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different interpolation by double image (#23124)

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* different interpolation by double image

* fixing scaling mapping

* fixing a test

* added an option to enable previous interpolation

* added doxygen entries for the new parameter

* ASSERT_TRUE -> ASSERT_EQ

* changed log message when using old upscale mode
This commit is contained in:
Vaclav Vavra
2023-02-17 08:35:54 +01:00
committed by GitHub
parent 6c235c8edb
commit 923dbcc58f
6 changed files with 88 additions and 21 deletions
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modules/features2d/src/sift.dispatch.cpp
+28 -11
View File
@@ -72,6 +72,7 @@
#include "precomp.hpp"
#include <opencv2/core/hal/hal.hpp>
#include <opencv2/core/utils/tls.hpp>
#include <opencv2/core/utils/logger.hpp>
#include "sift.simd.hpp"
#include "sift.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content
@@ -88,7 +89,8 @@ class SIFT_Impl : public SIFT
public:
explicit SIFT_Impl( int nfeatures = 0, int nOctaveLayers = 3,
double contrastThreshold = 0.04, double edgeThreshold = 10,
double sigma = 1.6, int descriptorType = CV_32F );
double sigma = 1.6, int descriptorType = CV_32F,
bool enable_precise_upscale = true );
//! returns the descriptor size in floats (128)
int descriptorSize() const CV_OVERRIDE;
@@ -136,24 +138,25 @@ protected:
CV_PROP_RW double edgeThreshold;
CV_PROP_RW double sigma;
CV_PROP_RW int descriptor_type;
CV_PROP_RW bool enable_precise_upscale;
};
Ptr<SIFT> SIFT::create( int _nfeatures, int _nOctaveLayers,
double _contrastThreshold, double _edgeThreshold, double _sigma )
double _contrastThreshold, double _edgeThreshold, double _sigma, bool enable_precise_upscale )
{
CV_TRACE_FUNCTION();
return makePtr<SIFT_Impl>(_nfeatures, _nOctaveLayers, _contrastThreshold, _edgeThreshold, _sigma, CV_32F);
return makePtr<SIFT_Impl>(_nfeatures, _nOctaveLayers, _contrastThreshold, _edgeThreshold, _sigma, CV_32F, enable_precise_upscale);
}
Ptr<SIFT> SIFT::create( int _nfeatures, int _nOctaveLayers,
double _contrastThreshold, double _edgeThreshold, double _sigma, int _descriptorType )
double _contrastThreshold, double _edgeThreshold, double _sigma, int _descriptorType, bool enable_precise_upscale )
{
CV_TRACE_FUNCTION();
// SIFT descriptor supports 32bit floating point and 8bit unsigned int.
CV_Assert(_descriptorType == CV_32F || _descriptorType == CV_8U);
return makePtr<SIFT_Impl>(_nfeatures, _nOctaveLayers, _contrastThreshold, _edgeThreshold, _sigma, _descriptorType);
return makePtr<SIFT_Impl>(_nfeatures, _nOctaveLayers, _contrastThreshold, _edgeThreshold, _sigma, _descriptorType, enable_precise_upscale);
}
String SIFT::getDefaultName() const
@@ -170,7 +173,7 @@ unpackOctave(const KeyPoint& kpt, int& octave, int& layer, float& scale)
scale = octave >= 0 ? 1.f/(1 << octave) : (float)(1 << -octave);
}
static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma )
static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma, bool enable_precise_upscale )
{
CV_TRACE_FUNCTION();
@@ -188,12 +191,22 @@ static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma
if( doubleImageSize )
{
sig_diff = sqrtf( std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4, 0.01f) );
Mat dbl;
if (enable_precise_upscale) {
dbl.create(Size(gray_fpt.cols*2, gray_fpt.rows*2), gray_fpt.type());
Mat H = Mat::zeros(2, 3, CV_32F);
H.at<float>(0, 0) = 0.5f;
H.at<float>(1, 1) = 0.5f;
cv::warpAffine(gray_fpt, dbl, H, dbl.size(), INTER_LINEAR | WARP_INVERSE_MAP, BORDER_REFLECT);
} else {
#if DoG_TYPE_SHORT
resize(gray_fpt, dbl, Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, INTER_LINEAR_EXACT);
resize(gray_fpt, dbl, Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, INTER_LINEAR_EXACT);
#else
resize(gray_fpt, dbl, Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, INTER_LINEAR);
resize(gray_fpt, dbl, Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, INTER_LINEAR);
#endif
}
Mat result;
GaussianBlur(dbl, result, Size(), sig_diff, sig_diff);
return result;
@@ -459,10 +472,14 @@ static void calcDescriptors(const std::vector<Mat>& gpyr, const std::vector<KeyP
//////////////////////////////////////////////////////////////////////////////////////////
SIFT_Impl::SIFT_Impl( int _nfeatures, int _nOctaveLayers,
double _contrastThreshold, double _edgeThreshold, double _sigma, int _descriptorType )
double _contrastThreshold, double _edgeThreshold, double _sigma, int _descriptorType, bool _enable_precise_upscale)
: nfeatures(_nfeatures), nOctaveLayers(_nOctaveLayers),
contrastThreshold(_contrastThreshold), edgeThreshold(_edgeThreshold), sigma(_sigma), descriptor_type(_descriptorType)
contrastThreshold(_contrastThreshold), edgeThreshold(_edgeThreshold), sigma(_sigma), descriptor_type(_descriptorType),
enable_precise_upscale(_enable_precise_upscale)
{
if (!enable_precise_upscale) {
CV_LOG_ONCE_INFO(NULL, "precise upscale disabled, this is now deprecated as it was found to induce a location bias");
}
}
int SIFT_Impl::descriptorSize() const
@@ -516,7 +533,7 @@ void SIFT_Impl::detectAndCompute(InputArray _image, InputArray _mask,
actualNOctaves = maxOctave - firstOctave + 1;
}
Mat base = createInitialImage(image, firstOctave < 0, (float)sigma);
Mat base = createInitialImage(image, firstOctave < 0, (float)sigma, enable_precise_upscale);
std::vector<Mat> gpyr;
int nOctaves = actualNOctaves > 0 ? actualNOctaves : cvRound(std::log( (double)std::min( base.cols, base.rows ) ) / std::log(2.) - 2) - firstOctave;