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vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

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modules/videostab/src/outlier_rejection.cpp
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/*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.
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// This software is provided by the copyright holders and contributors "as is" and
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// 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/videostab/outlier_rejection.hpp"
namespace cv
{
namespace videostab
{
void NullOutlierRejector::process(
Size /*frameSize*/, InputArray points0, InputArray points1, OutputArray mask)
{
CV_INSTRUMENT_REGION();
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_INSTRUMENT_REGION();
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);
// fill grid cells
grid_.assign(ncells.area(), Cell());
for (int i = 0; i < npoints; ++i)
{
int cx = std::min(cvRound(points0_[i].x / cellSize_.width), ncells.width - 1);
int 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();
std::vector<int> inliers;
for (size_t ci = 0; ci < grid_.size(); ++ci)
{
// estimate translation model at the current cell using RANSAC
float x1, y1;
const Cell &cell = grid_[ci];
int ninliers, ninliersMax = 0;
float dxBest = 0.f, dyBest = 0.f;
// find the best hypothesis
if (!cell.empty())
{
for (int iter = 0; iter < niters; ++iter)
{
int idx = cell[static_cast<unsigned>(rng) % cell.size()];
float dx = points1_[idx].x - points0_[idx].x;
float 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