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Added local outlier rejector. Added rigid motion estimator. Refactored videostab module.

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This commit is contained in:
Alexey Spizhevoy
2012-04-24 12:23:23 +00:00
parent 6e830cf8f8
commit 95efec7539
9 changed files with 631 additions and 154 deletions
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modules/videostab/include/opencv2/videostab/global_motion.hpp
+32 -57
View File
@@ -48,8 +48,10 @@
#include <fstream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/videostab/optical_flow.hpp"
#include "opencv2/opencv_modules.hpp"
#include "opencv2/videostab/optical_flow.hpp"
#include "opencv2/videostab/motion_core.hpp"
#include "opencv2/videostab/outlier_rejection.hpp"
#if HAVE_OPENCV_GPU
#include "opencv2/gpu/gpu.hpp"
@@ -60,44 +62,9 @@ namespace cv
namespace videostab
{
enum MotionModel
{
MM_TRANSLATION = 0,
MM_TRANSLATION_AND_SCALE = 1,
MM_SIMILARITY = 2,
MM_AFFINE = 3,
MM_HOMOGRAPHY = 4,
MM_UNKNOWN = 5
};
CV_EXPORTS Mat estimateGlobalMotionLeastSquares(
int npoints, Point2f *points0, Point2f *points1, int model = MM_AFFINE, float *rmse = 0);
struct CV_EXPORTS RansacParams
{
int size; // subset size
float thresh; // max error to classify as inlier
float eps; // max outliers ratio
float prob; // probability of success
RansacParams() : size(0), thresh(0), eps(0), prob(0) {}
RansacParams(int size, float thresh, float eps, float prob)
: size(size), thresh(thresh), eps(eps), prob(prob) {}
static RansacParams default2dMotion(MotionModel model)
{
CV_Assert(model < MM_UNKNOWN);
if (model == MM_TRANSLATION)
return RansacParams(1, 0.5f, 0.5f, 0.99f);
if (model == MM_TRANSLATION_AND_SCALE)
return RansacParams(2, 0.5f, 0.5f, 0.99f);
if (model == MM_SIMILARITY)
return RansacParams(2, 0.5f, 0.5f, 0.99f);
if (model == MM_AFFINE)
return RansacParams(3, 0.5f, 0.5f, 0.99f);
return RansacParams(4, 0.5f, 0.5f, 0.99f);
}
};
CV_EXPORTS Mat estimateGlobalMotionRobust(
const std::vector<Point2f> &points0, const std::vector<Point2f> &points1,
@@ -106,8 +73,7 @@ CV_EXPORTS Mat estimateGlobalMotionRobust(
class CV_EXPORTS GlobalMotionEstimatorBase
{
public:
GlobalMotionEstimatorBase() : motionModel_(MM_UNKNOWN) {}
public:
virtual ~GlobalMotionEstimatorBase() {}
virtual void setMotionModel(MotionModel val) { motionModel_ = val; }
@@ -116,6 +82,8 @@ public:
virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) = 0;
protected:
GlobalMotionEstimatorBase(MotionModel model) { setMotionModel(model); }
MotionModel motionModel_;
};
@@ -140,7 +108,27 @@ private:
Ptr<GlobalMotionEstimatorBase> estimator_;
};
class CV_EXPORTS PyrLkRobustMotionEstimator : public GlobalMotionEstimatorBase
class CV_EXPORTS PyrLkRobustMotionEstimatorBase : public GlobalMotionEstimatorBase
{
public:
virtual void setRansacParams(const RansacParams &val) { ransacParams_ = val; }
virtual RansacParams ransacParams() const { return ransacParams_; }
virtual void setOutlierRejector(Ptr<IOutlierRejector> val) { outlierRejector_ = val; }
virtual Ptr<IOutlierRejector> outlierRejector() const { return outlierRejector_; }
virtual void setMinInlierRatio(float val) { minInlierRatio_ = val; }
virtual float minInlierRatio() const { return minInlierRatio_; }
protected:
PyrLkRobustMotionEstimatorBase(MotionModel model);
RansacParams ransacParams_;
Ptr<IOutlierRejector> outlierRejector_;
float minInlierRatio_;
};
class CV_EXPORTS PyrLkRobustMotionEstimator : public PyrLkRobustMotionEstimatorBase
{
public:
PyrLkRobustMotionEstimator(MotionModel model = MM_AFFINE);
@@ -151,12 +139,6 @@ public:
void setOptFlowEstimator(Ptr<ISparseOptFlowEstimator> val) { optFlowEstimator_ = val; }
Ptr<ISparseOptFlowEstimator> optFlowEstimator() const { return optFlowEstimator_; }
void setRansacParams(const RansacParams &val) { ransacParams_ = val; }
RansacParams ransacParams() const { return ransacParams_; }
void setMinInlierRatio(float val) { minInlierRatio_ = val; }
float minInlierRatio() const { return minInlierRatio_; }
void setGridSize(Size val) { gridSize_ = val; }
Size gridSize() const { return gridSize_; }
@@ -165,8 +147,6 @@ public:
private:
Ptr<FeatureDetector> detector_;
Ptr<ISparseOptFlowEstimator> optFlowEstimator_;
RansacParams ransacParams_;
float minInlierRatio_;
Size gridSize_;
std::vector<uchar> status_;
@@ -176,30 +156,25 @@ private:
};
#if HAVE_OPENCV_GPU
class CV_EXPORTS PyrLkRobustMotionEstimatorGpu : public GlobalMotionEstimatorBase
class CV_EXPORTS PyrLkRobustMotionEstimatorGpu : public PyrLkRobustMotionEstimatorBase
{
public:
PyrLkRobustMotionEstimatorGpu(MotionModel model = MM_AFFINE);
void setRansacParams(const RansacParams &val) { ransacParams_ = val; }
RansacParams ransacParams() const { return ransacParams_; }
void setMinInlierRatio(float val) { minInlierRatio_ = val; }
float minInlierRatio() const { return minInlierRatio_; }
virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0);
Mat estimate(const gpu::GpuMat &frame0, const gpu::GpuMat &frame1, bool *ok = 0);
private:
gpu::GoodFeaturesToTrackDetector_GPU detector_;
SparsePyrLkOptFlowEstimatorGpu optFlowEstimator_;
RansacParams ransacParams_;
float minInlierRatio_;
gpu::GpuMat frame0_, grayFrame0_, frame1_;
gpu::GpuMat pointsPrev_, points_;
Mat hostPointsPrev_, hostPoints_;
gpu::GpuMat status_;
Mat hostPointsPrev_, hostPoints_;
std::vector<Point2f> hostPointsPrevGood_, hostPointsGood_;
std::vector<uchar> rejectionStatus_;
};
#endif
modules/videostab/include/opencv2/videostab/motion_core.hpp
+103
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@@ -0,0 +1,103 @@
/*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) 2009-2011, 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*/
#ifndef __OPENCV_VIDEOSTAB_MOTION_CORE_HPP__
#define __OPENCV_VIDEOSTAB_MOTION_CORE_HPP__
#include <cmath>
#include "opencv2/core/core.hpp"
namespace cv
{
namespace videostab
{
enum MotionModel
{
MM_TRANSLATION = 0,
MM_TRANSLATION_AND_SCALE = 1,
MM_RIGID = 2,
MM_SIMILARITY = 3,
MM_AFFINE = 4,
MM_HOMOGRAPHY = 5,
MM_UNKNOWN = 6
};
struct CV_EXPORTS RansacParams
{
int size; // subset size
float thresh; // max error to classify as inlier
float eps; // max outliers ratio
float prob; // probability of success
RansacParams() : size(0), thresh(0), eps(0), prob(0) {}
RansacParams(int size, float thresh, float eps, float prob)
: size(size), thresh(thresh), eps(eps), prob(prob) {}
int niters() const
{
return static_cast<int>(
std::ceil(std::log(1 - prob) / std::log(1 - std::pow(1 - eps, size))));
}
static RansacParams default2dMotion(MotionModel model)
{
CV_Assert(model < MM_UNKNOWN);
if (model == MM_TRANSLATION)
return RansacParams(1, 0.5f, 0.5f, 0.99f);
if (model == MM_TRANSLATION_AND_SCALE)
return RansacParams(2, 0.5f, 0.5f, 0.99f);
if (model == MM_RIGID)
return RansacParams(2, 0.5f, 0.5f, 0.99f);
if (model == MM_SIMILARITY)
return RansacParams(2, 0.5f, 0.5f, 0.99f);
if (model == MM_AFFINE)
return RansacParams(3, 0.5f, 0.5f, 0.99f);
return RansacParams(4, 0.5f, 0.5f, 0.99f);
}
};
} // namespace videostab
} // namespace cv
#endif
modules/videostab/include/opencv2/videostab/optical_flow.hpp
+4 -4
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@@ -78,11 +78,11 @@ class CV_EXPORTS PyrLkOptFlowEstimatorBase
public:
PyrLkOptFlowEstimatorBase() { setWinSize(Size(21, 21)); setMaxLevel(3); }
void setWinSize(Size val) { winSize_ = val; }
Size winSize() const { return winSize_; }
virtual void setWinSize(Size val) { winSize_ = val; }
virtual Size winSize() const { return winSize_; }
void setMaxLevel(int val) { maxLevel_ = val; }
int maxLevel() const { return maxLevel_; }
virtual void setMaxLevel(int val) { maxLevel_ = val; }
virtual int maxLevel() const { return maxLevel_; }
protected:
Size winSize_;
modules/videostab/include/opencv2/videostab/outlier_rejection.hpp
+96
View File
@@ -0,0 +1,96 @@
/*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) 2009-2011, 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*/
#ifndef __OPENCV_VIDEOSTAB_OUTLIER_REJECTION_HPP__
#define __OPENCV_VIDEOSTAB_OUTLIER_REJECTION_HPP__
#include <vector>
#include "opencv2/core/core.hpp"
#include "opencv2/videostab/motion_core.hpp"
namespace cv
{
namespace videostab
{
class CV_EXPORTS IOutlierRejector
{
public:
virtual ~IOutlierRejector() {}
virtual void process(
Size frameSize, InputArray points0, InputArray points1, OutputArray mask) = 0;
};
class CV_EXPORTS NullOutlierRejector : public IOutlierRejector
{
public:
virtual void process(
Size frameSize, InputArray points0, InputArray points1, OutputArray mask);
};
class CV_EXPORTS TranslationBasedLocalOutlierRejector : public IOutlierRejector
{
public:
TranslationBasedLocalOutlierRejector();
void setCellSize(Size val) { cellSize_ = val; }
Size cellSize() const { return cellSize_; }
void setRansacParams(RansacParams val) { ransacParams_ = val; }
RansacParams ransacParams() const { return ransacParams_; }
virtual void process(
Size frameSize, InputArray points0, InputArray points1, OutputArray mask);
private:
Size cellSize_;
RansacParams ransacParams_;
typedef std::vector<int> Cell;
std::vector<Cell> grid_;
};
} // namespace videostab
} // namespace cv
#endif
modules/videostab/include/opencv2/videostab/videostab.hpp
+1 -1
View File
@@ -40,7 +40,7 @@
//
//M*/
// References:
// REFERENCES
// 1. "Full-Frame Video Stabilization with Motion Inpainting"
// Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum
// 2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths"
modules/videostab/include/opencv2/videostab/wobble_suppression.hpp
+4 -4
View File
@@ -101,12 +101,12 @@ public:
class CV_EXPORTS MoreAccurateMotionWobbleSuppressorBase : public WobbleSuppressorBase
{
public:
MoreAccurateMotionWobbleSuppressorBase() { setPeriod(30); }
void setPeriod(int val) { period_ = val; }
int period() const { return period_; }
virtual void setPeriod(int val) { period_ = val; }
virtual int period() const { return period_; }
protected:
MoreAccurateMotionWobbleSuppressorBase() { setPeriod(30); }
int period_;
};
modules/videostab/src/global_motion.cpp
+125 -11
View File
@@ -43,6 +43,7 @@
#include "precomp.hpp"
#include "opencv2/videostab/global_motion.hpp"
#include "opencv2/videostab/ring_buffer.hpp"
#include "opencv2/videostab/outlier_rejection.hpp"
#include "opencv2/opencv_modules.hpp"
using namespace std;
@@ -150,6 +151,61 @@ static Mat estimateGlobMotionLeastSquaresTranslationAndScale(
}
static Mat estimateGlobMotionLeastSquaresRigid(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
Point2f mean0(0.f, 0.f);
Point2f mean1(0.f, 0.f);
for (int i = 0; i < npoints; ++i)
{
mean0 += points0[i];
mean1 += points1[i];
}
mean0 *= 1.f / npoints;
mean1 *= 1.f / npoints;
Mat_<float> A = Mat::zeros(2, 2, CV_32F);
Point2f pt0, pt1;
for (int i = 0; i < npoints; ++i)
{
pt0 = points0[i] - mean0;
pt1 = points1[i] - mean1;
A(0,0) += pt1.x * pt0.x;
A(0,1) += pt1.x * pt0.y;
A(1,0) += pt1.y * pt0.x;
A(1,1) += pt1.y * pt0.y;
}
Mat_<float> M = Mat::eye(3, 3, CV_32F);
SVD svd(A);
Mat_<float> R = svd.u * svd.vt;
Mat tmp(M(Rect(0,0,2,2)));
R.copyTo(tmp);
M(0,2) = mean1.x - R(0,0)*mean0.x - R(0,1)*mean0.y;
M(1,2) = mean1.y - R(1,0)*mean0.x - R(1,1)*mean0.y;
if (rmse)
{
*rmse = 0;
for (int i = 0; i < npoints; ++i)
{
pt0 = points0[i];
pt1 = points1[i];
*rmse += sqr(pt1.x - M(0,0)*pt0.x - M(0,1)*pt0.y - M(0,2)) +
sqr(pt1.y - M(1,0)*pt0.x - M(1,1)*pt0.y - M(1,2));
}
*rmse = sqrt(*rmse / npoints);
}
return M;
}
static Mat estimateGlobMotionLeastSquaresSimilarity(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
@@ -234,6 +290,7 @@ Mat estimateGlobalMotionLeastSquares(
typedef Mat (*Impl)(int, Point2f*, Point2f*, float*);
static Impl impls[] = { estimateGlobMotionLeastSquaresTranslation,
estimateGlobMotionLeastSquaresTranslationAndScale,
estimateGlobMotionLeastSquaresRigid,
estimateGlobMotionLeastSquaresSimilarity,
estimateGlobMotionLeastSquaresAffine };
@@ -247,8 +304,7 @@ Mat estimateGlobalMotionRobust(
{
CV_Assert(model <= MM_AFFINE);
const int niters = static_cast<int>(ceil(log(1 - params.prob) /
log(1 - pow(1 - params.eps, params.size))));
const int niters = params.niters();
// current hypothesis
vector<int> indices(params.size);
@@ -338,6 +394,7 @@ Mat estimateGlobalMotionRobust(
FromFileMotionReader::FromFileMotionReader(const string &path)
: GlobalMotionEstimatorBase(MM_UNKNOWN)
{
file_.open(path.c_str());
CV_Assert(file_.is_open());
@@ -357,6 +414,7 @@ Mat FromFileMotionReader::estimate(const Mat &/*frame0*/, const Mat &/*frame1*/,
ToFileMotionWriter::ToFileMotionWriter(const string &path, Ptr<GlobalMotionEstimatorBase> estimator)
: GlobalMotionEstimatorBase(estimator->motionModel())
{
file_.open(path.c_str());
CV_Assert(file_.is_open());
@@ -376,13 +434,20 @@ Mat ToFileMotionWriter::estimate(const Mat &frame0, const Mat &frame1, bool *ok)
}
PyrLkRobustMotionEstimatorBase::PyrLkRobustMotionEstimatorBase(MotionModel model)
: GlobalMotionEstimatorBase(model)
{
setRansacParams(RansacParams::default2dMotion(model));
setOutlierRejector(new NullOutlierRejector());
setMinInlierRatio(0.1f);
}
PyrLkRobustMotionEstimator::PyrLkRobustMotionEstimator(MotionModel model)
: PyrLkRobustMotionEstimatorBase(model)
{
setDetector(new GoodFeaturesToTrackDetector());
setOptFlowEstimator(new SparsePyrLkOptFlowEstimator());
setMotionModel(model);
setRansacParams(RansacParams::default2dMotion(model));
setMinInlierRatio(0.1f);
setGridSize(Size(0,0));
}
@@ -428,6 +493,29 @@ Mat PyrLkRobustMotionEstimator::estimate(const Mat &frame0, const Mat &frame1, b
}
}
// perfrom outlier rejection
IOutlierRejector *outlierRejector = static_cast<IOutlierRejector*>(outlierRejector_);
if (!dynamic_cast<NullOutlierRejector*>(outlierRejector))
{
pointsPrev_.swap(pointsPrevGood_);
points_.swap(pointsGood_);
outlierRejector_->process(frame0.size(), pointsPrev_, points_, status_);
pointsPrevGood_.clear(); pointsPrevGood_.reserve(points_.size());
pointsGood_.clear(); pointsGood_.reserve(points_.size());
for (size_t i = 0; i < points_.size(); ++i)
{
if (status_[i])
{
pointsPrevGood_.push_back(pointsPrev_[i]);
pointsGood_.push_back(points_[i]);
}
}
}
size_t npoints = pointsGood_.size();
// find motion
@@ -462,11 +550,9 @@ Mat PyrLkRobustMotionEstimator::estimate(const Mat &frame0, const Mat &frame1, b
#if HAVE_OPENCV_GPU
PyrLkRobustMotionEstimatorGpu::PyrLkRobustMotionEstimatorGpu(MotionModel model)
: PyrLkRobustMotionEstimatorBase(model)
{
CV_Assert(gpu::getCudaEnabledDeviceCount() > 0);
setMotionModel(model);
setRansacParams(RansacParams::default2dMotion(model));
setMinInlierRatio(0.1f);
}
@@ -506,8 +592,34 @@ Mat PyrLkRobustMotionEstimatorGpu::estimate(const gpu::GpuMat &frame0, const gpu
pointsPrev_.download(hostPointsPrev_);
points_.download(hostPoints_);
Point2f *points0 = hostPointsPrev_.ptr<Point2f>();
Point2f *points1 = hostPoints_.ptr<Point2f>();
int npoints = hostPointsPrev_.cols;
// perfrom outlier rejection
IOutlierRejector *outlierRejector = static_cast<IOutlierRejector*>(outlierRejector_);
if (!dynamic_cast<NullOutlierRejector*>(outlierRejector))
{
outlierRejector_->process(frame0.size(), hostPointsPrev_, hostPoints_, rejectionStatus_);
hostPointsPrevGood_.clear(); hostPointsPrevGood_.reserve(hostPoints_.cols);
hostPointsGood_.clear(); hostPointsGood_.reserve(hostPoints_.cols);
for (int i = 0; i < hostPoints_.cols; ++i)
{
if (rejectionStatus_[i])
{
hostPointsPrevGood_.push_back(hostPointsPrev_.at<Point2f>(0,i));
hostPointsGood_.push_back(hostPoints_.at<Point2f>(0,i));
}
}
points0 = &hostPointsPrevGood_[0];
points1 = &hostPointsGood_[0];
npoints = static_cast<int>(hostPointsGood_.size());
}
// find motion
int ninliers = 0;
@@ -515,12 +627,13 @@ Mat PyrLkRobustMotionEstimatorGpu::estimate(const gpu::GpuMat &frame0, const gpu
if (motionModel_ != MM_HOMOGRAPHY)
M = estimateGlobalMotionRobust(
npoints, hostPointsPrev_.ptr<Point2f>(0), hostPoints_.ptr<Point2f>(), motionModel_,
ransacParams_, 0, &ninliers);
npoints, points0, points1, motionModel_, ransacParams_, 0, &ninliers);
else
{
vector<uchar> mask;
M = findHomography(hostPointsPrev_, hostPoints_, mask, CV_RANSAC, ransacParams_.thresh);
M = findHomography(
Mat(1, npoints, CV_32FC2, points0), Mat(1, npoints, CV_32FC2, points1),
mask, CV_RANSAC, ransacParams_.thresh);
for (int i = 0; i < npoints; ++i)
if (mask[i]) ninliers++;
}
@@ -558,3 +671,4 @@ Mat getMotion(int from, int to, const vector<Mat> &motions)
} // namespace videostab
} // namespace cv
modules/videostab/src/outlier_rejection.cpp
+201
View File
@@ -0,0 +1,201 @@
/*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) 2009-2011, 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"
#include "opencv2/core/core.hpp"
#include "opencv2/videostab/outlier_rejection.hpp"
using namespace std;
namespace cv
{
namespace videostab
{
void NullOutlierRejector::process(
Size frameSize, InputArray points0, InputArray points1, OutputArray mask)
{
CV_Assert(points0.type() == points1.type());
CV_Assert(points0.getMat().checkVector(2) == points1.getMat().checkVector(2));
int npoints = points0.getMat().checkVector(2);
mask.create(1, npoints, CV_8U);
Mat mask_ = mask.getMat();
mask_.setTo(1);
}
TranslationBasedLocalOutlierRejector::TranslationBasedLocalOutlierRejector()
{
setCellSize(Size(50, 50));
setRansacParams(RansacParams::default2dMotion(MM_TRANSLATION));
}
void TranslationBasedLocalOutlierRejector::process(
Size frameSize, InputArray points0, InputArray points1, OutputArray mask)
{
CV_Assert(points0.type() == points1.type());
CV_Assert(points0.getMat().checkVector(2) == points1.getMat().checkVector(2));
int npoints = points0.getMat().checkVector(2);
const Point2f* points0_ = points0.getMat().ptr<Point2f>();
const Point2f* points1_ = points1.getMat().ptr<Point2f>();
mask.create(1, npoints, CV_8U);
uchar* mask_ = mask.getMat().ptr<uchar>();
Size ncells((frameSize.width + cellSize_.width - 1) / cellSize_.width,
(frameSize.height + cellSize_.height - 1) / cellSize_.height);
int cx, cy;
// fill grid cells
grid_.assign(ncells.area(), Cell());
for (int i = 0; i < npoints; ++i)
{
cx = std::min(cvRound(points0_[i].x / cellSize_.width), ncells.width - 1);
cy = std::min(cvRound(points0_[i].y / cellSize_.height), ncells.height - 1);
grid_[cy * ncells.width + cx].push_back(i);
}
// process each cell
RNG rng(0);
int niters = ransacParams_.niters();
int ninliers, ninliersMax;
vector<int> inliers;
float dx, dy, dxBest, dyBest;
float x1, y1;
int idx;
for (size_t ci = 0; ci < grid_.size(); ++ci)
{
// estimate translation model at the current cell using RANSAC
const Cell &cell = grid_[ci];
ninliersMax = 0;
dxBest = dyBest = 0.f;
// find the best hypothesis
if (!cell.empty())
{
for (int iter = 0; iter < niters; ++iter)
{
idx = cell[static_cast<unsigned>(rng) % cell.size()];
dx = points1_[idx].x - points0_[idx].x;
dy = points1_[idx].y - points0_[idx].y;
ninliers = 0;
for (size_t i = 0; i < cell.size(); ++i)
{
x1 = points0_[cell[i]].x + dx;
y1 = points0_[cell[i]].y + dy;
if (sqr(x1 - points1_[cell[i]].x) + sqr(y1 - points1_[cell[i]].y) <
sqr(ransacParams_.thresh))
{
ninliers++;
}
}
if (ninliers > ninliersMax)
{
ninliersMax = ninliers;
dxBest = dx;
dyBest = dy;
}
}
}
// get the best hypothesis inliers
ninliers = 0;
inliers.resize(ninliersMax);
for (size_t i = 0; i < cell.size(); ++i)
{
x1 = points0_[cell[i]].x + dxBest;
y1 = points0_[cell[i]].y + dyBest;
if (sqr(x1 - points1_[cell[i]].x) + sqr(y1 - points1_[cell[i]].y) <
sqr(ransacParams_.thresh))
{
inliers[ninliers++] = cell[i];
}
}
// refine the best hypothesis
dxBest = dyBest = 0.f;
for (size_t i = 0; i < inliers.size(); ++i)
{
dxBest += points1_[inliers[i]].x - points0_[inliers[i]].x;
dyBest += points1_[inliers[i]].y - points0_[inliers[i]].y;
}
if (!inliers.empty())
{
dxBest /= inliers.size();
dyBest /= inliers.size();
}
// set mask elements for refined model inliers
for (size_t i = 0; i < cell.size(); ++i)
{
x1 = points0_[cell[i]].x + dxBest;
y1 = points0_[cell[i]].y + dyBest;
if (sqr(x1 - points1_[cell[i]].x) + sqr(y1 - points1_[cell[i]].y) <
sqr(ransacParams_.thresh))
{
mask_[cell[i]] = 1;
}
else
{
mask_[cell[i]] = 0;
}
}
}
}
} // namespace videostab
} // namespace cv