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C-API cleanup: core module tests

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This commit is contained in:
Maksim Shabunin
2024-11-11 13:06:37 +03:00
parent 979428d590
commit 2d9c0c8592
12 changed files with 73 additions and 2374 deletions
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modules/core/test/test_math.cpp
+38 -78
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@@ -10,7 +10,6 @@
#include <float.h>
#include <math.h>
#include "opencv2/core/softfloat.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/core/hal/intrin.hpp"
namespace opencv_test { namespace {
@@ -604,24 +603,24 @@ void Core_GEMMTest::get_test_array_types_and_sizes( int test_case_idx, vector<ve
tabc_flag = cvtest::randInt(rng) & 7;
switch( tabc_flag & (CV_GEMM_A_T|CV_GEMM_B_T) )
switch( tabc_flag & (cv::GEMM_1_T|cv::GEMM_2_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:
case cv::GEMM_2_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:
case cv::GEMM_1_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:
case cv::GEMM_1_T | cv::GEMM_2_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;
@@ -632,7 +631,7 @@ void Core_GEMMTest::get_test_array_types_and_sizes( int test_case_idx, vector<ve
if( cvtest::randInt(rng) & 1 )
sizes[INPUT][4] = Size(0,0);
else if( !(tabc_flag & CV_GEMM_C_T) )
else if( !(tabc_flag & cv::GEMM_3_T) )
sizes[INPUT][4] = sizes[OUTPUT][0];
else
{
@@ -742,8 +741,8 @@ void Core_MulTransposedTest::get_test_array_types_and_sizes( int test_case_idx,
void Core_MulTransposedTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
low = cvScalarAll(-10.);
high = cvScalarAll(10.);
low = cv::Scalar::all(-10.);
high = cv::Scalar::all(10.);
}
@@ -1126,7 +1125,7 @@ void Core_MahalanobisTest::prepare_to_validation( int )
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])));
test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = cv::Scalar(sqrt(cvtest::crossCorr(test_mat[TEMP][0], test_mat[TEMP][1])));
}
@@ -1186,8 +1185,8 @@ void Core_DetTest::get_test_array_types_and_sizes( int test_case_idx, vector<vec
void Core_DetTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
low = cvScalarAll(-2.);
high = cvScalarAll(2.);
low = cv::Scalar::all(-2.);
high = cv::Scalar::all(2.);
}
@@ -1214,21 +1213,17 @@ void Core_DetTest::run_func()
// 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 )
static double cvTsLU( cv::Mat a )
{
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;
CV_Assert(a.type() == CV_64FC1);
int i, j, k, N = a.rows, N1 = a.cols, Nm = MIN(N, N1), step = (int)(a.step/sizeof(double));
double *a0 = a.ptr<double>();
double t, det = 1.;
CV_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;
double *a1, *a2;
k = i;
for( j = i+1; j < N; j++ )
@@ -1245,57 +1240,27 @@ static double cvTsLU( CvMat* a, CvMat* b=NULL, CvMat* x=NULL, int* rank=0 )
{
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 )
for( j = i+1; j < N; j++, a2 += 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 )
{
CV_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;
}
@@ -1303,8 +1268,7 @@ static double cvTsLU( CvMat* a, CvMat* b=NULL, CvMat* x=NULL, int* rank=0 )
void Core_DetTest::prepare_to_validation( int )
{
test_mat[INPUT][0].convertTo(test_mat[TEMP][0], test_mat[TEMP][0].type());
CvMat temp0 = cvMat(test_mat[TEMP][0]);
test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = cvRealScalar(cvTsLU(&temp0, 0, 0));
test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = cv::Scalar(cvTsLU(test_mat[TEMP][0]));
}
@@ -1384,7 +1348,7 @@ int Core_InvertTest::prepare_test_case( int test_case_idx )
if( method == cv::DECOMP_CHOLESKY )
{
cvtest::gemm( test_mat[INPUT][0], test_mat[INPUT][0], 1.,
Mat(), 0., test_mat[TEMP][0], CV_GEMM_B_T );
Mat(), 0., test_mat[TEMP][0], cv::GEMM_2_T );
cvtest::copy( test_mat[TEMP][0], test_mat[INPUT][0] );
}
}
@@ -1396,8 +1360,8 @@ int Core_InvertTest::prepare_test_case( int test_case_idx )
void Core_InvertTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
low = cvScalarAll(-1.);
high = cvScalarAll(1.);
low = cv::Scalar::all(-1.);
high = cv::Scalar::all(1.);
}
void Core_InvertTest::run_func()
@@ -1493,9 +1457,9 @@ void Core_SolveTest::get_test_array_types_and_sizes( int test_case_idx, vector<v
RNG& rng = cv::theRNG();
int bits = cvtest::randInt(rng);
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
CvSize in_sz = cvSize(sizes[INPUT][0]);
cv::Size in_sz = cv::Size(sizes[INPUT][0]);
if( in_sz.width > in_sz.height )
in_sz = cvSize(in_sz.height, in_sz.width);
in_sz = cv::Size(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 );
@@ -1534,8 +1498,8 @@ int Core_SolveTest::prepare_test_case( int test_case_idx )
void Core_SolveTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
low = cvScalarAll(-1.);
high = cvScalarAll(1.);
low = cv::Scalar::all(-1.);
high = cv::Scalar::all(1.);
}
@@ -1564,8 +1528,7 @@ void Core_SolveTest::prepare_to_validation( int )
Mat& temp1 = test_mat[TEMP][1];
cvtest::convert(input, temp1, temp1.type());
dst = Scalar::all(0);
CvMat _temp1 = cvMat(temp1);
double det = cvTsLU( &_temp1, 0, 0 );
double det = cvTsLU( temp1 );
dst0 = Scalar::all(det != 0);
return;
}
@@ -1584,7 +1547,7 @@ void Core_SolveTest::prepare_to_validation( int )
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 );
cvtest::gemm( input, *pdst, 1., Mat(), 0., dst, cv::GEMM_1_T );
dst0 = Scalar::all(0);
}
@@ -1624,7 +1587,6 @@ Core_SolvePolyTest::~Core_SolvePolyTest() {}
void Core_SolvePolyTest::run( int )
{
RNG& rng = cv::theRNG();
int fig = 100;
double range = 50;
double err_eps = 1e-4;
@@ -1673,10 +1635,8 @@ void Core_SolvePolyTest::run( int )
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);
Mat amat(n + 1, 1, CV_64FC1, &a[0]), umat(n, 1, CV_64FC2, &u[0]);
cv::solvePoly(amat, umat, maxiter);
for (int j = 0; j < n; ++j)
ar[j] = complex_type(u[j * 2], u[j * 2 + 1]);
@@ -1689,13 +1649,13 @@ void Core_SolvePolyTest::run( int )
{
ar2.resize(n);
cv::Mat _umat2(3, 1, CV_64F, &ar2[0]), umat2 = _umat2;
cvFlip(&amat, &amat, 0);
cv::flip(amat, amat, 0);
int nr2;
if( cubic_case == 0 )
nr2 = cv::solveCubic(cv::cvarrToMat(&amat),umat2);
nr2 = cv::solveCubic(amat,umat2);
else
nr2 = cv::solveCubic(cv::Mat_<float>(cv::cvarrToMat(&amat)), umat2);
cvFlip(&amat, &amat, 0);
nr2 = cv::solveCubic(cv::Mat_<float>(amat), umat2);
cv::flip(amat, amat, 0);
if(nr2 > 0)
std::sort(ar2.begin(), ar2.begin()+nr2, pred_double());
ar2.resize(nr2);
@@ -2317,7 +2277,7 @@ 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;
int singleMatFlags = cv::COVAR_ROWS;
cv::Mat gold;
cv::Mat goldMean;
@@ -2348,7 +2308,7 @@ 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;
int singleMatFlags = cv::COVAR_ROWS | cv::COVAR_USE_AVG;
cv::Mat gold;
cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
@@ -2448,7 +2408,7 @@ TEST(Core_Cholesky, accuracy64f)
for (int i = 0; i < A.rows; i++)
for (int j = i + 1; j < A.cols; j++)
A.at<double>(i, j) = 0.0;
EXPECT_LE(cvtest::norm(refA, A*A.t(), CV_RELATIVE_L2), FLT_EPSILON);
EXPECT_LE(cvtest::norm(refA, A*A.t(), cv::NORM_L2 | cv::NORM_RELATIVE), FLT_EPSILON);
}
TEST(Core_QR_Solver, accuracy64f)
@@ -2468,7 +2428,7 @@ TEST(Core_QR_Solver, accuracy64f)
//solve system with square matrix
solve(A, B, solutionQR, DECOMP_QR);
EXPECT_LE(cvtest::norm(A*solutionQR, B, CV_RELATIVE_L2), FLT_EPSILON);
EXPECT_LE(cvtest::norm(A*solutionQR, B, cv::NORM_L2 | cv::NORM_RELATIVE), FLT_EPSILON);
A = Mat(m, n, CV_64F);
B = Mat(m, n, CV_64F);
@@ -2477,13 +2437,13 @@ TEST(Core_QR_Solver, accuracy64f)
//solve normal system
solve(A, B, solutionQR, DECOMP_QR | DECOMP_NORMAL);
EXPECT_LE(cvtest::norm(A.t()*(A*solutionQR), A.t()*B, CV_RELATIVE_L2), FLT_EPSILON);
EXPECT_LE(cvtest::norm(A.t()*(A*solutionQR), A.t()*B, cv::NORM_L2 | cv::NORM_RELATIVE), FLT_EPSILON);
//solve overdeterminated system as a least squares problem
Mat solutionSVD;
solve(A, B, solutionQR, DECOMP_QR);
solve(A, B, solutionSVD, DECOMP_SVD);
EXPECT_LE(cvtest::norm(solutionQR, solutionSVD, CV_RELATIVE_L2), FLT_EPSILON);
EXPECT_LE(cvtest::norm(solutionQR, solutionSVD, cv::NORM_L2 | cv::NORM_RELATIVE), FLT_EPSILON);
//solve system with singular matrix
A = Mat(10, 10, CV_64F);