Commit 574ccbce authored by Richard Torenvliet's avatar Richard Torenvliet

Use a smart pointer for the keyframes. Makes sure that generated mesh belongs to a keyframe

parent de4c961c
......@@ -880,7 +880,6 @@ inline eos::core::Mesh generate_new_mesh(
return mesh;
}
inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParameters>> fit_shape_and_pose_multi_2(
const morphablemodel::MorphableModel& morphable_model,
const std::vector<morphablemodel::Blendshape>& blendshapes,
......@@ -1128,7 +1127,8 @@ inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParamete
inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParameters>> fit_shape_and_pose_multi_parallel(
const morphablemodel::MorphableModel& morphable_model,
const std::vector<morphablemodel::Blendshape>& blendshapes,
std::vector<eos::video::Keyframe>& keyframes,
std::vector<std::shared_ptr<eos::video::Keyframe>> keyframes,
// std::vector<eos::video::Keyframe>& keyframes,
const core::LandmarkMapper& landmark_mapper,
int image_width,
int image_height,
......@@ -1224,7 +1224,7 @@ inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParamete
// (equal to landmark coordinates), for every image / mesh.
std::tie(curr_model_points, curr_vertex_indices, curr_image_points) =
eos::core::get_landmark_coordinates<Vec2f, Vec4f>(
keyframes[j].fitting_result.landmarks, landmark_mapper, current_mesh);
keyframes[j].get()->fitting_result.landmarks, landmark_mapper, current_mesh);
// Start constructing a list of rendering parameters needed for reconstruction.
// Get the current points from the last added image points and model points
......@@ -1234,9 +1234,6 @@ inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParamete
fitting::RenderingParameters current_rendering_params(current_pose, image_width, image_height);
rendering_params[j] = current_rendering_params;
// update key frame rendering params
keyframes[j].fitting_result.rendering_parameters = current_rendering_params;
Mat affine_from_ortho = fitting::get_3x4_affine_camera_matrix(current_rendering_params, image_width, image_height);
// if no contour
......@@ -1289,8 +1286,8 @@ inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParamete
vector<Vec2f> image_points_contour;
vector<int> vertex_indices_contour;
auto curr_keyframe = keyframes[j];
auto landmarks = curr_keyframe.fitting_result.landmarks;
auto curr_keyframe = keyframes[j].get();
auto landmarks = curr_keyframe->fitting_result.landmarks;
auto yaw_angle = glm::degrees(glm::eulerAngles(rendering_params[j].get_rotation())[1]);
// For each 2D contour landmark, get the corresponding 3D vertex point and vertex id:
......@@ -1332,95 +1329,6 @@ inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParamete
VectorXf current_mean_plus_blendshapes = morphable_model.get_shape_model().get_mean() + blendshapes_as_basis * Eigen::Map<const Eigen::VectorXf>(blendshape_coefficients[j].data(),blendshape_coefficients[j].size());
mean_plus_blendshapes.push_back(current_mean_plus_blendshapes);
}
// Given the current pose, find 2D-3D contour correspondences of the front-facing face contour:
// vector<Vec2f> image_points_contour;
// vector<int> vertex_indices_contour;
//
// auto curr_keyframe = keyframes[j];
// auto landmarks = curr_keyframe.fitting_result.landmarks;
// auto yaw_angle = glm::degrees(glm::eulerAngles(rendering_params[j].get_rotation())[1]);
//
// // For each 2D contour landmark, get the corresponding 3D vertex point and vertex id:
// std::tie(image_points_contour, std::ignore, vertex_indices_contour) =
// fitting::get_contour_correspondences(
// landmarks,
// contour_landmarks,
// model_contour,
// yaw_angle,
// current_meshs[j],
// rendering_params[j].get_modelview(),
// rendering_params[j].get_projection(),
// fitting::get_opencv_viewport(image_width, image_height)
// );
//
// // Add the contour correspondences to the set of landmarks that we use for the fitting:
// vertex_indices[j] = fitting::concat(vertex_indices[j], vertex_indices_contour);
// image_points[j] = fitting::concat(image_points[j], image_points_contour);
//
// // Fit the occluding (away-facing) contour using the detected contour LMs:
// vector<Eigen::Vector2f> occluding_contour_landmarks;
//
// // positive yaw = subject looking to the left
// if (yaw_angle >= 0.0f)
// {
// // the left contour is the occluding one we want to use ("away-facing")
// auto contour_landmarks_ =
// core::filter(landmarks, contour_landmarks.left_contour); // Can do this outside of the loop
// std::for_each(begin(contour_landmarks_),
// end(contour_landmarks_),
// [&occluding_contour_landmarks](auto &&lm)
// {
// occluding_contour_landmarks.push_back({lm.coordinates[0], lm.coordinates[1]});
// });
// }
// else
// {
// auto contour_landmarks_ = core::filter(landmarks, contour_landmarks.right_contour);
// std::for_each(begin(contour_landmarks_),
// end(contour_landmarks_),
// [&occluding_contour_landmarks](auto &&lm)
// {
// occluding_contour_landmarks.push_back({lm.coordinates[0], lm.coordinates[1]});
// });
// }
//
// auto edge_correspondences = fitting::find_occluding_edge_correspondences_parallel(
// current_meshs[j], edge_topology, rendering_params[j], occluding_contour_landmarks, 180.0f
// );
//
// image_points[j] = fitting::concat(image_points[j], edge_correspondences.first);
// vertex_indices[j] = fitting::concat(vertex_indices[j], edge_correspondences.second);
//
// // Get the model points of the current mesh, for all correspondences that we've got:
// model_points[j].clear();
//
// for (const auto &v : vertex_indices[j])
// {
// model_points[j].push_back(
// {
// current_meshs[j].vertices[v][0],
// current_meshs[j].vertices[v][1],
// current_meshs[j].vertices[v][2],
// current_meshs[j].vertices[v][3]
// });
// }
//
// // Re-estimate the pose, using all correspondences:
// auto current_pose = fitting::estimate_orthographic_projection_linear(image_points[j],
// model_points[j],
// true,
// image_height);
// rendering_params[j] = fitting::RenderingParameters(current_pose, image_width, image_height);
//
// Mat affine_from_ortho =
// fitting::get_3x4_affine_camera_matrix(rendering_params[j], image_width, image_height);
// affine_from_orthos.push_back(affine_from_ortho);
//
// // Estimate the PCA shape coefficients with the current blendshape coefficients:
// VectorXf current_mean_plus_blendshapes = morphable_model.get_shape_model().get_mean() +
// blendshapes_as_basis * Eigen::Map<const Eigen::VectorXf>(blendshape_coefficients[j].data(),
// blendshape_coefficients[j].size());
// mean_plus_blendshapes.push_back(current_mean_plus_blendshapes);
pca_shape_coefficients = fitting::fit_shape_to_landmarks_linear_multi_parallel(
morphable_model,
......@@ -1457,8 +1365,8 @@ inline std::pair<std::vector<core::Mesh>, std::vector<fitting::RenderingParamete
// save it to the keyframe, we might need it for showing the reconstruction.
// we could make it optional
keyframes[j].fitting_result.mesh = current_meshs[j];
keyframes[j].fitting_result.rendering_parameters = rendering_params[j];
keyframes[j].get()->fitting_result.mesh = current_meshs[j];
keyframes[j].get()->fitting_result.rendering_parameters = rendering_params[j];
}
}
......
......@@ -610,7 +610,7 @@ inline void raster_triangle(TriangleToRasterize triangle, cv::Mat colourbuffer,
#ifdef _OPENMP
// vector<detail::TriangleToRasterize> triangles_to_raster;
inline void raster_triangle_parallel(vector<detail::TriangleToRasterize> triangles_to_raster, cv::Mat colourbuffer, cv::Mat depthbuffer, boost::optional<Texture> texture, bool enable_far_clipping)
inline void raster_triangle_parallel(std::vector<detail::TriangleToRasterize> triangles_to_raster, cv::Mat colourbuffer, cv::Mat depthbuffer, boost::optional<Texture> texture, bool enable_far_clipping)
{
using cv::Vec2f;
using cv::Vec3f;
......
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