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366 lines
11 KiB
C++
366 lines
11 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/videostab/global_motion.hpp"
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using namespace std;
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namespace cv
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{
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namespace videostab
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{
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static Mat estimateGlobMotionLeastSquaresTranslation(
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int npoints, const Point2f *points0, const Point2f *points1, float *rmse)
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{
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Mat_<float> M = Mat::eye(3, 3, CV_32F);
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for (int i = 0; i < npoints; ++i)
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{
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M(0,2) += points1[i].x - points0[i].x;
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M(1,2) += points1[i].y - points0[i].y;
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}
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M(0,2) /= npoints;
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M(1,2) /= npoints;
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if (rmse)
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{
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*rmse = 0;
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for (int i = 0; i < npoints; ++i)
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*rmse += sqr(points1[i].x - points0[i].x - M(0,2)) +
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sqr(points1[i].y - points0[i].y - M(1,2));
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*rmse = sqrt(*rmse / npoints);
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}
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return M;
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}
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static Mat estimateGlobMotionLeastSquaresTranslationAndScale(
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int npoints, const Point2f *points0, const Point2f *points1, float *rmse)
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{
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Mat_<float> A(2*npoints, 3), b(2*npoints, 1);
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float *a0, *a1;
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Point2f p0, p1;
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for (int i = 0; i < npoints; ++i)
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{
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a0 = A[2*i];
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a1 = A[2*i+1];
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p0 = points0[i];
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p1 = points1[i];
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a0[0] = p0.x; a0[1] = 1; a0[2] = 0;
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a1[0] = p0.y; a1[1] = 0; a1[2] = 1;
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b(2*i,0) = p1.x;
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b(2*i+1,0) = p1.y;
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}
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Mat_<float> sol;
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solve(A, b, sol, DECOMP_SVD);
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if (rmse)
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*rmse = static_cast<float>(norm(A*sol, b, NORM_L2) / sqrt(static_cast<double>(npoints)));
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Mat_<float> M = Mat::eye(3, 3, CV_32F);
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M(0,0) = M(1,1) = sol(0,0);
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M(0,2) = sol(1,0);
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M(1,2) = sol(2,0);
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return M;
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}
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static Mat estimateGlobMotionLeastSquaresLinearSimilarity(
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int npoints, const Point2f *points0, const Point2f *points1, float *rmse)
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{
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Mat_<float> A(2*npoints, 4), b(2*npoints, 1);
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float *a0, *a1;
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Point2f p0, p1;
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for (int i = 0; i < npoints; ++i)
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{
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a0 = A[2*i];
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a1 = A[2*i+1];
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p0 = points0[i];
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p1 = points1[i];
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a0[0] = p0.x; a0[1] = p0.y; a0[2] = 1; a0[3] = 0;
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a1[0] = p0.y; a1[1] = -p0.x; a1[2] = 0; a1[3] = 1;
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b(2*i,0) = p1.x;
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b(2*i+1,0) = p1.y;
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}
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Mat_<float> sol;
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solve(A, b, sol, DECOMP_SVD);
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if (rmse)
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*rmse = static_cast<float>(norm(A*sol, b, NORM_L2) / sqrt(static_cast<double>(npoints)));
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Mat_<float> M = Mat::eye(3, 3, CV_32F);
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M(0,0) = M(1,1) = sol(0,0);
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M(0,1) = sol(1,0);
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M(1,0) = -sol(1,0);
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M(0,2) = sol(2,0);
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M(1,2) = sol(3,0);
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return M;
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}
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static Mat estimateGlobMotionLeastSquaresAffine(
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int npoints, const Point2f *points0, const Point2f *points1, float *rmse)
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{
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Mat_<float> A(2*npoints, 6), b(2*npoints, 1);
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float *a0, *a1;
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Point2f p0, p1;
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for (int i = 0; i < npoints; ++i)
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{
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a0 = A[2*i];
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a1 = A[2*i+1];
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p0 = points0[i];
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p1 = points1[i];
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a0[0] = p0.x; a0[1] = p0.y; a0[2] = 1; a0[3] = a0[4] = a0[5] = 0;
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a1[0] = a1[1] = a1[2] = 0; a1[3] = p0.x; a1[4] = p0.y; a1[5] = 1;
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b(2*i,0) = p1.x;
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b(2*i+1,0) = p1.y;
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}
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Mat_<float> sol;
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solve(A, b, sol, DECOMP_SVD);
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if (rmse)
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*rmse = static_cast<float>(norm(A*sol, b, NORM_L2) / sqrt(static_cast<double>(npoints)));
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Mat_<float> M = Mat::eye(3, 3, CV_32F);
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for (int i = 0, k = 0; i < 2; ++i)
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for (int j = 0; j < 3; ++j, ++k)
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M(i,j) = sol(k,0);
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return M;
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}
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Mat estimateGlobalMotionLeastSquares(
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const vector<Point2f> &points0, const vector<Point2f> &points1, int model, float *rmse)
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{
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CV_Assert(points0.size() == points1.size());
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typedef Mat (*Impl)(int, const Point2f*, const Point2f*, float*);
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static Impl impls[] = { estimateGlobMotionLeastSquaresTranslation,
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estimateGlobMotionLeastSquaresTranslationAndScale,
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estimateGlobMotionLeastSquaresLinearSimilarity,
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estimateGlobMotionLeastSquaresAffine };
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int npoints = static_cast<int>(points0.size());
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return impls[model](npoints, &points0[0], &points1[0], rmse);
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}
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Mat estimateGlobalMotionRobust(
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const vector<Point2f> &points0, const vector<Point2f> &points1, int model,
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const RansacParams ¶ms, float *rmse, int *ninliers)
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{
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CV_Assert(points0.size() == points1.size());
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typedef Mat (*Impl)(int, const Point2f*, const Point2f*, float*);
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static Impl impls[] = { estimateGlobMotionLeastSquaresTranslation,
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estimateGlobMotionLeastSquaresTranslationAndScale,
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estimateGlobMotionLeastSquaresLinearSimilarity,
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estimateGlobMotionLeastSquaresAffine };
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const int npoints = static_cast<int>(points0.size());
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if (npoints < params.size)
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return Mat::eye(3, 3, CV_32F);
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const int niters = static_cast<int>(ceil(log(1 - params.prob) /
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log(1 - pow(1 - params.eps, params.size))));
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RNG rng(0);
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vector<int> indices(params.size);
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vector<Point2f> subset0(params.size), subset1(params.size);
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vector<Point2f> subset0best(params.size), subset1best(params.size);
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Mat_<float> bestM;
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int ninliersMax = -1;
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Point2f p0, p1;
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float x, y;
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for (int iter = 0; iter < niters; ++iter)
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{
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for (int i = 0; i < params.size; ++i)
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{
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bool ok = false;
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while (!ok)
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{
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ok = true;
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indices[i] = static_cast<unsigned>(rng) % npoints;
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for (int j = 0; j < i; ++j)
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if (indices[i] == indices[j])
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{ ok = false; break; }
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}
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}
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for (int i = 0; i < params.size; ++i)
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{
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subset0[i] = points0[indices[i]];
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subset1[i] = points1[indices[i]];
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}
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Mat_<float> M = impls[model](params.size, &subset0[0], &subset1[0], 0);
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int _ninliers = 0;
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for (int i = 0; i < npoints; ++i)
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{
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p0 = points0[i]; p1 = points1[i];
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x = M(0,0)*p0.x + M(0,1)*p0.y + M(0,2);
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y = M(1,0)*p0.x + M(1,1)*p0.y + M(1,2);
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if (sqr(x - p1.x) + sqr(y - p1.y) < params.thresh * params.thresh)
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_ninliers++;
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}
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if (_ninliers >= ninliersMax)
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{
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bestM = M;
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ninliersMax = _ninliers;
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subset0best.swap(subset0);
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subset1best.swap(subset1);
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}
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}
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if (ninliersMax < params.size)
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// compute rmse
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bestM = impls[model](params.size, &subset0best[0], &subset1best[0], rmse);
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else
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{
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subset0.resize(ninliersMax);
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subset1.resize(ninliersMax);
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for (int i = 0, j = 0; i < npoints; ++i)
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{
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p0 = points0[i]; p1 = points1[i];
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x = bestM(0,0)*p0.x + bestM(0,1)*p0.y + bestM(0,2);
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y = bestM(1,0)*p0.x + bestM(1,1)*p0.y + bestM(1,2);
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if (sqr(x - p1.x) + sqr(y - p1.y) < params.thresh * params.thresh)
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{
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subset0[j] = p0;
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subset1[j] = p1;
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j++;
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}
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}
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bestM = impls[model](ninliersMax, &subset0[0], &subset1[0], rmse);
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}
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if (ninliers)
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*ninliers = ninliersMax;
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return bestM;
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}
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PyrLkRobustMotionEstimator::PyrLkRobustMotionEstimator()
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: ransacParams_(RansacParams::affine2dMotionStd())
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{
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setDetector(new GoodFeaturesToTrackDetector());
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setOptFlowEstimator(new SparsePyrLkOptFlowEstimator());
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setMotionModel(AFFINE);
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setMaxRmse(0.5f);
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setMinInlierRatio(0.1f);
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}
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Mat PyrLkRobustMotionEstimator::estimate(const Mat &frame0, const Mat &frame1)
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{
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detector_->detect(frame0, keypointsPrev_);
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if (keypointsPrev_.empty())
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return Mat::eye(3, 3, CV_32F);
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pointsPrev_.resize(keypointsPrev_.size());
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for (size_t i = 0; i < keypointsPrev_.size(); ++i)
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pointsPrev_[i] = keypointsPrev_[i].pt;
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optFlowEstimator_->run(frame0, frame1, pointsPrev_, points_, status_, noArray());
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size_t npoints = points_.size();
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pointsPrevGood_.clear();
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pointsPrevGood_.reserve(npoints);
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pointsGood_.clear();
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pointsGood_.reserve(npoints);
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for (size_t i = 0; i < npoints; ++i)
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{
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if (status_[i])
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{
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pointsPrevGood_.push_back(pointsPrev_[i]);
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pointsGood_.push_back(points_[i]);
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}
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}
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float rmse;
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int ninliers;
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Mat M = estimateGlobalMotionRobust(
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pointsPrevGood_, pointsGood_, motionModel_, ransacParams_, &rmse, &ninliers);
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if (rmse > maxRmse_ || static_cast<float>(ninliers) / pointsGood_.size() < minInlierRatio_)
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M = Mat::eye(3, 3, CV_32F);
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return M;
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}
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Mat getMotion(int from, int to, const Mat *motions, int size)
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{
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Mat M = Mat::eye(3, 3, CV_32F);
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if (to > from)
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{
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for (int i = from; i < to; ++i)
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M = at(i, motions, size) * M;
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}
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else if (from > to)
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{
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for (int i = to; i < from; ++i)
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M = at(i, motions, size) * M;
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M = M.inv();
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}
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return M;
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}
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Mat getMotion(int from, int to, const vector<Mat> &motions)
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{
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return getMotion(from, to, &motions[0], (int)motions.size());
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}
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} // namespace videostab
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} // namespace cv
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