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Commit ea682d6f authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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Merge pull request #219 from cbalint13/brisk 

Remove AGAST (for push to opencv's main)
parents 0acab5db 459c05b7
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modules/xfeatures2d/doc/xfeatures2d.bib
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......@@ -45,15 +45,6 @@
organization={Ieee}
}
@inproceedings{mair2010_agast,
title={Adaptive and Generic Corner Detection Based on the Accelerated Segment Test"},
author={"Elmar Mair and Gregory D. Hager and Darius Burschka and Michael Suppa and Gerhard Hirzinger"},
year={"2010"},
month={"September"},
booktitle={"European Conference on Computer Vision (ECCV'10)"},
url={"http://www6.in.tum.de/Main/ResearchAgast"
}
@incollection{LUCID,
title={Locally uniform comparison image descriptor},
author={Ziegler, Andrew, Eric Christiansen, David Kriegman, and Serge J. Belongie}
......
modules/xfeatures2d/include/opencv2/xfeatures2d.hpp
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......@@ -129,53 +129,6 @@ public:
static Ptr<BriefDescriptorExtractor> create( int bytes = 32 );
};
/** @overload */
CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression=true );
/** @brief Detects corners using the AGAST algorithm
@param image grayscale image where keypoints (corners) are detected.
@param keypoints keypoints detected on the image.
@param threshold threshold on difference between intensity of the central pixel and pixels of a
circle around this pixel.
@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
(keypoints).
@param type one of the four neighborhoods as defined in the paper:
AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16
Detects corners using the AGAST algorithm by @cite mair2010_agast .
*/
CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression, int type );
/** @brief Wrapping class for feature detection using the AGAST method. :
*/
class CV_EXPORTS_W AgastFeatureDetector : public Feature2D
{
public:
enum
{
AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3,
THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001,
};
CV_WRAP static Ptr<AgastFeatureDetector> create( int threshold=10,
bool nonmaxSuppression=true,
int type=AgastFeatureDetector::OAST_9_16 );
CV_WRAP virtual void setThreshold(int threshold) = 0;
CV_WRAP virtual int getThreshold() const = 0;
CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
CV_WRAP virtual bool getNonmaxSuppression() const = 0;
CV_WRAP virtual void setType(int type) = 0;
CV_WRAP virtual int getType() const = 0;
};
/** @brief Class implementing the locally uniform comparison image descriptor, described in @cite LUCID
An image descriptor that can be computed very fast, while being
......
modules/xfeatures2d/perf/perf_agast.cpp deleted 100644 → 0
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#include "perf_precomp.hpp"
using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;
using namespace perf;
using std::tr1::make_tuple;
using std::tr1::get;
CV_ENUM(AgastType, AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16)
typedef std::tr1::tuple<string, AgastType> File_Type_t;
typedef perf::TestBaseWithParam<File_Type_t> agast;
#define AGAST_IMAGES \
"cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
"stitching/a3.png"
PERF_TEST_P(agast, detect, testing::Combine(
testing::Values(AGAST_IMAGES),
AgastType::all()
))
{
string filename = getDataPath(get<0>(GetParam()));
int type = get<1>(GetParam());
Mat frame = imread(filename, IMREAD_GRAYSCALE);
if (frame.empty())
FAIL() << "Unable to load source image " << filename;
declare.in(frame);
Ptr<FeatureDetector> fd = AgastFeatureDetector::create(20, true, type);
ASSERT_FALSE( fd.empty() );
vector<KeyPoint> points;
TEST_CYCLE() fd->detect(frame, points);
SANITY_CHECK_KEYPOINTS(points);
}
modules/xfeatures2d/src/agast.cpp deleted 100644 → 0
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modules/xfeatures2d/src/agast_score.cpp deleted 100644 → 0
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modules/xfeatures2d/src/agast_score.hpp deleted 100644 → 0
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/* This is AGAST and OAST, an optimal and accelerated corner detector
based on the accelerated segment tests
Below is the original copyright and the references */
/*
Copyright (C) 2010 Elmar Mair
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
*Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
*Redistributions 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.
*Neither the name of the University of Cambridge nor the names of
its contributors may 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 COPYRIGHT OWNER 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.
*/
/*
The references are:
* Adaptive and Generic Corner Detection Based on the Accelerated Segment Test,
Elmar Mair and Gregory D. Hager and Darius Burschka
and Michael Suppa and Gerhard Hirzinger ECCV 2010
URL: http://www6.in.tum.de/Main/ResearchAgast
*/
#ifndef __OPENCV_FEATURES_2D_AGAST_HPP__
#define __OPENCV_FEATURES_2D_AGAST_HPP__
#ifdef __cplusplus
#include "precomp.hpp"
namespace cv
{
namespace xfeatures2d
{
void makeAgastOffsets(int pixel[16], int row_stride, int type);
template<int type>
int agast_cornerScore(const uchar* ptr, const int pixel[], int threshold);
}
}
#endif
#endif
modules/xfeatures2d/test/test_agast.cpp deleted 100644 → 0
View file @ 0acab5db
/*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 "test_precomp.hpp"
using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;
class CV_AgastTest : public cvtest::BaseTest
{
public:
CV_AgastTest();
~CV_AgastTest();
protected:
void run(int);
};
CV_AgastTest::CV_AgastTest() {}
CV_AgastTest::~CV_AgastTest() {}
void CV_AgastTest::run( int )
{
for(int type=0; type <= 2; ++type) {
Mat image1 = imread(string(ts->get_data_path()) + "inpaint/orig.png");
Mat image2 = imread(string(ts->get_data_path()) + "cameracalibration/chess9.png");
string xml = string(ts->get_data_path()) + format("agast/result%d.xml", type);
if (image1.empty() || image2.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
Mat gray1, gray2;
cvtColor(image1, gray1, COLOR_BGR2GRAY);
cvtColor(image2, gray2, COLOR_BGR2GRAY);
vector<KeyPoint> keypoints1;
vector<KeyPoint> keypoints2;
AGAST(gray1, keypoints1, 30, true, type);
AGAST(gray2, keypoints2, (type > 0 ? 30 : 20), true, type);
for(size_t i = 0; i < keypoints1.size(); ++i)
{
const KeyPoint& kp = keypoints1[i];
cv::circle(image1, kp.pt, cvRound(kp.size/2), Scalar(255, 0, 0));
}
for(size_t i = 0; i < keypoints2.size(); ++i)
{
const KeyPoint& kp = keypoints2[i];
cv::circle(image2, kp.pt, cvRound(kp.size/2), Scalar(255, 0, 0));
}
Mat kps1(1, (int)(keypoints1.size() * sizeof(KeyPoint)), CV_8U, &keypoints1[0]);
Mat kps2(1, (int)(keypoints2.size() * sizeof(KeyPoint)), CV_8U, &keypoints2[0]);
FileStorage fs(xml, FileStorage::READ);
if (!fs.isOpened())
{
fs.open(xml, FileStorage::WRITE);
if (!fs.isOpened())
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
fs << "exp_kps1" << kps1;
fs << "exp_kps2" << kps2;
fs.release();
fs.open(xml, FileStorage::READ);
if (!fs.isOpened())
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
}
Mat exp_kps1, exp_kps2;
read( fs["exp_kps1"], exp_kps1, Mat() );
read( fs["exp_kps2"], exp_kps2, Mat() );
fs.release();
if ( exp_kps1.size != kps1.size || 0 != cvtest::norm(exp_kps1, kps1, NORM_L2) ||
exp_kps2.size != kps2.size || 0 != cvtest::norm(exp_kps2, kps2, NORM_L2))
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
return;
}
/*cv::namedWindow("Img1"); cv::imshow("Img1", image1);
cv::namedWindow("Img2"); cv::imshow("Img2", image2);
cv::waitKey(0);*/
}
ts->set_failed_test_info(cvtest::TS::OK);
}
TEST(Features2d_AGAST, regression) { CV_AgastTest test; test.safe_run(); }
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