outlier_rejection.cpp 6.57 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86
/*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/videostab/outlier_rejection.hpp"

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);

a  
Kai Westerkamp committed
87 88
    int cx, cy;

wester committed
89 90 91 92 93 94
    // fill grid cells

    grid_.assign(ncells.area(), Cell());

    for (int i = 0; i < npoints; ++i)
    {
a  
Kai Westerkamp committed
95 96
        cx = std::min(cvRound(points0_[i].x / cellSize_.width), ncells.width - 1);
        cy = std::min(cvRound(points0_[i].y / cellSize_.height), ncells.height - 1);
wester committed
97 98 99 100 101 102 103
        grid_[cy * ncells.width + cx].push_back(i);
    }

    // process each cell

    RNG rng(0);
    int niters = ransacParams_.niters();
a  
Kai Westerkamp committed
104
    int ninliers, ninliersMax;
wester committed
105
    std::vector<int> inliers;
a  
Kai Westerkamp committed
106 107 108
    float dx, dy, dxBest, dyBest;
    float x1, y1;
    int idx;
wester committed
109 110 111 112 113 114

    for (size_t ci = 0; ci < grid_.size(); ++ci)
    {
        // estimate translation model at the current cell using RANSAC

        const Cell &cell = grid_[ci];
a  
Kai Westerkamp committed
115 116
        ninliersMax = 0;
        dxBest = dyBest = 0.f;
wester committed
117 118 119 120 121 122 123

        // find the best hypothesis

        if (!cell.empty())
        {
            for (int iter = 0; iter < niters; ++iter)
            {
a  
Kai Westerkamp committed
124 125 126
                idx = cell[static_cast<unsigned>(rng) % cell.size()];
                dx = points1_[idx].x - points0_[idx].x;
                dy = points1_[idx].y - points0_[idx].y;
wester committed
127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198

                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