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py-3d-face-reconstruction
Commit 5116af65 authored by Richard Torenvliet's avatar Richard Torenvliet
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Use clang++ instead of g++

parent d99c5f0e
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makefile
View file @ 5116af65
......@@ -50,7 +50,7 @@ $(SITE_PACKAGES)/cv%:
src/reconstruction/fit.so: src/reconstruction/fit-model.cpp
$(BASE_DOCKER_CMD) /bin/bash -c \
'(cd reconstruction; \
g++ -fPIC -O3 -shared -std=c++11 \
clang++ -fPIC -O3 -shared -std=c++14 \
-I/usr/local/include/pybind11/include/ \
-I/usr/local/eos/include/ \
-I/usr/local/eos/3rdparty/glm/ \
......@@ -61,7 +61,7 @@ src/reconstruction/fit.so: src/reconstruction/fit-model.cpp
-L/usr/lib/x86_64-linux-gnu/ \
-L/usr/local/lib/ \
-lboost_program_options \
-lfile_system \
-lboost_filesystem \
-lopencv_world \
`python-config --cflags --ldflags` \
$(notdir $^) -o $(notdir $@) \
......
src/reconstruction/fit-model.cpp
View file @ 5116af65
......@@ -45,8 +45,10 @@ int add(int i, int j) {
#include <fstream>
using namespace eos;
namespace po = boost::program_options;
namespace fs = boost::filesystem;
using eos::core::Landmark;
using eos::core::LandmarkCollection;
using cv::Mat;
......@@ -68,82 +70,82 @@ using std::string;
* is estimated, and then, using this camera matrix, the shape is fitted
* to the landmarks.
*/
int fit(int argc, char *argv[])
{
// Load the image, landmarks, LandmarkMapper and the Morphable Model:
Mat image = cv::imread(imagefile.string());
LandmarkCollection<cv::Vec2f> landmarks;
try {
landmarks = read_pts_landmarks(landmarksfile.string());
}
catch (const std::runtime_error& e) {
cout << "Error reading the landmarks: " << e.what() << endl;
return EXIT_FAILURE;
}
morphablemodel::MorphableModel morphable_model;
try {
morphable_model = morphablemodel::load_model(modelfile.string());
}
catch (const std::runtime_error& e) {
cout << "Error loading the Morphable Model: " << e.what() << endl;
return EXIT_FAILURE;
}
core::LandmarkMapper landmark_mapper = mappingsfile.empty() ? core::LandmarkMapper() : core::LandmarkMapper(mappingsfile);
// Draw the loaded landmarks:
Mat outimg = image.clone();
for (auto&& lm : landmarks) {
cv::rectangle(outimg, cv::Point2f(lm.coordinates[0] - 2.0f, lm.coordinates[1] - 2.0f), cv::Point2f(lm.coordinates[0] + 2.0f, lm.coordinates[1] + 2.0f), { 255, 0, 0 });
}
// These will be the final 2D and 3D points used for the fitting:
vector<Vec4f> model_points; // the points in the 3D shape model
vector<int> vertex_indices; // their vertex indices
vector<Vec2f> image_points; // the corresponding 2D landmark points
// Sub-select all the landmarks which we have a mapping for (i.e. that are defined in the 3DMM):
for (int i = 0; i < landmarks.size(); ++i) {
auto converted_name = landmark_mapper.convert(landmarks[i].name);
if (!converted_name) { // no mapping defined for the current landmark
continue;
}
int vertex_idx = std::stoi(converted_name.get());
Vec4f vertex = morphable_model.get_shape_model().get_mean_at_point(vertex_idx);
model_points.emplace_back(vertex);
vertex_indices.emplace_back(vertex_idx);
image_points.emplace_back(landmarks[i].coordinates);
}
// Estimate the camera (pose) from the 2D - 3D point correspondences
fitting::ScaledOrthoProjectionParameters pose = fitting::estimate_orthographic_projection_linear(image_points, model_points, true, image.rows);
fitting::RenderingParameters rendering_params(pose, image.cols, image.rows);
// The 3D head pose can be recovered as follows:
float yaw_angle = glm::degrees(glm::yaw(rendering_params.get_rotation()));
// and similarly for pitch and roll.
// Estimate the shape coefficients by fitting the shape to the landmarks:
Mat affine_from_ortho = fitting::get_3x4_affine_camera_matrix(rendering_params, image.cols, image.rows);
vector<float> fitted_coeffs = fitting::fit_shape_to_landmarks_linear(morphable_model, affine_from_ortho, image_points, vertex_indices);
// Obtain the full mesh with the estimated coefficients:
render::Mesh mesh = morphable_model.draw_sample(fitted_coeffs, vector<float>());
// Extract the texture from the image using given mesh and camera parameters:
Mat isomap = render::extract_texture(mesh, affine_from_ortho, image);
// Save the mesh as textured obj:
outputfile += fs::path(".obj");
render::write_textured_obj(mesh, outputfile.string());
// And save the isomap:
outputfile.replace_extension(".isomap.png");
cv::imwrite(outputfile.string(), isomap);
cout << "Finished fitting and wrote result mesh and isomap to files with basename " << outputfile.stem().stem() << "." << endl;
return EXIT_SUCCESS;
}
//int fit(int argc, char *argv[])
//{
// // Load the image, landmarks, LandmarkMapper and the Morphable Model:
// Mat image = cv::imread(imagefile.string());
// LandmarkCollection<cv::Vec2f> landmarks;
// try {
// landmarks = read_pts_landmarks(landmarksfile.string());
// }
// catch (const std::runtime_error& e) {
// cout << "Error reading the landmarks: " << e.what() << endl;
// return EXIT_FAILURE;
// }
// morphablemodel::MorphableModel morphable_model;
// try {
// morphable_model = morphablemodel::load_model(modelfile.string());
// }
// catch (const std::runtime_error& e) {
// cout << "Error loading the Morphable Model: " << e.what() << endl;
// return EXIT_FAILURE;
// }
// core::LandmarkMapper landmark_mapper = mappingsfile.empty() ? core::LandmarkMapper() : core::LandmarkMapper(mappingsfile);
//
// // Draw the loaded landmarks:
// Mat outimg = image.clone();
// for (auto&& lm : landmarks) {
// cv::rectangle(outimg, cv::Point2f(lm.coordinates[0] - 2.0f, lm.coordinates[1] - 2.0f), cv::Point2f(lm.coordinates[0] + 2.0f, lm.coordinates[1] + 2.0f), { 255, 0, 0 });
// }
//
// // These will be the final 2D and 3D points used for the fitting:
// vector<Vec4f> model_points; // the points in the 3D shape model
// vector<int> vertex_indices; // their vertex indices
// vector<Vec2f> image_points; // the corresponding 2D landmark points
//
// // Sub-select all the landmarks which we have a mapping for (i.e. that are defined in the 3DMM):
// for (int i = 0; i < landmarks.size(); ++i) {
// auto converted_name = landmark_mapper.convert(landmarks[i].name);
// if (!converted_name) { // no mapping defined for the current landmark
// continue;
// }
// int vertex_idx = std::stoi(converted_name.get());
// Vec4f vertex = morphable_model.get_shape_model().get_mean_at_point(vertex_idx);
// model_points.emplace_back(vertex);
// vertex_indices.emplace_back(vertex_idx);
// image_points.emplace_back(landmarks[i].coordinates);
// }
//
// // Estimate the camera (pose) from the 2D - 3D point correspondences
// fitting::ScaledOrthoProjectionParameters pose = fitting::estimate_orthographic_projection_linear(image_points, model_points, true, image.rows);
// fitting::RenderingParameters rendering_params(pose, image.cols, image.rows);
//
// // The 3D head pose can be recovered as follows:
// float yaw_angle = glm::degrees(glm::yaw(rendering_params.get_rotation()));
// // and similarly for pitch and roll.
//
// // Estimate the shape coefficients by fitting the shape to the landmarks:
// Mat affine_from_ortho = fitting::get_3x4_affine_camera_matrix(rendering_params, image.cols, image.rows);
// vector<float> fitted_coeffs = fitting::fit_shape_to_landmarks_linear(morphable_model, affine_from_ortho, image_points, vertex_indices);
//
// // Obtain the full mesh with the estimated coefficients:
// render::Mesh mesh = morphable_model.draw_sample(fitted_coeffs, vector<float>());
//
// // Extract the texture from the image using given mesh and camera parameters:
// Mat isomap = render::extract_texture(mesh, affine_from_ortho, image);
//
// // Save the mesh as textured obj:
// outputfile += fs::path(".obj");
// render::write_textured_obj(mesh, outputfile.string());
//
// // And save the isomap:
// outputfile.replace_extension(".isomap.png");
// cv::imwrite(outputfile.string(), isomap);
//
// cout << "Finished fitting and wrote result mesh and isomap to files with basename " << outputfile.stem().stem() << "." << endl;
//
// return EXIT_SUCCESS;
//}
PYBIND11_PLUGIN(fit) {
py::module m("fit", "fit example");
......
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