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merged 2.4 into trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-30 14:33:52 +00:00
parent 3f1c6d7357
commit d5a0088bbe
194 changed files with 10158 additions and 8225 deletions
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119
modules/nonfree/doc/feature_detection.rst
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@@ -5,90 +5,45 @@ SIFT
----
.. ocv:class:: SIFT
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach. ::
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [Lowe04]_.
class CV_EXPORTS SIFT
{
public:
struct CommonParams
{
static const int DEFAULT_NOCTAVES = 4;
static const int DEFAULT_NOCTAVE_LAYERS = 3;
static const int DEFAULT_FIRST_OCTAVE = -1;
enum{ FIRST_ANGLE = 0, AVERAGE_ANGLE = 1 };
CommonParams();
CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave,
int _angleMode );
int nOctaves, nOctaveLayers, firstOctave;
int angleMode;
};
struct DetectorParams
{
static double GET_DEFAULT_THRESHOLD()
{ return 0.04 / SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS / 2.0; }
static double GET_DEFAULT_EDGE_THRESHOLD() { return 10.0; }
DetectorParams();
DetectorParams( double _threshold, double _edgeThreshold );
double threshold, edgeThreshold;
};
struct DescriptorParams
{
static double GET_DEFAULT_MAGNIFICATION() { return 3.0; }
static const bool DEFAULT_IS_NORMALIZE = true;
static const int DESCRIPTOR_SIZE = 128;
DescriptorParams();
DescriptorParams( double _magnification, bool _isNormalize,
bool _recalculateAngles );
double magnification;
bool isNormalize;
bool recalculateAngles;
};
SIFT();
//! sift-detector constructor
SIFT( double _threshold, double _edgeThreshold,
int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
int _angleMode=CommonParams::FIRST_ANGLE );
//! sift-descriptor constructor
SIFT( double _magnification, bool _isNormalize=true,
bool _recalculateAngles = true,
int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
int _angleMode=CommonParams::FIRST_ANGLE );
SIFT( const CommonParams& _commParams,
const DetectorParams& _detectorParams = DetectorParams(),
const DescriptorParams& _descriptorParams = DescriptorParams() );
//! returns the descriptor size in floats (128)
int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
//! finds the keypoints using the SIFT algorithm
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints) const;
//! finds the keypoints and computes descriptors for them using SIFT algorithm.
//! Optionally it can compute descriptors for the user-provided keypoints
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints,
Mat& descriptors,
bool useProvidedKeypoints=false) const;
CommonParams getCommonParams () const { return commParams; }
DetectorParams getDetectorParams () const { return detectorParams; }
DescriptorParams getDescriptorParams () const { return descriptorParams; }
protected:
...
};
.. [Lowe04] Lowe, D. G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.
SIFT::SIFT
----------
The SIFT constructors.
.. ocv:function:: SIFT::SIFT( int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6)
:param nfeatures: The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
:param nOctaveLayers: The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
:param contrastThreshold: The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
:param edgeThreshold: The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the ``edgeThreshold``, the less features are filtered out (more features are retained).
:param sigma: The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.
SIFT::operator ()
-----------------
Extract features and computes their descriptors using SIFT algorithm
.. ocv:function:: void SIFT::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
:param image: Input 8-bit grayscale image
:param mask: Optional input mask that marks the regions where we should detect features.
:param keypoints: The input/output vector of keypoints
:param descriptors: The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them.
:param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors.
SURF
----
.. ocv:class:: SURF
@@ -146,8 +101,8 @@ SURF::operator()
----------------
Detects keypoints and computes SURF descriptors for them.
.. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints)
.. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints, vector<float>& descriptors, bool useProvidedKeypoints=false)
.. ocv:function:: void SURF::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints) const
.. ocv:function:: void SURF::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
.. ocv:pyfunction:: cv2.SURF.detect(img, mask) -> keypoints
.. ocv:pyfunction:: cv2.SURF.detect(img, mask[, useProvidedKeypoints]) -> keypoints, descriptors
@@ -162,7 +117,7 @@ Detects keypoints and computes SURF descriptors for them.
:param keypoints: The input/output vector of keypoints
:param descriptors: The output concatenated vectors of descriptors. Each descriptor is 64- or 128-element vector, as returned by ``SURF::descriptorSize()``. So the total size of ``descriptors`` will be ``keypoints.size()*descriptorSize()``.
:param descriptors: The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them.
:param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors.

118
modules/nonfree/src/nonfree_init.cpp Normal file
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@@ -0,0 +1,118 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
///////////////////////////////////////////////////////////////////////////////////////////////////////////
static Algorithm* createSURF()
{
return new SURF;
}
static AlgorithmInfo& surf_info()
{
static AlgorithmInfo surf_info_var("Feature2D.SURF", createSURF);
return surf_info_var;
}
static AlgorithmInfo& surf_info_auto = surf_info();
AlgorithmInfo* SURF::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
SURF obj;
surf_info().addParam(obj, "hessianThreshold", obj.hessianThreshold);
surf_info().addParam(obj, "nOctaves", obj.nOctaves);
surf_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
surf_info().addParam(obj, "extended", obj.extended);
surf_info().addParam(obj, "upright", obj.upright);
initialized = true;
}
return &surf_info();
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////
static Algorithm* createSIFT() { return new SIFT; }
static AlgorithmInfo& sift_info()
{
static AlgorithmInfo sift_info_var("Feature2D.SIFT", createSIFT);
return sift_info_var;
}
static AlgorithmInfo& sift_info_auto = sift_info();
AlgorithmInfo* SIFT::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
SIFT obj;
sift_info().addParam(obj, "nFeatures", obj.nfeatures);
sift_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
sift_info().addParam(obj, "contrastThreshold", obj.contrastThreshold);
sift_info().addParam(obj, "edgeThreshold", obj.edgeThreshold);
sift_info().addParam(obj, "sigma", obj.sigma);
initialized = true;
}
return &sift_info();
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////
bool initModule_nonfree(void)
{
Ptr<Algorithm> sift = createSIFT(), surf = createSURF();
return sift->info() != 0 && surf->info() != 0;
}
}

7
modules/nonfree/src/precomp.cpp
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@@ -7,10 +7,11 @@
// copy or use the software.
//
//
// Intel License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
@@ -23,7 +24,7 @@
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and

7
modules/nonfree/src/sift.cpp
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@@ -7,10 +7,11 @@
// copy or use the software.
//
//
// Intel License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
@@ -23,7 +24,7 @@
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and

88
modules/nonfree/src/surf.cpp
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@@ -443,6 +443,22 @@ struct SURFFindInvoker
float hessianThreshold;
};
struct KeypointGreater
{
inline bool operator()(const KeyPoint& kp1, const KeyPoint& kp2) const
{
if(kp1.response > kp2.response) return true;
if(kp1.response < kp2.response) return false;
if(kp1.size > kp2.size) return true;
if(kp1.size < kp2.size) return false;
if(kp1.octave > kp2.octave) return true;
if(kp1.octave < kp2.octave) return false;
if(kp1.pt.y < kp2.pt.y) return false;
if(kp1.pt.y > kp2.pt.y) return true;
return kp1.pt.x < kp2.pt.y;
}
};
static void fastHessianDetector( const Mat& sum, const Mat& mask_sum, vector<KeyPoint>& keypoints,
int nOctaves, int nOctaveLayers, float hessianThreshold )
@@ -490,6 +506,8 @@ static void fastHessianDetector( const Mat& sum, const Mat& mask_sum, vector<Key
SURFFindInvoker(sum, mask_sum, dets, traces, sizes,
sampleSteps, middleIndices, keypoints,
nOctaveLayers, hessianThreshold) );
std::sort(keypoints.begin(), keypoints.end(), KeypointGreater());
}
@@ -938,76 +956,6 @@ void SURF::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat&
void SURF::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const
{
(*this)(image, Mat(), keypoints, descriptors, true);
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////
static Algorithm* createSURF()
{
return new SURF;
}
static AlgorithmInfo& surf_info()
{
static AlgorithmInfo surf_info_var("Feature2D.SURF", createSURF);
return surf_info_var;
}
static AlgorithmInfo& surf_info_auto = surf_info();
AlgorithmInfo* SURF::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
SURF obj;
surf_info().addParam(obj, "hessianThreshold", obj.hessianThreshold);
surf_info().addParam(obj, "nOctaves", obj.nOctaves);
surf_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
surf_info().addParam(obj, "extended", obj.extended);
surf_info().addParam(obj, "upright", obj.upright);
initialized = true;
}
return &surf_info();
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////
static Algorithm* createSIFT() { return new SIFT; }
static AlgorithmInfo& sift_info()
{
static AlgorithmInfo sift_info_var("Feature2D.SIFT", createSIFT);
return sift_info_var;
}
static AlgorithmInfo& sift_info_auto = sift_info();
AlgorithmInfo* SIFT::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
SIFT obj;
sift_info().addParam(obj, "nFeatures", obj.nfeatures);
sift_info().addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
sift_info().addParam(obj, "contrastThreshold", obj.contrastThreshold);
sift_info().addParam(obj, "edgeThreshold", obj.edgeThreshold);
sift_info().addParam(obj, "sigma", obj.sigma);
initialized = true;
}
return &sift_info();
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////
bool initModule_nonfree(void)
{
Ptr<Algorithm> sift = createSIFT(), surf = createSURF();
return sift->info() != 0 && surf->info() != 0;
}
}

14
modules/nonfree/test/test_features2d.cpp
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@@ -302,6 +302,8 @@ protected:
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
{
ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
ts->printf(cvtest::TS::LOG, "Valid size is (%d x %d) actual size is (%d x %d).\n", validDescriptors.rows, validDescriptors.cols, calcDescriptors.rows, calcDescriptors.cols);
ts->printf(cvtest::TS::LOG, "Valid type is %d actual type is %d.\n", validDescriptors.type(), calcDescriptors.type());
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
@@ -992,21 +994,21 @@ TEST( Features2d_DescriptorExtractor_SIFT, regression )
TEST( Features2d_DescriptorExtractor_SURF, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-surf", 0.035f,
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-surf", 0.05f,
DescriptorExtractor::create("SURF"), 0.147372f );
test.safe_run();
}
/*TEST( Features2d_DescriptorExtractor_OpponentSIFT, regression )
TEST( Features2d_DescriptorExtractor_OpponentSIFT, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-sift", 0.18f,
DescriptorExtractor::create("OpponentSIFT"), 8.06652f );
test.safe_run();
}*/
}
TEST( Features2d_DescriptorExtractor_OpponentSURF, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-surf", 0.18f,
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-surf", 0.3f,
DescriptorExtractor::create("OpponentSURF"), 0.147372f );
test.safe_run();
}
@@ -1066,11 +1068,11 @@ TEST(Features2d_BruteForceDescriptorMatcher_knnMatch, regression)
matcher->knnMatch(descQ, descT, matches, k);
//cout << "\nBest " << k << " matches to " << descT.rows << " train desc-s." << endl;
ASSERT_EQ(descQ.rows, matches.size());
ASSERT_EQ(descQ.rows, static_cast<int>(matches.size()));
for(size_t i = 0; i<matches.size(); i++)
{
//cout << "\nmatches[" << i << "].size()==" << matches[i].size() << endl;
ASSERT_GT(min(k, descT.rows), static_cast<int>(matches[i].size()));
ASSERT_GE(min(k, descT.rows), static_cast<int>(matches[i].size()));
for(size_t j = 0; j<matches[i].size(); j++)
{
//cout << "\t" << matches[i][j].queryIdx << " -> " << matches[i][j].trainIdx << endl;