potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 23:33:05 +04:00
Code Packages Releases Wiki Activity

Merge pull request #25075 from mshabunin:cleanup-imgproc-1

Browse Source 
C-API cleanup: apps, imgproc_c and some constants #25075

Merge with https://github.com/opencv/opencv_contrib/pull/3642

* Removed obsolete apps - traincascade and createsamples (please use older OpenCV versions if you need them). These apps relied heavily on C-API
* removed all mentions of imgproc C-API headers (imgproc_c.h, types_c.h) - they were empty, included core C-API headers
* replaced usage of several C constants with C++ ones (error codes, norm modes, RNG modes, PCA modes, ...) - most part of this PR (split into two parts - all modules and calib+3d - for easier backporting)
* removed imgproc C-API headers (as separate commit, so that other changes could be backported to 4.x)

Most of these changes can be backported to 4.x.
This commit is contained in:
Maksim Shabunin
2024-03-05 12:18:31 +03:00
committed by GitHub
parent 1d1faaabef
commit 8cbdd0c833
152 changed files with 820 additions and 18160 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/core/test/test_mat.cpp
+28 -27
View File
@@ -11,6 +11,7 @@
#include "opencv2/core/cuda.hpp"
#include "opencv2/core/bindings_utils.hpp"
#include "opencv2/core/core_c.h"
namespace opencv_test { namespace {
@@ -322,7 +323,7 @@ TEST(Core_PCA, accuracy)
rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA;
PCA rPCA( rPoints, Mat(), PCA::DATA_AS_ROW, maxComponents ), cPCA;
// 1. check C++ PCA & ROW
Mat rPrjTestPoints = rPCA.project( rTestPoints );
@@ -362,7 +363,7 @@ TEST(Core_PCA, accuracy)
// check pca eigenvalues
evalEps = 1e-5, evecEps = 5e-3;
err = cvtest::norm(rPCA.eigenvalues, subEval, NORM_L2 | NORM_RELATIVE);
EXPECT_LE(err , evalEps) << "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW)";
EXPECT_LE(err , evalEps) << "pca.eigenvalues is incorrect (PCA::DATA_AS_ROW)";
// check pca eigenvectors
for(int i = 0; i < subEvec.rows; i++)
{
@@ -379,7 +380,7 @@ TEST(Core_PCA, accuracy)
double mval = 0; Point mloc;
minMaxLoc(tmp, 0, &mval, 0, &mloc);
EXPECT_LE(err, evecEps) << "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW) at " << i << " "
EXPECT_LE(err, evecEps) << "pca.eigenvectors is incorrect (PCA::DATA_AS_ROW) at " << i << " "
<< cv::format("max diff is %g at (i=%d, j=%d) (%g vs %g)\n",
mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x),
subEvec.at<float>(mloc.y, mloc.x))
@@ -399,7 +400,7 @@ TEST(Core_PCA, accuracy)
err = cvtest::norm(rPrjTestPoints.row(i), prj, NORM_L2 | NORM_RELATIVE);
if (err < prjEps)
{
EXPECT_LE(err, prjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_ROW)";
EXPECT_LE(err, prjEps) << "bad accuracy of project() (PCA::DATA_AS_ROW)";
continue;
}
// check pca backProject
@@ -407,22 +408,22 @@ TEST(Core_PCA, accuracy)
err = cvtest::norm(rBackPrjTestPoints.row(i), backPrj, NORM_L2 | NORM_RELATIVE);
if (err > backPrjEps)
{
EXPECT_LE(err, backPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW)";
EXPECT_LE(err, backPrjEps) << "bad accuracy of backProject() (PCA::DATA_AS_ROW)";
continue;
}
}
// 2. check C++ PCA & COL
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents );
cPCA( rPoints.t(), Mat(), PCA::DATA_AS_COL, maxComponents );
diffPrjEps = 1, diffBackPrjEps = 1;
Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t());
err = cvtest::norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL)";
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (PCA::DATA_AS_COL)";
err = cvtest::norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL)";
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (PCA::DATA_AS_COL)";
// 3. check C++ PCA w/retainedVariance
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance );
cPCA( rPoints.t(), Mat(), PCA::DATA_AS_COL, retainedVariance );
diffPrjEps = 1, diffBackPrjEps = 1;
Mat rvPrjTestPoints = cPCA.project(rTestPoints.t());
@@ -431,9 +432,9 @@ TEST(Core_PCA, accuracy)
else
err = cvtest::norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (PCA::DATA_AS_COL); retainedVariance=" << retainedVariance;
err = cvtest::norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (PCA::DATA_AS_COL); retainedVariance=" << retainedVariance;
#ifdef CHECK_C
// 4. check C PCA & ROW
@@ -447,14 +448,14 @@ TEST(Core_PCA, accuracy)
_prjTestPoints = cvMat(prjTestPoints);
_backPrjTestPoints = cvMat(backPrjTestPoints);
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW );
cvCalcPCA( &_points, &_avg, &_eval, &_evec, PCA::DATA_AS_ROW );
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
err = cvtest::norm(prjTestPoints, rPrjTestPoints, NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW)";
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (PCA::DATA_AS_ROW)";
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints, NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW)";
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (PCA::DATA_AS_ROW)";
// 5. check C PCA & COL
_points = cvMat(cPoints);
@@ -465,14 +466,14 @@ TEST(Core_PCA, accuracy)
prjTestPoints = prjTestPoints.t(); _prjTestPoints = cvMat(prjTestPoints);
backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = cvMat(backPrjTestPoints);
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL );
cvCalcPCA( &_points, &_avg, &_eval, &_evec, PCA::DATA_AS_COL );
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
err = cvtest::norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL)";
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (PCA::DATA_AS_COL)";
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL)";
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (PCA::DATA_AS_COL)";
#endif
// Test read and write
const std::string filename = cv::tempfile("PCA_store.yml");
@@ -599,7 +600,7 @@ static void setValue(SparseMat& M, const int* idx, double value, RNG& rng)
else if( M.type() == CV_64F )
*(double*)ptr = value;
else
CV_Error(CV_StsUnsupportedFormat, "");
CV_Error(cv::Error::StsUnsupportedFormat, "");
}
#if defined(__GNUC__) && (__GNUC__ == 11 || __GNUC__ == 12 || __GNUC__ == 13)
@@ -651,8 +652,8 @@ void Core_ArrayOpTest::run( int /* start_from */)
MatND A(3, sz3, CV_32F), B(3, sz3, CV_16SC4);
CvMatND matA = cvMatND(A), matB = cvMatND(B);
RNG rng;
rng.fill(A, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
rng.fill(B, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10));
rng.fill(A, RNG::UNIFORM, Scalar::all(-10), Scalar::all(10));
rng.fill(B, RNG::UNIFORM, Scalar::all(-10), Scalar::all(10));
int idx0[] = {3,4,5}, idx1[] = {0, 9, 7};
float val0 = 130;
@@ -808,7 +809,7 @@ void Core_ArrayOpTest::run( int /* start_from */)
all_vals.resize(nz0);
all_vals2.resize(nz0);
Mat_<double> _all_vals(all_vals), _all_vals2(all_vals2);
rng.fill(_all_vals, CV_RAND_UNI, Scalar(-1000), Scalar(1000));
rng.fill(_all_vals, RNG::UNIFORM, Scalar(-1000), Scalar(1000));
if( depth == CV_32F )
{
Mat _all_vals_f;
@@ -824,9 +825,9 @@ void Core_ArrayOpTest::run( int /* start_from */)
}
minMaxLoc(_all_vals, &min_val, &max_val);
double _norm0 = cv/*test*/::norm(_all_vals, CV_C);
double _norm1 = cv/*test*/::norm(_all_vals, CV_L1);
double _norm2 = cv/*test*/::norm(_all_vals, CV_L2);
double _norm0 = cv/*test*/::norm(_all_vals, NORM_INF);
double _norm1 = cv/*test*/::norm(_all_vals, NORM_L1);
double _norm2 = cv/*test*/::norm(_all_vals, NORM_L2);
for( i = 0; i < nz0; i++ )
{
@@ -861,9 +862,9 @@ void Core_ArrayOpTest::run( int /* start_from */)
SparseMat M3; SparseMat(Md).convertTo(M3, Md.type(), 2);
int nz1 = (int)M.nzcount(), nz2 = (int)M3.nzcount();
double norm0 = cv/*test*/::norm(M, CV_C);
double norm1 = cv/*test*/::norm(M, CV_L1);
double norm2 = cv/*test*/::norm(M, CV_L2);
double norm0 = cv/*test*/::norm(M, NORM_INF);
double norm1 = cv/*test*/::norm(M, NORM_L1);
double norm2 = cv/*test*/::norm(M, NORM_L2);
double eps = depth == CV_32F ? FLT_EPSILON*100 : DBL_EPSILON*1000;
if( nz1 != nz0 || nz2 != nz0)