Commit d0cee5fe authored by Richard Torenvliet's avatar Richard Torenvliet

Add a beginning of the multi-frame reconstruction projec - commit is merely a...

Add a beginning of the multi-frame reconstruction projec - commit is merely a copy and backup of work
parent df1e1286
......@@ -41,6 +41,10 @@ target_link_libraries(fit-model-simple ${OpenCV_LIBS} ${Boost_LIBRARIES})
add_executable(fit-model fit-model.cpp)
target_link_libraries(fit-model ${OpenCV_LIBS} ${Boost_LIBRARIES})
# Multi frame fitting
add_executable(fit-model-multi-frame fit-model-multi-frame.cpp)
target_link_libraries(fit-model-multi-frame ${OpenCV_LIBS} ${Boost_LIBRARIES})
if(BUILD_CERES_EXAMPLE)
# Find Ceres, for the fit-model-ceres app:
find_package(Ceres REQUIRED)
......@@ -60,6 +64,7 @@ target_link_libraries(generate-obj ${OpenCV_LIBS} ${Boost_LIBRARIES})
# install target:
install(TARGETS fit-model-simple DESTINATION bin)
install(TARGETS fit-model-multi-frame DESTINATION bin)
install(TARGETS fit-model DESTINATION bin)
install(TARGETS generate-obj DESTINATION bin)
install(DIRECTORY ${CMAKE_SOURCE_DIR}/examples/data DESTINATION bin)
/*
* eos - A 3D Morphable Model fitting library written in modern C++11/14.
*
* File: examples/fit-model-simple.cpp
*
* Copyright 2015 Patrik Huber
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "glm/ext.hpp"
#include "eos/core/Landmark.hpp"
#include "eos/core/LandmarkMapper.hpp"
#include "eos/fitting/orthographic_camera_estimation_linear.hpp"
#include "eos/fitting/RenderingParameters.hpp"
#include "eos/fitting/linear_shape_fitting.hpp"
#include "eos/render/utils.hpp"
#include "eos/render/texture_extraction.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "boost/program_options.hpp"
#include "boost/filesystem.hpp"
#include <vector>
#include <iostream>
#include <fstream>
using namespace eos;
using namespace fitting;
namespace po = boost::program_options;
namespace fs = boost::filesystem;
using eos::core::Landmark;
using eos::core::LandmarkCollection;
using cv::Mat;
using cv::Vec2f;
using cv::Vec3f;
using cv::Vec4f;
using std::cout;
using std::endl;
using std::vector;
using std::string;
/**
* Reads an ibug .pts landmark file and returns an ordered vector with
* the 68 2D landmark coordinates.
*
* @param[in] filename Path to a .pts file.
* @return An ordered vector with the 68 ibug landmarks.
*/
LandmarkCollection<cv::Vec2f> read_pts_landmarks(std::string filename)
{
using std::getline;
using cv::Vec2f;
using std::string;
LandmarkCollection<Vec2f> landmarks;
landmarks.reserve(68);
std::ifstream file(filename);
if (!file.is_open()) {
throw std::runtime_error(string("Could not open landmark file: " + filename));
}
string line;
// Skip the first 3 lines, they're header lines:
getline(file, line); // 'version: 1'
getline(file, line); // 'n_points : 68'
getline(file, line); // '{'
int ibugId = 1;
while (getline(file, line))
{
if (line == "}") { // end of the file
break;
}
std::stringstream lineStream(line);
Landmark<Vec2f> landmark;
landmark.name = std::to_string(ibugId);
if (!(lineStream >> landmark.coordinates[0] >> landmark.coordinates[1])) {
throw std::runtime_error(string("Landmark format error while parsing the line: " + line));
}
// From the iBug website:
// "Please note that the re-annotated data for this challenge are saved in the Matlab convention of 1 being
// the first index, i.e. the coordinates of the top left pixel in an image are x=1, y=1."
// ==> So we shift every point by 1:
landmark.coordinates[0] -= 1.0f;
landmark.coordinates[1] -= 1.0f;
landmarks.emplace_back(landmark);
++ibugId;
}
return landmarks;
};
/**
* This app demonstrates estimation of the camera and fitting of the shape
* model of a 3D Morphable Model from an ibug LFPW image with its landmarks.
*
* First, the 68 ibug landmarks are loaded from the .pts file and converted
* to vertex indices using the LandmarkMapper. Then, an orthographic camera
* is estimated, and then, using this camera matrix, the shape is fitted
* to the landmarks.
*/
int main(int argc, char *argv[]) {
fs::path modelfile, isomapfile, imagefile, landmarksfile, mappingsfile, outputfile;
try {
po::options_description desc("Allowed options");
desc.add_options()
("help,h",
"display the help message")
("model,m", po::value<fs::path>(&modelfile)->required()->default_value("/data/share/sfm_shape_3448.bin"),
"a Morphable Model stored as cereal BinaryArchive")
("image,i", po::value<fs::path>(&imagefile)->required()->default_value("/data/image_0010.png"),
"an input image")
("landmarks,l", po::value<fs::path>(&landmarksfile)->required()->default_value("/data/image_0010.pts"),
"2D landmarks for the image, in ibug .pts format")
("mapping,p", po::value<fs::path>(&mappingsfile)->required()->default_value("/data/share/ibug2did.txt"),
"landmark identifier to model vertex number mapping")
("output,o", po::value<fs::path>(&outputfile)->required()->default_value("/data/out"),
"basename for the output rendering and obj files")
;
po::variables_map vm;
po::store(po::command_line_parser(argc, argv).options(desc).run(), vm);
if (vm.count("help")) {
cout << "Usage: fit-model-simple [options]" << endl;
cout << desc;
return EXIT_SUCCESS;
}
po::notify(vm);
}
catch (const po::error& e) {
cout << "Error while parsing command-line arguments: " << e.what() << endl;
cout << "Use --help to display a list of options." << endl;
return EXIT_SUCCESS;
}
// 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;
}
......@@ -172,7 +172,7 @@ public:
* Returns a sample from the model with the given shape- and
* colour PCA coefficients.
*
* If one of the given vectors is empty, the mean is used.
* If an empty vector is given, the mean of the occording vector is used.
* The coefficient vectors should contain normalised, i.e. standard normal distributed coefficients.
* If the Morphable Model is a shape-only model (without colour model), make sure to
* leave \c color_coefficients empty.
......
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