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26 changed files with 1380 additions and 2091 deletions
-1
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@@ -63,7 +63,6 @@ A general overview can be found in the paper [RapidPoseTriangulation: Multi-view
## Extras
- For usage in combination with ROS2 see [ros](extras/ros/README.md) directory.
<br>
+307 -6
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@@ -1,6 +1,8 @@
#include <algorithm>
#include <array>
#include <cstdint>
#include <stdexcept>
#include <vector>
#include <nanobind/nanobind.h>
#include <nanobind/ndarray.h>
@@ -19,7 +21,10 @@ using PoseArray2D =
nb::ndarray<nb::numpy, const float, nb::shape<-1, -1, -1, 3>, nb::c_contig>;
using CountArray = nb::ndarray<nb::numpy, const uint32_t, nb::shape<-1>, nb::c_contig>;
using RoomArray = nb::ndarray<nb::numpy, const float, nb::shape<2, 3>, nb::c_contig>;
using PoseArray3DConst =
nb::ndarray<nb::numpy, const float, nb::shape<-1, -1, 4>, nb::c_contig>;
using PoseArray3D = nb::ndarray<nb::numpy, float, nb::shape<-1, -1, 4>, nb::c_contig>;
using PoseArray2DOut = nb::ndarray<nb::numpy, float, nb::shape<-1, 4>, nb::c_contig>;
PoseBatch2DView pose_batch_view_from_numpy(const PoseArray2D &poses_2d, const CountArray &person_counts)
{
@@ -45,6 +50,15 @@ PoseBatch2DView pose_batch_view_from_numpy(const PoseArray2D &poses_2d, const Co
};
}
PoseBatch3DView pose_batch3d_view_from_numpy(const PoseArray3DConst &poses_3d)
{
return PoseBatch3DView {
poses_3d.data(),
static_cast<size_t>(poses_3d.shape(0)),
static_cast<size_t>(poses_3d.shape(1)),
};
}
std::array<std::array<float, 3>, 2> roomparams_from_numpy(const RoomArray &roomparams)
{
std::array<std::array<float, 3>, 2> result {};
@@ -69,10 +83,75 @@ PoseArray3D pose_batch_to_numpy(PoseBatch3D batch)
const size_t shape[3] = {batch.num_persons, batch.num_joints, 4};
return PoseArray3D(storage->data(), 3, shape, owner);
}
PoseArray3D pose_batch_to_numpy_copy(const PoseBatch3D &batch)
{
PoseBatch3D copy = batch;
return pose_batch_to_numpy(std::move(copy));
}
PoseArray2DOut pose_rows_to_numpy_copy(const std::vector<std::array<float, 4>> &rows)
{
auto *storage = new std::vector<float>(rows.size() * 4, 0.0f);
for (size_t row = 0; row < rows.size(); ++row)
{
for (size_t coord = 0; coord < 4; ++coord)
{
(*storage)[row * 4 + coord] = rows[row][coord];
}
}
nb::capsule owner(storage, [](void *value) noexcept
{
delete static_cast<std::vector<float> *>(value);
});
const size_t shape[2] = {rows.size(), 4};
return PoseArray2DOut(storage->data(), 2, shape, owner);
}
PoseArray3D merged_poses_to_numpy_copy(const std::vector<std::vector<std::array<float, 4>>> &poses)
{
size_t num_poses = poses.size();
size_t num_joints = 0;
if (!poses.empty())
{
num_joints = poses[0].size();
}
auto *storage = new std::vector<float>(num_poses * num_joints * 4, 0.0f);
for (size_t pose = 0; pose < num_poses; ++pose)
{
if (poses[pose].size() != num_joints)
{
delete storage;
throw std::invalid_argument("Merged poses must use a consistent joint count.");
}
for (size_t joint = 0; joint < num_joints; ++joint)
{
for (size_t coord = 0; coord < 4; ++coord)
{
(*storage)[((pose * num_joints) + joint) * 4 + coord] = poses[pose][joint][coord];
}
}
}
nb::capsule owner(storage, [](void *value) noexcept
{
delete static_cast<std::vector<float> *>(value);
});
const size_t shape[3] = {num_poses, num_joints, 4};
return PoseArray3D(storage->data(), 3, shape, owner);
}
} // namespace
NB_MODULE(_core, m)
{
nb::enum_<CameraModel>(m, "CameraModel")
.value("PINHOLE", CameraModel::Pinhole)
.value("FISHEYE", CameraModel::Fisheye);
nb::class_<Camera>(m, "Camera")
.def(nb::init<>())
.def_rw("name", &Camera::name)
@@ -82,12 +161,195 @@ NB_MODULE(_core, m)
.def_rw("T", &Camera::T)
.def_rw("width", &Camera::width)
.def_rw("height", &Camera::height)
.def_rw("type", &Camera::type)
.def_rw("model", &Camera::model)
.def("__repr__", [](const Camera &camera)
{
return camera.to_string();
});
nb::class_<TriangulationOptions>(m, "TriangulationOptions")
.def(nb::init<>())
.def_rw("min_match_score", &TriangulationOptions::min_match_score)
.def_rw("min_group_size", &TriangulationOptions::min_group_size);
nb::class_<PairCandidate>(m, "PairCandidate")
.def(nb::init<>())
.def_rw("view1", &PairCandidate::view1)
.def_rw("view2", &PairCandidate::view2)
.def_rw("person1", &PairCandidate::person1)
.def_rw("person2", &PairCandidate::person2)
.def_rw("global_person1", &PairCandidate::global_person1)
.def_rw("global_person2", &PairCandidate::global_person2);
nb::class_<PreviousPoseMatch>(m, "PreviousPoseMatch")
.def(nb::init<>())
.def_rw("previous_pose_index", &PreviousPoseMatch::previous_pose_index)
.def_rw("score_view1", &PreviousPoseMatch::score_view1)
.def_rw("score_view2", &PreviousPoseMatch::score_view2)
.def_rw("matched_view1", &PreviousPoseMatch::matched_view1)
.def_rw("matched_view2", &PreviousPoseMatch::matched_view2)
.def_rw("kept", &PreviousPoseMatch::kept)
.def_rw("decision", &PreviousPoseMatch::decision);
nb::class_<PreviousPoseFilterDebug>(m, "PreviousPoseFilterDebug")
.def(nb::init<>())
.def_rw("used_previous_poses", &PreviousPoseFilterDebug::used_previous_poses)
.def_rw("matches", &PreviousPoseFilterDebug::matches)
.def_rw("kept_pair_indices", &PreviousPoseFilterDebug::kept_pair_indices)
.def_rw("kept_pairs", &PreviousPoseFilterDebug::kept_pairs);
nb::class_<CoreProposalDebug>(m, "CoreProposalDebug")
.def(nb::init<>())
.def_rw("pair_index", &CoreProposalDebug::pair_index)
.def_rw("pair", &CoreProposalDebug::pair)
.def_rw("score", &CoreProposalDebug::score)
.def_rw("kept", &CoreProposalDebug::kept)
.def_rw("drop_reason", &CoreProposalDebug::drop_reason)
.def_prop_ro("pose_3d", [](const CoreProposalDebug &proposal)
{
return pose_rows_to_numpy_copy(proposal.pose_3d);
}, nb::rv_policy::move);
nb::class_<ProposalGroupDebug>(m, "ProposalGroupDebug")
.def(nb::init<>())
.def_rw("center", &ProposalGroupDebug::center)
.def_rw("proposal_indices", &ProposalGroupDebug::proposal_indices)
.def_prop_ro("pose_3d", [](const ProposalGroupDebug &group)
{
return pose_rows_to_numpy_copy(group.pose_3d);
}, nb::rv_policy::move);
nb::class_<GroupingDebug>(m, "GroupingDebug")
.def(nb::init<>())
.def_rw("initial_groups", &GroupingDebug::initial_groups)
.def_rw("duplicate_pair_drops", &GroupingDebug::duplicate_pair_drops)
.def_rw("groups", &GroupingDebug::groups);
nb::class_<FullProposalDebug>(m, "FullProposalDebug")
.def(nb::init<>())
.def_rw("source_core_proposal_index", &FullProposalDebug::source_core_proposal_index)
.def_rw("pair", &FullProposalDebug::pair)
.def_prop_ro("pose_3d", [](const FullProposalDebug &proposal)
{
return pose_rows_to_numpy_copy(proposal.pose_3d);
}, nb::rv_policy::move);
nb::class_<MergeDebug>(m, "MergeDebug")
.def(nb::init<>())
.def_rw("group_proposal_indices", &MergeDebug::group_proposal_indices)
.def_prop_ro("merged_poses", [](const MergeDebug &merge)
{
return merged_poses_to_numpy_copy(merge.merged_poses);
}, nb::rv_policy::move);
nb::class_<TriangulationTrace>(m, "TriangulationTrace")
.def(nb::init<>())
.def_rw("pairs", &TriangulationTrace::pairs)
.def_rw("previous_filter", &TriangulationTrace::previous_filter)
.def_rw("core_proposals", &TriangulationTrace::core_proposals)
.def_rw("grouping", &TriangulationTrace::grouping)
.def_rw("full_proposals", &TriangulationTrace::full_proposals)
.def_rw("merge", &TriangulationTrace::merge)
.def_prop_ro("final_poses", [](const TriangulationTrace &trace)
{
return pose_batch_to_numpy_copy(trace.final_poses);
}, nb::rv_policy::move);
m.def(
"build_pair_candidates",
[](const PoseArray2D &poses_2d, const CountArray &person_counts)
{
return build_pair_candidates(pose_batch_view_from_numpy(poses_2d, person_counts));
},
"poses_2d"_a,
"person_counts"_a);
m.def(
"filter_pairs_with_previous_poses",
[](const PoseArray2D &poses_2d,
const CountArray &person_counts,
const std::vector<Camera> &cameras,
const std::vector<std::string> &joint_names,
const PoseArray3DConst &previous_poses_3d,
float min_match_score)
{
TriangulationOptions options;
options.min_match_score = min_match_score;
return filter_pairs_with_previous_poses(
pose_batch_view_from_numpy(poses_2d, person_counts),
cameras,
joint_names,
pose_batch3d_view_from_numpy(previous_poses_3d),
options);
},
"poses_2d"_a,
"person_counts"_a,
"cameras"_a,
"joint_names"_a,
"previous_poses_3d"_a,
"min_match_score"_a = 0.95f);
m.def(
"triangulate_debug",
[](const PoseArray2D &poses_2d,
const CountArray &person_counts,
const std::vector<Camera> &cameras,
const RoomArray &roomparams,
const std::vector<std::string> &joint_names,
float min_match_score,
size_t min_group_size)
{
TriangulationOptions options;
options.min_match_score = min_match_score;
options.min_group_size = min_group_size;
return triangulate_debug(
pose_batch_view_from_numpy(poses_2d, person_counts),
cameras,
roomparams_from_numpy(roomparams),
joint_names,
nullptr,
options);
},
"poses_2d"_a,
"person_counts"_a,
"cameras"_a,
"roomparams"_a,
"joint_names"_a,
"min_match_score"_a = 0.95f,
"min_group_size"_a = 1);
m.def(
"triangulate_debug",
[](const PoseArray2D &poses_2d,
const CountArray &person_counts,
const std::vector<Camera> &cameras,
const RoomArray &roomparams,
const std::vector<std::string> &joint_names,
const PoseArray3DConst &previous_poses_3d,
float min_match_score,
size_t min_group_size)
{
const PoseBatch3DView previous_view = pose_batch3d_view_from_numpy(previous_poses_3d);
TriangulationOptions options;
options.min_match_score = min_match_score;
options.min_group_size = min_group_size;
return triangulate_debug(
pose_batch_view_from_numpy(poses_2d, person_counts),
cameras,
roomparams_from_numpy(roomparams),
joint_names,
&previous_view,
options);
},
"poses_2d"_a,
"person_counts"_a,
"cameras"_a,
"roomparams"_a,
"joint_names"_a,
"previous_poses_3d"_a,
"min_match_score"_a = 0.95f,
"min_group_size"_a = 1);
m.def(
"triangulate_poses",
[](const PoseArray2D &poses_2d,
@@ -98,11 +360,17 @@ NB_MODULE(_core, m)
float min_match_score,
size_t min_group_size)
{
const PoseBatch2DView pose_batch = pose_batch_view_from_numpy(poses_2d, person_counts);
const auto room = roomparams_from_numpy(roomparams);
PoseBatch3D poses_3d = triangulate_poses(
pose_batch, cameras, room, joint_names, min_match_score, min_group_size);
return pose_batch_to_numpy(std::move(poses_3d));
TriangulationOptions options;
options.min_match_score = min_match_score;
options.min_group_size = min_group_size;
const PoseBatch3D poses_3d = triangulate_poses(
pose_batch_view_from_numpy(poses_2d, person_counts),
cameras,
roomparams_from_numpy(roomparams),
joint_names,
nullptr,
options);
return pose_batch_to_numpy(poses_3d);
},
"poses_2d"_a,
"person_counts"_a,
@@ -111,4 +379,37 @@ NB_MODULE(_core, m)
"joint_names"_a,
"min_match_score"_a = 0.95f,
"min_group_size"_a = 1);
m.def(
"triangulate_poses",
[](const PoseArray2D &poses_2d,
const CountArray &person_counts,
const std::vector<Camera> &cameras,
const RoomArray &roomparams,
const std::vector<std::string> &joint_names,
const PoseArray3DConst &previous_poses_3d,
float min_match_score,
size_t min_group_size)
{
const PoseBatch3DView previous_view = pose_batch3d_view_from_numpy(previous_poses_3d);
TriangulationOptions options;
options.min_match_score = min_match_score;
options.min_group_size = min_group_size;
const PoseBatch3D poses_3d = triangulate_poses(
pose_batch_view_from_numpy(poses_2d, person_counts),
cameras,
roomparams_from_numpy(roomparams),
joint_names,
&previous_view,
options);
return pose_batch_to_numpy(poses_3d);
},
"poses_2d"_a,
"person_counts"_a,
"cameras"_a,
"roomparams"_a,
"joint_names"_a,
"previous_poses_3d"_a,
"min_match_score"_a = 0.95f,
"min_group_size"_a = 1);
}
-22
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@@ -1,22 +0,0 @@
# ROS Wrapper
Run the 3D triangulator with ROS topics as inputs and publish detected poses.
<br>
- Build container:
```bash
docker build --progress=plain -t rapidposetriangulation_ros3d -f extras/ros/dockerfile_3d .
```
- Update or remove the `ROS_DOMAIN_ID` in the `docker-compose.yml` files to fit your environment
- Run and test:
```bash
xhost + && docker compose -f extras/ros/docker-compose-3d.yml up
docker exec -it ros-test_node-1 bash
export ROS_DOMAIN_ID=18
```
-70
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@@ -1,70 +0,0 @@
services:
test_node:
image: rapidposetriangulation_ros3d
network_mode: "host"
ipc: "host"
privileged: true
volumes:
- ../../:/RapidPoseTriangulation/
- /tmp/.X11-unix:/tmp/.X11-unix
- /dev/shm:/dev/shm
environment:
- DISPLAY
- QT_X11_NO_MITSHM=1
- "PYTHONUNBUFFERED=1"
command: /bin/bash -i -c 'sleep infinity'
triangulator:
image: rapidposetriangulation_ros3d
network_mode: "host"
ipc: "host"
privileged: true
volumes:
- ../../:/RapidPoseTriangulation/
- /tmp/.X11-unix:/tmp/.X11-unix
- /dev/shm:/dev/shm
environment:
- DISPLAY
- QT_X11_NO_MITSHM=1
- "PYTHONUNBUFFERED=1"
command: /bin/bash -i -c 'export ROS_DOMAIN_ID=18 && ros2 run rpt3d_wrapper_cpp rpt3d_wrapper'
skeleton_markers:
image: rapidposetriangulation_ros3d
network_mode: "host"
ipc: "host"
privileged: true
volumes:
- ../../:/RapidPoseTriangulation/
- /tmp/.X11-unix:/tmp/.X11-unix
- /dev/shm:/dev/shm
environment:
- "PYTHONUNBUFFERED=1"
command: /bin/bash -i -c 'export ROS_DOMAIN_ID=18 && ros2 run marker_publishers skeleton_markers'
cube_markers:
image: rapidposetriangulation_ros3d
network_mode: "host"
ipc: "host"
privileged: true
volumes:
- ../../:/RapidPoseTriangulation/
- /tmp/.X11-unix:/tmp/.X11-unix
- /dev/shm:/dev/shm
environment:
- "PYTHONUNBUFFERED=1"
command: /bin/bash -i -c 'export ROS_DOMAIN_ID=18 && ros2 run marker_publishers cube_markers'
skeleton_tfs:
image: rapidposetriangulation_ros3d
network_mode: "host"
ipc: "host"
privileged: true
volumes:
- ../../:/RapidPoseTriangulation/
- /tmp/.X11-unix:/tmp/.X11-unix
- /dev/shm:/dev/shm
environment:
- "PYTHONUNBUFFERED=1"
command: /bin/bash -i -c 'export ROS_DOMAIN_ID=18 && ros2 run marker_publishers skeleton_tfs'
-46
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@@ -1,46 +0,0 @@
FROM ros:humble-ros-base-jammy
ARG DEBIAN_FRONTEND=noninteractive
# Set the working directory to /project
WORKDIR /project/
# Update and install basic tools
RUN apt-get update && apt-get upgrade -y
RUN apt-get update && apt-get install -y --no-install-recommends git nano wget
RUN apt-get update && apt-get install -y --no-install-recommends python3-pip
RUN pip3 install --upgrade pip
# Fix ros package building error
RUN pip3 install --no-cache-dir "setuptools<=58.2.0"
# Add ROS2 sourcing by default
RUN echo "source /opt/ros/humble/setup.bash" >> ~/.bashrc
# Create ROS2 workspace for project packages
RUN mkdir -p /project/dev_ws/src/
RUN cd /project/dev_ws/; colcon build
RUN echo "source /project/dev_ws/install/setup.bash" >> ~/.bashrc
# Copy modules
COPY ./extras/include/ /RapidPoseTriangulation/extras/include/
COPY ./rpt/ /RapidPoseTriangulation/rpt/
COPY ./extras/ros/marker_publishers/ /RapidPoseTriangulation/extras/ros/marker_publishers/
COPY ./extras/ros/rpt_msgs/ /RapidPoseTriangulation/extras/ros/rpt_msgs/
COPY ./extras/ros/rpt3d_wrapper_cpp/ /RapidPoseTriangulation/extras/ros/rpt3d_wrapper_cpp/
# Link and build as ros package
RUN ln -s /RapidPoseTriangulation/extras/ros/marker_publishers/ /project/dev_ws/src/
RUN ln -s /RapidPoseTriangulation/extras/ros/rpt_msgs/ /project/dev_ws/src/
RUN ln -s /RapidPoseTriangulation/extras/ros/rpt3d_wrapper_cpp/ /project/dev_ws/src/
RUN /bin/bash -i -c 'cd /project/dev_ws/; colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release'
# Update ros packages -> autocompletion and check
RUN /bin/bash -i -c 'ros2 pkg list'
# Clear cache to save space, only has an effect if image is squashed
RUN apt-get autoremove -y \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /RapidPoseTriangulation/
CMD ["/bin/bash"]
@@ -1,63 +0,0 @@
cmake_minimum_required(VERSION 3.5)
project(marker_publishers)
# Default to C99
if(NOT CMAKE_C_STANDARD)
set(CMAKE_C_STANDARD 99)
endif()
# Default to C++14
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 14)
endif()
if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
add_compile_options(-Wall -Wextra -Wpedantic)
endif()
# find dependencies
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(rpt_msgs REQUIRED)
find_package(geometry_msgs REQUIRED)
find_package(tf2_ros REQUIRED)
find_package(visualization_msgs REQUIRED)
add_executable(cube_markers src/cube_markers.cpp)
ament_target_dependencies(cube_markers rclcpp geometry_msgs visualization_msgs)
target_include_directories(cube_markers PUBLIC
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
$<INSTALL_INTERFACE:include>)
add_executable(skeleton_markers src/skeleton_markers.cpp)
ament_target_dependencies(skeleton_markers rclcpp rpt_msgs geometry_msgs visualization_msgs)
target_include_directories(skeleton_markers PUBLIC
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
$<INSTALL_INTERFACE:include>)
add_executable(skeleton_tfs src/skeleton_tfs.cpp)
ament_target_dependencies(skeleton_tfs rclcpp rpt_msgs geometry_msgs tf2_ros)
target_include_directories(skeleton_tfs PUBLIC
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
$<INSTALL_INTERFACE:include>)
install(TARGETS cube_markers
DESTINATION lib/${PROJECT_NAME})
install(TARGETS skeleton_markers
DESTINATION lib/${PROJECT_NAME})
install(TARGETS skeleton_tfs
DESTINATION lib/${PROJECT_NAME})
if(BUILD_TESTING)
find_package(ament_lint_auto REQUIRED)
# the following line skips the linter which checks for copyrights
# uncomment the line when a copyright and license is not present in all source files
#set(ament_cmake_copyright_FOUND TRUE)
# the following line skips cpplint (only works in a git repo)
# uncomment the line when this package is not in a git repo
#set(ament_cmake_cpplint_FOUND TRUE)
ament_lint_auto_find_test_dependencies()
endif()
ament_package()
-24
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@@ -1,24 +0,0 @@
<?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>marker_publishers</name>
<version>0.0.0</version>
<description>TODO: Package description</description>
<maintainer email="root@todo.todo">root</maintainer>
<license>TODO: License declaration</license>
<buildtool_depend>ament_cmake</buildtool_depend>
<depend>rclcpp</depend>
<depend>rpt_msgs</depend>
<depend>geometry_msgs</depend>
<depend>tf2_ros</depend>
<depend>visualization_msgs</depend>
<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
@@ -1,122 +0,0 @@
#include <chrono>
#include <vector>
#include <rclcpp/rclcpp.hpp>
#include <geometry_msgs/msg/point.hpp>
#include <visualization_msgs/msg/marker.hpp>
#include <visualization_msgs/msg/marker_array.hpp>
// =================================================================================================
const std::string output_topic = "/markers_cube";
static const std::array<std::array<float, 3>, 2> roomparams = {{
{4.0, 5.0, 2.2},
{1.0, 0.0, 1.1},
}};
// =================================================================================================
// =================================================================================================
class CubePublisher : public rclcpp::Node
{
public:
CubePublisher() : Node("cube_publisher")
{
publisher_ = this->create_publisher<visualization_msgs::msg::MarkerArray>(output_topic, 1);
timer_ = this->create_wall_timer(
std::chrono::seconds(1), std::bind(&CubePublisher::timer_callback, this));
cube_edges = generate_cube_edges();
}
private:
visualization_msgs::msg::MarkerArray cube_edges;
rclcpp::Publisher<visualization_msgs::msg::MarkerArray>::SharedPtr publisher_;
rclcpp::TimerBase::SharedPtr timer_;
void timer_callback()
{
publisher_->publish(cube_edges);
}
visualization_msgs::msg::MarkerArray generate_cube_edges();
};
// =================================================================================================
visualization_msgs::msg::MarkerArray CubePublisher::generate_cube_edges()
{
visualization_msgs::msg::MarkerArray marker_array;
visualization_msgs::msg::Marker marker;
marker.header.frame_id = "world";
marker.type = visualization_msgs::msg::Marker::LINE_LIST;
marker.action = visualization_msgs::msg::Marker::ADD;
marker.scale.x = 0.05;
marker.id = 0;
marker.color.a = 1.0;
marker.color.r = 1.0;
std::vector<std::vector<double>> corners = {
{-1, -1, -1},
{1, -1, -1},
{1, 1, -1},
{-1, 1, -1},
{-1, -1, 1},
{1, -1, 1},
{1, 1, 1},
{-1, 1, 1},
};
for (auto &corner : corners)
{
for (size_t i = 0; i < corner.size(); ++i)
{
corner[i] = 0.5 * roomparams[0][i] * corner[i] + roomparams[1][i];
}
}
std::vector<std::pair<int, int>> edge_indices = {
{0, 1},
{1, 2},
{2, 3},
{3, 0},
{4, 5},
{5, 6},
{6, 7},
{7, 4},
{0, 4},
{1, 5},
{2, 6},
{3, 7},
};
for (const auto &edge : edge_indices)
{
geometry_msgs::msg::Point p1, p2;
p1.x = corners[edge.first][0];
p1.y = corners[edge.first][1];
p1.z = corners[edge.first][2];
p2.x = corners[edge.second][0];
p2.y = corners[edge.second][1];
p2.z = corners[edge.second][2];
marker.points.push_back(p1);
marker.points.push_back(p2);
}
marker_array.markers.push_back(marker);
return marker_array;
}
// =================================================================================================
// =================================================================================================
int main(int argc, char *argv[])
{
rclcpp::init(argc, argv);
rclcpp::spin(std::make_shared<CubePublisher>());
rclcpp::shutdown();
return 0;
}
@@ -1,255 +0,0 @@
#include <cstdint>
#include <string>
#include <vector>
#include <rclcpp/rclcpp.hpp>
#include <geometry_msgs/msg/point.hpp>
#include <visualization_msgs/msg/marker.hpp>
#include <visualization_msgs/msg/marker_array.hpp>
#include <std_msgs/msg/color_rgba.hpp>
#include "rpt_msgs/msg/poses.hpp"
// =================================================================================================
const std::string input_topic = "/poses/humans3d";
const std::string output_topic = "/markers_skeleton";
// =================================================================================================
// =================================================================================================
std::array<uint8_t, 3> hue_to_rgb(double hue)
{
double r, g, b;
int h = static_cast<int>(hue * 6);
double f = hue * 6 - h;
double q = 1 - f;
switch (h % 6)
{
case 0:
r = 1;
g = f;
b = 0;
break;
case 1:
r = q;
g = 1;
b = 0;
break;
case 2:
r = 0;
g = 1;
b = f;
break;
case 3:
r = 0;
g = q;
b = 1;
break;
case 4:
r = f;
g = 0;
b = 1;
break;
case 5:
r = 1;
g = 0;
b = q;
break;
default:
r = g = b = 0;
break;
}
std::array<uint8_t, 3> rgb = {
static_cast<uint8_t>(r * 255),
static_cast<uint8_t>(g * 255),
static_cast<uint8_t>(b * 255)};
return rgb;
}
// =================================================================================================
// =================================================================================================
class SkeletonPublisher : public rclcpp::Node
{
public:
SkeletonPublisher() : Node("skeleton_publisher")
{
publisher_ = this->create_publisher<visualization_msgs::msg::MarkerArray>(output_topic, 1);
subscriber_ = this->create_subscription<rpt_msgs::msg::Poses>(
input_topic, 1,
std::bind(&SkeletonPublisher::listener_callback, this, std::placeholders::_1));
connections = {
{"shoulder_middle", "head"},
{"head", "nose"},
{"nose", "eye_left"},
{"nose", "eye_right"},
{"eye_left", "ear_left"},
{"eye_right", "ear_right"},
{"shoulder_left", "shoulder_right"},
{"hip_middle", "shoulder_left"},
{"hip_middle", "shoulder_right"},
{"shoulder_left", "elbow_left"},
{"elbow_left", "wrist_left"},
{"hip_middle", "hip_left"},
{"hip_left", "knee_left"},
{"knee_left", "ankle_left"},
{"shoulder_right", "elbow_right"},
{"elbow_right", "wrist_right"},
{"hip_middle", "hip_right"},
{"hip_right", "knee_right"},
{"knee_right", "ankle_right"},
};
}
private:
std::vector<std::array<std::string, 2>> connections;
rclcpp::Publisher<visualization_msgs::msg::MarkerArray>::SharedPtr publisher_;
rclcpp::Subscription<rpt_msgs::msg::Poses>::SharedPtr subscriber_;
void listener_callback(const rpt_msgs::msg::Poses::SharedPtr msg);
visualization_msgs::msg::MarkerArray generate_msg(
const std::vector<std::vector<std::array<float, 4>>> &poses,
const std::vector<std::string> &joint_names);
};
// =================================================================================================
void SkeletonPublisher::listener_callback(const rpt_msgs::msg::Poses::SharedPtr msg)
{
std::vector<std::vector<std::array<float, 4>>> poses;
const std::vector<std::string> &joint_names = msg->joint_names;
// Unflatten poses
size_t idx = 0;
std::vector<int32_t> &bshape = msg->bodies_shape;
for (int32_t i = 0; i < bshape[0]; ++i)
{
std::vector<std::array<float, 4>> body;
for (int32_t j = 0; j < bshape[1]; ++j)
{
std::array<float, 4> joint;
for (int32_t k = 0; k < bshape[2]; ++k)
{
joint[k] = msg->bodies_flat[idx];
++idx;
}
body.push_back(std::move(joint));
}
poses.push_back(std::move(body));
}
auto skelmsg = generate_msg(poses, joint_names);
publisher_->publish(skelmsg);
}
// =================================================================================================
visualization_msgs::msg::MarkerArray SkeletonPublisher::generate_msg(
const std::vector<std::vector<std::array<float, 4>>> &poses,
const std::vector<std::string> &joint_names)
{
visualization_msgs::msg::MarkerArray marker_array;
visualization_msgs::msg::Marker marker;
marker.header.frame_id = "world";
marker.type = visualization_msgs::msg::Marker::LINE_LIST;
marker.action = visualization_msgs::msg::Marker::ADD;
marker.scale.x = 0.02;
marker.id = 0;
size_t num_bodies = poses.size();
for (size_t i = 0; i < num_bodies; ++i)
{
std_msgs::msg::ColorRGBA color;
double hue = static_cast<double>(i) / num_bodies;
auto rgb = hue_to_rgb(hue);
color.r = rgb[0] / 255.0;
color.g = rgb[1] / 255.0;
color.b = rgb[2] / 255.0;
color.a = 1.0;
for (size_t j = 0; j < connections.size(); ++j)
{
std::string joint1 = connections[j][0];
std::string joint2 = connections[j][1];
int sidx = -1;
int eidx = -1;
for (size_t k = 0; k < joint_names.size(); ++k)
{
if (joint_names[k] == joint1)
{
sidx = k;
}
if (joint_names[k] == joint2)
{
eidx = k;
}
}
if (sidx == -1 || eidx == -1)
{
continue;
}
if (poses[i][sidx][3] <= 0 || poses[i][eidx][3] <= 0)
{
continue;
}
geometry_msgs::msg::Point p1, p2;
p1.x = poses[i][sidx][0];
p1.y = poses[i][sidx][1];
p1.z = poses[i][sidx][2];
p2.x = poses[i][eidx][0];
p2.y = poses[i][eidx][1];
p2.z = poses[i][eidx][2];
marker.points.push_back(p1);
marker.points.push_back(p2);
marker.colors.push_back(color);
marker.colors.push_back(color);
}
}
if (num_bodies == 0)
{
// Create an invisible line to remove any old skeletons
std_msgs::msg::ColorRGBA color;
color.r = 0.0;
color.g = 0.0;
color.b = 0.0;
color.a = 0.0;
geometry_msgs::msg::Point p1, p2;
p1.x = 0.0;
p1.y = 0.0;
p1.z = 0.0;
p2.x = 0.0;
p2.y = 0.0;
p2.z = 0.001;
marker.points.push_back(p1);
marker.points.push_back(p2);
marker.colors.push_back(color);
marker.colors.push_back(color);
}
marker_array.markers.push_back(marker);
return marker_array;
}
// =================================================================================================
// =================================================================================================
int main(int argc, char *argv[])
{
rclcpp::init(argc, argv);
rclcpp::spin(std::make_shared<SkeletonPublisher>());
rclcpp::shutdown();
return 0;
}
@@ -1,335 +0,0 @@
#include <array>
#include <cstdint>
#include <map>
#include <string>
#include <vector>
#include <rclcpp/rclcpp.hpp>
#include <tf2_ros/transform_broadcaster.h>
#include <geometry_msgs/msg/transform_stamped.hpp>
#include "rpt_msgs/msg/poses.hpp"
// =================================================================================================
const std::string input_topic = "/poses/humans3d";
// =================================================================================================
// =================================================================================================
class SkeletonTFPublisher : public rclcpp::Node
{
public:
SkeletonTFPublisher() : Node("skeleton_tf_publisher")
{
broadcaster_ = std::make_shared<tf2_ros::TransformBroadcaster>(this);
subscriber_ = this->create_subscription<rpt_msgs::msg::Poses>(
input_topic, 1,
std::bind(&SkeletonTFPublisher::listener_callback, this, std::placeholders::_1));
pc_connections = {
// main joints
{"hip_middle", "shoulder_middle"},
{"shoulder_middle", "head"},
{"head", "nose"},
{"head", "eye_left"},
{"head", "eye_right"},
{"head", "ear_left"},
{"head", "ear_right"},
{"shoulder_middle", "shoulder_left"},
{"shoulder_middle", "shoulder_right"},
{"shoulder_left", "elbow_left"},
{"elbow_left", "wrist_left"},
{"hip_middle", "hip_left"},
{"hip_left", "knee_left"},
{"knee_left", "ankle_left"},
{"shoulder_right", "elbow_right"},
{"elbow_right", "wrist_right"},
{"hip_middle", "hip_right"},
{"hip_right", "knee_right"},
{"knee_right", "ankle_right"},
// whole-body joints
{"ankle_left", "foot_toe_big_left"},
{"ankle_left", "foot_toe_small_left"},
{"ankle_left", "foot_heel_left"},
{"ankle_right", "foot_toe_big_right"},
{"ankle_right", "foot_toe_small_right"},
{"ankle_right", "foot_heel_right"},
{"ear_right", "face_jaw_right_1"},
{"ear_right", "face_jaw_right_2"},
{"ear_right", "face_jaw_right_3"},
{"ear_right", "face_jaw_right_4"},
{"ear_right", "face_jaw_right_5"},
{"ear_right", "face_jaw_right_6"},
{"ear_right", "face_jaw_right_7"},
{"ear_right", "face_jaw_right_8"},
{"head", "face_jaw_middle"},
{"ear_left", "face_jaw_left_1"},
{"ear_left", "face_jaw_left_2"},
{"ear_left", "face_jaw_left_3"},
{"ear_left", "face_jaw_left_4"},
{"ear_left", "face_jaw_left_5"},
{"ear_left", "face_jaw_left_6"},
{"ear_left", "face_jaw_left_7"},
{"ear_left", "face_jaw_left_8"},
{"eye_right", "face_eyebrow_right_1"},
{"eye_right", "face_eyebrow_right_2"},
{"eye_right", "face_eyebrow_right_3"},
{"eye_right", "face_eyebrow_right_4"},
{"eye_right", "face_eyebrow_right_5"},
{"eye_left", "face_eyebrow_left_1"},
{"eye_left", "face_eyebrow_left_2"},
{"eye_left", "face_eyebrow_left_3"},
{"eye_left", "face_eyebrow_left_4"},
{"eye_left", "face_eyebrow_left_5"},
{"nose", "face_nose_1"},
{"nose", "face_nose_2"},
{"nose", "face_nose_3"},
{"nose", "face_nose_4"},
{"nose", "face_nose_5"},
{"nose", "face_nose_6"},
{"nose", "face_nose_7"},
{"nose", "face_nose_8"},
{"nose", "face_nose_9"},
{"eye_right", "face_eye_right_1"},
{"eye_right", "face_eye_right_2"},
{"eye_right", "face_eye_right_3"},
{"eye_right", "face_eye_right_4"},
{"eye_right", "face_eye_right_5"},
{"eye_right", "face_eye_right_6"},
{"eye_left", "face_eye_left_1"},
{"eye_left", "face_eye_left_2"},
{"eye_left", "face_eye_left_3"},
{"eye_left", "face_eye_left_4"},
{"eye_left", "face_eye_left_5"},
{"eye_left", "face_eye_left_6"},
{"head", "face_mouth_1"},
{"head", "face_mouth_2"},
{"head", "face_mouth_3"},
{"head", "face_mouth_4"},
{"head", "face_mouth_5"},
{"head", "face_mouth_6"},
{"head", "face_mouth_7"},
{"head", "face_mouth_8"},
{"head", "face_mouth_9"},
{"head", "face_mouth_10"},
{"head", "face_mouth_11"},
{"head", "face_mouth_12"},
{"head", "face_mouth_13"},
{"head", "face_mouth_14"},
{"head", "face_mouth_15"},
{"head", "face_mouth_16"},
{"head", "face_mouth_17"},
{"head", "face_mouth_18"},
{"head", "face_mouth_19"},
{"head", "face_mouth_20"},
{"wrist_left", "hand_wrist_left"},
{"wrist_left", "hand_finger_thumb_left_1"},
{"wrist_left", "hand_finger_thumb_left_2"},
{"wrist_left", "hand_finger_thumb_left_3"},
{"wrist_left", "hand_finger_thumb_left_4"},
{"wrist_left", "hand_finger_index_left_1"},
{"wrist_left", "hand_finger_index_left_2"},
{"wrist_left", "hand_finger_index_left_3"},
{"wrist_left", "hand_finger_index_left_4"},
{"wrist_left", "hand_finger_middle_left_1"},
{"wrist_left", "hand_finger_middle_left_2"},
{"wrist_left", "hand_finger_middle_left_3"},
{"wrist_left", "hand_finger_middle_left_4"},
{"wrist_left", "hand_finger_ring_left_1"},
{"wrist_left", "hand_finger_ring_left_2"},
{"wrist_left", "hand_finger_ring_left_3"},
{"wrist_left", "hand_finger_ring_left_4"},
{"wrist_left", "hand_finger_pinky_left_1"},
{"wrist_left", "hand_finger_pinky_left_2"},
{"wrist_left", "hand_finger_pinky_left_3"},
{"wrist_left", "hand_finger_pinky_left_4"},
{"wrist_right", "hand_wrist_right"},
{"wrist_right", "hand_finger_thumb_right_1"},
{"wrist_right", "hand_finger_thumb_right_2"},
{"wrist_right", "hand_finger_thumb_right_3"},
{"wrist_right", "hand_finger_thumb_right_4"},
{"wrist_right", "hand_finger_index_right_1"},
{"wrist_right", "hand_finger_index_right_2"},
{"wrist_right", "hand_finger_index_right_3"},
{"wrist_right", "hand_finger_index_right_4"},
{"wrist_right", "hand_finger_middle_right_1"},
{"wrist_right", "hand_finger_middle_right_2"},
{"wrist_right", "hand_finger_middle_right_3"},
{"wrist_right", "hand_finger_middle_right_4"},
{"wrist_right", "hand_finger_ring_right_1"},
{"wrist_right", "hand_finger_ring_right_2"},
{"wrist_right", "hand_finger_ring_right_3"},
{"wrist_right", "hand_finger_ring_right_4"},
{"wrist_right", "hand_finger_pinky_right_1"},
{"wrist_right", "hand_finger_pinky_right_2"},
{"wrist_right", "hand_finger_pinky_right_3"},
{"wrist_right", "hand_finger_pinky_right_4"},
};
for (auto &pair : pc_connections)
{
cp_map[pair[1]] = pair[0];
}
}
private:
std::vector<std::array<std::string, 2>> pc_connections;
std::map<std::string, std::string> cp_map;
std::shared_ptr<tf2_ros::TransformBroadcaster> broadcaster_;
rclcpp::Subscription<rpt_msgs::msg::Poses>::SharedPtr subscriber_;
void listener_callback(const rpt_msgs::msg::Poses::SharedPtr msg);
std::vector<geometry_msgs::msg::TransformStamped> generate_msg(
const std::vector<std::vector<std::array<float, 4>>> &poses,
const std::vector<std::string> &joint_names);
};
// =================================================================================================
void SkeletonTFPublisher::listener_callback(const rpt_msgs::msg::Poses::SharedPtr msg)
{
std::vector<std::vector<std::array<float, 4>>> poses;
const std::vector<std::string> &joint_names = msg->joint_names;
// Unflatten poses
size_t idx = 0;
std::vector<int32_t> &bshape = msg->bodies_shape;
for (int32_t i = 0; i < bshape[0]; ++i)
{
std::vector<std::array<float, 4>> body;
for (int32_t j = 0; j < bshape[1]; ++j)
{
std::array<float, 4> joint;
for (int32_t k = 0; k < bshape[2]; ++k)
{
joint[k] = msg->bodies_flat[idx];
++idx;
}
body.push_back(std::move(joint));
}
poses.push_back(std::move(body));
}
if (poses.empty())
{
return;
}
auto tf_msgs = generate_msg(poses, joint_names);
if (!tf_msgs.empty())
{
broadcaster_->sendTransform(tf_msgs);
}
}
// =================================================================================================
std::vector<geometry_msgs::msg::TransformStamped> SkeletonTFPublisher::generate_msg(
const std::vector<std::vector<std::array<float, 4>>> &poses,
const std::vector<std::string> &joint_names)
{
std::vector<geometry_msgs::msg::TransformStamped> tf_msgs;
tf_msgs.reserve(poses.size() * pc_connections.size());
rclcpp::Time now = this->get_clock()->now();
for (size_t i = 0; i < poses.size(); ++i)
{
const auto &body = poses[i];
// Find index of "hip_middle" in joint_names
int hip_mid_idx = -1;
for (size_t k = 0; k < joint_names.size(); ++k)
{
if (joint_names[k] == "hip_middle")
{
hip_mid_idx = static_cast<int>(k);
break;
}
}
// Set "hip_middle" as child of "world"
const auto &joint = body[hip_mid_idx];
geometry_msgs::msg::TransformStamped tf_stamped;
tf_stamped.header.stamp = now;
tf_stamped.header.frame_id = "world";
tf_stamped.child_frame_id = "s" + std::to_string(i) + "-hip_middle";
tf_stamped.transform.translation.x = joint[0];
tf_stamped.transform.translation.y = joint[1];
tf_stamped.transform.translation.z = joint[2];
tf_stamped.transform.rotation.x = 0.0;
tf_stamped.transform.rotation.y = 0.0;
tf_stamped.transform.rotation.z = 0.0;
tf_stamped.transform.rotation.w = 1.0;
tf_msgs.push_back(std::move(tf_stamped));
// Add other joints
for (size_t j = 0; j < body.size(); ++j)
{
// Skip "hip_middle"
if (static_cast<int>(j) == hip_mid_idx)
{
continue;
}
const auto &joint_child = body[j];
const std::string &child_name = joint_names[j];
float conf = joint_child[3];
if (conf <= 0.0f)
{
continue;
}
// Look up its parent
auto it = cp_map.find(child_name);
if (it == cp_map.end())
{
continue;
}
const std::string &parent_name = it->second;
// Ensure the parent frame was actually published
int parent_idx = -1;
for (size_t k = 0; k < joint_names.size(); ++k)
{
if (joint_names[k] == parent_name)
{
parent_idx = static_cast<int>(k);
break;
}
}
if (parent_name != "hip_middle" && body[parent_idx][3] <= 0.0f)
{
// Parent not visible, skip this child
continue;
}
const auto &joint_parent = body[parent_idx];
// Publish child frame
geometry_msgs::msg::TransformStamped tf_stamped;
tf_stamped.header.stamp = now;
tf_stamped.header.frame_id = "s" + std::to_string(i) + "-" + parent_name;
tf_stamped.child_frame_id = "s" + std::to_string(i) + "-" + child_name;
tf_stamped.transform.translation.x = joint_child[0] - joint_parent[0];
tf_stamped.transform.translation.y = joint_child[1] - joint_parent[1];
tf_stamped.transform.translation.z = joint_child[2] - joint_parent[2];
tf_stamped.transform.rotation.x = 0.0;
tf_stamped.transform.rotation.y = 0.0;
tf_stamped.transform.rotation.z = 0.0;
tf_stamped.transform.rotation.w = 1.0;
tf_msgs.push_back(std::move(tf_stamped));
}
}
return tf_msgs;
}
// =================================================================================================
// =================================================================================================
int main(int argc, char *argv[])
{
rclcpp::init(argc, argv);
rclcpp::spin(std::make_shared<SkeletonTFPublisher>());
rclcpp::shutdown();
return 0;
}
@@ -1,64 +0,0 @@
cmake_minimum_required(VERSION 3.5)
project(rpt3d_wrapper_cpp)
# Default to C99
if(NOT CMAKE_C_STANDARD)
set(CMAKE_C_STANDARD 99)
endif()
# Default to C++20
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 20)
endif()
if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
add_compile_options(-Wall -Wextra -Wpedantic)
endif()
# Find dependencies
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(rpt_msgs REQUIRED)
find_package(std_msgs REQUIRED)
# Add RapidPoseTriangulation implementation
set(RAPID_POSE_TRIANGULATION_DIR "/RapidPoseTriangulation")
add_library(rapid_pose_triangulation
${RAPID_POSE_TRIANGULATION_DIR}/rpt/camera.cpp
${RAPID_POSE_TRIANGULATION_DIR}/rpt/interface.cpp
${RAPID_POSE_TRIANGULATION_DIR}/rpt/triangulator.cpp
)
target_include_directories(rapid_pose_triangulation PUBLIC
${RAPID_POSE_TRIANGULATION_DIR}/extras/include
${RAPID_POSE_TRIANGULATION_DIR}/rpt
)
target_compile_options(rapid_pose_triangulation PUBLIC
-fPIC -O3 -march=native -Wall -Werror
)
# Build the executable
add_executable(rpt3d_wrapper src/rpt3d_wrapper.cpp)
ament_target_dependencies(rpt3d_wrapper rclcpp std_msgs rpt_msgs)
target_include_directories(rpt3d_wrapper PUBLIC
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
$<INSTALL_INTERFACE:include>)
target_link_libraries(rpt3d_wrapper
rapid_pose_triangulation
)
install(TARGETS rpt3d_wrapper
DESTINATION lib/${PROJECT_NAME})
if(BUILD_TESTING)
find_package(ament_lint_auto REQUIRED)
# the following line skips the linter which checks for copyrights
# uncomment the line when a copyright and license is not present in all source files
#set(ament_cmake_copyright_FOUND TRUE)
# the following line skips cpplint (only works in a git repo)
# uncomment the line when this package is not in a git repo
#set(ament_cmake_cpplint_FOUND TRUE)
ament_lint_auto_find_test_dependencies()
endif()
ament_package()
-24
View File
@@ -1,24 +0,0 @@
<?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>rpt3d_wrapper_cpp</name>
<version>0.0.0</version>
<description>TODO: Package description</description>
<maintainer email="root@todo.todo">root</maintainer>
<license>TODO: License declaration</license>
<buildtool_depend>ament_cmake</buildtool_depend>
<depend>rclcpp</depend>
<depend>rpt_msgs</depend>
<depend>std_msgs</depend>
<depend>OpenCV</depend>
<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
@@ -1,323 +0,0 @@
#include <atomic>
#include <chrono>
#include <iostream>
#include <mutex>
#include <string>
#include <vector>
// ROS2
#include <rclcpp/rclcpp.hpp>
#include <std_msgs/msg/string.hpp>
// JSON library
#include "/RapidPoseTriangulation/extras/include/nlohmann/json.hpp"
using json = nlohmann::json;
#include "rpt_msgs/msg/poses.hpp"
#include "/RapidPoseTriangulation/rpt/camera.hpp"
#include "/RapidPoseTriangulation/rpt/interface.hpp"
#include "/RapidPoseTriangulation/rpt/tracker.hpp"
// =================================================================================================
static const std::vector<std::string> cam_ids = {
"camera01",
"camera02",
"camera03",
"camera04",
"camera05",
"camera06",
"camera07",
"camera08",
"camera09",
"camera10",
};
static const std::string pose_in_topic = "/poses/{}";
static const std::string cam_info_topic = "/{}/calibration";
static const std::string pose_out_topic = "/poses/humans3d";
static const float min_match_score = 0.94;
static const size_t min_group_size = 4;
static const bool use_tracking = true;
static const float max_movement_speed = 2.0 * 1.5;
static const float cam_fps = 50;
static const float max_track_distance = 0.3 + max_movement_speed / cam_fps;
static const std::array<std::array<float, 3>, 2> roomparams = {{
{4.0, 5.0, 2.2},
{1.0, 0.0, 1.1},
}};
// =================================================================================================
// =================================================================================================
class Rpt3DWrapperNode : public rclcpp::Node
{
public:
Rpt3DWrapperNode()
: Node("rpt3d_wrapper")
{
this->all_cameras.resize(cam_ids.size());
this->all_poses.resize(cam_ids.size());
this->all_timestamps.resize(cam_ids.size());
this->joint_names = {};
this->all_poses_set.resize(cam_ids.size(), false);
// Load 3D models
pose_tracker = std::make_unique<PoseTracker>(
max_movement_speed, max_track_distance);
// QoS
rclcpp::QoS qos_profile(1);
qos_profile.reliable();
qos_profile.keep_last(1);
// Parallel executable callbacks
auto my_callback_group = create_callback_group(rclcpp::CallbackGroupType::Reentrant);
rclcpp::SubscriptionOptions options;
options.callback_group = my_callback_group;
// Setup subscribers
for (size_t i = 0; i < cam_ids.size(); i++)
{
std::string cam_id = cam_ids[i];
std::string topic_pose = pose_in_topic;
std::string topic_cam = cam_info_topic;
topic_pose.replace(topic_pose.find("{}"), 2, cam_id);
topic_cam.replace(topic_cam.find("{}"), 2, cam_id);
auto sub_pose = this->create_subscription<rpt_msgs::msg::Poses>(
topic_pose, qos_profile,
[this, i](const rpt_msgs::msg::Poses::SharedPtr msg)
{
this->callback_poses(msg, i);
},
options);
sub_pose_list_.push_back(sub_pose);
auto sub_cam = this->create_subscription<std_msgs::msg::String>(
topic_cam, qos_profile,
[this, i](const std_msgs::msg::String::SharedPtr msg)
{
this->callback_cam_info(msg, i);
},
options);
sub_cam_list_.push_back(sub_cam);
}
// Setup publisher
pose_pub_ = this->create_publisher<rpt_msgs::msg::Poses>(pose_out_topic, qos_profile);
RCLCPP_INFO(this->get_logger(), "Finished initialization of pose triangulator.");
}
private:
std::vector<rclcpp::Subscription<rpt_msgs::msg::Poses>::SharedPtr> sub_pose_list_;
std::vector<rclcpp::Subscription<std_msgs::msg::String>::SharedPtr> sub_cam_list_;
rclcpp::Publisher<rpt_msgs::msg::Poses>::SharedPtr pose_pub_;
std::unique_ptr<PoseTracker> pose_tracker;
std::vector<Camera> all_cameras;
std::mutex cams_mutex, pose_mutex, model_mutex;
std::vector<std::vector<std::vector<std::array<float, 3>>>> all_poses;
std::vector<double> all_timestamps;
std::vector<std::string> joint_names;
std::vector<bool> all_poses_set;
void callback_poses(const rpt_msgs::msg::Poses::SharedPtr msg, size_t cam_idx);
void callback_cam_info(const std_msgs::msg::String::SharedPtr msg, size_t cam_idx);
void call_model();
};
// =================================================================================================
void Rpt3DWrapperNode::callback_cam_info(const std_msgs::msg::String::SharedPtr msg, size_t cam_idx)
{
json cam = json::parse(msg->data);
Camera camera;
camera.name = cam["name"].get<std::string>();
camera.K = cam["K"].get<std::array<std::array<float, 3>, 3>>();
camera.DC = cam["DC"].get<std::vector<float>>();
camera.R = cam["R"].get<std::array<std::array<float, 3>, 3>>();
camera.T = cam["T"].get<std::array<std::array<float, 1>, 3>>();
camera.width = cam["width"].get<int>();
camera.height = cam["height"].get<int>();
camera.type = cam["type"].get<std::string>();
cams_mutex.lock();
all_cameras[cam_idx] = camera;
cams_mutex.unlock();
}
// =================================================================================================
void Rpt3DWrapperNode::callback_poses(const rpt_msgs::msg::Poses::SharedPtr msg, size_t cam_idx)
{
std::vector<std::vector<std::array<float, 3>>> poses;
if (this->joint_names.empty())
{
this->joint_names = msg->joint_names;
}
// Unflatten poses
size_t idx = 0;
std::vector<int32_t> &bshape = msg->bodies_shape;
for (int32_t i = 0; i < bshape[0]; ++i)
{
std::vector<std::array<float, 3>> body;
for (int32_t j = 0; j < bshape[1]; ++j)
{
std::array<float, 3> joint;
for (int32_t k = 0; k < bshape[2]; ++k)
{
joint[k] = msg->bodies_flat[idx];
++idx;
}
body.push_back(std::move(joint));
}
poses.push_back(std::move(body));
}
// If no pose was detected, create an empty placeholder
if (poses.size() == 0)
{
std::vector<std::array<float, 3>> body(joint_names.size(), {0, 0, 0});
poses.push_back(std::move(body));
}
pose_mutex.lock();
this->all_poses[cam_idx] = std::move(poses);
this->all_poses_set[cam_idx] = true;
this->all_timestamps[cam_idx] = msg->header.stamp.sec + msg->header.stamp.nanosec / 1.0e9;
pose_mutex.unlock();
// Trigger model callback
model_mutex.lock();
call_model();
model_mutex.unlock();
}
// =================================================================================================
void Rpt3DWrapperNode::call_model()
{
auto ts_msg = std::chrono::high_resolution_clock::now();
// Check if all cameras are set
cams_mutex.lock();
for (size_t i = 0; i < cam_ids.size(); i++)
{
if (all_cameras[i].name.empty())
{
RCLCPP_WARN(this->get_logger(), "Skipping frame, still waiting for cameras...");
cams_mutex.unlock();
return;
}
}
cams_mutex.unlock();
// If not all poses are set, return and wait for the others
pose_mutex.lock();
for (size_t i = 0; i < cam_ids.size(); i++)
{
if (!this->all_poses_set[i])
{
pose_mutex.unlock();
return;
}
}
pose_mutex.unlock();
// Call model, and meanwhile lock updates of the inputs
// Since the prediction is very fast, parallel callback threads only need to wait a short time
cams_mutex.lock();
pose_mutex.lock();
PoseBatch2D pose_batch_2d = PoseBatch2D::from_nested(all_poses);
auto poses_3d = triangulate_poses(
pose_batch_2d, all_cameras, roomparams, joint_names, min_match_score, min_group_size)
.to_nested();
double min_ts = *std::min_element(all_timestamps.begin(), all_timestamps.end());
this->all_poses_set = std::vector<bool>(cam_ids.size(), false);
std::vector<std::vector<std::array<float, 4>>> valid_poses;
std::vector<size_t> track_ids;
if (use_tracking)
{
auto pose_tracks = pose_tracker->track_poses(poses_3d, joint_names, min_ts);
std::vector<std::vector<std::array<float, 4>>> poses_3d_refined;
for (size_t j = 0; j < pose_tracks.size(); j++)
{
auto &pose = std::get<1>(pose_tracks[j]);
poses_3d_refined.push_back(pose);
auto &track_id = std::get<0>(pose_tracks[j]);
track_ids.push_back(track_id);
}
valid_poses = std::move(poses_3d_refined);
}
else
{
valid_poses = std::move(poses_3d);
track_ids = {};
}
pose_mutex.unlock();
cams_mutex.unlock();
// Calculate timings
auto ts_pose = std::chrono::high_resolution_clock::now();
double ts_pose_sec = std::chrono::duration<double>(ts_pose.time_since_epoch()).count();
double z_trigger_pose3d = ts_pose_sec - min_ts;
json jdata;
jdata["timestamps"] = {
{"trigger", min_ts},
{"pose3d", ts_pose_sec},
{"z-trigger-pose3d", z_trigger_pose3d}};
// Publish message
auto pose_msg = rpt_msgs::msg::Poses();
pose_msg.header.stamp.sec = static_cast<int>(min_ts);
pose_msg.header.stamp.nanosec = (min_ts - pose_msg.header.stamp.sec) * 1.0e9;
pose_msg.header.frame_id = "world";
std::vector<int32_t> pshape = {(int)valid_poses.size(), (int)joint_names.size(), 4};
pose_msg.bodies_shape = pshape;
pose_msg.bodies_flat.reserve(pshape[0] * pshape[1] * pshape[2]);
for (int32_t i = 0; i < pshape[0]; i++)
{
for (int32_t j = 0; j < pshape[1]; j++)
{
for (int32_t k = 0; k < pshape[2]; k++)
{
pose_msg.bodies_flat.push_back(valid_poses[i][j][k]);
}
}
}
pose_msg.joint_names = joint_names;
jdata["track_ids"] = track_ids;
pose_msg.extra_data = jdata.dump();
pose_pub_->publish(pose_msg);
// Print info
double elapsed_time = std::chrono::duration<double>(
std::chrono::high_resolution_clock::now() - ts_msg)
.count();
std::cout << "Detected persons: " << valid_poses.size()
<< " - Prediction time: " << elapsed_time << "s" << std::endl;
}
// =================================================================================================
// =================================================================================================
int main(int argc, char **argv)
{
rclcpp::init(argc, argv);
auto node = std::make_shared<Rpt3DWrapperNode>();
rclcpp::executors::MultiThreadedExecutor exec;
exec.add_node(node);
exec.spin();
rclcpp::shutdown();
return 0;
}
-33
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@@ -1,33 +0,0 @@
cmake_minimum_required(VERSION 3.8)
project(rpt_msgs)
if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
add_compile_options(-Wall -Wextra -Wpedantic)
endif()
# find dependencies
find_package(ament_cmake REQUIRED)
find_package(std_msgs REQUIRED)
find_package(rosidl_default_generators REQUIRED)
# uncomment the following section in order to fill in
# further dependencies manually.
# find_package(<dependency> REQUIRED)
rosidl_generate_interfaces(${PROJECT_NAME}
"msg/Poses.msg"
DEPENDENCIES std_msgs
)
if(BUILD_TESTING)
find_package(ament_lint_auto REQUIRED)
# the following line skips the linter which checks for copyrights
# comment the line when a copyright and license is added to all source files
set(ament_cmake_copyright_FOUND TRUE)
# the following line skips cpplint (only works in a git repo)
# comment the line when this package is in a git repo and when
# a copyright and license is added to all source files
set(ament_cmake_cpplint_FOUND TRUE)
ament_lint_auto_find_test_dependencies()
endif()
ament_package()
-7
View File
@@ -1,7 +0,0 @@
std_msgs/Header header
float32[] bodies_flat
int32[] bodies_shape
string[] joint_names
string extra_data
-23
View File
@@ -1,23 +0,0 @@
<?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>rpt_msgs</name>
<version>0.0.0</version>
<description>TODO: Package description</description>
<maintainer email="root@todo.todo">root</maintainer>
<license>TODO: License declaration</license>
<depend>std_msgs</depend>
<buildtool_depend>rosidl_default_generators</buildtool_depend>
<exec_depend>rosidl_default_runtime</exec_depend>
<member_of_group>rosidl_interface_packages</member_of_group>
<buildtool_depend>ament_cmake</buildtool_depend>
<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
+19
View File
@@ -0,0 +1,19 @@
#pragma once
#include <array>
#include "camera.hpp"
struct CachedCamera
{
const Camera cam;
const std::array<std::array<float, 3>, 3> invR;
const std::array<float, 3> center;
const std::array<std::array<float, 3>, 3> newK;
const std::array<std::array<float, 3>, 3> invK;
};
CachedCamera cache_camera(const Camera &camera);
void undistort_point_pinhole(std::array<float, 3> &point, const std::array<float, 5> &distortion);
void undistort_point_fisheye(std::array<float, 3> &point, const std::array<float, 5> &distortion);
+97 -78
View File
@@ -1,11 +1,60 @@
#include <array>
#include <cmath>
#include <iomanip>
#include <stdexcept>
#include <sstream>
#include <vector>
#include <cmath>
#include "camera.hpp"
#include "cached_camera.hpp"
namespace
{
std::array<std::array<float, 3>, 3> transpose3x3(const std::array<std::array<float, 3>, 3> &M)
{
return {{{M[0][0], M[1][0], M[2][0]},
{M[0][1], M[1][1], M[2][1]},
{M[0][2], M[1][2], M[2][2]}}};
}
std::array<std::array<float, 3>, 3> invert3x3(const std::array<std::array<float, 3>, 3> &M)
{
std::array<std::array<float, 3>, 3> adj = {
{{
M[1][1] * M[2][2] - M[1][2] * M[2][1],
M[0][2] * M[2][1] - M[0][1] * M[2][2],
M[0][1] * M[1][2] - M[0][2] * M[1][1],
},
{
M[1][2] * M[2][0] - M[1][0] * M[2][2],
M[0][0] * M[2][2] - M[0][2] * M[2][0],
M[0][2] * M[1][0] - M[0][0] * M[1][2],
},
{
M[1][0] * M[2][1] - M[1][1] * M[2][0],
M[0][1] * M[2][0] - M[0][0] * M[2][1],
M[0][0] * M[1][1] - M[0][1] * M[1][0],
}}};
float det = M[0][0] * adj[0][0] + M[0][1] * adj[1][0] + M[0][2] * adj[2][0];
if (std::fabs(det) < 1e-6f)
{
return {{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}};
}
float idet = 1.0f / det;
return {{
{{adj[0][0] * idet, adj[0][1] * idet, adj[0][2] * idet}},
{{adj[1][0] * idet, adj[1][1] * idet, adj[1][2] * idet}},
{{adj[2][0] * idet, adj[2][1] * idet, adj[2][2] * idet}},
}};
}
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_fisheye(
const Camera &cam, float balance, std::pair<int, int> new_size);
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_pinhole(
const Camera &cam, float alpha, std::pair<int, int> new_size);
} // namespace
// =================================================================================================
// =================================================================================================
@@ -63,7 +112,7 @@ std::string Camera::to_string() const
out << "'width': " << width << ", ";
out << "'height': " << height << ", ";
out << "'type': " << type;
out << "'model': '" << camera_model_name(model) << "'";
out << "}";
return out.str();
@@ -71,6 +120,33 @@ std::string Camera::to_string() const
// =================================================================================================
const char *camera_model_name(CameraModel model)
{
switch (model)
{
case CameraModel::Pinhole:
return "pinhole";
case CameraModel::Fisheye:
return "fisheye";
}
return "unknown";
}
CameraModel parse_camera_model(const std::string &value)
{
if (value == "pinhole")
{
return CameraModel::Pinhole;
}
if (value == "fisheye")
{
return CameraModel::Fisheye;
}
throw std::invalid_argument("Unsupported camera model: " + value);
}
// =================================================================================================
std::ostream &operator<<(std::ostream &out, const Camera &cam)
{
out << cam.to_string();
@@ -80,93 +156,33 @@ std::ostream &operator<<(std::ostream &out, const Camera &cam)
// =================================================================================================
// =================================================================================================
CameraInternal::CameraInternal(const Camera &cam)
CachedCamera cache_camera(const Camera &cam)
{
this->cam = cam;
this->invR = transpose3x3(cam.R);
const std::array<std::array<float, 3>, 3> invR = transpose3x3(cam.R);
// Camera center:
// C = -(Rᵀ * t) = -(Rᵀ * (R * (T * -1))) = -(Rᵀ * (R * -T)) = -(Rᵀ * -R * T) = -(-T) = T
this->center = {cam.T[0][0], cam.T[1][0], cam.T[2][0]};
const std::array<float, 3> center = {cam.T[0][0], cam.T[1][0], cam.T[2][0]};
// Undistort camera matrix
// As with the undistortion, the own implementation avoids some overhead compared to OpenCV
if (cam.type == "fisheye")
std::array<std::array<float, 3>, 3> newK;
if (cam.model == CameraModel::Fisheye)
{
newK = calc_optimal_camera_matrix_fisheye(1.0, {cam.width, cam.height});
newK = calc_optimal_camera_matrix_fisheye(cam, 1.0f, {cam.width, cam.height});
}
else
{
newK = calc_optimal_camera_matrix_pinhole(1.0, {cam.width, cam.height});
newK = calc_optimal_camera_matrix_pinhole(cam, 1.0f, {cam.width, cam.height});
}
this->invK = invert3x3(newK);
const std::array<std::array<float, 3>, 3> invK = invert3x3(newK);
return CachedCamera {cam, invR, center, newK, invK};
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::transpose3x3(
const std::array<std::array<float, 3>, 3> &M)
{
return {{{M[0][0], M[1][0], M[2][0]},
{M[0][1], M[1][1], M[2][1]},
{M[0][2], M[1][2], M[2][2]}}};
}
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::invert3x3(
const std::array<std::array<float, 3>, 3> &M)
{
// Compute the inverse using the adjugate method
// See: https://scicomp.stackexchange.com/a/29206
std::array<std::array<float, 3>, 3> adj = {
{{
M[1][1] * M[2][2] - M[1][2] * M[2][1],
M[0][2] * M[2][1] - M[0][1] * M[2][2],
M[0][1] * M[1][2] - M[0][2] * M[1][1],
},
{
M[1][2] * M[2][0] - M[1][0] * M[2][2],
M[0][0] * M[2][2] - M[0][2] * M[2][0],
M[0][2] * M[1][0] - M[0][0] * M[1][2],
},
{
M[1][0] * M[2][1] - M[1][1] * M[2][0],
M[0][1] * M[2][0] - M[0][0] * M[2][1],
M[0][0] * M[1][1] - M[0][1] * M[1][0],
}}};
float det = M[0][0] * adj[0][0] + M[0][1] * adj[1][0] + M[0][2] * adj[2][0];
if (std::fabs(det) < 1e-6f)
{
return {{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}};
}
float idet = 1.0f / det;
std::array<std::array<float, 3>, 3> inv = {
{{
adj[0][0] * idet,
adj[0][1] * idet,
adj[0][2] * idet,
},
{
adj[1][0] * idet,
adj[1][1] * idet,
adj[1][2] * idet,
},
{
adj[2][0] * idet,
adj[2][1] * idet,
adj[2][2] * idet,
}}};
return inv;
}
// =================================================================================================
void CameraInternal::undistort_point_pinhole(std::array<float, 3> &p, const std::vector<float> &k)
void undistort_point_pinhole(std::array<float, 3> &p, const std::array<float, 5> &k)
{
// Following: cv::cvUndistortPointsInternal
// Uses only the distortion coefficients: [k1, k2, p1, p2, k3]
@@ -202,7 +218,7 @@ void CameraInternal::undistort_point_pinhole(std::array<float, 3> &p, const std:
// =================================================================================================
void CameraInternal::undistort_point_fisheye(std::array<float, 3> &p, const std::vector<float> &k)
void undistort_point_fisheye(std::array<float, 3> &p, const std::array<float, 5> &k)
{
// Following: cv::fisheye::undistortPoints
// Uses only the distortion coefficients: [k1, k2, k3, k4]
@@ -250,8 +266,10 @@ void CameraInternal::undistort_point_fisheye(std::array<float, 3> &p, const std:
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_fisheye(
float balance, std::pair<int, int> new_size)
namespace
{
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_fisheye(
const Camera &cam, float balance, std::pair<int, int> new_size)
{
// Following: cv::fisheye::estimateNewCameraMatrixForUndistortRectify
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/fisheye.cpp#L630
@@ -355,8 +373,8 @@ std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_f
// =================================================================================================
std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_pinhole(
float alpha, std::pair<int, int> new_size)
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_pinhole(
const Camera &cam, float alpha, std::pair<int, int> new_size)
{
// Following: cv::getOptimalNewCameraMatrix
// https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/calibration_base.cpp#L1565
@@ -479,3 +497,4 @@ std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_p
{0.0, 0.0, 1.0}}};
return newK;
}
} // namespace
+13 -31
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@@ -7,46 +7,28 @@
// =================================================================================================
enum class CameraModel
{
Pinhole,
Fisheye,
};
const char *camera_model_name(CameraModel model);
CameraModel parse_camera_model(const std::string &value);
// =================================================================================================
struct Camera
{
std::string name;
std::array<std::array<float, 3>, 3> K;
std::vector<float> DC;
std::array<float, 5> DC = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
std::array<std::array<float, 3>, 3> R;
std::array<std::array<float, 1>, 3> T;
int width;
int height;
std::string type;
CameraModel model = CameraModel::Pinhole;
friend std::ostream &operator<<(std::ostream &out, Camera const &camera);
std::string to_string() const;
};
// =================================================================================================
class CameraInternal
{
public:
CameraInternal(const Camera &cam);
Camera cam;
std::array<std::array<float, 3>, 3> invR;
std::array<float, 3> center;
std::array<std::array<float, 3>, 3> newK;
std::array<std::array<float, 3>, 3> invK;
static std::array<std::array<float, 3>, 3> transpose3x3(
const std::array<std::array<float, 3>, 3> &M);
static std::array<std::array<float, 3>, 3> invert3x3(
const std::array<std::array<float, 3>, 3> &M);
static void undistort_point_pinhole(std::array<float, 3> &p, const std::vector<float> &k);
static void undistort_point_fisheye(std::array<float, 3> &p, const std::vector<float> &k);
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_fisheye(
float balance, std::pair<int, int> new_size);
std::array<std::array<float, 3>, 3> calc_optimal_camera_matrix_pinhole(
float alpha, std::pair<int, int> new_size);
};
+10
View File
@@ -38,6 +38,11 @@ const float &PoseBatch2DView::at(size_t view, size_t person, size_t joint, size_
return data[pose2d_offset(view, person, joint, coord, max_persons, num_joints)];
}
const float &PoseBatch3DView::at(size_t person, size_t joint, size_t coord) const
{
return data[pose3d_offset(person, joint, coord, num_joints)];
}
const float &PoseBatch2D::at(size_t view, size_t person, size_t joint, size_t coord) const
{
return data[pose2d_offset(view, person, joint, coord, max_persons, num_joints)];
@@ -109,6 +114,11 @@ const float &PoseBatch3D::at(size_t person, size_t joint, size_t coord) const
return data[pose3d_offset(person, joint, coord, num_joints)];
}
PoseBatch3DView PoseBatch3D::view() const
{
return PoseBatch3DView {data.data(), num_persons, num_joints};
}
NestedPoses3D PoseBatch3D::to_nested() const
{
NestedPoses3D poses_3d(num_persons);
+159 -7
View File
@@ -25,6 +25,15 @@ struct PoseBatch2DView
const float &at(size_t view, size_t person, size_t joint, size_t coord) const;
};
struct PoseBatch3DView
{
const float *data = nullptr;
size_t num_persons = 0;
size_t num_joints = 0;
const float &at(size_t person, size_t joint, size_t coord) const;
};
struct PoseBatch2D
{
std::vector<float> data;
@@ -48,6 +57,7 @@ struct PoseBatch3D
float &at(size_t person, size_t joint, size_t coord);
const float &at(size_t person, size_t joint, size_t coord) const;
PoseBatch3DView view() const;
NestedPoses3D to_nested() const;
static PoseBatch3D from_nested(const NestedPoses3D &poses_3d);
@@ -55,6 +65,142 @@ struct PoseBatch3D
// =================================================================================================
struct PairCandidate
{
int view1 = -1;
int view2 = -1;
int person1 = -1;
int person2 = -1;
int global_person1 = -1;
int global_person2 = -1;
};
struct PreviousPoseMatch
{
int previous_pose_index = -1;
float score_view1 = 0.0f;
float score_view2 = 0.0f;
bool matched_view1 = false;
bool matched_view2 = false;
bool kept = true;
std::string decision = "unfiltered";
};
struct PreviousPoseFilterDebug
{
bool used_previous_poses = false;
std::vector<PreviousPoseMatch> matches;
std::vector<int> kept_pair_indices;
std::vector<PairCandidate> kept_pairs;
};
struct CoreProposalDebug
{
int pair_index = -1;
PairCandidate pair;
std::vector<std::array<float, 4>> pose_3d;
float score = 0.0f;
bool kept = false;
std::string drop_reason = "uninitialized";
};
struct ProposalGroupDebug
{
std::array<float, 3> center = {0.0f, 0.0f, 0.0f};
std::vector<std::array<float, 4>> pose_3d;
std::vector<int> proposal_indices;
};
struct GroupingDebug
{
std::vector<ProposalGroupDebug> initial_groups;
std::vector<int> duplicate_pair_drops;
std::vector<ProposalGroupDebug> groups;
};
struct FullProposalDebug
{
int source_core_proposal_index = -1;
PairCandidate pair;
std::vector<std::array<float, 4>> pose_3d;
};
struct MergeDebug
{
std::vector<std::vector<std::array<float, 4>>> merged_poses;
std::vector<std::vector<int>> group_proposal_indices;
};
struct TriangulationTrace
{
std::vector<PairCandidate> pairs;
PreviousPoseFilterDebug previous_filter;
std::vector<CoreProposalDebug> core_proposals;
GroupingDebug grouping;
std::vector<FullProposalDebug> full_proposals;
MergeDebug merge;
PoseBatch3D final_poses;
};
// =================================================================================================
struct TriangulationOptions
{
float min_match_score = 0.95f;
size_t min_group_size = 1;
};
// =================================================================================================
std::vector<PairCandidate> build_pair_candidates(const PoseBatch2DView &poses_2d);
PreviousPoseFilterDebug filter_pairs_with_previous_poses(
const PoseBatch2DView &poses_2d,
const std::vector<Camera> &cameras,
const std::vector<std::string> &joint_names,
const PoseBatch3DView &previous_poses_3d,
const TriangulationOptions &options = {});
inline PreviousPoseFilterDebug filter_pairs_with_previous_poses(
const PoseBatch2D &poses_2d,
const std::vector<Camera> &cameras,
const std::vector<std::string> &joint_names,
const PoseBatch3D &previous_poses_3d,
const TriangulationOptions &options = {})
{
return filter_pairs_with_previous_poses(
poses_2d.view(), cameras, joint_names, previous_poses_3d.view(), options);
}
TriangulationTrace triangulate_debug(
const PoseBatch2DView &poses_2d,
const std::vector<Camera> &cameras,
const std::array<std::array<float, 3>, 2> &roomparams,
const std::vector<std::string> &joint_names,
const PoseBatch3DView *previous_poses_3d = nullptr,
const TriangulationOptions &options = {});
inline TriangulationTrace triangulate_debug(
const PoseBatch2D &poses_2d,
const std::vector<Camera> &cameras,
const std::array<std::array<float, 3>, 2> &roomparams,
const std::vector<std::string> &joint_names,
const PoseBatch3D *previous_poses_3d = nullptr,
const TriangulationOptions &options = {})
{
PoseBatch3DView previous_view_storage;
const PoseBatch3DView *previous_view = nullptr;
if (previous_poses_3d != nullptr)
{
previous_view_storage = previous_poses_3d->view();
previous_view = &previous_view_storage;
}
return triangulate_debug(
poses_2d.view(), cameras, roomparams, joint_names, previous_view, options);
}
// =================================================================================================
/**
* Calculate a triangulation using a padded pose tensor.
*
@@ -62,8 +208,7 @@ struct PoseBatch3D
* @param cameras List of cameras.
* @param roomparams Room parameters (room size, room center).
* @param joint_names List of 2D joint names.
* @param min_match_score Minimum score to consider a triangulated joint as valid.
* @param min_group_size Minimum number of camera pairs that need to see a person.
* @param options Triangulation options.
*
* @return Pose tensor of shape [persons, joints, 4].
*/
@@ -72,17 +217,24 @@ PoseBatch3D triangulate_poses(
const std::vector<Camera> &cameras,
const std::array<std::array<float, 3>, 2> &roomparams,
const std::vector<std::string> &joint_names,
float min_match_score = 0.95f,
size_t min_group_size = 1);
const PoseBatch3DView *previous_poses_3d = nullptr,
const TriangulationOptions &options = {});
inline PoseBatch3D triangulate_poses(
const PoseBatch2D &poses_2d,
const std::vector<Camera> &cameras,
const std::array<std::array<float, 3>, 2> &roomparams,
const std::vector<std::string> &joint_names,
float min_match_score = 0.95f,
size_t min_group_size = 1)
const PoseBatch3D *previous_poses_3d = nullptr,
const TriangulationOptions &options = {})
{
PoseBatch3DView previous_view_storage;
const PoseBatch3DView *previous_view = nullptr;
if (previous_poses_3d != nullptr)
{
previous_view_storage = previous_poses_3d->view();
previous_view = &previous_view_storage;
}
return triangulate_poses(
poses_2d.view(), cameras, roomparams, joint_names, min_match_score, min_group_size);
poses_2d.view(), cameras, roomparams, joint_names, previous_view, options);
}
-325
View File
@@ -1,325 +0,0 @@
#pragma once
#include <array>
#include <string>
#include <vector>
#include <algorithm>
#include <limits>
#include <cmath>
#include <iostream>
// =================================================================================================
struct Track
{
std::vector<std::vector<std::array<float, 4>>> core_poses;
std::vector<std::vector<std::array<float, 4>>> full_poses;
std::vector<double> timestamps;
size_t id;
};
// =================================================================================================
class PoseTracker
{
public:
PoseTracker(float max_movement_speed, float max_distance);
std::vector<std::tuple<size_t, std::vector<std::array<float, 4>>>> track_poses(
const std::vector<std::vector<std::array<float, 4>>> &poses_3d,
const std::vector<std::string> &joint_names,
const double timestamp);
void reset();
private:
float max_distance;
float max_movement_speed;
size_t history_size = 3;
std::vector<double> timestamps;
std::vector<Track> pose_tracks;
const std::vector<std::string> core_joints = {
"shoulder_left",
"shoulder_right",
"hip_left",
"hip_right",
"elbow_left",
"elbow_right",
"knee_left",
"knee_right",
"wrist_left",
"wrist_right",
"ankle_left",
"ankle_right",
};
std::tuple<int, float> match_to_track(const std::vector<std::array<float, 4>> &core_pose_3d);
std::vector<std::array<float, 4>> refine_pose(const Track &track);
};
// =================================================================================================
// =================================================================================================
PoseTracker::PoseTracker(float max_movement_speed, float max_distance)
{
this->max_movement_speed = max_movement_speed;
this->max_distance = max_distance;
}
// =================================================================================================
void PoseTracker::reset()
{
pose_tracks.clear();
timestamps.clear();
}
// =================================================================================================
std::vector<std::tuple<size_t, std::vector<std::array<float, 4>>>> PoseTracker::track_poses(
const std::vector<std::vector<std::array<float, 4>>> &poses_3d,
const std::vector<std::string> &joint_names,
const double timestamp)
{
// Extract core joints
std::vector<size_t> core_joint_idx;
for (const auto &joint : core_joints)
{
auto it = std::find(joint_names.begin(), joint_names.end(), joint);
core_joint_idx.push_back(std::distance(joint_names.begin(), it));
}
std::vector<std::vector<std::array<float, 4>>> core_poses;
core_poses.resize(poses_3d.size());
for (size_t i = 0; i < poses_3d.size(); ++i)
{
core_poses[i].resize(core_joint_idx.size());
for (size_t j = 0; j < core_joint_idx.size(); ++j)
{
for (size_t k = 0; k < 4; ++k)
{
core_poses[i][j][k] = poses_3d[i][core_joint_idx[j]][k];
}
}
}
// Match core poses to tracks
std::vector<std::tuple<size_t, int, float>> matches;
for (size_t i = 0; i < core_poses.size(); ++i)
{
auto [track_idx, distance_sq] = match_to_track(core_poses[i]);
matches.emplace_back(i, track_idx, distance_sq);
}
std::sort(matches.begin(), matches.end(),
[](const auto &a, const auto &b)
{ return std::get<2>(a) < std::get<2>(b); });
// If track is matched multiple times, only add the best and create new tracks for the rest
std::vector<bool> used(pose_tracks.size(), false);
for (size_t i = 0; i < matches.size(); ++i)
{
auto [pose_idx, track_idx, distance_sq] = matches[i];
if (track_idx != -1 && !used[track_idx])
{
used[track_idx] = true;
}
else
{
std::get<1>(matches[i]) = -1;
}
}
// Update tracks
for (size_t i = 0; i < matches.size(); ++i)
{
auto [pose_idx, track_idx, distance_sq] = matches[i];
if (track_idx == -1)
{
// Create a new track
Track new_track;
new_track.core_poses.push_back(core_poses[pose_idx]);
new_track.full_poses.push_back(poses_3d[pose_idx]);
new_track.timestamps.push_back(timestamp);
new_track.id = pose_tracks.size();
pose_tracks.push_back(new_track);
}
else
{
// Update existing track
auto &track = pose_tracks[track_idx];
track.core_poses.push_back(core_poses[pose_idx]);
track.full_poses.push_back(poses_3d[pose_idx]);
track.timestamps.push_back(timestamp);
}
}
// Remove old track entries
timestamps.push_back(timestamp);
if (timestamps.size() > history_size)
{
timestamps.erase(timestamps.begin());
}
double max_age = timestamps.front();
for (size_t i = 0; i < pose_tracks.size();)
{
auto &track = pose_tracks[i];
for (size_t j = 0; j < track.timestamps.size();)
{
double ts = track.timestamps[j];
if (ts < max_age)
{
track.core_poses.erase(track.core_poses.begin() + j);
track.full_poses.erase(track.full_poses.begin() + j);
track.timestamps.erase(track.timestamps.begin() + j);
}
else
{
j++;
}
}
if (track.timestamps.size() == 0)
{
pose_tracks.erase(pose_tracks.begin() + i);
}
else
{
++i;
}
}
// Refine poses
std::vector<std::tuple<size_t, std::vector<std::array<float, 4>>>> tracked_poses;
for (size_t i = 0; i < pose_tracks.size(); ++i)
{
auto &track = pose_tracks[i];
// Create a refined pose for current tracks, or old tracks with a bit history,
// to avoid continuing tracks of flickering persons
if (track.core_poses.size() >= std::ceil(history_size / 2.0) ||
track.timestamps.back() == timestamps.back())
{
std::vector<std::array<float, 4>> refined_pose = refine_pose(track);
tracked_poses.emplace_back(track.id, refined_pose);
}
}
return tracked_poses;
}
// =================================================================================================
std::tuple<int, float> PoseTracker::match_to_track(
const std::vector<std::array<float, 4>> &core_pose_3d)
{
int best_track = -1;
float best_distance_sq = max_distance * max_distance;
for (size_t i = 0; i < pose_tracks.size(); ++i)
{
const auto &track = pose_tracks[i];
if (track.core_poses.size() == 0)
{
continue;
}
// Calculate distance to the last pose in the track
const auto &last_pose = track.core_poses.back();
float distance_sq = 0.0;
for (size_t j = 0; j < core_pose_3d.size(); ++j)
{
float dx = core_pose_3d[j][0] - last_pose[j][0];
float dy = core_pose_3d[j][1] - last_pose[j][1];
float dz = core_pose_3d[j][2] - last_pose[j][2];
distance_sq += dx * dx + dy * dy + dz * dz;
}
distance_sq /= core_pose_3d.size();
if (distance_sq < best_distance_sq)
{
best_distance_sq = distance_sq;
best_track = static_cast<int>(i);
}
}
return {best_track, best_distance_sq};
}
// =================================================================================================
std::vector<std::array<float, 4>> PoseTracker::refine_pose(const Track &track)
{
// Restrict maximum movement by physical constraints, by clipping the pose to the maximum
// movement distance from one of the track's history poses
//
// While advanced sensor filtering techniques, like using a Kalman-Filter, might improve the
// average accuracy of the pose, they introduce update delays on fast movement changes. For
// example, if a person stands still for a while and then suddenly moves, the first few frames
// will likely be treated as outliers, since the filter will not be able to adapt fast enough.
// This behaviour is not desired in a real-time critical applications, where the pose needs to
// be updated to the real physical position of the person as fast as possible. Therefore, only
// the movement speed is limited here.
if (track.core_poses.size() < 2)
{
return track.full_poses.back();
}
// Calculate maximum possible movement distance from each history pose
std::vector<float> max_movement_dists_sq;
max_movement_dists_sq.resize(track.core_poses.size() - 1);
double last_timestamp = track.timestamps.back();
for (size_t i = 0; i < track.core_poses.size() - 1; ++i)
{
double ts = track.timestamps[i];
float max_movement = max_movement_speed * (last_timestamp - ts);
max_movement_dists_sq[i] = max_movement * max_movement;
}
// Clip joint if required
std::vector<std::array<float, 4>> refined_pose = track.full_poses.back();
for (size_t i = 0; i < refined_pose.size(); ++i)
{
float min_dist_sq = std::numeric_limits<float>::infinity();
size_t closest_idx = 0;
bool clip = true;
for (size_t j = 0; j < max_movement_dists_sq.size(); ++j)
{
float dx = refined_pose[i][0] - track.full_poses[j][i][0];
float dy = refined_pose[i][1] - track.full_poses[j][i][1];
float dz = refined_pose[i][2] - track.full_poses[j][i][2];
float dist_sq = dx * dx + dy * dy + dz * dz;
if (dist_sq < min_dist_sq)
{
min_dist_sq = dist_sq;
closest_idx = j;
}
if (dist_sq <= max_movement_dists_sq[j])
{
clip = false;
break;
}
}
if (clip)
{
float dist_sq = min_dist_sq;
float scale = max_movement_dists_sq[closest_idx] / dist_sq;
float dx = refined_pose[i][0] - track.full_poses[closest_idx][i][0];
float dy = refined_pose[i][1] - track.full_poses[closest_idx][i][1];
float dz = refined_pose[i][2] - track.full_poses[closest_idx][i][2];
refined_pose[i][0] = track.full_poses[closest_idx][i][0] + dx * scale;
refined_pose[i][1] = track.full_poses[closest_idx][i][1] + dy * scale;
refined_pose[i][2] = track.full_poses[closest_idx][i][2] + dz * scale;
// Set confidence to a low value if the joint is clipped
refined_pose[i][3] = 0.1;
}
}
return refined_pose;
}
+640 -225
View File
File diff suppressed because it is too large Load Diff
+32 -1
View File
@@ -3,7 +3,24 @@ from __future__ import annotations
from collections.abc import Sequence
from typing import TYPE_CHECKING
from ._core import Camera, triangulate_poses
from ._core import (
Camera,
CameraModel,
TriangulationOptions,
CoreProposalDebug,
FullProposalDebug,
GroupingDebug,
MergeDebug,
PairCandidate,
PreviousPoseFilterDebug,
PreviousPoseMatch,
ProposalGroupDebug,
TriangulationTrace,
build_pair_candidates,
filter_pairs_with_previous_poses,
triangulate_debug,
triangulate_poses,
)
if TYPE_CHECKING:
import numpy as np
@@ -28,7 +45,21 @@ def pack_poses_2d(
__all__ = [
"Camera",
"CameraModel",
"TriangulationOptions",
"CoreProposalDebug",
"FullProposalDebug",
"GroupingDebug",
"MergeDebug",
"PairCandidate",
"PreviousPoseFilterDebug",
"PreviousPoseMatch",
"ProposalGroupDebug",
"TriangulationTrace",
"build_pair_candidates",
"convert_cameras",
"filter_pairs_with_previous_poses",
"pack_poses_2d",
"triangulate_debug",
"triangulate_poses",
]
+33 -5
View File
@@ -1,12 +1,12 @@
from __future__ import annotations
from collections.abc import Sequence
from typing import TypeAlias, TypedDict
from typing import Literal, TypeAlias, TypedDict
import numpy as np
import numpy.typing as npt
from ._core import Camera
from ._core import Camera, CameraModel
Matrix3x3Like: TypeAlias = Sequence[Sequence[float]]
VectorLike: TypeAlias = Sequence[float]
@@ -21,12 +21,39 @@ class CameraDict(TypedDict, total=False):
T: Sequence[Sequence[float]]
width: int
height: int
type: str
type: Literal["pinhole", "fisheye"]
model: Literal["pinhole", "fisheye"] | CameraModel
CameraModelLike: TypeAlias = CameraModel | Literal["pinhole", "fisheye"]
CameraLike = Camera | CameraDict
def _coerce_camera_model(model: CameraModelLike) -> CameraModel:
if isinstance(model, CameraModel):
return model
if model == "pinhole":
return CameraModel.PINHOLE
if model == "fisheye":
return CameraModel.FISHEYE
raise ValueError(f"Unsupported camera model: {model}")
def _coerce_distortion(distortion: VectorLike, camera_model: CameraModel) -> tuple[float, float, float, float, float]:
values = tuple(float(value) for value in distortion)
expected = 4 if camera_model is CameraModel.FISHEYE else 5
if len(values) not in {expected, 5}:
raise ValueError(
f"{camera_model.name.lower()} cameras require {expected} distortion coefficients"
+ (" (or 5 with a trailing zero)." if camera_model is CameraModel.FISHEYE else ".")
)
if camera_model is CameraModel.FISHEYE and len(values) == 4:
values = values + (0.0,)
if len(values) != 5:
raise ValueError("Distortion coefficients must normalize to exactly 5 values.")
return values
def convert_cameras(cameras: Sequence[CameraLike]) -> list[Camera]:
"""Normalize mappings or existing Camera objects into bound Camera instances."""
@@ -39,12 +66,13 @@ def convert_cameras(cameras: Sequence[CameraLike]) -> list[Camera]:
camera = Camera()
camera.name = str(cam["name"])
camera.K = cam["K"]
camera.DC = cam["DC"]
camera_model = _coerce_camera_model(cam.get("model", cam.get("type", "pinhole")))
camera.DC = _coerce_distortion(cam["DC"], camera_model)
camera.R = cam["R"]
camera.T = cam["T"]
camera.width = int(cam["width"])
camera.height = int(cam["height"])
camera.type = str(cam.get("type", "pinhole"))
camera.model = camera_model
converted.append(camera)
return converted
+70 -1
View File
@@ -52,7 +52,7 @@ def test_camera_structure_repr():
camera.T = [[1], [2], [3]]
camera.width = 640
camera.height = 480
camera.type = "pinhole"
camera.model = rpt.CameraModel.PINHOLE
rendered = repr(camera)
assert "Camera 1" in rendered
@@ -100,6 +100,75 @@ def test_triangulate_repeatability():
np.testing.assert_allclose(first, second, rtol=1e-5, atol=1e-5)
def test_build_pair_candidates_exposes_cartesian_view_pairs():
poses_2d, person_counts = rpt.pack_poses_2d(
[
np.zeros((2, 2, 3), dtype=np.float32),
np.zeros((1, 2, 3), dtype=np.float32),
np.zeros((3, 2, 3), dtype=np.float32),
],
joint_count=2,
)
pairs = rpt.build_pair_candidates(poses_2d, person_counts)
assert len(pairs) == (2 * 1) + (2 * 3) + (1 * 3)
assert (pairs[0].view1, pairs[0].view2, pairs[0].person1, pairs[0].person2) == (0, 1, 0, 0)
assert pairs[-1].global_person2 == 5
def test_triangulate_accepts_empty_previous_poses():
poses_2d, person_counts, cameras = load_case("data/p1/sample.json", "tests/poses_p1.json")
roomparams = np.asarray([[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]], dtype=np.float32)
empty_previous = np.zeros((0, len(JOINT_NAMES), 4), dtype=np.float32)
baseline = rpt.triangulate_poses(poses_2d, person_counts, cameras, roomparams, JOINT_NAMES)
with_previous = rpt.triangulate_poses(
poses_2d,
person_counts,
cameras,
roomparams,
JOINT_NAMES,
empty_previous,
)
np.testing.assert_allclose(with_previous, baseline, rtol=1e-5, atol=1e-5)
def test_triangulate_debug_matches_final_output():
poses_2d, person_counts, cameras = load_case("data/h1/sample.json", "tests/poses_h1.json")
roomparams = np.asarray([[4.8, 6.0, 2.0], [0.0, 0.0, 1.0]], dtype=np.float32)
final_poses = rpt.triangulate_poses(poses_2d, person_counts, cameras, roomparams, JOINT_NAMES)
trace = rpt.triangulate_debug(poses_2d, person_counts, cameras, roomparams, JOINT_NAMES)
np.testing.assert_allclose(trace.final_poses, final_poses, rtol=1e-5, atol=1e-5)
assert len(trace.pairs) >= len(trace.core_proposals)
for group in trace.grouping.groups:
assert all(0 <= index < len(trace.core_proposals) for index in group.proposal_indices)
for merge_indices in trace.merge.group_proposal_indices:
assert all(0 <= index < len(trace.core_proposals) for index in merge_indices)
def test_filter_pairs_with_previous_poses_returns_debug_matches():
poses_2d, person_counts, cameras = load_case("data/p1/sample.json", "tests/poses_p1.json")
roomparams = np.asarray([[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]], dtype=np.float32)
previous_poses = rpt.triangulate_poses(poses_2d, person_counts, cameras, roomparams, JOINT_NAMES)
debug = rpt.filter_pairs_with_previous_poses(
poses_2d,
person_counts,
cameras,
JOINT_NAMES,
previous_poses,
)
assert debug.used_previous_poses is True
assert len(debug.matches) == len(rpt.build_pair_candidates(poses_2d, person_counts))
assert len(debug.kept_pairs) == len(debug.kept_pair_indices)
assert any(match.matched_view1 or match.matched_view2 for match in debug.matches)
def test_triangulate_does_not_mutate_inputs():
poses_2d, person_counts, cameras = load_case("data/h1/sample.json", "tests/poses_h1.json")
roomparams = np.asarray([[4.8, 6.0, 2.0], [0.0, 0.0, 1.0]], dtype=np.float32)