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Refactored videostab module. Added normalization into motion estimators.
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@ -66,8 +66,7 @@ enum MotionModel
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};
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CV_EXPORTS Mat estimateGlobalMotionLeastSquares(
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const std::vector<Point2f> &points0, const std::vector<Point2f> &points1,
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int model = AFFINE, float *rmse = 0);
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int npoints, Point2f *points0, Point2f *points1, int model = AFFINE, float *rmse = 0);
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struct CV_EXPORTS RansacParams
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{
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@ -80,16 +79,24 @@ struct CV_EXPORTS RansacParams
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RansacParams(int size, float thresh, float eps, float prob)
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: size(size), thresh(thresh), eps(eps), prob(prob) {}
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static RansacParams translation2dMotionStd() { return RansacParams(1, 0.5f, 0.5f, 0.99f); }
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static RansacParams translationAndScale2dMotionStd() { return RansacParams(2, 0.5f, 0.5f, 0.99f); }
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static RansacParams linearSimilarity2dMotionStd() { return RansacParams(2, 0.5f, 0.5f, 0.99f); }
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static RansacParams affine2dMotionStd() { return RansacParams(3, 0.5f, 0.5f, 0.99f); }
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static RansacParams homography2dMotionStd() { return RansacParams(4, 0.5f, 0.5f, 0.99f); }
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static RansacParams default2dMotion(MotionModel model)
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{
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CV_Assert(model < UNKNOWN);
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if (model == TRANSLATION)
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return RansacParams(1, 0.5f, 0.5f, 0.99f);
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if (model == TRANSLATION_AND_SCALE)
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return RansacParams(2, 0.5f, 0.5f, 0.99f);
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if (model == LINEAR_SIMILARITY)
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return RansacParams(2, 0.5f, 0.5f, 0.99f);
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if (model == AFFINE)
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return RansacParams(3, 0.5f, 0.5f, 0.99f);
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return RansacParams(4, 0.5f, 0.5f, 0.99f);
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}
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};
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CV_EXPORTS Mat estimateGlobalMotionRobust(
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const std::vector<Point2f> &points0, const std::vector<Point2f> &points1,
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int model = AFFINE, const RansacParams ¶ms = RansacParams::affine2dMotionStd(),
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int model = AFFINE, const RansacParams ¶ms = RansacParams::default2dMotion(AFFINE),
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float *rmse = 0, int *ninliers = 0);
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class CV_EXPORTS GlobalMotionEstimatorBase
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@ -51,8 +51,44 @@ namespace cv
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namespace videostab
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{
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// does isotropic normalization
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static Mat normalizePoints(int npoints, Point2f *points)
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{
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float cx = 0.f, cy = 0.f;
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for (int i = 0; i < npoints; ++i)
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{
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cx += points[i].x;
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cy += points[i].y;
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}
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cx /= npoints;
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cy /= npoints;
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float d = 0.f;
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for (int i = 0; i < npoints; ++i)
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{
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points[i].x -= cx;
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points[i].y -= cy;
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d += sqrt(sqr(points[i].x) + sqr(points[i].y));
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}
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d /= npoints;
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float s = sqrt(2.f) / d;
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for (int i = 0; i < npoints; ++i)
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{
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points[i].x *= s;
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points[i].y *= s;
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}
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Mat_<float> T = Mat::eye(3, 3, CV_32F);
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T(0,0) = T(1,1) = s;
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T(0,2) = -cx*s;
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T(1,2) = -cy*s;
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return T;
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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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int npoints, Point2f *points0, 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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@ -62,6 +98,7 @@ static Mat estimateGlobMotionLeastSquaresTranslation(
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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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@ -70,13 +107,17 @@ static Mat estimateGlobMotionLeastSquaresTranslation(
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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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int npoints, Point2f *points0, Point2f *points1, float *rmse)
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{
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Mat_<float> T0 = normalizePoints(npoints, points0);
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Mat_<float> T1 = normalizePoints(npoints, points1);
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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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@ -103,13 +144,17 @@ static Mat estimateGlobMotionLeastSquaresTranslationAndScale(
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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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return T1.inv() * M * T0;
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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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int npoints, Point2f *points0, Point2f *points1, float *rmse)
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{
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Mat_<float> T0 = normalizePoints(npoints, points0);
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Mat_<float> T1 = normalizePoints(npoints, points1);
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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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@ -138,13 +183,17 @@ static Mat estimateGlobMotionLeastSquaresLinearSimilarity(
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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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return T1.inv() * M * T0;
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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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int npoints, Point2f *points0, Point2f *points1, float *rmse)
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{
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Mat_<float> T0 = normalizePoints(npoints, points0);
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Mat_<float> T1 = normalizePoints(npoints, points1);
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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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@ -172,24 +221,22 @@ static Mat estimateGlobMotionLeastSquaresAffine(
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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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return T1.inv() * M * T0;
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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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int npoints, Point2f *points0, Point2f *points1, int model, float *rmse)
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{
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CV_Assert(model <= AFFINE);
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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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typedef Mat (*Impl)(int, Point2f*, 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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return impls[model](npoints, points0, points1, rmse);
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}
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@ -200,22 +247,22 @@ Mat estimateGlobalMotionRobust(
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CV_Assert(model <= AFFINE);
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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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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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// current hypothesis
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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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vector<Point2f> subset0(params.size);
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vector<Point2f> subset1(params.size);
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// best hypothesis
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vector<Point2f> subset0best(params.size);
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vector<Point2f> subset1best(params.size);
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Mat_<float> bestM;
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int ninliersMax = -1;
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RNG rng(0);
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Point2f p0, p1;
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float x, y;
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@ -239,7 +286,8 @@ Mat estimateGlobalMotionRobust(
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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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Mat_<float> M = estimateGlobalMotionLeastSquares(
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params.size, &subset0[0], &subset1[0], model, 0);
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int ninliers = 0;
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for (int i = 0; i < npoints; ++i)
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@ -260,8 +308,9 @@ Mat estimateGlobalMotionRobust(
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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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// compute RMSE
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bestM = estimateGlobalMotionLeastSquares(
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params.size, &subset0best[0], &subset1best[0], model, rmse);
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else
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{
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subset0.resize(ninliersMax);
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@ -278,7 +327,8 @@ Mat estimateGlobalMotionRobust(
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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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bestM = estimateGlobalMotionLeastSquares(
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ninliersMax, &subset0[0], &subset1[0], model, rmse);
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}
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if (ninliers)
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@ -332,16 +382,11 @@ PyrLkRobustMotionEstimator::PyrLkRobustMotionEstimator(MotionModel model)
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setDetector(new GoodFeaturesToTrackDetector());
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setOptFlowEstimator(new SparsePyrLkOptFlowEstimator());
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setMotionModel(model);
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if (model == TRANSLATION)
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setRansacParams(RansacParams::translation2dMotionStd());
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else if (model == TRANSLATION_AND_SCALE)
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setRansacParams(RansacParams::translationAndScale2dMotionStd());
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else if (model == LINEAR_SIMILARITY)
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setRansacParams(RansacParams::linearSimilarity2dMotionStd());
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else if (model == AFFINE)
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setRansacParams(RansacParams::affine2dMotionStd());
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else if (model == HOMOGRAPHY)
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setRansacParams(RansacParams::homography2dMotionStd());
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RansacParams ransac = RansacParams::default2dMotion(model);
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ransac.size *= 2; // we use more points than needed, but result looks better
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setRansacParams(ransac);
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setMaxRmse(0.5f);
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setMinInlierRatio(0.1f);
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setGridSize(Size(0,0));
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@ -362,6 +407,7 @@ Mat PyrLkRobustMotionEstimator::estimate(const Mat &frame0, const Mat &frame1, b
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keypointsPrev_.push_back(KeyPoint((x+1)*dx, (y+1)*dy, 0.f));
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}
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// draw keypoints
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/*Mat img;
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drawKeypoints(frame0, keypointsPrev_, img);
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imshow("frame0_keypoints", img);
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@ -374,10 +420,9 @@ Mat PyrLkRobustMotionEstimator::estimate(const Mat &frame0, const Mat &frame1, b
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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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pointsPrevGood_.clear(); pointsPrevGood_.reserve(npoints);
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pointsGood_.clear(); 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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@ -338,16 +338,18 @@ void TwoPassStabilizer::runPrePassIfNecessary()
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WobbleSuppressorBase *wobbleSuppressor = static_cast<WobbleSuppressorBase*>(wobbleSuppressor_);
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doWobbleSuppression_ = dynamic_cast<NullWobbleSuppressor*>(wobbleSuppressor) == 0;
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bool okEst;
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bool ok = true, ok2 = true;
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while (!(frame = frameSource_->nextFrame()).empty())
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{
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if (frameCount_ > 0)
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{
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motions_.push_back(motionEstimator_->estimate(prevFrame, frame));
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motions_.push_back(motionEstimator_->estimate(prevFrame, frame, &ok));
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if (doWobbleSuppression_)
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{
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Mat M = wobbleSuppressor_->motionEstimator()->estimate(prevFrame, frame, &okEst);
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if (okEst)
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Mat M = wobbleSuppressor_->motionEstimator()->estimate(prevFrame, frame, &ok2);
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if (ok2)
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motions2_.push_back(M);
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else
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motions2_.push_back(motions_.back());
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@ -363,7 +365,12 @@ void TwoPassStabilizer::runPrePassIfNecessary()
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prevFrame = frame;
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frameCount_++;
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log_->print(".");
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if (ok)
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{
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if (ok2) log_->print(".");
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else log_->print("?");
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}
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else log_->print("x");
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}
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for (int i = 0; i < radius_; ++i)
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@ -55,7 +55,7 @@ WobbleSuppressorBase::WobbleSuppressorBase() : motions_(0), stabilizationMotions
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{
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PyrLkRobustMotionEstimator *est = new PyrLkRobustMotionEstimator();
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est->setMotionModel(HOMOGRAPHY);
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est->setRansacParams(RansacParams::homography2dMotionStd());
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est->setRansacParams(RansacParams::default2dMotion(HOMOGRAPHY));
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setMotionEstimator(est);
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}
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@ -67,6 +67,8 @@ void printHelp()
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"Arguments:\n"
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" -m, --model=(transl|transl_and_scale|linear_sim|affine|homography)\n"
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" Set motion model. The default is affine.\n"
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" --subset=(<int_number>|auto)\n"
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" Number of random samples per one motion hypothesis. The default is auto.\n"
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" --outlier-ratio=<float_number>\n"
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" Motion estimation outlier ratio hypothesis. The default is 0.5.\n"
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" --min-inlier-ratio=<float_number>\n"
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@ -116,10 +118,12 @@ void printHelp()
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" --ws-model=(transl|transl_and_scale|linear_sim|affine|homography)\n"
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" Set wobble suppression motion model (must have more DOF than motion \n"
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" estimation model). The default is homography.\n"
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" --ws-min-inlier-ratio=<float_number>\n"
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" Minimum inlier ratio to decide if estimated motion is OK. The default is 0.1.\n"
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" --ws-subset=(<int_number>|auto)\n"
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" Number of random samples per one motion hypothesis. The default is auto.\n"
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" --ws-outlier-ratio=<float_number>\n"
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" Motion estimation outlier ratio hypothesis. The default is 0.5.\n"
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" --ws-min-inlier-ratio=<float_number>\n"
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" Minimum inlier ratio to decide if estimated motion is OK. The default is 0.1.\n"
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" --ws-nkps=<int_number>\n"
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" Number of keypoints to find in each frame. The default is 1000.\n"
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" --ws-extra-kps=<int_number>\n"
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@ -147,8 +151,9 @@ int main(int argc, const char **argv)
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const char *keys =
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"{ 1 | | | | }"
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"{ m | model | affine| }"
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"{ | min-inlier-ratio | 0.1 | }"
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"{ | subset | auto | }"
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"{ | outlier-ratio | 0.5 | }"
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"{ | min-inlier-ratio | 0.1 | }"
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"{ | nkps | 1000 | }"
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"{ | extra-kps | 0 | }"
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"{ sm | save-motions | no | }"
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@ -170,8 +175,9 @@ int main(int argc, const char **argv)
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"{ ws | wobble-suppress | no | }"
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"{ | ws-period | 30 | }"
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"{ | ws-model | homography | }"
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"{ | ws-min-inlier-ratio | 0.1 | }"
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"{ | ws-subset | auto | }"
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"{ | ws-outlier-ratio | 0.5 | }"
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"{ | ws-min-inlier-ratio | 0.1 | }"
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"{ | ws-nkps | 1000 | }"
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"{ | ws-extra-kps | 0 | }"
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"{ sm2 | save-motions2 | no | }"
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@ -230,6 +236,8 @@ int main(int argc, const char **argv)
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est->setDetector(new GoodFeaturesToTrackDetector(argi("ws-nkps")));
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RansacParams ransac = est->ransacParams();
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if (arg("ws-subset") != "auto")
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ransac.size = argi("ws-subset");
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ransac.eps = argf("ws-outlier-ratio");
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est->setRansacParams(ransac);
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est->setMinInlierRatio(argf("ws-min-inlier-ratio"));
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@ -288,6 +296,8 @@ int main(int argc, const char **argv)
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est->setDetector(new GoodFeaturesToTrackDetector(argi("nkps")));
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RansacParams ransac = est->ransacParams();
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if (arg("subset") != "auto")
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ransac.size = argi("subset");
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ransac.eps = argf("outlier-ratio");
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||||
est->setRansacParams(ransac);
|
||||
est->setMinInlierRatio(argf("min-inlier-ratio"));
|
||||
|
Loading…
Reference in New Issue
Block a user