Use pybind11 instead of c headers

Dockerfile

@@ -32,6 +32,12 @@ RUN cmake ../ \
     -DPYTHON_EXECUTABLE=/usr/bin/python
 
 RUN make && make install
+
 ENV PYTHONPATH=/usr/local/eos/bin/:$PYTHONPATH
 
+WORKDIR /libs
+RUN git clone https://github.com/pybind/pybind11.git
+RUN (cd pybind11; mkdir build; cd build; cmake -DPYBIND11_PYTHON_VERSION=2.7 ..);
+RUN (cd pybind11/build; make -j4 && make install);
+
 WORKDIR /src

makefile

@@ -44,5 +44,25 @@ $(SITE_PACKAGES)/cv%:
 	@ls $@
 
 
+#src/reconstruction/fit.so: src/reconstruction/fit-model.cpp
+#	$(BASE_DOCKER_CMD) /bin/bash -c '(cd reconstruction; python setup.py build_ext --inplace)'
+
 src/reconstruction/fit.so: src/reconstruction/fit-model.cpp
-	$(BASE_DOCKER_CMD) /bin/bash -c '(cd reconstruction; python setup.py build_ext --inplace)'
+	$(BASE_DOCKER_CMD) /bin/bash -c \
+		'(cd reconstruction; \
+			g++ -fPIC -O3 -shared -std=c++11 \
+				-I/usr/local/include/pybind11/include/ \
+				-I/usr/local/eos/include/ \
+				-I/usr/local/eos/3rdparty/glm/ \
+				-I/usr/local/eos/3rdparty/cereal-1.1.1/include/ \
+				-I/usr/local/include/opencv2/ \
+				-I/usr/include/boost/ \
+				-L/usr/local/eos/bin/ \
+				-L/usr/lib/x86_64-linux-gnu/ \
+				-L/usr/local/lib/ \
+				-lboost_program_options \
+				-lfile_system \
+				-lopencv_world \
+				`python-config --cflags --ldflags` \
+			$(notdir $^) -o $(notdir $@) \
+		)'

src/reconstruction/fit-model.cpp

+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+int add(int i, int j) {
+    return i + j;
+
+}
+
 /*
  * eos - A 3D Morphable Model fitting library written in modern C++11/14.
  *
  * File: examples/fit-model.cpp
  *
- * Copyright 2015 Patrik Huber *
+ * 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
@@ -16,8 +26,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include <Python.h>
-
 #include "eos/core/Landmark.hpp"
 #include "eos/core/LandmarkMapper.hpp"
 #include "eos/fitting/orthographic_camera_estimation_linear.hpp"
@@ -41,7 +49,6 @@ namespace po = boost::program_options;
 namespace fs = boost::filesystem;
 using eos::core::Landmark;
 using eos::core::LandmarkCollection;
-using eos::core::LandmarkMapper;
 using cv::Mat;
 using cv::Vec2f;
 using cv::Vec3f;
@@ -51,56 +58,6 @@ 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
@@ -111,40 +68,8 @@ LandmarkCollection<cv::Vec2f> read_pts_landmarks(std::string filename)
  * is estimated, and then, using this camera matrix, the shape is fitted
  * to the landmarks.
  */
-int main(int argc, char *argv[])
+int fit(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("../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("../share/ibug2did.txt"),
-                "landmark identifier to model vertex number mapping")
-            ("output,o", po::value<fs::path>(&outputfile)->required()->default_value("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 [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;
@@ -156,7 +81,6 @@ int main(int argc, char *argv[])
         return EXIT_FAILURE;
     }
     morphablemodel::MorphableModel morphable_model;
-
     try {
         morphable_model = morphablemodel::load_model(modelfile.string());
     }
@@ -164,19 +88,12 @@ int main(int argc, char *argv[])
         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 }
-        );
+        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:
@@ -207,9 +124,7 @@ int main(int argc, char *argv[])
 
     // 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
-    );
+    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>());
@@ -230,84 +145,10 @@ int main(int argc, char *argv[])
     return EXIT_SUCCESS;
 }
 
-static PyObject * fit_model(PyObject *self, PyObject *args) {
-    LandmarkCollection<cv::Vec2f> landmarks;
-    morphablemodel::MorphableModel morphable_model;
-
-    LandmarkMapper("/usr/local/eos/share/ibug2did.txt");
-    morphable_model = morphablemodel::load_model("/usr/local/eos/share/sfm_shape_3448.bin");
-
-    // 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;
-    Py_RETURN_NONE;
-}
-
-static PyMethodDef module_methods[] = {
-    // name of function, function_name
-    {"fit_model", fit_model, METH_VARARGS, "Fits an image with given landmarks to a 3DMM"},
-    {NULL, NULL, 0, NULL}
-};
+PYBIND11_PLUGIN(fit) {
+    py::module m("fit", "fit example");
 
+    m.def("add", &add, "A function which adds two numbers");
 
-PyMODINIT_FUNC initfit(void) {
-    (void) Py_InitModule("fit", module_methods);
+    return m.ptr();
 }

src/test/reconstruction_test.py

@@ -49,8 +49,7 @@ def fit_model():
     input_points = dataset_module.factory(filename=image_filename)
     input_image = input_points.get_image()
 
-    fit.fit_model(image)
+    print(fit.add(1, 3))
 
 if __name__ == '__main__':
-    #fit_model()
-    shape()
+    fit_model()