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Merge pull request #17683 from ivashmak:homography
[GSoC] New RANSAC. Homography part * change enum and squash commits * add small improvements * change function to static, update magsac * remove path from samples, remove license, small updates * update pnp solver, small improvements * fix warnings * add tutorial, comments * fix markdown warnings * fix markdown warnings * fix markdown warnings
This commit is contained in:
Maksym Ivashechkin
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GitHub
GitHub
parent
b45273eccb
commit
a66f61748f
@@ -0,0 +1,526 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "../precomp.hpp"
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#include "../usac.hpp"
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#include "opencv2/flann/miniflann.hpp"
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#include <map>
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namespace cv { namespace usac {
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double Utils::getCalibratedThreshold (double threshold, const Mat &K1, const Mat &K2) {
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return threshold / ((K1.at<double>(0, 0) + K1.at<double>(1, 1) +
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K2.at<double>(0, 0) + K2.at<double>(1, 1)) / 4.0);
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}
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/*
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* K1, K2 are 3x3 intrinsics matrices
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* points is matrix of size |N| x 4
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* Assume K = [k11 k12 k13
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* 0 k22 k23
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* 0 0 1]
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*/
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void Utils::calibratePoints (const Mat &K1, const Mat &K2, const Mat &points, Mat &calib_points) {
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const auto * const points_ = (float *) points.data;
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const auto * const k1 = (double *) K1.data;
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const auto inv1_k11 = float(1 / k1[0]); // 1 / k11
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const auto inv1_k12 = float(-k1[1] / (k1[0]*k1[4])); // -k12 / (k11*k22)
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// (-k13*k22 + k12*k23) / (k11*k22)
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const auto inv1_k13 = float((-k1[2]*k1[4] + k1[1]*k1[5]) / (k1[0]*k1[4]));
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const auto inv1_k22 = float(1 / k1[4]); // 1 / k22
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const auto inv1_k23 = float(-k1[5] / k1[4]); // -k23 / k22
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const auto * const k2 = (double *) K2.data;
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const auto inv2_k11 = float(1 / k2[0]);
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const auto inv2_k12 = float(-k2[1] / (k2[0]*k2[4]));
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const auto inv2_k13 = float((-k2[2]*k2[4] + k2[1]*k2[5]) / (k2[0]*k2[4]));
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const auto inv2_k22 = float(1 / k2[4]);
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const auto inv2_k23 = float(-k2[5] / k2[4]);
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calib_points = Mat ( points.rows, 4, points.type());
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auto * calib_points_ = (float *) calib_points.data;
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for (int i = 0; i < points.rows; i++) {
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const int idx = 4*i;
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(*calib_points_++) = inv1_k11 * points_[idx ] + inv1_k12 * points_[idx+1] + inv1_k13;
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(*calib_points_++) = inv1_k22 * points_[idx+1] + inv1_k23;
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(*calib_points_++) = inv2_k11 * points_[idx+2] + inv2_k12 * points_[idx+3] + inv2_k13;
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(*calib_points_++) = inv2_k22 * points_[idx+3] + inv2_k23;
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}
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}
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/*
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* K is 3x3 intrinsic matrix
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* points is matrix of size |N| x 5, first two columns are image points [u_i, v_i]
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* calib_norm_pts are K^-1 [u v 1]^T / ||K^-1 [u v 1]^T||
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*/
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void Utils::calibrateAndNormalizePointsPnP (const Mat &K, const Mat &pts, Mat &calib_norm_pts) {
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const auto * const points = (float *) pts.data;
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const auto * const k = (double *) K.data;
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const auto inv_k11 = float(1 / k[0]);
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const auto inv_k12 = float(-k[1] / (k[0]*k[4]));
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const auto inv_k13 = float((-k[2]*k[4] + k[1]*k[5]) / (k[0]*k[4]));
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const auto inv_k22 = float(1 / k[4]);
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const auto inv_k23 = float(-k[5] / k[4]);
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calib_norm_pts = Mat (pts.rows, 3, pts.type());
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auto * calib_norm_pts_ = (float *) calib_norm_pts.data;
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for (int i = 0; i < pts.rows; i++) {
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const int idx = 5 * i;
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const float k_inv_u = inv_k11 * points[idx] + inv_k12 * points[idx+1] + inv_k13;
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const float k_inv_v = inv_k22 * points[idx+1] + inv_k23;
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const float norm = 1.f / sqrtf(k_inv_u*k_inv_u + k_inv_v*k_inv_v + 1);
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(*calib_norm_pts_++) = k_inv_u * norm;
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(*calib_norm_pts_++) = k_inv_v * norm;
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(*calib_norm_pts_++) = norm;
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}
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}
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void Utils::normalizeAndDecalibPointsPnP (const Mat &K_, Mat &pts, Mat &calib_norm_pts) {
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const auto * const K = (double *) K_.data;
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const auto k11 = (float)K[0], k12 = (float)K[1], k13 = (float)K[2],
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k22 = (float)K[4], k23 = (float)K[5];
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calib_norm_pts = Mat (pts.rows, 3, pts.type());
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auto * points = (float *) pts.data;
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auto * calib_norm_pts_ = (float *) calib_norm_pts.data;
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for (int i = 0; i < pts.rows; i++) {
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const int idx = 5 * i;
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const float k_inv_u = points[idx ];
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const float k_inv_v = points[idx+1];
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const float norm = 1.f / sqrtf(k_inv_u*k_inv_u + k_inv_v*k_inv_v + 1);
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(*calib_norm_pts_++) = k_inv_u * norm;
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(*calib_norm_pts_++) = k_inv_v * norm;
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(*calib_norm_pts_++) = norm;
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points[idx ] = k11 * k_inv_u + k12 * k_inv_v + k13;
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points[idx+1] = k22 * k_inv_v + k23;
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}
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}
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/*
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* decompose Projection Matrix to calibration, rotation and translation
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* Assume K = [fx 0 tx
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* 0 fy ty
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* 0 0 1]
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*/
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void Utils::decomposeProjection (const Mat &P, Mat &K_, Mat &R, Mat &t, bool same_focal) {
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const Mat M = P.colRange(0,3);
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double scale = norm(M.row(2)); scale *= scale;
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Matx33d K = Matx33d::eye();
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K(1,2) = M.row(1).dot(M.row(2)) / scale;
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K(0,2) = M.row(0).dot(M.row(2)) / scale;
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K(1,1) = sqrt(M.row(1).dot(M.row(1)) / scale - K(1,2)*K(1,2));
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K(0,0) = sqrt(M.row(0).dot(M.row(0)) / scale - K(0,2)*K(0,2));
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if (same_focal)
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K(0,0) = K(1,1) = (K(0,0) + K(1,1)) / 2;
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R = K.inv() * M / sqrt(scale);
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if (determinant(M) < 0) R *= -1;
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t = R * M.inv() * P.col(3);
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K_ = Mat(K);
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}
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Matx33d Math::getSkewSymmetric(const Vec3d &v) {
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return Matx33d(0, -v[2], v[1],
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v[2], 0, -v[0],
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-v[1], v[0], 0);
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}
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Matx33d Math::rotVec2RotMat (const Vec3d &v) {
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const double phi = sqrt(v[0]*v[0]+v[1]*v[1]+v[2]*v[2]);
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const double x = v[0] / phi, y = v[1] / phi, z = v[2] / phi;
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const double a = sin(phi), b = cos(phi);
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// R = I + sin(phi) * skew(v) + (1 - cos(phi) * skew(v)^2
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return Matx33d((b - 1)*y*y + (b - 1)*z*z + 1, -a*z - x*y*(b - 1), a*y - x*z*(b - 1),
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a*z - x*y*(b - 1), (b - 1)*x*x + (b - 1)*z*z + 1, -a*x - y*z*(b - 1),
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-a*y - x*z*(b - 1), a*x - y*z*(b - 1), (b - 1)*x*x + (b - 1)*y*y + 1);
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}
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Vec3d Math::rotMat2RotVec (const Matx33d &R) {
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// https://math.stackexchange.com/questions/83874/efficient-and-accurate-numerical-implementation-of-the-inverse-rodrigues-rotatio?rq=1
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Vec3d rot_vec;
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const double trace = R(0,0)+R(1,1)+R(2,2);
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if (trace >= 3 - FLT_EPSILON) {
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rot_vec = (0.5 * (trace-3)/12)*Vec3d(R(2,1)-R(1,2),
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R(0,2)-R(2,0),
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R(1,0)-R(0,1));
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} else if (3 - FLT_EPSILON > trace && trace > -1 + FLT_EPSILON) {
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double theta = acos((trace - 1) / 2);
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rot_vec = (theta / (2 * sin(theta))) * Vec3d(R(2,1)-R(1,2),
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R(0,2)-R(2,0),
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R(1,0)-R(0,1));
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} else {
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int a;
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if (R(0,0) > R(1,1))
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a = R(0,0) > R(2,2) ? 0 : 2;
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else
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a = R(1,1) > R(2,2) ? 1 : 2;
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Vec3d v;
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int b = (a + 1) % 3, c = (a + 2) % 3;
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double s = sqrt(R(a,a) - R(b,b) - R(c,c) + 1);
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v[a] = s / 2;
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v[b] = (R(b,a) + R(a,b)) / (2 * s);
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v[c] = (R(c,a) + R(a,c)) / (2 * s);
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rot_vec = M_PI * v / norm(v);
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}
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return rot_vec;
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}
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/*
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* Eliminate matrix of m rows and n columns to be upper triangular.
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*/
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void Math::eliminateUpperTriangular (std::vector<double> &a, int m, int n) {
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for (int r = 0; r < m; r++){
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double pivot = a[r*n+r];
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int row_with_pivot = r;
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// find the maximum pivot value among r-th column
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for (int k = r+1; k < m; k++)
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if (fabs(pivot) < fabs(a[k*n+r])) {
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pivot = a[k*n+r];
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row_with_pivot = k;
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}
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// if pivot value is 0 continue
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if (fabs(pivot) < DBL_EPSILON)
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continue;
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// swap row with maximum pivot value with current row
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for (int c = r; c < n; c++)
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std::swap(a[row_with_pivot*n+c], a[r*n+c]);
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// eliminate other rows
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for (int j = r+1; j < m; j++){
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const auto fac = a[j*n+r] / pivot;
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for (int c = r; c < n; c++)
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a[j*n+c] -= fac * a[r*n+c];
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}
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}
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}
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//////////////////////////////////////// RANDOM GENERATOR /////////////////////////////
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class UniformRandomGeneratorImpl : public UniformRandomGenerator {
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private:
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int subset_size = 0, max_range = 0;
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std::vector<int> subset;
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RNG rng;
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public:
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explicit UniformRandomGeneratorImpl (int state) : rng(state) {}
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// interval is <0; max_range);
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UniformRandomGeneratorImpl (int state, int max_range_, int subset_size_) : rng(state) {
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subset_size = subset_size_;
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max_range = max_range_;
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subset = std::vector<int>(subset_size_);
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}
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int getRandomNumber () override {
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return rng.uniform(0, max_range);
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}
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int getRandomNumber (int max_rng) override {
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return rng.uniform(0, max_rng);
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}
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// closed range
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void resetGenerator (int max_range_) override {
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CV_CheckGE(0, max_range_, "max range must be greater than 0");
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max_range = max_range_;
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}
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void generateUniqueRandomSet (std::vector<int>& sample) override {
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CV_CheckLE(subset_size, max_range, "RandomGenerator. Subset size must be LE than range!");
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int j, num;
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sample[0] = rng.uniform(0, max_range);
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for (int i = 1; i < subset_size;) {
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num = rng.uniform(0, max_range);
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// check if value is in array
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for (j = i - 1; j >= 0; j--)
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if (num == sample[j])
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// if so, generate again
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break;
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// success, value is not in array, so it is unique, add to sample.
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if (j == -1) sample[i++] = num;
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}
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}
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// interval is <0; max_range)
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void generateUniqueRandomSet (std::vector<int>& sample, int max_range_) override {
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/*
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* necessary condition:
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* if subset size is bigger than range then array cannot be unique,
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* so function has infinite loop.
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*/
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CV_CheckLE(subset_size, max_range_, "RandomGenerator. Subset size must be LE than range!");
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int num, j;
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sample[0] = rng.uniform(0, max_range_);
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for (int i = 1; i < subset_size;) {
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num = rng.uniform(0, max_range_);
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for (j = i - 1; j >= 0; j--)
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if (num == sample[j])
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break;
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if (j == -1) sample[i++] = num;
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}
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}
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// interval is <0, max_range)
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void generateUniqueRandomSet (std::vector<int>& sample, int subset_size_, int max_range_) override {
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CV_CheckLE(subset_size_, max_range_, "RandomGenerator. Subset size must be LE than range!");
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int num, j;
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sample[0] = rng.uniform(0, max_range_);
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for (int i = 1; i < subset_size_;) {
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num = rng.uniform(0, max_range_);
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for (j = i - 1; j >= 0; j--)
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if (num == sample[j])
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break;
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if (j == -1) sample[i++] = num;
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}
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}
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const std::vector<int> &generateUniqueRandomSubset (std::vector<int> &array1, int size1) override {
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CV_CheckLE(subset_size, size1, "RandomGenerator. Subset size must be LE than range!");
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int temp_size1 = size1;
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for (int i = 0; i < subset_size; i++) {
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const int idx1 = rng.uniform(0, temp_size1);
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subset[i] = array1[idx1];
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std::swap(array1[idx1], array1[--temp_size1]);
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}
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return subset;
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}
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void setSubsetSize (int subset_size_) override {
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subset_size = subset_size_;
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}
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int getSubsetSize () const override { return subset_size; }
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Ptr<RandomGenerator> clone (int state) const override {
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return makePtr<UniformRandomGeneratorImpl>(state, max_range, subset_size);
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}
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};
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Ptr<UniformRandomGenerator> UniformRandomGenerator::create (int state) {
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return makePtr<UniformRandomGeneratorImpl>(state);
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}
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Ptr<UniformRandomGenerator> UniformRandomGenerator::create
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(int state, int max_range, int subset_size_) {
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return makePtr<UniformRandomGeneratorImpl>(state, max_range, subset_size_);
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}
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// @k_minth - desired k-th minimal element. For median is half of array
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// closed working interval of array <@left; @right>
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float quicksort_median (std::vector<float> &array, int k_minth, int left, int right);
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float quicksort_median (std::vector<float> &array, int k_minth, int left, int right) {
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// length is 0, return single value
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if (right - left == 0) return array[left];
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// get pivot, the rightest value in array
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const auto pivot = array[right];
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int right_ = right - 1; // -1, not including pivot
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// counter of values smaller equal than pivot
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int j = left, values_less_eq_pivot = 1; // 1, inludes pivot already
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for (; j <= right_;) {
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if (array[j] <= pivot) {
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j++;
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values_less_eq_pivot++;
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} else
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// value is bigger than pivot, swap with right_ value
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// swap values in array and decrease interval
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std::swap(array[j], array[right_--]);
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}
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if (values_less_eq_pivot == k_minth) return pivot;
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if (k_minth > values_less_eq_pivot)
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return quicksort_median(array, k_minth - values_less_eq_pivot, j, right-1);
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else
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return quicksort_median(array, k_minth, left, j-1);
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}
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// find median using quicksort with complexity O(log n)
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// Note, function changes order of values in array
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float Utils::findMedian (std::vector<float> &array) {
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const int length = static_cast<int>(array.size());
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if (length % 2) {
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// odd number of values
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return quicksort_median (array, length/2+1, 0, length-1);
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} else {
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// even: return average
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return (quicksort_median(array, length/2 , 0, length-1) +
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quicksort_median(array, length/2+1, 0, length-1))/2;
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}
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////
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///////////////////////////////// Radius Search Graph /////////////////////////////////////////////
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///////////////////////////////////////////////////////////////////////////////////////////////////
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class RadiusSearchNeighborhoodGraphImpl : public RadiusSearchNeighborhoodGraph {
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private:
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std::vector<std::vector<int>> graph;
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public:
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RadiusSearchNeighborhoodGraphImpl (const Mat &container_, int points_size,
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double radius, int flann_search_params, int num_kd_trees) {
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// Radius search OpenCV works only with float data
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CV_Assert(container_.type() == CV_32F);
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FlannBasedMatcher flann(makePtr<flann::KDTreeIndexParams>(num_kd_trees), makePtr<flann::SearchParams>(flann_search_params));
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std::vector<std::vector<DMatch>> neighbours;
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flann.radiusMatch(container_, container_, neighbours, (float)radius);
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// allocate graph
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graph = std::vector<std::vector<int>> (points_size);
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int pt = 0;
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for (const auto &n : neighbours) {
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auto &graph_row = graph[pt];
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graph_row = std::vector<int>(n.size()-1);
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int j = 0;
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for (const auto &idx : n)
|
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// skip neighbor which has the same index as requested point
|
||||
if (idx.trainIdx != pt)
|
||||
graph_row[j++] = idx.trainIdx;
|
||||
pt++;
|
||||
}
|
||||
}
|
||||
|
||||
inline const std::vector<int> &getNeighbors(int point_idx) const override {
|
||||
return graph[point_idx];
|
||||
}
|
||||
};
|
||||
Ptr<RadiusSearchNeighborhoodGraph> RadiusSearchNeighborhoodGraph::create (const Mat &points,
|
||||
int points_size, double radius_, int flann_search_params, int num_kd_trees) {
|
||||
return makePtr<RadiusSearchNeighborhoodGraphImpl> (points, points_size, radius_,
|
||||
flann_search_params, num_kd_trees);
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////// FLANN Graph /////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
class FlannNeighborhoodGraphImpl : public FlannNeighborhoodGraph {
|
||||
private:
|
||||
std::vector<std::vector<int>> graph;
|
||||
std::vector<std::vector<double>> distances;
|
||||
public:
|
||||
FlannNeighborhoodGraphImpl (const Mat &container_, int points_size, int k_nearest_neighbors,
|
||||
bool get_distances, int flann_search_params_, int num_kd_trees) {
|
||||
CV_Assert(k_nearest_neighbors <= points_size);
|
||||
// FLANN works only with float data
|
||||
CV_Assert(container_.type() == CV_32F);
|
||||
|
||||
flann::Index flannIndex (container_.reshape(1), flann::KDTreeIndexParams(num_kd_trees));
|
||||
Mat dists, nearest_neighbors;
|
||||
|
||||
flannIndex.knnSearch(container_, nearest_neighbors, dists, k_nearest_neighbors+1,
|
||||
flann::SearchParams(flann_search_params_));
|
||||
|
||||
// first nearest neighbor of point is this point itself.
|
||||
// remove this first column
|
||||
nearest_neighbors.colRange(1, k_nearest_neighbors+1).copyTo (nearest_neighbors);
|
||||
|
||||
graph = std::vector<std::vector<int>>(points_size, std::vector<int>(k_nearest_neighbors));
|
||||
const auto * const nn = (int *) nearest_neighbors.data;
|
||||
const auto * const dists_ptr = (float *) dists.data;
|
||||
|
||||
if (get_distances)
|
||||
distances = std::vector<std::vector<double>>(points_size, std::vector<double>(k_nearest_neighbors));
|
||||
|
||||
for (int pt = 0; pt < points_size; pt++) {
|
||||
std::copy(nn + k_nearest_neighbors*pt, nn + k_nearest_neighbors*pt + k_nearest_neighbors, &graph[pt][0]);
|
||||
if (get_distances)
|
||||
std::copy(dists_ptr + k_nearest_neighbors*pt, dists_ptr + k_nearest_neighbors*pt + k_nearest_neighbors,
|
||||
&distances[pt][0]);
|
||||
}
|
||||
}
|
||||
const std::vector<double>& getNeighborsDistances (int idx) const override {
|
||||
return distances[idx];
|
||||
}
|
||||
inline const std::vector<int> &getNeighbors(int point_idx) const override {
|
||||
// CV_Assert(point_idx_ < num_vertices);
|
||||
return graph[point_idx];
|
||||
}
|
||||
};
|
||||
|
||||
Ptr<FlannNeighborhoodGraph> FlannNeighborhoodGraph::create(const Mat &points,
|
||||
int points_size, int k_nearest_neighbors_, bool get_distances,
|
||||
int flann_search_params_, int num_kd_trees) {
|
||||
return makePtr<FlannNeighborhoodGraphImpl>(points, points_size,
|
||||
k_nearest_neighbors_, get_distances, flann_search_params_, num_kd_trees);
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////// Grid Neighborhood Graph /////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
class GridNeighborhoodGraphImpl : public GridNeighborhoodGraph {
|
||||
private:
|
||||
// This struct is used for the nearest neighbors search by griding two images.
|
||||
struct CellCoord {
|
||||
int c1x, c1y, c2x, c2y;
|
||||
CellCoord (int c1x_, int c1y_, int c2x_, int c2y_) {
|
||||
c1x = c1x_; c1y = c1y_; c2x = c2x_; c2y = c2y_;
|
||||
}
|
||||
bool operator==(const CellCoord &o) const {
|
||||
return c1x == o.c1x && c1y == o.c1y && c2x == o.c2x && c2y == o.c2y;
|
||||
}
|
||||
bool operator<(const CellCoord &o) const {
|
||||
if (c1x < o.c1x) return true;
|
||||
if (c1x == o.c1x && c1y < o.c1y) return true;
|
||||
if (c1x == o.c1x && c1y == o.c1y && c2x < o.c2x) return true;
|
||||
return c1x == o.c1x && c1y == o.c1y && c2x == o.c2x && c2y < o.c2y;
|
||||
}
|
||||
};
|
||||
|
||||
std::map<CellCoord, std::vector<int >> neighbors_map;
|
||||
std::vector<std::vector<int>> graph;
|
||||
public:
|
||||
GridNeighborhoodGraphImpl (const Mat &container_, int points_size,
|
||||
int cell_size_x_img1, int cell_size_y_img1, int cell_size_x_img2, int cell_size_y_img2) {
|
||||
|
||||
const auto * const container = (float *) container_.data;
|
||||
// <int, int, int, int> -> {neighbors set}
|
||||
// Key is cell position. The value is indexes of neighbors.
|
||||
|
||||
const float cell_sz_x1 = 1.f / (float) cell_size_x_img1,
|
||||
cell_sz_y1 = 1.f / (float) cell_size_y_img1,
|
||||
cell_sz_x2 = 1.f / (float) cell_size_x_img2,
|
||||
cell_sz_y2 = 1.f / (float) cell_size_y_img2;
|
||||
const int dimension = container_.cols;
|
||||
for (int i = 0; i < points_size; i++) {
|
||||
const int idx = dimension * i;
|
||||
neighbors_map[CellCoord((int)(container[idx ] * cell_sz_x1),
|
||||
(int)(container[idx+1] * cell_sz_y1),
|
||||
(int)(container[idx+2] * cell_sz_x2),
|
||||
(int)(container[idx+3] * cell_sz_y2))].emplace_back(i);
|
||||
}
|
||||
|
||||
//--------- create a graph ----------
|
||||
graph = std::vector<std::vector<int>>(points_size);
|
||||
|
||||
// store neighbors cells into graph (2D vector)
|
||||
for (const auto &cell : neighbors_map) {
|
||||
const int neighbors_in_cell = static_cast<int>(cell.second.size());
|
||||
|
||||
// only one point in cell -> no neighbors
|
||||
if (neighbors_in_cell < 2) continue;
|
||||
|
||||
const std::vector<int> &neighbors = cell.second;
|
||||
// ---------- fill graph -----
|
||||
for (int v_in_cell : neighbors) {
|
||||
// there is always at least one neighbor
|
||||
auto &graph_row = graph[v_in_cell];
|
||||
graph_row = std::vector<int>(neighbors_in_cell-1);
|
||||
int j = 0;
|
||||
for (int n : neighbors)
|
||||
if (n != v_in_cell)
|
||||
graph_row[j++] = n;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
inline const std::vector<int> &getNeighbors(int point_idx) const override {
|
||||
// Note, neighbors vector also includes point_idx!
|
||||
// return neighbors_map[vertices_to_cells[point_idx]];
|
||||
return graph[point_idx];
|
||||
}
|
||||
};
|
||||
|
||||
Ptr<GridNeighborhoodGraph> GridNeighborhoodGraph::create(const Mat &points,
|
||||
int points_size, int cell_size_x_img1_, int cell_size_y_img1_,
|
||||
int cell_size_x_img2_, int cell_size_y_img2_) {
|
||||
return makePtr<GridNeighborhoodGraphImpl>(points, points_size,
|
||||
cell_size_x_img1_, cell_size_y_img1_, cell_size_x_img2_, cell_size_y_img2_);
|
||||
}
|
||||
}}
|
||||
Reference in New Issue
Block a user