mirror of
https://github.com/introlab/rtabmap.git
synced 2026-09-03 01:50:24 +08:00
* Add FlannIndex abstract interface and implement NanoFlannIndex subclass * Refactored: made NanoFlann a new NN type instead of inheriting FlannIndex. Added tests. Vendoring nanoflann.h directly in the repo. RegistrationVis now use NANOFLANN_INDEX_KDTREE_SINGLE (instead of FLANN_INDEX_KDTREE_SINGLE) flann index for 2d points matching. * cleanup comments, added FlannIndex doxygen * Fixing windows tests * updating flaky test * Simplified interface, added flann kdtree single approach selectable by parameters. * RegVis: symmetry of nanoflann for two branches of guess feature matching * cv::BFMatcher baseline * Small cmake optimization FLANN_KDTREE_MEM_OPT only defined for FlannIndex * Refactored where FLANN_KDTREE_MEM_OPT is defined * fixed file name already exist * cleanup * fixup build --------- Co-authored-by: matlabbe <matlabbe@gmail.com>
172 lines
6.8 KiB
YAML
172 lines
6.8 KiB
YAML
name: CMake-Windows
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on:
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push:
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branches:
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- master
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pull_request:
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branches:
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- '**'
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workflow_dispatch:
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env:
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BUILD_TYPE: Release
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concurrency:
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group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
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cancel-in-progress: ${{ github.event_name == 'pull_request' }}
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jobs:
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build:
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name: ${{ matrix.build_name }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: true
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matrix:
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build_name: [windows-2022, windows-2022-cuda]
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include:
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- build_name: windows-2022
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os: windows-2022
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extra_deps: ""
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extra_cmake_def: '-DWITH_TORCH=OFF -DWITH_ZED=OFF'
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- build_name: windows-2022-cuda
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os: windows-2022
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extra_deps: ""
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extra_cmake_def: '-DWITH_TORCH=ON -DWITH_ZED=ON -DWITH_CUDASIFT=ON'
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steps:
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- uses: actions/checkout@v4
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- name: Restore test data
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id: cache-testdata
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if: matrix.build_name != 'windows-2022-cuda'
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uses: actions/cache@v4
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with:
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# SQLite DBs are binary-portable -> share the cache across
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# linux / macos / windows. Key omits runner.os on purpose so all
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# three OSes hit the same entry.
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path: data/tests/*.db
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key: testdata-${{ hashFiles('data/tests/manifest.txt') }}
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- name: Fetch test data
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if: matrix.build_name != 'windows-2022-cuda' && steps.cache-testdata.outputs.cache-hit != 'true'
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shell: bash
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run: bash scripts/fetch_test_data.sh
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- name: Install Windows Dependencies
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if: matrix.build_name == 'windows-2022'
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uses: ./.github/actions/install-windows-deps
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- name: Install Windows Dependencies with CUDA
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if: matrix.build_name == 'windows-2022-cuda'
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uses: ./.github/actions/install-windows-cuda-deps
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- name: Configure CMake
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run: |
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# K4A, K4W2 and ZED are located via these env vars (FindK4A.cmake / FindKinectSDK2.cmake
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# and the install rules), pointing at the export where the bundle staged their SDK files.
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$env:K4A_ROOT_DIR = "${{env.VCPKG_EXPORT_PATH}}/installed/x64-windows-release"
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$env:KINECTSDK20_DIR = "${{env.VCPKG_EXPORT_PATH}}/installed/x64-windows-release"
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$env:ZED_SDK_ROOT_DIR = "${{env.VCPKG_EXPORT_PATH}}/installed/x64-windows-release"
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cmake `
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-B ${{github.workspace}}/build `
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-DCMAKE_BUILD_TYPE=${{env.BUILD_TYPE}} `
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-DBUILD_AS_BUNDLE=ON `
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-DWITH_PYTHON=ON `
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-DWITH_CERES=ON `
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-DWITH_ORBBEC_SDK=ON `
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-DWITH_FREENECT2=ON `
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-DWITH_K4W2=ON `
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-DWITH_K4A=ON `
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-DWITH_DEPTHAI=ON `
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-DWITH_REALSENSE2=ON `
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-DWITH_CCCORELIB=ON `
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${{ matrix.extra_cmake_def }} `
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-DBUILD_TESTING=ON `
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-DVCPKG_MANIFEST_INSTALL=OFF `
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-DVCPKG_TARGET_TRIPLET=x64-windows-release `
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-DVCPKG_INSTALLED_DIR="${{env.VCPKG_EXPORT_PATH}}/installed" `
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-DCMAKE_TOOLCHAIN_FILE=${{env.VCPKG_EXPORT_PATH}}/scripts/buildsystems/vcpkg.cmake `
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-DTorch_DIR=${{env.VCPKG_EXPORT_PATH}}/installed/x64-windows-release/tools/python3/Lib/site-packages/torch/share/cmake/Torch
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- name: Build
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run: cmake --build ${{github.workspace}}/build --config ${{env.BUILD_TYPE}} --target ALL_BUILD
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- name: Test
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# Not run on the CUDA build, which is a build+package job only.
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#
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# The runner has no NVIDIA GPU or driver (see the driver-DLL note in
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# app/src/CMakeLists.txt), so no cv::cuda / CUDASIFT path can actually
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# execute there -- everything falls back to CPU and the run just repeats
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# the coverage the non-CUDA job already gives, on the same sources. It
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# used to cost more than it returned: the two cv::cuda-touching tests
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# (test_util3d_features, test_localgrid) had to be excluded because lazy
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# CUDA-init stalled them past the 300 s timeout, and once WITH_ZED=ON was
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# added to this job every remaining corelib test died in the loader with
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# 0xc0000135 (STATUS_DLL_NOT_FOUND) before reaching main(): rtabmap_core
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# links sl_zed64.dll, which imports the driver-only nvcuvid.dll /
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# nvEncodeAPI64.dll that a driver-less runner does not have.
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#
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# So this job compiles, links and packages the CUDA artifact; the
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# non-CUDA job is what verifies behaviour. Same reason the Info step
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# below is skipped here.
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if: matrix.build_name != 'windows-2022-cuda'
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working-directory: ${{github.workspace}}/build
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# PYTHONHOME points the embedded interpreter at vcpkg's bundled
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# Python install (where Lib/, DLLs/ live). Without it Python emits
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# "Could not find platform independent libraries <prefix>" at init
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# and runs without a stdlib, breaking every numpy import.
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# PYTHONNOUSERSITE=1: prevent the embedded Python interpreter from
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# loading numpy / other site-packages from %APPDATA%\Python that
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# were compiled against a different ABI than the build-time Python
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# (numpy 1.x/2.x mismatch crashes in test_pydetector /
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# test_pydescriptor / test_pymatcher).
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env:
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PYTHONHOME: ${{env.VCPKG_EXPORT_PATH}}/installed/x64-windows-release/tools/python3
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PYTHONNOUSERSITE: 1
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run: |
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ctest -C ${{env.BUILD_TYPE}} -V --timeout 300 -LE performance
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- name: Info
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# Skipped for CUDA: the binary links ZED (sl_zed64.dll -> nvcuvid/nvEncodeAPI64),
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# which need the NVIDIA driver; the GPU-less runner can't load the exe.
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if: matrix.build_name != 'windows-2022-cuda'
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working-directory: ${{github.workspace}}/build/bin
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run: |
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./rtabmap-console --version
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- name: Build Windows Package
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shell: pwsh
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run: |
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if ("${{ github.event_name }}" -eq "pull_request") {
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cpack --config build/CPackConfig.cmake -G ZIP -B build
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} else {
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cmake --build ${{ github.workspace }}/build --config ${{ env.BUILD_TYPE }} --target package
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}
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- name: Rename CUDA artifacts
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if: matrix.build_name == 'windows-2022-cuda'
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shell: pwsh
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run: Get-ChildItem -Path "build" -Filter "RTABMap-*" | Rename-Item -NewName { $_.BaseName + "_cuda" + $_.Extension }
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- name: Upload RTABMap Artifacts (ZIP)
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uses: actions/upload-artifact@v4
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with:
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name: RTABMap-Binaries-${{ matrix.build_name }}-zip
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path: |
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build/RTABMap-*.zip
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compression-level: 0
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if-no-files-found: warn
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retention-days: ${{ github.event_name == 'pull_request' && 1 || 90 }}
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- name: Upload RTABMap Artifacts (Installer)
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if: github.event_name != 'pull_request'
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uses: actions/upload-artifact@v4
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with:
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name: RTABMap-Binaries-${{ matrix.build_name }}-exe
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path: |
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build/RTABMap-*.exe
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compression-level: 0
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if-no-files-found: warn
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retention-days: ${{ github.event_name == 'pull_request' && 1 || 90 }}
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