
|
|
{
|
|
cvtest::gemm( test_mat[TEMP][1], *w, 1., Mat(), 0., test_mat[REF_OUTPUT][0],
|
|
flags & CV_SVD_U_T ? CV_GEMM_A_T : 0 );
|
|
cvtest::gemm( test_mat[REF_OUTPUT][0], test_mat[TEMP][2], 1., Mat(), 0.,
|
|
test_mat[OUTPUT][0], flags & CV_SVD_V_T ? 0 : CV_GEMM_B_T );
|
|
}
|
|
else
|
|
{
|
|
cvtest::gemm( *w, test_mat[TEMP][2], 1., Mat(), 0., test_mat[REF_OUTPUT][0],
|
|
flags & CV_SVD_V_T ? 0 : CV_GEMM_B_T );
|
|
cvtest::gemm( test_mat[TEMP][1], test_mat[REF_OUTPUT][0], 1., Mat(), 0.,
|
|
test_mat[OUTPUT][0], flags & CV_SVD_U_T ? CV_GEMM_A_T : 0 );
|
|
}
|
|
|
|
cvtest::copy( test_mat[INPUT][0], test_mat[REF_OUTPUT][0] );
|
|
}
|
|
}
|
|
|
|
|
|
|
|
///////////////// SVBkSb /////////////////////
|
|
|
|
class Core_SVBkSbTest : public Core_MatrixTest
|
|
{
|
|
public:
|
|
typedef Core_MatrixTest Base;
|
|
Core_SVBkSbTest();
|
|
protected:
|
|
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
|
|
double get_success_error_level( int test_case_idx, int i, int j );
|
|
void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
|
|
int prepare_test_case( int test_case_idx );
|
|
void run_func();
|
|
void prepare_to_validation( int test_case_idx );
|
|
int flags;
|
|
bool have_b, symmetric, compact, vector_w;
|
|
};
|
|
|
|
|
|
Core_SVBkSbTest::Core_SVBkSbTest() : Core_MatrixTest( 2, 1, false, false, 1 ),
|
|
flags(0), have_b(false), symmetric(false), compact(false), vector_w(false)
|
|
{
|
|
test_case_count = 100;
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
test_array[TEMP].push_back(NULL);
|
|
}
|
|
|
|
|
|
void Core_SVBkSbTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes,
|
|
vector<vector<int> >& types )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int bits = cvtest::randInt(rng);
|
|
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
|
|
int min_size, i, m, n;
|
|
CvSize b_size;
|
|
|
|
min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );
|
|
|
|
flags = bits & (CV_SVD_MODIFY_A+CV_SVD_U_T+CV_SVD_V_T);
|
|
have_b = (bits & 16) != 0;
|
|
symmetric = (bits & 32) != 0;
|
|
compact = (bits & 64) != 0;
|
|
vector_w = (bits & 128) != 0;
|
|
|
|
if( symmetric )
|
|
sizes[INPUT][0] = Size(min_size, min_size);
|
|
|
|
m = sizes[INPUT][0].height;
|
|
n = sizes[INPUT][0].width;
|
|
|
|
sizes[INPUT][1] = Size(0,0);
|
|
b_size = Size(m,m);
|
|
if( have_b )
|
|
{
|
|
sizes[INPUT][1].height = sizes[INPUT][0].height;
|
|
sizes[INPUT][1].width = cvtest::randInt(rng) % 100 + 1;
|
|
b_size = sizes[INPUT][1];
|
|
}
|
|
|
|
if( compact )
|
|
sizes[TEMP][0] = Size(min_size, min_size);
|
|
else
|
|
sizes[TEMP][0] = sizes[INPUT][0];
|
|
|
|
if( vector_w )
|
|
{
|
|
if( bits & 256 )
|
|
sizes[TEMP][0] = Size(1, min_size);
|
|
else
|
|
sizes[TEMP][0] = Size(min_size, 1);
|
|
}
|
|
|
|
sizes[TEMP][1] = compact ? Size(min_size, m) : Size(m, m);
|
|
|
|
if( flags & CV_SVD_U_T )
|
|
CV_SWAP( sizes[TEMP][1].width, sizes[TEMP][1].height, i );
|
|
|
|
sizes[TEMP][2] = compact ? Size(n, min_size) : Size(n, n);
|
|
|
|
if( !(flags & CV_SVD_V_T) )
|
|
CV_SWAP( sizes[TEMP][2].width, sizes[TEMP][2].height, i );
|
|
|
|
types[TEMP][0] = types[TEMP][1] = types[TEMP][2] = types[INPUT][0];
|
|
types[OUTPUT][0] = types[REF_OUTPUT][0] = types[INPUT][0];
|
|
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = Size( b_size.width, n );
|
|
}
|
|
|
|
|
|
int Core_SVBkSbTest::prepare_test_case( int test_case_idx )
|
|
{
|
|
int code = Base::prepare_test_case( test_case_idx );
|
|
if( code > 0 )
|
|
{
|
|
Mat& input = test_mat[INPUT][0];
|
|
cvTsFloodWithZeros( input, ts->get_rng() );
|
|
|
|
if( symmetric )
|
|
{
|
|
Mat& temp = test_mat[TEMP][1];
|
|
cvtest::gemm( input, input, 1., Mat(), 0., temp, CV_GEMM_B_T );
|
|
cvtest::copy( temp, input );
|
|
}
|
|
|
|
CvMat _input = input;
|
|
cvSVD( &_input, test_array[TEMP][0], test_array[TEMP][1], test_array[TEMP][2], flags );
|
|
}
|
|
|
|
return code;
|
|
}
|
|
|
|
|
|
void Core_SVBkSbTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
|
|
{
|
|
low = cvScalarAll(-2.);
|
|
high = cvScalarAll(2.);
|
|
}
|
|
|
|
|
|
double Core_SVBkSbTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
|
{
|
|
return CV_MAT_DEPTH(cvGetElemType(test_array[INPUT][0])) == CV_32F ? 1e-3 : 1e-7;
|
|
}
|
|
|
|
|
|
void Core_SVBkSbTest::run_func()
|
|
{
|
|
cvSVBkSb( test_array[TEMP][0], test_array[TEMP][1], test_array[TEMP][2],
|
|
test_array[INPUT][1], test_array[OUTPUT][0], flags );
|
|
}
|
|
|
|
|
|
void Core_SVBkSbTest::prepare_to_validation( int )
|
|
{
|
|
Mat& input = test_mat[INPUT][0];
|
|
int i, m = input.rows, n = input.cols, min_size = MIN(m, n);
|
|
bool is_float = input.type() == CV_32F;
|
|
Size w_size = compact ? Size(min_size,min_size) : Size(m,n);
|
|
Mat& w = test_mat[TEMP][0];
|
|
Mat wdb( w_size.height, w_size.width, CV_64FC1 );
|
|
CvMat _w = w, _wdb = wdb;
|
|
// use exactly the same threshold as in icvSVD... ,
|
|
// so the changes in the library and here should be synchronized.
|
|
double threshold = cv::sum(w)[0]*(DBL_EPSILON*2);//(is_float ? FLT_EPSILON*10 : DBL_EPSILON*2);
|
|
|
|
wdb = Scalar::all(0);
|
|
for( i = 0; i < min_size; i++ )
|
|
{
|
|
double wii = vector_w ? cvGetReal1D(&_w,i) : cvGetReal2D(&_w,i,i);
|
|
cvSetReal2D( &_wdb, i, i, wii > threshold ? 1./wii : 0. );
|
|
}
|
|
|
|
Mat u = test_mat[TEMP][1];
|
|
Mat v = test_mat[TEMP][2];
|
|
Mat b = test_mat[INPUT][1];
|
|
|
|
if( is_float )
|
|
{
|
|
test_mat[TEMP][1].convertTo(u, CV_64F);
|
|
test_mat[TEMP][2].convertTo(v, CV_64F);
|
|
if( !b.empty() )
|
|
test_mat[INPUT][1].convertTo(b, CV_64F);
|
|
}
|
|
|
|
Mat t0, t1;
|
|
|
|
if( !b.empty() )
|
|
cvtest::gemm( u, b, 1., Mat(), 0., t0, !(flags & CV_SVD_U_T) ? CV_GEMM_A_T : 0 );
|
|
else if( flags & CV_SVD_U_T )
|
|
cvtest::copy( u, t0 );
|
|
else
|
|
cvtest::transpose( u, t0 );
|
|
|
|
cvtest::gemm( wdb, t0, 1, Mat(), 0, t1, 0 );
|
|
|
|
cvtest::gemm( v, t1, 1, Mat(), 0, t0, flags & CV_SVD_V_T ? CV_GEMM_A_T : 0 );
|
|
Mat& dst0 = test_mat[REF_OUTPUT][0];
|
|
t0.convertTo(dst0, dst0.type() );
|
|
}
|
|
|
|
|
|
typedef std::complex<double> complex_type;
|
|
|
|
struct pred_complex
|
|
{
|
|
bool operator() (const complex_type& lhs, const complex_type& rhs) const
|
|
{
|
|
return fabs(lhs.real() - rhs.real()) > fabs(rhs.real())*FLT_EPSILON ? lhs.real() < rhs.real() : lhs.imag() < rhs.imag();
|
|
}
|
|
};
|
|
|
|
struct pred_double
|
|
{
|
|
bool operator() (const double& lhs, const double& rhs) const
|
|
{
|
|
return lhs < rhs;
|
|
}
|
|
};
|
|
|
|
class Core_SolvePolyTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
Core_SolvePolyTest();
|
|
~Core_SolvePolyTest();
|
|
protected:
|
|
virtual void run( int start_from );
|
|
};
|
|
|
|
Core_SolvePolyTest::Core_SolvePolyTest() {}
|
|
|
|
Core_SolvePolyTest::~Core_SolvePolyTest() {}
|
|
|
|
void Core_SolvePolyTest::run( int )
|
|
{
|
|
RNG& rng = ts->get_rng();
|
|
int fig = 100;
|
|
double range = 50;
|
|
double err_eps = 1e-4;
|
|
|
|
for (int idx = 0, max_idx = 1000, progress = 0; idx < max_idx; ++idx)
|
|
{
|
|
progress = update_progress(progress, idx-1, max_idx, 0);
|
|
int n = cvtest::randInt(rng) % 13 + 1;
|
|
std::vector<complex_type> r(n), ar(n), c(n + 1, 0);
|
|
std::vector<double> a(n + 1), u(n * 2), ar1(n), ar2(n);
|
|
|
|
int rr_odds = 3; // odds that we get a real root
|
|
for (int j = 0; j < n;)
|
|
{
|
|
if (cvtest::randInt(rng) % rr_odds == 0 || j == n - 1)
|
|
r[j++] = cvtest::randReal(rng) * range;
|
|
else
|
|
{
|
|
r[j] = complex_type(cvtest::randReal(rng) * range,
|
|
cvtest::randReal(rng) * range + 1);
|
|
r[j + 1] = std::conj(r[j]);
|
|
j += 2;
|
|
}
|
|
}
|
|
|
|
for (int j = 0, k = 1 << n, jj, kk; j < k; ++j)
|
|
{
|
|
int p = 0;
|
|
complex_type v(1);
|
|
for (jj = 0, kk = 1; jj < n && !(j & kk); ++jj, ++p, kk <<= 1)
|
|
;
|
|
for (; jj < n; ++jj, kk <<= 1)
|
|
{
|
|
if (j & kk)
|
|
v *= -r[jj];
|
|
else
|
|
++p;
|
|
}
|
|
c[p] += v;
|
|
}
|
|
|
|
bool pass = false;
|
|
double div = 0, s = 0;
|
|
int cubic_case = idx & 1;
|
|
for (int maxiter = 100; !pass && maxiter < 10000; maxiter *= 2, cubic_case = (cubic_case + 1) % 2)
|
|
{
|
|
for (int j = 0; j < n + 1; ++j)
|
|
a[j] = c[j].real();
|
|
|
|
CvMat amat, umat;
|
|
cvInitMatHeader(&amat, n + 1, 1, CV_64FC1, &a[0]);
|
|
cvInitMatHeader(&umat, n, 1, CV_64FC2, &u[0]);
|
|
cvSolvePoly(&amat, &umat, maxiter, fig);
|
|
|
|
for (int j = 0; j < n; ++j)
|
|
ar[j] = complex_type(u[j * 2], u[j * 2 + 1]);
|
|
|
|
std::sort(r.begin(), r.end(), pred_complex());
|
|
std::sort(ar.begin(), ar.end(), pred_complex());
|
|
|
|
pass = true;
|
|
if( n == 3 )
|
|
{
|
|
ar2.resize(n);
|
|
cv::Mat _umat2(3, 1, CV_64F, &ar2[0]), umat2 = _umat2;
|
|
cvFlip(&amat, &amat, 0);
|
|
int nr2;
|
|
if( cubic_case == 0 )
|
|
nr2 = cv::solveCubic(cv::Mat(&amat),umat2);
|
|
else
|
|
nr2 = cv::solveCubic(cv::Mat_<float>(cv::Mat(&amat)), umat2);
|
|
cvFlip(&amat, &amat, 0);
|
|
if(nr2 > 0)
|
|
std::sort(ar2.begin(), ar2.begin()+nr2, pred_double());
|
|
ar2.resize(nr2);
|
|
|
|
int nr1 = 0;
|
|
for(int j = 0; j < n; j++)
|
|
if( fabs(r[j].imag()) < DBL_EPSILON )
|
|
ar1[nr1++] = r[j].real();
|
|
|
|
pass = pass && nr1 == nr2;
|
|
if( nr2 > 0 )
|
|
{
|
|
div = s = 0;
|
|
for(int j = 0; j < nr1; j++)
|
|
{
|
|
s += fabs(ar1[j]);
|
|
div += fabs(ar1[j] - ar2[j]);
|
|
}
|
|
div /= s;
|
|
pass = pass && div < err_eps;
|
|
}
|
|
}
|
|
|
|
div = s = 0;
|
|
for (int j = 0; j < n; ++j)
|
|
{
|
|
s += fabs(r[j].real()) + fabs(r[j].imag());
|
|
div += sqrt(pow(r[j].real() - ar[j].real(), 2) + pow(r[j].imag() - ar[j].imag(), 2));
|
|
}
|
|
div /= s;
|
|
pass = pass && div < err_eps;
|
|
}
|
|
|
|
//test bug #5623 - solves equation x^3 = 0
|
|
cv::Mat coeffs_5623(4, 1, CV_64FC1);
|
|
cv::Mat r_5623(3, 1, CV_64FC2);
|
|
coeffs_5623.at<double>(0) = 1;
|
|
coeffs_5623.at<double>(1) = 0;
|
|
coeffs_5623.at<double>(2) = 0;
|
|
coeffs_5623.at<double>(3) = 0;
|
|
double prec_5623 = cv::solveCubic(coeffs_5623, r_5623);
|
|
pass = pass && r_5623.at<double>(0) == 0 && r_5623.at<double>(1) == 0 && r_5623.at<double>(2) == 0;
|
|
pass = pass && prec_5623 == 1;
|
|
|
|
if (!pass)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
ts->printf( cvtest::TS::LOG, "too big diff = %g\n", div );
|
|
|
|
for (size_t j=0;j<ar2.size();++j)
|
|
ts->printf( cvtest::TS::LOG, "ar2[%d]=%g\n", j, ar2[j]);
|
|
ts->printf(cvtest::TS::LOG, "\n");
|
|
|
|
for (size_t j=0;j<r.size();++j)
|
|
ts->printf( cvtest::TS::LOG, "r[%d]=(%g, %g)\n", j, r[j].real(), r[j].imag());
|
|
ts->printf( cvtest::TS::LOG, "\n" );
|
|
for (size_t j=0;j<ar.size();++j)
|
|
ts->printf( cvtest::TS::LOG, "ar[%d]=(%g, %g)\n", j, ar[j].real(), ar[j].imag());
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
template<typename T>
|
|
static void checkRoot(Mat& r, T re, T im)
|
|
{
|
|
for (int i = 0; i < r.cols*r.rows; i++)
|
|
{
|
|
Vec<T, 2> v = *(Vec<T, 2>*)r.ptr(i);
|
|
if (fabs(re - v[0]) < 1e-6 && fabs(im - v[1]) < 1e-6)
|
|
{
|
|
v[0] = std::numeric_limits<T>::quiet_NaN();
|
|
v[1] = std::numeric_limits<T>::quiet_NaN();
|
|
return;
|
|
}
|
|
}
|
|
GTEST_NONFATAL_FAILURE_("Can't find root") << "(" << re << ", " << im << ")";
|
|
}
|
|
TEST(Core_SolvePoly, regression_5599)
|
|
{
|
|
// x^4 - x^2 = 0, roots: 1, -1, 0, 0
|
|
cv::Mat coefs = (cv::Mat_<float>(1,5) << 0, 0, -1, 0, 1 );
|
|
{
|
|
cv::Mat r;
|
|
double prec;
|
|
prec = cv::solvePoly(coefs, r);
|
|
EXPECT_LE(prec, 1e-6);
|
|
EXPECT_EQ(4u, r.total());
|
|
//std::cout << "Preciseness = " << prec << std::endl;
|
|
//std::cout << "roots:\n" << r << "\n" << std::endl;
|
|
ASSERT_EQ(CV_32FC2, r.type());
|
|
checkRoot<float>(r, 1, 0);
|
|
checkRoot<float>(r, -1, 0);
|
|
checkRoot<float>(r, 0, 0);
|
|
checkRoot<float>(r, 0, 0);
|
|
}
|
|
// x^2 - 2x + 1 = 0, roots: 1, 1
|
|
coefs = (cv::Mat_<float>(1,3) << 1, -2, 1 );
|
|
{
|
|
cv::Mat r;
|
|
double prec;
|
|
prec = cv::solvePoly(coefs, r);
|
|
EXPECT_LE(prec, 1e-6);
|
|
EXPECT_EQ(2u, r.total());
|
|
//std::cout << "Preciseness = " << prec << std::endl;
|
|
//std::cout << "roots:\n" << r << "\n" << std::endl;
|
|
ASSERT_EQ(CV_32FC2, r.type());
|
|
checkRoot<float>(r, 1, 0);
|
|
checkRoot<float>(r, 1, 0);
|
|
}
|
|
}
|
|
|
|
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);
|
|
|
|
TYPED_TEST_P(Core_CheckRange, Negative)
|
|
{
|
|
double min_bound = 4.5;
|
|
double max_bound = 16.0;
|
|
|
|
TypeParam data[] = {5, 10, 15, 4, 10, 2, 8, 12, 14};
|
|
cv::Mat src = cv::Mat(3,3, cv::DataDepth<TypeParam>::value, data);
|
|
|
|
cv::Point bad_pt(0, 0);
|
|
|
|
ASSERT_FALSE(checkRange(src, true, &bad_pt, min_bound, max_bound));
|
|
ASSERT_EQ(bad_pt.x, 0);
|
|
ASSERT_EQ(bad_pt.y, 1);
|
|
}
|
|
|
|
TYPED_TEST_P(Core_CheckRange, Positive)
|
|
{
|
|
double min_bound = -1;
|
|
double max_bound = 16.0;
|
|
|
|
TypeParam data[] = {5, 10, 15, 4, 10, 2, 8, 12, 14};
|
|
cv::Mat src = cv::Mat(3,3, cv::DataDepth<TypeParam>::value, data);
|
|
|
|
cv::Point bad_pt(0, 0);
|
|
|
|
ASSERT_TRUE(checkRange(src, true, &bad_pt, min_bound, max_bound));
|
|
ASSERT_EQ(bad_pt.x, 0);
|
|
ASSERT_EQ(bad_pt.y, 0);
|
|
}
|
|
|
|
TYPED_TEST_P(Core_CheckRange, Bounds)
|
|
{
|
|
double min_bound = 24.5;
|
|
double max_bound = 1.0;
|
|
|
|
TypeParam data[] = {5, 10, 15, 4, 10, 2, 8, 12, 14};
|
|
cv::Mat src = cv::Mat(3,3, cv::DataDepth<TypeParam>::value, data);
|
|
|
|
cv::Point bad_pt(0, 0);
|
|
|
|
ASSERT_FALSE(checkRange(src, true, &bad_pt, min_bound, max_bound));
|
|
ASSERT_EQ(bad_pt.x, 0);
|
|
ASSERT_EQ(bad_pt.y, 0);
|
|
}
|
|
|
|
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);
|
|
|
|
TEST(Core_Invert, small)
|
|
{
|
|
cv::Mat a = (cv::Mat_<float>(3,3) << 2.42104644730331, 1.81444796521479, -3.98072565304758, 0, 7.08389214348967e-3, 5.55326770986007e-3, 0,0, 7.44556154284261e-3);
|
|
//cv::randu(a, -1, 1);
|
|
|
|
cv::Mat b = a.t()*a;
|
|
cv::Mat c, i = Mat_<float>::eye(3, 3);
|
|
cv::invert(b, c, cv::DECOMP_LU); //std::cout << b*c << std::endl;
|
|
ASSERT_LT( cv::norm(b*c, i, CV_C), 0.1 );
|
|
cv::invert(b, c, cv::DECOMP_SVD); //std::cout << b*c << std::endl;
|
|
ASSERT_LT( cv::norm(b*c, i, CV_C), 0.1 );
|
|
cv::invert(b, c, cv::DECOMP_CHOLESKY); //std::cout << b*c << std::endl;
|
|
ASSERT_LT( cv::norm(b*c, i, CV_C), 0.1 );
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(Core_CovarMatrix, accuracy) { Core_CovarMatrixTest test; test.safe_run(); }
|
|
TEST(Core_CrossProduct, accuracy) { Core_CrossProductTest test; test.safe_run(); }
|
|
TEST(Core_Determinant, accuracy) { Core_DetTest test; test.safe_run(); }
|
|
TEST(Core_DotProduct, accuracy) { Core_DotProductTest test; test.safe_run(); }
|
|
TEST(Core_GEMM, accuracy) { Core_GEMMTest test; test.safe_run(); }
|
|
TEST(Core_Invert, accuracy) { Core_InvertTest test; test.safe_run(); }
|
|
TEST(Core_Mahalanobis, accuracy) { Core_MahalanobisTest test; test.safe_run(); }
|
|
TEST(Core_MulTransposed, accuracy) { Core_MulTransposedTest test; test.safe_run(); }
|
|
TEST(Core_Transform, accuracy) { Core_TransformTest test; test.safe_run(); }
|
|
TEST(Core_PerspectiveTransform, accuracy) { Core_PerspectiveTransformTest test; test.safe_run(); }
|
|
TEST(Core_Pow, accuracy) { Core_PowTest test; test.safe_run(); }
|
|
TEST(Core_SolveLinearSystem, accuracy) { Core_SolveTest test; test.safe_run(); }
|
|
TEST(Core_SVD, accuracy) { Core_SVDTest test; test.safe_run(); }
|
|
TEST(Core_SVBkSb, accuracy) { Core_SVBkSbTest test; test.safe_run(); }
|
|
TEST(Core_Trace, accuracy) { Core_TraceTest test; test.safe_run(); }
|
|
TEST(Core_SolvePoly, accuracy) { Core_SolvePolyTest test; test.safe_run(); }
|
|
|
|
|
|
TEST(Core_SVD, flt)
|
|
{
|
|
float a[] = {
|
|
1.23377746e+011f, -7.05490125e+010f, -4.18380882e+010f, -11693456.f,
|
|
-39091328.f, 77492224.f, -7.05490125e+010f, 2.36211143e+011f,
|
|
-3.51093473e+010f, 70773408.f, -4.83386156e+005f, -129560368.f,
|
|
-4.18380882e+010f, -3.51093473e+010f, 9.25311222e+010f, -49052424.f,
|
|
43922752.f, 12176842.f, -11693456.f, 70773408.f, -49052424.f, 8.40836094e+004f,
|
|
5.17475293e+003f, -1.16122949e+004f, -39091328.f, -4.83386156e+005f,
|
|
43922752.f, 5.17475293e+003f, 5.16047969e+004f, 5.68887842e+003f, 77492224.f,
|
|
-129560368.f, 12176842.f, -1.16122949e+004f, 5.68887842e+003f,
|
|
1.28060578e+005f
|
|
};
|
|
|
|
float b[] = {
|
|
283751232.f, 2.61604198e+009f, -745033216.f, 2.31125625e+005f,
|
|
-4.52429188e+005f, -1.37596525e+006f
|
|
};
|
|
|
|
Mat A(6, 6, CV_32F, a);
|
|
Mat B(6, 1, CV_32F, b);
|
|
Mat X, B1;
|
|
solve(A, B, X, DECOMP_SVD);
|
|
B1 = A*X;
|
|
EXPECT_LE(norm(B1, B, NORM_L2 + NORM_RELATIVE), FLT_EPSILON*10);
|
|
}
|
|
|
|
|
|
// 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
|
|
{
|
|
public:
|
|
CV_KMeansSingularTest() {}
|
|
~CV_KMeansSingularTest() {}
|
|
protected:
|
|
void run(int inVariant)
|
|
{
|
|
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(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);
|
|
|
|
if (inVariant == VECTOR)
|
|
{
|
|
dims = 2;
|
|
|
|
std::vector<cv::Point2f> data0(N0);
|
|
rng.fill(data0, RNG::UNIFORM, -1, 1);
|
|
|
|
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++ )
|
|
{
|
|
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(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)
|
|
{
|
|
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. */
|