1.换opencv4
2.修改程序
改程序针对slambook2/ch8/direct_method.cpp
#include <opencv2/opencv.hpp>
#include <sophus/se3.hpp>
#include <boost/format.hpp>
#include <pangolin/pangolin.h>
using namespace std;
typedef vector<Eigen::Vector2d, Eigen::aligned_allocator<Eigen::Vector2d>> VecVector2d;
// Camera intrinsics
double fx = 718.856, fy = 718.856, cx = 607.1928, cy = 185.2157;
// baseline
double baseline = 0.573;
// paths
string left_file = "../left.png";
string disparity_file = "../disparity.png";
boost::format fmt_others("../%06d.png"); // other files
// useful typedefs
typedef Eigen::Matrix<double, 6, 6> Matrix6d;
typedef Eigen::Matrix<double, 2, 6> Matrix26d;
typedef Eigen::Matrix<double, 6, 1> Vector6d;
/**
* pose estimation using direct method
* @param img1
* @param img2
* @param px_ref
* @param depth_ref
* @param T21
*/
void DirectPoseEstimationMultiLayer(
const cv::Mat &img1,
const cv::Mat &img2,
const VecVector2d &px_ref,
const vector<double> depth_ref,
Sophus::SE3d &T21
);
/**
* pose estimation using direct method
* @param img1
* @param img2
* @param px_ref
* @param depth_ref
* @param T21
*/
void DirectPoseEstimationSingleLayer(
const cv::Mat &img1,
const cv::Mat &img2,
const VecVector2d &px_ref,
const vector<double> depth_ref,
Sophus::SE3d &T21
);
// bilinear interpolation
inline float GetPixelValue(const cv::Mat &img, float x, float y) {
// boundary check
if (x < 0) x = 0;
if (y < 0) y = 0;
if (x >= img.cols) x = img.cols - 1;
if (y >= img.rows) y = img.rows - 1;
uchar *data = &img.data[int(y) * img.step + int(x)];
float xx = x - floor(x);
float yy = y - floor(y);
return float(
(1 - xx) * (1 - yy) * data[0] +
xx * (1 - yy) * data[1] +
(1 - xx) * yy * data[img.step] +
xx * yy * data[img.step + 1]
);
}
/// class for accumulator jacobians in parallel
class JacobianAccumulator: public cv::ParallelLoopBody {
private:
const cv::Mat &img1;
const cv::Mat &img2;
const VecVector2d &px_ref;
const vector<double> depth_ref;
Sophus::SE3d &T21;
mutable VecVector2d projection; // projected points
mutable std::mutex hessian_mutex;
mutable Matrix6d H = Matrix6d::Zero();
mutable Vector6d b = Vector6d::Zero();
mutable double cost = 0;
public:
JacobianAccumulator(
const cv::Mat &img1_,
const cv::Mat &img2_,
const VecVector2d &px_ref_,
const vector<double> depth_ref_,
Sophus::SE3d &T21_) :
img1(img1_), img2(img2_), px_ref(px_ref_), depth_ref(depth_ref_), T21(T21_) {
projection = VecVector2d(px_ref.size(), Eigen::Vector2d(0, 0));
}
/// accumulate jacobians in a range
// void accumulate_jacobian(const cv::Range &range);
/// get hessian matrix
Matrix6d hessian() const { return H; }
/// get bias
Vector6d bias() const { return b; }
/// get total cost
double cost_func() const { return cost; }
/// get projected points
VecVector2d projected_points() const { return projection; }
/// reset h, b, cost to zero
void reset() {
H = Matrix6d::Zero();
b = Vector6d::Zero();
cost = 0;
}
virtual void operator()(const cv::Range& range) const {
// parameters
const int half_patch_size = 1;
int cnt_good = 0;
Matrix6d hessian = Matrix6d::Zero();
Vector6d bias = Vector6d::Zero();
double cost_tmp = 0;
for (size_t i = range.start; i < range.end; i++) {
// compute the projection in the second image
Eigen::Vector3d point_ref =
depth_ref[i] * Eigen::Vector3d((px_ref[i][0] - cx) / fx, (px_ref[i][1] - cy) / fy, 1);
Eigen::Vector3d point_cur = T21 * point_ref;
if (point_cur[2] < 0) // depth invalid
continue;
float u = fx * point_cur[0] / point_cur[2] + cx, v = fy * point_cur[1] / point_cur[2] + cy;
if (u < half_patch_size || u > img2.cols - half_patch_size || v < half_patch_size ||
v > img2.rows - half_patch_size)
continue;
projection[i] = Eigen::Vector2d(u, v);
double X = point_cur[0], Y = point_cur[1], Z = point_cur[2],
Z2 = Z * Z, Z_inv = 1.0 / Z, Z2_inv = Z_inv * Z_inv;
cnt_good++;
// and compute error and jacobian
for (int x = -half_patch_size; x <= half_patch_size; x++)
for (int y = -half_patch_size; y <= half_patch_size; y++) {
double error = GetPixelValue(img1, px_ref[i][0] + x, px_ref[i][1] + y) -
GetPixelValue(img2, u + x, v + y);
Matrix26d J_pixel_xi;
Eigen::Vector2d J_img_pixel;
J_pixel_xi(0, 0) = fx * Z_inv;
J_pixel_xi(0, 1) = 0;
J_pixel_xi(0, 2) = -fx * X * Z2_inv;
J_pixel_xi(0, 3) = -fx * X * Y * Z2_inv;
J_pixel_xi(0, 4) = fx + fx * X * X * Z2_inv;
J_pixel_xi(0, 5) = -fx * Y * Z_inv;
J_pixel_xi(1, 0) = 0;
J_pixel_xi(1, 1) = fy * Z_inv;
J_pixel_xi(1, 2) = -fy * Y * Z2_inv;
J_pixel_xi(1, 3) = -fy - fy * Y * Y * Z2_inv;
J_pixel_xi(1, 4) = fy * X * Y * Z2_inv;
J_pixel_xi(1, 5) = fy * X * Z_inv;
J_img_pixel = Eigen::Vector2d(
0.5 * (GetPixelValue(img2, u + 1 + x, v + y) - GetPixelValue(img2, u - 1 + x, v + y)),
0.5 * (GetPixelValue(img2, u + x, v + 1 + y) - GetPixelValue(img2, u + x, v - 1 + y))
);
// total jacobian
Vector6d J = -1.0 * (J_img_pixel.transpose() * J_pixel_xi).transpose();
hessian += J * J.transpose();
bias += -error * J;
cost_tmp += error * error;
}
}
if (cnt_good) {
// set hessian, bias and cost
unique_lock<mutex> lck(hessian_mutex);
H += hessian;
b += bias;
cost += cost_tmp / cnt_good;
}
}
};
int main(int argc, char **argv) {
cv::Mat left_img = cv::imread(left_file, 0);
cv::Mat disparity_img = cv::imread(disparity_file, 0);
if (left_img.empty() || disparity_img.empty())
{
std::cout << "!!! Failed imread(): image not found" << std::endl;
return 1;
}
// let's randomly pick pixels in the first image and generate some 3d points in the first image's frame
cv::RNG rng;
int nPoints = 2000;
int boarder = 20;
VecVector2d pixels_ref;
vector<double> depth_ref;
cout << "left_img.cols" << left_img.cols << endl;
cout << "left_img: " << left_img << endl;
// generate pixels in ref and load depth data
for (int i = 0; i < nPoints; i++) {
int x = rng.uniform(boarder, left_img.cols - boarder); // don't pick pixels close to boarder
int y = rng.uniform(boarder, left_img.rows - boarder); // don't pick pixels close to boarder
int disparity = disparity_img.at<uchar>(y, x);
double depth = fx * baseline / disparity; // you know this is disparity to depth
depth_ref.push_back(depth);
pixels_ref.push_back(Eigen::Vector2d(x, y));
}
// estimates 01~05.png's pose using this information
Sophus::SE3d T_cur_ref;
for (int i = 1; i < 6; i++) { // 1~10
cv::Mat img = cv::imread((fmt_others % i).str(), 0);
// try single layer by uncomment this line
// DirectPoseEstimationSingleLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref);
DirectPoseEstimationMultiLayer(left_img, img, pixels_ref, depth_ref, T_cur_ref);
}
return 0;
}
void DirectPoseEstimationSingleLayer(
const cv::Mat &img1,
const cv::Mat &img2,
const VecVector2d &px_ref,
const vector<double> depth_ref,
Sophus::SE3d &T21) {
const int iterations = 10;
double cost = 0, lastCost = 0;
auto t1 = chrono::steady_clock::now();
JacobianAccumulator jaco_accu(img1, img2, px_ref, depth_ref, T21);
for (int iter = 0; iter < iterations; iter++) {
jaco_accu.reset();
cv::parallel_for_(cv::Range(0, px_ref.size()), jaco_accu);
Matrix6d H = jaco_accu.hessian();
Vector6d b = jaco_accu.bias();
// solve update and put it into estimation
Vector6d update = H.ldlt().solve(b);;
T21 = Sophus::SE3d::exp(update) * T21;
cost = jaco_accu.cost_func();
if (std::isnan(update[0])) {
// sometimes occurred when we have a black or white patch and H is irreversible
cout << "update is nan" << endl;
break;
}
if (iter > 0 && cost > lastCost) {
cout << "cost increased: " << cost << ", " << lastCost << endl;
break;
}
if (update.norm() < 1e-3) {
// converge
break;
}
lastCost = cost;
cout << "iteration: " << iter << ", cost: " << cost << endl;
}
cout << "T21 =
" << T21.matrix() << endl;
auto t2 = chrono::steady_clock::now();
auto time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "direct method for single layer: " << time_used.count() << endl;
// plot the projected pixels here
cv::Mat img2_show;
cv::cvtColor(img2, img2_show, CV_GRAY2BGR);
VecVector2d projection = jaco_accu.projected_points();
for (size_t i = 0; i < px_ref.size(); ++i) {
auto p_ref = px_ref[i];
auto p_cur = projection[i];
if (p_cur[0] > 0 && p_cur[1] > 0) {
cv::circle(img2_show, cv::Point2f(p_cur[0], p_cur[1]), 2, cv::Scalar(0, 250, 0), 2);
cv::line(img2_show, cv::Point2f(p_ref[0], p_ref[1]), cv::Point2f(p_cur[0], p_cur[1]),
cv::Scalar(0, 250, 0));
}
}
cv::imshow("current", img2_show);
cv::waitKey();
}
void DirectPoseEstimationMultiLayer(
const cv::Mat &img1,
const cv::Mat &img2,
const VecVector2d &px_ref,
const vector<double> depth_ref,
Sophus::SE3d &T21) {
// parameters
int pyramids = 4;
double pyramid_scale = 0.5;
double scales[] = {1.0, 0.5, 0.25, 0.125};
// create pyramids
vector<cv::Mat> pyr1, pyr2; // image pyramids
for (int i = 0; i < pyramids; i++) {
if (i == 0) {
pyr1.push_back(img1);
pyr2.push_back(img2);
} else {
cv::Mat img1_pyr, img2_pyr;
cv::resize(pyr1[i - 1], img1_pyr,
cv::Size(pyr1[i - 1].cols * pyramid_scale, pyr1[i - 1].rows * pyramid_scale));
cv::resize(pyr2[i - 1], img2_pyr,
cv::Size(pyr2[i - 1].cols * pyramid_scale, pyr2[i - 1].rows * pyramid_scale));
pyr1.push_back(img1_pyr);
pyr2.push_back(img2_pyr);
}
}
double fxG = fx, fyG = fy, cxG = cx, cyG = cy; // backup the old values
for (int level = pyramids - 1; level >= 0; level--) {
VecVector2d px_ref_pyr; // set the keypoints in this pyramid level
for (auto &px: px_ref) {
px_ref_pyr.push_back(scales[level] * px);
}
// scale fx, fy, cx, cy in different pyramid levels
fx = fxG * scales[level];
fy = fyG * scales[level];
cx = cxG * scales[level];
cy = cyG * scales[level];
DirectPoseEstimationSingleLayer(pyr1[level], pyr2[level], px_ref_pyr, depth_ref, T21);
}
}