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Commit 40fb71b0 authored by Patrik Huber's avatar Patrik Huber
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Added colour coefficients fitting using Ceres 

Fits camera, shape, blendshapes and colour coefficients with a cost function containing a landmark constraint, priors, and minimising the error between the input image's RGB values and the model estimate (rendered at each vertex).

Note: The image cost function cannot be used with the model on GitHub, as it doesn't include the colour PCA.
parent 17ef4b84
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Showing with 159 additions and 24 deletions
examples/fit-model-ceres.cpp
View file @ 40fb71b0
......@@ -22,6 +22,7 @@
#include "eos/morphablemodel/Blendshape.hpp"
#include "eos/fitting/contour_correspondence.hpp"
#include "eos/fitting/ceres_nonlinear.hpp"
#include "eos/fitting/fitting.hpp"
#include "glm/glm.hpp"
#include "glm/ext.hpp"
......@@ -143,7 +144,7 @@ int main(int argc, char *argv[])
("model-contour,c", po::value<fs::path>(&contourfile)->required()->default_value("../share/model_contours.json"),
"file with model contour indices")
("output,o", po::value<fs::path>(&outputfile)->required()->default_value("out"),
"basename for the output rendering and obj files")
"basename for the output obj file")
;
po::variables_map vm;
po::store(po::command_line_parser(argc, argv).options(desc).run(), vm);
......@@ -224,6 +225,9 @@ int main(int argc, char *argv[])
}
// Estimate the camera (pose) from the 2D - 3D point correspondences
std::stringstream fitting_log;
auto start = std::chrono::steady_clock::now();
std::vector<double> camera_rotation; // Quaternion, [w x y z]. Todo: Actually, use std::array for all of these.
camera_rotation.resize(4); // initialises with zeros
camera_rotation[0] = 1.0;
......@@ -264,46 +268,177 @@ int main(int argc, char *argv[])
else {
camera_costfunction.SetParameterLowerBound(&camera_translation_and_intrinsics[0], 2, 1.0); // frustum_scale must be > 0
}
QuaternionParameterization* quaternion_parameterisation = new QuaternionParameterization;
camera_costfunction.SetParameterization(&camera_rotation[0], quaternion_parameterisation);
QuaternionParameterization* camera_fit_quaternion_parameterisation = new QuaternionParameterization;
camera_costfunction.SetParameterization(&camera_rotation[0], camera_fit_quaternion_parameterisation);
Solver::Options solver_options;
solver_options.linear_solver_type = ITERATIVE_SCHUR;
solver_options.num_threads = 8;
solver_options.num_linear_solver_threads = 8; // only SPARSE_SCHUR can use this
solver_options.minimizer_progress_to_stdout = true;
//solver_options.max_num_iterations = 100;
Solver::Summary solver_summary;
Solve(solver_options, &camera_costfunction, &solver_summary);
std::cout << solver_summary.BriefReport() << "\n";
auto end = std::chrono::steady_clock::now();
auto mean_mesh = morphable_model.get_mean();
for (auto&& idx : vertex_indices)
{
glm::dvec3 point_3d(mean_mesh.vertices[idx][0], mean_mesh.vertices[idx][1], mean_mesh.vertices[idx][2]); // The 3D model point
glm::dvec3 projected_point;
glm::dquat q(camera_rotation[0], camera_rotation[1], camera_rotation[2], camera_rotation[3]);
auto rot_mtx = glm::mat4_cast(q);
glm::dvec4 viewport(0, image.rows, image.cols, -image.rows); // OpenCV convention
const double aspect = static_cast<double>(image.cols) / image.rows;
// Draw the mean-face landmarks projected using the estimated camera:
// Construct the rotation & translation (model-view) matrices, projection matrix, and viewport:
glm::dquat estimated_rotation(camera_rotation[0], camera_rotation[1], camera_rotation[2], camera_rotation[3]);
auto rot_mtx = glm::mat4_cast(estimated_rotation);
const double aspect = static_cast<double>(image.cols) / image.rows;
auto get_translation_matrix = [](auto&& camera_translation_and_intrinsics, auto &&use_perspective) {
if (use_perspective)
{
return glm::translate(glm::dvec3(camera_translation_and_intrinsics[0], camera_translation_and_intrinsics[1], camera_translation_and_intrinsics[2]));
}
else {
return glm::translate(glm::dvec3(camera_translation_and_intrinsics[0], camera_translation_and_intrinsics[1], 0.0));
}
};
auto get_projection_matrix = [](auto&& camera_translation_and_intrinsics, auto&& aspect, auto&& use_perspective) {
if (use_perspective)
{
auto t_mtx = glm::translate(glm::dvec3(camera_translation_and_intrinsics[0], camera_translation_and_intrinsics[1], camera_translation_and_intrinsics[2]));
const auto& focal = camera_translation_and_intrinsics[3];
auto persp_mtx = glm::perspective(focal, aspect, 0.1, 1000.0);
projected_point = glm::project(point_3d, t_mtx * rot_mtx, persp_mtx, viewport);
return glm::perspective(focal, aspect, 0.1, 1000.0);
}
else {
const auto& frustum_scale = camera_translation_and_intrinsics[2];
auto t_mtx = glm::translate(glm::dvec3(camera_translation_and_intrinsics[0], camera_translation_and_intrinsics[1], 0.0));
auto ortho_mtx = glm::ortho(-1.0 * aspect * frustum_scale, 1.0 * aspect * frustum_scale, -1.0 * frustum_scale, 1.0 * frustum_scale);
projected_point = glm::project(point_3d, t_mtx * rot_mtx, ortho_mtx, viewport);
return glm::ortho(-1.0 * aspect * frustum_scale, 1.0 * aspect * frustum_scale, -1.0 * frustum_scale, 1.0 * frustum_scale);
}
cv::circle(outimg, cv::Point2f(projected_point.x, projected_point.y), 3, { 255.0f, 0.0f, 127.0f }); // violet
};
auto t_mtx = get_translation_matrix(camera_translation_and_intrinsics, use_perspective);
auto projection_mtx = get_projection_matrix(camera_translation_and_intrinsics, aspect, use_perspective);
const glm::dvec4 viewport(0, image.rows, image.cols, -image.rows); // OpenCV convention
auto mean_mesh = morphable_model.get_mean();
for (auto&& idx : vertex_indices)
{
glm::dvec3 point_3d(mean_mesh.vertices[idx][0], mean_mesh.vertices[idx][1], mean_mesh.vertices[idx][2]); // The 3D model point
glm::dvec3 projected_point = glm::project(point_3d, t_mtx * rot_mtx, projection_mtx, viewport);
cv::circle(outimg, cv::Point2f(projected_point.x, projected_point.y), 3, { 0.0f, 0.0f, 255.0f }); // red
}
for (auto&& lm : image_points) {
cv::circle(outimg, cv::Point2f(lm), 3, { 255.0f, 0.0f, 0.0f }); // blue - used landmarks
cv::circle(outimg, cv::Point2f(lm), 3, { 0.0f, 255.0f, 255.0f }); // yellow: subset of the detected LMs that we use
}
auto euler_angles = glm::eulerAngles(estimated_rotation); // returns [P, Y, R]
fitting_log << "Pose fit with mean shape:\tYaw " << glm::degrees(euler_angles[1]) << ", Pitch " << glm::degrees(euler_angles[0]) << ", Roll " << glm::degrees(euler_angles[2]) << "; t & f: " << camera_translation_and_intrinsics << '\n';
fitting_log << "Ceres took: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms.\n";
// Contour fitting:
// These are the additional contour-correspondences we're going to find and then use:
vector<Vec4f> model_points_contour; // the points in the 3D shape model
vector<int> vertex_indices_contour; // their vertex indices
vector<Vec2f> image_points_contour; // the corresponding 2D landmark points
// For each 2D contour landmark, get the corresponding 3D vertex point and vertex id:
std::tie(image_points_contour, model_points_contour, vertex_indices_contour) = fitting::get_contour_correspondences(landmarks, ibug_contour, model_contour, glm::degrees(euler_angles[1]), morphable_model, t_mtx * rot_mtx, projection_mtx, viewport);
using eos::fitting::concat;
model_points = concat(model_points, model_points_contour);
vertex_indices = concat(vertex_indices, vertex_indices_contour);
image_points = concat(image_points, image_points_contour);
// Full fitting - Estimate shape and pose, given the previous pose estimate:
start = std::chrono::steady_clock::now();
Problem fitting_costfunction;
// Landmark constraint:
for (int i = 0; i < model_points.size(); ++i)
{
CostFunction* cost_function = new AutoDiffCostFunction<fitting::LandmarkCost, 2 /* num residuals */, 4 /* camera rotation (quaternion) */, num_cam_trans_intr_params /* camera translation & focal length */, 10 /* shape-coeffs */, 6 /* bs-coeffs */>(new fitting::LandmarkCost(morphable_model.get_shape_model(), blendshapes, image_points[i], vertex_indices[i], image.cols, image.rows, use_perspective));
fitting_costfunction.AddResidualBlock(cost_function, NULL, &camera_rotation[0], &camera_translation_and_intrinsics[0], &shape_coefficients[0], &blendshape_coefficients[0]);
}
// Shape prior:
CostFunction* shape_prior_cost = new AutoDiffCostFunction<fitting::PriorCost, 10 /* num residuals */, 10 /* shape-coeffs */>(new fitting::PriorCost(10, 35.0));
fitting_costfunction.AddResidualBlock(shape_prior_cost, NULL, &shape_coefficients[0]);
for (int i=0; i < 10; ++i)
{
fitting_costfunction.SetParameterLowerBound(&shape_coefficients[0], i, -3.0);
fitting_costfunction.SetParameterUpperBound(&shape_coefficients[0], i, 3.0);
}
// Prior and constraints on blendshapes:
CostFunction* blendshapes_prior_cost = new AutoDiffCostFunction<fitting::PriorCost, 6 /* num residuals */, 6 /* bs-coeffs */>(new fitting::PriorCost(6, 10.0));
fitting_costfunction.AddResidualBlock(blendshapes_prior_cost, NULL, &blendshape_coefficients[0]);
fitting_costfunction.SetParameterLowerBound(&blendshape_coefficients[0], 0, 0.0);
fitting_costfunction.SetParameterLowerBound(&blendshape_coefficients[0], 1, 0.0);
fitting_costfunction.SetParameterLowerBound(&blendshape_coefficients[0], 2, 0.0);
fitting_costfunction.SetParameterLowerBound(&blendshape_coefficients[0], 3, 0.0);
fitting_costfunction.SetParameterLowerBound(&blendshape_coefficients[0], 4, 0.0);
fitting_costfunction.SetParameterLowerBound(&blendshape_coefficients[0], 5, 0.0);
// Some constraints on the camera translation and fov/scale:
if (use_perspective)
{
fitting_costfunction.SetParameterUpperBound(&camera_translation_and_intrinsics[0], 2, -std::numeric_limits<double>::epsilon()); // t_z has to be negative
fitting_costfunction.SetParameterLowerBound(&camera_translation_and_intrinsics[0], 3, 0.01); // fov in radians, must be > 0
}
else {
fitting_costfunction.SetParameterLowerBound(&camera_translation_and_intrinsics[0], 2, 1.0); // frustum_scale must be > 0
}
QuaternionParameterization* full_fit_quaternion_parameterisation = new QuaternionParameterization;
fitting_costfunction.SetParameterization(&camera_rotation[0], full_fit_quaternion_parameterisation);
// Colour model fitting:
std::vector<double> colour_coefficients;
colour_coefficients.resize(10);
// Add a residual for each vertex:
for (int i = 0; i < morphable_model.get_shape_model().get_data_dimension() / 3; ++i)
{
CostFunction* cost_function = new AutoDiffCostFunction<fitting::ImageCost, 3 /* Residuals: [R, G, B] */, 4 /* camera rotation (quaternion) */, num_cam_trans_intr_params /* camera translation & focal length */, 10 /* shape-coeffs */, 6 /* bs-coeffs */, 10 /* colour coeffs */>(new fitting::ImageCost(morphable_model, blendshapes, image, i, use_perspective));
fitting_costfunction.AddResidualBlock(cost_function, NULL, &camera_rotation[0], &camera_translation_and_intrinsics[0], &shape_coefficients[0], &blendshape_coefficients[0], &colour_coefficients[0]);
}
// Prior for the colour coefficients:
CostFunction* colour_prior_cost = new AutoDiffCostFunction<fitting::PriorCost, 10 /* num residuals */, 10 /* colour-coeffs */>(new fitting::PriorCost(10, 35.0));
fitting_costfunction.AddResidualBlock(colour_prior_cost, NULL, &colour_coefficients[0]);
for (int i = 0; i < 10; ++i)
{
fitting_costfunction.SetParameterLowerBound(&colour_coefficients[0], i, -3.0);
fitting_costfunction.SetParameterUpperBound(&colour_coefficients[0], i, 3.0);
}
// Set different options for the full fitting:
/* solver_options.linear_solver_type = ceres::ITERATIVE_SCHUR;
//solver_options.linear_solver_type = ceres::DENSE_QR;
//solver_options.minimizer_type = ceres::TRUST_REGION; // default I think
//solver_options.minimizer_type = ceres::LINE_SEARCH;
solver_options.num_threads = 8;
solver_options.num_linear_solver_threads = 8; // only SPARSE_SCHUR can use this
solver_options.minimizer_progress_to_stdout = true;
solver_options.max_num_iterations = 100;
*/
Solve(solver_options, &fitting_costfunction, &solver_summary);
std::cout << solver_summary.BriefReport() << "\n";
end = std::chrono::steady_clock::now();
// Draw the landmarks projected using all estimated parameters:
// Construct the rotation & translation (model-view) matrices, projection matrix, and viewport:
estimated_rotation = glm::dquat(camera_rotation[0], camera_rotation[1], camera_rotation[2], camera_rotation[3]);
rot_mtx = glm::mat4_cast(estimated_rotation);
t_mtx = get_translation_matrix(camera_translation_and_intrinsics, use_perspective);
projection_mtx = get_projection_matrix(camera_translation_and_intrinsics, aspect, use_perspective);
auto vectord_to_vectorf = [](const std::vector<double>& vec) {
return std::vector<float>(std::begin(vec), std::end(vec));
};
auto shape_ceres = morphable_model.get_shape_model().draw_sample(shape_coefficients) + to_matrix(blendshapes) * Mat(vectord_to_vectorf(blendshape_coefficients), true);
render::Mesh mesh = morphablemodel::detail::sample_to_mesh(shape_ceres, morphable_model.get_color_model().draw_sample(colour_coefficients), morphable_model.get_shape_model().get_triangle_list(), morphable_model.get_color_model().get_triangle_list(), morphable_model.get_texture_coordinates());
for (auto&& idx : vertex_indices)
{
glm::dvec3 point_3d(mesh.vertices[idx][0], mesh.vertices[idx][1], mesh.vertices[idx][2]); // The 3D model point
glm::dvec3 projected_point = glm::project(point_3d, t_mtx * rot_mtx, projection_mtx, viewport);
cv::circle(outimg, cv::Point2f(projected_point.x, projected_point.y), 3, { 0.0f, 76.0f, 255.0f }); // orange
}
for (auto&& lm : image_points) {
cv::circle(outimg, cv::Point2f(lm), 3, { 0.0f, 255.0f, 255.0f }); // yellow: subset of the detected LMs that we use (including contour landmarks)
}
estimated_rotation = glm::dquat(camera_rotation[0], camera_rotation[1], camera_rotation[2], camera_rotation[3]);
euler_angles = glm::eulerAngles(estimated_rotation); // returns [P, Y, R]
fitting_log << "Final fit:\t\t\tYaw " << glm::degrees(euler_angles[1]) << ", Pitch " << glm::degrees(euler_angles[0]) << ", Roll " << glm::degrees(euler_angles[2]) << "; t & f: " << camera_translation_and_intrinsics << '\n';
fitting_log << "Ceres took: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms.\n";
cout << fitting_log.str();
auto euler_angles = glm::eulerAngles(glm::dquat(camera_rotation[0], camera_rotation[1], camera_rotation[2], camera_rotation[3])); // returns [P, Y, R]
std::cout << "Pose fit with mean shape: Yaw " << glm::degrees(euler_angles[1]) << ", Pitch " << glm::degrees(euler_angles[0]) << ", Roll " << glm::degrees(euler_angles[2]) << "; t & f: " << camera_translation_and_intrinsics << '\n';
outputfile.replace_extension(".obj");
render::write_obj(mesh, outputfile.string());
return EXIT_SUCCESS;
}
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