1373 lines
45 KiB
C++
1373 lines
45 KiB
C++
#include <numeric>
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#include <omp.h>
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#include <opencv2/opencv.hpp>
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#include "camera.hpp"
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#include "triangulator.hpp"
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// =================================================================================================
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// =================================================================================================
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[[maybe_unused]] static void print_2d_mat(const cv::Mat &mat)
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{
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// Ensure the matrix is 2D
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if (mat.dims != 2)
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{
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std::cerr << "Error: The matrix is not 2D." << std::endl;
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return;
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}
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// Retrieve matrix dimensions
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int rows = mat.rows;
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int cols = mat.cols;
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// Print the matrix in a NumPy-like style
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std::cout << "cv::Mat('shape': (" << rows << ", " << cols << ")";
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std::cout << ", 'data': [" << std::endl;
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for (int i = 0; i < rows; ++i)
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{
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std::cout << " [";
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for (int j = 0; j < cols; ++j)
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{
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std::cout << std::fixed << std::setprecision(3) << mat.at<float>(i, j);
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if (j < cols - 1)
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{
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std::cout << ", ";
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}
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}
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std::cout << "]";
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if (i < rows - 1)
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{
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std::cout << "," << std::endl;
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}
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}
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std::cout << "])" << std::endl;
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}
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// =================================================================================================
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[[maybe_unused]] static void print_allpairs(
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const std::vector<std::pair<std::tuple<int, int, int, int>, std::pair<int, int>>> &all_pairs)
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{
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std::cout << "All pairs: [" << std::endl;
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for (const auto &pair : all_pairs)
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{
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const auto &tuple_part = pair.first;
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const auto &pair_part = pair.second;
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// Print the tuple part
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std::cout << "("
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<< std::get<0>(tuple_part) << ", "
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<< std::get<1>(tuple_part) << ", "
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<< std::get<2>(tuple_part) << ", "
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<< std::get<3>(tuple_part) << ")";
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// Print the pair part
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std::cout << ", ("
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<< pair_part.first << ", "
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<< pair_part.second << ")"
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<< std::endl;
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}
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std::cout << "]" << std::endl;
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}
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// =================================================================================================
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// =================================================================================================
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CameraInternal::CameraInternal(const Camera &cam)
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{
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this->cam = cam;
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// Convert camera matrix to cv::Mat for OpenCV
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K = cv::Mat(3, 3, CV_32FC1, const_cast<float *>(&cam.K[0][0])).clone();
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DC = cv::Mat(cam.DC.size(), 1, CV_32FC1, const_cast<float *>(cam.DC.data())).clone();
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R = cv::Mat(3, 3, CV_32FC1, const_cast<float *>(&cam.R[0][0])).clone();
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T = cv::Mat(3, 1, CV_32FC1, const_cast<float *>(&cam.T[0][0])).clone();
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}
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// =================================================================================================
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void CameraInternal::update_projection_matrix()
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{
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// Calculate opencv-style projection matrix
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cv::Mat Tr, RT;
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Tr = R * (T * -1);
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cv::hconcat(R, Tr, RT);
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P = K * RT;
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}
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// =================================================================================================
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// =================================================================================================
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TriangulatorInternal::TriangulatorInternal(float min_score)
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{
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this->min_score = min_score;
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}
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// =================================================================================================
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std::vector<std::vector<std::array<float, 4>>> TriangulatorInternal::triangulate_poses(
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const std::vector<std::vector<std::vector<std::array<float, 3>>>> &poses_2d,
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const std::vector<Camera> &cameras,
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const std::array<std::array<float, 3>, 2> &roomparams,
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const std::vector<std::string> &joint_names)
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{
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// Check inputs
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if (poses_2d.empty())
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{
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throw std::invalid_argument("No 2D poses provided.");
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}
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if (poses_2d.size() != cameras.size())
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{
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throw std::invalid_argument("Number of cameras and 2D poses must be the same.");
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}
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if (joint_names.size() != poses_2d[0][0].size())
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{
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throw std::invalid_argument("Number of joint names and 2D poses must be the same.");
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}
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for (const auto &joint : core_joints)
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{
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if (std::find(joint_names.begin(), joint_names.end(), joint) == joint_names.end())
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{
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throw std::invalid_argument("Core joint '" + joint + "' not found in joint names.");
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}
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}
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// Convert inputs to internal formats
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std::vector<std::vector<cv::Mat>> poses_2d_mats;
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poses_2d_mats.reserve(cameras.size());
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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size_t num_persons = poses_2d[i].size();
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size_t num_joints = poses_2d[i][0].size();
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std::vector<cv::Mat> camera_poses;
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camera_poses.reserve(num_persons);
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for (size_t j = 0; j < num_persons; ++j)
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{
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std::vector<int> dims = {(int)num_joints, 3};
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cv::Mat pose_mat = cv::Mat(dims, CV_32F);
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// Use pointer to copy data efficiently
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for (size_t k = 0; k < num_joints; ++k)
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{
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float *mat_ptr = pose_mat.ptr<float>(k);
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const auto &joint = poses_2d[i][j][k];
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mat_ptr[0] = joint[0];
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mat_ptr[1] = joint[1];
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mat_ptr[2] = joint[2];
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}
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camera_poses.push_back(std::move(pose_mat));
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}
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poses_2d_mats.push_back(std::move(camera_poses));
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}
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std::vector<CameraInternal> internal_cameras;
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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internal_cameras.emplace_back(cameras[i]);
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}
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std::vector<size_t> core_joint_idx;
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for (const auto &joint : core_joints)
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{
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auto it = std::find(joint_names.begin(), joint_names.end(), joint);
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core_joint_idx.push_back(std::distance(joint_names.begin(), it));
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}
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std::vector<std::array<size_t, 2>> core_limbs_idx;
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for (const auto &limb : core_limbs)
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{
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auto it1 = std::find(core_joints.begin(), core_joints.end(), limb.first);
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auto it2 = std::find(core_joints.begin(), core_joints.end(), limb.second);
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core_limbs_idx.push_back({(size_t)std::distance(core_joints.begin(), it1),
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(size_t)std::distance(core_joints.begin(), it2)});
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}
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// Undistort 2D poses
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#pragma omp parallel for
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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undistort_poses(poses_2d_mats[i], internal_cameras[i]);
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internal_cameras[i].update_projection_matrix();
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}
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// Project last 3D poses to 2D
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std::vector<std::tuple<std::vector<cv::Mat>, std::vector<cv::Mat>>> last_poses_2d;
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if (!last_poses_3d.empty())
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{
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// Select core joints
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std::vector<cv::Mat> last_core_poses;
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last_core_poses.resize(last_poses_3d.size());
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#pragma omp parallel for
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for (size_t i = 0; i < last_poses_3d.size(); ++i)
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{
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cv::Mat &pose = last_poses_3d[i];
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std::vector<int> dims = {(int)core_joint_idx.size(), 4};
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cv::Mat last_poses_core(dims, pose.type());
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for (size_t j = 0; j < core_joint_idx.size(); ++j)
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{
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pose.row(core_joint_idx[j]).copyTo(last_poses_core.row(j));
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}
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last_core_poses[i] = last_poses_core;
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}
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// Project
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last_poses_2d.resize(cameras.size());
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#pragma omp parallel for
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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auto [poses2d, dists] = project_poses(last_core_poses, internal_cameras[i], true);
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last_poses_2d[i] = std::make_tuple(poses2d, dists);
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}
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}
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// Check matches to old poses
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float threshold = min_score - 0.2;
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std::map<size_t, std::map<size_t, std::vector<size_t>>> scored_pasts;
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if (!last_poses_3d.empty())
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{
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// Calculate index pairs and initialize vectors
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std::vector<std::array<size_t, 3>> indices_ijk;
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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size_t num_last_persons = std::get<0>(last_poses_2d[i]).size();
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scored_pasts[i] = std::map<size_t, std::vector<size_t>>();
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for (size_t j = 0; j < num_last_persons; ++j)
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{
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size_t num_new_persons = poses_2d_mats[i].size();
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scored_pasts[i][j] = std::vector<size_t>();
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for (size_t k = 0; k < num_new_persons; ++k)
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{
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indices_ijk.push_back({i, j, k});
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}
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}
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}
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std::vector<std::array<size_t, 2>> indices_ik;
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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size_t num_new_persons = poses_2d_mats[i].size();
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for (size_t k = 0; k < num_new_persons; ++k)
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{
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indices_ik.push_back({i, k});
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}
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}
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// Precalculate core poses
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std::vector<cv::Mat> poses_2d_mats_core_list;
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poses_2d_mats_core_list.resize(indices_ik.size());
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std::vector<std::vector<size_t>> mats_core_map;
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mats_core_map.resize(cameras.size());
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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size_t num_new_persons = poses_2d_mats[i].size();
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for (size_t k = 0; k < num_new_persons; ++k)
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{
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mats_core_map[i].push_back(0);
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}
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}
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#pragma omp parallel for
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for (size_t e = 0; e < indices_ik.size(); ++e)
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{
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const auto [i, k] = indices_ik[e];
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const cv::Mat &pose = poses_2d_mats[i][k];
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std::vector<int> dims = {(int)core_joint_idx.size(), 3};
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cv::Mat pose_core(dims, pose.type());
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for (size_t j = 0; j < core_joint_idx.size(); ++j)
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{
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pose.row(core_joint_idx[j]).copyTo(pose_core.row(j));
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}
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poses_2d_mats_core_list[e] = pose_core;
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mats_core_map[i][k] = e;
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}
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// Calculate matching score
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#pragma omp parallel for
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for (size_t e = 0; e < indices_ijk.size(); ++e)
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{
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const auto [i, j, k] = indices_ijk[e];
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const cv::Mat &last_pose = std::get<0>(last_poses_2d[i])[j];
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const cv::Mat &last_dist = std::get<1>(last_poses_2d[i])[j];
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const cv::Mat &new_pose = poses_2d_mats_core_list[mats_core_map[i][k]];
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double score = calc_pose_score(new_pose, last_pose, last_dist, internal_cameras[i]);
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if (score > threshold)
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{
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#pragma omp critical
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{
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scored_pasts[i][j].push_back(k);
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}
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}
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}
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}
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// Create pairs of persons
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// Checks if the person was already matched to the last frame and if so only creates pairs
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// with those, else it creates all possible pairs
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std::vector<int> num_persons_sum;
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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int nsum = poses_2d[i].size();
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if (i > 0)
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{
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nsum += num_persons_sum[i - 1];
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}
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num_persons_sum.push_back(nsum);
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}
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std::vector<std::pair<std::tuple<int, int, int, int>, std::pair<int, int>>> all_pairs;
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std::vector<std::array<size_t, 4>> indices;
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for (size_t i = 0; i < cameras.size(); ++i)
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{
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for (size_t j = i + 1; j < cameras.size(); ++j)
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{
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for (size_t k = 0; k < poses_2d[i].size(); ++k)
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{
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for (size_t l = 0; l < poses_2d[j].size(); ++l)
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{
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indices.push_back({i, j, k, l});
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}
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}
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}
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}
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#pragma omp parallel for ordered schedule(dynamic)
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for (size_t e = 0; e < indices.size(); ++e)
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{
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const auto [i, j, k, l] = indices[e];
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int pid1 = num_persons_sum[i] + k;
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int pid2 = num_persons_sum[k] + l;
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bool match = false;
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if (!last_poses_3d.empty())
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{
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for (size_t m = 0; m < last_poses_3d.size(); ++m)
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{
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auto &smi = scored_pasts[i][m];
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auto &smj = scored_pasts[j][m];
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bool in_smi = std::find(smi.begin(), smi.end(), k) != smi.end();
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bool in_smj = std::find(smj.begin(), smj.end(), l) != smj.end();
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if (in_smi && in_smj)
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{
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match = true;
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auto item = std::make_pair(
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std::make_tuple(i, j, k, l), std::make_pair(pid1, pid2));
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#pragma omp ordered
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all_pairs.push_back(item);
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}
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else if (in_smi || in_smj)
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{
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match = true;
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}
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}
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}
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if (!match)
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{
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auto item = std::make_pair(
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std::make_tuple(i, j, k, l), std::make_pair(pid1, pid2));
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// Needed to prevent randomized grouping/merging with slightly different results
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#pragma omp ordered
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all_pairs.push_back(item);
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}
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}
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// Calculate pair scores
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std::vector<std::pair<cv::Mat, float>> all_scored_poses;
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all_scored_poses.resize(all_pairs.size());
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#pragma omp parallel for
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for (size_t i = 0; i < all_pairs.size(); ++i)
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{
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const auto &pids = all_pairs[i].first;
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// Extract camera parameters
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const auto &cam1 = internal_cameras[std::get<0>(pids)];
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const auto &cam2 = internal_cameras[std::get<1>(pids)];
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// Extract 2D poses
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const cv::Mat &pose1 = poses_2d_mats[std::get<0>(pids)][std::get<2>(pids)];
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const cv::Mat &pose2 = poses_2d_mats[std::get<1>(pids)][std::get<3>(pids)];
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// Select core joints
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std::vector<int> dims = {(int)core_joint_idx.size(), 3};
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cv::Mat pose1_core(dims, pose1.type());
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cv::Mat pose2_core(dims, pose2.type());
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for (size_t j = 0; j < core_joint_idx.size(); ++j)
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{
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size_t idx = core_joint_idx[j];
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pose1.row(idx).copyTo(pose1_core.row(j));
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pose2.row(idx).copyTo(pose2_core.row(j));
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}
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// Triangulate and score
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auto [pose3d, score] = triangulate_and_score(
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pose1_core, pose2_core, cam1, cam2, roomparams, core_limbs_idx);
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all_scored_poses[i] = std::make_pair(pose3d, score);
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}
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// Drop low scoring poses
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std::vector<size_t> drop_indices;
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for (size_t i = 0; i < all_scored_poses.size(); ++i)
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{
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if (all_scored_poses[i].second < min_score)
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{
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drop_indices.push_back(i);
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}
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}
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if (!drop_indices.empty())
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{
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for (size_t i = drop_indices.size(); i > 0; --i)
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{
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all_scored_poses.erase(all_scored_poses.begin() + drop_indices[i - 1]);
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all_pairs.erase(all_pairs.begin() + drop_indices[i - 1]);
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}
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}
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// Group pairs that share a person
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std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> groups;
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groups = calc_grouping(all_pairs, all_scored_poses, min_score);
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// Calculate full 3D poses
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std::vector<cv::Mat> all_full_poses;
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all_full_poses.resize(all_pairs.size());
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#pragma omp parallel for
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for (size_t i = 0; i < all_pairs.size(); ++i)
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{
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const auto &pids = all_pairs[i].first;
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const auto &cam1 = internal_cameras[std::get<0>(pids)];
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const auto &cam2 = internal_cameras[std::get<1>(pids)];
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const auto &pose1 = poses_2d_mats[std::get<0>(pids)][std::get<2>(pids)];
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const auto &pose2 = poses_2d_mats[std::get<1>(pids)][std::get<3>(pids)];
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auto [pose3d, score] = triangulate_and_score(
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pose1, pose2, cam1, cam2, roomparams, {});
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all_full_poses[i] = (pose3d);
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}
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// Merge groups
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std::vector<cv::Mat> all_merged_poses;
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all_merged_poses.resize(groups.size());
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#pragma omp parallel for
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for (size_t i = 0; i < groups.size(); ++i)
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{
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const auto &group = groups[i];
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std::vector<cv::Mat> poses;
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poses.reserve(std::get<2>(group).size());
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for (const auto &idx : std::get<2>(group))
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{
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poses.push_back(all_full_poses[idx]);
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}
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auto merged_pose = merge_group(poses, min_score);
|
|
all_merged_poses[i] = (merged_pose);
|
|
}
|
|
last_poses_3d = all_merged_poses;
|
|
|
|
// Convert to output format
|
|
std::vector<std::vector<std::array<float, 4>>> poses_3d;
|
|
poses_3d.reserve(all_merged_poses.size());
|
|
for (size_t i = 0; i < all_merged_poses.size(); ++i)
|
|
{
|
|
const auto &mat = all_merged_poses[i];
|
|
std::vector<std::array<float, 4>> pose;
|
|
size_t num_joints = mat.rows;
|
|
pose.reserve(num_joints);
|
|
size_t num_valid = 0;
|
|
|
|
for (size_t j = 0; j < num_joints; ++j)
|
|
{
|
|
const float *mat_ptr = mat.ptr<float>(j);
|
|
std::array<float, 4> point;
|
|
for (size_t k = 0; k < 4; ++k)
|
|
{
|
|
point[k] = mat_ptr[k];
|
|
}
|
|
pose.push_back(point);
|
|
|
|
if (point[3] > min_score)
|
|
{
|
|
num_valid++;
|
|
}
|
|
}
|
|
|
|
if (num_valid > 0)
|
|
{
|
|
poses_3d.push_back(std::move(pose));
|
|
}
|
|
}
|
|
|
|
return poses_3d;
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
void TriangulatorInternal::reset()
|
|
{
|
|
last_poses_3d.clear();
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
void TriangulatorInternal::undistort_poses(std::vector<cv::Mat> &poses, CameraInternal &icam)
|
|
{
|
|
int width = icam.cam.width;
|
|
int height = icam.cam.height;
|
|
|
|
// Undistort camera matrix
|
|
cv::Mat newK = cv::getOptimalNewCameraMatrix(
|
|
icam.K, icam.DC, cv::Size(width, height), 1, cv::Size(width, height));
|
|
|
|
for (size_t p = 0; p < poses.size(); ++p)
|
|
{
|
|
// Extract the (x, y) coordinates
|
|
cv::Mat points = poses[p].colRange(0, 2).clone();
|
|
points = points.reshape(2);
|
|
|
|
// Undistort the points
|
|
cv::undistortPoints(points, points, icam.K, icam.DC, cv::noArray(), newK);
|
|
|
|
// Update the original poses with the undistorted points
|
|
points = points.reshape(1);
|
|
points.copyTo(poses[p].colRange(0, 2));
|
|
|
|
// Mask out points that are far outside the image (points slightly outside are still valid)
|
|
float mask_offset = (width + height) / 40.0;
|
|
int num_joints = poses[p].rows;
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
float *poses_ptr = poses[p].ptr<float>(j);
|
|
float x = poses_ptr[0];
|
|
float y = poses_ptr[1];
|
|
bool in_x = x >= -mask_offset && x < width + mask_offset;
|
|
bool in_y = y >= -mask_offset && y < height + mask_offset;
|
|
if (!in_x || !in_y)
|
|
{
|
|
poses_ptr[0] = 0.0;
|
|
poses_ptr[1] = 0.0;
|
|
poses_ptr[2] = 0.0;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Update the camera matrix
|
|
icam.K = newK.clone();
|
|
icam.DC = cv::Mat::zeros(5, 1, CV_32F);
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
std::tuple<std::vector<cv::Mat>, std::vector<cv::Mat>> TriangulatorInternal::project_poses(
|
|
const std::vector<cv::Mat> &bodies3D, const CameraInternal &icam, bool calc_dists)
|
|
{
|
|
size_t num_persons = bodies3D.size();
|
|
size_t num_joints = bodies3D[0].rows;
|
|
|
|
std::vector<cv::Mat> bodies2D_list(num_persons);
|
|
std::vector<cv::Mat> dists_list(num_persons);
|
|
|
|
cv::Mat T_repeated = cv::repeat(icam.T, 1, num_joints).t();
|
|
cv::Mat R_transposed = icam.R.t();
|
|
|
|
for (size_t i = 0; i < num_persons; ++i)
|
|
{
|
|
const cv::Mat &body3D = bodies3D[i];
|
|
|
|
// Extract coordinates
|
|
const cv::Mat points3d = body3D.colRange(0, 3);
|
|
|
|
// Project from world to camera coordinate system
|
|
cv::Mat xyz = (points3d - T_repeated) * R_transposed;
|
|
|
|
// Set points behind the camera to zero
|
|
for (size_t j = 0; j < num_joints; ++j)
|
|
{
|
|
float *xyz_row_ptr = xyz.ptr<float>(j);
|
|
float z = xyz_row_ptr[2];
|
|
if (z <= 0)
|
|
{
|
|
xyz_row_ptr[0] = 0.0;
|
|
xyz_row_ptr[1] = 0.0;
|
|
xyz_row_ptr[2] = 0.0;
|
|
}
|
|
}
|
|
|
|
// Calculate distance from camera center if required
|
|
cv::Mat dists;
|
|
if (calc_dists)
|
|
{
|
|
cv::multiply(xyz, xyz, dists);
|
|
cv::reduce(dists, dists, 1, cv::REDUCE_SUM, CV_32F);
|
|
cv::sqrt(dists, dists);
|
|
}
|
|
else
|
|
{
|
|
dists = cv::Mat::zeros(num_joints, 1, CV_32F);
|
|
}
|
|
|
|
// Project points to image plane
|
|
cv::Mat uv;
|
|
if (icam.cam.type == "fisheye")
|
|
{
|
|
}
|
|
else
|
|
{
|
|
cv::Mat DCc = icam.DC.rowRange(0, 5);
|
|
cv::projectPoints(
|
|
xyz, cv::Mat::zeros(3, 1, CV_32F), cv::Mat::zeros(3, 1, CV_32F), icam.K, DCc, uv);
|
|
}
|
|
uv = uv.reshape(1, {xyz.rows, 2});
|
|
|
|
// Add scores again
|
|
std::vector<int> dimsB = {(int)num_joints, 3};
|
|
cv::Mat bodies2D = cv::Mat(dimsB, CV_32F);
|
|
for (size_t j = 0; j < num_joints; ++j)
|
|
{
|
|
float *bodies2D_row_ptr = bodies2D.ptr<float>(j);
|
|
const float *uv_row_ptr = uv.ptr<float>(j);
|
|
const float *bodies3D_row_ptr = body3D.ptr<float>(j);
|
|
|
|
bodies2D_row_ptr[0] = uv_row_ptr[0];
|
|
bodies2D_row_ptr[1] = uv_row_ptr[1];
|
|
bodies2D_row_ptr[2] = bodies3D_row_ptr[3];
|
|
}
|
|
|
|
// Filter invalid projections
|
|
for (size_t j = 0; j < num_joints; ++j)
|
|
{
|
|
float *bodies2D_row_ptr = bodies2D.ptr<float>(j);
|
|
float x = bodies2D_row_ptr[0];
|
|
float y = bodies2D_row_ptr[1];
|
|
bool in_x = x >= 0.0 && x < icam.cam.width;
|
|
bool in_y = y >= 0.0 && y < icam.cam.height;
|
|
if (!in_x || !in_y)
|
|
{
|
|
bodies2D_row_ptr[0] = 0.0;
|
|
bodies2D_row_ptr[1] = 0.0;
|
|
bodies2D_row_ptr[2] = 0.0;
|
|
}
|
|
}
|
|
|
|
// Store results
|
|
bodies2D_list[i] = bodies2D;
|
|
dists_list[i] = dists;
|
|
}
|
|
|
|
return std::make_tuple(bodies2D_list, dists_list);
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
float TriangulatorInternal::calc_pose_score(
|
|
const cv::Mat &pose1,
|
|
const cv::Mat &pose2,
|
|
const cv::Mat &dist1,
|
|
const CameraInternal &icam)
|
|
{
|
|
const float min_score = 0.1;
|
|
|
|
// Create mask for valid points
|
|
size_t num_joints = pose1.rows;
|
|
cv::Mat mask(num_joints, 1, CV_8U);
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
const float *pose1_ptr = pose1.ptr<float>(i);
|
|
const float *pose2_ptr = pose2.ptr<float>(i);
|
|
|
|
mask_ptr[0] = (pose1_ptr[2] > min_score) && (pose2_ptr[2] > min_score);
|
|
}
|
|
|
|
// Drop if not enough valid points
|
|
int valid_count = cv::countNonZero(mask);
|
|
if (valid_count < 3)
|
|
{
|
|
return 0.0;
|
|
}
|
|
|
|
// Calculate scores
|
|
float iscale = (icam.cam.width + icam.cam.height) / 2;
|
|
cv::Mat scores = score_projection(pose1, pose2, dist1, mask, iscale);
|
|
|
|
// Drop lowest scores
|
|
size_t drop_k = static_cast<size_t>(pose1.rows * 0.2);
|
|
const size_t min_k = 3;
|
|
std::vector<float> scores_vec;
|
|
scores_vec.reserve(valid_count);
|
|
for (int i = 0; i < scores.rows; ++i)
|
|
{
|
|
const float *scores_ptr = scores.ptr<float>(i);
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
if (mask_ptr[0] > 0)
|
|
{
|
|
scores_vec.push_back(scores_ptr[0]);
|
|
}
|
|
}
|
|
size_t scores_size = scores_vec.size();
|
|
if (scores_size >= min_k)
|
|
{
|
|
drop_k = std::min(drop_k, scores_size - min_k);
|
|
std::partial_sort(scores_vec.begin(), scores_vec.begin() + drop_k, scores_vec.end());
|
|
scores_vec.erase(scores_vec.begin(), scores_vec.begin() + drop_k);
|
|
}
|
|
|
|
// Calculate final score
|
|
float score = 0.0;
|
|
size_t n_items = scores_vec.size();
|
|
if (n_items > 0)
|
|
{
|
|
float sum_scores = std::accumulate(scores_vec.begin(), scores_vec.end(), 0.0);
|
|
score = sum_scores / static_cast<float>(n_items);
|
|
}
|
|
|
|
return score;
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
cv::Mat TriangulatorInternal::score_projection(
|
|
const cv::Mat &pose,
|
|
const cv::Mat &repro,
|
|
const cv::Mat &dists,
|
|
const cv::Mat &mask,
|
|
float iscale)
|
|
{
|
|
const float min_score = 0.1;
|
|
const size_t num_joints = pose.rows;
|
|
|
|
// Calculate error
|
|
cv::Mat error = cv::Mat::zeros(num_joints, 1, CV_32F);
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
const float *pose_ptr = pose.ptr<float>(i);
|
|
const float *repro_ptr = repro.ptr<float>(i);
|
|
float *error_ptr = error.ptr<float>(i);
|
|
|
|
if (mask_ptr)
|
|
{
|
|
float dx = pose_ptr[0] - repro_ptr[0];
|
|
float dy = pose_ptr[1] - repro_ptr[1];
|
|
float err = std::sqrt(dx * dx + dy * dy);
|
|
|
|
// Set errors of invisible reprojections to a high value
|
|
float score = repro_ptr[2];
|
|
if (score < min_score)
|
|
{
|
|
err = iscale;
|
|
}
|
|
|
|
error_ptr[0] = err;
|
|
}
|
|
}
|
|
|
|
// Scale error by image size
|
|
const float inv_iscale = 1.0 / iscale;
|
|
const float iscale_quarter = iscale / 4.0;
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
float *error_ptr = error.ptr<float>(i);
|
|
|
|
float err = error_ptr[0];
|
|
err = std::max(0.0f, std::min(err, iscale_quarter)) * inv_iscale;
|
|
error_ptr[0] = err;
|
|
}
|
|
|
|
// Scale error by distance to camera
|
|
float mean_dist = 0.0;
|
|
int count = 0;
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
const float *dists_ptr = dists.ptr<float>(i);
|
|
|
|
if (mask_ptr)
|
|
{
|
|
mean_dist += dists_ptr[0];
|
|
count++;
|
|
}
|
|
}
|
|
if (count > 0)
|
|
{
|
|
mean_dist /= count;
|
|
}
|
|
const float dscale = std::sqrt(mean_dist / 3.5);
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
float *error_ptr = error.ptr<float>(i);
|
|
|
|
float err = error_ptr[0];
|
|
err *= dscale;
|
|
error_ptr[0] = err;
|
|
}
|
|
|
|
// Convert error to score
|
|
cv::Mat score = error;
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
float *score_ptr = score.ptr<float>(i);
|
|
score_ptr[0] = 1.0 / (1.0 + score_ptr[0] * 10.0);
|
|
}
|
|
|
|
return score;
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
std::pair<cv::Mat, float> TriangulatorInternal::triangulate_and_score(
|
|
const cv::Mat &pose1,
|
|
const cv::Mat &pose2,
|
|
const CameraInternal &cam1,
|
|
const CameraInternal &cam2,
|
|
const std::array<std::array<float, 3>, 2> &roomparams,
|
|
const std::vector<std::array<size_t, 2>> &core_limbs_idx)
|
|
{
|
|
const float min_score = 0.1;
|
|
const size_t num_joints = pose1.rows;
|
|
|
|
// Create mask for valid points
|
|
cv::Mat mask(num_joints, 1, CV_8U);
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
const float *pose1_ptr = pose1.ptr<float>(i);
|
|
const float *pose2_ptr = pose2.ptr<float>(i);
|
|
|
|
mask_ptr[0] = (pose1_ptr[2] > min_score) && (pose2_ptr[2] > min_score);
|
|
}
|
|
|
|
// If too few joints are visible, return a low score
|
|
int num_visible = cv::countNonZero(mask);
|
|
if (num_visible < 3)
|
|
{
|
|
cv::Mat pose3d = cv::Mat::zeros(num_joints, 4, CV_32F);
|
|
float score = 0.0;
|
|
return std::make_pair(pose3d, score);
|
|
}
|
|
|
|
// Extract coordinates of visible joints
|
|
std::vector<int> dims = {2, num_visible};
|
|
cv::Mat points1 = cv::Mat(dims, CV_32F);
|
|
cv::Mat points2 = cv::Mat(dims, CV_32F);
|
|
int idx = 0;
|
|
float *points1_ptr = points1.ptr<float>(0);
|
|
float *points2_ptr = points2.ptr<float>(0);
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
const float *pose1_ptr = pose1.ptr<float>(i);
|
|
const float *pose2_ptr = pose2.ptr<float>(i);
|
|
|
|
if (mask_ptr[0])
|
|
{
|
|
points1_ptr[idx + 0 * num_visible] = pose1_ptr[0];
|
|
points1_ptr[idx + 1 * num_visible] = pose1_ptr[1];
|
|
points2_ptr[idx + 0 * num_visible] = pose2_ptr[0];
|
|
points2_ptr[idx + 1 * num_visible] = pose2_ptr[1];
|
|
idx++;
|
|
}
|
|
}
|
|
|
|
// Triangulate points
|
|
cv::Mat points4d_h, points3d;
|
|
cv::triangulatePoints(cam1.P, cam2.P, points1, points2, points4d_h);
|
|
cv::convertPointsFromHomogeneous(points4d_h.t(), points3d);
|
|
|
|
// Create the 3D pose matrix
|
|
cv::Mat pose3d = cv::Mat::zeros(num_joints, 4, CV_32F);
|
|
idx = 0;
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
float *pose3d_ptr = pose3d.ptr<float>(i);
|
|
const float *points3d_ptr = points3d.ptr<float>(idx);
|
|
|
|
if (mask_ptr[0])
|
|
{
|
|
pose3d_ptr[0] = points3d_ptr[0];
|
|
pose3d_ptr[1] = points3d_ptr[1];
|
|
pose3d_ptr[2] = points3d_ptr[2];
|
|
pose3d_ptr[3] = 1.0;
|
|
idx++;
|
|
}
|
|
}
|
|
|
|
// Check if mean of the points is inside the room bounds
|
|
std::array<float, 3> mean = {0.0, 0.0, 0.0};
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
const float *pose3d_ptr = pose3d.ptr<float>(i);
|
|
|
|
if (mask_ptr[0])
|
|
{
|
|
mean[0] += pose3d_ptr[0];
|
|
mean[1] += pose3d_ptr[1];
|
|
mean[2] += pose3d_ptr[2];
|
|
}
|
|
}
|
|
float inv_num_vis = 1.0 / num_visible;
|
|
for (int j = 0; j < 3; ++j)
|
|
{
|
|
mean[j] *= inv_num_vis;
|
|
}
|
|
const std::array<float, 3> ¢er = roomparams[0];
|
|
const std::array<float, 3> &size = roomparams[1];
|
|
for (int j = 0; j < 3; ++j)
|
|
{
|
|
if (mean[j] > center[j] + size[j] / 2.0 ||
|
|
mean[j] < center[j] - size[j] / 2.0)
|
|
{
|
|
// Very low score if outside room
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
float *pose3d_ptr = pose3d.ptr<float>(i);
|
|
pose3d_ptr[3] = 0.001;
|
|
}
|
|
return {pose3d, 0.001};
|
|
}
|
|
}
|
|
|
|
// Calculate reprojections
|
|
std::vector<cv::Mat> poses_3d = {pose3d};
|
|
cv::Mat repro1, dists1, repro2, dists2;
|
|
auto [repro1s, dists1s] = project_poses(poses_3d, cam1, true);
|
|
auto [repro2s, dists2s] = project_poses(poses_3d, cam2, true);
|
|
repro1 = repro1s[0];
|
|
dists1 = dists1s[0];
|
|
repro2 = repro2s[0];
|
|
dists2 = dists2s[0];
|
|
|
|
// Calculate scores for each view
|
|
float iscale = (cam1.cam.width + cam1.cam.height) / 2.0;
|
|
cv::Mat score1 = score_projection(pose1, repro1, dists1, mask, iscale);
|
|
cv::Mat score2 = score_projection(pose2, repro2, dists2, mask, iscale);
|
|
|
|
// Combine scores
|
|
cv::Mat scores = (score1 + score2) * 0.5;
|
|
|
|
// Add scores to 3D pose
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
float *pose3d_ptr = pose3d.ptr<float>(i);
|
|
const float *scores_ptr = scores.ptr<float>(i);
|
|
|
|
if (mask_ptr[0])
|
|
{
|
|
pose3d_ptr[3] = scores_ptr[0];
|
|
}
|
|
}
|
|
|
|
// Set scores outside the room to a low value
|
|
const float wdist = 0.1;
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
float *pose3d_ptr = pose3d.ptr<float>(i);
|
|
|
|
if (mask_ptr[0])
|
|
{
|
|
for (int j = 0; j < 3; ++j)
|
|
{
|
|
if (pose3d_ptr[j] > center[j] + size[j] / 2.0 + wdist ||
|
|
pose3d_ptr[j] < center[j] - size[j] / 2.0 - wdist)
|
|
{
|
|
pose3d_ptr[3] = 0.001;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Filter clearly wrong limbs
|
|
if (!core_limbs_idx.empty())
|
|
{
|
|
const float max_length_sq = 0.9 * 0.9;
|
|
for (size_t i = 0; i < core_limbs_idx.size(); ++i)
|
|
{
|
|
size_t limb1 = core_limbs_idx[i][0];
|
|
size_t limb2 = core_limbs_idx[i][1];
|
|
float *pose3d_ptr1 = pose3d.ptr<float>(limb1);
|
|
float *pose3d_ptr2 = pose3d.ptr<float>(limb2);
|
|
|
|
if (pose3d_ptr1[3] > min_score && pose3d_ptr2[3] > min_score)
|
|
{
|
|
float dx = pose3d_ptr1[0] - pose3d_ptr2[0];
|
|
float dy = pose3d_ptr1[1] - pose3d_ptr2[1];
|
|
float dz = pose3d_ptr1[2] - pose3d_ptr2[2];
|
|
float length_sq = dx * dx + dy * dy + dz * dz;
|
|
|
|
if (length_sq > max_length_sq)
|
|
{
|
|
pose3d_ptr2[3] = 0.001;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Drop lowest scores
|
|
size_t drop_k = static_cast<size_t>(num_joints * 0.2);
|
|
const size_t min_k = 3;
|
|
std::vector<float> scores_vec;
|
|
for (size_t i = 0; i < num_joints; ++i)
|
|
{
|
|
const float *pose3d_ptr = pose3d.ptr<float>(i);
|
|
if (pose3d_ptr[3] > min_score)
|
|
{
|
|
scores_vec.push_back(pose3d_ptr[3]);
|
|
}
|
|
}
|
|
size_t scores_size = scores_vec.size();
|
|
if (scores_size >= min_k)
|
|
{
|
|
drop_k = std::min(drop_k, scores_size - min_k);
|
|
std::partial_sort(scores_vec.begin(), scores_vec.begin() + drop_k, scores_vec.end());
|
|
scores_vec.erase(scores_vec.begin(), scores_vec.begin() + drop_k);
|
|
}
|
|
|
|
// Calculate final score
|
|
float score = 0.0;
|
|
size_t n_items = scores_vec.size();
|
|
if (n_items > 0)
|
|
{
|
|
float sum_scores = std::accumulate(scores_vec.begin(), scores_vec.end(), 0.0);
|
|
score = sum_scores / static_cast<float>(n_items);
|
|
}
|
|
|
|
return std::make_pair(pose3d, score);
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInternal::calc_grouping(
|
|
const std::vector<std::pair<std::tuple<int, int, int, int>, std::pair<int, int>>> &all_pairs,
|
|
const std::vector<std::pair<cv::Mat, float>> &all_scored_poses,
|
|
float min_score)
|
|
{
|
|
float max_center_dist_sq = 0.6 * 0.6;
|
|
float max_joint_avg_dist = 0.3;
|
|
|
|
size_t num_pairs = all_pairs.size();
|
|
size_t num_joints = all_scored_poses[0].first.rows;
|
|
|
|
// Calculate pose centers
|
|
std::vector<cv::Point3d> centers;
|
|
centers.resize(num_pairs);
|
|
for (size_t i = 0; i < num_pairs; ++i)
|
|
{
|
|
const cv::Mat &pose_3d = all_scored_poses[i].first;
|
|
cv::Point3d center(0, 0, 0);
|
|
size_t num_valid = 0;
|
|
for (size_t j = 0; j < num_joints; ++j)
|
|
{
|
|
const float *pose_3d_ptr = pose_3d.ptr<float>(j);
|
|
|
|
float score = pose_3d_ptr[3];
|
|
if (score > min_score)
|
|
{
|
|
center.x += pose_3d_ptr[0];
|
|
center.y += pose_3d_ptr[1];
|
|
center.z += pose_3d_ptr[2];
|
|
num_valid++;
|
|
}
|
|
}
|
|
if (num_valid > 0)
|
|
{
|
|
float inv_num_valid = 1.0 / num_valid;
|
|
center.x *= inv_num_valid;
|
|
center.y *= inv_num_valid;
|
|
center.z *= inv_num_valid;
|
|
}
|
|
centers[i] = center;
|
|
}
|
|
|
|
// Calculate Groups
|
|
// defined as a tuple of center, pose, and all-pairs-indices of members
|
|
std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> groups;
|
|
for (size_t i = 0; i < num_pairs; ++i)
|
|
{
|
|
const cv::Mat &pose_3d = all_scored_poses[i].first;
|
|
const cv::Point3d ¢er = centers[i];
|
|
float best_dist = std::numeric_limits<float>::infinity();
|
|
int best_group = -1;
|
|
|
|
for (size_t j = 0; j < groups.size(); ++j)
|
|
{
|
|
auto &group = groups[j];
|
|
cv::Point3d &group_center = std::get<0>(group);
|
|
|
|
// Check if the center is close enough
|
|
float dx = group_center.x - center.x;
|
|
float dy = group_center.y - center.y;
|
|
float dz = group_center.z - center.z;
|
|
float center_dist_sq = dx * dx + dy * dy + dz * dz;
|
|
|
|
if (center_dist_sq < max_center_dist_sq)
|
|
{
|
|
cv::Mat &group_pose = std::get<1>(group);
|
|
|
|
// Calculate average joint distance
|
|
float dist_sum = 0.0;
|
|
size_t count = 0;
|
|
for (size_t row = 0; row < num_joints; ++row)
|
|
{
|
|
const float *pose_3d_ptr = pose_3d.ptr<float>(row);
|
|
const float *group_pose_ptr = group_pose.ptr<float>(row);
|
|
|
|
float score1 = pose_3d_ptr[3];
|
|
float score2 = group_pose_ptr[3];
|
|
|
|
if (score1 > min_score && score2 > min_score)
|
|
{
|
|
float dx = pose_3d_ptr[0] - group_pose_ptr[0];
|
|
float dy = pose_3d_ptr[1] - group_pose_ptr[1];
|
|
float dz = pose_3d_ptr[2] - group_pose_ptr[2];
|
|
float dist_sq = dx * dx + dy * dy + dz * dz;
|
|
dist_sum += std::sqrt(dist_sq);
|
|
count++;
|
|
}
|
|
}
|
|
|
|
if (count > 0)
|
|
{
|
|
// Check if the average joint distance is close enough
|
|
float avg_dist = dist_sum / count;
|
|
if (avg_dist < max_joint_avg_dist && avg_dist < best_dist)
|
|
{
|
|
best_dist = avg_dist;
|
|
best_group = static_cast<int>(j);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (best_group == -1)
|
|
{
|
|
// Create a new group
|
|
std::vector<int> new_indices{static_cast<int>(i)};
|
|
groups.emplace_back(center, pose_3d.clone(), std::move(new_indices));
|
|
}
|
|
else
|
|
{
|
|
// Update existing group
|
|
auto &group = groups[best_group];
|
|
cv::Point3d &group_center = std::get<0>(group);
|
|
cv::Mat &group_pose = std::get<1>(group);
|
|
std::vector<int> &group_indices = std::get<2>(group);
|
|
|
|
float n_elems = static_cast<float>(group_indices.size());
|
|
float inv_n1 = 1.0 / (n_elems + 1.0);
|
|
|
|
// Update group center
|
|
group_center.x = (group_center.x * n_elems + center.x) * inv_n1;
|
|
group_center.y = (group_center.y * n_elems + center.y) * inv_n1;
|
|
group_center.z = (group_center.z * n_elems + center.z) * inv_n1;
|
|
|
|
// Update group pose
|
|
for (size_t row = 0; row < num_joints; ++row)
|
|
{
|
|
const float *pose_3d_ptr = pose_3d.ptr<float>(row);
|
|
float *group_pose_ptr = group_pose.ptr<float>(row);
|
|
|
|
group_pose_ptr[0] = (group_pose_ptr[0] * n_elems + pose_3d_ptr[0]) * inv_n1;
|
|
group_pose_ptr[1] = (group_pose_ptr[1] * n_elems + pose_3d_ptr[1]) * inv_n1;
|
|
group_pose_ptr[2] = (group_pose_ptr[2] * n_elems + pose_3d_ptr[2]) * inv_n1;
|
|
group_pose_ptr[3] = (group_pose_ptr[3] * n_elems + pose_3d_ptr[3]) * inv_n1;
|
|
}
|
|
group_indices.push_back(static_cast<int>(i));
|
|
}
|
|
}
|
|
|
|
return groups;
|
|
}
|
|
|
|
// =================================================================================================
|
|
|
|
cv::Mat TriangulatorInternal::merge_group(const std::vector<cv::Mat> &poses_3d, float min_score)
|
|
{
|
|
int num_poses = poses_3d.size();
|
|
int num_joints = poses_3d[0].rows;
|
|
|
|
// Merge poses to create initial pose
|
|
// Use only those triangulations with a high score
|
|
cv::Mat sum_poses = cv::Mat::zeros(num_joints, 4, CV_32F);
|
|
std::vector<int> sum_mask(num_joints, 0);
|
|
for (int i = 0; i < num_poses; ++i)
|
|
{
|
|
const cv::Mat &pose = poses_3d[i];
|
|
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
const float *pose_ptr = pose.ptr<float>(j);
|
|
float *sum_ptr = sum_poses.ptr<float>(j);
|
|
|
|
float score = pose_ptr[3];
|
|
if (score > min_score)
|
|
{
|
|
sum_ptr[0] += pose_ptr[0];
|
|
sum_ptr[1] += pose_ptr[1];
|
|
sum_ptr[2] += pose_ptr[2];
|
|
sum_ptr[3] += pose_ptr[3];
|
|
sum_mask[j]++;
|
|
}
|
|
}
|
|
}
|
|
cv::Mat initial_pose_3d = cv::Mat::zeros(num_joints, 4, CV_32F);
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
if (sum_mask[j] > 0)
|
|
{
|
|
float *initial_ptr = initial_pose_3d.ptr<float>(j);
|
|
const float *sum_ptr = sum_poses.ptr<float>(j);
|
|
|
|
float inv_count = 1.0 / sum_mask[j];
|
|
initial_ptr[0] = sum_ptr[0] * inv_count;
|
|
initial_ptr[1] = sum_ptr[1] * inv_count;
|
|
initial_ptr[2] = sum_ptr[2] * inv_count;
|
|
initial_ptr[3] = sum_ptr[3] * inv_count;
|
|
}
|
|
}
|
|
|
|
// Use center as default if the initial pose is empty
|
|
std::vector<u_char> jmask(num_joints, 0);
|
|
cv::Point3d center(0, 0, 0);
|
|
int valid_joints = 0;
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
const float *initial_ptr = initial_pose_3d.ptr<float>(j);
|
|
float score = initial_ptr[3];
|
|
|
|
if (score > min_score)
|
|
{
|
|
jmask[j] = 1;
|
|
center.x += initial_ptr[0];
|
|
center.y += initial_ptr[1];
|
|
center.z += initial_ptr[2];
|
|
valid_joints++;
|
|
}
|
|
}
|
|
if (valid_joints > 0)
|
|
{
|
|
center *= 1.0 / valid_joints;
|
|
}
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
if (!jmask[j])
|
|
{
|
|
float *initial_ptr = initial_pose_3d.ptr<float>(j);
|
|
initial_ptr[0] = center.x;
|
|
initial_ptr[1] = center.y;
|
|
initial_ptr[2] = center.z;
|
|
}
|
|
}
|
|
|
|
// Drop joints with low scores and filter outlying joints using distance threshold
|
|
float offset = 0.1;
|
|
float max_dist_sq = 1.2 * 1.2;
|
|
cv::Mat mask = cv::Mat::zeros(num_poses, num_joints, CV_8U);
|
|
cv::Mat distances = cv::Mat::zeros(num_poses, num_joints, CV_32F);
|
|
for (int i = 0; i < num_poses; ++i)
|
|
{
|
|
float *distances_ptr = distances.ptr<float>(i);
|
|
u_char *mask_ptr = mask.ptr<u_char>(i);
|
|
const cv::Mat &pose = poses_3d[i];
|
|
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
const float *initial_ptr = initial_pose_3d.ptr<float>(j);
|
|
const float *pose_ptr = pose.ptr<float>(j);
|
|
|
|
float dx = pose_ptr[0] - initial_ptr[0];
|
|
float dy = pose_ptr[1] - initial_ptr[1];
|
|
float dz = pose_ptr[2] - initial_ptr[2];
|
|
float dist_sq = dx * dx + dy * dy + dz * dz;
|
|
distances_ptr[j] = dist_sq;
|
|
|
|
float score = pose_ptr[3];
|
|
if (dist_sq <= max_dist_sq && score > (min_score - offset))
|
|
{
|
|
mask_ptr[j] = 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Select the best-k proposals for each joint that are closest to the initial pose
|
|
int keep_best = 3;
|
|
cv::Mat best_k_mask = cv::Mat::zeros(num_poses, num_joints, CV_8U);
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
std::vector<std::pair<float, int>> valid_indices;
|
|
valid_indices.reserve(num_poses);
|
|
for (int i = 0; i < num_poses; ++i)
|
|
{
|
|
if (mask.at<u_char>(i, j))
|
|
{
|
|
valid_indices.emplace_back(distances.at<float>(i, j), i);
|
|
}
|
|
}
|
|
std::partial_sort(
|
|
valid_indices.begin(),
|
|
valid_indices.begin() + std::min(keep_best, static_cast<int>(valid_indices.size())),
|
|
valid_indices.end());
|
|
|
|
for (int k = 0; k < std::min(keep_best, static_cast<int>(valid_indices.size())); ++k)
|
|
{
|
|
best_k_mask.at<u_char>(valid_indices[k].second, j) = 1;
|
|
}
|
|
}
|
|
|
|
// Combine masks
|
|
mask &= best_k_mask;
|
|
|
|
// Compute the final pose
|
|
sum_poses = cv::Mat::zeros(num_joints, 4, CV_32F);
|
|
sum_mask.assign(num_joints, 0);
|
|
for (int i = 0; i < num_poses; ++i)
|
|
{
|
|
const u_char *mask_row_ptr = mask.ptr<u_char>(i);
|
|
const cv::Mat &pose = poses_3d[i];
|
|
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
if (mask_row_ptr[j])
|
|
{
|
|
float *sum_ptr = sum_poses.ptr<float>(j);
|
|
const float *pose_ptr = pose.ptr<float>(j);
|
|
|
|
sum_ptr[0] += pose_ptr[0];
|
|
sum_ptr[1] += pose_ptr[1];
|
|
sum_ptr[2] += pose_ptr[2];
|
|
sum_ptr[3] += pose_ptr[3];
|
|
|
|
sum_mask[j]++;
|
|
}
|
|
}
|
|
}
|
|
cv::Mat final_pose_3d = cv::Mat::zeros(num_joints, 4, CV_32F);
|
|
for (int j = 0; j < num_joints; ++j)
|
|
{
|
|
if (sum_mask[j] > 0)
|
|
{
|
|
float *final_pose_ptr = final_pose_3d.ptr<float>(j);
|
|
const float *sum_ptr = sum_poses.ptr<float>(j);
|
|
|
|
float inv_count = 1.0 / sum_mask[j];
|
|
final_pose_ptr[0] = sum_ptr[0] * inv_count;
|
|
final_pose_ptr[1] = sum_ptr[1] * inv_count;
|
|
final_pose_ptr[2] = sum_ptr[2] * inv_count;
|
|
final_pose_ptr[3] = sum_ptr[3] * inv_count;
|
|
}
|
|
}
|
|
|
|
return final_pose_3d;
|
|
}
|