Remove unused extras directory

Document Python API and ship package stubs
Add tracked triangulation association reports
2026-03-26 11:22:44 +08:00 · 2026-03-12 14:53:42 +08:00 · 2026-03-12 14:16:31 +08:00 · 2026-03-12 00:24:02 +08:00 · 2026-03-12 00:08:56 +08:00 · 2026-03-11 23:55:04 +08:00
86 changed files with 3252 additions and 35950 deletions
@@ -1,4 +1,5 @@
 rpt_wrap.*
+rptPYTHON_wrap.*
 rpt.py
 *.bin

@@ -1,3 +0,0 @@
-[submodule "skelda"]
-	path = skelda
-	url = https://gitlab.com/Percipiote/skelda.git
@@ -0,0 +1,17 @@
+cmake_minimum_required(VERSION 3.18)
+
+project(RapidPoseTriangulation
+    VERSION 0.1.0
+    LANGUAGES CXX
+    DESCRIPTION "Rapid Pose Triangulation library with Python bindings"
+)
+
+# Global settings
+set(CMAKE_CXX_STANDARD 23)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_CXX_EXTENSIONS OFF)
+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
+
+# Add subdirectories
+add_subdirectory(rpt)
+add_subdirectory(bindings)
@@ -16,10 +16,10 @@ A general overview can be found in the paper [RapidPoseTriangulation: Multi-view

 ## Build

- Clone this project with submodules:
+- Clone this project:

    ```bash
-    git clone --recurse-submodules https://gitlab.com/Percipiote/RapidPoseTriangulation.git
+    git clone https://gitlab.com/Percipiote/RapidPoseTriangulation.git
    cd RapidPoseTriangulation/
    ```

@@ -53,57 +53,12 @@ A general overview can be found in the paper [RapidPoseTriangulation: Multi-view
 - Build triangulator:

  ```bash
-  cd /RapidPoseTriangulation/swig/ && make all && cd ../tests/ && python3 test_interface.py && cd ..
-
-  cd /RapidPoseTriangulation/scripts/ && \
-  g++ -std=c++2a -fPIC -O3 -march=native -Wall -Werror -flto=auto \
-    -I /RapidPoseTriangulation/rpt/ \
-    -isystem /usr/include/opencv4/ \
-    -isystem /onnxruntime/include/ \
-    -isystem /onnxruntime/include/onnxruntime/core/session/ \
-    -isystem /onnxruntime/include/onnxruntime/core/providers/tensorrt/ \
-    -L /onnxruntime/build/Linux/Release/ \
-    test_skelda_dataset.cpp \
-    /RapidPoseTriangulation/rpt/*.cpp \
-    -o test_skelda_dataset.bin \
-    -Wl,--start-group \
-      -lonnxruntime_providers_tensorrt \
-      -lonnxruntime_providers_shared \
-      -lonnxruntime_providers_cuda \
-      -lonnxruntime \
-    -Wl,--end-group \
-    $(pkg-config --libs opencv4) \
-    -Wl,-rpath,/onnxruntime/build/Linux/Release/ \
-  && cd ..
+  cd /RapidPoseTriangulation/
+  uv sync --group dev
+  uv run pytest tests/test_interface.py
+  uv build
  ```

- Download _ONNX_ models from [model registry](https://gitlab.com/Percipiote/RapidPoseTriangulation/-/ml/models) and save them to `mmdeploy/extras/exports/`.
-  Upon the first usage, they will be converted to _TensorRT_ models, which will take a few minutes. \
-  (Note that this conversion is not deterministic and will each time result in slightly different models and therefore also slightly different benchmark results.)
-
- Test with samples:
-
-  ```bash
-  python3 /RapidPoseTriangulation/scripts/test_triangulate.py
-  ```
-
- Test with [skelda](https://gitlab.com/Percipiote/skelda/) dataset:
-
-  ```bash
-  export CUDA_VISIBLE_DEVICES=0
-  python3 /RapidPoseTriangulation/scripts/test_skelda_dataset.py
-  ```
-
-<br>
-
-## Extras
-
- Exporting tools for 2D models are at [mmdeploy](extras/mmdeploy/README.md) directory.
-
- For usage in combination with ROS2 see [ros](extras/ros/README.md) directory.
-
- Running on a Nvidia Jetson is also possible following [jetson](extras/jetson/README.md) directory.
-
 <br>

 ## Citation
@@ -0,0 +1,45 @@
+find_package(Python 3.10 REQUIRED COMPONENTS Interpreter Development.Module)
+
+if(NOT nanobind_DIR)
+    execute_process(
+        COMMAND "${Python_EXECUTABLE}" -c "import nanobind; print(nanobind.cmake_dir())"
+        OUTPUT_VARIABLE nanobind_DIR
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+        RESULT_VARIABLE nanobind_dir_result
+    )
+    if(NOT nanobind_dir_result EQUAL 0 OR NOT nanobind_DIR)
+        message(FATAL_ERROR "Failed to resolve nanobind CMake directory from Python.")
+    endif()
+endif()
+
+find_package(nanobind CONFIG REQUIRED)
+
+set(RPT_PYTHON_PACKAGE_DIR "${CMAKE_CURRENT_BINARY_DIR}/rpt")
+file(MAKE_DIRECTORY "${RPT_PYTHON_PACKAGE_DIR}")
+configure_file("${PROJECT_SOURCE_DIR}/src/rpt/__init__.py" "${RPT_PYTHON_PACKAGE_DIR}/__init__.py" COPYONLY)
+configure_file("${PROJECT_SOURCE_DIR}/src/rpt/__init__.pyi" "${RPT_PYTHON_PACKAGE_DIR}/__init__.pyi" COPYONLY)
+configure_file("${PROJECT_SOURCE_DIR}/src/rpt/_helpers.py" "${RPT_PYTHON_PACKAGE_DIR}/_helpers.py" COPYONLY)
+configure_file("${PROJECT_SOURCE_DIR}/src/rpt/py.typed" "${RPT_PYTHON_PACKAGE_DIR}/py.typed" COPYONLY)
+
+nanobind_add_module(rpt_core_ext "${CMAKE_CURRENT_SOURCE_DIR}/rpt_module.cpp")
+
+set_target_properties(rpt_core_ext PROPERTIES
+    OUTPUT_NAME "_core"
+    LIBRARY_OUTPUT_DIRECTORY "${RPT_PYTHON_PACKAGE_DIR}"
+)
+
+target_link_libraries(rpt_core_ext PRIVATE rpt_core)
+target_include_directories(rpt_core_ext PRIVATE
+    "${PROJECT_SOURCE_DIR}/rpt"
+)
+
+nanobind_add_stub(rpt_core_stub
+    MODULE rpt._core
+    OUTPUT "${RPT_PYTHON_PACKAGE_DIR}/_core.pyi"
+    PYTHON_PATH "${CMAKE_CURRENT_BINARY_DIR}"
+    DEPENDS rpt_core_ext
+)
+
+install(TARGETS rpt_core_ext LIBRARY DESTINATION rpt)
+install(FILES "${RPT_PYTHON_PACKAGE_DIR}/__init__.pyi" DESTINATION rpt)
+install(FILES "${RPT_PYTHON_PACKAGE_DIR}/_core.pyi" DESTINATION rpt)
@@ -0,0 +1,454 @@
+#include <algorithm>
+#include <array>
+#include <cstdint>
+#include <stdexcept>
+#include <vector>
+
+#include <nanobind/nanobind.h>
+#include <nanobind/ndarray.h>
+#include <nanobind/stl/array.h>
+#include <nanobind/stl/string.h>
+#include <nanobind/stl/vector.h>
+
+#include "interface.hpp"
+
+namespace nb = nanobind;
+using namespace nb::literals;
+
+namespace
+{
+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 TrackIdArray = nb::ndarray<nb::numpy, const int64_t, nb::shape<-1>, 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)
+{
+    if (poses_2d.shape(0) != person_counts.shape(0))
+    {
+        throw std::invalid_argument("poses_2d and person_counts must have the same number of views.");
+    }
+
+    for (size_t i = 0; i < static_cast<size_t>(person_counts.shape(0)); ++i)
+    {
+        if (person_counts(i) > poses_2d.shape(1))
+        {
+            throw std::invalid_argument("person_counts entries must not exceed the padded person dimension.");
+        }
+    }
+
+    return PoseBatch2DView {
+        poses_2d.data(),
+        person_counts.data(),
+        static_cast<size_t>(poses_2d.shape(0)),
+        static_cast<size_t>(poses_2d.shape(1)),
+        static_cast<size_t>(poses_2d.shape(2)),
+    };
+}
+
+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)),
+    };
+}
+
+TrackedPoseBatch3DView tracked_pose_batch_view_from_numpy(
+    const PoseArray3DConst &poses_3d,
+    const TrackIdArray &track_ids)
+{
+    if (poses_3d.shape(0) != track_ids.shape(0))
+    {
+        throw std::invalid_argument(
+            "previous_poses_3d and previous_track_ids must have the same number of tracks.");
+    }
+
+    return TrackedPoseBatch3DView {
+        track_ids.data(),
+        poses_3d.data(),
+        static_cast<size_t>(poses_3d.shape(0)),
+        static_cast<size_t>(poses_3d.shape(1)),
+    };
+}
+
+PoseArray3D pose_batch_to_numpy(PoseBatch3D batch)
+{
+    auto *storage = new std::vector<float>(std::move(batch.data));
+    nb::capsule owner(storage, [](void *value) noexcept
+    {
+        delete static_cast<std::vector<float> *>(value);
+    });
+
+    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_prop_ro("name", [](const Camera &camera)
+        {
+            return camera.name;
+        })
+        .def_prop_ro("K", [](const Camera &camera)
+        {
+            return camera.K;
+        })
+        .def_prop_ro("DC", [](const Camera &camera)
+        {
+            return camera.DC;
+        })
+        .def_prop_ro("R", [](const Camera &camera)
+        {
+            return camera.R;
+        })
+        .def_prop_ro("T", [](const Camera &camera)
+        {
+            return camera.T;
+        })
+        .def_prop_ro("width", [](const Camera &camera)
+        {
+            return camera.width;
+        })
+        .def_prop_ro("height", [](const Camera &camera)
+        {
+            return camera.height;
+        })
+        .def_prop_ro("model", [](const Camera &camera)
+        {
+            return camera.model;
+        })
+        .def_prop_ro("invR", [](const Camera &camera)
+        {
+            return camera.invR;
+        })
+        .def_prop_ro("center", [](const Camera &camera)
+        {
+            return camera.center;
+        })
+        .def_prop_ro("newK", [](const Camera &camera)
+        {
+            return camera.newK;
+        })
+        .def_prop_ro("invK", [](const Camera &camera)
+        {
+            return camera.invK;
+        })
+        .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_<TriangulationConfig>(m, "TriangulationConfig")
+        .def(nb::init<>())
+        .def_rw("cameras", &TriangulationConfig::cameras)
+        .def_rw("roomparams", &TriangulationConfig::roomparams)
+        .def_rw("joint_names", &TriangulationConfig::joint_names)
+        .def_rw("options", &TriangulationConfig::options);
+
+    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("previous_track_id", &PreviousPoseMatch::previous_track_id)
+        .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::enum_<AssociationStatus>(m, "AssociationStatus")
+        .value("MATCHED", AssociationStatus::Matched)
+        .value("NEW", AssociationStatus::New)
+        .value("AMBIGUOUS", AssociationStatus::Ambiguous);
+
+    nb::class_<AssociationReport>(m, "AssociationReport")
+        .def(nb::init<>())
+        .def_rw("pose_previous_indices", &AssociationReport::pose_previous_indices)
+        .def_rw("pose_previous_track_ids", &AssociationReport::pose_previous_track_ids)
+        .def_rw("pose_status", &AssociationReport::pose_status)
+        .def_rw("pose_candidate_previous_indices", &AssociationReport::pose_candidate_previous_indices)
+        .def_rw("pose_candidate_previous_track_ids", &AssociationReport::pose_candidate_previous_track_ids)
+        .def_rw("unmatched_previous_indices", &AssociationReport::unmatched_previous_indices)
+        .def_rw("unmatched_previous_track_ids", &AssociationReport::unmatched_previous_track_ids)
+        .def_rw("new_pose_indices", &AssociationReport::new_pose_indices)
+        .def_rw("ambiguous_pose_indices", &AssociationReport::ambiguous_pose_indices);
+
+    nb::class_<FinalPoseAssociationDebug>(m, "FinalPoseAssociationDebug")
+        .def(nb::init<>())
+        .def_rw("final_pose_index", &FinalPoseAssociationDebug::final_pose_index)
+        .def_rw("source_core_proposal_indices", &FinalPoseAssociationDebug::source_core_proposal_indices)
+        .def_rw("source_pair_indices", &FinalPoseAssociationDebug::source_pair_indices)
+        .def_rw("candidate_previous_indices", &FinalPoseAssociationDebug::candidate_previous_indices)
+        .def_rw("candidate_previous_track_ids", &FinalPoseAssociationDebug::candidate_previous_track_ids)
+        .def_rw("resolved_previous_index", &FinalPoseAssociationDebug::resolved_previous_index)
+        .def_rw("resolved_previous_track_id", &FinalPoseAssociationDebug::resolved_previous_track_id)
+        .def_rw("status", &FinalPoseAssociationDebug::status);
+
+    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_rw("association", &TriangulationTrace::association)
+        .def_rw("final_pose_associations", &TriangulationTrace::final_pose_associations)
+        .def_prop_ro("final_poses", [](const TriangulationTrace &trace)
+        {
+            return pose_batch_to_numpy_copy(trace.final_poses);
+        }, nb::rv_policy::move);
+
+    nb::class_<TriangulationResult>(m, "TriangulationResult")
+        .def(nb::init<>())
+        .def_rw("association", &TriangulationResult::association)
+        .def_prop_ro("poses_3d", [](const TriangulationResult &result)
+        {
+            return pose_batch_to_numpy_copy(result.poses);
+        }, nb::rv_policy::move);
+
+    m.def(
+        "make_camera",
+        &make_camera,
+        "name"_a,
+        "K"_a,
+        "DC"_a,
+        "R"_a,
+        "T"_a,
+        "width"_a,
+        "height"_a,
+        "model"_a);
+
+    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 TriangulationConfig &config,
+           const PoseArray3DConst &previous_poses_3d,
+           const TrackIdArray &previous_track_ids)
+        {
+            return filter_pairs_with_previous_poses(
+                pose_batch_view_from_numpy(poses_2d, person_counts),
+                config,
+                tracked_pose_batch_view_from_numpy(previous_poses_3d, previous_track_ids));
+        },
+        "poses_2d"_a,
+        "person_counts"_a,
+        "config"_a,
+        "previous_poses_3d"_a,
+        "previous_track_ids"_a);
+
+    m.def(
+        "triangulate_debug",
+        [](const PoseArray2D &poses_2d,
+           const CountArray &person_counts,
+           const TriangulationConfig &config)
+        {
+            return triangulate_debug(pose_batch_view_from_numpy(poses_2d, person_counts), config);
+        },
+        "poses_2d"_a,
+        "person_counts"_a,
+        "config"_a);
+
+    m.def(
+        "triangulate_debug",
+        [](const PoseArray2D &poses_2d,
+           const CountArray &person_counts,
+           const TriangulationConfig &config,
+           const PoseArray3DConst &previous_poses_3d,
+           const TrackIdArray &previous_track_ids)
+        {
+            const TrackedPoseBatch3DView previous_view =
+                tracked_pose_batch_view_from_numpy(previous_poses_3d, previous_track_ids);
+            return triangulate_debug(
+                pose_batch_view_from_numpy(poses_2d, person_counts),
+                config,
+                &previous_view);
+        },
+        "poses_2d"_a,
+        "person_counts"_a,
+        "config"_a,
+        "previous_poses_3d"_a,
+        "previous_track_ids"_a);
+
+    m.def(
+        "triangulate_poses",
+        [](const PoseArray2D &poses_2d,
+           const CountArray &person_counts,
+           const TriangulationConfig &config)
+        {
+            const PoseBatch3D poses_3d =
+                triangulate_poses(pose_batch_view_from_numpy(poses_2d, person_counts), config);
+            return pose_batch_to_numpy(poses_3d);
+        },
+        "poses_2d"_a,
+        "person_counts"_a,
+        "config"_a);
+
+    m.def(
+        "triangulate_with_report",
+        [](const PoseArray2D &poses_2d,
+           const CountArray &person_counts,
+           const TriangulationConfig &config,
+           const PoseArray3DConst &previous_poses_3d,
+           const TrackIdArray &previous_track_ids)
+        {
+            const TriangulationResult result = triangulate_with_report(
+                pose_batch_view_from_numpy(poses_2d, person_counts),
+                config,
+                tracked_pose_batch_view_from_numpy(previous_poses_3d, previous_track_ids));
+            return result;
+        },
+        "poses_2d"_a,
+        "person_counts"_a,
+        "config"_a,
+        "previous_poses_3d"_a,
+        "previous_track_ids"_a);
+}
@@ -1,326 +0,0 @@
-import json
-import os
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-
-from skelda import utils_pose, utils_view
-
-# ==================================================================================================
-
-filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
-
-core_triangs = [
-    [
-        [0.287, -0.282, 1.264, 1.000],
-        [0.504, -0.052, 1.272, 1.000],
-        [0.276, -0.160, 0.764, 1.000],
-        [0.443, -0.099, 0.768, 1.000],
-        [0.258, -0.313, 0.999, 1.000],
-        [0.513, -0.009, 1.008, 1.000],
-        [0.204, -0.126, 0.439, 1.000],
-        [0.422, -0.132, 0.436, 1.000],
-        [0.195, -0.265, 0.807, 1.000],
-        [0.415, 0.039, 0.823, 1.000],
-        [0.113, -0.103, 0.096, 1.000],
-        [0.389, -0.175, 0.097, 1.000],
-    ],
-    [
-        [0.322, -0.192, 1.349, 1.000],
-        [0.268, -0.594, 1.336, 1.000],
-        [0.272, -0.100, 0.882, 1.000],
-        [0.281, -0.379, 0.870, 1.000],
-        [0.336, -0.104, 1.124, 1.000],
-        [0.249, -0.578, 1.089, 1.000],
-        [0.229, 0.009, 0.571, 1.000],
-        [0.269, -0.345, 0.553, 1.000],
-        [0.289, -0.016, 0.951, 1.000],
-        [0.216, -0.327, 0.908, 1.000],
-        [0.188, 0.128, 0.268, 1.000],
-        [0.267, -0.273, 0.243, 1.000],
-    ],
-    [
-        [0.865, 1.058, 1.613, 1.000],
-        [0.862, 0.870, 1.604, 1.000],
-        [0.927, 1.562, 1.491, 1.000],
-        [0.954, 1.505, 1.486, 1.000],
-        [0.908, 1.309, 1.542, 1.000],
-        [0.905, 1.170, 1.525, 1.000],
-        [0.968, 1.911, 1.454, 1.000],
-        [1.019, 1.919, 1.457, 1.000],
-        [0.921, 1.542, 1.514, 1.000],
-        [0.931, 1.539, 1.506, 1.000],
-        [1.008, 2.230, 1.455, 1.000],
-        [1.071, 2.271, 1.460, 1.000],
-    ],
-    [
-        [-0.260, 0.789, 1.316, 1.000],
-        [0.039, 1.073, 1.322, 1.000],
-        [-0.236, 0.798, 0.741, 1.000],
-        [-0.048, 0.952, 0.759, 1.000],
-        [-0.315, 0.734, 0.995, 1.000],
-        [0.080, 1.026, 1.046, 1.000],
-        [-0.291, 0.721, 0.339, 1.000],
-        [-0.101, 0.887, 0.366, 1.000],
-        [-0.300, 0.600, 0.742, 1.000],
-        [0.066, 0.768, 0.897, 1.000],
-        [-0.381, 0.685, -0.113, 1.000],
-        [-0.169, 0.775, -0.040, 1.000],
-    ],
-    [
-        [-0.199, 0.854, 1.414, 1.000],
-        [-0.401, 0.566, 1.409, 1.000],
-        [-0.242, 0.818, 0.870, 1.000],
-        [-0.343, 0.654, 0.856, 1.000],
-        [-0.176, 0.903, 1.140, 1.000],
-        [-0.398, 0.480, 1.132, 1.000],
-        [-0.245, 0.812, 0.492, 1.000],
-        [-0.380, 0.642, 0.471, 1.000],
-        [-0.145, 0.817, 0.912, 1.000],
-        [-0.251, 0.396, 0.973, 1.000],
-        [-0.255, 0.879, 0.107, 1.000],
-        [-0.383, 0.633, 0.116, 1.000],
-    ],
-    [
-        [0.641, 1.796, 1.681, 1.000],
-        [0.603, 1.719, 1.680, 1.000],
-        [0.711, 2.000, 1.518, 1.000],
-        [0.706, 1.970, 1.515, 1.000],
-        [0.689, 1.920, 1.588, 1.000],
-        [0.651, 1.784, 1.585, 1.000],
-        [0.786, 2.190, 1.448, 1.000],
-        [0.780, 2.167, 1.444, 1.000],
-        [0.747, 1.994, 1.531, 1.000],
-        [0.720, 1.783, 1.546, 1.000],
-        [0.868, 2.432, 1.427, 1.000],
-        [0.849, 2.341, 1.410, 1.000],
-    ],
-]
-
-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",
-]
-
-poses_2d = [
-    [
-        [
-            [383.443, 144.912, 0.923],
-            [382.629, 135.143, 0.83],
-            [374.488, 134.329, 1.0],
-            [349.251, 136.771, 0.478],
-            [343.552, 139.213, 1.0],
-            [356.578, 201.899, 0.73],
-            [323.2, 201.899, 0.825],
-            [357.392, 282.494, 0.663],
-            [324.014, 289.821, 0.854],
-            [378.558, 339.481, 0.621],
-            [355.764, 356.577, 0.821],
-            [370.417, 357.391, 0.714],
-            [332.155, 359.834, 0.71],
-            [391.584, 452.641, 0.768],
-            [331.341, 458.34, 0.789],
-            [414.379, 547.076, 0.864],
-            [332.969, 550.333, 0.9],
-            [351.286, 358.613, 0.71],
-            [339.889, 201.899, 0.73],
-            [346.402, 137.992, 0.478],
-        ],
-        [
-            [640.948, 116.443, 0.908],
-            [650.057, 100.249, 0.788],
-            [642.972, 100.249, 0.681],
-            [682.445, 103.285, 0.862],
-            [684.469, 100.249, 0.518],
-            [707.748, 181.219, 0.836],
-            [693.578, 180.207, 0.705],
-            [702.688, 290.528, 0.867],
-            [664.227, 276.358, 0.786],
-            [662.202, 375.547, 0.847],
-            [605.523, 319.88, 0.881],
-            [692.566, 373.522, 0.758],
-            [679.409, 372.51, 0.739],
-            [679.409, 500.038, 0.844],
-            [672.324, 494.978, 0.837],
-            [679.409, 635.663, 0.913],
-            [659.166, 599.226, 0.894],
-            [685.987, 373.016, 0.739],
-            [700.663, 180.713, 0.705],
-            [683.457, 101.767, 0.518],
-        ],
-    ],
-    [
-        [
-            [495.125, 304.671, 0.581],
-            [492.338, 301.885, 0.462],
-            [502.091, 301.885, 0.295],
-            [495.125, 308.851, 0.92],
-            [528.562, 306.064, 0.754],
-            [477.013, 359.703, 0.822],
-            [557.819, 355.523, 0.855],
-            [466.564, 431.452, 0.855],
-            [565.481, 425.879, 0.836],
-            [458.902, 480.911, 0.85],
-            [544.583, 464.889, 0.596],
-            [491.642, 490.663, 0.741],
-            [539.707, 492.056, 0.746],
-            [480.496, 569.379, 0.779],
-            [531.348, 577.041, 0.784],
-            [464.475, 646.005, 0.872],
-            [518.809, 661.33, 0.913],
-            [515.675, 491.36, 0.741],
-            [517.416, 357.613, 0.822],
-            [511.843, 307.458, 0.754],
-        ],
-        [
-            [472.982, 273.983, 0.911],
-            [477.875, 266.645, 0.848],
-            [464.421, 266.645, 0.896],
-            [483.378, 268.48, 0.599],
-            [448.521, 268.48, 0.88],
-            [493.163, 308.841, 0.753],
-            [425.894, 311.899, 0.837],
-            [502.336, 363.268, 0.625],
-            [417.944, 368.16, 0.847],
-            [499.278, 407.91, 0.495],
-            [438.736, 410.357, 0.844],
-            [484.602, 426.868, 0.682],
-            [448.521, 427.48, 0.681],
-            [485.825, 504.534, 0.745],
-            [441.794, 505.757, 0.796],
-            [489.494, 571.803, 0.688],
-            [442.405, 577.918, 0.867],
-            [466.561, 427.174, 0.681],
-            [459.528, 310.37, 0.753],
-            [465.95, 268.48, 0.599],
-        ],
-        [
-            [702.349, 208.747, 0.215],
-            [705.862, 207.944, 0.212],
-            [700.341, 207.944, 0.209],
-            [708.472, 196.399, 0.182],
-            [699.538, 196.299, 0.193],
-            [708.071, 196.7, 0.194],
-            [696.927, 196.098, 0.22],
-            [709.175, 206.137, 0.191],
-            [696.526, 206.94, 0.188],
-            [707.368, 210.052, 0.128],
-            [699.738, 209.751, 0.145],
-            [704.658, 215.172, 0.172],
-            [701.445, 215.273, 0.168],
-            [705.159, 224.007, 0.179],
-            [703.654, 225.211, 0.185],
-            [705.059, 225.914, 0.23],
-            [704.457, 230.532, 0.241],
-            [703.051, 215.223, 0.168],
-            [702.499, 196.399, 0.194],
-            [704.005, 196.349, 0.182],
-        ],
-    ],
-]
-
-joints_2d = [
-    "nose",
-    "eye_left",
-    "eye_right",
-    "ear_left",
-    "ear_right",
-    "shoulder_left",
-    "shoulder_right",
-    "elbow_left",
-    "elbow_right",
-    "wrist_left",
-    "wrist_right",
-    "hip_left",
-    "hip_right",
-    "knee_left",
-    "knee_right",
-    "ankle_left",
-    "ankle_right",
-    "hip_middle",
-    "shoulder_middle",
-    "head",
-]
-
-
-# ==================================================================================================
-
-
-def main():
-
-    with open(os.path.join(filepath, "sample.json"), "r", encoding="utf-8") as file:
-        sample = json.load(file)
-
-    camparams = sample["cameras"]
-    roomparams = {
-        "room_size": sample["room_size"],
-        "room_center": sample["room_center"],
-    }
-
-    fig2 = utils_view.draw_poses3d(core_triangs, core_joints, roomparams, camparams)
-    fig2.axes[0].view_init(elev=30, azim=0)
-    fig2.savefig(os.path.join(filepath, "core-triangs.png"), dpi=fig2.dpi)
-
-    core_projections = []
-    for i in range(len(camparams)):
-        b2d, _ = utils_pose.project_poses(np.array(core_triangs), camparams[i])
-        core_projections.append(b2d)
-
-    img_size = [900, 900]
-    scale = 0.66
-    fig_size = 2
-    plotsize = (35, 30)
-    fig, axs = plt.subplots(fig_size, fig_size, figsize=plotsize)
-    fig.suptitle("core reprojections", fontsize=20)
-    fig.tight_layout(rect=[0, 0, 1, 0.97])
-
-    num_persons = max((len(b2d) for b2d in core_projections))
-    colors = plt.cm.hsv(np.linspace(0, 1, num_persons, endpoint=False)).tolist()
-    colors = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
-
-    for i in range(len(camparams)):
-        img = np.ones((img_size[0], img_size[1], 3), dtype=np.uint8) * 255
-
-        for j in range(len(core_projections[i])):
-            color = colors[j]
-            body = np.array(core_projections[i][j])
-            img = utils_view.draw_body_in_image(img, body, core_joints, color)
-
-        for k in range(len(poses_2d[i])):
-            body = np.array(poses_2d[i][k])
-            cjids = [joints_2d.index(j) for j in core_joints]
-            body = body[cjids]
-            img = utils_view.draw_body_in_image(
-                img, body, core_joints, [0, 0, 0], thickness=2
-            )
-
-        img = cv2.resize(img, (int(img.shape[1] * scale), int(img.shape[0] * scale)))
-        title = str(i)
-
-        x, y = divmod(i, fig_size)
-        axs[x][y].imshow(img)
-        axs[x][y].set_title(title)
-
-    # Delete empty plots
-    for i in range(2, fig_size**2):
-        x, y = divmod(i, fig_size)
-        fig.delaxes(axs[x][y])
-
-    fig.savefig(os.path.join(filepath, "core-reprojections.png"), dpi=fig.dpi)
-
-
-# ==================================================================================================
-
-if __name__ == "__main__":
-    main()
@@ -1,52 +0,0 @@
-FROM nvcr.io/nvidia/tensorrt:24.10-py3
-
-ARG DEBIAN_FRONTEND=noninteractive
-ENV LANG=C.UTF-8
-ENV LC_ALL=C.UTF-8
-WORKDIR /
-
-RUN apt-get update && apt-get install -y --no-install-recommends feh
-RUN apt-get update && apt-get install -y --no-install-recommends python3-opencv
-RUN pip uninstall -y opencv-python && pip install --no-cache-dir "opencv-python<4.3"
-
-# Show matplotlib images
-RUN apt-get update && apt-get install -y --no-install-recommends python3-tk
-
-# Update pip to allow installation of skelda in editable mode
-RUN pip3 install --upgrade --no-cache-dir pip
-
-# Install swig and later dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends build-essential
-RUN apt-get update && apt-get install -y --no-install-recommends swig
-RUN apt-get update && apt-get install -y --no-install-recommends libopencv-dev
-
-# Install ONNX runtime
-# See: https://github.com/microsoft/onnxruntime/blob/main/dockerfiles/Dockerfile.tensorrt
-RUN pip3 uninstall -y onnxruntime-gpu
-RUN pip3 install --no-cache-dir psutil
-RUN git clone --recursive --depth=1 --branch=v1.20.1 https://github.com/Microsoft/onnxruntime.git
-# Next line fixes: https://github.com/microsoft/onnxruntime/issues/24861
-RUN cat /onnxruntime/cmake/deps.txt && \
-    sed -i 's/;be8be39fdbc6e60e94fa7870b280707069b5b81a/;32b145f525a8308d7ab1c09388b2e288312d8eba/g' /onnxruntime/cmake/deps.txt && \
-    cat /onnxruntime/cmake/deps.txt
-ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/cmake-3.30.1-linux-x86_64/bin:${PATH}
-ARG CMAKE_CUDA_ARCHITECTURES=75;80;90
-ENV TRT_VERSION=10.5.0.18
-RUN /bin/sh onnxruntime/dockerfiles/scripts/install_common_deps.sh
-RUN /bin/sh onnxruntime/dockerfiles/scripts/checkout_submodules.sh ${trt_version}
-RUN ls
-RUN cd onnxruntime && \
-    /bin/sh build.sh --allow_running_as_root --parallel --build_shared_lib \
-      --cuda_home /usr/local/cuda --cudnn_home /usr/lib/x86_64-linux-gnu/ --use_tensorrt \
-      --tensorrt_home /usr/lib/x86_64-linux-gnu/ --config Release --build_wheel --skip_tests \
-      --skip_submodule_sync --cmake_extra_defines '"CMAKE_CUDA_ARCHITECTURES='${CMAKE_CUDA_ARCHITECTURES}'"'
-RUN cd onnxruntime && pip install build/Linux/Release/dist/*.whl
-
-# Install skelda
-RUN pip3 install --upgrade --no-cache-dir scipy
-RUN apt-get update && apt-get install -y --no-install-recommends xvfb
-COPY ./skelda/ /skelda/
-RUN pip3 install --no-cache-dir -e /skelda/
-
-WORKDIR /RapidPoseTriangulation/
-CMD ["/bin/bash"]
@@ -1,142 +0,0 @@
-# Setup with Nvidia-Jetson-Orin
-
-Initial setup and installation of _RapidPoseTriangulation_ on a _Nvidia Jetson_ device. \
-Tested with a _Jetson AGX Orin Developer Kit_ module.
-
-<br>
-
-## Base installation
-
- Install newest software image: \
-  (<https://developer.nvidia.com/sdk-manager>)
-
-  - Use manual recovery mode setup for first installation
-
-  - Find out the _ip-address_ of the _Jetson_ for the runtime component installation with:
-
-    ```bash
-    sudo nmap -sn $(ip route get 1 | awk '{print $(NF-2);exit}')/24
-    ```
-
- Initialize system: \
-  (<https://developer.nvidia.com/embedded/learn/get-started-jetson-agx-orin-devkit>)
-
-  - Connect via _ssh_, because using _screen_ did not work, skip _oem-config_ step
-
-  - Skip installation of _nvidia-jetpack_
-
- Install basic tools:
-
-  ```bash
-  sudo apt install -y curl nano wget git
-  ```
-
- Update hostname:
-
-  ```bash
-  sudo nano /etc/hostname
-  sudo nano /etc/hosts
-  sudo reboot
-  ```
-
- Enable maximum performance mode:
-
-  ```bash
-  sudo nvpmodel -m 0
-  sudo jetson_clocks
-  ```
-
- Test docker is working:
-
-  ```bash
-  sudo docker run --rm hello-world
-  ```
-
- Enable _docker_ without _sudo_: \
-  (<https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user>)
-
- Enable GPU-access for docker building:
-
-  - Run `sudo nano /etc/docker/daemon.json` and add:
-
-    ```json
-    {
-      "runtimes": {
-        "nvidia": {
-          "args": [],
-          "path": "nvidia-container-runtime"
-        }
-      },
-      "default-runtime": "nvidia"
-    }
-    ```
-
-  - Restart docker: `sudo systemctl restart docker`
-
- Test docker is working:
-
-  ```bash
-  docker run --rm hello-world
-  docker run -it --rm --runtime=nvidia --network=host dustynv/onnxruntime:1.20-r36.4.0
-  ```
-
-<br>
-
-## RPT installation
-
- Build docker container:
-
-  ```bash
-  docker build --progress=plain -f extras/jetson/dockerfile -t rapidposetriangulation .
-  ./run_container.sh
-  ```
-
- Build _rpt_ package inside container:
-
-  ```bash
-  cd /RapidPoseTriangulation/swig/ && make all && cd ../tests/ && python3 test_interface.py && cd ..
-
-  cd /RapidPoseTriangulation/scripts/ && \
-  g++ -std=c++2a -fPIC -O3 -march=native -Wall -Werror -flto=auto \
-    -I /RapidPoseTriangulation/rpt/ \
-    -isystem /usr/include/opencv4/ \
-    -isystem /usr/local/include/onnxruntime/ \
-    -L /usr/local/lib/ \
-    test_skelda_dataset.cpp \
-    /RapidPoseTriangulation/rpt/*.cpp \
-    -o test_skelda_dataset.bin \
-    -Wl,--start-group \
-      -lonnxruntime_providers_tensorrt \
-      -lonnxruntime_providers_shared \
-      -lonnxruntime_providers_cuda \
-      -lonnxruntime \
-    -Wl,--end-group \
-    $(pkg-config --libs opencv4) \
-    -Wl,-rpath,/onnxruntime/build/Linux/Release/ \
-  && cd ..
-  ```
-
- Test with samples:
-
-  ```bash
-  python3 /RapidPoseTriangulation/scripts/test_triangulate.py
-  ```
-
-<br>
-
-## ROS interface
-
- Build docker container:
-
-  ```bash
-  docker build --progress=plain -f extras/ros/dockerfile_2d -t rapidposetriangulation_ros2d .
-  ```
-
- Run and test:
-
-  ```bash
-  export CAMID="camera01" && docker compose -f extras/jetson/docker-compose-2d.yml up
-
-  docker exec -it jetson-test_node-1 bash
-  export ROS_DOMAIN_ID=18
-  ```
@@ -1,37 +0,0 @@
-services:
-
-    test_node:
-        image: rapidposetriangulation_ros2d
-        network_mode: "host"
-        ipc: "host"
-        runtime: nvidia
-        privileged: true
-        volumes:
-            - ../../:/RapidPoseTriangulation/
-            - ../../skelda/:/skelda/
-            - /tmp/.X11-unix:/tmp/.X11-unix
-            - /dev/shm:/dev/shm
-        environment:
-            - CAMID
-            - DISPLAY
-            - QT_X11_NO_MITSHM=1
-            - "PYTHONUNBUFFERED=1"
-        command: /bin/bash -i -c 'sleep infinity'
-
-    estimator:
-        image: rapidposetriangulation_ros2d
-        network_mode: "host"
-        ipc: "host"
-        runtime: nvidia
-        privileged: true
-        volumes:
-            - ../../:/RapidPoseTriangulation/
-            - ../../skelda/:/skelda/
-            - /tmp/.X11-unix:/tmp/.X11-unix
-            - /dev/shm:/dev/shm
-        environment:
-            - CAMID
-            - DISPLAY
-            - QT_X11_NO_MITSHM=1
-            - "PYTHONUNBUFFERED=1"
-        command: /bin/bash -i -c 'export ROS_DOMAIN_ID=18 && ros2 run rpt2d_wrapper_cpp rpt2d_wrapper'
@@ -1,25 +0,0 @@
-FROM dustynv/onnxruntime:1.20-r36.4.0
-
-ARG DEBIAN_FRONTEND=noninteractive
-ENV LANG=C.UTF-8
-ENV LC_ALL=C.UTF-8
-WORKDIR /
-
-RUN apt-get update && apt-get install -y --no-install-recommends feh
-RUN apt-get update && apt-get install -y --no-install-recommends python3-opencv
-RUN apt-get update && apt-get install -y --no-install-recommends libatlas-base-dev
-
-# Show matplotlib images
-RUN apt-get update && apt-get install -y --no-install-recommends python3-tk
-
-# Install swig and later dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends build-essential
-RUN apt-get update && apt-get install -y --no-install-recommends swig
-RUN apt-get update && apt-get install -y --no-install-recommends libopencv-dev
-
-RUN pip3 install --no-cache-dir scipy
-COPY ./skelda/ /skelda/
-RUN pip3 install --no-cache-dir -e /skelda/
-
-WORKDIR /RapidPoseTriangulation/
-CMD ["/bin/bash"]
@@ -1,131 +0,0 @@
-# Exporting MMPose models
-
-```bash
-docker build --progress=plain -f extras/mmdeploy/dockerfile -t rpt_mmdeploy .
-
-./extras/mmdeploy/run_container.sh
-```
-
-<br>
-
-## ONNX
-
-```bash
-cd /mmdeploy/
-export withFP16="_fp16"
-cp /RapidPoseTriangulation/extras/mmdeploy/configs/detection_onnxruntime_static-320x320"$withFP16".py configs/mmdet/detection/
-
-python3 ./tools/deploy.py \
-    configs/mmdet/detection/detection_onnxruntime_static-320x320"$withFP16".py \
-    /mmpose/projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py \
-    https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth \
-    /mmpose/projects/rtmpose/examples/onnxruntime/human-pose.jpeg \
-    --work-dir work_dir \
-    --show
-mv /mmdeploy/work_dir/end2end.onnx /RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x3x320x320"$withFP16".onnx
-
-python3 ./tools/deploy.py \
-    configs/mmdet/detection/detection_onnxruntime_static-320x320"$withFP16".py \
-    /mmpose/projects/rtmpose/rtmdet/person/rtmdet_m_640-8xb32_coco-person.py \
-    https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
-    /mmpose/projects/rtmpose/examples/onnxruntime/human-pose.jpeg \
-    --work-dir work_dir \
-    --show
-mv /mmdeploy/work_dir/end2end.onnx /RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-m_1x3x320x320"$withFP16".onnx
-```
-
-```bash
-cd /mmdeploy/
-export withFP16="_fp16"
-cp /RapidPoseTriangulation/extras/mmdeploy/configs/pose-detection_simcc_onnxruntime_static-384x288"$withFP16".py configs/mmpose/
-cp /RapidPoseTriangulation/extras/mmdeploy/configs/pose-detection_simcc_onnxruntime_dynamic-384x288"$withFP16".py configs/mmpose/
-
-python3 ./tools/deploy.py \
-    configs/mmpose/pose-detection_simcc_onnxruntime_static-384x288"$withFP16".py \
-    /mmpose/projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py \
-    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-384x288-65e718c4_20230504.pth \
-    /mmpose/projects/rtmpose/examples/onnxruntime/human-pose.jpeg \
-    --work-dir work_dir \
-    --show
-mv /mmdeploy/work_dir/end2end.onnx /RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_1x3x384x288"$withFP16".onnx
-
-python3 ./tools/deploy.py \
-    configs/mmpose/pose-detection_simcc_onnxruntime_dynamic-384x288"$withFP16".py \
-    /mmpose/projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py \
-    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-384x288-65e718c4_20230504.pth \
-    /mmpose/projects/rtmpose/examples/onnxruntime/human-pose.jpeg \
-    --work-dir work_dir \
-    --show
-mv /mmdeploy/work_dir/end2end.onnx /RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_Bx3x384x288"$withFP16".onnx
-
-python3 ./tools/deploy.py \
-    configs/mmpose/pose-detection_simcc_onnxruntime_static-384x288"$withFP16".py \
-    /mmpose/projects/rtmpose/rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py \
-    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-ucoco_dw-ucoco_270e-384x288-2438fd99_20230728.pth \
-    /mmpose/projects/rtmpose/examples/onnxruntime/human-pose.jpeg \
-    --work-dir work_dir \
-    --show
-mv /mmdeploy/work_dir/end2end.onnx /RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-l_wb_1x3x384x288"$withFP16".onnx
-
-python3 ./tools/deploy.py \
-    configs/mmpose/pose-detection_simcc_onnxruntime_dynamic-384x288"$withFP16".py \
-    /mmpose/projects/rtmpose/rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py \
-    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-ucoco_dw-ucoco_270e-384x288-2438fd99_20230728.pth \
-    /mmpose/projects/rtmpose/examples/onnxruntime/human-pose.jpeg \
-    --work-dir work_dir \
-    --show
-mv /mmdeploy/work_dir/end2end.onnx /RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-l_wb_Bx3x384x288"$withFP16".onnx
-```
-
-```bash
-python3 /RapidPoseTriangulation/extras/mmdeploy/make_extra_graphs_pt.py
-python3 /RapidPoseTriangulation/extras/mmdeploy/make_extra_graphs_tf.py
-```
-
-```bash
-python3 /RapidPoseTriangulation/extras/mmdeploy/add_extra_steps.py
-```
-
-<br>
-
-## TensorRT
-
-Run this directly in the inference container (the TensorRT versions need to be the same)
-
-```bash
-export withFP16="_fp16"
-
-trtexec --fp16 \
-  --onnx=/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x320x320x3"$withFP16"_extra-steps.onnx \
-  --saveEngine=end2end.engine
-
-mv ./end2end.engine /RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x320x320x3"$withFP16"_extra-steps.engine
-
-trtexec --fp16 \
-  --onnx=/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_Bx384x288x3"$withFP16"_extra-steps.onnx \
-  --saveEngine=end2end.engine \
-  --minShapes=image_input:1x384x288x3 \
-  --optShapes=image_input:1x384x288x3 \
-  --maxShapes=image_input:1x384x288x3
-
-mv ./end2end.engine /RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_1x384x288x3"$withFP16"_extra-steps.engine
-```
-
-<br>
-
-## Benchmark
-
-```bash
-cd /mmdeploy/
-export withFP16="_fp16"
-
-python3 ./tools/profiler.py \
-    configs/mmpose/pose-detection_simcc_onnxruntime_static-384x288"$withFP16".py \
-    /mmpose/projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py \
-    /RapidPoseTriangulation/extras/mmdeploy/testimages/ \
-    --model /RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_1x3x384x288"$withFP16".onnx \
-    --shape 384x288 \
-    --device cuda \
-    --warmup 50 \
-    --num-iter 200
-```
@@ -1,233 +0,0 @@
-import re
-
-import numpy as np
-import onnx
-from onnx import TensorProto, helper, numpy_helper
-
-# ==================================================================================================
-
-base_path = "/RapidPoseTriangulation/extras/mmdeploy/exports/"
-det_model_path1 = base_path + "rtmdet-nano_1x3x320x320.onnx"
-det_model_path2 = base_path + "rtmdet-m_1x3x320x320.onnx"
-pose_model_path1 = base_path + "rtmpose-m_Bx3x384x288.onnx"
-pose_model_path2 = base_path + "rtmpose-m_1x3x384x288.onnx"
-pose_model_path3 = base_path + "rtmpose-l_wb_Bx3x384x288.onnx"
-pose_model_path4 = base_path + "rtmpose-l_wb_1x3x384x288.onnx"
-
-norm_mean = -1 * (np.array([0.485, 0.456, 0.406]) * 255)
-norm_std = 1.0 / (np.array([0.229, 0.224, 0.225]) * 255)
-
-
-# ==================================================================================================
-
-
-def add_steps_to_onnx(model_path):
-
-    # Load existing model
-    model = onnx.load(model_path)
-    graph = model.graph
-
-    mean = norm_mean.astype(np.float32)
-    std = norm_std.astype(np.float32)
-
-    mean = np.reshape(mean, (1, 3, 1, 1)).astype(np.float32)
-    std = np.reshape(std, (1, 3, 1, 1)).astype(np.float32)
-
-    use_fp16 = bool("fp16" in model_path)
-    if use_fp16:
-        mean = mean.astype(np.float16)
-        std = std.astype(np.float16)
-
-    # Add the initializers to the graph
-    mean_initializer = numpy_helper.from_array(mean, name="norm_mean")
-    std_initializer = numpy_helper.from_array(std, name="norm_std")
-    graph.initializer.extend([mean_initializer, std_initializer])
-
-    # Define layer names, assuming the first input is the image tensor
-    input_name = graph.input[0].name
-
-    # Cast to internal type
-    # This has to be the first node, because tensorrt does not support uint8 layers
-    cast_type = 10 if use_fp16 else 1
-    casted_output = "casted_output"
-    cast_node = helper.make_node(
-        "Cast",
-        inputs=[input_name],
-        outputs=[casted_output],
-        to=cast_type,
-        name="Cast_Input",
-    )
-
-    # Node to transpose
-    transpose_output = "transpose_output"
-    transpose_node = helper.make_node(
-        "Transpose",
-        inputs=[casted_output],
-        outputs=[transpose_output],
-        perm=[0, 3, 1, 2],
-        name="Transpose",
-    )
-
-    # Node to add mean
-    mean_added_output = "mean_added_output"
-    mean_add_node = helper.make_node(
-        "Add",
-        inputs=[transpose_output, "norm_mean"],
-        outputs=[mean_added_output],
-        name="Mean_Addition",
-    )
-
-    # Node to multiply by std
-    std_mult_output = "std_mult_output"
-    std_mul_node = helper.make_node(
-        "Mul",
-        inputs=[mean_added_output, "norm_std"],
-        outputs=[std_mult_output],
-        name="Std_Multiplication",
-    )
-
-    # Replace original input of the model with the output of normalization
-    for node in graph.node:
-        for idx, input_name_in_node in enumerate(node.input):
-            if input_name_in_node == input_name:
-                node.input[idx] = std_mult_output
-
-    # Add the new nodes to the graph
-    graph.node.insert(0, cast_node)
-    graph.node.insert(1, transpose_node)
-    graph.node.insert(2, mean_add_node)
-    graph.node.insert(3, std_mul_node)
-
-    # Transpose the input shape
-    input_shape = graph.input[0].type.tensor_type.shape.dim
-    dims = [dim.dim_value for dim in input_shape]
-    for i, j in enumerate([0, 3, 1, 2]):
-        input_shape[j].dim_value = dims[i]
-
-    # Set the batch size to a defined string
-    input_shape = graph.input[0].type.tensor_type.shape.dim
-    if input_shape[0].dim_value == 0:
-        input_shape[0].dim_param = "batch_size"
-
-    # Rename the input tensor
-    main_input_image_name = model.graph.input[0].name
-    for node in model.graph.node:
-        for idx, name in enumerate(node.input):
-            if name == main_input_image_name:
-                node.input[idx] = "image_input"
-    model.graph.input[0].name = "image_input"
-
-    # Set input image type to int8
-    model.graph.input[0].type.tensor_type.elem_type = TensorProto.UINT8
-
-    # Cast all outputs to fp32 to avoid half precision issues in cpp code
-    for output in graph.output:
-        orig_output_name = output.name
-        internal_output_name = orig_output_name + "_internal"
-
-        # Rename the output tensor
-        for node in model.graph.node:
-            for idx, name in enumerate(node.output):
-                if name == orig_output_name:
-                    node.output[idx] = internal_output_name
-
-        # Insert a Cast node that casts the internal output to fp32
-        cast_fp32_name = orig_output_name
-        cast_node_output = helper.make_node(
-            "Cast",
-            inputs=[internal_output_name],
-            outputs=[cast_fp32_name],
-            to=1,
-            name="Cast_Output_" + orig_output_name,
-        )
-        # Append the cast node to the graph
-        graph.node.append(cast_node_output)
-
-        # Update the output's data type info
-        output.type.tensor_type.elem_type = TensorProto.FLOAT
-
-    # Merge the two outputs
-    if "det" in model_path:
-        r1_output = "dets"
-        r2_output = "labels"
-        out_name = "bboxes"
-        out_dim = 6
-    if "pose" in model_path:
-        r1_output = "kpts"
-        r2_output = "scores"
-        out_name = "keypoints"
-        out_dim = 3
-    if "det" in model_path or "pose" in model_path:
-        # Node to expand
-        r2_expanded = r2_output + "_expanded"
-        unsqueeze_node = helper.make_node(
-            "Unsqueeze",
-            inputs=[r2_output],
-            outputs=[r2_expanded],
-            axes=[2],
-            name="Unsqueeze",
-        )
-
-        # Node to concatenate
-        r12_merged = out_name
-        concat_node = helper.make_node(
-            "Concat",
-            inputs=[r1_output, r2_expanded],
-            outputs=[r12_merged],
-            axis=2,
-            name="Merged",
-        )
-
-        # Define the new concatenated output
-        merged_output = helper.make_tensor_value_info(
-            r12_merged,
-            TensorProto.FLOAT,
-            [
-                (
-                    graph.input[0].type.tensor_type.shape.dim[0].dim_value
-                    if graph.input[0].type.tensor_type.shape.dim[0].dim_value > 0
-                    else None
-                ),
-                (
-                    graph.output[0].type.tensor_type.shape.dim[1].dim_value
-                    if graph.output[0].type.tensor_type.shape.dim[1].dim_value > 0
-                    else None
-                ),
-                out_dim,
-            ],
-        )
-
-        # Update the graph
-        graph.node.append(unsqueeze_node)
-        graph.node.append(concat_node)
-        graph.output.pop()
-        graph.output.pop()
-        graph.output.append(merged_output)
-
-    path = re.sub(r"(x)(\d+)x(\d+)x(\d+)", r"\1\3x\4x\2", model_path)
-    path = path.replace(".onnx", "_extra-steps.onnx")
-    onnx.save(model, path)
-
-
-# ==================================================================================================
-
-
-def main():
-    add_steps_to_onnx(det_model_path1)
-    add_steps_to_onnx(det_model_path2)
-    add_steps_to_onnx(pose_model_path1)
-    add_steps_to_onnx(pose_model_path2)
-    add_steps_to_onnx(pose_model_path3)
-    add_steps_to_onnx(pose_model_path4)
-    add_steps_to_onnx(det_model_path1.replace(".onnx", "_fp16.onnx"))
-    add_steps_to_onnx(det_model_path2.replace(".onnx", "_fp16.onnx"))
-    add_steps_to_onnx(pose_model_path1.replace(".onnx", "_fp16.onnx"))
-    add_steps_to_onnx(pose_model_path2.replace(".onnx", "_fp16.onnx"))
-    add_steps_to_onnx(pose_model_path3.replace(".onnx", "_fp16.onnx"))
-    add_steps_to_onnx(pose_model_path4.replace(".onnx", "_fp16.onnx"))
-
-
-# ==================================================================================================
-
-if __name__ == "__main__":
-    main()
@@ -1,18 +0,0 @@
-_base_ = ["../_base_/base_static.py", "../../_base_/backends/onnxruntime.py"]
-
-onnx_config = dict(
-    input_shape=[320, 320],
-)
-
-codebase_config = dict(
-    # For later TensorRT inference, the number of output boxes needs to be as stable as possible,
-    # because a drop in the box count leads to a re-optimization which takes a lot of time,
-    # therefore reduce the maximum number of output boxes to the smallest usable value and sort out
-    # low confidence boxes outside the model.
-    post_processing=dict(
-        score_threshold=0.0,
-        confidence_threshold=0.0,
-        iou_threshold=0.5,
-        max_output_boxes_per_class=10,
-    ),
-)
@@ -1,18 +0,0 @@
-_base_ = ["../_base_/base_static.py", "../../_base_/backends/onnxruntime-fp16.py"]
-
-onnx_config = dict(
-    input_shape=[320, 320],
-)
-
-codebase_config = dict(
-    # For later TensorRT inference, the number of output boxes needs to be as stable as possible,
-    # because a drop in the box count leads to a re-optimization which takes a lot of time,
-    # therefore reduce the maximum number of output boxes to the smallest usable value and sort out
-    # low confidence boxes outside the model.
-    post_processing=dict(
-        score_threshold=0.0,
-        confidence_threshold=0.0,
-        iou_threshold=0.5,
-        max_output_boxes_per_class=10,
-    ),
-)
@@ -1,19 +0,0 @@
-_base_ = ["./pose-detection_static.py", "../_base_/backends/onnxruntime.py"]
-
-onnx_config = dict(
-    input_shape=[288, 384],
-    output_names=["kpts", "scores"],
-    dynamic_axes={
-        "input": {
-            0: "batch",
-        },
-        "kpts": {
-            0: "batch",
-        },
-        "scores": {
-            0: "batch",
-        },
-    },
-)
-
-codebase_config = dict(export_postprocess=True)  # export get_simcc_maximum
@@ -1,19 +0,0 @@
-_base_ = ["./pose-detection_static.py", "../_base_/backends/onnxruntime-fp16.py"]
-
-onnx_config = dict(
-    input_shape=[288, 384],
-    output_names=["kpts", "scores"],
-    dynamic_axes={
-        "input": {
-            0: "batch",
-        },
-        "kpts": {
-            0: "batch",
-        },
-        "scores": {
-            0: "batch",
-        },
-    },
-)
-
-codebase_config = dict(export_postprocess=True)  # export get_simcc_maximum
@@ -1,8 +0,0 @@
-_base_ = ["./pose-detection_static.py", "../_base_/backends/onnxruntime.py"]
-
-onnx_config = dict(
-    input_shape=[288, 384],
-    output_names=["kpts", "scores"],
-)
-
-codebase_config = dict(export_postprocess=True)  # export get_simcc_maximum
@@ -1,8 +0,0 @@
-_base_ = ["./pose-detection_static.py", "../_base_/backends/onnxruntime-fp16.py"]
-
-onnx_config = dict(
-    input_shape=[288, 384],
-    output_names=["kpts", "scores"],
-)
-
-codebase_config = dict(export_postprocess=True)  # export get_simcc_maximum
@@ -1,41 +0,0 @@
-FROM openmmlab/mmdeploy:ubuntu20.04-cuda11.8-mmdeploy1.3.1
-
-ARG DEBIAN_FRONTEND=noninteractive
-ENV LANG=C.UTF-8
-ENV LC_ALL=C.UTF-8
-WORKDIR /
-
-RUN apt-get update && apt-get install -y --no-install-recommends feh
-
-RUN git clone https://github.com/open-mmlab/mmdeploy.git --depth=1
-RUN cd mmdeploy/; python3 tools/scripts/build_ubuntu_x64_ort.py
-
-# Install MMPose
-ENV FORCE_CUDA="1"
-ENV MMCV_WITH_OPS=1
-RUN pip3 install --upgrade --no-cache-dir openmim
-RUN mim install mmengine
-RUN mim install "mmcv>=2,<2.2.0"
-RUN mim install "mmdet>=3"
-RUN mim install "mmpose>=1.1.0"
-# Fix an error when importing mmpose
-RUN pip3 install --upgrade --no-cache-dir "numpy<2" scipy
-RUN git clone --depth=1 --branch=main https://github.com/open-mmlab/mmpose.git
-
-RUN echo 'export PYTHONPATH=/mmdeploy/build/lib:$PYTHONPATH' >> ~/.bashrc
-RUN echo 'export LD_LIBRARY_PATH=/mmdeploy/../mmdeploy-dep/onnxruntime-linux-x64-1.8.1/lib/:$LD_LIBRARY_PATH' >> ~/.bashrc
-
-# Show images
-RUN apt-get update && apt-get install -y --no-install-recommends python3-tk
-
-# Tool for fp16 conversion
-RUN pip3 install --upgrade --no-cache-dir onnxconverter_common
-
-# Fix an error when profiling
-RUN pip3 install --upgrade --no-cache-dir "onnxruntime-gpu<1.17"
-
-RUN pip3 install --upgrade --no-cache-dir tensorflow
-RUN pip3 install --upgrade --no-cache-dir tf2onnx
-
-WORKDIR /mmdeploy/
-CMD ["/bin/bash"]
@@ -1,2 +0,0 @@
-*
-!.gitignore
@@ -1,338 +0,0 @@
-import cv2
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.ops import roi_align
-
-# ==================================================================================================
-
-base_path = "/RapidPoseTriangulation/extras/mmdeploy/exports/"
-det_target_size = (320, 320)
-pose_target_size = (384, 288)
-
-# ==================================================================================================
-
-
-class Letterbox(nn.Module):
-    def __init__(self, target_size, fill_value=128):
-        """Resize and pad image while keeping aspect ratio"""
-        super(Letterbox, self).__init__()
-
-        self.target_size = target_size
-        self.fill_value = fill_value
-
-    def calc_params(self, ishape):
-        ih, iw = ishape[1], ishape[2]
-        th, tw = self.target_size
-
-        scale = torch.min(tw / iw, th / ih)
-        nw = torch.round(iw * scale)
-        nh = torch.round(ih * scale)
-
-        pad_w = tw - nw
-        pad_h = th - nh
-        pad_left = pad_w // 2
-        pad_top = pad_h // 2
-        pad_right = pad_w - pad_left
-        pad_bottom = pad_h - pad_top
-        paddings = (pad_left, pad_right, pad_top, pad_bottom)
-
-        return paddings, scale, (nw, nh)
-
-    def forward(self, img):
-        paddings, _, (nw, nh) = self.calc_params(img.shape)
-
-        # Resize the image
-        img = img.to(torch.float32)
-        img = img.permute(0, 3, 1, 2)
-        img = F.interpolate(
-            img,
-            size=(nh, nw),
-            mode="bilinear",
-            align_corners=False,
-        )
-        img = img.permute(0, 2, 3, 1)
-        img = img.round()
-
-        # Pad the image
-        img = F.pad(
-            img.permute(0, 3, 1, 2),
-            pad=paddings,
-            mode="constant",
-            value=self.fill_value,
-        )
-        img = img.permute(0, 2, 3, 1)
-
-        return img
-
-
-# ==================================================================================================
-
-
-class BoxCrop(nn.Module):
-    def __init__(self, target_size):
-        """Crop bounding box from image"""
-        super(BoxCrop, self).__init__()
-
-        self.target_size = target_size
-        self.padding_scale = 1.25
-
-    def calc_params(self, bbox):
-        start_x, start_y, end_x, end_y = bbox[0, 0], bbox[0, 1], bbox[0, 2], bbox[0, 3]
-        target_h, target_w = self.target_size
-
-        # Calculate original bounding box width, height and center
-        bbox_w = end_x - start_x
-        bbox_h = end_y - start_y
-        center_x = (start_x + end_x) / 2.0
-        center_y = (start_y + end_y) / 2.0
-
-        # Calculate the aspect ratios
-        bbox_aspect = bbox_w / bbox_h
-        target_aspect = target_w / target_h
-
-        # Adjust the scaled bounding box to match the target aspect ratio
-        if bbox_aspect > target_aspect:
-            adjusted_h = bbox_w / target_aspect
-            adjusted_w = bbox_w
-        else:
-            adjusted_w = bbox_h * target_aspect
-            adjusted_h = bbox_h
-
-        # Scale the bounding box by the padding_scale
-        scaled_bbox_w = adjusted_w * self.padding_scale
-        scaled_bbox_h = adjusted_h * self.padding_scale
-
-        # Calculate scaled bounding box coordinates
-        new_start_x = center_x - scaled_bbox_w / 2.0
-        new_start_y = center_y - scaled_bbox_h / 2.0
-        new_end_x = center_x + scaled_bbox_w / 2.0
-        new_end_y = center_y + scaled_bbox_h / 2.0
-
-        # Define the new box coordinates
-        new_box = torch.stack((new_start_x, new_start_y, new_end_x, new_end_y), dim=0)
-        new_box = new_box.unsqueeze(0)
-        scale = torch.stack(
-            ((target_w / scaled_bbox_w), (target_h / scaled_bbox_h)), dim=0
-        )
-
-        return scale, new_box
-
-    def forward(self, img, bbox):
-        _, bbox = self.calc_params(bbox)
-
-        batch_indices = torch.zeros(bbox.shape[0], 1)
-        rois = torch.cat([batch_indices, bbox], dim=1)
-
-        # Resize and crop
-        img = img.to(torch.float32)
-        img = img.permute(0, 3, 1, 2)
-        img = roi_align(
-            img,
-            rois,
-            output_size=self.target_size,
-            spatial_scale=1.0,
-            sampling_ratio=0,
-        )
-        img = img.permute(0, 2, 3, 1)
-        img = img.round()
-
-        return img
-
-
-# ==================================================================================================
-
-
-class DetPreprocess(nn.Module):
-    def __init__(self, target_size, fill_value=114):
-        super(DetPreprocess, self).__init__()
-        self.letterbox = Letterbox(target_size, fill_value)
-
-    def forward(self, img):
-        # img: torch.Tensor of shape [batch, H, W, C], dtype=torch.uint8
-        img = self.letterbox(img)
-        return img
-
-
-# ==================================================================================================
-
-
-class DetPostprocess(nn.Module):
-    def __init__(self, target_size):
-        super(DetPostprocess, self).__init__()
-
-        self.target_size = target_size
-        self.letterbox = Letterbox(target_size)
-
-    def forward(self, img, boxes):
-        paddings, scale, _ = self.letterbox.calc_params(img.shape)
-
-        boxes = boxes.float()
-        boxes[:, :, 0] -= paddings[0]
-        boxes[:, :, 2] -= paddings[0]
-        boxes[:, :, 1] -= paddings[2]
-        boxes[:, :, 3] -= paddings[2]
-
-        zero = torch.tensor(0)
-        boxes = torch.max(boxes, zero)
-
-        th, tw = self.target_size
-        pad_w = paddings[0] + paddings[1]
-        pad_h = paddings[2] + paddings[3]
-        max_w = tw - pad_w - 1
-        max_h = th - pad_h - 1
-        b0 = boxes[:, :, 0]
-        b1 = boxes[:, :, 1]
-        b2 = boxes[:, :, 2]
-        b3 = boxes[:, :, 3]
-        b0 = torch.min(b0, max_w)
-        b1 = torch.min(b1, max_h)
-        b2 = torch.min(b2, max_w)
-        b3 = torch.min(b3, max_h)
-        boxes[:, :, 0] = b0
-        boxes[:, :, 1] = b1
-        boxes[:, :, 2] = b2
-        boxes[:, :, 3] = b3
-
-        boxes[:, :, 0:4] /= scale
-        return boxes
-
-
-# ==================================================================================================
-
-
-class PosePreprocess(nn.Module):
-    def __init__(self, target_size, fill_value=114):
-        super(PosePreprocess, self).__init__()
-        self.boxcrop = BoxCrop(target_size)
-
-    def forward(self, img, bbox):
-        # img: torch.Tensor of shape [1, H, W, C], dtype=torch.uint8
-        # bbox: torch.Tensor of shape [1, 4], dtype=torch.float32
-        img = self.boxcrop(img, bbox)
-        return img
-
-
-# ==================================================================================================
-
-
-class PosePostprocess(nn.Module):
-    def __init__(self, target_size):
-        super(PosePostprocess, self).__init__()
-        self.boxcrop = BoxCrop(target_size)
-        self.target_size = target_size
-
-    def forward(self, img, bbox, keypoints):
-        scale, bbox = self.boxcrop.calc_params(bbox)
-
-        kp = keypoints.float()
-        kp[:, :, 0:2] /= scale
-        kp[:, :, 0] += bbox[0, 0]
-        kp[:, :, 1] += bbox[0, 1]
-
-        zero = torch.tensor(0)
-        kp = torch.max(kp, zero)
-
-        max_w = img.shape[2] - 1
-        max_h = img.shape[1] - 1
-        k0 = kp[:, :, 0]
-        k1 = kp[:, :, 1]
-        k0 = torch.min(k0, max_w)
-        k1 = torch.min(k1, max_h)
-        kp[:, :, 0] = k0
-        kp[:, :, 1] = k1
-
-        return kp
-
-
-# ==================================================================================================
-
-
-def main():
-
-    img_path = "/RapidPoseTriangulation/scripts/../data/h1/54138969-img_003201.jpg"
-    image = cv2.imread(img_path, 3)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    # Initialize the DetPreprocess module
-    preprocess_model = DetPreprocess(target_size=det_target_size)
-    det_dummy_input_a0 = torch.from_numpy(image).unsqueeze(0)
-
-    # Export to ONNX
-    torch.onnx.export(
-        preprocess_model,
-        det_dummy_input_a0,
-        base_path + "det_preprocess.onnx",
-        opset_version=11,
-        input_names=["input_image"],
-        output_names=["preprocessed_image"],
-        dynamic_axes={
-            "input_image": {0: "batch_size", 1: "height", 2: "width"},
-            "preprocessed_image": {0: "batch_size"},
-        },
-    )
-
-    # Initialize the DetPostprocess module
-    postprocess_model = DetPostprocess(target_size=det_target_size)
-    det_dummy_input_b0 = torch.from_numpy(image).unsqueeze(0)
-    det_dummy_input_b1 = torch.rand(1, 10, 5)
-
-    # Export to ONNX
-    torch.onnx.export(
-        postprocess_model,
-        (det_dummy_input_b0, det_dummy_input_b1),
-        base_path + "det_postprocess.onnx",
-        opset_version=11,
-        input_names=["input_image", "boxes"],
-        output_names=["output_boxes"],
-        dynamic_axes={
-            "input_image": {0: "batch_size", 1: "height", 2: "width"},
-            "boxes": {0: "batch_size", 1: "num_boxes"},
-            "output_boxes": {0: "batch_size", 1: "num_boxes"},
-        },
-    )
-
-    # Initialize the PosePreprocess module
-    preprocess_model = PosePreprocess(target_size=pose_target_size)
-    det_dummy_input_c0 = torch.from_numpy(image).unsqueeze(0)
-    det_dummy_input_c1 = torch.tensor([[352, 339, 518, 594]]).to(torch.int32)
-
-    # Export to ONNX
-    torch.onnx.export(
-        preprocess_model,
-        (det_dummy_input_c0, det_dummy_input_c1),
-        base_path + "pose_preprocess.onnx",
-        opset_version=11,
-        input_names=["input_image", "bbox"],
-        output_names=["preprocessed_image"],
-        dynamic_axes={
-            "input_image": {0: "batch_size", 1: "height", 2: "width"},
-            "preprocessed_image": {0: "batch_size"},
-        },
-    )
-
-    # Initialize the PosePostprocess module
-    postprocess_model = PosePostprocess(target_size=pose_target_size)
-    det_dummy_input_d0 = torch.from_numpy(image).unsqueeze(0)
-    det_dummy_input_d1 = torch.tensor([[352, 339, 518, 594]]).to(torch.int32)
-    det_dummy_input_d2 = torch.rand(1, 17, 2)
-
-    # Export to ONNX
-    torch.onnx.export(
-        postprocess_model,
-        (det_dummy_input_d0, det_dummy_input_d1, det_dummy_input_d2),
-        base_path + "pose_postprocess.onnx",
-        opset_version=11,
-        input_names=["input_image", "bbox", "keypoints"],
-        output_names=["output_keypoints"],
-        dynamic_axes={
-            "input_image": {0: "batch_size", 1: "height", 2: "width"},
-            "output_keypoints": {0: "batch_size"},
-        },
-    )
-
-
-# ==================================================================================================
-
-if __name__ == "__main__":
-    main()
@@ -1,276 +0,0 @@
-import cv2
-
-import numpy as np
-import tensorflow as tf
-import tf2onnx
-
-# ==================================================================================================
-
-base_path = "/RapidPoseTriangulation/extras/mmdeploy/exports/"
-det_target_size = (320, 320)
-
-# ==================================================================================================
-
-
-class BayerToRGB(tf.keras.layers.Layer):
-    """Convert Bayer image to RGB
-    See: https://stanford.edu/class/ee367/reading/Demosaicing_ICASSP04.pdf
-    See: https://github.com/cheind/pytorch-debayer/blob/master/debayer/modules.py#L231
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.layout = "RGGB"
-        self.max_val = 255.0
-
-        self.kernels = tf.constant(
-            np.array(
-                [
-                    # G at R/B locations
-                    [
-                        [0, 0, -1, 0, 0],
-                        [0, 0, 2, 0, 0],
-                        [-1, 2, 4, 2, -1],
-                        [0, 0, 2, 0, 0],
-                        [0, 0, -1, 0, 0],
-                    ],
-                    # R/B at G in R/B rows and B/R columns
-                    [
-                        [0, 0, 0.5, 0, 0],
-                        [0, -1, 0, -1, 0],
-                        [-1, 4, 5, 4, -1],
-                        [0, -1, 0, -1, 0],
-                        [0, 0, 0.5, 0, 0],
-                    ],
-                    # R/B at G in B/R rows and R/B columns
-                    [
-                        [0, 0, 0.5, 0, 0],
-                        [0, -1, 4, -1, 0],
-                        [-1, 0, 5, 0, -1],
-                        [0, -1, 4, -1, 0],
-                        [0, 0, 0.5, 0, 0],
-                    ],
-                    # R/B at B/R in B/R rows and B/R columns
-                    [
-                        [0, 0, -1.5, 0, 0],
-                        [0, 2, 0, 2, 0],
-                        [-1.5, 0, 6, 0, -1.5],
-                        [0, 2, 0, 2, 0],
-                        [0, 0, -1.5, 0, 0],
-                    ],
-                ],
-                dtype=np.float32,
-            )
-            .reshape(1, 4, 5, 5)
-            .transpose(2, 3, 0, 1)
-            / 8.0
-        )
-        self.index = tf.constant(
-            np.array(
-                # Describes the kernel indices that calculate the corresponding RGB values for
-                # the 2x2 layout (RGGB)  sub-structure
-                [
-                    # Destination R
-                    [
-                        [4, 1],  # identity, R at G in R row  B column
-                        [2, 3],  # R at G in B row R column, R at B in B row R column
-                    ],
-                    # Destination G
-                    [
-                        [0, 4],
-                        [4, 0],
-                    ],
-                    # Destination B
-                    [
-                        [3, 2],
-                        [1, 4],
-                    ],
-                ]
-            ).reshape(1, 3, 2, 2)
-        )
-
-    def call(self, img):
-        H, W = tf.shape(img)[1], tf.shape(img)[2]
-
-        # Pad the image
-        tpad = img[:, 0:2, :, :]
-        bpad = img[:, H - 2 : H, :, :]
-        ipad = tf.concat([tpad, img, bpad], axis=1)
-        lpad = ipad[:, :, 0:2, :]
-        rpad = ipad[:, :, W - 2 : W, :]
-        ipad = tf.concat([lpad, ipad, rpad], axis=2)
-
-        # Convolve with kernels
-        planes = tf.nn.conv2d(ipad, self.kernels, strides=[1, 1, 1, 1], padding="VALID")
-
-        # Concatenate identity kernel
-        planes = tf.concat([planes, img], axis=-1)
-
-        # Gather values
-        index_repeated = tf.tile(self.index, multiples=[1, 1, H // 2, W // 2])
-        index_repeated = tf.transpose(index_repeated, perm=[0, 2, 3, 1])
-        row_indices, col_indices = tf.meshgrid(tf.range(H), tf.range(W), indexing="ij")
-        index_tensor = tf.stack([row_indices, col_indices], axis=-1)
-        index_tensor = tf.expand_dims(index_tensor, axis=0)
-        index_tensor = tf.expand_dims(index_tensor, axis=-2)
-        index_tensor = tf.repeat(index_tensor, repeats=3, axis=-2)
-        index_repeated = tf.expand_dims(index_repeated, axis=-1)
-        indices = tf.concat([tf.cast(index_tensor, tf.int64), index_repeated], axis=-1)
-        rgb = tf.gather_nd(planes, indices, batch_dims=1)
-
-        if self.max_val == 255.0:
-            # Make value range valid again
-            rgb = tf.round(rgb)
-
-        return rgb
-
-
-# ==================================================================================================
-
-
-def bayer_resize(img, size):
-    """Resize a Bayer image by splitting color channels"""
-
-    # Split the image into 4 channels
-    r = img[:, 0::2, 0::2, 0]
-    g1 = img[:, 0::2, 1::2, 0]
-    g2 = img[:, 1::2, 0::2, 0]
-    b = img[:, 1::2, 1::2, 0]
-    bsplit = tf.stack([r, g1, g2, b], axis=-1)
-
-    # Resize the image
-    # Make sure the target size is divisible by 2
-    size = (size[0] // 2, size[1] // 2)
-    bsized = tf.image.resize(bsplit, size=size, method="bilinear")
-
-    # Create a bayer image again
-    img = tf.nn.depth_to_space(bsized, block_size=2)
-
-    return img
-
-
-# ==================================================================================================
-
-
-class Letterbox(tf.keras.layers.Layer):
-    def __init__(self, target_size, fill_value=128):
-        """Resize and pad image while keeping aspect ratio"""
-        super(Letterbox, self).__init__()
-
-        self.b2rgb = BayerToRGB()
-        self.target_size = target_size
-        self.fill_value = fill_value
-
-    def calc_params(self, ishape):
-        img_h, img_w = ishape[1], ishape[2]
-        target_h, target_w = self.target_size
-
-        scale = tf.minimum(target_w / img_w, target_h / img_h)
-        new_w = tf.round(tf.cast(img_w, scale.dtype) * scale)
-        new_h = tf.round(tf.cast(img_h, scale.dtype) * scale)
-        new_w = tf.cast(new_w, tf.int32)
-        new_h = tf.cast(new_h, tf.int32)
-        new_w = new_w - (new_w % 2)
-        new_h = new_h - (new_h % 2)
-
-        pad_w = target_w - new_w
-        pad_h = target_h - new_h
-        pad_left = tf.cast(tf.floor(tf.cast(pad_w, tf.float32) / 2.0), tf.int32)
-        pad_top = tf.cast(tf.floor(tf.cast(pad_h, tf.float32) / 2.0), tf.int32)
-        pad_right = pad_w - pad_left
-        pad_bottom = pad_h - pad_top
-        paddings = [pad_top, pad_bottom, pad_left, pad_right]
-
-        return paddings, scale, (new_w, new_h)
-
-    def call(self, img):
-        paddings, _, (nw, nh) = self.calc_params(tf.shape(img))
-
-        # Resize the image and convert to RGB
-        img = bayer_resize(img, (nh, nw))
-        img = self.b2rgb(img)
-
-        # Pad the image
-        pad_top, pad_bottom, pad_left, pad_right = paddings
-        img = tf.pad(
-            img,
-            paddings=[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]],
-            mode="CONSTANT",
-            constant_values=self.fill_value,
-        )
-
-        return img
-
-
-# ==================================================================================================
-
-
-class DetPreprocess(tf.keras.layers.Layer):
-    def __init__(self, target_size, fill_value=114):
-        super(DetPreprocess, self).__init__()
-        self.letterbox = Letterbox(target_size, fill_value)
-
-    def call(self, img):
-        """img: tf.Tensor of shape [batch, H, W, C], dtype=tf.uint8"""
-
-        # Cast to float32 since TensorRT does not support uint8 layers
-        img = tf.cast(img, tf.float32)
-
-        img = self.letterbox(img)
-        return img
-
-
-# ==================================================================================================
-
-
-def rgb2bayer(img):
-    bayer = np.zeros((img.shape[0], img.shape[1]), dtype=img.dtype)
-    bayer[0::2, 0::2] = img[0::2, 0::2, 0]
-    bayer[0::2, 1::2] = img[0::2, 1::2, 1]
-    bayer[1::2, 0::2] = img[1::2, 0::2, 1]
-    bayer[1::2, 1::2] = img[1::2, 1::2, 2]
-    return bayer
-
-
-# ==================================================================================================
-
-
-def main():
-
-    img_path = "/RapidPoseTriangulation/scripts/../data/h1/54138969-img_003201.jpg"
-    image = cv2.imread(img_path, 3)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    image = rgb2bayer(image)
-    image = np.expand_dims(image, axis=-1)
-    image = np.asarray(image, dtype=np.uint8)
-
-    # Initialize the DetPreprocess module
-    preprocess_model = tf.keras.Sequential()
-    preprocess_model.add(DetPreprocess(target_size=det_target_size))
-    det_dummy_input_a0 = tf.convert_to_tensor(
-        np.expand_dims(image, axis=0), dtype=tf.uint8
-    )
-    det_dummy_output_a0 = preprocess_model(det_dummy_input_a0)
-    print("\n", det_dummy_output_a0.shape, "\n")
-
-    output_a0 = det_dummy_output_a0.numpy()
-    output_a0 = np.squeeze(output_a0, axis=0)
-    output_a0 = np.asarray(output_a0, dtype=np.uint8)
-    output_a0 = cv2.cvtColor(output_a0, cv2.COLOR_RGB2BGR)
-    cv2.imwrite(base_path + "det_preprocess.jpg", output_a0)
-
-    # Export to ONNX
-    input_signature = [tf.TensorSpec([None, None, None, 1], tf.uint8, name="x")]
-    _, _ = tf2onnx.convert.from_keras(
-        preprocess_model,
-        input_signature,
-        opset=11,
-        output_path=base_path + "det_preprocess.onnx",
-        target=["tensorrt"],
-    )
-
-
-# ==================================================================================================
-
-if __name__ == "__main__":
-    main()
@@ -1,9 +0,0 @@
-#! /bin/bash
-
-xhost +
-docker run --privileged --rm --network host -it \
-  --gpus all --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
-  --volume "$(pwd)"/:/RapidPoseTriangulation/ \
-  --volume /tmp/.X11-unix:/tmp/.X11-unix \
-  --env DISPLAY --env QT_X11_NO_MITSHM=1 \
-  rpt_mmdeploy
@@ -1,23 +0,0 @@
-# Finetuning MMPose models
-
-See: <https://mmpose.readthedocs.io/en/latest/user_guides/train_and_test.html>
-
-<br>
-
-```bash
-docker build --progress=plain -f extras/mmpose/dockerfile -t rpt_mmpose .
-
-./extras/mmpose/run_container.sh
-```
-
-```bash
-cd /mmpose/
-export CUDA_VISIBLE_DEVICES=0
-
-python3 ./tools/train.py \
-    /RapidPoseTriangulation/extras/mmpose/configs/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py \
-    --amp \
-    --cfg-options \
-      load_from=https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth \
-      base_lr=0.00004
-```
@@ -1,235 +0,0 @@
-_base_ = ['mmpose::_base_/default_runtime.py']
-
-val_interval=1
-max_epochs = 3
-
-# common setting
-num_keypoints = 133
-input_size = (288, 384)
-
-# runtime
-stage2_num_epochs = 30
-base_lr = 4e-3
-train_batch_size = 32
-val_batch_size = 32
-
-train_cfg = dict(max_epochs=max_epochs, val_interval=val_interval)
-randomness = dict(seed=21)
-
-# optimizer
-optim_wrapper = dict(
-    type='OptimWrapper',
-    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
-    clip_grad=dict(max_norm=35, norm_type=2),
-    paramwise_cfg=dict(
-        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
-
-# learning rate
-param_scheduler = [
-    dict(
-        type='LinearLR',
-        start_factor=1.0e-5,
-        by_epoch=False,
-        begin=0,
-        end=1000),
-    dict(
-        type='CosineAnnealingLR',
-        eta_min=base_lr * 0.05,
-        begin=max_epochs // 2,
-        end=max_epochs,
-        T_max=max_epochs // 2,
-        by_epoch=True,
-        convert_to_iter_based=True),
-]
-
-# automatically scaling LR based on the actual training batch size
-auto_scale_lr = dict(base_batch_size=512)
-
-# codec settings
-codec = dict(
-    type='SimCCLabel',
-    input_size=input_size,
-    sigma=(6., 6.93),
-    simcc_split_ratio=2.0,
-    normalize=False,
-    use_dark=False)
-
-# model settings
-model = dict(
-    type='TopdownPoseEstimator',
-    data_preprocessor=dict(
-        type='PoseDataPreprocessor',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        bgr_to_rgb=True),
-    backbone=dict(
-        _scope_='mmdet',
-        type='CSPNeXt',
-        arch='P5',
-        expand_ratio=0.5,
-        deepen_factor=1.,
-        widen_factor=1.,
-        out_indices=(4, ),
-        channel_attention=True,
-        norm_cfg=dict(type='SyncBN'),
-        act_cfg=dict(type='SiLU'),
-        init_cfg=dict(
-            type='Pretrained',
-            prefix='backbone.',
-            checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
-            'rtmposev1/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth'  # noqa
-        )),
-    head=dict(
-        type='RTMCCHead',
-        in_channels=1024,
-        out_channels=num_keypoints,
-        input_size=codec['input_size'],
-        in_featuremap_size=tuple([s // 32 for s in codec['input_size']]),
-        simcc_split_ratio=codec['simcc_split_ratio'],
-        final_layer_kernel_size=7,
-        gau_cfg=dict(
-            hidden_dims=256,
-            s=128,
-            expansion_factor=2,
-            dropout_rate=0.,
-            drop_path=0.,
-            act_fn='SiLU',
-            use_rel_bias=False,
-            pos_enc=False),
-        loss=dict(
-            type='KLDiscretLoss',
-            use_target_weight=True,
-            beta=10.,
-            label_softmax=True),
-        decoder=codec),
-    test_cfg=dict(flip_test=True, ))
-
-# base dataset settings
-dataset_type = 'CocoWholeBodyDataset'
-data_mode = 'topdown'
-data_root = 'data/coco/'
-
-backend_args = dict(backend='local')
-
-# pipelines
-train_pipeline = [
-    dict(type='LoadImage', backend_args=backend_args),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='RandomFlip', direction='horizontal'),
-    dict(type='RandomHalfBody'),
-    dict(
-        type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80),
-    dict(type='TopdownAffine', input_size=codec['input_size']),
-    dict(type='mmdet.YOLOXHSVRandomAug'),
-    dict(
-        type='Albumentation',
-        transforms=[
-            dict(type='Blur', p=0.1),
-            dict(type='MedianBlur', p=0.1),
-            dict(
-                type='CoarseDropout',
-                max_holes=1,
-                max_height=0.4,
-                max_width=0.4,
-                min_holes=1,
-                min_height=0.2,
-                min_width=0.2,
-                p=1.0),
-        ]),
-    dict(type='GenerateTarget', encoder=codec),
-    dict(type='PackPoseInputs')
-]
-val_pipeline = [
-    dict(type='LoadImage', backend_args=backend_args),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='TopdownAffine', input_size=codec['input_size']),
-    dict(type='PackPoseInputs')
-]
-
-train_pipeline_stage2 = [
-    dict(type='LoadImage', backend_args=backend_args),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='RandomFlip', direction='horizontal'),
-    dict(type='RandomHalfBody'),
-    dict(
-        type='RandomBBoxTransform',
-        shift_factor=0.,
-        scale_factor=[0.75, 1.25],
-        rotate_factor=60),
-    dict(type='TopdownAffine', input_size=codec['input_size']),
-    dict(type='mmdet.YOLOXHSVRandomAug'),
-    dict(
-        type='Albumentation',
-        transforms=[
-            dict(type='Blur', p=0.1),
-            dict(type='MedianBlur', p=0.1),
-            dict(
-                type='CoarseDropout',
-                max_holes=1,
-                max_height=0.4,
-                max_width=0.4,
-                min_holes=1,
-                min_height=0.2,
-                min_width=0.2,
-                p=0.5),
-        ]),
-    dict(type='GenerateTarget', encoder=codec),
-    dict(type='PackPoseInputs')
-]
-
-# data loaders
-train_dataloader = dict(
-    batch_size=train_batch_size,
-    num_workers=10,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_mode=data_mode,
-        ann_file='annotations/coco_wholebody_train_v1.0.json',
-        data_prefix=dict(img='train2017/'),
-        pipeline=train_pipeline,
-    ))
-val_dataloader = dict(
-    batch_size=val_batch_size,
-    num_workers=10,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
-    dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_mode=data_mode,
-        ann_file='annotations/coco_wholebody_val_v1.0.json',
-        data_prefix=dict(img='val2017/'),
-        test_mode=True,
-        # bbox_file='data/coco/person_detection_results/'
-        # 'COCO_val2017_detections_AP_H_56_person.json',
-        pipeline=val_pipeline,
-    ))
-test_dataloader = val_dataloader
-
-# hooks
-default_hooks = dict(
-    checkpoint=dict(
-        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))
-
-custom_hooks = [
-    dict(
-        type='EMAHook',
-        ema_type='ExpMomentumEMA',
-        momentum=0.0002,
-        update_buffers=True,
-        priority=49),
-    dict(
-        type='mmdet.PipelineSwitchHook',
-        switch_epoch=max_epochs - stage2_num_epochs,
-        switch_pipeline=train_pipeline_stage2)
-]
-
-# evaluators
-val_evaluator = dict(
-    type='CocoWholeBodyMetric',
-    ann_file=data_root + 'annotations/coco_wholebody_val_v1.0.json')
-test_evaluator = val_evaluator
@@ -1,9 +0,0 @@
-FROM rpt_mmdeploy
-
-RUN apt-get update && apt-get install -y --no-install-recommends nano
-RUN pip3 install --upgrade --no-cache-dir "albumentations<1.4"
-
-RUN sed -i '94i\        self.runner.val_loop.run()' /usr/local/lib/python3.8/dist-packages/mmengine/runner/loops.py
-
-WORKDIR /mmpose/
-CMD ["/bin/bash"]
@@ -1,11 +0,0 @@
-#! /bin/bash
-
-xhost +
-docker run --privileged --rm --network host -it \
-  --gpus all --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
-  --volume "$(pwd)"/:/RapidPoseTriangulation/ \
-  --volume "$(pwd)"/../datasets/coco2017/annotations/:/mmpose/data/coco/annotations/ \
-  --volume "$(pwd)"/../datasets/coco2017/images/:/mmpose/data/coco/ \
-  --volume /tmp/.X11-unix:/tmp/.X11-unix \
-  --env DISPLAY --env QT_X11_NO_MITSHM=1 \
-  rpt_mmpose
@@ -1,24 +0,0 @@
-# ROS-Wrapper
-
-Run pose estimator with ros topics as inputs and publish detected poses.
-
-<br>
-
- Build container:
-
-  ```bash
-  docker build --progress=plain -t rapidposetriangulation_ros2d -f extras/ros/dockerfile_2d .
-  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 + && export CAMID="camera01" && docker compose -f extras/ros/docker-compose-2d.yml up
-  xhost + && docker compose -f extras/ros/docker-compose-3d.yml up
-
-  docker exec -it ros-test_node-1 bash
-  export ROS_DOMAIN_ID=18
-  ```
@@ -1,71 +0,0 @@
-services:
-
-    test_node:
-        image: rapidposetriangulation_ros2d
-        network_mode: "host"
-        ipc: "host"
-        runtime: nvidia
-        privileged: true
-        volumes:
-            - ../../:/RapidPoseTriangulation/
-            - ../../skelda/:/skelda/
-            - /tmp/.X11-unix:/tmp/.X11-unix
-            - /dev/shm:/dev/shm
-        environment:
-            - CAMID
-            - DISPLAY
-            - QT_X11_NO_MITSHM=1
-            - "PYTHONUNBUFFERED=1"
-        command: /bin/bash -i -c 'sleep infinity'
-
-    estimator:
-        image: rapidposetriangulation_ros2d
-        network_mode: "host"
-        ipc: "host"
-        runtime: nvidia
-        privileged: true
-        volumes:
-            - ../../:/RapidPoseTriangulation/
-            - ../../skelda/:/skelda/
-            - /tmp/.X11-unix:/tmp/.X11-unix
-            - /dev/shm:/dev/shm
-        environment:
-            - CAMID
-            - DISPLAY
-            - QT_X11_NO_MITSHM=1
-            - "PYTHONUNBUFFERED=1"
-        command: /bin/bash -i -c 'export ROS_DOMAIN_ID=18 && ros2 run rpt2d_wrapper_cpp rpt2d_wrapper'
-
-    pose_visualizer:
-        image: rapidposetriangulation_ros2d
-        network_mode: "host"
-        ipc: "host"
-        runtime: nvidia
-        privileged: true
-        volumes:
-            - ../../:/RapidPoseTriangulation/
-            - ../../skelda/:/skelda/
-            - /tmp/.X11-unix:/tmp/.X11-unix
-            - /dev/shm:/dev/shm
-        environment:
-            - CAMID
-            - DISPLAY
-            - QT_X11_NO_MITSHM=1
-            - "PYTHONUNBUFFERED=1"
-        command: /bin/bash -i -c 'sleep 2 && export ROS_DOMAIN_ID=18 && ros2 run pose2d_visualizer pose2d_visualizer $CAMID'
-
-    pose_viewer:
-        image: rapidposetriangulation_ros2d
-        network_mode: "host"
-        ipc: "host"
-        runtime: nvidia
-        privileged: true
-        volumes:
-            - /tmp/.X11-unix:/tmp/.X11-unix
-            - /dev/shm:/dev/shm
-        environment:
-            - CAMID
-            - DISPLAY
-            - QT_X11_NO_MITSHM=1
-            - "PYTHONUNBUFFERED=1"
-        command: /bin/bash -i -c 'sleep 2 && export ROS_DOMAIN_ID=18 && ros2 run image_view image_view --ros-args --remap image:=/$CAMID/img_with_pose -p autosize:=True -p window_name:=MyPoseImage'
@@ -1,72 +0,0 @@
-services:
-
-    test_node:
-        image: rapidposetriangulation_ros3d
-        network_mode: "host"
-        ipc: "host"
-        privileged: true
-        volumes:
-            - ../../:/RapidPoseTriangulation/
-            - ../../skelda/:/skelda/
-            - /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/
-            - ../../skelda/:/skelda/
-            - /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'
@@ -1,67 +0,0 @@
-FROM rapidposetriangulation
-WORKDIR /
-
-# Install ROS2
-# https://docs.ros.org/en/humble/Installation/Ubuntu-Install-Debians.html
-RUN apt-get update && apt-get install -y --no-install-recommends locales
-RUN locale-gen en_US en_US.UTF-8 && update-locale LC_ALL=en_US.UTF-8 LANG=en_US.UTF-8
-RUN apt-get update && apt-get install -y --no-install-recommends software-properties-common
-RUN add-apt-repository universe
-RUN apt-get update && apt-get install -y --no-install-recommends curl
-RUN curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg
-RUN echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(. /etc/os-release && echo $UBUNTU_CODENAME) main" > /etc/apt/sources.list.d/ros2.list
-RUN apt-get update && apt-get install -y --no-install-recommends ros-humble-ros-base python3-argcomplete
-RUN apt-get update && apt-get install -y --no-install-recommends ros-dev-tools
-RUN echo "source /opt/ros/humble/setup.bash" >> ~/.bashrc
-
-# Fix ros package building error
-RUN pip3 install --no-cache-dir "setuptools<=58.2.0"
-
-# Create ROS2 workspace for basic packages
-RUN mkdir -p /project/base/src/
-RUN cd /project/base/; colcon build
-RUN echo "source /project/base/install/setup.bash" >> ~/.bashrc
-
-# Install opencv and cv_bridge
-RUN apt-get update && apt-get install -y --no-install-recommends libboost-dev
-RUN apt-get update && apt-get install -y --no-install-recommends libboost-python-dev
-RUN apt-get update && apt-get install -y --no-install-recommends libopencv-dev
-RUN cd /project/base/src/; git clone --branch humble --depth=1 https://github.com/ros-perception/vision_opencv.git
-RUN /bin/bash -i -c 'cd /project/base/; colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release'
-
-# Install ROS2 image viewer
-RUN cd /project/base/src/; git clone --branch=humble --depth=1 https://github.com/ros-perception/image_pipeline.git
-RUN cd /project/base/src/; git clone --branch=humble --depth=1 https://github.com/ros-perception/image_common.git
-RUN /bin/bash -i -c 'cd /project/base/; colcon build --symlink-install --packages-select camera_calibration_parsers image_transport image_view --cmake-args -DCMAKE_BUILD_TYPE=Release'
-
-# Fix module not found error when displaying images
-RUN apt-get update && apt-get install -y --no-install-recommends libcanberra-gtk-module libcanberra-gtk3-module
-
-# 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 ./scripts/ /RapidPoseTriangulation/scripts/
-COPY ./extras/ros/rpt_msgs/ /RapidPoseTriangulation/extras/ros/rpt_msgs/
-COPY ./extras/ros/pose2d_visualizer/ /RapidPoseTriangulation/extras/ros/pose2d_visualizer/
-COPY ./extras/ros/rpt2d_wrapper_cpp/ /RapidPoseTriangulation/extras/ros/rpt2d_wrapper_cpp/
-
-# Link and build as ros package
-RUN ln -s /RapidPoseTriangulation/extras/ros/rpt_msgs/ /project/dev_ws/src/
-RUN ln -s /RapidPoseTriangulation/extras/ros/pose2d_visualizer/ /project/dev_ws/src/
-RUN ln -s /RapidPoseTriangulation/extras/ros/rpt2d_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"]
@@ -1,46 +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()
@@ -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;
-}
@@ -1,20 +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>pose2d_visualizer</name>
-  <version>0.0.0</version>
-  <description>TODO: Package description</description>
-  <maintainer email="root@todo.todo">root</maintainer>
-  <license>TODO: License declaration</license>
-
-  <exec_depend>rpt_msgs</exec_depend>
-
-  <test_depend>ament_copyright</test_depend>
-  <test_depend>ament_flake8</test_depend>
-  <test_depend>ament_pep257</test_depend>
-  <test_depend>python3-pytest</test_depend>
-
-  <export>
-    <build_type>ament_python</build_type>
-  </export>
-</package>
@@ -1,158 +0,0 @@
-import sys
-import threading
-import time
-
-import cv2
-from matplotlib import pyplot as plt
-import numpy as np
-import rclpy
-from cv_bridge import CvBridge
-from rclpy.qos import QoSHistoryPolicy, QoSProfile, QoSReliabilityPolicy
-from sensor_msgs.msg import Image
-
-from rpt_msgs.msg import Poses
-from skelda import utils_view
-
-# ==================================================================================================
-
-bridge = CvBridge()
-node = None
-publisher_img = None
-
-img_input_topic = "/{}/pylon_ros2_camera_node/image_raw"
-pose_input_topic = "/poses/{}"
-img_output_topic = "/{}/img_with_pose"
-
-last_input_image = None
-lock = threading.Lock()
-
-# ==================================================================================================
-
-
-def bayer2rgb(bayer):
-    img = cv2.cvtColor(bayer, cv2.COLOR_BayerBG2RGB)
-    return img
-
-
-# ==================================================================================================
-
-
-def callback_images(image_data):
-    global last_input_image, lock
-
-    # Convert into cv images from image string
-    if image_data.encoding == "bayer_rggb8":
-        bayer_image = bridge.imgmsg_to_cv2(image_data, "bayer_rggb8")
-        color_image = bayer2rgb(bayer_image)
-    elif image_data.encoding == "mono8":
-        gray_image = bridge.imgmsg_to_cv2(image_data, "mono8")
-        color_image = cv2.cvtColor(gray_image, cv2.COLOR_GRAY2RGB)
-    elif image_data.encoding == "rgb8":
-        color_image = bridge.imgmsg_to_cv2(image_data, "rgb8")
-    else:
-        raise ValueError("Unknown image encoding:", image_data.encoding)
-
-    time_stamp = image_data.header.stamp.sec + image_data.header.stamp.nanosec / 1.0e9
-
-    with lock:
-        last_input_image = (color_image, time_stamp)
-
-
-# ==================================================================================================
-
-
-def callback_poses(pose_data):
-    global last_input_image, lock
-
-    ptime = time.time()
-    if last_input_image is None:
-        return
-
-    # Extract pose data
-    joint_names = pose_data.joint_names
-    bodies2D = np.array(pose_data.bodies_flat).reshape(pose_data.bodies_shape).tolist()
-
-    # Collect inputs
-    images_2d = []
-    timestamps = []
-    with lock:
-        img = np.copy(last_input_image[0])
-        ts = last_input_image[1]
-        images_2d.append(img)
-        timestamps.append(ts)
-
-    # Visualize 2D poses
-    colors = plt.cm.hsv(np.linspace(0, 1, len(bodies2D), endpoint=False)).tolist()
-    colors = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
-    for i, body in enumerate(bodies2D):
-        color = list(reversed(colors[i]))
-        img = utils_view.draw_body_in_image(img, body, joint_names, color)
-
-    # Publish image with poses
-    publish(img)
-
-    msg = "Visualization time: {:.3f}s"
-    print(msg.format(time.time() - ptime))
-
-
-# ==================================================================================================
-
-
-def publish(img):
-    # Publish image data
-    msg = bridge.cv2_to_imgmsg(img, "rgb8")
-    publisher_img.publish(msg)
-
-
-# ==================================================================================================
-
-
-def main():
-    global node, publisher_img
-
-    # Start node
-    rclpy.init(args=sys.argv)
-    cam_id = sys.argv[1]
-    node = rclpy.create_node("pose2d_visualizer")
-
-    # Quality of service settings
-    qos_profile = QoSProfile(
-        reliability=QoSReliabilityPolicy.RELIABLE,
-        history=QoSHistoryPolicy.KEEP_LAST,
-        depth=1,
-    )
-
-    # Create subscribers
-    _ = node.create_subscription(
-        Image,
-        img_input_topic.format(cam_id),
-        callback_images,
-        qos_profile,
-    )
-    _ = node.create_subscription(
-        Poses,
-        pose_input_topic.format(cam_id),
-        callback_poses,
-        qos_profile,
-    )
-
-    # Create publishers
-    publisher_img = node.create_publisher(
-        Image,
-        img_output_topic.format(cam_id),
-        qos_profile,
-    )
-
-    node.get_logger().info("Finished initialization of pose visualizer")
-
-    # Run ros update thread
-    rclpy.spin(node)
-
-    node.destroy_node()
-    rclpy.shutdown()
-
-
-# ==================================================================================================
-
-if __name__ == "__main__":
-    main()
@@ -1,4 +0,0 @@
-[develop]
-script_dir=$base/lib/pose2d_visualizer
-[install]
-install_scripts=$base/lib/pose2d_visualizer
@@ -1,23 +0,0 @@
-from setuptools import setup
-
-package_name = "pose2d_visualizer"
-
-setup(
-    name=package_name,
-    version="0.0.0",
-    packages=[package_name],
-    data_files=[
-        ("share/ament_index/resource_index/packages", ["resource/" + package_name]),
-        ("share/" + package_name, ["package.xml"]),
-    ],
-    install_requires=["setuptools"],
-    zip_safe=True,
-    maintainer="root",
-    maintainer_email="root@todo.todo",
-    description="TODO: Package description",
-    license="TODO: License declaration",
-    tests_require=["pytest"],
-    entry_points={
-        "console_scripts": ["pose2d_visualizer = pose2d_visualizer.pose2d_visualizer:main"],
-    },
-)
@@ -1,27 +0,0 @@
-# Copyright 2015 Open Source Robotics Foundation, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pytest
-from ament_copyright.main import main
-
-
-# Remove the `skip` decorator once the source file(s) have a copyright header
-@pytest.mark.skip(
-    reason="No copyright header has been placed in the generated source file."
-)
-@pytest.mark.copyright
-@pytest.mark.linter
-def test_copyright():
-    rc = main(argv=[".", "test"])
-    assert rc == 0, "Found errors"
@@ -1,25 +0,0 @@
-# Copyright 2017 Open Source Robotics Foundation, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pytest
-from ament_flake8.main import main_with_errors
-
-
-@pytest.mark.flake8
-@pytest.mark.linter
-def test_flake8():
-    rc, errors = main_with_errors(argv=[])
-    assert rc == 0, "Found %d code style errors / warnings:\n" % len(
-        errors
-    ) + "\n".join(errors)
@@ -1,23 +0,0 @@
-# Copyright 2015 Open Source Robotics Foundation, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pytest
-from ament_pep257.main import main
-
-
-@pytest.mark.linter
-@pytest.mark.pep257
-def test_pep257():
-    rc = main(argv=[".", "test"])
-    assert rc == 0, "Found code style errors / warnings"
@@ -1,69 +0,0 @@
-cmake_minimum_required(VERSION 3.5)
-project(rpt2d_wrapper_cpp)
-
-# Default to C99
-if(NOT CMAKE_C_STANDARD)
-  set(CMAKE_C_STANDARD 99)
-endif()
-
-# Default to C++17
-if(NOT CMAKE_CXX_STANDARD)
-  set(CMAKE_CXX_STANDARD 17)
-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(sensor_msgs REQUIRED)
-find_package(cv_bridge REQUIRED)
-find_package(OpenCV REQUIRED)
-
-### 3) ONNX Runtime
-# for desktop
-include_directories(/onnxruntime/include/
-                    /onnxruntime/include/onnxruntime/core/session/
-                    /onnxruntime/include/onnxruntime/core/providers/tensorrt/)
-link_directories(/onnxruntime/build/Linux/Release/)
-# for jetson
-include_directories(/usr/local/include/onnxruntime/)
-link_directories(/usr/local/lib/)
-
-add_executable(rpt2d_wrapper src/rpt2d_wrapper.cpp)
-ament_target_dependencies(rpt2d_wrapper rclcpp sensor_msgs rpt_msgs cv_bridge)
-target_include_directories(rpt2d_wrapper PUBLIC
-  $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
-  $<INSTALL_INTERFACE:include>)
-
-target_link_libraries(rpt2d_wrapper
-  ${OpenCV_LIBS}
-  onnxruntime_providers_tensorrt
-  onnxruntime_providers_shared
-  onnxruntime_providers_cuda
-  onnxruntime
-)
-
-set_target_properties(rpt2d_wrapper PROPERTIES
-  BUILD_WITH_INSTALL_RPATH TRUE
-  INSTALL_RPATH "/onnxruntime/build/Linux/Release"
-)
-
-install(TARGETS rpt2d_wrapper
-  DESTINATION lib/${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()
@@ -1,25 +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>rpt2d_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>sensor_msgs</depend>
-
-  <depend>cv_bridge</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>
@@ -1,237 +0,0 @@
-#include <atomic>
-#include <chrono>
-#include <iostream>
-#include <string>
-#include <vector>
-
-// ROS2
-#include <rclcpp/rclcpp.hpp>
-#include <sensor_msgs/msg/image.hpp>
-
-// OpenCV / cv_bridge
-#include <cv_bridge/cv_bridge.h>
-#include <opencv2/opencv.hpp>
-
-// JSON library
-#include "/RapidPoseTriangulation/extras/include/nlohmann/json.hpp"
-using json = nlohmann::json;
-
-#include "rpt_msgs/msg/poses.hpp"
-#include "/RapidPoseTriangulation/scripts/utils_2d_pose.hpp"
-#include "/RapidPoseTriangulation/scripts/utils_pipeline.hpp"
-
-// =================================================================================================
-
-static const std::string img_input_topic = "/{}/pylon_ros2_camera_node/image_raw";
-static const std::string pose_out_topic = "/poses/{}";
-
-static const float min_bbox_score = 0.4;
-static const float min_bbox_area = 0.1 * 0.1;
-static const bool batch_poses = true;
-
-static const std::map<std::string, bool> whole_body = {
-    {"foots", true},
-    {"face", true},
-    {"hands", true},
-};
-
-// =================================================================================================
-// =================================================================================================
-
-class Rpt2DWrapperNode : public rclcpp::Node
-{
-public:
-    Rpt2DWrapperNode(const std::string &cam_id)
-        : Node("rpt2d_wrapper_" + cam_id)
-    {
-        this->is_busy = false;
-        std::string img_topic = std::string(img_input_topic)
-                                    .replace(img_input_topic.find("{}"), 2, cam_id);
-        std::string pose_topic = std::string(pose_out_topic)
-                                     .replace(pose_out_topic.find("{}"), 2, cam_id);
-
-        // QoS
-        rclcpp::QoS qos_profile(1);
-        qos_profile.reliable();
-        qos_profile.keep_last(1);
-
-        // Setup subscriber
-        image_sub_ = this->create_subscription<sensor_msgs::msg::Image>(
-            img_topic, qos_profile,
-            std::bind(&Rpt2DWrapperNode::callback_images, this, std::placeholders::_1));
-
-        // Setup publisher
-        pose_pub_ = this->create_publisher<rpt_msgs::msg::Poses>(pose_topic, qos_profile);
-
-        // Load model
-        bool use_wb = utils_pipeline::use_whole_body(whole_body);
-        this->kpt_model = std::make_unique<utils_2d_pose::PosePredictor>(
-            use_wb, min_bbox_score, min_bbox_area, batch_poses);
-
-        RCLCPP_INFO(this->get_logger(), "Finished initialization of pose estimator.");
-    }
-
-private:
-    rclcpp::Subscription<sensor_msgs::msg::Image>::SharedPtr image_sub_;
-    rclcpp::Publisher<rpt_msgs::msg::Poses>::SharedPtr pose_pub_;
-    std::atomic<bool> is_busy;
-
-    // Pose model pointer
-    std::unique_ptr<utils_2d_pose::PosePredictor> kpt_model;
-    const std::vector<std::string> joint_names_2d = utils_pipeline::get_joint_names(whole_body);
-
-    void callback_images(const sensor_msgs::msg::Image::SharedPtr msg);
-
-    std::vector<std::vector<std::array<float, 3>>> call_model(const cv::Mat &image);
-};
-
-// =================================================================================================
-
-void Rpt2DWrapperNode::callback_images(const sensor_msgs::msg::Image::SharedPtr msg)
-{
-    if (this->is_busy)
-    {
-        RCLCPP_WARN(this->get_logger(), "Skipping frame, still processing...");
-        return;
-    }
-    this->is_busy = true;
-    auto ts_image = std::chrono::high_resolution_clock::now();
-
-    // Load or convert image to Bayer format
-    cv::Mat bayer_image;
-    try
-    {
-        if (msg->encoding == "mono8")
-        {
-            cv_bridge::CvImageConstPtr cv_ptr = cv_bridge::toCvShare(msg, msg->encoding);
-            bayer_image = cv_ptr->image;
-        }
-        else if (msg->encoding == "bayer_rggb8")
-        {
-            cv_bridge::CvImageConstPtr cv_ptr = cv_bridge::toCvShare(msg, msg->encoding);
-            bayer_image = cv_ptr->image;
-        }
-        else if (msg->encoding == "rgb8")
-        {
-            cv_bridge::CvImageConstPtr cv_ptr = cv_bridge::toCvShare(msg, "rgb8");
-            cv::Mat color_image = cv_ptr->image;
-            bayer_image = utils_pipeline::rgb2bayer(color_image);
-        }
-        else
-        {
-            throw std::runtime_error("Unknown image encoding: " + msg->encoding);
-        }
-    }
-    catch (const std::exception &e)
-    {
-        RCLCPP_ERROR(this->get_logger(), "cv_bridge exception: %s", e.what());
-        return;
-    }
-
-    // Call model
-    const auto &valid_poses = this->call_model(bayer_image);
-
-    // Calculate timings
-    double time_stamp = msg->header.stamp.sec + msg->header.stamp.nanosec / 1.0e9;
-    auto ts_image_sec = std::chrono::duration<double>(ts_image.time_since_epoch()).count();
-    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_image = ts_image_sec - time_stamp;
-    double z_trigger_pose = ts_pose_sec - time_stamp;
-    double z_image_pose = ts_pose_sec - ts_image_sec;
-    json jdata;
-    jdata["timestamps"] = {
-        {"trigger", time_stamp},
-        {"image", ts_image_sec},
-        {"pose2d", ts_pose_sec},
-        {"z-trigger-image", z_trigger_image},
-        {"z-image-pose2d", z_image_pose},
-        {"z-trigger-pose2d", z_trigger_pose}};
-
-    // Publish message
-    auto pose_msg = rpt_msgs::msg::Poses();
-    pose_msg.header = msg->header;
-    std::vector<int32_t> bshape = {(int)valid_poses.size(), (int)joint_names_2d.size(), 3};
-    pose_msg.bodies_shape = bshape;
-    pose_msg.bodies_flat.reserve(bshape[0] * bshape[1] * bshape[2]);
-    for (int32_t i = 0; i < bshape[0]; i++)
-    {
-        for (int32_t j = 0; j < bshape[1]; j++)
-        {
-            for (int32_t k = 0; k < bshape[2]; k++)
-            {
-                pose_msg.bodies_flat.push_back(valid_poses[i][j][k]);
-            }
-        }
-    }
-    pose_msg.joint_names = joint_names_2d;
-    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_image)
-                              .count();
-    std::cout << "Detected persons: " << valid_poses.size()
-              << " - Prediction time: " << elapsed_time << "s" << std::endl;
-
-    this->is_busy = false;
-}
-
-// =================================================================================================
-
-std::vector<std::vector<std::array<float, 3>>> Rpt2DWrapperNode::call_model(const cv::Mat &image)
-{
-    // Create image vector
-    cv::Mat rgb_image = utils_pipeline::bayer2rgb(image);
-    std::vector<cv::Mat> images_2d = {rgb_image};
-
-    // Predict 2D poses
-    auto poses_2d_all = kpt_model->predict(images_2d);
-    auto poses_2d_upd = utils_pipeline::update_keypoints(
-        poses_2d_all, joint_names_2d, whole_body);
-    auto &poses_2d = poses_2d_upd[0];
-
-    // Drop persons with no joints
-    std::vector<std::vector<std::array<float, 3>>> valid_poses;
-    for (auto &person : poses_2d)
-    {
-        float sum_conf = 0.0;
-        for (auto &kp : person)
-        {
-            sum_conf += kp[2];
-        }
-        if (sum_conf > 0.0)
-        {
-            valid_poses.push_back(person);
-        }
-    }
-
-    // Round poses to 3 decimal places
-    for (auto &person : valid_poses)
-    {
-        for (auto &kp : person)
-        {
-            kp[0] = std::round(kp[0] * 1000.0) / 1000.0;
-            kp[1] = std::round(kp[1] * 1000.0) / 1000.0;
-            kp[2] = std::round(kp[2] * 1000.0) / 1000.0;
-        }
-    }
-
-    return valid_poses;
-}
-
-// =================================================================================================
-// =================================================================================================
-
-int main(int argc, char **argv)
-{
-    rclcpp::init(argc, argv);
-    const std::string cam_id = std::getenv("CAMID");
-
-    auto node = std::make_shared<Rpt2DWrapperNode>(cam_id);
-    rclcpp::spin(node);
-
-    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()
@@ -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>
@@ -1,324 +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
-        tri_model = std::make_unique<Triangulator>(
-            min_match_score, min_group_size);
-        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<Triangulator> tri_model;
-    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();
-    const auto poses_3d = tri_model->triangulate_poses(
-        all_poses, all_cameras, roomparams, joint_names);
-
-    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;
-}
@@ -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()
@@ -1,7 +0,0 @@
-std_msgs/Header header
-
-float32[] bodies_flat
-int32[] bodies_shape
-string[] joint_names
-
-string extra_data
@@ -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>
@@ -1,3 +1,25 @@
+[build-system]
+requires = [
+    "nanobind>=2.11",
+    "scikit-build-core>=0.10",
+]
+build-backend = "scikit_build_core.build"
+
+[project]
+name = "rapid-pose-triangulation"
+version = "0.1.0"
+description = "Rapid Pose Triangulation library with nanobind Python bindings"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = ["numpy>=2.0"]
+
+[dependency-groups]
+dev = ["pytest>=8.3"]
+
+[tool.scikit-build]
+minimum-version = "build-system.requires"
+wheel.packages = ["src/rpt"]
+
 [tool.isort]
 profile = "black"
-known_first_party = "skelda,draw_utils,test_triangulate,utils_2d_pose,triangulate_poses"
+known_first_party = "rpt"
@@ -0,0 +1,24 @@
+# RPT Core Library
+
+set(RPT_SOURCES
+    camera.cpp
+    interface.cpp
+    triangulator.cpp
+)
+
+add_library(rpt_core STATIC ${RPT_SOURCES})
+
+target_include_directories(rpt_core PUBLIC
+    ${CMAKE_CURRENT_SOURCE_DIR}
+)
+
+target_compile_options(rpt_core PRIVATE
+    -fPIC
+    -march=native
+    -Wall
+)
+
+# Release mode optimizations
+target_compile_options(rpt_core PRIVATE
+    $<$<CONFIG:Release>:-O3;-flto=auto>
+)
@@ -2,10 +2,60 @@
 #include <cmath>
 #include <iomanip>
 #include <sstream>
+#include <stdexcept>
+#include <utility>
 #include <vector>

 #include "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
+
 // =================================================================================================
 // =================================================================================================

@@ -62,7 +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();
@@ -70,6 +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();
@@ -79,93 +156,51 @@ std::ostream &operator<<(std::ostream &out, const Camera &cam)
 // =================================================================================================
 // =================================================================================================

-CameraInternal::CameraInternal(const Camera &cam)
+Camera make_camera(
+    std::string name,
+    std::array<std::array<float, 3>, 3> K,
+    std::array<float, 5> DC,
+    std::array<std::array<float, 3>, 3> R,
+    std::array<std::array<float, 1>, 3> T,
+    int width,
+    int height,
+    CameraModel model)
 {
-    this->cam = cam;
-    this->invR = transpose3x3(cam.R);
+    Camera cam {
+        .name = std::move(name),
+        .K = K,
+        .DC = DC,
+        .R = R,
+        .T = T,
+        .width = width,
+        .height = height,
+        .model = model,
+    };
+
+    cam.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]};
+    cam.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")
+    if (cam.model == CameraModel::Fisheye)
    {
-        newK = calc_optimal_camera_matrix_fisheye(1.0, {cam.width, cam.height});
+        cam.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});
+        cam.newK = calc_optimal_camera_matrix_pinhole(cam, 1.0f, {cam.width, cam.height});
    }
-    this->invK = invert3x3(newK);
+    cam.invK = invert3x3(cam.newK);
+
+    return cam;
 }

 // =================================================================================================

-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]
@@ -201,14 +236,14 @@ 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]
    // https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/fisheye.cpp#L429

    float theta_d = std::sqrt(p[0] * p[0] + p[1] * p[1]);
-    float pi_half = std::numbers::pi * 0.5;
+    float pi_half = M_PI * 0.5;
    theta_d = std::min(std::max(-pi_half, theta_d), pi_half);

    if (theta_d < 1e-6)
@@ -249,8 +284,10 @@ 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
@@ -354,8 +391,8 @@ 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
@@ -478,3 +515,4 @@ std::array<std::array<float, 3>, 3> CameraInternal::calc_optimal_camera_matrix_p
         {0.0, 0.0, 1.0}}};
    return newK;
 }
+} // namespace
@@ -7,46 +7,45 @@

 // =================================================================================================

+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;
+    int width = 0;
+    int height = 0;
+    CameraModel model = CameraModel::Pinhole;
+    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 {};

    friend std::ostream &operator<<(std::ostream &out, Camera const &camera);
    std::string to_string() const;
 };

-// =================================================================================================
+Camera make_camera(
+    std::string name,
+    std::array<std::array<float, 3>, 3> K,
+    std::array<float, 5> DC,
+    std::array<std::array<float, 3>, 3> R,
+    std::array<std::array<float, 1>, 3> T,
+    int width,
+    int height,
+    CameraModel model);

-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);
-};
+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);
@@ -1,35 +1,174 @@
-#include "triangulator.hpp"
+#include <algorithm>
+#include <stdexcept>
+#include <utility>
+
 #include "interface.hpp"

+// =================================================================================================
+
+namespace
+{
+size_t pose2d_offset(
+    size_t view,
+    size_t person,
+    size_t joint,
+    size_t coord,
+    size_t max_persons,
+    size_t num_joints)
+{
+    return ((((view * max_persons) + person) * num_joints) + joint) * 3 + coord;
+}
+
+size_t pose3d_offset(size_t person, size_t joint, size_t coord, size_t num_joints)
+{
+    return (((person * num_joints) + joint) * 4) + coord;
+}
+} // namespace
+
 // =================================================================================================
 // =================================================================================================

-Triangulator::Triangulator(float min_match_score, size_t min_group_size)
+float &PoseBatch2D::at(size_t view, size_t person, size_t joint, size_t coord)
 {
-    this->triangulator = new TriangulatorInternal(min_match_score, min_group_size);
+    return data[pose2d_offset(view, person, joint, coord, max_persons, num_joints)];
+}
+
+const float &PoseBatch2DView::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)];
+}
+
+const float &PoseBatch3DView::at(size_t person, size_t joint, size_t coord) const
+{
+    return data[pose3d_offset(person, joint, coord, num_joints)];
+}
+
+int64_t TrackedPoseBatch3DView::track_id(size_t person) const
+{
+    return track_ids[person];
+}
+
+const float &TrackedPoseBatch3DView::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)];
+}
+
+PoseBatch2DView PoseBatch2D::view() const
+{
+    return PoseBatch2DView {data.data(), person_counts.data(), num_views, max_persons, num_joints};
+}
+
+PoseBatch2D PoseBatch2D::from_nested(const RaggedPoses2D &poses_2d)
+{
+    PoseBatch2D batch;
+    batch.num_views = poses_2d.size();
+    for (const auto &view : poses_2d)
+    {
+        batch.max_persons = std::max(batch.max_persons, view.size());
+        if (!view.empty())
+        {
+            if (batch.num_joints == 0)
+            {
+                batch.num_joints = view[0].size();
+            }
+            else if (batch.num_joints != view[0].size())
+            {
+                throw std::invalid_argument("All views must use the same joint count.");
+            }
+
+            for (const auto &person : view)
+            {
+                if (person.size() != batch.num_joints)
+                {
+                    throw std::invalid_argument("All persons must use the same joint count.");
+                }
+            }
+        }
+    }
+
+    batch.person_counts.resize(batch.num_views);
+    batch.data.assign(batch.num_views * batch.max_persons * batch.num_joints * 3, 0.0f);
+
+    for (size_t view = 0; view < batch.num_views; ++view)
+    {
+        batch.person_counts[view] = static_cast<uint32_t>(poses_2d[view].size());
+        for (size_t person = 0; person < poses_2d[view].size(); ++person)
+        {
+            for (size_t joint = 0; joint < batch.num_joints; ++joint)
+            {
+                for (size_t coord = 0; coord < 3; ++coord)
+                {
+                    batch.at(view, person, joint, coord) = poses_2d[view][person][joint][coord];
+                }
+            }
+        }
+    }
+
+    return batch;
 }

 // =================================================================================================

-std::vector<std::vector<std::array<float, 4>>> Triangulator::triangulate_poses(
-    const std::vector<std::vector<std::vector<std::array<float, 3>>>> &poses_2d,
-    const std::vector<Camera> &cameras,
-    const std::array<std::array<float, 3>, 2> &roomparams,
-    const std::vector<std::string> &joint_names)
+float &PoseBatch3D::at(size_t person, size_t joint, size_t coord)
 {
-    return this->triangulator->triangulate_poses(poses_2d, cameras, roomparams, joint_names);
+    return data[pose3d_offset(person, joint, coord, num_joints)];
 }

-// =================================================================================================
-
-void Triangulator::reset()
+const float &PoseBatch3D::at(size_t person, size_t joint, size_t coord) const
 {
-    this->triangulator->reset();
+    return data[pose3d_offset(person, joint, coord, num_joints)];
 }

-// =================================================================================================
-
-void Triangulator::print_stats()
+PoseBatch3DView PoseBatch3D::view() const
 {
-    this->triangulator->print_stats();
+    return PoseBatch3DView {data.data(), num_persons, num_joints};
+}
+
+NestedPoses3D PoseBatch3D::to_nested() const
+{
+    NestedPoses3D poses_3d(num_persons);
+    for (size_t person = 0; person < num_persons; ++person)
+    {
+        poses_3d[person].resize(num_joints);
+        for (size_t joint = 0; joint < num_joints; ++joint)
+        {
+            for (size_t coord = 0; coord < 4; ++coord)
+            {
+                poses_3d[person][joint][coord] = at(person, joint, coord);
+            }
+        }
+    }
+    return poses_3d;
+}
+
+PoseBatch3D PoseBatch3D::from_nested(const NestedPoses3D &poses_3d)
+{
+    PoseBatch3D batch;
+    batch.num_persons = poses_3d.size();
+    if (!poses_3d.empty())
+    {
+        batch.num_joints = poses_3d[0].size();
+    }
+
+    batch.data.resize(batch.num_persons * batch.num_joints * 4);
+    for (size_t person = 0; person < batch.num_persons; ++person)
+    {
+        if (poses_3d[person].size() != batch.num_joints)
+        {
+            throw std::invalid_argument("All 3D poses must use the same joint count.");
+        }
+        for (size_t joint = 0; joint < batch.num_joints; ++joint)
+        {
+            for (size_t coord = 0; coord < 4; ++coord)
+            {
+                batch.at(person, joint, coord) = poses_3d[person][joint][coord];
+            }
+        }
+    }
+    return batch;
 }
@@ -1,5 +1,7 @@
 #pragma once

+#include <array>
+#include <cstdint>
 #include <string>
 #include <vector>

@@ -7,48 +9,266 @@

 // =================================================================================================

-// Forward declaration of the class, that swig does not try to parse all its dependencies.
-class TriangulatorInternal;
+using RaggedPoses2D = std::vector<std::vector<std::vector<std::array<float, 3>>>>;
+using NestedPoses3D = std::vector<std::vector<std::array<float, 4>>>;

 // =================================================================================================

-class Triangulator
+struct PoseBatch2DView
 {
-public:
-    /**
-     * Triangulator to predict poses from multiple views.
-     *
-     *
-     * @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.
-     */
-    Triangulator(
-        float min_match_score = 0.95,
-        size_t min_group_size = 1);
+    const float *data = nullptr;
+    const uint32_t *person_counts = nullptr;
+    size_t num_views = 0;
+    size_t max_persons = 0;
+    size_t num_joints = 0;

-    /**
-     * Calculate a triangulation.
-     *
-     *
-     * @param poses_2d List of shape [views, persons, joints, 3], containing the 2D poses.
-     * @param cameras List of cameras.
-     * @param roomparams Room parameters (room size, room center).
-     * @param joint_names List of 2D joint names.
-     *
-     * @return List of shape [persons, joints, 4], containing the 3D poses.
-     */
-    std::vector<std::vector<std::array<float, 4>>> triangulate_poses(
-        const std::vector<std::vector<std::vector<std::array<float, 3>>>> &poses_2d,
-        const std::vector<Camera> &cameras,
-        const std::array<std::array<float, 3>, 2> &roomparams,
-        const std::vector<std::string> &joint_names);
-
-    /** Reset the triangulator. */
-    void reset();
-
-    /** Print triangulation statistics. */
-    void print_stats();
-
-private:
-    TriangulatorInternal *triangulator;
+    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 TrackedPoseBatch3DView
+{
+    const int64_t *track_ids = nullptr;
+    const float *data = nullptr;
+    size_t num_persons = 0;
+    size_t num_joints = 0;
+
+    int64_t track_id(size_t person) const;
+    const float &at(size_t person, size_t joint, size_t coord) const;
+};
+
+struct PoseBatch2D
+{
+    std::vector<float> data;
+    std::vector<uint32_t> person_counts;
+    size_t num_views = 0;
+    size_t max_persons = 0;
+    size_t num_joints = 0;
+
+    float &at(size_t view, size_t person, size_t joint, size_t coord);
+    const float &at(size_t view, size_t person, size_t joint, size_t coord) const;
+    PoseBatch2DView view() const;
+
+    static PoseBatch2D from_nested(const RaggedPoses2D &poses_2d);
+};
+
+struct PoseBatch3D
+{
+    std::vector<float> data;
+    size_t num_persons = 0;
+    size_t num_joints = 0;
+
+    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);
+};
+
+// =================================================================================================
+
+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;
+    int64_t previous_track_id = -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;
+};
+
+enum class AssociationStatus
+{
+    Matched,
+    New,
+    Ambiguous,
+};
+
+struct AssociationReport
+{
+    std::vector<int> pose_previous_indices;
+    std::vector<int64_t> pose_previous_track_ids;
+    std::vector<AssociationStatus> pose_status;
+    std::vector<std::vector<int>> pose_candidate_previous_indices;
+    std::vector<std::vector<int64_t>> pose_candidate_previous_track_ids;
+    std::vector<int> unmatched_previous_indices;
+    std::vector<int64_t> unmatched_previous_track_ids;
+    std::vector<int> new_pose_indices;
+    std::vector<int> ambiguous_pose_indices;
+};
+
+struct FinalPoseAssociationDebug
+{
+    int final_pose_index = -1;
+    std::vector<int> source_core_proposal_indices;
+    std::vector<int> source_pair_indices;
+    std::vector<int> candidate_previous_indices;
+    std::vector<int64_t> candidate_previous_track_ids;
+    int resolved_previous_index = -1;
+    int64_t resolved_previous_track_id = -1;
+    AssociationStatus status = AssociationStatus::New;
+};
+
+struct TriangulationTrace
+{
+    std::vector<PairCandidate> pairs;
+    PreviousPoseFilterDebug previous_filter;
+    std::vector<CoreProposalDebug> core_proposals;
+    GroupingDebug grouping;
+    std::vector<FullProposalDebug> full_proposals;
+    MergeDebug merge;
+    AssociationReport association;
+    std::vector<FinalPoseAssociationDebug> final_pose_associations;
+    PoseBatch3D final_poses;
+};
+
+struct TriangulationResult
+{
+    PoseBatch3D poses;
+    AssociationReport association;
+};
+
+// =================================================================================================
+
+struct TriangulationOptions
+{
+    float min_match_score = 0.95f;
+    size_t min_group_size = 1;
+};
+
+struct TriangulationConfig
+{
+    std::vector<Camera> cameras;
+    std::array<std::array<float, 3>, 2> roomparams {{{0.0f, 0.0f, 0.0f}, {0.0f, 0.0f, 0.0f}}};
+    std::vector<std::string> joint_names;
+    TriangulationOptions options;
+};
+
+// =================================================================================================
+
+std::vector<PairCandidate> build_pair_candidates(const PoseBatch2DView &poses_2d);
+
+PreviousPoseFilterDebug filter_pairs_with_previous_poses(
+    const PoseBatch2DView &poses_2d,
+    const TriangulationConfig &config,
+    const TrackedPoseBatch3DView &previous_poses_3d,
+    const TriangulationOptions *options_override = nullptr);
+
+TriangulationTrace triangulate_debug(
+    const PoseBatch2DView &poses_2d,
+    const TriangulationConfig &config,
+    const TrackedPoseBatch3DView *previous_poses_3d = nullptr,
+    const TriangulationOptions *options_override = nullptr);
+
+// =================================================================================================
+
+/**
+ * Calculate a triangulation using a padded pose tensor.
+ *
+ * @param poses_2d Padded poses of shape [views, max_persons, joints, 3].
+ * @param config Triangulation configuration (cameras, room parameters, joint names, options).
+ * @param options_override Optional per-call options override. Defaults to config.options.
+ *
+ * @return Pose tensor of shape [persons, joints, 4].
+ */
+PoseBatch3D triangulate_poses(
+    const PoseBatch2DView &poses_2d,
+    const TriangulationConfig &config,
+    const TriangulationOptions *options_override = nullptr);
+
+TriangulationResult triangulate_with_report(
+    const PoseBatch2DView &poses_2d,
+    const TriangulationConfig &config,
+    const TrackedPoseBatch3DView &previous_poses_3d,
+    const TriangulationOptions *options_override = nullptr);
+
+inline PoseBatch3D triangulate_poses(
+    const PoseBatch2D &poses_2d,
+    const TriangulationConfig &config,
+    const TriangulationOptions *options_override = nullptr)
+{
+    return triangulate_poses(poses_2d.view(), config, options_override);
+}
+
+inline TriangulationTrace triangulate_debug(
+    const PoseBatch2D &poses_2d,
+    const TriangulationConfig &config,
+    const TriangulationOptions *options_override = nullptr)
+{
+    return triangulate_debug(poses_2d.view(), config, nullptr, options_override);
+}
+
+inline TriangulationResult triangulate_with_report(
+    const PoseBatch2D &poses_2d,
+    const TriangulationConfig &config,
+    const TrackedPoseBatch3DView &previous_poses_3d,
+    const TriangulationOptions *options_override = nullptr)
+{
+    return triangulate_with_report(poses_2d.view(), config, previous_poses_3d, options_override);
+}
@@ -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;
-}
@@ -1,130 +0,0 @@
-#pragma once
-
-#include <array>
-#include <string>
-#include <vector>
-
-#include "camera.hpp"
-
-// =================================================================================================
-
-class TriangulatorInternal
-{
-public:
-    TriangulatorInternal(float min_match_score, size_t min_group_size);
-
-    std::vector<std::vector<std::array<float, 4>>> triangulate_poses(
-        const std::vector<std::vector<std::vector<std::array<float, 3>>>> &poses_2d,
-        const std::vector<Camera> &cameras,
-        const std::array<std::array<float, 3>, 2> &roomparams,
-        const std::vector<std::string> &joint_names);
-
-    void reset();
-    void print_stats();
-
-private:
-    float min_match_score;
-    float min_group_size;
-    size_t num_cams;
-
-    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",
-    };
-    const std::vector<std::pair<std::string, std::string>> core_limbs = {
-        {"knee_left", "ankle_left"},
-        {"hip_left", "knee_left"},
-        {"hip_right", "knee_right"},
-        {"knee_right", "ankle_right"},
-        {"elbow_left", "wrist_left"},
-        {"elbow_right", "wrist_right"},
-        {"shoulder_left", "elbow_left"},
-        {"shoulder_right", "elbow_right"},
-    };
-    std::vector<std::vector<std::array<float, 4>>> last_poses_3d;
-
-    void undistort_poses(
-        std::vector<std::vector<std::array<float, 3>>> &poses_2d, CameraInternal &icam);
-
-    std::tuple<std::vector<std::vector<std::array<float, 3>>>, std::vector<std::vector<float>>>
-    project_poses(
-        const std::vector<std::vector<std::array<float, 4>>> &poses_3d,
-        const CameraInternal &icam,
-        bool calc_dists);
-
-    float calc_pose_score(
-        const std::vector<std::array<float, 3>> &pose1,
-        const std::vector<std::array<float, 3>> &pose2,
-        const std::vector<float> &dist1,
-        const CameraInternal &icam);
-
-    std::vector<float> score_projection(
-        const std::vector<std::array<float, 3>> &pose,
-        const std::vector<std::array<float, 3>> &repro,
-        const std::vector<float> &dists,
-        const std::vector<bool> &mask,
-        float iscale);
-
-    std::pair<std::vector<std::array<float, 4>>, float> triangulate_and_score(
-        const std::vector<std::array<float, 3>> &pose1,
-        const std::vector<std::array<float, 3>> &pose2,
-        const CameraInternal &cam1,
-        const CameraInternal &cam2,
-        const std::array<std::array<float, 3>, 2> &roomparams,
-        const std::vector<std::array<size_t, 2>> &core_limbs_idx);
-
-    std::vector<std::tuple<
-        std::array<float, 3>, std::vector<std::array<float, 4>>, std::vector<int>>>
-    calc_grouping(
-        const std::vector<std::pair<
-            std::tuple<int, int, int, int>, std::pair<int, int>>> &all_pairs,
-        const std::vector<std::pair<std::vector<std::array<float, 4>>, float>> &all_scored_poses,
-        float min_score);
-
-    std::vector<std::array<float, 4>> merge_group(
-        const std::vector<std::vector<std::array<float, 4>>> &poses_3d,
-        float min_score);
-
-    void add_extra_joints(
-        std::vector<std::vector<std::array<float, 4>>> &poses,
-        const std::vector<std::string> &joint_names);
-
-    void filter_poses(
-        std::vector<std::vector<std::array<float, 4>>> &poses,
-        const std::array<std::array<float, 3>, 2> &roomparams,
-        const std::vector<size_t> &core_joint_idx,
-        const std::vector<std::array<size_t, 2>> &core_limbs_idx);
-
-    void add_missing_joints(
-        std::vector<std::vector<std::array<float, 4>>> &poses,
-        const std::vector<std::string> &joint_names);
-
-    void replace_far_joints(
-        std::vector<std::vector<std::array<float, 4>>> &poses,
-        const std::vector<std::string> &joint_names);
-
-    // Statistics
-    double num_calls = 0;
-    double total_time = 0;
-    double init_time = 0;
-    double undistort_time = 0;
-    double project_time = 0;
-    double match_time = 0;
-    double pairs_time = 0;
-    double pair_scoring_time = 0;
-    double grouping_time = 0;
-    double full_time = 0;
-    double merge_time = 0;
-    double post_time = 0;
-    double convert_time = 0;
-};
@@ -5,7 +5,6 @@ docker run --privileged --rm --network host -it \
  --runtime nvidia --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
  --volume "$(pwd)"/:/RapidPoseTriangulation/ \
  --volume "$(pwd)"/../datasets/:/datasets/ \
-  --volume "$(pwd)"/skelda/:/skelda/ \
  --volume /tmp/.X11-unix:/tmp/.X11-unix \
  --env DISPLAY --env QT_X11_NO_MITSHM=1 \
  rapidposetriangulation
@@ -1,325 +0,0 @@
-#include <chrono>
-#include <cmath>
-#include <fstream>
-#include <iostream>
-#include <memory>
-#include <sstream>
-#include <stdexcept>
-#include <string>
-#include <vector>
-
-// OpenCV
-#include <opencv2/opencv.hpp>
-
-// JSON library
-#include "/RapidPoseTriangulation/extras/include/nlohmann/json.hpp"
-using json = nlohmann::json;
-
-#include "/RapidPoseTriangulation/rpt/camera.hpp"
-#include "/RapidPoseTriangulation/rpt/interface.hpp"
-#include "/RapidPoseTriangulation/rpt/tracker.hpp"
-#include "/RapidPoseTriangulation/scripts/utils_2d_pose.hpp"
-#include "/RapidPoseTriangulation/scripts/utils_pipeline.hpp"
-
-// =================================================================================================
-
-static const std::string path_data = "/tmp/rpt/all.json";
-static const std::string path_cfg = "/tmp/rpt/config.json";
-
-// =================================================================================================
-
-std::vector<cv::Mat> load_images(json &item)
-{
-    // Load images
-    std::vector<cv::Mat> images;
-    for (size_t j = 0; j < item["imgpaths"].size(); j++)
-    {
-        auto ipath = item["imgpaths"][j].get<std::string>();
-        cv::Mat image = cv::imread(ipath, cv::IMREAD_COLOR);
-        cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
-        images.push_back(image);
-    }
-
-    if (item["dataset_name"] == "human36m")
-    {
-        // Since the images don't have the same shape, rescale some of them
-        for (size_t i = 0; i < images.size(); i++)
-        {
-            cv::Mat &img = images[i];
-            cv::Size ishape = img.size();
-            if (ishape != cv::Size(1000, 1000))
-            {
-                auto cam = item["cameras"][i];
-                cam["K"][1][1] = cam["K"][1][1].get<float>() * (1000.0 / ishape.height);
-                cam["K"][1][2] = cam["K"][1][2].get<float>() * (1000.0 / ishape.height);
-                cam["K"][0][0] = cam["K"][0][0].get<float>() * (1000.0 / ishape.width);
-                cam["K"][0][2] = cam["K"][0][2].get<float>() * (1000.0 / ishape.width);
-                cv::resize(img, img, cv::Size(1000, 1000));
-                images[i] = img;
-            }
-        }
-    }
-
-    // Convert image format to Bayer encoding to simulate real camera input
-    // This also resulted in notably better MPJPE results in most cases, presumably since the
-    // demosaicing algorithm from OpenCV is better than the default one from the cameras
-    for (size_t i = 0; i < images.size(); i++)
-    {
-        cv::Mat &img = images[i];
-        cv::Mat bayer_image = utils_pipeline::rgb2bayer(img);
-        images[i] = std::move(bayer_image);
-    }
-
-    return images;
-}
-
-// =================================================================================================
-
-std::string read_file(const std::string &path)
-{
-    std::ifstream file_stream(path);
-    if (!file_stream.is_open())
-    {
-        throw std::runtime_error("Unable to open file: " + path);
-    }
-
-    std::stringstream buffer;
-    buffer << file_stream.rdbuf();
-    return buffer.str();
-}
-
-void write_file(const std::string &path, const std::string &content)
-{
-    std::ofstream file_stream(path, std::ios::out | std::ios::binary);
-    if (!file_stream.is_open())
-    {
-        throw std::runtime_error("Unable to open file for writing: " + path);
-    }
-
-    file_stream << content;
-
-    if (!file_stream)
-    {
-        throw std::runtime_error("Error occurred while writing to file: " + path);
-    }
-    file_stream.close();
-}
-
-// =================================================================================================
-
-int main(int argc, char **argv)
-{
-    // Load the files
-    auto dataset = json::parse(read_file(path_data));
-    auto config = json::parse(read_file(path_cfg));
-
-    // Load the configuration
-    const std::map<std::string, bool> whole_body = config["whole_body"];
-    const float min_bbox_score = config["min_bbox_score"];
-    const float min_bbox_area = config["min_bbox_area"];
-    const bool batch_poses = config["batch_poses"];
-    const std::vector<std::string> joint_names_2d = utils_pipeline::get_joint_names(whole_body);
-    const float min_match_score = config["min_match_score"];
-    const size_t min_group_size = config["min_group_size"];
-    const int take_interval = config["take_interval"];
-    const float ifps = config["fps"];
-    const float max_movement_speed = config["max_movement_speed"];
-    const float max_track_distance = config["max_track_distance"];
-
-    // Load 2D model
-    bool use_wb = utils_pipeline::use_whole_body(whole_body);
-    std::unique_ptr<utils_2d_pose::PosePredictor> kpt_model =
-        std::make_unique<utils_2d_pose::PosePredictor>(
-            use_wb, min_bbox_score, min_bbox_area, batch_poses);
-
-    // Load 3D models
-    std::unique_ptr<Triangulator> tri_model = std::make_unique<Triangulator>(
-        min_match_score, min_group_size);
-    std::unique_ptr<PoseTracker> pose_tracker = std::make_unique<PoseTracker>(
-        max_movement_speed, max_track_distance);
-
-    // Timers
-    size_t time_count = dataset.size();
-    std::vector<double> times_image;
-    std::vector<double> times_debayer;
-    std::vector<double> times_pose2d;
-    std::vector<double> times_pose3d;
-    std::vector<double> times_tracks;
-    times_image.reserve(time_count);
-    times_debayer.reserve(time_count);
-    times_pose2d.reserve(time_count);
-    times_pose3d.reserve(time_count);
-    times_tracks.reserve(time_count);
-    size_t print_steps = (size_t)std::floor((float)time_count / 100.0f);
-    print_steps = std::max((size_t)1, print_steps);
-
-    std::cout << "Running predictions:  |";
-    size_t bar_width = (size_t)std::ceil((float)time_count / (float)print_steps);
-    for (size_t i = 0; i < bar_width; i++)
-    {
-        std::cout << "-";
-    }
-    std::cout << "|" << std::endl;
-
-    // Calculate 2D poses [items, views, persons, joints, 3]
-    std::vector<std::vector<std::vector<std::vector<std::array<float, 3>>>>> all_poses_2d;
-    std::cout << "Calculating 2D poses: |";
-    for (size_t i = 0; i < dataset.size(); i++)
-    {
-        if (i % print_steps == 0)
-        {
-            std::cout << "#" << std::flush;
-        }
-        std::chrono::duration<float> elapsed;
-        auto &item = dataset[i];
-
-        // Load images
-        auto stime = std::chrono::high_resolution_clock::now();
-        std::vector<cv::Mat> images = load_images(item);
-        elapsed = std::chrono::high_resolution_clock::now() - stime;
-        times_image.push_back(elapsed.count());
-
-        // Demosaic images
-        stime = std::chrono::high_resolution_clock::now();
-        for (size_t i = 0; i < images.size(); i++)
-        {
-            cv::Mat &img = images[i];
-            cv::Mat rgb = utils_pipeline::bayer2rgb(img);
-            images[i] = std::move(rgb);
-        }
-        elapsed = std::chrono::high_resolution_clock::now() - stime;
-        times_debayer.push_back(elapsed.count());
-
-        // Predict 2D poses
-        stime = std::chrono::high_resolution_clock::now();
-        auto poses_2d_all = kpt_model->predict(images);
-        auto poses_2d_upd = utils_pipeline::update_keypoints(
-            poses_2d_all, joint_names_2d, whole_body);
-        elapsed = std::chrono::high_resolution_clock::now() - stime;
-        times_pose2d.push_back(elapsed.count());
-
-        all_poses_2d.push_back(std::move(poses_2d_upd));
-    }
-    std::cout << "|" << std::endl;
-
-    // Calculate 3D poses [items, persons, joints, 4]
-    std::vector<std::vector<std::vector<std::array<float, 4>>>> all_poses_3d;
-    std::vector<std::string> all_ids;
-    std::string old_scene = "";
-    int old_id = -1;
-    std::cout << "Calculating 3D poses: |";
-    for (size_t i = 0; i < dataset.size(); i++)
-    {
-        if (i % print_steps == 0)
-        {
-            std::cout << "#" << std::flush;
-        }
-        std::chrono::duration<float> elapsed;
-        auto &item = dataset[i];
-        auto &poses_2d = all_poses_2d[i];
-
-        if (old_scene != item["scene"] || old_id + take_interval != item["index"])
-        {
-            // Reset last poses if scene changes
-            tri_model->reset();
-            pose_tracker->reset();
-            old_scene = item["scene"];
-        }
-
-        auto stime = std::chrono::high_resolution_clock::now();
-        std::vector<Camera> cameras;
-        for (size_t j = 0; j < item["cameras"].size(); j++)
-        {
-            auto &cam = item["cameras"][j];
-            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>();
-            cameras.push_back(camera);
-        }
-        std::array<std::array<float, 3>, 2> roomparams = {
-            item["room_size"].get<std::array<float, 3>>(),
-            item["room_center"].get<std::array<float, 3>>()};
-
-        auto poses_3d = tri_model->triangulate_poses(poses_2d, cameras, roomparams, joint_names_2d);
-        elapsed = std::chrono::high_resolution_clock::now() - stime;
-        times_pose3d.push_back(elapsed.count());
-
-        if (ifps <= 0)
-        {
-            // Disable pose tracking if frame rate is too low
-            times_tracks.push_back(0.0);
-        }
-        else
-        {
-            stime = std::chrono::high_resolution_clock::now();
-            double ts = ((int)item["index"]) / ifps;
-            auto pose_tracks = pose_tracker->track_poses(poses_3d, joint_names_2d, 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);
-            }
-            poses_3d = poses_3d_refined;
-            elapsed = std::chrono::high_resolution_clock::now() - stime;
-            times_tracks.push_back(elapsed.count());
-        }
-
-        all_poses_3d.push_back(std::move(poses_3d));
-        all_ids.push_back(item["id"].get<std::string>());
-        old_id = item["index"];
-    }
-    std::cout << "|" << std::endl;
-
-    // Print timing stats
-    std::cout << "\nMetrics:" << std::endl;
-    size_t warmup = std::min((size_t)10, time_count - 1);
-    double time_image = 0.0;
-    double time_debayer = 0.0;
-    double time_pose2d = 0.0;
-    double time_pose3d = 0.0;
-    double time_tracks = 0.0;
-    for (size_t i = warmup; i < time_count; i++)
-    {
-        time_image += times_image[i];
-        time_debayer += times_debayer[i];
-        time_pose2d += times_pose2d[i];
-        time_pose3d += times_pose3d[i];
-        time_tracks += times_tracks[i];
-    }
-    double avg_time_image = time_image / (time_count - warmup);
-    double avg_time_debayer = time_debayer / (time_count - warmup);
-    double avg_time_pose2d = time_pose2d / (time_count - warmup);
-    double avg_time_pose3d = time_pose3d / (time_count - warmup);
-    double avg_time_tracks = time_tracks / (time_count - warmup);
-    double fps = 1.0 / (avg_time_debayer + avg_time_pose2d + avg_time_pose3d + avg_time_tracks);
-    std::cout << "{\n"
-              << "  \"img_loading\": " << avg_time_image << ",\n"
-              << "  \"demosaicing\": " << avg_time_debayer << ",\n"
-              << "  \"avg_time_2d\": " << avg_time_pose2d << ",\n"
-              << "  \"avg_time_3d\": " << avg_time_pose3d << ",\n"
-              << "  \"time_tracks\": " << avg_time_tracks << ",\n"
-              << "  \"fps\": " << fps << "\n"
-              << "}" << std::endl;
-    tri_model->print_stats();
-
-    // Store the results as json
-    json all_results;
-    all_results["all_ids"] = all_ids;
-    all_results["all_poses_2d"] = all_poses_2d;
-    all_results["all_poses_3d"] = all_poses_3d;
-    all_results["joint_names_2d"] = joint_names_2d;
-    all_results["joint_names_3d"] = joint_names_2d;
-
-    // Save the results
-    std::string path_results = "/tmp/rpt/results.json";
-    write_file(path_results, all_results.dump(0));
-
-    return 0;
-}
@@ -1,513 +0,0 @@
-import json
-import os
-
-import numpy as np
-
-import utils_pipeline
-from skelda import evals
-from skelda.writers import json_writer
-
-# ==================================================================================================
-
-whole_body = {
-    "foots": False,
-    "face": False,
-    "hands": False,
-}
-
-dataset_use = "human36m"
-# dataset_use = "panoptic"
-# dataset_use = "mvor"
-# dataset_use = "shelf"
-# dataset_use = "campus"
-# dataset_use = "ikeaasm"
-# dataset_use = "chi3d"
-# dataset_use = "tsinghua"
-# dataset_use = "human36m_wb"
-# dataset_use = "egohumans_tagging"
-# dataset_use = "egohumans_legoassemble"
-# dataset_use = "egohumans_fencing"
-# dataset_use = "egohumans_basketball"
-# dataset_use = "egohumans_volleyball"
-# dataset_use = "egohumans_badminton"
-# dataset_use = "egohumans_tennis"
-
-
-# Describes the minimum area as fraction of the image size for a 2D bounding box to be considered
-# If the persons are small in the image, use a lower value
-default_min_bbox_area = 0.1 * 0.1
-
-# Describes how confident a 2D bounding box needs to be to be considered
-# If the persons are small in the image, or poorly recognizable, use a lower value
-default_min_bbox_score = 0.3
-
-# Describes how good two 2D poses need to match each other to create a valid triangulation
-# If the quality of the 2D detections is poor, use a lower value
-default_min_match_score = 0.94
-
-# Describes the minimum number of camera pairs that need to detect the same person
-# If the number of cameras is high, and the views are not occluded, use a higher value
-default_min_group_size = 1
-
-# Batch poses per image for faster processing
-# If most of the time only one person is in a image, disable it, because it is slightly slower then
-default_batch_poses = True
-
-# Approach speed of EN ISO 13855 with 2000 mm/sec for hand speed
-# and an additional factor to compensate for noise-based jumps
-default_max_movement_speed = 2.0 * 1.5
-
-# The size of an A4 sheet of paper which is assumed to fit between two different persons
-# and additionally the distance a person can move between two frames (here at 10 fps)
-default_max_track_distance = 0.3 + default_max_movement_speed / 10
-
-
-datasets = {
-    "human36m": {
-        "path": "/datasets/human36m/skelda/pose_test.json",
-        "take_interval": 5,
-        "fps": 50,
-        "min_match_score": 0.95,
-        "min_group_size": 1,
-        "min_bbox_score": 0.4,
-        "min_bbox_area": 0.1 * 0.1,
-        "batch_poses": False,
-        "max_movement_speed": 2.0 * 1.5,
-        "max_track_distance": 0.3 + default_max_movement_speed / (50 / 5),
-    },
-    "panoptic": {
-        "path": "/datasets/panoptic/skelda/test.json",
-        "cams": ["00_03", "00_06", "00_12", "00_13", "00_23"],
-        # "cams": ["00_03", "00_06", "00_12"],
-        # "cams": ["00_03", "00_06", "00_12", "00_13", "00_23", "00_15", "00_10"],
-        # "cams": ["00_03", "00_06", "00_12", "00_13", "00_23", "00_15", "00_10", "00_21", "00_09", "00_01"],
-        # "cams": [],
-        "take_interval": 3,
-        "fps": 30,
-        "min_match_score": 0.95,
-        "use_scenes": ["160906_pizza1", "160422_haggling1", "160906_ian5"],
-        "min_group_size": 1,
-        # "min_group_size": 1,
-        # "min_group_size": 1,
-        # "min_group_size": 2,
-        # "min_group_size": 11,
-        "min_bbox_area": 0.05 * 0.05,
-        "max_track_distance": 0.3 + default_max_movement_speed / (30 / 3),
-    },
-    "mvor": {
-        "path": "/datasets/mvor/skelda/all.json",
-        "take_interval": 1,
-        "fps": -1,
-        "min_match_score": 0.81,
-        "min_bbox_score": 0.25,
-    },
-    "campus": {
-        "path": "/datasets/campus/skelda/test.json",
-        "fps": 25,
-        "take_interval": 1,
-        "min_match_score": 0.91,
-        "min_bbox_score": 0.5,
-        "max_track_distance": 0.3 + default_max_movement_speed / 25,
-    },
-    "shelf": {
-        "path": "/datasets/shelf/skelda/test.json",
-        "take_interval": 1,
-        "fps": 25,
-        "min_match_score": 0.95,
-        "min_group_size": 3,
-        "max_track_distance": 0.3 + default_max_movement_speed / 25,
-    },
-    "ikeaasm": {
-        "path": "/datasets/ikeaasm/skelda/test.json",
-        "take_interval": 2,
-        "fps": -1,
-        "min_match_score": 0.81,
-        "min_bbox_score": 0.20,
-    },
-    "chi3d": {
-        "path": "/datasets/chi3d/skelda/all.json",
-        "take_interval": 5,
-        "fps": 50,
-        "min_match_score": 0.92,
-        "min_bbox_area": 0.2 * 0.2,
-        "max_track_distance": 0.3 + default_max_movement_speed / (50 / 5),
-    },
-    "tsinghua": {
-        "path": "/datasets/tsinghua/skelda/test.json",
-        "take_interval": 3,
-        "fps": 30,
-        "min_match_score": 0.95,
-        "min_group_size": 2,
-        "max_track_distance": 0.3 + default_max_movement_speed / (30 / 3),
-    },
-    "human36m_wb": {
-        "path": "/datasets/human36m/skelda/wb/test.json",
-        "take_interval": 100,
-        "fps": -1,
-        "min_bbox_score": 0.4,
-        "batch_poses": False,
-    },
-    "egohumans_tagging": {
-        "path": "/datasets/egohumans/skelda/all.json",
-        "take_interval": 2,
-        "fps": 20,
-        "subset": "tagging",
-        "min_match_score": 0.89,
-        "min_group_size": 1,
-        "min_bbox_score": 0.2,
-        "min_bbox_area": 0.05 * 0.05,
-        "max_movement_speed": 4.0 * 1.5,
-        "max_track_distance": 0.3 + (4.0 * 1.5) / (20 / 2),
-    },
-    "egohumans_legoassemble": {
-        "path": "/datasets/egohumans/skelda/all.json",
-        "take_interval": 2,
-        "fps": 20,
-        "subset": "legoassemble",
-        "min_group_size": 2,
-        "max_track_distance": 0.3 + default_max_movement_speed / (20 / 2),
-    },
-    "egohumans_fencing": {
-        "path": "/datasets/egohumans/skelda/all.json",
-        "take_interval": 2,
-        "fps": 20,
-        "subset": "fencing",
-        "min_group_size": 7,
-        "min_bbox_score": 0.5,
-        "min_bbox_area": 0.05 * 0.05,
-        "max_track_distance": 0.3 + default_max_movement_speed / (20 / 2),
-    },
-    "egohumans_basketball": {
-        "path": "/datasets/egohumans/skelda/all.json",
-        "take_interval": 2,
-        "fps": 20,
-        "subset": "basketball",
-        "min_group_size": 4,
-        "min_bbox_score": 0.25,
-        "min_bbox_area": 0.025 * 0.025,
-        "max_movement_speed": 4.0 * 1.5,
-        "max_track_distance": 0.3 + (4.0 * 1.5) / (20 / 2),
-    },
-    "egohumans_volleyball": {
-        "path": "/datasets/egohumans/skelda/all.json",
-        "take_interval": 2,
-        "fps": 20,
-        "subset": "volleyball",
-        "min_match_score": 0.95,
-        "min_group_size": 7,
-        "min_bbox_score": 0.20,
-        "min_bbox_area": 0.05 * 0.05,
-        "max_movement_speed": 4.0 * 1.5,
-        "max_track_distance": 0.3 + (4.0 * 1.5) / (20 / 2),
-    },
-    "egohumans_badminton": {
-        "path": "/datasets/egohumans/skelda/all.json",
-        "take_interval": 2,
-        "fps": 20,
-        "subset": "badminton",
-        "min_group_size": 7,
-        "min_bbox_score": 0.25,
-        "min_bbox_area": 0.05 * 0.05,
-        "max_movement_speed": 4.0 * 1.5,
-        "max_track_distance": 0.3 + (4.0 * 1.5) / (20 / 2),
-    },
-    "egohumans_tennis": {
-        "path": "/datasets/egohumans/skelda/all.json",
-        "take_interval": 2,
-        "fps": 20,
-        "subset": "tennis",
-        "min_group_size": 11,
-        "min_bbox_area": 0.025 * 0.025,
-        "max_movement_speed": 4.0 * 1.5,
-        "max_track_distance": 0.3 + (4.0 * 1.5) / (20 / 2),
-    },
-}
-
-joint_names_2d = utils_pipeline.get_joint_names(whole_body)
-joint_names_3d = list(joint_names_2d)
-eval_joints = [
-    "head",
-    "shoulder_left",
-    "shoulder_right",
-    "elbow_left",
-    "elbow_right",
-    "wrist_left",
-    "wrist_right",
-    "hip_left",
-    "hip_right",
-    "knee_left",
-    "knee_right",
-    "ankle_left",
-    "ankle_right",
-]
-if dataset_use == "human36m":
-    eval_joints[eval_joints.index("head")] = "nose"
-if dataset_use == "panoptic":
-    eval_joints[eval_joints.index("head")] = "nose"
-if dataset_use == "human36m_wb":
-    if utils_pipeline.use_whole_body(whole_body):
-        eval_joints = list(joint_names_2d)
-    else:
-        eval_joints[eval_joints.index("head")] = "nose"
-
-# output_dir = "/RapidPoseTriangulation/data/testoutput/"
-output_dir = ""
-
-# pred_export_path = f"/datasets/predictions/{dataset_use}/RapidPoseTriangulation.json"
-pred_export_path = ""
-
-
-# ==================================================================================================
-
-
-def load_labels(dataset: dict):
-    """Load labels by dataset description"""
-
-    if "panoptic" in dataset:
-        labels = utils_pipeline.load_json(dataset["panoptic"]["path"])
-        labels = [lb for i, lb in enumerate(labels) if i % 1500 < 90]
-
-        # Filter by maximum number of persons
-        labels = [l for l in labels if len(l["bodies3D"]) <= 10]
-
-        # Filter scenes
-        if "use_scenes" in dataset["panoptic"]:
-            labels = [
-                l for l in labels if l["scene"] in dataset["panoptic"]["use_scenes"]
-            ]
-
-        # Filter cameras
-        if not "cameras_depth" in labels[0] and len(dataset["panoptic"]["cams"]) > 0:
-            for label in labels:
-                for i, cam in reversed(list(enumerate(label["cameras"]))):
-                    if cam["name"] not in dataset["panoptic"]["cams"]:
-                        label["cameras"].pop(i)
-                        label["imgpaths"].pop(i)
-
-    elif "human36m" in dataset:
-        labels = utils_pipeline.load_json(dataset["human36m"]["path"])
-        labels = [lb for lb in labels if lb["subject"] == "S9"]
-        labels = [lb for i, lb in enumerate(labels) if i % 4000 < 150]
-
-    elif "mvor" in dataset:
-        labels = utils_pipeline.load_json(dataset["mvor"]["path"])
-
-        # Rename keys
-        for label in labels:
-            label["cameras_color"] = label["cameras"]
-            label["imgpaths_color"] = label["imgpaths"]
-
-    elif "ikeaasm" in dataset:
-        labels = utils_pipeline.load_json(dataset["ikeaasm"]["path"])
-        cams0 = str(labels[0]["cameras"])
-        labels = [lb for lb in labels if str(lb["cameras"]) == cams0]
-
-    elif "shelf" in dataset:
-        labels = utils_pipeline.load_json(dataset["shelf"]["path"])
-        labels = [lb for lb in labels if "test" in lb["splits"]]
-
-    elif "campus" in dataset:
-        labels = utils_pipeline.load_json(dataset["campus"]["path"])
-        labels = [lb for lb in labels if "test" in lb["splits"]]
-
-    elif "tsinghua" in dataset:
-        labels = utils_pipeline.load_json(dataset["tsinghua"]["path"])
-        labels = [lb for lb in labels if "test" in lb["splits"]]
-        labels = [lb for lb in labels if lb["seq"] == "seq_1"]
-        labels = [lb for i, lb in enumerate(labels) if i % 300 < 90]
-
-        for label in labels:
-            label["bodyids"] = list(range(len(label["bodies3D"])))
-
-    elif "chi3d" in dataset:
-        labels = utils_pipeline.load_json(dataset["chi3d"]["path"])
-        labels = [lb for lb in labels if lb["setup"] == "s03"]
-        labels = [lb for i, lb in enumerate(labels) if i % 2000 < 150]
-
-    elif "human36m_wb" in dataset:
-        labels = utils_pipeline.load_json(dataset["human36m_wb"]["path"])
-
-    elif any(("egohumans" in key for key in dataset)):
-        labels = utils_pipeline.load_json(dataset[dataset_use]["path"])
-        labels = [lb for lb in labels if "test" in lb["splits"]]
-        labels = [lb for lb in labels if dataset[dataset_use]["subset"] in lb["seq"]]
-        if dataset[dataset_use]["subset"] in ["volleyball", "tennis"]:
-            labels = [lb for i, lb in enumerate(labels) if i % 150 < 60]
-
-    else:
-        raise ValueError("Dataset not available")
-
-    # Optionally drop samples to speed up train/eval
-    if "take_interval" in dataset:
-        take_interval = dataset["take_interval"]
-        if take_interval > 1:
-            labels = [l for i, l in enumerate(labels) if i % take_interval == 0]
-
-    # Add default values
-    for label in labels:
-        if "scene" not in label:
-            label["scene"] = "default"
-        for cam in label["cameras"]:
-            if not "type" in cam:
-                cam["type"] = "pinhole"
-
-    return labels
-
-
-# ==================================================================================================
-
-
-def main():
-    global joint_names_3d, eval_joints
-
-    print("Loading dataset ...")
-    labels = load_labels(
-        {
-            dataset_use: datasets[dataset_use],
-            "take_interval": datasets[dataset_use]["take_interval"],
-        }
-    )
-
-    # Print a dataset sample for debugging
-    print("Amount of samples:", len(labels))
-    print(labels[0])
-
-    # Save dataset
-    tmp_export_dir = "/tmp/rpt/"
-    for label in labels:
-        if "splits" in label:
-            label.pop("splits")
-    json_writer.save_dataset(labels, tmp_export_dir)
-
-    # Load dataset specific parameters
-    min_match_score = datasets[dataset_use].get(
-        "min_match_score", default_min_match_score
-    )
-    min_group_size = datasets[dataset_use].get("min_group_size", default_min_group_size)
-    min_bbox_score = datasets[dataset_use].get("min_bbox_score", default_min_bbox_score)
-    min_bbox_area = datasets[dataset_use].get("min_bbox_area", default_min_bbox_area)
-    batch_poses = datasets[dataset_use].get("batch_poses", default_batch_poses)
-    max_movement_speed = datasets[dataset_use].get(
-        "max_movement_speed", default_max_movement_speed
-    )
-    max_track_distance = datasets[dataset_use].get(
-        "max_track_distance", default_max_track_distance
-    )
-
-    # Save config
-    config_path = tmp_export_dir + "config.json"
-    config = {
-        "min_match_score": min_match_score,
-        "min_group_size": min_group_size,
-        "min_bbox_score": min_bbox_score,
-        "min_bbox_area": min_bbox_area,
-        "batch_poses": batch_poses,
-        "max_movement_speed": max_movement_speed,
-        "max_track_distance": max_track_distance,
-        "whole_body": whole_body,
-        "take_interval": datasets[dataset_use]["take_interval"],
-        "fps": datasets[dataset_use]["fps"],
-    }
-    utils_pipeline.save_json(config, config_path)
-
-    # Call the CPP binary
-    os.system("/RapidPoseTriangulation/scripts/test_skelda_dataset.bin")
-
-    # Load the results
-    print("Loading exports ...")
-    res_path = tmp_export_dir + "results.json"
-    results = utils_pipeline.load_json(res_path)
-    all_poses_3d = results["all_poses_3d"]
-    all_poses_2d = results["all_poses_2d"]
-    all_ids = results["all_ids"]
-    joint_names_3d = results["joint_names_3d"]
-
-    # # Visualize labels and predictions
-    # from skelda import utils_view
-    # for i in range(0, len(labels), 1):
-    #     posesL = []
-    #     posesR = []
-    #     jnames = []
-    #     for j in labels[i]["joints"]:
-    #         if "->" in j:
-    #             jnames.append(j.split("->")[-1])
-    #         else:
-    #             jnames.append(j)
-    #     for j in range(len(labels[i]["bodies3D"])):
-    #         pose = []
-    #         for k in range(len(eval_joints)):
-    #             n = eval_joints[k]
-    #             pose.append(labels[i]["bodies3D"][j][jnames.index(n)])
-    #         posesL.append(pose)
-    #     for j in range(len(all_poses_3d[i])):
-    #         pose = []
-    #         for k in range(len(eval_joints)):
-    #             n = eval_joints[k]
-    #             pose.append(all_poses_3d[i][j][joint_names_3d.index(n)])
-    #         posesR.append(pose)
-    #     poses_3d = posesL + posesR
-    #     sample = labels[i]
-    #     sample["bodies3D"] = np.array(poses_3d).round(3).tolist()
-    #     sample["joints"] = eval_joints
-    #     sample["num_persons"] = len(poses_3d)
-    #     print(sample)
-    #     utils_view.draw_sample_3d(sample)
-    #     utils_view.draw_many_images(
-    #         sample["imgpaths"],
-    #         sample["cameras"],
-    #         [],
-    #         all_poses_2d[i],
-    #         joint_names_3d,
-    #         "2D detections",
-    #     )
-    #     utils_view.show_plots()
-
-    if pred_export_path != "":
-        # Export predictions
-        print("\nExporting predictions ...")
-        all_poses_3d = [np.array(poses).round(3).tolist() for poses in all_poses_3d]
-        data = {
-            "poses3D": all_poses_3d,
-            "ids": all_ids,
-            "joint_names": joint_names_3d,
-        }
-        os.makedirs(os.path.dirname(pred_export_path), exist_ok=True)
-        with open(pred_export_path, "w") as file:
-            json.dump(data, file, indent=0)
-
-    # Run evaluation
-    _ = evals.mpjpe.run_eval(
-        labels,
-        all_poses_3d,
-        all_ids,
-        joint_names_net=joint_names_3d,
-        joint_names_use=eval_joints,
-        save_error_imgs=output_dir,
-        debug_2D_preds=all_poses_2d,
-    )
-    _ = evals.pcp.run_eval(
-        labels,
-        all_poses_3d,
-        all_ids,
-        joint_names_net=joint_names_3d,
-        joint_names_use=eval_joints,
-        replace_head_with_nose=True,
-    )
-
-    if dataset_use == "shelf":
-        # Also run old-style evaluation for shelf dataset
-        odir = os.path.join(output_dir, "pcp/") if output_dir != "" else ""
-        _ = evals.campus_shelf.run_eval(
-            labels,
-            all_poses_3d,
-            all_ids,
-            joint_names_net=joint_names_3d,
-            save_error_imgs=odir,
-            debug_2D_preds=all_poses_2d,
-        )
-
-
-# ==================================================================================================
-
-if __name__ == "__main__":
-    main()
@@ -1,144 +0,0 @@
-import copy
-import json
-import os
-
-import numpy as np
-
-import utils_pipeline
-from skelda import utils_pose, utils_view
-from skelda.writers import json_writer
-
-# ==================================================================================================
-
-filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
-test_img_dir = filepath + "../data/"
-
-whole_body = {
-    "foots": False,
-    "face": False,
-    "hands": False,
-}
-config = {
-    "min_match_score": 0.94,
-    "min_group_size": 1,
-    "min_bbox_score": 0.3,
-    "min_bbox_area": 0.1 * 0.1,
-    "batch_poses": True,
-    "whole_body": whole_body,
-    "take_interval": 1,
-    "fps": -1,
-    "max_movement_speed": 0,
-    "max_track_distance": 0,
-}
-
-joint_names_2d = utils_pipeline.get_joint_names(whole_body)
-joint_names_3d = list(joint_names_2d)
-
-
-# ==================================================================================================
-
-
-def update_sample(sample, new_dir=""):
-    sample = copy.deepcopy(sample)
-
-    # Rename image paths
-    sample["imgpaths"] = [
-        os.path.join(new_dir, os.path.basename(v)) for v in sample["imgpaths"]
-    ]
-
-    # Add placeholders for missing keys
-    if not "scene" in sample:
-        sample["scene"] = "default"
-    if not "id" in sample:
-        sample["id"] = "0"
-    if not "index" in sample:
-        sample["index"] = 0
-    for cam in sample["cameras"]:
-        if not "type" in cam:
-            cam["type"] = "pinhole"
-
-    return sample
-
-
-# ==================================================================================================
-
-
-def main():
-
-    for dirname in sorted(os.listdir(test_img_dir)):
-        dirpath = os.path.join(test_img_dir, dirname)
-
-        if not os.path.isdir(dirpath):
-            continue
-
-        if (dirname[0] not in ["p", "h", "e", "q"]) or len(dirname) != 2:
-            continue
-
-        # Load sample infos
-        print("\n" + dirpath)
-        with open(os.path.join(dirpath, "sample.json"), "r", encoding="utf-8") as file:
-            sample = json.load(file)
-        sample = update_sample(sample, dirpath)
-
-        if len(sample["imgpaths"]) == 1:
-            # At least two images are required
-            continue
-
-        # Save dataset
-        labels = [sample]
-        tmp_export_dir = "/tmp/rpt/"
-        for label in labels:
-            if "splits" in label:
-                label.pop("splits")
-        json_writer.save_dataset(labels, tmp_export_dir)
-
-        # Save config
-        config_path = tmp_export_dir + "config.json"
-        utils_pipeline.save_json(config, config_path)
-
-        # Call the CPP binary
-        os.system("/RapidPoseTriangulation/scripts/test_skelda_dataset.bin")
-
-        # Load the results
-        print("Loading exports ...")
-        res_path = tmp_export_dir + "results.json"
-        results = utils_pipeline.load_json(res_path)
-        poses_3d = results["all_poses_3d"][0]
-        poses_2d = results["all_poses_2d"][0]
-        joint_names_3d = results["joint_names_3d"]
-
-        # Visualize the 2D results
-        fig1 = utils_view.draw_many_images(
-            sample["imgpaths"], [], [], poses_2d, joint_names_2d, "2D detections"
-        )
-        fig1.savefig(os.path.join(dirpath, "2d-k.png"), dpi=fig1.dpi)
-
-        # Visualize the 3D results
-        print("Detected 3D poses:")
-        poses_3d = np.array(poses_3d)
-        print(poses_3d.round(3))
-        if len(poses_3d) == 0:
-            utils_view.show_plots()
-            continue
-        camparams = sample["cameras"]
-        roomparams = {
-            "room_size": sample["room_size"],
-            "room_center": sample["room_center"],
-        }
-        poses_2d_proj = []
-        for cam in camparams:
-            poses_2d_cam, _ = utils_pose.project_poses(poses_3d, cam)
-            poses_2d_proj.append(poses_2d_cam)
-        fig2 = utils_view.draw_poses3d(poses_3d, joint_names_3d, roomparams, camparams)
-        fig3 = utils_view.draw_many_images(
-            sample["imgpaths"], [], [], poses_2d_proj, joint_names_3d, "2D projections"
-        )
-        fig2.savefig(os.path.join(dirpath, "3d-p.png"), dpi=fig2.dpi)
-        fig3.savefig(os.path.join(dirpath, "2d-p.png"), dpi=fig3.dpi)
-        utils_view.show_plots()
-
-
-# ==================================================================================================
-
-if __name__ == "__main__":
-    main()
@@ -1,316 +0,0 @@
-#pragma once
-
-#include <string>
-#include <vector>
-
-#include <opencv2/opencv.hpp>
-
-// =================================================================================================
-
-namespace utils_pipeline
-{
-    bool use_whole_body(const std::map<std::string, bool> &whole_body)
-    {
-        for (const auto &pair : whole_body)
-        {
-            if (pair.second)
-            {
-                return true;
-            }
-        }
-        return false;
-    }
-
-    // =============================================================================================
-
-    std::vector<std::string> get_joint_names(const std::map<std::string, bool> &whole_body)
-    {
-        std::vector<std::string> joint_names_2d = {
-            "nose",
-            "eye_left",
-            "eye_right",
-            "ear_left",
-            "ear_right",
-            "shoulder_left",
-            "shoulder_right",
-            "elbow_left",
-            "elbow_right",
-            "wrist_left",
-            "wrist_right",
-            "hip_left",
-            "hip_right",
-            "knee_left",
-            "knee_right",
-            "ankle_left",
-            "ankle_right",
-        };
-
-        if (whole_body.at("foots"))
-        {
-            joint_names_2d.insert(
-                joint_names_2d.end(),
-                {
-                    "foot_toe_big_left",
-                    "foot_toe_small_left",
-                    "foot_heel_left",
-                    "foot_toe_big_right",
-                    "foot_toe_small_right",
-                    "foot_heel_right",
-                });
-        }
-        if (whole_body.at("face"))
-        {
-            joint_names_2d.insert(
-                joint_names_2d.end(),
-                {
-                    "face_jaw_right_1",
-                    "face_jaw_right_2",
-                    "face_jaw_right_3",
-                    "face_jaw_right_4",
-                    "face_jaw_right_5",
-                    "face_jaw_right_6",
-                    "face_jaw_right_7",
-                    "face_jaw_right_8",
-                    "face_jaw_middle",
-                    "face_jaw_left_1",
-                    "face_jaw_left_2",
-                    "face_jaw_left_3",
-                    "face_jaw_left_4",
-                    "face_jaw_left_5",
-                    "face_jaw_left_6",
-                    "face_jaw_left_7",
-                    "face_jaw_left_8",
-                    "face_eyebrow_right_1",
-                    "face_eyebrow_right_2",
-                    "face_eyebrow_right_3",
-                    "face_eyebrow_right_4",
-                    "face_eyebrow_right_5",
-                    "face_eyebrow_left_1",
-                    "face_eyebrow_left_2",
-                    "face_eyebrow_left_3",
-                    "face_eyebrow_left_4",
-                    "face_eyebrow_left_5",
-                    "face_nose_1",
-                    "face_nose_2",
-                    "face_nose_3",
-                    "face_nose_4",
-                    "face_nose_5",
-                    "face_nose_6",
-                    "face_nose_7",
-                    "face_nose_8",
-                    "face_nose_9",
-                    "face_eye_right_1",
-                    "face_eye_right_2",
-                    "face_eye_right_3",
-                    "face_eye_right_4",
-                    "face_eye_right_5",
-                    "face_eye_right_6",
-                    "face_eye_left_1",
-                    "face_eye_left_2",
-                    "face_eye_left_3",
-                    "face_eye_left_4",
-                    "face_eye_left_5",
-                    "face_eye_left_6",
-                    "face_mouth_1",
-                    "face_mouth_2",
-                    "face_mouth_3",
-                    "face_mouth_4",
-                    "face_mouth_5",
-                    "face_mouth_6",
-                    "face_mouth_7",
-                    "face_mouth_8",
-                    "face_mouth_9",
-                    "face_mouth_10",
-                    "face_mouth_11",
-                    "face_mouth_12",
-                    "face_mouth_13",
-                    "face_mouth_14",
-                    "face_mouth_15",
-                    "face_mouth_16",
-                    "face_mouth_17",
-                    "face_mouth_18",
-                    "face_mouth_19",
-                    "face_mouth_20",
-                });
-        }
-        if (whole_body.at("hands"))
-        {
-            joint_names_2d.insert(
-                joint_names_2d.end(),
-                {
-                    "hand_wrist_left",
-                    "hand_finger_thumb_left_1",
-                    "hand_finger_thumb_left_2",
-                    "hand_finger_thumb_left_3",
-                    "hand_finger_thumb_left_4",
-                    "hand_finger_index_left_1",
-                    "hand_finger_index_left_2",
-                    "hand_finger_index_left_3",
-                    "hand_finger_index_left_4",
-                    "hand_finger_middle_left_1",
-                    "hand_finger_middle_left_2",
-                    "hand_finger_middle_left_3",
-                    "hand_finger_middle_left_4",
-                    "hand_finger_ring_left_1",
-                    "hand_finger_ring_left_2",
-                    "hand_finger_ring_left_3",
-                    "hand_finger_ring_left_4",
-                    "hand_finger_pinky_left_1",
-                    "hand_finger_pinky_left_2",
-                    "hand_finger_pinky_left_3",
-                    "hand_finger_pinky_left_4",
-                    "hand_wrist_right",
-                    "hand_finger_thumb_right_1",
-                    "hand_finger_thumb_right_2",
-                    "hand_finger_thumb_right_3",
-                    "hand_finger_thumb_right_4",
-                    "hand_finger_index_right_1",
-                    "hand_finger_index_right_2",
-                    "hand_finger_index_right_3",
-                    "hand_finger_index_right_4",
-                    "hand_finger_middle_right_1",
-                    "hand_finger_middle_right_2",
-                    "hand_finger_middle_right_3",
-                    "hand_finger_middle_right_4",
-                    "hand_finger_ring_right_1",
-                    "hand_finger_ring_right_2",
-                    "hand_finger_ring_right_3",
-                    "hand_finger_ring_right_4",
-                    "hand_finger_pinky_right_1",
-                    "hand_finger_pinky_right_2",
-                    "hand_finger_pinky_right_3",
-                    "hand_finger_pinky_right_4",
-                });
-        }
-
-        joint_names_2d.insert(
-            joint_names_2d.end(),
-            {
-                "hip_middle",
-                "shoulder_middle",
-                "head",
-            });
-
-        return joint_names_2d;
-    }
-
-    // =============================================================================================
-
-    cv::Mat bayer2rgb(const cv::Mat &bayer)
-    {
-        cv::Mat rgb;
-        cv::cvtColor(bayer, rgb, cv::COLOR_BayerBG2RGB);
-        return rgb;
-    }
-
-    cv::Mat rgb2bayer(const cv::Mat &img)
-    {
-        CV_Assert(img.type() == CV_8UC3);
-        cv::Mat bayer(img.rows, img.cols, CV_8UC1);
-
-        for (int r = 0; r < img.rows; ++r)
-        {
-            const uchar *imgData = img.ptr<uchar>(r);
-            uchar *bayerRowPtr = bayer.ptr<uchar>(r);
-
-            for (int c = 0; c < img.cols; ++c)
-            {
-                int pixelIndex = 3 * c;
-
-                // Use faster bit operation instead of modulo+if
-                // Even row, even col => R = 0
-                // Even row, odd col => G = 1
-                // Odd row, even col => G = 1
-                // Odd row, odd col => B = 2
-                int row_mod = r & 1;
-                int col_mod = c & 1;
-                int component = row_mod + col_mod;
-
-                bayerRowPtr[c] = imgData[pixelIndex + component];
-            }
-        }
-
-        return bayer;
-    }
-
-    // =============================================================================================
-
-    inline int find_index(const std::vector<std::string> &vec, const std::string &key)
-    {
-        auto it = std::find(vec.begin(), vec.end(), key);
-        if (it == vec.end())
-        {
-            throw std::runtime_error("Cannot find \"" + key + "\" in joint_names.");
-        }
-        return static_cast<int>(std::distance(vec.begin(), it));
-    }
-
-    std::vector<std::vector<std::vector<std::array<float, 3>>>> update_keypoints(
-        const std::vector<std::vector<std::vector<std::array<float, 3>>>> &poses_2d,
-        const std::vector<std::string> &joint_names,
-        const std::map<std::string, bool> &whole_body)
-    {
-        std::vector<std::vector<std::vector<std::array<float, 3>>>> new_views;
-        new_views.reserve(poses_2d.size());
-
-        for (const auto &view : poses_2d)
-        {
-            // "view" is a list of bodies => each body is Nx3
-            std::vector<std::vector<std::array<float, 3>>> new_bodies;
-            new_bodies.reserve(view.size());
-
-            for (const auto &body : view)
-            {
-                // 1) Copy first 17 keypoints
-                std::vector<std::array<float, 3>> new_body;
-                new_body.insert(new_body.end(), body.begin(), body.begin() + 17);
-
-                // 2) Optionally append extra keypoints
-                if (whole_body.at("foots"))
-                {
-                    new_body.insert(new_body.end(), body.begin() + 17, body.begin() + 23);
-                }
-                if (whole_body.at("face"))
-                {
-                    new_body.insert(new_body.end(), body.begin() + 23, body.begin() + 91);
-                }
-                if (whole_body.at("hands"))
-                {
-                    new_body.insert(new_body.end(), body.begin() + 91, body.end());
-                }
-
-                // 3) Compute mid_hip
-                int hlid = find_index(joint_names, "hip_left");
-                int hrid = find_index(joint_names, "hip_right");
-                float mid_hip_x = 0.5 * (new_body[hlid][0] + new_body[hrid][0]);
-                float mid_hip_y = 0.5 * (new_body[hlid][1] + new_body[hrid][1]);
-                float mid_hip_c = std::min(new_body[hlid][2], new_body[hrid][2]);
-                new_body.push_back({mid_hip_x, mid_hip_y, mid_hip_c});
-
-                // 4) Compute mid_shoulder
-                int slid = find_index(joint_names, "shoulder_left");
-                int srid = find_index(joint_names, "shoulder_right");
-                float mid_shoulder_x = 0.5 * (new_body[slid][0] + new_body[srid][0]);
-                float mid_shoulder_y = 0.5 * (new_body[slid][1] + new_body[srid][1]);
-                float mid_shoulder_c = std::min(new_body[slid][2], new_body[srid][2]);
-                new_body.push_back({mid_shoulder_x, mid_shoulder_y, mid_shoulder_c});
-
-                // 5) Compute head
-                int elid = find_index(joint_names, "ear_left");
-                int erid = find_index(joint_names, "ear_right");
-                float head_x = 0.5 * (new_body[elid][0] + new_body[erid][0]);
-                float head_y = 0.5 * (new_body[elid][1] + new_body[erid][1]);
-                float head_c = std::min(new_body[elid][2], new_body[erid][2]);
-                new_body.push_back({head_x, head_y, head_c});
-
-                // Add this updated body into new_bodies
-                new_bodies.push_back(new_body);
-            }
-
-            // Add all updated bodies for this view
-            new_views.push_back(new_bodies);
-        }
-
-        return new_views;
-    }
-}
@@ -1,189 +0,0 @@
-import json
-
-# ==================================================================================================
-
-
-def load_json(path: str):
-    with open(path, "r", encoding="utf-8") as file:
-        data = json.load(file)
-    return data
-
-
-def save_json(data: dict, path: str):
-    with open(path, "w+", encoding="utf-8") as file:
-        json.dump(data, file, indent=0)
-
-
-# ==================================================================================================
-
-
-def use_whole_body(whole_body: dict) -> bool:
-    return any((whole_body[k] for k in whole_body))
-
-
-# ==================================================================================================
-
-
-def get_joint_names(whole_body: dict):
-
-    joint_names_2d = [
-        "nose",
-        "eye_left",
-        "eye_right",
-        "ear_left",
-        "ear_right",
-        "shoulder_left",
-        "shoulder_right",
-        "elbow_left",
-        "elbow_right",
-        "wrist_left",
-        "wrist_right",
-        "hip_left",
-        "hip_right",
-        "knee_left",
-        "knee_right",
-        "ankle_left",
-        "ankle_right",
-    ]
-
-    if whole_body["foots"]:
-        joint_names_2d.extend(
-            [
-                "foot_toe_big_left",
-                "foot_toe_small_left",
-                "foot_heel_left",
-                "foot_toe_big_right",
-                "foot_toe_small_right",
-                "foot_heel_right",
-            ]
-        )
-    if whole_body["face"]:
-        joint_names_2d.extend(
-            [
-                "face_jaw_right_1",
-                "face_jaw_right_2",
-                "face_jaw_right_3",
-                "face_jaw_right_4",
-                "face_jaw_right_5",
-                "face_jaw_right_6",
-                "face_jaw_right_7",
-                "face_jaw_right_8",
-                "face_jaw_middle",
-                "face_jaw_left_1",
-                "face_jaw_left_2",
-                "face_jaw_left_3",
-                "face_jaw_left_4",
-                "face_jaw_left_5",
-                "face_jaw_left_6",
-                "face_jaw_left_7",
-                "face_jaw_left_8",
-                "face_eyebrow_right_1",
-                "face_eyebrow_right_2",
-                "face_eyebrow_right_3",
-                "face_eyebrow_right_4",
-                "face_eyebrow_right_5",
-                "face_eyebrow_left_1",
-                "face_eyebrow_left_2",
-                "face_eyebrow_left_3",
-                "face_eyebrow_left_4",
-                "face_eyebrow_left_5",
-                "face_nose_1",
-                "face_nose_2",
-                "face_nose_3",
-                "face_nose_4",
-                "face_nose_5",
-                "face_nose_6",
-                "face_nose_7",
-                "face_nose_8",
-                "face_nose_9",
-                "face_eye_right_1",
-                "face_eye_right_2",
-                "face_eye_right_3",
-                "face_eye_right_4",
-                "face_eye_right_5",
-                "face_eye_right_6",
-                "face_eye_left_1",
-                "face_eye_left_2",
-                "face_eye_left_3",
-                "face_eye_left_4",
-                "face_eye_left_5",
-                "face_eye_left_6",
-                "face_mouth_1",
-                "face_mouth_2",
-                "face_mouth_3",
-                "face_mouth_4",
-                "face_mouth_5",
-                "face_mouth_6",
-                "face_mouth_7",
-                "face_mouth_8",
-                "face_mouth_9",
-                "face_mouth_10",
-                "face_mouth_11",
-                "face_mouth_12",
-                "face_mouth_13",
-                "face_mouth_14",
-                "face_mouth_15",
-                "face_mouth_16",
-                "face_mouth_17",
-                "face_mouth_18",
-                "face_mouth_19",
-                "face_mouth_20",
-            ]
-        )
-    if whole_body["hands"]:
-        joint_names_2d.extend(
-            [
-                "hand_wrist_left",
-                "hand_finger_thumb_left_1",
-                "hand_finger_thumb_left_2",
-                "hand_finger_thumb_left_3",
-                "hand_finger_thumb_left_4",
-                "hand_finger_index_left_1",
-                "hand_finger_index_left_2",
-                "hand_finger_index_left_3",
-                "hand_finger_index_left_4",
-                "hand_finger_middle_left_1",
-                "hand_finger_middle_left_2",
-                "hand_finger_middle_left_3",
-                "hand_finger_middle_left_4",
-                "hand_finger_ring_left_1",
-                "hand_finger_ring_left_2",
-                "hand_finger_ring_left_3",
-                "hand_finger_ring_left_4",
-                "hand_finger_pinky_left_1",
-                "hand_finger_pinky_left_2",
-                "hand_finger_pinky_left_3",
-                "hand_finger_pinky_left_4",
-                "hand_wrist_right",
-                "hand_finger_thumb_right_1",
-                "hand_finger_thumb_right_2",
-                "hand_finger_thumb_right_3",
-                "hand_finger_thumb_right_4",
-                "hand_finger_index_right_1",
-                "hand_finger_index_right_2",
-                "hand_finger_index_right_3",
-                "hand_finger_index_right_4",
-                "hand_finger_middle_right_1",
-                "hand_finger_middle_right_2",
-                "hand_finger_middle_right_3",
-                "hand_finger_middle_right_4",
-                "hand_finger_ring_right_1",
-                "hand_finger_ring_right_2",
-                "hand_finger_ring_right_3",
-                "hand_finger_ring_right_4",
-                "hand_finger_pinky_right_1",
-                "hand_finger_pinky_right_2",
-                "hand_finger_pinky_right_3",
-                "hand_finger_pinky_right_4",
-            ]
-        )
-
-    joint_names_2d.extend(
-        [
-            "hip_middle",
-            "shoulder_middle",
-            "head",
-        ]
-    )
-
-    return joint_names_2d
@@ -0,0 +1,225 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
+
+from ._core import (
+    AssociationReport,
+    AssociationStatus,
+    Camera,
+    CameraModel,
+    FinalPoseAssociationDebug,
+    TriangulationConfig,
+    TriangulationOptions,
+    TriangulationResult,
+    CoreProposalDebug,
+    FullProposalDebug,
+    GroupingDebug,
+    MergeDebug,
+    PairCandidate,
+    PreviousPoseFilterDebug,
+    PreviousPoseMatch,
+    ProposalGroupDebug,
+    TriangulationTrace,
+    build_pair_candidates as _build_pair_candidates,
+    filter_pairs_with_previous_poses as _filter_pairs_with_previous_poses,
+    make_camera as _make_camera,
+    triangulate_debug as _triangulate_debug,
+    triangulate_poses as _triangulate_poses,
+    triangulate_with_report as _triangulate_with_report,
+)
+
+if TYPE_CHECKING:
+    import numpy as np
+    import numpy.typing as npt
+
+    from ._helpers import CameraLike, CameraModelLike, Matrix3x3Like, PoseViewLike, VectorLike
+
+    PoseArray2D = npt.NDArray[np.float32]
+    PoseArray3D = npt.NDArray[np.float32]
+    PersonCountArray = npt.NDArray[np.uint32]
+    TrackIdArray = npt.NDArray[np.int64]
+
+
+Camera.__doc__ = """Immutable camera calibration with precomputed projection cache fields."""
+TriangulationConfig.__doc__ = """Stable scene configuration used for triangulation."""
+TriangulationOptions.__doc__ = """Score and grouping thresholds used by triangulation."""
+TriangulationResult.__doc__ = """Tracked triangulation output containing poses and association metadata."""
+AssociationReport.__doc__ = """Track-association summary for a tracked triangulation call."""
+TriangulationTrace.__doc__ = """Full debug trace for triangulation, including pair, grouping, and association stages."""
+
+
+def convert_cameras(cameras: "Sequence[CameraLike]") -> list[Camera]:
+    """Normalize mapping-like camera inputs into immutable bound `Camera` instances."""
+
+    from ._helpers import convert_cameras as _convert_cameras
+
+    return _convert_cameras(cameras)
+
+
+def make_camera(
+    name: str,
+    K: "Matrix3x3Like",
+    DC: "VectorLike",
+    R: "Matrix3x3Like",
+    T: "Sequence[Sequence[float]]",
+    width: int,
+    height: int,
+    model: "CameraModel | CameraModelLike",
+) -> Camera:
+    """Create an immutable camera and precompute its cached projection fields."""
+
+    from ._helpers import _coerce_camera_model, _coerce_distortion
+
+    camera_model = _coerce_camera_model(model)
+    return _make_camera(
+        name,
+        K,
+        _coerce_distortion(DC, camera_model),
+        R,
+        T,
+        width,
+        height,
+        camera_model,
+    )
+
+
+def build_pair_candidates(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+) -> list[PairCandidate]:
+    """Enumerate all cross-view person pairs implied by the padded 2D pose batch."""
+
+    return _build_pair_candidates(poses_2d, person_counts)
+
+
+def pack_poses_2d(
+    views: "Sequence[PoseViewLike]", *, joint_count: int | None = None
+) -> "tuple[npt.NDArray[np.float32], npt.NDArray[np.uint32]]":
+    """Pack ragged per-view pose detections into the padded tensor expected by the core API."""
+
+    from ._helpers import pack_poses_2d as _pack_poses_2d
+
+    return _pack_poses_2d(views, joint_count=joint_count)
+
+
+def make_triangulation_config(
+    cameras: "Sequence[CameraLike]",
+    roomparams: "npt.NDArray[np.generic] | Sequence[Sequence[float]]",
+    joint_names: "Sequence[str]",
+    *,
+    min_match_score: float = 0.95,
+    min_group_size: int = 1,
+) -> TriangulationConfig:
+    """Build a triangulation config from cameras, room parameters, and joint names."""
+
+    from ._helpers import make_triangulation_config as _make_triangulation_config
+
+    return _make_triangulation_config(
+        cameras,
+        roomparams,
+        joint_names,
+        min_match_score=min_match_score,
+        min_group_size=min_group_size,
+    )
+
+
+def filter_pairs_with_previous_poses(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+    previous_poses_3d: "PoseArray3D",
+    previous_track_ids: "TrackIdArray",
+) -> PreviousPoseFilterDebug:
+    """Filter raw cross-view pairs against caller-owned previous 3D tracks."""
+
+    return _filter_pairs_with_previous_poses(
+        poses_2d,
+        person_counts,
+        config,
+        previous_poses_3d,
+        previous_track_ids,
+    )
+
+
+def triangulate_debug(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+    previous_poses_3d: "PoseArray3D | None" = None,
+    previous_track_ids: "TrackIdArray | None" = None,
+) -> TriangulationTrace:
+    """Run triangulation and return the full debug trace.
+
+    If previous-frame 3D tracks are supplied, `previous_track_ids` must be supplied as an
+    aligned `int64` array of the same length.
+    """
+
+    if previous_poses_3d is None:
+        return _triangulate_debug(poses_2d, person_counts, config)
+    if previous_track_ids is None:
+        raise ValueError("previous_track_ids is required when previous_poses_3d is provided.")
+    return _triangulate_debug(poses_2d, person_counts, config, previous_poses_3d, previous_track_ids)
+
+
+def triangulate_poses(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+) -> "PoseArray3D":
+    """Triangulate a frame into anonymous 3D poses without track association."""
+
+    return _triangulate_poses(poses_2d, person_counts, config)
+
+
+def triangulate_with_report(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+    previous_poses_3d: "PoseArray3D",
+    previous_track_ids: "TrackIdArray",
+) -> TriangulationResult:
+    """Triangulate a frame and return caller-owned track association results.
+
+    The previous-frame poses and IDs are treated as immutable caller state. The returned
+    association report classifies each final pose as matched, new, or ambiguous, and lists
+    unmatched previous tracks as deletion candidates.
+    """
+
+    return _triangulate_with_report(
+        poses_2d,
+        person_counts,
+        config,
+        previous_poses_3d,
+        previous_track_ids,
+    )
+
+
+__all__ = [
+    "Camera",
+    "CameraModel",
+    "AssociationReport",
+    "AssociationStatus",
+    "FinalPoseAssociationDebug",
+    "TriangulationConfig",
+    "TriangulationOptions",
+    "TriangulationResult",
+    "CoreProposalDebug",
+    "FullProposalDebug",
+    "GroupingDebug",
+    "MergeDebug",
+    "PairCandidate",
+    "PreviousPoseFilterDebug",
+    "PreviousPoseMatch",
+    "ProposalGroupDebug",
+    "TriangulationTrace",
+    "build_pair_candidates",
+    "convert_cameras",
+    "filter_pairs_with_previous_poses",
+    "make_camera",
+    "make_triangulation_config",
+    "pack_poses_2d",
+    "triangulate_debug",
+    "triangulate_poses",
+    "triangulate_with_report",
+]
@@ -0,0 +1,115 @@
+from collections.abc import Sequence
+from typing import TypeAlias, overload
+
+import numpy as np
+import numpy.typing as npt
+
+from ._core import (
+    AssociationReport,
+    AssociationStatus,
+    Camera,
+    CameraModel,
+    CoreProposalDebug,
+    FinalPoseAssociationDebug,
+    FullProposalDebug,
+    GroupingDebug,
+    MergeDebug,
+    PairCandidate,
+    PreviousPoseFilterDebug,
+    PreviousPoseMatch,
+    ProposalGroupDebug,
+    TriangulationConfig,
+    TriangulationOptions,
+    TriangulationResult,
+    TriangulationTrace,
+)
+from ._helpers import CameraLike, CameraModelLike, Matrix3x3Like, PoseViewLike, RoomParamsLike, VectorLike
+
+PoseArray2D: TypeAlias = npt.NDArray[np.float32]
+PoseArray3D: TypeAlias = npt.NDArray[np.float32]
+PersonCountArray: TypeAlias = npt.NDArray[np.uint32]
+TrackIdArray: TypeAlias = npt.NDArray[np.int64]
+
+
+def convert_cameras(cameras: Sequence[CameraLike]) -> list[Camera]: ...
+
+
+def make_camera(
+    name: str,
+    K: Matrix3x3Like,
+    DC: VectorLike,
+    R: Matrix3x3Like,
+    T: Sequence[Sequence[float]],
+    width: int,
+    height: int,
+    model: CameraModel | CameraModelLike,
+) -> Camera: ...
+
+
+def build_pair_candidates(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+) -> list[PairCandidate]: ...
+
+
+def pack_poses_2d(
+    views: Sequence[PoseViewLike],
+    *,
+    joint_count: int | None = None,
+) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.uint32]]: ...
+
+
+def make_triangulation_config(
+    cameras: Sequence[CameraLike],
+    roomparams: RoomParamsLike,
+    joint_names: Sequence[str],
+    *,
+    min_match_score: float = 0.95,
+    min_group_size: int = 1,
+) -> TriangulationConfig: ...
+
+
+def filter_pairs_with_previous_poses(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+    previous_poses_3d: PoseArray3D,
+    previous_track_ids: TrackIdArray,
+) -> PreviousPoseFilterDebug: ...
+
+
+@overload
+def triangulate_debug(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+) -> TriangulationTrace: ...
+
+
+@overload
+def triangulate_debug(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+    previous_poses_3d: PoseArray3D,
+    previous_track_ids: TrackIdArray,
+) -> TriangulationTrace: ...
+
+
+def triangulate_poses(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+) -> PoseArray3D: ...
+
+
+def triangulate_with_report(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+    previous_poses_3d: PoseArray3D,
+    previous_track_ids: TrackIdArray,
+) -> TriangulationResult: ...
+
+
+__all__: list[str]
@@ -0,0 +1,159 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Literal, TypeAlias, TypedDict
+
+import numpy as np
+import numpy.typing as npt
+
+from ._core import Camera, CameraModel, TriangulationConfig, TriangulationOptions, make_camera
+
+Matrix3x3Like: TypeAlias = Sequence[Sequence[float]]
+VectorLike: TypeAlias = Sequence[float]
+RoomParamsLike: TypeAlias = npt.NDArray[np.generic] | Sequence[Sequence[float]]
+PoseViewLike: TypeAlias = npt.NDArray[np.generic] | Sequence[Sequence[Sequence[float]]] | Sequence[Sequence[float]]
+
+
+class CameraDict(TypedDict, total=False):
+    name: str
+    K: Matrix3x3Like
+    DC: VectorLike
+    R: Matrix3x3Like
+    T: Sequence[Sequence[float]]
+    width: int
+    height: int
+    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."""
+
+    converted: list[Camera] = []
+    for cam in cameras:
+        if isinstance(cam, Camera):
+            converted.append(cam)
+            continue
+
+        camera_model = _coerce_camera_model(cam.get("model", cam.get("type", "pinhole")))
+        converted.append(
+            make_camera(
+                str(cam["name"]),
+                cam["K"],
+                _coerce_distortion(cam["DC"], camera_model),
+                cam["R"],
+                cam["T"],
+                int(cam["width"]),
+                int(cam["height"]),
+                camera_model,
+            )
+        )
+    return converted
+
+
+def pack_poses_2d(
+    views: Sequence[PoseViewLike], *, joint_count: int | None = None
+) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.uint32]]:
+    """Pack ragged per-view pose detections into the padded tensor expected by the core API."""
+
+    normalized: list[npt.NDArray[np.float32]] = []
+    inferred_joint_count = joint_count
+
+    for view in views:
+        array = np.asarray(view, dtype=np.float32)
+
+        if array.size == 0:
+            normalized.append(np.zeros((0, 0, 3), dtype=np.float32))
+            continue
+
+        if array.ndim == 2:
+            if array.shape[-1] != 3:
+                raise ValueError("Single-person pose inputs must have shape [joints, 3].")
+            array = array[np.newaxis, :, :]
+        elif array.ndim != 3 or array.shape[-1] != 3:
+            raise ValueError("Each view must have shape [persons, joints, 3] or [joints, 3].")
+
+        if inferred_joint_count is None:
+            inferred_joint_count = int(array.shape[1])
+        elif array.shape[1] != inferred_joint_count:
+            raise ValueError("All views must use the same joint count.")
+
+        normalized.append(np.ascontiguousarray(array, dtype=np.float32))
+
+    if inferred_joint_count is None:
+        raise ValueError("joint_count is required when all views are empty.")
+
+    fixed_views: list[npt.NDArray[np.float32]] = []
+    max_persons = 0
+    for array in normalized:
+        if array.size == 0:
+            array = np.zeros((0, inferred_joint_count, 3), dtype=np.float32)
+        elif array.shape[1] != inferred_joint_count:
+            raise ValueError("All views must use the same joint count.")
+        max_persons = max(max_persons, int(array.shape[0]))
+        fixed_views.append(array)
+
+    packed = np.zeros((len(fixed_views), max_persons, inferred_joint_count, 3), dtype=np.float32)
+    counts = np.zeros((len(fixed_views),), dtype=np.uint32)
+
+    for view_idx, array in enumerate(fixed_views):
+        person_count = int(array.shape[0])
+        counts[view_idx] = person_count
+        if person_count:
+            packed[view_idx, :person_count, :, :] = array
+
+    return packed, counts
+
+
+def make_triangulation_config(
+    cameras: Sequence[CameraLike],
+    roomparams: RoomParamsLike,
+    joint_names: Sequence[str],
+    *,
+    min_match_score: float = 0.95,
+    min_group_size: int = 1,
+) -> TriangulationConfig:
+    config = TriangulationConfig()
+    config.cameras = convert_cameras(cameras)
+
+    roomparams_array = np.asarray(roomparams, dtype=np.float32)
+    if roomparams_array.shape != (2, 3):
+        raise ValueError("roomparams must have shape [2, 3].")
+    config.roomparams = roomparams_array.tolist()
+
+    config.joint_names = [str(joint_name) for joint_name in joint_names]
+
+    options = TriangulationOptions()
+    options.min_match_score = float(min_match_score)
+    options.min_group_size = int(min_group_size)
+    config.options = options
+    return config
@@ -0,0 +1 @@
+
@@ -1,14 +0,0 @@
-# Standard compile options for the C++ executable
-FLAGS = -fPIC -O3 -march=native -Wall -Werror -flto=auto
-
-# The Python interface through SWIG
-PYTHON_VERSION = $(shell python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}");')
-PYTHONI = -I/usr/include/python$(PYTHON_VERSION)/
-PYTHONL = -Xlinker -export-dynamic
-
-# Default super-target
-all:
-	cd ../rpt/ && g++ $(FLAGS) -std=c++2a -c *.cpp ; cd ../swig/
-	swig -c++ -python -keyword -o rpt_wrap.cxx rpt.i
-	g++ $(FLAGS) $(PYTHONI) -c rpt_wrap.cxx -o rpt_wrap.o
-	g++ $(FLAGS) $(PYTHONL) -shared ../rpt/*.o rpt_wrap.o -o _rpt.so
@@ -1,70 +0,0 @@
-%module rpt
-%{
-    // Includes the header in the wrapper code
-    #include "../rpt/camera.hpp"
-    #include "../rpt/interface.hpp"
-%}
-
-// Some modules need extra imports beside the main .hpp file
-%include "std_array.i"
-%include "std_string.i"
-%include "std_vector.i"
-
-// Instantiate templates used by example
-// If the template is too nested (>2), parts of it need to be declared as well
-namespace std {
-    %template(FloatMatrix_3x3) array<array<float, 3>, 3>;
-    %template(VectorFloat) vector<float>;
-    %template(FloatMatrix_3x1) array<array<float, 1>, 3>;
-    %template(FloatMatrix_3x4) array<array<float, 3>, 4>;
-    %template(Matrix_Jx4) vector<array<float, 4>>;
-    %template(Matrix_NxJx4) vector<vector<array<float, 4>>>;
-    %template(Matrix_Jx3) vector<array<float, 3>>;
-    %template(Matrix_VxNxJx3) vector<vector<vector<array<float, 3>>>>;
-    %template(VectorCamera) vector<Camera>;
-    %template(FloatMatrix_2x3) array<array<float, 3>, 2>;
-    %template(VectorString) vector<std::string>;
-}
-
-// Convert vector to native (python) list
-%naturalvar Camera::K;
-%naturalvar Camera::DC;
-%naturalvar Camera::R;
-%naturalvar Camera::T;
-
-// Improve printing of result objects
-%extend Camera {
-    std::string __str__() const {
-         return $self->to_string();
-    }
-}
-
-// Ignore: Warning 503: Can't wrap 'operator <<' unless renamed to a valid identifier.
-%warnfilter(503) Camera;
-
-// Ignore: Warning 511: Can't use keyword arguments with overloaded functions.
-// The warning is cause by enabling keyword arguments, which doesn't work for vectors.
-#pragma SWIG nowarn=511
-
-// Parse the header file to generate wrappers
-%include "../rpt/camera.hpp"
-%include "../rpt/interface.hpp"
-
-// Add additional Python code to the module
-%pythoncode %{
-def convert_cameras(cameras):
-    """Convert cameras from Python to C++."""
-    c_cameras = []
-    for cam in cameras:
-        camera = Camera()
-        camera.name = cam["name"]
-        camera.K = cam["K"]
-        camera.DC = cam["DC"]
-        camera.R = cam["R"]
-        camera.T = cam["T"]
-        camera.width = cam["width"]
-        camera.height = cam["height"]
-        camera.type = cam.get("type", "pinhole")
-        c_cameras.append(camera)
-    return c_cameras
-%}
@@ -2,33 +2,10 @@

 Various module tests

-### Triangulator
+### Python Interface

  ```bash
-  cd /RapidPoseTriangulation/tests/ && python3 test_interface.py && cd ..
+  cd /RapidPoseTriangulation/
+  uv sync --group dev
+  uv run pytest tests/test_interface.py
  ```
-
-### Onnx C++ Interface
-
-```bash
-cd /RapidPoseTriangulation/tests/
-
-g++ -std=c++17 -O3 -march=native -Wall \
-    $(pkg-config --cflags opencv4) \
-    -I /onnxruntime/include \
-    -I /onnxruntime/include/onnxruntime/core/session \
-    -I /onnxruntime/include/onnxruntime/core/providers/tensorrt \
-    -L /onnxruntime/build/Linux/Release \
-    test_utils2d.cpp \
-    -o my_app.bin \
-    -Wl,--start-group \
-      -lonnxruntime_providers_tensorrt \
-      -lonnxruntime_providers_shared \
-      -lonnxruntime_providers_cuda \
-      -lonnxruntime \
-    -Wl,--end-group \
-    $(pkg-config --libs opencv4) \
-    -Wl,-rpath,/onnxruntime/build/Linux/Release
-
-./my_app.bin
-```
@@ -1,132 +1,281 @@
 import json
-import sys
-import time
+from pathlib import Path

 import numpy as np
+import pytest

-sys.path.append("../swig/")
 import rpt

-# ==================================================================================================
+ROOT = Path(__file__).resolve().parents[1]
+
+JOINT_NAMES = [
+    "nose",
+    "eye_left",
+    "eye_right",
+    "ear_left",
+    "ear_right",
+    "shoulder_left",
+    "shoulder_right",
+    "elbow_left",
+    "elbow_right",
+    "wrist_left",
+    "wrist_right",
+    "hip_left",
+    "hip_right",
+    "knee_left",
+    "knee_right",
+    "ankle_left",
+    "ankle_right",
+    "hip_middle",
+    "shoulder_middle",
+    "head",
+]


-def main():
-    print("")
+def load_case(camera_path: str, pose_path: str):
+    with (ROOT / camera_path).open("r", encoding="utf-8") as file:
+        camera_data = json.load(file)
+    with (ROOT / pose_path).open("r", encoding="utf-8") as file:
+        pose_data = json.load(file)

-    # Test camera structure
-    camera = rpt.Camera()
-    camera.name = "Camera 1"
-    camera.K = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
-    camera.DC = [0, 0, 0, 0, 0]
-    camera.R = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
-    camera.T = [[1], [2], [3]]
-    camera.width = 640
-    camera.height = 480
-    print(camera)
-    print("")
+    poses_2d, person_counts = rpt.pack_poses_2d(pose_data["2D"], joint_count=len(JOINT_NAMES))
+    return poses_2d, person_counts, camera_data["cameras"]

-    # Load input data
-    roomparams = [[4.8, 6.0, 2.0], [0, 0, 1.0]]
-    joint_names = [
-        "nose",
-        "eye_left",
-        "eye_right",
-        "ear_left",
-        "ear_right",
-        "shoulder_left",
-        "shoulder_right",
-        "elbow_left",
-        "elbow_right",
-        "wrist_left",
-        "wrist_right",
-        "hip_left",
-        "hip_right",
-        "knee_left",
-        "knee_right",
-        "ankle_left",
-        "ankle_right",
-        "hip_middle",
-        "shoulder_middle",
-        "head",
-    ]
-    cpath = "/RapidPoseTriangulation/data/h1/sample.json"
-    ppath = "/RapidPoseTriangulation/tests/poses_h1.json"
-    with open(cpath, "r") as file:
-        cdata = json.load(file)
-    with open(ppath, "r") as file:
-        pdata = json.load(file)
-    cams = cdata["cameras"]
-    poses_2d = pdata["2D"]
-    cameras = rpt.convert_cameras(cams)

-    # Run triangulation
-    triangulator = rpt.Triangulator(min_match_score=0.95)
-    stime = time.time()
-    poses_3d = triangulator.triangulate_poses(
-        poses_2d, cameras, roomparams, joint_names
+def make_config(cameras, roomparams) -> rpt.TriangulationConfig:
+    return rpt.make_triangulation_config(
+        cameras,
+        np.asarray(roomparams, dtype=np.float32),
+        JOINT_NAMES,
    )
-    print("3D time:", time.time() - stime)
-    print(np.array(poses_3d))
-    print("")

-    # Load input data
-    roomparams = [[5.6, 6.4, 2.4], [0, -0.5, 1.2]]
-    cpath = "/RapidPoseTriangulation/data/p1/sample.json"
-    ppath = "/RapidPoseTriangulation/tests/poses_p1.json"
-    with open(cpath, "r") as file:
-        cdata = json.load(file)
-    with open(ppath, "r") as file:
-        pdata = json.load(file)
-    cams = cdata["cameras"]
-    poses_2d = pdata["2D"]
-    cameras = rpt.convert_cameras(cams)

-    # Run triangulation
-    triangulator.reset()
-    stime = time.time()
-    poses_3d = triangulator.triangulate_poses(
-        poses_2d, cameras, roomparams, joint_names
+def test_camera_structure_repr():
+    camera = rpt.make_camera(
+        "Camera 1",
+        [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
+        [0, 0, 0, 0, 0],
+        [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
+        [[1], [2], [3]],
+        640,
+        480,
+        rpt.CameraModel.PINHOLE,
    )
-    print("3D time:", time.time() - stime)
-    print(np.array(poses_3d))
-    print("")

-    # Run again to test last pose cache
-    stime = time.time()
-    poses_3d = triangulator.triangulate_poses(
-        poses_2d, cameras, roomparams, joint_names
+    rendered = repr(camera)
+    assert "Camera 1" in rendered
+    assert "pinhole" in rendered
+
+
+@pytest.mark.parametrize(
+    ("camera_path", "pose_path", "roomparams"),
+    [
+        ("data/h1/sample.json", "tests/poses_h1.json", [[4.8, 6.0, 2.0], [0.0, 0.0, 1.0]]),
+        ("data/p1/sample.json", "tests/poses_p1.json", [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]]),
+        ("data/e1/sample.json", "tests/poses_e1.json", [[6.0, 5.0, 2.0], [1.5, 1.0, -0.5]]),
+    ],
+)
+def test_triangulate_samples(camera_path: str, pose_path: str, roomparams):
+    poses_2d, person_counts, cameras = load_case(camera_path, pose_path)
+    config = make_config(cameras, roomparams)
+    poses_3d = rpt.triangulate_poses(poses_2d, person_counts, config)
+
+    assert isinstance(poses_3d, np.ndarray)
+    assert poses_3d.dtype == np.float32
+    assert poses_3d.ndim == 3
+    assert poses_3d.shape[1:] == (len(JOINT_NAMES), 4)
+    assert poses_3d.shape[0] > 0
+    assert np.isfinite(poses_3d).all()
+
+
+def test_triangulate_repeatability():
+    poses_2d, person_counts, cameras = load_case("data/p1/sample.json", "tests/poses_p1.json")
+    config = make_config(cameras, [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]])
+
+    first = rpt.triangulate_poses(poses_2d, person_counts, config)
+    second = rpt.triangulate_poses(poses_2d, person_counts, config)
+
+    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,
    )
-    print("3D time:", time.time() - stime)
-    print(np.array(poses_3d))
-    print("")

-    # Load input data
-    roomparams = [[6.0, 5.0, 2.0], [1.5, 1.0, -0.5]]
-    cpath = "/RapidPoseTriangulation/data/e1/sample.json"
-    ppath = "/RapidPoseTriangulation/tests/poses_e1.json"
-    with open(cpath, "r") as file:
-        cdata = json.load(file)
-    with open(ppath, "r") as file:
-        pdata = json.load(file)
-    cams = cdata["cameras"]
-    poses_2d = pdata["2D"]
-    cameras = rpt.convert_cameras(cams)
+    pairs = rpt.build_pair_candidates(poses_2d, person_counts)

-    # Run triangulation
-    triangulator.reset()
-    stime = time.time()
-    poses_3d = triangulator.triangulate_poses(
-        poses_2d, cameras, roomparams, joint_names
+    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")
+    config = make_config(cameras, [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]])
+    empty_previous = np.zeros((0, len(JOINT_NAMES), 4), dtype=np.float32)
+    empty_previous_ids = np.zeros((0,), dtype=np.int64)
+
+    baseline = rpt.triangulate_poses(poses_2d, person_counts, config)
+    result = rpt.triangulate_with_report(
+        poses_2d,
+        person_counts,
+        config,
+        empty_previous,
+        empty_previous_ids,
    )
-    print("3D time:", time.time() - stime)
-    print(np.array(poses_3d))
-    print("")

-    triangulator.print_stats()
-    print("")
+    np.testing.assert_allclose(result.poses_3d, baseline, rtol=1e-5, atol=1e-5)
+    assert result.association.unmatched_previous_track_ids == []


-# ==================================================================================================
+def test_triangulate_debug_matches_final_output():
+    poses_2d, person_counts, cameras = load_case("data/h1/sample.json", "tests/poses_h1.json")
+    config = make_config(cameras, [[4.8, 6.0, 2.0], [0.0, 0.0, 1.0]])

-if __name__ == "__main__":
-    main()
+    final_poses = rpt.triangulate_poses(poses_2d, person_counts, config)
+    trace = rpt.triangulate_debug(poses_2d, person_counts, config)
+
+    np.testing.assert_allclose(trace.final_poses, final_poses, rtol=1e-5, atol=1e-5)
+    assert len(trace.pairs) >= len(trace.core_proposals)
+    assert trace.association.pose_previous_track_ids == []
+    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")
+    config = make_config(cameras, [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]])
+    previous_poses = rpt.triangulate_poses(poses_2d, person_counts, config)
+    previous_track_ids = np.arange(previous_poses.shape[0], dtype=np.int64) + 100
+
+    debug = rpt.filter_pairs_with_previous_poses(
+        poses_2d,
+        person_counts,
+        config,
+        previous_poses,
+        previous_track_ids,
+    )
+
+    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)
+    assert any(match.previous_track_id >= 100 for match in debug.matches if match.previous_pose_index >= 0)
+
+
+def test_triangulate_with_report_resolves_previous_track_ids():
+    poses_2d, person_counts, cameras = load_case("data/p1/sample.json", "tests/poses_p1.json")
+    config = make_config(cameras, [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]])
+    previous_poses = rpt.triangulate_poses(poses_2d, person_counts, config)
+    previous_track_ids = np.arange(previous_poses.shape[0], dtype=np.int64) + 100
+
+    result = rpt.triangulate_with_report(
+        poses_2d,
+        person_counts,
+        config,
+        previous_poses,
+        previous_track_ids,
+    )
+
+    assert result.poses_3d.shape == previous_poses.shape
+    assert len(result.association.pose_previous_track_ids) == result.poses_3d.shape[0]
+    matched_track_ids = sorted(
+        track_id for track_id in result.association.pose_previous_track_ids if track_id >= 0
+    )
+    unmatched_track_ids = sorted(result.association.unmatched_previous_track_ids)
+
+    for pose_index in result.association.new_pose_indices:
+        assert result.association.pose_previous_track_ids[pose_index] == -1
+    for pose_index in result.association.ambiguous_pose_indices:
+        assert result.association.pose_previous_track_ids[pose_index] == -1
+
+    assert matched_track_ids == sorted(set(matched_track_ids))
+    assert sorted(matched_track_ids + unmatched_track_ids) == list(previous_track_ids)
+
+
+def test_triangulate_with_report_rejects_duplicate_previous_track_ids():
+    poses_2d, person_counts, cameras = load_case("data/p1/sample.json", "tests/poses_p1.json")
+    config = make_config(cameras, [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]])
+    previous_poses = rpt.triangulate_poses(poses_2d, person_counts, config)
+    duplicate_ids = np.zeros((previous_poses.shape[0],), dtype=np.int64)
+
+    with pytest.raises(ValueError, match="unique"):
+        rpt.triangulate_with_report(
+            poses_2d,
+            person_counts,
+            config,
+            previous_poses,
+            duplicate_ids,
+        )
+
+
+def test_triangulate_does_not_mutate_inputs():
+    poses_2d, person_counts, cameras = load_case("data/h1/sample.json", "tests/poses_h1.json")
+    config = make_config(cameras, [[4.8, 6.0, 2.0], [0.0, 0.0, 1.0]])
+
+    poses_before = poses_2d.copy()
+    counts_before = person_counts.copy()
+
+    rpt.triangulate_poses(poses_2d, person_counts, config)
+
+    np.testing.assert_array_equal(poses_2d, poses_before)
+    np.testing.assert_array_equal(person_counts, counts_before)
+
+
+def test_pack_poses_2d_from_ragged_inputs():
+    packed, counts = rpt.pack_poses_2d(
+        [
+            [[[1, 2, 0.5], [3, 4, 0.6]]],
+            np.asarray(
+                [
+                    [[5, 6, 0.7], [7, 8, 0.8]],
+                    [[9, 10, 0.9], [11, 12, 1.0]],
+                ],
+                dtype=np.float32,
+            ),
+            [],
+        ],
+        joint_count=2,
+    )
+
+    assert packed.shape == (3, 2, 2, 3)
+    assert packed.dtype == np.float32
+    np.testing.assert_array_equal(counts, np.asarray([1, 2, 0], dtype=np.uint32))
+    np.testing.assert_allclose(packed[0, 0], np.asarray([[1, 2, 0.5], [3, 4, 0.6]], dtype=np.float32))
+    np.testing.assert_allclose(packed[1, 1], np.asarray([[9, 10, 0.9], [11, 12, 1.0]], dtype=np.float32))
+    np.testing.assert_array_equal(packed[2], np.zeros((2, 2, 3), dtype=np.float32))
+
+
+def test_pack_poses_2d_rejects_inconsistent_joint_count():
+    with pytest.raises(ValueError, match="joint count"):
+        rpt.pack_poses_2d(
+            [
+                [[[1, 2, 0.5], [3, 4, 0.6]]],
+                [[[5, 6, 0.7]]],
+            ]
+        )
+
+
+def test_pack_poses_2d_rejects_invalid_last_dimension():
+    with pytest.raises(ValueError, match="shape"):
+        rpt.pack_poses_2d([np.zeros((1, 2, 2), dtype=np.float32)])
+
+
+def test_make_triangulation_config_builds_bound_struct():
+    _, _, cameras = load_case("data/p1/sample.json", "tests/poses_p1.json")
+    config = make_config(cameras, [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]])
+
+    assert isinstance(config, rpt.TriangulationConfig)
+    assert len(config.cameras) > 0
+    np.testing.assert_allclose(config.roomparams, [[5.6, 6.4, 2.4], [0.0, -0.5, 1.2]])
+    assert config.joint_names == JOINT_NAMES
+    assert config.options.min_match_score == pytest.approx(0.95)
+    assert config.options.min_group_size == 1
@@ -1,96 +0,0 @@
-#include <filesystem>
-#include <vector>
-#include <string>
-#include <memory>
-
-#include <opencv2/opencv.hpp>
-
-#include "/RapidPoseTriangulation/scripts/utils_2d_pose.hpp"
-
-// =================================================================================================
-// =================================================================================================
-
-int main(int argc, char **argv)
-{
-    using namespace utils_2d_pose;
-
-    std::string base_path = "/RapidPoseTriangulation/extras/mmdeploy/exports/";
-    std::string model_path1 = base_path + "rtmdet-nano_1x320x320x3_fp16_extra-steps.onnx";
-    std::string model_path2 = base_path + "rtmpose-m_1x384x288x3_fp16_extra-steps.onnx";
-
-    std::vector<std::string> img_paths = {
-        "/RapidPoseTriangulation/data/h1/54138969-img_003201.jpg",
-        "/RapidPoseTriangulation/data/h1/55011271-img_003201.jpg",
-        "/RapidPoseTriangulation/data/h1/58860488-img_003201.jpg",
-        "/RapidPoseTriangulation/data/h1/60457274-img_003201.jpg",
-    };
-
-    // {
-    //     std::cout << "\nTesting RTMDet and RTMPose" << std::endl;
-    //     RTMDet model1(model_path1, 0.3, 0.1 * 0.1, 30);
-    //     RTMPose model2(model_path2, 30);
-    //     for (size_t i = 0; i < img_paths.size(); i++)
-    //     {
-    //         cv::Mat img = cv::imread(img_paths[i]);
-    //         cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
-
-    //         auto outputs1 = model1.call(img);
-    //         std::cout << "Model1 outputs: " << outputs1[0][0] << " " << outputs1[0][1] << " "
-    //                   << outputs1[0][2] << " " << outputs1[0][3] << " " << outputs1[0][4] << " "
-    //                   << std::endl;
-
-    //         for (size_t j = 0; j < outputs1.size(); j++)
-    //         {
-    //             std::vector<std::array<float, 5>> bboxes = {outputs1[j]};
-    //             auto outputs2 = model2.call(img, bboxes);
-    //             std::cout << "Model2 outputs: " << outputs2[0][0][0] << " "
-    //                       << outputs2[0][0][1] << " " << outputs2[0][0][2] << " " << std::endl;
-    //         }
-    //     }
-    // }
-
-    // {
-    //     std::cout << "\nTesting TopDown" << std::endl;
-    //     TopDown model3(model_path1, model_path2, 0.3, 0.1 * 0.1, false, 30);
-    //     for (size_t i = 0; i < img_paths.size(); i++)
-    //     {
-    //         cv::Mat img = cv::imread(img_paths[i]);
-    //         cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
-
-    //         auto outputs3 = model3.predict(img);
-    //         std::cout << "Model3 outputs: " << outputs3[0][0][0] << " "
-    //                   << outputs3[0][0][1] << " " << outputs3[0][0][2] << " " << std::endl;
-    //     }
-    // }
-
-    {
-        std::cout << "\nTesting PosePredictor 1" << std::endl;
-        PosePredictor model4(false, 0.3, 0.1 * 0.1, false);
-        std::vector<cv::Mat> images;
-        for (size_t i = 0; i < img_paths.size(); i++)
-        {
-            cv::Mat img = cv::imread(img_paths[i]);
-            cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
-            images.push_back(img);
-        }
-        auto outputs4 = model4.predict(images);
-        std::cout << "Model4 outputs: " << outputs4[0][0][0][0] << " "
-                  << outputs4[0][0][0][1] << " " << outputs4[0][0][0][2] << " " << std::endl;
-    }
-
-    {
-        std::cout << "\nTesting PosePredictor 2" << std::endl;
-        PosePredictor model5(false, 0.3, 0.1 * 0.1, true);
-        std::vector<cv::Mat> images;
-        for (size_t i = 0; i < img_paths.size(); i++)
-        {
-            cv::Mat img = cv::imread(img_paths[i]);
-            cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
-            images.push_back(img);
-        }
-        auto outputs5 = model5.predict(images);
-        std::cout << "Model5 outputs: " << outputs5[0][0][0][0] << " "
-                  << outputs5[0][0][0][1] << " " << outputs5[0][0][0][2] << " " << std::endl;
-    }
-    return 0;
-}
@@ -0,0 +1,312 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.11'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
+]
+
+[[package]]
+name = "exceptiongroup"
+version = "1.3.1"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+dependencies = [
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/8a/0e/97c33bf5009bdbac74fd2beace167cab3f978feb69cc36f1ef79360d6c4e/exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598", size = 16740, upload-time = "2025-11-21T23:01:53.443Z" },
+]
+
+[[package]]
+name = "iniconfig"
+version = "2.3.0"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.2.6"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+resolution-markers = [
+    "python_full_version < '3.11'",
+]
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/76/21/7d2a95e4bba9dc13d043ee156a356c0a8f0c6309dff6b21b4d71a073b8a8/numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd", size = 20276440, upload-time = "2025-05-17T22:38:04.611Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/9a/3e/ed6db5be21ce87955c0cbd3009f2803f59fa08df21b5df06862e2d8e2bdd/numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb", size = 21165245, upload-time = "2025-05-17T21:27:58.555Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/22/c2/4b9221495b2a132cc9d2eb862e21d42a009f5a60e45fc44b00118c174bff/numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90", size = 14360048, upload-time = "2025-05-17T21:28:21.406Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/fd/77/dc2fcfc66943c6410e2bf598062f5959372735ffda175b39906d54f02349/numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163", size = 5340542, upload-time = "2025-05-17T21:28:30.931Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/7a/4f/1cb5fdc353a5f5cc7feb692db9b8ec2c3d6405453f982435efc52561df58/numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf", size = 6878301, upload-time = "2025-05-17T21:28:41.613Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/eb/17/96a3acd228cec142fcb8723bd3cc39c2a474f7dcf0a5d16731980bcafa95/numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83", size = 14297320, upload-time = "2025-05-17T21:29:02.78Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b4/63/3de6a34ad7ad6646ac7d2f55ebc6ad439dbbf9c4370017c50cf403fb19b5/numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915", size = 16801050, upload-time = "2025-05-17T21:29:27.675Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/07/b6/89d837eddef52b3d0cec5c6ba0456c1bf1b9ef6a6672fc2b7873c3ec4e2e/numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680", size = 15807034, upload-time = "2025-05-17T21:29:51.102Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/01/c8/dc6ae86e3c61cfec1f178e5c9f7858584049b6093f843bca541f94120920/numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289", size = 18614185, upload-time = "2025-05-17T21:30:18.703Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/5b/c5/0064b1b7e7c89137b471ccec1fd2282fceaae0ab3a9550f2568782d80357/numpy-2.2.6-cp310-cp310-win32.whl", hash = "sha256:b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d", size = 6527149, upload-time = "2025-05-17T21:30:29.788Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a3/dd/4b822569d6b96c39d1215dbae0582fd99954dcbcf0c1a13c61783feaca3f/numpy-2.2.6-cp310-cp310-win_amd64.whl", hash = "sha256:f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3", size = 12904620, upload-time = "2025-05-17T21:30:48.994Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/da/a8/4f83e2aa666a9fbf56d6118faaaf5f1974d456b1823fda0a176eff722839/numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae", size = 21176963, upload-time = "2025-05-17T21:31:19.36Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b3/2b/64e1affc7972decb74c9e29e5649fac940514910960ba25cd9af4488b66c/numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a", size = 14406743, upload-time = "2025-05-17T21:31:41.087Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/4a/9f/0121e375000b5e50ffdd8b25bf78d8e1a5aa4cca3f185d41265198c7b834/numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42", size = 5352616, upload-time = "2025-05-17T21:31:50.072Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/31/0d/b48c405c91693635fbe2dcd7bc84a33a602add5f63286e024d3b6741411c/numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491", size = 6889579, upload-time = "2025-05-17T21:32:01.712Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/52/b8/7f0554d49b565d0171eab6e99001846882000883998e7b7d9f0d98b1f934/numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a", size = 14312005, upload-time = "2025-05-17T21:32:23.332Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b3/dd/2238b898e51bd6d389b7389ffb20d7f4c10066d80351187ec8e303a5a475/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf", size = 16821570, upload-time = "2025-05-17T21:32:47.991Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/83/6c/44d0325722cf644f191042bf47eedad61c1e6df2432ed65cbe28509d404e/numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1", size = 15818548, upload-time = "2025-05-17T21:33:11.728Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ae/9d/81e8216030ce66be25279098789b665d49ff19eef08bfa8cb96d4957f422/numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab", size = 18620521, upload-time = "2025-05-17T21:33:39.139Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/6a/fd/e19617b9530b031db51b0926eed5345ce8ddc669bb3bc0044b23e275ebe8/numpy-2.2.6-cp311-cp311-win32.whl", hash = "sha256:0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47", size = 6525866, upload-time = "2025-05-17T21:33:50.273Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/31/0a/f354fb7176b81747d870f7991dc763e157a934c717b67b58456bc63da3df/numpy-2.2.6-cp311-cp311-win_amd64.whl", hash = "sha256:e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303", size = 12907455, upload-time = "2025-05-17T21:34:09.135Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/82/5d/c00588b6cf18e1da539b45d3598d3557084990dcc4331960c15ee776ee41/numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff", size = 20875348, upload-time = "2025-05-17T21:34:39.648Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/66/ee/560deadcdde6c2f90200450d5938f63a34b37e27ebff162810f716f6a230/numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c", size = 14119362, upload-time = "2025-05-17T21:35:01.241Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3c/65/4baa99f1c53b30adf0acd9a5519078871ddde8d2339dc5a7fde80d9d87da/numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3", size = 5084103, upload-time = "2025-05-17T21:35:10.622Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/cc/89/e5a34c071a0570cc40c9a54eb472d113eea6d002e9ae12bb3a8407fb912e/numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282", size = 6625382, upload-time = "2025-05-17T21:35:21.414Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f8/35/8c80729f1ff76b3921d5c9487c7ac3de9b2a103b1cd05e905b3090513510/numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87", size = 14018462, upload-time = "2025-05-17T21:35:42.174Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/8c/3d/1e1db36cfd41f895d266b103df00ca5b3cbe965184df824dec5c08c6b803/numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249", size = 16527618, upload-time = "2025-05-17T21:36:06.711Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/61/c6/03ed30992602c85aa3cd95b9070a514f8b3c33e31124694438d88809ae36/numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49", size = 15505511, upload-time = "2025-05-17T21:36:29.965Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b7/25/5761d832a81df431e260719ec45de696414266613c9ee268394dd5ad8236/numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de", size = 18313783, upload-time = "2025-05-17T21:36:56.883Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/57/0a/72d5a3527c5ebffcd47bde9162c39fae1f90138c961e5296491ce778e682/numpy-2.2.6-cp312-cp312-win32.whl", hash = "sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4", size = 6246506, upload-time = "2025-05-17T21:37:07.368Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/36/fa/8c9210162ca1b88529ab76b41ba02d433fd54fecaf6feb70ef9f124683f1/numpy-2.2.6-cp312-cp312-win_amd64.whl", hash = "sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2", size = 12614190, upload-time = "2025-05-17T21:37:26.213Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f9/5c/6657823f4f594f72b5471f1db1ab12e26e890bb2e41897522d134d2a3e81/numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84", size = 20867828, upload-time = "2025-05-17T21:37:56.699Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/dc/9e/14520dc3dadf3c803473bd07e9b2bd1b69bc583cb2497b47000fed2fa92f/numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b", size = 14143006, upload-time = "2025-05-17T21:38:18.291Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/4f/06/7e96c57d90bebdce9918412087fc22ca9851cceaf5567a45c1f404480e9e/numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d", size = 5076765, upload-time = "2025-05-17T21:38:27.319Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/73/ed/63d920c23b4289fdac96ddbdd6132e9427790977d5457cd132f18e76eae0/numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566", size = 6617736, upload-time = "2025-05-17T21:38:38.141Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/85/c5/e19c8f99d83fd377ec8c7e0cf627a8049746da54afc24ef0a0cb73d5dfb5/numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f", size = 14010719, upload-time = "2025-05-17T21:38:58.433Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f", size = 16526072, upload-time = "2025-05-17T21:39:22.638Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b2/6c/04b5f47f4f32f7c2b0e7260442a8cbcf8168b0e1a41ff1495da42f42a14f/numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868", size = 15503213, upload-time = "2025-05-17T21:39:45.865Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/17/0a/5cd92e352c1307640d5b6fec1b2ffb06cd0dabe7d7b8227f97933d378422/numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d", size = 18316632, upload-time = "2025-05-17T21:40:13.331Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f0/3b/5cba2b1d88760ef86596ad0f3d484b1cbff7c115ae2429678465057c5155/numpy-2.2.6-cp313-cp313-win32.whl", hash = "sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd", size = 6244532, upload-time = "2025-05-17T21:43:46.099Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/cb/3b/d58c12eafcb298d4e6d0d40216866ab15f59e55d148a5658bb3132311fcf/numpy-2.2.6-cp313-cp313-win_amd64.whl", hash = "sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c", size = 12610885, upload-time = "2025-05-17T21:44:05.145Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/6b/9e/4bf918b818e516322db999ac25d00c75788ddfd2d2ade4fa66f1f38097e1/numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6", size = 20963467, upload-time = "2025-05-17T21:40:44Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/61/66/d2de6b291507517ff2e438e13ff7b1e2cdbdb7cb40b3ed475377aece69f9/numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda", size = 14225144, upload-time = "2025-05-17T21:41:05.695Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e4/25/480387655407ead912e28ba3a820bc69af9adf13bcbe40b299d454ec011f/numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40", size = 5200217, upload-time = "2025-05-17T21:41:15.903Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/aa/4a/6e313b5108f53dcbf3aca0c0f3e9c92f4c10ce57a0a721851f9785872895/numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8", size = 6712014, upload-time = "2025-05-17T21:41:27.321Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b7/30/172c2d5c4be71fdf476e9de553443cf8e25feddbe185e0bd88b096915bcc/numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f", size = 14077935, upload-time = "2025-05-17T21:41:49.738Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/12/fb/9e743f8d4e4d3c710902cf87af3512082ae3d43b945d5d16563f26ec251d/numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa", size = 16600122, upload-time = "2025-05-17T21:42:14.046Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/12/75/ee20da0e58d3a66f204f38916757e01e33a9737d0b22373b3eb5a27358f9/numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571", size = 15586143, upload-time = "2025-05-17T21:42:37.464Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/76/95/bef5b37f29fc5e739947e9ce5179ad402875633308504a52d188302319c8/numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1", size = 18385260, upload-time = "2025-05-17T21:43:05.189Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/09/04/f2f83279d287407cf36a7a8053a5abe7be3622a4363337338f2585e4afda/numpy-2.2.6-cp313-cp313t-win32.whl", hash = "sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff", size = 6377225, upload-time = "2025-05-17T21:43:16.254Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/67/0e/35082d13c09c02c011cf21570543d202ad929d961c02a147493cb0c2bdf5/numpy-2.2.6-cp313-cp313t-win_amd64.whl", hash = "sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06", size = 12771374, upload-time = "2025-05-17T21:43:35.479Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/9e/3b/d94a75f4dbf1ef5d321523ecac21ef23a3cd2ac8b78ae2aac40873590229/numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d", size = 21040391, upload-time = "2025-05-17T21:44:35.948Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/17/f4/09b2fa1b58f0fb4f7c7963a1649c64c4d315752240377ed74d9cd878f7b5/numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db", size = 6786754, upload-time = "2025-05-17T21:44:47.446Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/af/30/feba75f143bdc868a1cc3f44ccfa6c4b9ec522b36458e738cd00f67b573f/numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543", size = 16643476, upload-time = "2025-05-17T21:45:11.871Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/37/48/ac2a9584402fb6c0cd5b5d1a91dcf176b15760130dd386bbafdbfe3640bf/numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00", size = 12812666, upload-time = "2025-05-17T21:45:31.426Z" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.4.3"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+resolution-markers = [
+    "python_full_version >= '3.11'",
+]
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/10/8b/c265f4823726ab832de836cdd184d0986dcf94480f81e8739692a7ac7af2/numpy-2.4.3.tar.gz", hash = "sha256:483a201202b73495f00dbc83796c6ae63137a9bdade074f7648b3e32613412dd", size = 20727743, upload-time = "2026-03-09T07:58:53.426Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f9/51/5093a2df15c4dc19da3f79d1021e891f5dcf1d9d1db6ba38891d5590f3fe/numpy-2.4.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:33b3bf58ee84b172c067f56aeadc7ee9ab6de69c5e800ab5b10295d54c581adb", size = 16957183, upload-time = "2026-03-09T07:55:57.774Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b5/7c/c061f3de0630941073d2598dc271ac2f6cbcf5c83c74a5870fea07488333/numpy-2.4.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8ba7b51e71c05aa1f9bc3641463cd82308eab40ce0d5c7e1fd4038cbf9938147", size = 14968734, upload-time = "2026-03-09T07:56:00.494Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ef/27/d26c85cbcd86b26e4f125b0668e7a7c0542d19dd7d23ee12e87b550e95b5/numpy-2.4.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:a1988292870c7cb9d0ebb4cc96b4d447513a9644801de54606dc7aabf2b7d920", size = 5475288, upload-time = "2026-03-09T07:56:02.857Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/2b/09/3c4abbc1dcd8010bf1a611d174c7aa689fc505585ec806111b4406f6f1b1/numpy-2.4.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:23b46bb6d8ecb68b58c09944483c135ae5f0e9b8d8858ece5e4ead783771d2a9", size = 6805253, upload-time = "2026-03-09T07:56:04.53Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/21/bc/e7aa3f6817e40c3f517d407742337cbb8e6fc4b83ce0b55ab780c829243b/numpy-2.4.3-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a016db5c5dba78fa8fe9f5d80d6708f9c42ab087a739803c0ac83a43d686a470", size = 15969479, upload-time = "2026-03-09T07:56:06.638Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/78/51/9f5d7a41f0b51649ddf2f2320595e15e122a40610b233d51928dd6c92353/numpy-2.4.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:715de7f82e192e8cae5a507a347d97ad17598f8e026152ca97233e3666daaa71", size = 16901035, upload-time = "2026-03-09T07:56:09.405Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/64/6e/b221dd847d7181bc5ee4857bfb026182ef69499f9305eb1371cbb1aea626/numpy-2.4.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:2ddb7919366ee468342b91dea2352824c25b55814a987847b6c52003a7c97f15", size = 17325657, upload-time = "2026-03-09T07:56:12.067Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/eb/b8/8f3fd2da596e1063964b758b5e3c970aed1949a05200d7e3d46a9d46d643/numpy-2.4.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a315e5234d88067f2d97e1f2ef670a7569df445d55400f1e33d117418d008d52", size = 18635512, upload-time = "2026-03-09T07:56:14.629Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/5c/24/2993b775c37e39d2f8ab4125b44337ab0b2ba106c100980b7c274a22bee7/numpy-2.4.3-cp311-cp311-win32.whl", hash = "sha256:2b3f8d2c4589b1a2028d2a770b0fc4d1f332fb5e01521f4de3199a896d158ddd", size = 6238100, upload-time = "2026-03-09T07:56:17.243Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/76/1d/edccf27adedb754db7c4511d5eac8b83f004ae948fe2d3509e8b78097d4c/numpy-2.4.3-cp311-cp311-win_amd64.whl", hash = "sha256:77e76d932c49a75617c6d13464e41203cd410956614d0a0e999b25e9e8d27eec", size = 12609816, upload-time = "2026-03-09T07:56:19.089Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/92/82/190b99153480076c8dce85f4cfe7d53ea84444145ffa54cb58dcd460d66b/numpy-2.4.3-cp311-cp311-win_arm64.whl", hash = "sha256:eb610595dd91560905c132c709412b512135a60f1851ccbd2c959e136431ff67", size = 10485757, upload-time = "2026-03-09T07:56:21.753Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a9/ed/6388632536f9788cea23a3a1b629f25b43eaacd7d7377e5d6bc7b9deb69b/numpy-2.4.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:61b0cbabbb6126c8df63b9a3a0c4b1f44ebca5e12ff6997b80fcf267fb3150ef", size = 16669628, upload-time = "2026-03-09T07:56:24.252Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/74/1b/ee2abfc68e1ce728b2958b6ba831d65c62e1b13ce3017c13943f8f9b5b2e/numpy-2.4.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7395e69ff32526710748f92cd8c9849b361830968ea3e24a676f272653e8983e", size = 14696872, upload-time = "2026-03-09T07:56:26.991Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ba/d1/780400e915ff5638166f11ca9dc2c5815189f3d7cf6f8759a1685e586413/numpy-2.4.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:abdce0f71dcb4a00e4e77f3faf05e4616ceccfe72ccaa07f47ee79cda3b7b0f4", size = 5203489, upload-time = "2026-03-09T07:56:29.414Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0b/bb/baffa907e9da4cc34a6e556d6d90e032f6d7a75ea47968ea92b4858826c4/numpy-2.4.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:48da3a4ee1336454b07497ff7ec83903efa5505792c4e6d9bf83d99dc07a1e18", size = 6550814, upload-time = "2026-03-09T07:56:32.225Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/7b/12/8c9f0c6c95f76aeb20fc4a699c33e9f827fa0d0f857747c73bb7b17af945/numpy-2.4.3-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:32e3bef222ad6b052280311d1d60db8e259e4947052c3ae7dd6817451fc8a4c5", size = 15666601, upload-time = "2026-03-09T07:56:34.461Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/bd/79/cc665495e4d57d0aa6fbcc0aa57aa82671dfc78fbf95fe733ed86d98f52a/numpy-2.4.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e7dd01a46700b1967487141a66ac1a3cf0dd8ebf1f08db37d46389401512ca97", size = 16621358, upload-time = "2026-03-09T07:56:36.852Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a8/40/b4ecb7224af1065c3539f5ecfff879d090de09608ad1008f02c05c770cb3/numpy-2.4.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:76f0f283506c28b12bba319c0fab98217e9f9b54e6160e9c79e9f7348ba32e9c", size = 17016135, upload-time = "2026-03-09T07:56:39.337Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f7/b1/6a88e888052eed951afed7a142dcdf3b149a030ca59b4c71eef085858e43/numpy-2.4.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:737f630a337364665aba3b5a77e56a68cc42d350edd010c345d65a3efa3addcc", size = 18345816, upload-time = "2026-03-09T07:56:42.31Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f3/8f/103a60c5f8c3d7fc678c19cd7b2476110da689ccb80bc18050efbaeae183/numpy-2.4.3-cp312-cp312-win32.whl", hash = "sha256:26952e18d82a1dbbc2f008d402021baa8d6fc8e84347a2072a25e08b46d698b9", size = 5960132, upload-time = "2026-03-09T07:56:44.851Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d7/7c/f5ee1bf6ed888494978046a809df2882aad35d414b622893322df7286879/numpy-2.4.3-cp312-cp312-win_amd64.whl", hash = "sha256:65f3c2455188f09678355f5cae1f959a06b778bc66d535da07bf2ef20cd319d5", size = 12316144, upload-time = "2026-03-09T07:56:47.057Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/71/46/8d1cb3f7a00f2fb6394140e7e6623696e54c6318a9d9691bb4904672cf42/numpy-2.4.3-cp312-cp312-win_arm64.whl", hash = "sha256:2abad5c7fef172b3377502bde47892439bae394a71bc329f31df0fd829b41a9e", size = 10220364, upload-time = "2026-03-09T07:56:49.849Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b6/d0/1fe47a98ce0df229238b77611340aff92d52691bcbc10583303181abf7fc/numpy-2.4.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b346845443716c8e542d54112966383b448f4a3ba5c66409771b8c0889485dd3", size = 16665297, upload-time = "2026-03-09T07:56:52.296Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/27/d9/4e7c3f0e68dfa91f21c6fb6cf839bc829ec920688b1ce7ec722b1a6202fb/numpy-2.4.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2629289168f4897a3c4e23dc98d6f1731f0fc0fe52fb9db19f974041e4cc12b9", size = 14691853, upload-time = "2026-03-09T07:56:54.992Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3a/66/bd096b13a87549683812b53ab211e6d413497f84e794fb3c39191948da97/numpy-2.4.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:bb2e3cf95854233799013779216c57e153c1ee67a0bf92138acca0e429aefaee", size = 5198435, upload-time = "2026-03-09T07:56:57.184Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a2/2f/687722910b5a5601de2135c891108f51dfc873d8e43c8ed9f4ebb440b4a2/numpy-2.4.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:7f3408ff897f8ab07a07fbe2823d7aee6ff644c097cc1f90382511fe982f647f", size = 6546347, upload-time = "2026-03-09T07:56:59.531Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/bf/ec/7971c4e98d86c564750393fab8d7d83d0a9432a9d78bb8a163a6dc59967a/numpy-2.4.3-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:decb0eb8a53c3b009b0962378065589685d66b23467ef5dac16cbe818afde27f", size = 15664626, upload-time = "2026-03-09T07:57:01.385Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/7e/eb/7daecbea84ec935b7fc732e18f532073064a3816f0932a40a17f3349185f/numpy-2.4.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d5f51900414fc9204a0e0da158ba2ac52b75656e7dce7e77fb9f84bfa343b4cc", size = 16608916, upload-time = "2026-03-09T07:57:04.008Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/df/58/2a2b4a817ffd7472dca4421d9f0776898b364154e30c95f42195041dc03b/numpy-2.4.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6bd06731541f89cdc01b261ba2c9e037f1543df7472517836b78dfb15bd6e476", size = 17015824, upload-time = "2026-03-09T07:57:06.347Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/4a/ca/627a828d44e78a418c55f82dd4caea8ea4a8ef24e5144d9e71016e52fb40/numpy-2.4.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:22654fe6be0e5206f553a9250762c653d3698e46686eee53b399ab90da59bd92", size = 18334581, upload-time = "2026-03-09T07:57:09.114Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/cd/c0/76f93962fc79955fcba30a429b62304332345f22d4daec1cb33653425643/numpy-2.4.3-cp313-cp313-win32.whl", hash = "sha256:d71e379452a2f670ccb689ec801b1218cd3983e253105d6e83780967e899d687", size = 5958618, upload-time = "2026-03-09T07:57:11.432Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b1/3c/88af0040119209b9b5cb59485fa48b76f372c73068dbf9254784b975ac53/numpy-2.4.3-cp313-cp313-win_amd64.whl", hash = "sha256:0a60e17a14d640f49146cb38e3f105f571318db7826d9b6fef7e4dce758faecd", size = 12312824, upload-time = "2026-03-09T07:57:13.586Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/58/ce/3d07743aced3d173f877c3ef6a454c2174ba42b584ab0b7e6d99374f51ed/numpy-2.4.3-cp313-cp313-win_arm64.whl", hash = "sha256:c9619741e9da2059cd9c3f206110b97583c7152c1dc9f8aafd4beb450ac1c89d", size = 10221218, upload-time = "2026-03-09T07:57:16.183Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/62/09/d96b02a91d09e9d97862f4fc8bfebf5400f567d8eb1fe4b0cc4795679c15/numpy-2.4.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7aa4e54f6469300ebca1d9eb80acd5253cdfa36f2c03d79a35883687da430875", size = 14819570, upload-time = "2026-03-09T07:57:18.564Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b5/ca/0b1aba3905fdfa3373d523b2b15b19029f4f3031c87f4066bd9d20ef6c6b/numpy-2.4.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d1b90d840b25874cf5cd20c219af10bac3667db3876d9a495609273ebe679070", size = 5326113, upload-time = "2026-03-09T07:57:21.052Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c0/63/406e0fd32fcaeb94180fd6a4c41e55736d676c54346b7efbce548b94a914/numpy-2.4.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:a749547700de0a20a6718293396ec237bb38218049cfce788e08fcb716e8cf73", size = 6646370, upload-time = "2026-03-09T07:57:22.804Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b6/d0/10f7dc157d4b37af92720a196be6f54f889e90dcd30dce9dc657ed92c257/numpy-2.4.3-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:94f3c4a151a2e529adf49c1d54f0f57ff8f9b233ee4d44af623a81553ab86368", size = 15723499, upload-time = "2026-03-09T07:57:24.693Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/66/f1/d1c2bf1161396629701bc284d958dc1efa3a5a542aab83cf11ee6eb4cba5/numpy-2.4.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22c31dc07025123aedf7f2db9e91783df13f1776dc52c6b22c620870dc0fab22", size = 16657164, upload-time = "2026-03-09T07:57:27.676Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/1a/be/cca19230b740af199ac47331a21c71e7a3d0ba59661350483c1600d28c37/numpy-2.4.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:148d59127ac95979d6f07e4d460f934ebdd6eed641db9c0db6c73026f2b2101a", size = 17081544, upload-time = "2026-03-09T07:57:30.664Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b9/c5/9602b0cbb703a0936fb40f8a95407e8171935b15846de2f0776e08af04c7/numpy-2.4.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:a97cbf7e905c435865c2d939af3d93f99d18eaaa3cabe4256f4304fb51604349", size = 18380290, upload-time = "2026-03-09T07:57:33.763Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ed/81/9f24708953cd30be9ee36ec4778f4b112b45165812f2ada4cc5ea1c1f254/numpy-2.4.3-cp313-cp313t-win32.whl", hash = "sha256:be3b8487d725a77acccc9924f65fd8bce9af7fac8c9820df1049424a2115af6c", size = 6082814, upload-time = "2026-03-09T07:57:36.491Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e2/9e/52f6eaa13e1a799f0ab79066c17f7016a4a8ae0c1aefa58c82b4dab690b4/numpy-2.4.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1ec84fd7c8e652b0f4aaaf2e6e9cc8eaa9b1b80a537e06b2e3a2fb176eedcb26", size = 12452673, upload-time = "2026-03-09T07:57:38.281Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c4/04/b8cece6ead0b30c9fbd99bb835ad7ea0112ac5f39f069788c5558e3b1ab2/numpy-2.4.3-cp313-cp313t-win_arm64.whl", hash = "sha256:120df8c0a81ebbf5b9020c91439fccd85f5e018a927a39f624845be194a2be02", size = 10290907, upload-time = "2026-03-09T07:57:40.747Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/70/ae/3936f79adebf8caf81bd7a599b90a561334a658be4dcc7b6329ebf4ee8de/numpy-2.4.3-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:5884ce5c7acfae1e4e1b6fde43797d10aa506074d25b531b4f54bde33c0c31d4", size = 16664563, upload-time = "2026-03-09T07:57:43.817Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/9b/62/760f2b55866b496bb1fa7da2a6db076bef908110e568b02fcfc1422e2a3a/numpy-2.4.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:297837823f5bc572c5f9379b0c9f3a3365f08492cbdc33bcc3af174372ebb168", size = 14702161, upload-time = "2026-03-09T07:57:46.169Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/32/af/a7a39464e2c0a21526fb4fb76e346fb172ebc92f6d1c7a07c2c139cc17b1/numpy-2.4.3-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:a111698b4a3f8dcbe54c64a7708f049355abd603e619013c346553c1fd4ca90b", size = 5208738, upload-time = "2026-03-09T07:57:48.506Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/29/8c/2a0cf86a59558fa078d83805589c2de490f29ed4fb336c14313a161d358a/numpy-2.4.3-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:4bd4741a6a676770e0e97fe9ab2e51de01183df3dcbcec591d26d331a40de950", size = 6543618, upload-time = "2026-03-09T07:57:50.591Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/aa/b8/612ce010c0728b1c363fa4ea3aa4c22fe1c5da1de008486f8c2f5cb92fae/numpy-2.4.3-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:54f29b877279d51e210e0c80709ee14ccbbad647810e8f3d375561c45ef613dd", size = 15680676, upload-time = "2026-03-09T07:57:52.34Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a9/7e/4f120ecc54ba26ddf3dc348eeb9eb063f421de65c05fc961941798feea18/numpy-2.4.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:679f2a834bae9020f81534671c56fd0cc76dd7e5182f57131478e23d0dc59e24", size = 16613492, upload-time = "2026-03-09T07:57:54.91Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/2c/86/1b6020db73be330c4b45d5c6ee4295d59cfeef0e3ea323959d053e5a6909/numpy-2.4.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d84f0f881cb2225c2dfd7f78a10a5645d487a496c6668d6cc39f0f114164f3d0", size = 17031789, upload-time = "2026-03-09T07:57:57.641Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/07/3a/3b90463bf41ebc21d1b7e06079f03070334374208c0f9a1f05e4ae8455e7/numpy-2.4.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d213c7e6e8d211888cc359bab7199670a00f5b82c0978b9d1c75baf1eddbeac0", size = 18339941, upload-time = "2026-03-09T07:58:00.577Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a8/74/6d736c4cd962259fd8bae9be27363eb4883a2f9069763747347544c2a487/numpy-2.4.3-cp314-cp314-win32.whl", hash = "sha256:52077feedeff7c76ed7c9f1a0428558e50825347b7545bbb8523da2cd55c547a", size = 6007503, upload-time = "2026-03-09T07:58:03.331Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/48/39/c56ef87af669364356bb011922ef0734fc49dad51964568634c72a009488/numpy-2.4.3-cp314-cp314-win_amd64.whl", hash = "sha256:0448e7f9caefb34b4b7dd2b77f21e8906e5d6f0365ad525f9f4f530b13df2afc", size = 12444915, upload-time = "2026-03-09T07:58:06.353Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/9d/1f/ab8528e38d295fd349310807496fabb7cf9fe2e1f70b97bc20a483ea9d4a/numpy-2.4.3-cp314-cp314-win_arm64.whl", hash = "sha256:b44fd60341c4d9783039598efadd03617fa28d041fc37d22b62d08f2027fa0e7", size = 10494875, upload-time = "2026-03-09T07:58:08.734Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e6/ef/b7c35e4d5ef141b836658ab21a66d1a573e15b335b1d111d31f26c8ef80f/numpy-2.4.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:0a195f4216be9305a73c0e91c9b026a35f2161237cf1c6de9b681637772ea657", size = 14822225, upload-time = "2026-03-09T07:58:11.034Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/cd/8d/7730fa9278cf6648639946cc816e7cc89f0d891602584697923375f801ed/numpy-2.4.3-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:cd32fbacb9fd1bf041bf8e89e4576b6f00b895f06d00914820ae06a616bdfef7", size = 5328769, upload-time = "2026-03-09T07:58:13.67Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/47/01/d2a137317c958b074d338807c1b6a383406cdf8b8e53b075d804cc3d211d/numpy-2.4.3-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:2e03c05abaee1f672e9d67bc858f300b5ccba1c21397211e8d77d98350972093", size = 6649461, upload-time = "2026-03-09T07:58:15.912Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/5c/34/812ce12bc0f00272a4b0ec0d713cd237cb390666eb6206323d1cc9cedbb2/numpy-2.4.3-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7d1ce23cce91fcea443320a9d0ece9b9305d4368875bab09538f7a5b4131938a", size = 15725809, upload-time = "2026-03-09T07:58:17.787Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/25/c0/2aed473a4823e905e765fee3dc2cbf504bd3e68ccb1150fbdabd5c39f527/numpy-2.4.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c59020932feb24ed49ffd03704fbab89f22aa9c0d4b180ff45542fe8918f5611", size = 16655242, upload-time = "2026-03-09T07:58:20.476Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f2/c8/7e052b2fc87aa0e86de23f20e2c42bd261c624748aa8efd2c78f7bb8d8c6/numpy-2.4.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9684823a78a6cd6ad7511fc5e25b07947d1d5b5e2812c93fe99d7d4195130720", size = 17080660, upload-time = "2026-03-09T07:58:23.067Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f3/3d/0876746044db2adcb11549f214d104f2e1be00f07a67edbb4e2812094847/numpy-2.4.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:0200b25c687033316fb39f0ff4e3e690e8957a2c3c8d22499891ec58c37a3eb5", size = 18380384, upload-time = "2026-03-09T07:58:25.839Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/07/12/8160bea39da3335737b10308df4f484235fd297f556745f13092aa039d3b/numpy-2.4.3-cp314-cp314t-win32.whl", hash = "sha256:5e10da9e93247e554bb1d22f8edc51847ddd7dde52d85ce31024c1b4312bfba0", size = 6154547, upload-time = "2026-03-09T07:58:28.289Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/42/f3/76534f61f80d74cc9cdf2e570d3d4eeb92c2280a27c39b0aaf471eda7b48/numpy-2.4.3-cp314-cp314t-win_amd64.whl", hash = "sha256:45f003dbdffb997a03da2d1d0cb41fbd24a87507fb41605c0420a3db5bd4667b", size = 12633645, upload-time = "2026-03-09T07:58:30.384Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/1f/b6/7c0d4334c15983cec7f92a69e8ce9b1e6f31857e5ee3a413ac424e6bd63d/numpy-2.4.3-cp314-cp314t-win_arm64.whl", hash = "sha256:4d382735cecd7bcf090172489a525cd7d4087bc331f7df9f60ddc9a296cf208e", size = 10565454, upload-time = "2026-03-09T07:58:33.031Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/64/e4/4dab9fb43c83719c29241c535d9e07be73bea4bc0c6686c5816d8e1b6689/numpy-2.4.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:c6b124bfcafb9e8d3ed09130dbee44848c20b3e758b6bbf006e641778927c028", size = 16834892, upload-time = "2026-03-09T07:58:35.334Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c9/29/f8b6d4af90fed3dfda84ebc0df06c9833d38880c79ce954e5b661758aa31/numpy-2.4.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:76dbb9d4e43c16cf9aa711fcd8de1e2eeb27539dcefb60a1d5e9f12fae1d1ed8", size = 14893070, upload-time = "2026-03-09T07:58:37.7Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/9a/04/a19b3c91dbec0a49269407f15d5753673a09832daed40c45e8150e6fa558/numpy-2.4.3-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:29363fbfa6f8ee855d7569c96ce524845e3d726d6c19b29eceec7dd555dab152", size = 5399609, upload-time = "2026-03-09T07:58:39.853Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/79/34/4d73603f5420eab89ea8a67097b31364bf7c30f811d4dd84b1659c7476d9/numpy-2.4.3-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:bc71942c789ef415a37f0d4eab90341425a00d538cd0642445d30b41023d3395", size = 6714355, upload-time = "2026-03-09T07:58:42.365Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/58/ad/1100d7229bb248394939a12a8074d485b655e8ed44207d328fdd7fcebc7b/numpy-2.4.3-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7e58765ad74dcebd3ef0208a5078fba32dc8ec3578fe84a604432950cd043d79", size = 15800434, upload-time = "2026-03-09T07:58:44.837Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0c/fd/16d710c085d28ba4feaf29ac60c936c9d662e390344f94a6beaa2ac9899b/numpy-2.4.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8e236dbda4e1d319d681afcbb136c0c4a8e0f1a5c58ceec2adebb547357fe857", size = 16729409, upload-time = "2026-03-09T07:58:47.972Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/57/a7/b35835e278c18b85206834b3aa3abe68e77a98769c59233d1f6300284781/numpy-2.4.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:4b42639cdde6d24e732ff823a3fa5b701d8acad89c4142bc1d0bd6dc85200ba5", size = 12504685, upload-time = "2026-03-09T07:58:50.525Z" },
+]
+
+[[package]]
+name = "packaging"
+version = "26.0"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/65/ee/299d360cdc32edc7d2cf530f3accf79c4fca01e96ffc950d8a52213bd8e4/packaging-26.0.tar.gz", hash = "sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4", size = 143416, upload-time = "2026-01-21T20:50:39.064Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b7/b9/c538f279a4e237a006a2c98387d081e9eb060d203d8ed34467cc0f0b9b53/packaging-26.0-py3-none-any.whl", hash = "sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529", size = 74366, upload-time = "2026-01-21T20:50:37.788Z" },
+]
+
+[[package]]
+name = "pluggy"
+version = "1.6.0"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" },
+]
+
+[[package]]
+name = "pygments"
+version = "2.19.2"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631, upload-time = "2025-06-21T13:39:12.283Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" },
+]
+
+[[package]]
+name = "pytest"
+version = "9.0.2"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
+    { name = "iniconfig" },
+    { name = "packaging" },
+    { name = "pluggy" },
+    { name = "pygments" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" },
+]
+
+[[package]]
+name = "rapid-pose-triangulation"
+version = "0.1.0"
+source = { editable = "." }
+dependencies = [
+    { name = "numpy", version = "2.2.6", source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.4.3", source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }, marker = "python_full_version >= '3.11'" },
+]
+
+[package.dev-dependencies]
+dev = [
+    { name = "pytest" },
+]
+
+[package.metadata]
+requires-dist = [{ name = "numpy", specifier = ">=2.0" }]
+
+[package.metadata.requires-dev]
+dev = [{ name = "pytest", specifier = ">=8.3" }]
+
+[[package]]
+name = "tomli"
+version = "2.4.0"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/82/30/31573e9457673ab10aa432461bee537ce6cef177667deca369efb79df071/tomli-2.4.0.tar.gz", hash = "sha256:aa89c3f6c277dd275d8e243ad24f3b5e701491a860d5121f2cdd399fbb31fc9c", size = 17477, upload-time = "2026-01-11T11:22:38.165Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3c/d9/3dc2289e1f3b32eb19b9785b6a006b28ee99acb37d1d47f78d4c10e28bf8/tomli-2.4.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b5ef256a3fd497d4973c11bf142e9ed78b150d36f5773f1ca6088c230ffc5867", size = 153663, upload-time = "2026-01-11T11:21:45.27Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/51/32/ef9f6845e6b9ca392cd3f64f9ec185cc6f09f0a2df3db08cbe8809d1d435/tomli-2.4.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:5572e41282d5268eb09a697c89a7bee84fae66511f87533a6f88bd2f7b652da9", size = 148469, upload-time = "2026-01-11T11:21:46.873Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d6/c2/506e44cce89a8b1b1e047d64bd495c22c9f71f21e05f380f1a950dd9c217/tomli-2.4.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:551e321c6ba03b55676970b47cb1b73f14a0a4dce6a3e1a9458fd6d921d72e95", size = 236039, upload-time = "2026-01-11T11:21:48.503Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b3/40/e1b65986dbc861b7e986e8ec394598187fa8aee85b1650b01dd925ca0be8/tomli-2.4.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5e3f639a7a8f10069d0e15408c0b96a2a828cfdec6fca05296ebcdcc28ca7c76", size = 243007, upload-time = "2026-01-11T11:21:49.456Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/9c/6f/6e39ce66b58a5b7ae572a0f4352ff40c71e8573633deda43f6a379d56b3e/tomli-2.4.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1b168f2731796b045128c45982d3a4874057626da0e2ef1fdd722848b741361d", size = 240875, upload-time = "2026-01-11T11:21:50.755Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/aa/ad/cb089cb190487caa80204d503c7fd0f4d443f90b95cf4ef5cf5aa0f439b0/tomli-2.4.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:133e93646ec4300d651839d382d63edff11d8978be23da4cc106f5a18b7d0576", size = 246271, upload-time = "2026-01-11T11:21:51.81Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0b/63/69125220e47fd7a3a27fd0de0c6398c89432fec41bc739823bcc66506af6/tomli-2.4.0-cp311-cp311-win32.whl", hash = "sha256:b6c78bdf37764092d369722d9946cb65b8767bfa4110f902a1b2542d8d173c8a", size = 96770, upload-time = "2026-01-11T11:21:52.647Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/1e/0d/a22bb6c83f83386b0008425a6cd1fa1c14b5f3dd4bad05e98cf3dbbf4a64/tomli-2.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:d3d1654e11d724760cdb37a3d7691f0be9db5fbdaef59c9f532aabf87006dbaa", size = 107626, upload-time = "2026-01-11T11:21:53.459Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/2f/6d/77be674a3485e75cacbf2ddba2b146911477bd887dda9d8c9dfb2f15e871/tomli-2.4.0-cp311-cp311-win_arm64.whl", hash = "sha256:cae9c19ed12d4e8f3ebf46d1a75090e4c0dc16271c5bce1c833ac168f08fb614", size = 94842, upload-time = "2026-01-11T11:21:54.831Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3c/43/7389a1869f2f26dba52404e1ef13b4784b6b37dac93bac53457e3ff24ca3/tomli-2.4.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:920b1de295e72887bafa3ad9f7a792f811847d57ea6b1215154030cf131f16b1", size = 154894, upload-time = "2026-01-11T11:21:56.07Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e9/05/2f9bf110b5294132b2edf13fe6ca6ae456204f3d749f623307cbb7a946f2/tomli-2.4.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7d6d9a4aee98fac3eab4952ad1d73aee87359452d1c086b5ceb43ed02ddb16b8", size = 149053, upload-time = "2026-01-11T11:21:57.467Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e8/41/1eda3ca1abc6f6154a8db4d714a4d35c4ad90adc0bcf700657291593fbf3/tomli-2.4.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:36b9d05b51e65b254ea6c2585b59d2c4cb91c8a3d91d0ed0f17591a29aaea54a", size = 243481, upload-time = "2026-01-11T11:21:58.661Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d2/6d/02ff5ab6c8868b41e7d4b987ce2b5f6a51d3335a70aa144edd999e055a01/tomli-2.4.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1c8a885b370751837c029ef9bc014f27d80840e48bac415f3412e6593bbc18c1", size = 251720, upload-time = "2026-01-11T11:22:00.178Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/7b/57/0405c59a909c45d5b6f146107c6d997825aa87568b042042f7a9c0afed34/tomli-2.4.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8768715ffc41f0008abe25d808c20c3d990f42b6e2e58305d5da280ae7d1fa3b", size = 247014, upload-time = "2026-01-11T11:22:01.238Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/2c/0e/2e37568edd944b4165735687cbaf2fe3648129e440c26d02223672ee0630/tomli-2.4.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:7b438885858efd5be02a9a133caf5812b8776ee0c969fea02c45e8e3f296ba51", size = 251820, upload-time = "2026-01-11T11:22:02.727Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/5a/1c/ee3b707fdac82aeeb92d1a113f803cf6d0f37bdca0849cb489553e1f417a/tomli-2.4.0-cp312-cp312-win32.whl", hash = "sha256:0408e3de5ec77cc7f81960c362543cbbd91ef883e3138e81b729fc3eea5b9729", size = 97712, upload-time = "2026-01-11T11:22:03.777Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/69/13/c07a9177d0b3bab7913299b9278845fc6eaaca14a02667c6be0b0a2270c8/tomli-2.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:685306e2cc7da35be4ee914fd34ab801a6acacb061b6a7abca922aaf9ad368da", size = 108296, upload-time = "2026-01-11T11:22:04.86Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/18/27/e267a60bbeeee343bcc279bb9e8fbed0cbe224bc7b2a3dc2975f22809a09/tomli-2.4.0-cp312-cp312-win_arm64.whl", hash = "sha256:5aa48d7c2356055feef06a43611fc401a07337d5b006be13a30f6c58f869e3c3", size = 94553, upload-time = "2026-01-11T11:22:05.854Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/34/91/7f65f9809f2936e1f4ce6268ae1903074563603b2a2bd969ebbda802744f/tomli-2.4.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:84d081fbc252d1b6a982e1870660e7330fb8f90f676f6e78b052ad4e64714bf0", size = 154915, upload-time = "2026-01-11T11:22:06.703Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/20/aa/64dd73a5a849c2e8f216b755599c511badde80e91e9bc2271baa7b2cdbb1/tomli-2.4.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:9a08144fa4cba33db5255f9b74f0b89888622109bd2776148f2597447f92a94e", size = 149038, upload-time = "2026-01-11T11:22:07.56Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/9e/8a/6d38870bd3d52c8d1505ce054469a73f73a0fe62c0eaf5dddf61447e32fa/tomli-2.4.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c73add4bb52a206fd0c0723432db123c0c75c280cbd67174dd9d2db228ebb1b4", size = 242245, upload-time = "2026-01-11T11:22:08.344Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/59/bb/8002fadefb64ab2669e5b977df3f5e444febea60e717e755b38bb7c41029/tomli-2.4.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1fb2945cbe303b1419e2706e711b7113da57b7db31ee378d08712d678a34e51e", size = 250335, upload-time = "2026-01-11T11:22:09.951Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a5/3d/4cdb6f791682b2ea916af2de96121b3cb1284d7c203d97d92d6003e91c8d/tomli-2.4.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:bbb1b10aa643d973366dc2cb1ad94f99c1726a02343d43cbc011edbfac579e7c", size = 245962, upload-time = "2026-01-11T11:22:11.27Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f2/4a/5f25789f9a460bd858ba9756ff52d0830d825b458e13f754952dd15fb7bb/tomli-2.4.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4cbcb367d44a1f0c2be408758b43e1ffb5308abe0ea222897d6bfc8e8281ef2f", size = 250396, upload-time = "2026-01-11T11:22:12.325Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/aa/2f/b73a36fea58dfa08e8b3a268750e6853a6aac2a349241a905ebd86f3047a/tomli-2.4.0-cp313-cp313-win32.whl", hash = "sha256:7d49c66a7d5e56ac959cb6fc583aff0651094ec071ba9ad43df785abc2320d86", size = 97530, upload-time = "2026-01-11T11:22:13.865Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3b/af/ca18c134b5d75de7e8dc551c5234eaba2e8e951f6b30139599b53de9c187/tomli-2.4.0-cp313-cp313-win_amd64.whl", hash = "sha256:3cf226acb51d8f1c394c1b310e0e0e61fecdd7adcb78d01e294ac297dd2e7f87", size = 108227, upload-time = "2026-01-11T11:22:15.224Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/22/c3/b386b832f209fee8073c8138ec50f27b4460db2fdae9ffe022df89a57f9b/tomli-2.4.0-cp313-cp313-win_arm64.whl", hash = "sha256:d20b797a5c1ad80c516e41bc1fb0443ddb5006e9aaa7bda2d71978346aeb9132", size = 94748, upload-time = "2026-01-11T11:22:16.009Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f3/c4/84047a97eb1004418bc10bdbcfebda209fca6338002eba2dc27cc6d13563/tomli-2.4.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:26ab906a1eb794cd4e103691daa23d95c6919cc2fa9160000ac02370cc9dd3f6", size = 154725, upload-time = "2026-01-11T11:22:17.269Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a8/5d/d39038e646060b9d76274078cddf146ced86dc2b9e8bbf737ad5983609a0/tomli-2.4.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:20cedb4ee43278bc4f2fee6cb50daec836959aadaf948db5172e776dd3d993fc", size = 148901, upload-time = "2026-01-11T11:22:18.287Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/73/e5/383be1724cb30f4ce44983d249645684a48c435e1cd4f8b5cded8a816d3c/tomli-2.4.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:39b0b5d1b6dd03684b3fb276407ebed7090bbec989fa55838c98560c01113b66", size = 243375, upload-time = "2026-01-11T11:22:19.154Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/31/f0/bea80c17971c8d16d3cc109dc3585b0f2ce1036b5f4a8a183789023574f2/tomli-2.4.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a26d7ff68dfdb9f87a016ecfd1e1c2bacbe3108f4e0f8bcd2228ef9a766c787d", size = 250639, upload-time = "2026-01-11T11:22:20.168Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/2c/8f/2853c36abbb7608e3f945d8a74e32ed3a74ee3a1f468f1ffc7d1cb3abba6/tomli-2.4.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:20ffd184fb1df76a66e34bd1b36b4a4641bd2b82954befa32fe8163e79f1a702", size = 246897, upload-time = "2026-01-11T11:22:21.544Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/49/f0/6c05e3196ed5337b9fe7ea003e95fd3819a840b7a0f2bf5a408ef1dad8ed/tomli-2.4.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:75c2f8bbddf170e8effc98f5e9084a8751f8174ea6ccf4fca5398436e0320bc8", size = 254697, upload-time = "2026-01-11T11:22:23.058Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f3/f5/2922ef29c9f2951883525def7429967fc4d8208494e5ab524234f06b688b/tomli-2.4.0-cp314-cp314-win32.whl", hash = "sha256:31d556d079d72db7c584c0627ff3a24c5d3fb4f730221d3444f3efb1b2514776", size = 98567, upload-time = "2026-01-11T11:22:24.033Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/7b/31/22b52e2e06dd2a5fdbc3ee73226d763b184ff21fc24e20316a44ccc4d96b/tomli-2.4.0-cp314-cp314-win_amd64.whl", hash = "sha256:43e685b9b2341681907759cf3a04e14d7104b3580f808cfde1dfdb60ada85475", size = 108556, upload-time = "2026-01-11T11:22:25.378Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/48/3d/5058dff3255a3d01b705413f64f4306a141a8fd7a251e5a495e3f192a998/tomli-2.4.0-cp314-cp314-win_arm64.whl", hash = "sha256:3d895d56bd3f82ddd6faaff993c275efc2ff38e52322ea264122d72729dca2b2", size = 96014, upload-time = "2026-01-11T11:22:26.138Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b8/4e/75dab8586e268424202d3a1997ef6014919c941b50642a1682df43204c22/tomli-2.4.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:5b5807f3999fb66776dbce568cc9a828544244a8eb84b84b9bafc080c99597b9", size = 163339, upload-time = "2026-01-11T11:22:27.143Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/06/e3/b904d9ab1016829a776d97f163f183a48be6a4deb87304d1e0116a349519/tomli-2.4.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c084ad935abe686bd9c898e62a02a19abfc9760b5a79bc29644463eaf2840cb0", size = 159490, upload-time = "2026-01-11T11:22:28.399Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e3/5a/fc3622c8b1ad823e8ea98a35e3c632ee316d48f66f80f9708ceb4f2a0322/tomli-2.4.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0f2e3955efea4d1cfbcb87bc321e00dc08d2bcb737fd1d5e398af111d86db5df", size = 269398, upload-time = "2026-01-11T11:22:29.345Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/fd/33/62bd6152c8bdd4c305ad9faca48f51d3acb2df1f8791b1477d46ff86e7f8/tomli-2.4.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0e0fe8a0b8312acf3a88077a0802565cb09ee34107813bba1c7cd591fa6cfc8d", size = 276515, upload-time = "2026-01-11T11:22:30.327Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/4b/ff/ae53619499f5235ee4211e62a8d7982ba9e439a0fb4f2f351a93d67c1dd2/tomli-2.4.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:413540dce94673591859c4c6f794dfeaa845e98bf35d72ed59636f869ef9f86f", size = 273806, upload-time = "2026-01-11T11:22:32.56Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/47/71/cbca7787fa68d4d0a9f7072821980b39fbb1b6faeb5f5cf02f4a5559fa28/tomli-2.4.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:0dc56fef0e2c1c470aeac5b6ca8cc7b640bb93e92d9803ddaf9ea03e198f5b0b", size = 281340, upload-time = "2026-01-11T11:22:33.505Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f5/00/d595c120963ad42474cf6ee7771ad0d0e8a49d0f01e29576ee9195d9ecdf/tomli-2.4.0-cp314-cp314t-win32.whl", hash = "sha256:d878f2a6707cc9d53a1be1414bbb419e629c3d6e67f69230217bb663e76b5087", size = 108106, upload-time = "2026-01-11T11:22:34.451Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/de/69/9aa0c6a505c2f80e519b43764f8b4ba93b5a0bbd2d9a9de6e2b24271b9a5/tomli-2.4.0-cp314-cp314t-win_amd64.whl", hash = "sha256:2add28aacc7425117ff6364fe9e06a183bb0251b03f986df0e78e974047571fd", size = 120504, upload-time = "2026-01-11T11:22:35.764Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b3/9f/f1668c281c58cfae01482f7114a4b88d345e4c140386241a1a24dcc9e7bc/tomli-2.4.0-cp314-cp314t-win_arm64.whl", hash = "sha256:2b1e3b80e1d5e52e40e9b924ec43d81570f0e7d09d11081b797bc4692765a3d4", size = 99561, upload-time = "2026-01-11T11:22:36.624Z" },
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/23/d1/136eb2cb77520a31e1f64cbae9d33ec6df0d78bdf4160398e86eec8a8754/tomli-2.4.0-py3-none-any.whl", hash = "sha256:1f776e7d669ebceb01dee46484485f43a4048746235e683bcdffacdf1fb4785a", size = 14477, upload-time = "2026-01-11T11:22:37.446Z" },
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.15.0"
+source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/" }
+sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" }
+wheels = [
+    { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" },
+]
Author	SHA1	Message	Date
crosstyan	6c09f7044b	Remove unused extras directory	2026-03-26 11:22:44 +08:00
crosstyan	68b2b32510	Document Python API and ship package stubs	2026-03-12 14:53:42 +08:00
crosstyan	0209986a2c	Add tracked triangulation association reports	2026-03-12 14:16:31 +08:00
crosstyan	31c4690121	Unify camera model into immutable factory-built type	2026-03-12 00:24:02 +08:00
crosstyan	c23f25f871	Simplify triangulation API with config struct	2026-03-12 00:08:56 +08:00
crosstyan	7df34b18c3	Simplify triangulation core and remove integrations	2026-03-11 23:55:04 +08:00
crosstyan	103aeb5887	Refactor triangulation stages and camera model	2026-03-11 23:42:07 +08:00
crosstyan	24f74c87f1	Make triangulation a zero-copy pure function	2026-03-11 22:29:21 +08:00
crosstyan	5bed0f0aaf	Remove ONNX and Skelda integration	2026-03-11 22:03:42 +08:00
crosstyan	d7769794fb	Migrate Python bindings from SWIG to nanobind	2026-03-11 21:56:30 +08:00
crosstyan	0ec6a85921	Fix move semantics in triangulate_poses to improve performance	2026-01-29 17:55:59 +08:00
crosstyan	cab98936dd	Refactor CMake configuration: separate RPT core library into its own CMakeLists.txt and streamline SWIG integration	2026-01-29 17:54:21 +08:00
crosstyan	7405454480	Add CMake support and SWIG bindings for RapidPoseTriangulation	2026-01-29 17:37:38 +08:00