From fcb7baf04e7eab6aac273bd6eb2f3dde52867692 Mon Sep 17 00:00:00 2001 From: Ignacio Vizzo Date: Thu, 25 Mar 2021 15:50:45 +0100 Subject: [PATCH] Initial open source commit --- .dockerignore | 5 + .gitignore | 174 +++++++++++++++++ 3rdparty/Open3D.sh | 86 ++++++++ 3rdparty/cmake.sh | 20 ++ 3rdparty/embree.sh | 16 ++ 3rdparty/patch_pykitti.sh | 20 ++ INSTALL.md | 102 ++++++++++ LICENSE.txt | 21 ++ README.md | 184 ++++++++++++++++++ apps/config/gicp.yml | 18 ++ apps/config/p2l_icp.yml | 18 ++ apps/config/p2p_icp.yml | 18 ++ apps/config/puma.yml | 25 +++ apps/data_conversion/bin2bag.py | 116 +++++++++++ apps/data_conversion/bin2ply.py | 111 +++++++++++ apps/data_conversion/ply2bin.py | 59 ++++++ apps/data_conversion/ros2ply.py | 81 ++++++++ apps/pipelines/mapping/build_gt_cloud.py | 97 +++++++++ apps/pipelines/mapping/build_gt_mesh.py | 152 +++++++++++++++ .../mapping/build_gt_mesh_incremental.py | 113 +++++++++++ apps/pipelines/odometry/icp_frame_2_frame.py | 96 +++++++++ apps/pipelines/odometry/icp_frame_2_map.py | 110 +++++++++++ apps/pipelines/odometry/icp_frame_2_mesh.py | 111 +++++++++++ apps/pipelines/slam/puma_pipeline.py | 176 +++++++++++++++++ apps/run_poisson.py | 65 +++++++ docker-compose.yml | 19 ++ docker/README.md | 56 ++++++ docker/apps/Dockerfile | 27 +++ docker/builder/Dockerfile | 51 +++++ pics/puma.png | Bin 0 -> 392302 bytes pics/suma.png | Bin 0 -> 374234 bytes pics/tsdf.png | Bin 0 -> 504334 bytes puma/__init__.py | 1 + puma/cpp/gen_normal_map.cpp | 115 +++++++++++ puma/mesh/__init__.py | 2 + puma/mesh/poisson.py | 46 +++++ puma/mesh/size.py | 20 ++ puma/preprocessing/__init__.py | 1 + puma/preprocessing/preprocess_cloud.py | 43 ++++ puma/preprocessing/range_image_normal.py | 28 +++ puma/projections/__init__.py | 2 + puma/projections/range_image.py | 97 +++++++++ puma/projections/scan2mesh.py | 69 +++++++ puma/registration/__init__.py | 5 + puma/registration/method_selector.py | 14 ++ puma/registration/o3d_aliases.py | 10 + puma/registration/run_icp.py | 15 ++ puma/registration/scan2mesh.py | 31 +++ puma/registration/scan2mesh_icp.py | 76 ++++++++ puma/utils/__init__.py | 7 + puma/utils/calibration.py | 34 ++++ puma/utils/circular_buffer.py | 11 ++ puma/utils/config.py | 28 +++ puma/utils/kitti_poses.py | 28 +++ puma/utils/progress_bar.py | 17 ++ puma/utils/save_geoms.py | 18 ++ puma/utils/timeit.py | 17 ++ pyproject.toml | 16 ++ requirements.txt | 9 + setup.cfg | 33 ++++ setup.py | 18 ++ 61 files changed, 2958 insertions(+) create mode 100644 .dockerignore create mode 100644 .gitignore create mode 100644 3rdparty/Open3D.sh create mode 100644 3rdparty/cmake.sh create mode 100644 3rdparty/embree.sh create mode 100644 3rdparty/patch_pykitti.sh create mode 100644 INSTALL.md create mode 100644 LICENSE.txt create mode 100644 README.md create mode 100644 apps/config/gicp.yml create mode 100644 apps/config/p2l_icp.yml create mode 100644 apps/config/p2p_icp.yml create mode 100644 apps/config/puma.yml create mode 100755 apps/data_conversion/bin2bag.py create mode 100755 apps/data_conversion/bin2ply.py create mode 100755 apps/data_conversion/ply2bin.py create mode 100755 apps/data_conversion/ros2ply.py create mode 100755 apps/pipelines/mapping/build_gt_cloud.py create mode 100755 apps/pipelines/mapping/build_gt_mesh.py create mode 100755 apps/pipelines/mapping/build_gt_mesh_incremental.py create mode 100755 apps/pipelines/odometry/icp_frame_2_frame.py create mode 100755 apps/pipelines/odometry/icp_frame_2_map.py create mode 100755 apps/pipelines/odometry/icp_frame_2_mesh.py create mode 100755 apps/pipelines/slam/puma_pipeline.py create mode 100755 apps/run_poisson.py create mode 100644 docker-compose.yml create mode 100644 docker/README.md create mode 100644 docker/apps/Dockerfile create mode 100644 docker/builder/Dockerfile create mode 100644 pics/puma.png create mode 100644 pics/suma.png create mode 100644 pics/tsdf.png create mode 100644 puma/__init__.py create mode 100644 puma/cpp/gen_normal_map.cpp create mode 100644 puma/mesh/__init__.py create mode 100644 puma/mesh/poisson.py create mode 100644 puma/mesh/size.py create mode 100644 puma/preprocessing/__init__.py create mode 100755 puma/preprocessing/preprocess_cloud.py create mode 100755 puma/preprocessing/range_image_normal.py create mode 100644 puma/projections/__init__.py create mode 100755 puma/projections/range_image.py create mode 100644 puma/projections/scan2mesh.py create mode 100644 puma/registration/__init__.py create mode 100644 puma/registration/method_selector.py create mode 100644 puma/registration/o3d_aliases.py create mode 100644 puma/registration/run_icp.py create mode 100644 puma/registration/scan2mesh.py create mode 100644 puma/registration/scan2mesh_icp.py create mode 100644 puma/utils/__init__.py create mode 100644 puma/utils/calibration.py create mode 100644 puma/utils/circular_buffer.py create mode 100644 puma/utils/config.py create mode 100644 puma/utils/kitti_poses.py create mode 100644 puma/utils/progress_bar.py create mode 100644 puma/utils/save_geoms.py create mode 100644 puma/utils/timeit.py create mode 100644 pyproject.toml create mode 100644 requirements.txt create mode 100644 setup.cfg create mode 100644 setup.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..12d6434 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,5 @@ +**/.git +**/.bin +**/.ply +**/*results/ +**/*data/ diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1c14b0e --- /dev/null +++ b/.gitignore @@ -0,0 +1,174 @@ +**/*results/ +**/*data/ + +# Created by https://www.toptal.com/developers/gitignore/api/python,c++ +# Edit at https://www.toptal.com/developers/gitignore?templates=python,c++ + +### C++ ### +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# End of https://www.toptal.com/developers/gitignore/api/python,c++ diff --git a/3rdparty/Open3D.sh b/3rdparty/Open3D.sh new file mode 100644 index 0000000..a7b3395 --- /dev/null +++ b/3rdparty/Open3D.sh @@ -0,0 +1,86 @@ +#!/bin/bash +# Install Open3D with GeneralizedICP support +# Based on Open3D/util/ci_utils.sh + +set -ex + +NPROC=${NPROC:-$(getconf _NPROCESSORS_ONLN)} # POSIX: MacOS + Linux + +# Patch for PSR +patch_psr() { + git apply --ignore-whitespace <::Initialize( FEMTreeNode& root , InputPo + pointStream.reset(); + } + if( outOfBoundPoints ) WARN( "Found out-of-bound points: " , outOfBoundPoints ); +- if( badData ) WARN( "Found bad data: " , badData ); ++ /* if( badData ) WARN( "Found bad data: " , badData ); */ + if( std::is_same< Real , float >::value ) + { + std::vector< size_t > badNodeCounts( ThreadPool::NumThreads() , 0 ); +diff --git a/Src/FEMTree.IsoSurface.specialized.inl b/Src/FEMTree.IsoSurface.specialized.inl +index 28b5ef0..88710e4 100644 +--- a/Src/FEMTree.IsoSurface.specialized.inl ++++ b/Src/FEMTree.IsoSurface.specialized.inl +@@ -1855,7 +1855,7 @@ public: + FEMTree< Dim , Real >::MemoryUsage(); + if( pointEvaluator ) delete pointEvaluator; + size_t badRootCount = _BadRootCount; +- if( badRootCount!=0 ) WARN( "bad average roots: " , badRootCount ); ++ /* if( badRootCount!=0 ) WARN( "bad average roots: " , badRootCount ); */ + return isoStats; + } + }; +EOF +} + +build_all() { + + echo "Using cmake: $(which cmake)" + cmake --version + + mkdir -p build + cd build + + cmakeOptions=( + -DCMAKE_BUILD_TYPE=Release + -DPYTHON_EXECUTABLE="$(which python)" + -DBUILD_LIBREALSENSE=OFF + -DBUILD_UNIT_TESTS=OFF + -DBUILD_BENCHMARKS=OFF + -DBUILD_EXAMPLES=OFF + -DENABLE_HEADLESS_RENDERING=ON + -DBUILD_GUI=OFF + -DUSE_SYSTEM_GLEW=OFF + -DUSE_SYSTEM_GLFW=OFF + ) + + echo Running cmake "${cmakeOptions[@]}" .. + cmake "${cmakeOptions[@]}" .. + echo "Build & install Open3D..." + make VERBOSE=1 -j"$NPROC" + make install -j"$NPROC" + make VERBOSE=1 install-pip-package -j"$NPROC" +} + +# install open3d with python bindings +git clone --depth 1 --recurse-submodules \ + https://github.com/nachovizzo/Open3D.git /tmp/Open3D -b nacho/generalized_icp && + cd /tmp/Open3D/ + +# Patch Poisson Surface Reconstruction so it doesn't fill up the console. +cd 3rdparty/PoissonRecon/PoissonRecon/ && patch_psr && cd - + +# Was impossible to pass the SUDO env variable to the script, so remove it :) +sed -i 's/sudo//' util/install_deps_ubuntu.sh + +# install Open3D dependencies +bash util/install_deps_ubuntu.sh assume-yes + +# Build and cleanup to save space +build_all +rm -rf /tmp/Open3D && rm -rf /var/lib/apt/lists/* diff --git a/3rdparty/cmake.sh b/3rdparty/cmake.sh new file mode 100644 index 0000000..7533928 --- /dev/null +++ b/3rdparty/cmake.sh @@ -0,0 +1,20 @@ +#!/bin/bash +# Instructions from https://apt.kitware.com/ + +set -ex + +apt-get update && apt-get install --no-install-recommends -y \ + apt-transport-https \ + ca-certificates \ + gnupg \ + software-properties-common && + rm -rf /var/lib/apt/lists/* + +wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | + gpg --dearmor - | + tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null + +apt-add-repository 'deb https://apt.kitware.com/ubuntu/ focal main' +apt-get update && apt-get install --no-install-recommends -y \ + cmake && + rm -rf /var/lib/apt/lists/* diff --git a/3rdparty/embree.sh b/3rdparty/embree.sh new file mode 100644 index 0000000..6a823c8 --- /dev/null +++ b/3rdparty/embree.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +# Taken from the trimesh library, all copyrights belong to the original authors +# https://raw.githubusercontent.com/mikedh/trimesh/master/docker/builds/embree.bash +set -xe + +# Fetch the archive from GitHub releases. +wget https://github.com/embree/embree/releases/download/v2.17.7/embree-2.17.7.x86_64.linux.tar.gz -O /tmp/embree.tar.gz -nv +echo "2c4bdacd8f3c3480991b99e85b8f584975ac181373a75f3e9675bf7efae501fe /tmp/embree.tar.gz" | sha256sum --check +tar -xzf /tmp/embree.tar.gz --strip-components=1 -C /usr/local +# remove archive +rm -rf /tmp/embree.tar.gz + +# Install python bindings for embree (and upstream requirements). +pip install --no-cache-dir numpy cython +pip install --no-cache-dir https://github.com/scopatz/pyembree/releases/download/0.1.6/pyembree-0.1.6.tar.gz diff --git a/3rdparty/patch_pykitti.sh b/3rdparty/patch_pykitti.sh new file mode 100644 index 0000000..fefef32 --- /dev/null +++ b/3rdparty/patch_pykitti.sh @@ -0,0 +1,20 @@ +#!/bin/bash +set -ex + +# Obtain the location of the pykitti package +pykitti_dir=$( +python - <<-EOF + +import sys +import traceback +import os + +try: + import pykitti +except ModuleNotFoundError as err: + print(os.path.dirname(traceback.extract_tb(sys.exc_info()[2])[-1].filename)) +EOF +) + +echo "Patching pykitti in $pykitti_dir" +sed -i '/import cv2/d' "${pykitti_dir}/tracking.py" diff --git a/INSTALL.md b/INSTALL.md new file mode 100644 index 0000000..e297e91 --- /dev/null +++ b/INSTALL.md @@ -0,0 +1,102 @@ +# Installation + +- [Docker](#docker) +- [Local Installation](#local-installation) + - [Hard dependencies](#hard-dependencies) + - [Open3D](#open3d) + - [Embree](#embree) + - [puma](#puma) +- [Testing Installation](#testing-installation) +- [Troubleshooting](#troubleshooting) + +## Docker + +The easiest way to try out our code without pulling all the depdendencies is +by running our docker container. For that you need to install `docker` and +`docker-compose`. + +```sh +docker-compose build apps +``` + +This will build a docker container with all the pre-packaged dependencies. The +`apps/` directory is shared between host and container, so there are 2 specific +folder [data](./apps/data) and [results](./apps/results) in there where you can +put your data and inspect the results (from both, host and container). + +Additionally an env variable called `$DATASETS` is also shared with the docker +container in case you have your data already somewhere else and you don't want +to duplicate it on the [data](./apps/data) directory. + +## Local Installation + +If your some reason you plan to develop on top of this code, then the best +idea is to build the dependencies from source and work "locally". All the +commands from the example are still valid, the only difference is that you +don't need to use any `docker-compose` command at all. Just run the plain +python scripts. + +### Hard dependencies + +- Open3D, our fork with [Generalized ICP support][gicp] +- [Intel Embree][embree] >= v2.17.7 # but not 3.x +- [`pyembree`][pyembree] library +- `pybind11` python API + +The rest dependencies should be solved by the build system. + +### Open3D + +For the moment you will need to manually install our own [fork of Open3D][gicp] + +```sh +bash 3rdparty/Open3D.sh +``` + +That should give you the power of `Open3D` with the Generalized ICP algorithm +implementation. If you can't install `Open3D` please reffer to the [original +documentation](http://www.open3d.org/docs/release/compilation.html) + +### Embree + +Just run the installation script provied by the author of `trimesh`: + +```sh +bash 3rdparty/embree.sh +``` + +That should give you the pyembree library + +### puma + +There are different ways of intalling this package, probably the most easy is to +clone this source and just run + +```shell +pip install pybind11 +pip install --user . +``` + +## Testing Installation + +Just `cd` out of the source dir and run a interactive python shell: + +```python +import puma +``` + +If that doesn't fail then you can assume you've installed puma succesfully. + +## Troubleshooting + +If for any reason you wish to re-build the docker container just run: + +```sh +docker-compose up --build builder +``` + + + +[pyembree]: https://github.com/scopatz/pyembree +[embree]: https://www.embree.org/ +[gicp]: https://github.com/nachovizzo/Open3D/tree/nacho/generalized_icp diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..3f9f11b --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Ignacio Vizzo, Cyrill Stachniss, Photogrammetry & Robotics Bonn, University of Bonn + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..5c42ad0 --- /dev/null +++ b/README.md @@ -0,0 +1,184 @@ +# Poisson Surface Reconstruction for LiDAR Odometry and Mapping + +| Surfels | TSDF | Our Approach | +| :--------------------: | :--------------------: | :--------------------: | +| ![suma](pics/suma.png) | ![tsdf](pics/tsdf.png) | ![puma](pics/puma.png) | + +Table: Qualitative comparison between the different mapping techniques for +sequence `00` of the KITTI odometry benchmark. + +This repository implements the algorithms described in our paper [Poisson +Surface Reconstruction for LiDAR Odometry and Mapping][paper]. + +This is a LiDAR Odometry and Mapping pipeline that uses the [Poisson Surface +Reconstruction][psr] algorithm to build the map as a triangular mesh. + +We propose a novel `frame-to-mesh` registration algorithm where we compute +the poses of the vehicle by estimating the 6 degrees of freedom of the LiDAR. +To achieve this, we project each scan to the triangular mesh by computing the +ray-to-triangle intersections between each point in the input scan and the +map mesh. We accelerate this ray-casting technique using a [python +wrapper][pyembree] of the [Intel® Embree][embree] library. + +The main application of our research is intended for autonomous driving vehicles. + +## Table of Contentes + +- [Running the code](#running-the-code) + - [1) Datasets](#1-datasets) + - [2) Converting from `.bin` to `.ply`](#2-converting-from-bin-to-ply) + - [3) Runing the `puma` pipeline](#3-runing-the-puma-pipeline) + - [4) Inspecting the results](#4-inspecting-the-results) +- [Where to go next](#where-to-go-next) +- [Citation](#citation) + +## Running the code + +If you don't want to use docker and install `puma` locally you might want to +visit the [Installation Instructions](./INSTALL.md) + +**NOTE:** All the commands assume you are working on this shared worksspace, +therefore, first `cd apps/` before running anything. + +### 1) Datasets + +First you need to indicate where are all your datasets, for doing so just: + +```sh +export DATASETS= +``` + +This env variable is shared between the docker container and your host +system(in a read-only fashion). + +So far we've only tested our approach on the [KITTI Odometry +benchmark][kitti] dataset and on the [Mai city dataset][mai-city]. Both +datasets are using a 64-beam velodyne like LiDAR. + +### 2) Converting from `.bin` to `.ply` + +All our apps use the [PLY][ply] which is also binary but has much +better support than just raw binary files. Therefore, you will need to +convert all your data before running any of the [apps](./apps/) available in +this repo. + +```sh +docker-compose run --rm apps bash -c '\ + ./data_conversion/bin2ply.py \ + --dataset $DATASETS/kitti-odometry/dataset/ \ # <-- path to where you have the kitti dataset + --out_dir ./data/kitti-odometry/ply/ \ + --sequence 07 + ' +``` + +### 3) Runing the `puma` pipeline + +Go grab a coffe/mate, this will take some time... + +```sh +docker-compose run --rm apps bash -c '\ + ./pipelines/slam/puma_pipeline.py \ + --dataset ./data/kitti-odometry/ply \ + --sequence 07 \ + --n_scans 40 + ' +``` + +### 4) Inspecting the results + +The `pipelines/slam/puma_pipeline.py` will generate 3 files on your host sytem: + +```sh +results +├── kitti-odometry_07_depth_10_cropped_p2l_raycasting.ply # <- Generated Model +├── kitti-odometry_07_depth_10_cropped_p2l_raycasting.txt # <- Estimated poses +└── kitti-odometry_07_depth_10_cropped_p2l_raycasting.yml # <- Configuration +``` + +You can open the `.ply` with [Open3D][open3d], [Meshlab][meshlab], +[CloudCompare][cloudcompare], or the tool you like the most. + +## Where to go next + +If you already installed [puma](./INSTALL.md) then it's time to look for the +[standalone apps](./apps/). Theese apps are basically executable command line +interfaces (CLI) to interact with the core puma code: + +```sh +├── data_conversion +│ ├── bin2bag.py +│ ├── kitti2ply.py +│ ├── ply2bin.py +│ └── ros2ply.py +├── pipelines +│ ├── mapping +│ │ ├── build_gt_cloud.py +│ │ ├── build_gt_mesh_incremental.py +│ │ └── build_gt_mesh.py +│ ├── odometry +│ │ ├── icp_frame_2_frame.py +│ │ ├── icp_frame_2_map.py +│ │ └── icp_frame_2_mesh.py +│ └── slam +│ └── puma_pipeline.py +└── run_poisson.py +``` + +All the apps should have an usable command line interface, so if you need +help you only need to pass the `--help` flag to the app you wish to use. For +example let's see the help message of the data conversion app `bin2ply.py` +used above: + +```text +Usage: bin2ply.py [OPTIONS] + + Utility script to convert from the binary form found in the KITTI odometry + dataset to .ply files. The intensity value for each measurement is encoded + in the color channel of the output PointCloud. + + If a given sequence it's specified then it assumes you have a clean copy + of the KITTI odometry benchmark, because it uses pykitti. If you only have + a folder with just .bin files the script will most likely fail. + + If no sequence is specified then it blindly reads all the *.bin file in + the specified dataset directory + +Options: + -d, --dataset PATH Location of the KITTI dataset [default: + /home/ivizzo/data/kitti-odometry/dataset/] + + -o, --out_dir PATH Where to store the results [default: + /home/ivizzo/data/kitti-odometry/ply/] + + -s, --sequence TEXT Sequence number + --use_intensity Encode the intensity value in the color channel + --help Show this message and exit. +``` + +## Citation + +If you use this library for any academic work, please cite the original [paper][paper]. + +```bibtex +@inproceedings{vizzo2021icra, +author = {I. Vizzo and X. Chen and N. Chebrolu and J. Behley and C. Stachniss}, +title = {{Poisson Surface Reconstruction for LiDAR Odometry and Mapping}}, +booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)}, +codeurl = {https://github.com/PRBonn/puma/}, +year = 2021, +} +``` + + + +[kitti]: http://www.cvlibs.net/datasets/kitti/eval_odometry.php +[mai-city]: https://www.ipb.uni-bonn.de/data/mai-city-dataset/ +[ply]: https://en.wikipedia.org/wiki/PLY_(file_format) +[paper]: http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vizzo2021icra.pdf +[psr]: http://sites.fas.harvard.edu/~cs277/papers/poissonrecon.pdf +[pyembree]: https://github.com/scopatz/pyembree +[embree]: https://www.embree.org/ +[gicp]: https://github.com/nachovizzo/Open3D/tree/nacho/generalized_icp +[open3d]: http://www.open3d.org/ +[meshlab]: https://www.meshlab.net/ +[cloudcompare]: https://www.danielgm.net/cc/ diff --git a/apps/config/gicp.yml b/apps/config/gicp.yml new file mode 100644 index 0000000..597f89e --- /dev/null +++ b/apps/config/gicp.yml @@ -0,0 +1,18 @@ +# ICP params +threshold: 1.0 +method: gicp +warm_start: true +acc_frame_count: 30 +loss: L2 + +# Processing +downsample: true +normals: false # false, range_image, kdtree +voxel_size: 0.25 +max_nn: 20 +W: 1024 +H: 64 + +# misc +out_dir: results/ +debug: false diff --git a/apps/config/p2l_icp.yml b/apps/config/p2l_icp.yml new file mode 100644 index 0000000..c84473d --- /dev/null +++ b/apps/config/p2l_icp.yml @@ -0,0 +1,18 @@ +# ICP params +threshold: 1.0 +method: p2l +warm_start: true +acc_frame_count: 30 +loss: huber + +# Processing +downsample: true +normals: range_image # none, kdtree +voxel_size: 0.25 +max_nn: 20 +W: 1024 +H: 64 + +# misc +out_dir: results/ +debug: false diff --git a/apps/config/p2p_icp.yml b/apps/config/p2p_icp.yml new file mode 100644 index 0000000..93688d8 --- /dev/null +++ b/apps/config/p2p_icp.yml @@ -0,0 +1,18 @@ +# ICP params +threshold: 1.0 +method: p2p +warm_start: true +acc_frame_count: 30 +loss: L2 + +# Processing +downsample: true +normals: false # false, range_image, kdtree +voxel_size: 0.25 +max_nn: 20 +W: 1024 +H: 64 + +# misc +out_dir: results/ +debug: false diff --git a/apps/config/puma.yml b/apps/config/puma.yml new file mode 100644 index 0000000..646cbbe --- /dev/null +++ b/apps/config/puma.yml @@ -0,0 +1,25 @@ +# ICP params +threshold: 1.0 +method: p2l +warm_start: true +acc_frame_count: 30 +strategy: raycasting # sample +loss: huber + +# Mapping +depth: 10 +min_density: 0.1 +n_threads: -1 +acc_map_count: 30 + +# Pre Processing +downsample: false +normals: range_image +voxel_size: 0.25 +max_nn: 20 +W: 1024 +H: 64 + +# misc +out_dir: results/ +debug: false diff --git a/apps/data_conversion/bin2bag.py b/apps/data_conversion/bin2bag.py new file mode 100755 index 0000000..4f78ce5 --- /dev/null +++ b/apps/data_conversion/bin2bag.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 + +import os +from pathlib import Path + +import click +import numpy as np +import pykitti +import rosbag +import rospy +import sensor_msgs.point_cloud2 as pcl2 +import tf +from geometry_msgs.msg import Transform, TransformStamped +from sensor_msgs.msg import PointField +from std_msgs.msg import Header +from tf2_msgs.msg import TFMessage +from tqdm import tqdm + + +def write_gt_pose_to_bag(bag, gt_pose, timestamp): + tf_msg = TFMessage() + tf_stamped = TransformStamped() + tf_stamped.header.stamp = rospy.Time.from_sec(timestamp) + tf_stamped.header.frame_id = "world" + tf_stamped.child_frame_id = "velodyne" + + t = gt_pose[0:3, 3] + q = tf.transformations.quaternion_from_matrix(gt_pose) + transform = Transform() + + transform.translation.x = t[0] + transform.translation.y = t[1] + transform.translation.z = t[2] + + transform.rotation.x = q[0] + transform.rotation.y = q[1] + transform.rotation.z = q[2] + transform.rotation.w = q[3] + + tf_stamped.transform = transform + tf_msg.transforms.append(tf_stamped) + + bag.write("/tf", tf_msg, tf_msg.transforms[0].header.stamp) + + +def write_scan_to_bag(bag, scan, timestamp): + # create header + header = Header() + header.frame_id = "velodyne" + header.stamp = rospy.Time.from_sec(timestamp) + + # fill pcl msg + fields = [ + PointField("x", 0, PointField.FLOAT32, 1), + PointField("y", 4, PointField.FLOAT32, 1), + PointField("z", 8, PointField.FLOAT32, 1), + PointField("intensity", 12, PointField.FLOAT32, 1), + ] + pcl_msg = pcl2.create_cloud(header, fields, scan) + + bag.write("/velodyne_points", pcl_msg, t=pcl_msg.header.stamp) + + +def get_kitti_odometry(dataset, sequence): + data = pykitti.odometry(dataset, sequence) + n_scans = len(data) + T_cam_velo = data.calib.T_cam0_velo + T_velo_cam = np.linalg.inv(T_cam_velo) + + gt_poses = T_velo_cam @ data.poses @ T_cam_velo + timestamps = [dt.total_seconds() for dt in data.timestamps] + # Hack: Add 1 nano sec in the first timestamp, ros python has bug in the + # implementation, this doesn't happen in the C++ one + timestamps[0] += 1e-9 + + return n_scans, gt_poses, timestamps, data.velo + + +@click.command() +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + help="Location of the KITTI-like dataset", +) +@click.option( + "--out_dir", + "-o", + default="./results/", + type=click.Path(exists=True), + help="name of the rosbag file", +) +@click.option( + "--sequence", "-s", type=str, default="00", help="Sequence number" +) +def main(dataset, out_dir, sequence): + filename = Path(dataset).parent.name + "_seq_" + sequence + ".bag" + rosbag_filename = os.path.join(out_dir, filename) + + n_scans, gt_poses, timestamps, scans = get_kitti_odometry(dataset, sequence) + + bag = rosbag.Bag(rosbag_filename, "w") + try: + + iterable_data = tqdm(zip(timestamps, scans, gt_poses), total=n_scans) + for dt, scan, gt_pose in iterable_data: + write_scan_to_bag(bag, scan, dt) + write_gt_pose_to_bag(bag, gt_pose, dt) + + finally: + print(bag) + bag.close() + + +if __name__ == "__main__": + main() diff --git a/apps/data_conversion/bin2ply.py b/apps/data_conversion/bin2ply.py new file mode 100755 index 0000000..47634f7 --- /dev/null +++ b/apps/data_conversion/bin2ply.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python3 + +import glob +import os + +import click +import numpy as np +import open3d as o3d +import pykitti +from tqdm import tqdm + + +def load_velo_scan(file): + """Load and parse a velodyne binary file.""" + scan = np.fromfile(file, dtype=np.float32) + return scan.reshape((-1, 4)) + + +def yield_velo_scans(velo_files): + """Generator to parse velodyne binary files into arrays.""" + for file in velo_files: + yield load_velo_scan(file) + + +def vel2ply(points, use_intensity=False): + pcd = o3d.geometry.PointCloud() + points_xyz = points[:, :3] + points_i = points[:, -1].reshape(-1, 1) + pcd.points = o3d.utility.Vector3dVector(points_xyz) + if use_intensity: + pcd.colors = o3d.utility.Vector3dVector( + np.full_like(points_xyz, points_i) + ) + return pcd + + +@click.command() +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["DATASETS"] + "/kitti-odometry/dataset/", + show_default=True, + help="Location of the KITTI dataset", +) +@click.option( + "--out_dir", + "-o", + type=click.Path(exists=False), + default="data/kitti-odometry/ply/", + show_default=True, + help="Where to store the results", +) +@click.option( + "--sequence", + "-s", + type=str, + default=None, + required=False, + help="Sequence number", +) +@click.option( + "--use_intensity", + is_flag=True, + default=False, + help="Encode the intensity value in the color channel", +) +def main(dataset, out_dir, sequence, use_intensity): + """Utility script to convert from the binary form found in the KITTI + odometry dataset to .ply files. The intensity value for each measurement is + encoded in the color channel of the output PointCloud. + + If a given sequence it's specified then it assumes you have a clean copy of + the KITTI odometry benchmark, because it uses pykitti. If you only have a + folder with just .bin files the script will most likely fail. + + If no sequence is specified then it blindly reads all the *.bin file in the + specified dataset directory + """ + print( + "Converting .bin scans into .ply fromat from:{orig} to:{dest}".format( + orig=dataset, dest=out_dir + ) + ) + + os.makedirs(out_dir, exist_ok=True) + if sequence: + # Means KITTI like format + base_path = os.path.join(out_dir, "sequences", sequence, "velodyne", "") + os.makedirs(base_path, exist_ok=True) + data = pykitti.odometry(dataset, sequence) + velo_files = data.velo_files + scans = data.velo + else: + # Read all the *.bin scans from the dataset folder + base_path = os.path.join(out_dir, "") + os.makedirs(base_path, exist_ok=True) + velo_files = sorted(glob.glob(os.path.join(dataset, "*.bin"))) + scans = yield_velo_scans(velo_files) + + for points, scan_name in tqdm( + zip(scans, velo_files), total=len(velo_files) + ): + pcd = vel2ply(points, use_intensity) + stem = os.path.splitext(scan_name.split("/")[-1])[0] + filename = base_path + stem + ".ply" + o3d.io.write_point_cloud(filename, pcd) + + +if __name__ == "__main__": + main() diff --git a/apps/data_conversion/ply2bin.py b/apps/data_conversion/ply2bin.py new file mode 100755 index 0000000..147613f --- /dev/null +++ b/apps/data_conversion/ply2bin.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +import glob +import os +import struct + +import click +import numpy as np +import open3d as o3d +from tqdm import tqdm + + +def save_bin_file(file, points): + with open(file, "wb") as bin_file: + for point in points.tolist(): + bin_file.write(struct.pack("f" * len(point), *point)) + + +def get_bin_filaname(dataset_out, scan_name): + return os.path.join( + dataset_out, os.path.split(os.path.splitext(scan_name)[0])[1] + ".bin" + ) + + +@click.command() +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["HOME"] + "/data/kitti-odometry/ply/", + help="Location of the KITTI-like dataset", +) +@click.option( + "--out_dir", + type=click.Path(exists=True), + default="./results/", + help="Location of the output directory", +) +@click.option( + "--sequence", "-s", type=str, default="00", help="Sequence number" +) +def main(dataset, out_dir, sequence): + dataset_in = os.path.join(dataset, "sequences", sequence, "velodyne", "") + dataset_out = os.path.join(out_dir, "sequences", sequence, "velodyne", "") + print("Converting ply files from " + dataset_in) + + scan_names = sorted(glob.glob(dataset_in + "*.ply")) + for scan_name in tqdm(scan_names): + scan = o3d.io.read_point_cloud(scan_name) + points = np.asarray(scan.points) + intensity = np.ones(points.shape[0]) / 2.0 + # convert from (size,) to (size,1) + intensity = intensity.reshape(-1, 1) + xyzi_points = np.hstack((points, intensity)) + out_file = get_bin_filaname(dataset_out, scan_name) + save_bin_file(out_file, xyzi_points) + + +if __name__ == "__main__": + main() diff --git a/apps/data_conversion/ros2ply.py b/apps/data_conversion/ros2ply.py new file mode 100755 index 0000000..eee3db4 --- /dev/null +++ b/apps/data_conversion/ros2ply.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 + + +import os + +import click +import numpy as np +import open3d as o3d +import rosbag +import sensor_msgs.point_cloud2 as pc2 +from tqdm import tqdm + + +def convert_bag_to_ply(bag, topic, out_dir): + field_names = ["x", "y", "z", "intensity"] + max_intensity = 1000 # HACK: expose parameter + bag_msgs = bag.read_messages(topics=[topic]) + msg_count = bag.get_message_count(topic) + + for idx, (_, msg, _) in tqdm(enumerate(bag_msgs), total=msg_count): + points_xyz = [] + points_i = [] + points = pc2.read_points_list( + cloud=msg, field_names=field_names, skip_nans=True + ) + + for point_xyzi in points: + x, y, z, i = point_xyzi + points_xyz.append([x, y, z]) + points_i.append([i, i, i]) + + points_xyz = np.asarray(points_xyz) + points_i = (np.asarray(points_i) / max_intensity).clip(0.0, 1.0) + assert points_xyz.shape == points_i.shape, "Dimension Missmatch" + + filename = out_dir + str(idx).zfill(6) + ".ply" + o3d_cloud = o3d.geometry.PointCloud() + o3d_cloud.points = o3d.utility.Vector3dVector(points_xyz) + o3d_cloud.colors = o3d.utility.Vector3dVector(points_i) + o3d.io.write_point_cloud(filename, o3d_cloud) + + +@click.command() +@click.argument("bagfile", type=click.Path(exists=True)) +@click.option( + "--topic", + type=str, + default="/os1_cloud_node/points", + help="Name of the topic to subscribe and convert the clouds", +) +@click.option( + "--out_dir", + type=click.Path(exists=False), + default="results/", + help="Where to store the results", +) +def main(bagfile, topic, out_dir): + """Convert a .bag file into multiple .ply files, one for each scan, + including intensity information encoded on the color channel of the + PointCloud. We use Open3D to convert the data from ROS to + o3d.geometry.PointCloud. + + To run it, start this script, then launch the rosbag file. Make sure you + pass the right option to this script to select the point cloud topic. + + \b + $ ./ros2ply.py --topic /points output.bag + """ + print("Saving results to", out_dir) + os.makedirs(out_dir, exist_ok=True) + bag = rosbag.Bag(bagfile) + assert topic in bag.get_type_and_topic_info().topics, ( + 'topic: "' + topic + '" is not recorded in: ' + bagfile + ) + + convert_bag_to_ply(bag, topic, out_dir) + bag.close() + + +if __name__ == "__main__": + main() diff --git a/apps/pipelines/mapping/build_gt_cloud.py b/apps/pipelines/mapping/build_gt_cloud.py new file mode 100755 index 0000000..237b954 --- /dev/null +++ b/apps/pipelines/mapping/build_gt_cloud.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +import glob +import os +from pathlib import Path + +import click +import numpy as np +import open3d as o3d +import pykitti +from tqdm import tqdm + +from puma.preprocessing import preprocess_cloud + + +@click.command() +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["HOME"] + "/data/kitti-odometry/ply/", + help="Location of the KITTI-like dataset", +) +@click.option( + "--out_dir", + type=click.Path(exists=True), + default="./results/", + help="Location of the output directory", +) +@click.option( + "--sequence", "-s", type=str, default="00", help="Sequence number" +) +@click.option( + "--n_scans", "-n", type=int, default=-1, help="Number of scans to integrate" +) +@click.option( + "--start", "-sp", type=int, default=0, help="Start from this scan on" +) +@click.option( + "--visualize", + is_flag=True, + default=False, + help="Visualize all the preprocess pipeline", +) +@click.option( + "--normals", + type=click.Choice(["range_image", "kdtree"], case_sensitive=False), + default=None, + help="Which normal computation to use", +) +def main(dataset, out_dir, sequence, n_scans, start, visualize, normals): + """From a set of input PointClouds create one unique aggreated map cloud.""" + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Info) + dataset = os.path.join(dataset, "") + + dataset_name = Path(dataset).parent.name + + data = pykitti.odometry(dataset, sequence) + T_cam_velo = data.calib.T_cam0_velo + T_velo_cam = np.linalg.inv(T_cam_velo) + + gt_poses = T_velo_cam @ data.poses @ T_cam_velo + + scans = os.path.join(dataset, "sequences", sequence, "velodyne", "") + scan_names = sorted(glob.glob(scans + "*.ply")) + + # Use the whole sequence if -1 is specified + n_scans = len(scan_names) if n_scans == -1 else n_scans + print("Processing " + str(n_scans) + " in " + scans) + cloud_map = o3d.geometry.PointCloud() + for idx in tqdm(range(start, start + n_scans)): + source = preprocess_cloud( + o3d.io.read_point_cloud(scan_names[idx]), + normals=normals, + ) + + source.transform(gt_poses[idx]) + cloud_map += source + + map_name = ( + "gt_" + + str(n_scans) + + "_scans_" + + sequence + + "_" + + dataset_name + + ".ply" + ) + out_file = os.path.join(out_dir, map_name) + print("saving map to " + out_file) + o3d.io.write_point_cloud(out_file, cloud_map) + + if visualize: + o3d.visualization.draw_geometries([cloud_map]) + + +if __name__ == "__main__": + main() diff --git a/apps/pipelines/mapping/build_gt_mesh.py b/apps/pipelines/mapping/build_gt_mesh.py new file mode 100755 index 0000000..1f67f52 --- /dev/null +++ b/apps/pipelines/mapping/build_gt_mesh.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 + +import glob +import os +from pathlib import Path + +import click +import numpy as np +import open3d as o3d +import pykitti +from tqdm import tqdm + +from puma.mesh import run_poisson +from puma.preprocessing import preprocess_cloud + + +@click.command() +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["HOME"] + "/data/kitti-odometry/ply/", + help="Location of the KITTI-like dataset", +) +@click.option( + "--out_dir", + type=click.Path(exists=True), + default="./results/", + help="Location of the output directory", +) +@click.option( + "--sequence", "-s", type=str, default="00", help="Sequence number" +) +@click.option( + "--n_scans", "-n", type=int, default=-1, help="Number of scans to integrate" +) +@click.option( + "--start", "-sp", type=int, default=0, help="Start from this scan on" +) +@click.option( + "--depth", + type=int, + default=10, + help="Depth of the tree that will be used for reconstruction", +) +@click.option( + "--visualize", + is_flag=True, + default=False, + help="Visualize all the preprocess pipeline", +) +@click.option( + "--normals", + type=click.Choice(["range_image", "kdtree"], case_sensitive=False), + default="range_image", + help="Which normal computation to use", +) +@click.option( + "--min_density", + "-md", + type=float, + default=0.1, + help="The minimun vertex density of the final mesh", +) +def main( + dataset, + out_dir, + sequence, + n_scans, + start, + depth, + visualize, + normals, + min_density, +): + """This script can be used to create GT mesh-model maps using GT poses. It + assumes you have the data in the kitti-like format and all the scans where + already pre-converted to '.ply', for example: + + \b + kitti/ply + ├── poses + │   └── 00.txt + └── sequences + └── 00 + ├── calib.txt + ├── poses.txt + ├── times.txt + └── velodyne + ├── 000000.ply + ├── 000001.ply + └── ... + + You would also need the .txt files calib.txt and times.txt, altough not + really used in this script, for convince, we reuse pykitti functionality + that requires these files to be created. + + How to run it and check a quick example: + + \b + $ ./build_gt_mesh.py -d $DATASETS/kitti/ply/ -s 00 -n 200 --depth 10 + """ + dataset = os.path.join(dataset, "") + dataset_name = Path(dataset).parent.name + + data = pykitti.odometry(dataset, sequence) + T_cam_velo = data.calib.T_cam0_velo + T_velo_cam = np.linalg.inv(T_cam_velo) + gt_poses = T_velo_cam @ data.poses @ T_cam_velo + + scans = os.path.join(dataset, "sequences", sequence, "velodyne", "") + scan_names = sorted(glob.glob(scans + "*.ply")) + + # Use the whole sequence if -1 is specified + n_scans = len(scan_names) if n_scans == -1 else n_scans + print("Processing " + str(n_scans) + " scans in " + scans) + cloud_map = o3d.geometry.PointCloud() + for idx in tqdm(range(start, start + n_scans)): + source = preprocess_cloud( + o3d.io.read_point_cloud(scan_names[idx]), + normals=normals, + ) + source.transform(gt_poses[idx]) + cloud_map += source + + print("Running Poisson Surface Reconstruction, go grab a coffee") + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) + + mesh, _ = run_poisson(cloud_map, depth, -1, min_density) + map_name = ( + "gt_" + + dataset_name + + "_seq_" + + sequence + + "_scans_" + + str(n_scans) + + "_depth_" + + str(depth) + + "_" + + normals + ) + mesh_file = os.path.join(out_dir, map_name + "_mesh.ply") + print("Saving mesh to " + mesh_file) + o3d.io.write_triangle_mesh(mesh_file, mesh) + if visualize: + o3d.visualization.draw_geometries([mesh]) + + +if __name__ == "__main__": + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Info) + +main() diff --git a/apps/pipelines/mapping/build_gt_mesh_incremental.py b/apps/pipelines/mapping/build_gt_mesh_incremental.py new file mode 100755 index 0000000..56f1986 --- /dev/null +++ b/apps/pipelines/mapping/build_gt_mesh_incremental.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 + +import csv +import glob +import os +from collections import deque +from pathlib import Path + +import click +import numpy as np +import open3d as o3d + +from puma.mesh import create_mesh_from_map, get_mesh_size_mb +from puma.preprocessing import preprocess +from puma.utils import ( + get_progress_bar, + load_config_from_yaml, + load_kitti_gt_poses, + print_progress, +) + + +@click.command() +@click.option("--config", "-c", default="config/puma.yml") +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["HOME"] + "/data/kitti-odometry/ply/", + help="Location of the KITTI-like dataset", +) +@click.option( + "--n_scans", "-n", type=int, default=-1, help="Number of scans to integrate" +) +@click.option("--sequence", "-s", type=str, default="00") +def main(config, dataset, n_scans, sequence): + """Similar to the slam/puma_pipeline.py but uses GT poses intead of + estimate the ego-motion of the vehivle. Build an incremental map using + the same technique used in the original puma pipeline.""" + config = load_config_from_yaml(config) + dataset = os.path.join(dataset, "") + os.makedirs(config.out_dir, exist_ok=True) + + map_name = Path(dataset).parent.name + map_name += "_" + sequence + map_name += "_depth_" + str(config.depth) + map_name += "_cropped" if config.min_density else "" + gt_poses = load_kitti_gt_poses(dataset, sequence) + map_name += "_gt" + + poses_file = map_name + ".txt" + poses_file = os.path.join(config.out_dir, poses_file) + print("Results will be saved to", poses_file) + + scans = os.path.join(dataset, "sequences", sequence, "velodyne", "") + scan_names = sorted(glob.glob(scans + "*.ply")) + + # Create data containers to store the map + mesh = o3d.geometry.TriangleMesh() + + # Create a circular buffer, the same way we do in the C++ implementation + local_map = deque(maxlen=config.acc_frame_count) + global_mesh = o3d.geometry.TriangleMesh() + + poses = [np.eye(4, 4, dtype=np.float64)] + + # Open csv file to output map size + csv_file = os.path.join(config.out_dir, map_name + ".csv") + print("Saving Map Size to", csv_file) + csv_file = open(csv_file, "w") + csv_writer = csv.writer(csv_file, delimiter=" ", lineterminator="\n") + csv_writer.writerow(["frames", "vertices", "triangles", "size"]) + + # Start the mapping pipeline + scan_count = 0 + map_count = 0 + pbar = get_progress_bar(1, n_scans) + for idx in pbar: + str_size = print_progress(pbar, idx, n_scans) + scan = preprocess(o3d.io.read_point_cloud(scan_names[idx]), config) + poses.append(gt_poses[idx]) + scan.transform(poses[-1]) + local_map.append(scan) + + scan_count += 1 + if scan_count >= config.acc_frame_count or idx == n_scans - 1: + scan_count = 0 + msg = "[scan #{}] Running PSR over local_map".format(idx) + pbar.set_description(msg.rjust(str_size)) + mesh, _ = create_mesh_from_map( + local_map, config.depth, config.n_threads, config.min_density + ) + + map_count += 1 + if map_count >= config.acc_map_count or idx == n_scans - 1: + map_count = 0 + global_mesh += mesh + global_mesh = global_mesh.remove_duplicated_triangles() + global_mesh = global_mesh.remove_duplicated_vertices() + vertices = len(global_mesh.vertices) + triangles = len(global_mesh.triangles) + size_mb = get_mesh_size_mb(global_mesh) + csv_writer.writerow([idx, vertices, triangles, size_mb]) + + # Save map to file + mesh_map_file = os.path.join(config.out_dir, map_name + ".ply") + print("Saving Map to", mesh_map_file) + o3d.io.write_triangle_mesh(mesh_map_file, global_mesh) + csv_file.close() + + +if __name__ == "__main__": + main() diff --git a/apps/pipelines/odometry/icp_frame_2_frame.py b/apps/pipelines/odometry/icp_frame_2_frame.py new file mode 100755 index 0000000..8491d5a --- /dev/null +++ b/apps/pipelines/odometry/icp_frame_2_frame.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 + +import glob +import os +from pathlib import Path + +import click +import numpy as np +import open3d as o3d +from tqdm import trange + +from puma.preprocessing import preprocess +from puma.registration import run_icp +from puma.utils import ( + load_config_from_yaml, + save_config_yaml, + save_poses, + vel2cam, +) + + +def get_scan_names(dataset): + dataset = os.path.join(dataset, "") + scan_names = sorted(glob.glob(dataset + "*.ply")) + return scan_names + + +@click.command() +@click.option("--config", "-c", default="config/p2p_icp.yml") +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + help="Dataset location, format:ply", +) +@click.option( + "--n_scans", "-n", type=int, default=-1, help="Number of scans to integrate" +) +@click.option( + "--start_scan", "-st", type=int, default=0, help="Start from scan" +) +@click.option("--sequence", "-s", type=str, help="Sequence number") +def main(config, dataset, n_scans, start_scan, sequence): + config = load_config_from_yaml(config) + if config.debug: + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) + dname = Path(dataset).parent.name + approach = Path(dataset).name + dataset = os.path.join(dataset, "sequences", sequence, "velodyne", "") + map_name = dname + "_" + sequence + "_" + map_name += config.method + "_frame2frame_icp" + + # Save config + config_file = map_name + ".yml" + config_file = os.path.join(config.out_dir, config_file) + save_config_yaml(config_file, dict(config)) + + if not config.out_dir: + config.out_dir = os.path.join("results", approach) + os.makedirs(config.out_dir, exist_ok=True) + + poses_file = map_name + ".txt" + poses_file = os.path.join(config.out_dir, poses_file) + print("Results will be saved to", poses_file) + + poses = [np.eye(4, 4, dtype=np.float64)] + deltas = [np.eye(4, 4, dtype=np.float64)] + + # Get dataset + scan_names = get_scan_names(dataset) + + # Use the whole sequence if -1 is specified + if n_scans == -1: + n_scans = len(scan_names) + + print("Processing " + str(n_scans) + " in " + dataset) + target = preprocess(o3d.io.read_point_cloud(scan_names[0]), config) + for idx in trange(start_scan + 1, start_scan + n_scans): + source = preprocess(o3d.io.read_point_cloud(scan_names[idx]), config) + + # Run ICP + initial_guess = deltas[-1] if config.warm_start else np.eye(4) + pose = run_icp(source, target, initial_guess, config) + deltas.append(pose) + poses.append(poses[-1] @ pose) + + # This scan will be the target in the next iteration + target = source + + # Save results to txt file + print("Saving estimated poses [camera frame] to " + poses_file) + save_poses(poses_file, vel2cam(poses)) + + +if __name__ == "__main__": + main() diff --git a/apps/pipelines/odometry/icp_frame_2_map.py b/apps/pipelines/odometry/icp_frame_2_map.py new file mode 100755 index 0000000..afbf598 --- /dev/null +++ b/apps/pipelines/odometry/icp_frame_2_map.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 + +import glob +import os +from collections import deque +from pathlib import Path + +import click +import numpy as np +import open3d as o3d + +from puma.preprocessing import preprocess +from puma.registration import run_icp +from puma.utils import ( + buffer_to_pointcloud, + get_progress_bar, + load_config_from_yaml, + print_progress, + save_config_yaml, + save_poses, + vel2cam, +) + + +def get_map_name(config, dataset, sequence): + dataset_name = Path(dataset).parent.name + map_name = dataset_name + "_" + sequence + "_frame2map_icp_" + map_name += config.normals + "_" if config.normals else "" + map_name += config.method + return map_name + + +@click.command() +@click.option("--config", "-c", default="config/p2p_icp.yml") +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["HOME"] + "/data/kitti-odometry/ply/", + help="Location of the KITTI-like dataset", +) +@click.option( + "--sequence", "-s", type=str, default="00", help="Sequence number" +) +@click.option( + "--n_scans", "-n", type=int, default=-1, help="Number of scans to integrate" +) +@click.option( + "--start", "-sp", type=int, default=0, help="Start from this scan on" +) +def main(config, dataset, sequence, n_scans, start): + config = load_config_from_yaml(config) + if config.debug: + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) + dataset = os.path.join(dataset, "") + scans = os.path.join(dataset, "sequences", sequence, "velodyne", "") + scan_names = sorted(glob.glob(scans + "*.ply")) + + os.makedirs(config.out_dir, exist_ok=True) + map_name = get_map_name(config, dataset, sequence) + poses_file = map_name + ".txt" + poses_file = os.path.join(config.out_dir, poses_file) + + # Save config + config_file = map_name + ".yml" + config_file = os.path.join(config.out_dir, config_file) + save_config_yaml(config_file, dict(config)) + + # Use the whole sequence if -1 is specified + if n_scans == -1: + n_scans = len(scan_names) - start + + first_scan_id = start + last_scan_id = start + n_scans + + print("Processing " + str(n_scans) + " scans in " + scans) + poses = [np.eye(4, 4, dtype=np.float64)] + deltas = [np.eye(4, 4, dtype=np.float64)] + + first_scan = preprocess(o3d.io.read_point_cloud(scan_names[0]), config) + local_map = deque(maxlen=config.acc_frame_count) + local_map.append(first_scan) + + # Start the mapping pipeline + pbar = get_progress_bar(first_scan_id + 1, last_scan_id) + for idx in pbar: + print_progress(pbar, idx, n_scans) + scan = preprocess(o3d.io.read_point_cloud(scan_names[idx]), config) + # The target model is the local map that is used on the PSR pipeline but + # running standard ICP. + target = buffer_to_pointcloud(local_map) + target.transform(np.linalg.inv(poses[-1])) + + # run frame-to-local-map ICP + initial_guess = deltas[-1] if config.warm_start else np.eye(4) + pose = run_icp(scan, target, initial_guess, config) + deltas.append(pose) + poses.append(poses[-1] @ pose) + + # This will never exceed config.acc_frame_count scans + scan.transform(poses[-1]) + local_map.append(scan) + + # Save results to txt file + print("Saving estimated poses [camera frame] to " + poses_file) + save_poses(poses_file, vel2cam(poses)) + + +if __name__ == "__main__": + main() diff --git a/apps/pipelines/odometry/icp_frame_2_mesh.py b/apps/pipelines/odometry/icp_frame_2_mesh.py new file mode 100755 index 0000000..b4f19f6 --- /dev/null +++ b/apps/pipelines/odometry/icp_frame_2_mesh.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python3 + +import copy +import glob +import os +from collections import deque +from pathlib import Path + +import click +import numpy as np +import open3d as o3d + +from puma.mesh import create_mesh_from_map +from puma.preprocessing import preprocess +from puma.registration import register_scan_to_mesh, run_icp +from puma.utils import ( + get_progress_bar, + load_config_from_yaml, + print_progress, + save_config_yaml, + save_poses, + vel2cam, +) + + +@click.command() +@click.option("--config", "-c", default="config/puma.yml") +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["HOME"] + "/data/kitti-odometry/ply/", + help="Location of the KITTI-like dataset", +) +@click.option( + "--n_scans", "-n", type=int, default=-1, help="Number of scans to integrate" +) +@click.option("--sequence", "-s", type=str, default="00") +def main(config, dataset, n_scans, sequence): + config = load_config_from_yaml(config) + if config.debug: + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) + dataset = os.path.join(dataset, "") + os.makedirs(config.out_dir, exist_ok=True) + + map_name = Path(dataset).parent.name + map_name += "_" + sequence + map_name += "_depth_" + str(config.depth) + map_name += "_cropped" if config.min_density else "" + map_name += "_" + config.method + map_name += "_" + config.strategy + + # Save config + config_file = map_name + ".yml" + config_file = os.path.join(config.out_dir, config_file) + save_config_yaml(config_file, dict(config)) + + poses_file = map_name + ".txt" + poses_file = os.path.join(config.out_dir, poses_file) + print("Results will be saved to", poses_file) + + scans = os.path.join(dataset, "sequences", sequence, "velodyne", "") + scan_names = sorted(glob.glob(scans + "*.ply")) + + # Use the whole sequence if -1 is specified + n_scans = len(scan_names) if n_scans == -1 else n_scans + + # Create data containers to store the map + mesh = o3d.geometry.TriangleMesh() + + # Create a circular buffer, the same way we do in the C++ implementation + local_map = deque(maxlen=config.acc_frame_count) + + poses = [np.eye(4, 4, dtype=np.float64)] + deltas = [np.eye(4, 4, dtype=np.float64)] + last_scan = preprocess(o3d.io.read_point_cloud(scan_names[0]), config) + + # Start the Odometry and Mapping pipeline + scan_count = 0 + pbar = get_progress_bar(1, n_scans) + for idx in pbar: + str_size = print_progress(pbar, idx, n_scans) + scan = preprocess(o3d.io.read_point_cloud(scan_names[idx]), config) + initial_guess = deltas[-1].copy() if config.warm_start else np.eye(4) + if mesh.has_vertices(): + msg = "[scan #{}] Registering scan to mesh model".format(idx) + pbar.set_description(msg.rjust(str_size)) + mesh.transform(np.linalg.inv(poses[-1])) + pose = register_scan_to_mesh( + scan, mesh, initial_guess, deltas, last_scan, config + ) + else: + pose = run_icp(scan, last_scan, initial_guess, config) + deltas.append(pose) + poses.append(poses[-1] @ pose) + last_scan = copy.deepcopy(scan) + scan.transform(poses[-1]) + local_map.append(scan) + + scan_count += 1 + if scan_count >= config.acc_frame_count or idx == n_scans - 1: + save_poses(poses_file, vel2cam(poses)) + msg = "[scan #{}] Running PSR over local_map".format(idx) + pbar.set_description(msg.rjust(str_size)) + mesh, _ = create_mesh_from_map( + local_map, config.depth, config.n_threads, config.min_density + ) + + +if __name__ == "__main__": + main() diff --git a/apps/pipelines/slam/puma_pipeline.py b/apps/pipelines/slam/puma_pipeline.py new file mode 100755 index 0000000..08baa42 --- /dev/null +++ b/apps/pipelines/slam/puma_pipeline.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +import copy +import glob +import os +from collections import deque +from pathlib import Path + +import click +import numpy as np +import open3d as o3d + +from puma.mesh import create_mesh_from_map +from puma.preprocessing import preprocess +from puma.registration import register_scan_to_mesh, run_icp +from puma.utils import ( + get_progress_bar, + load_config_from_yaml, + print_progress, + save_config_yaml, + save_poses, + vel2cam, +) + + +@click.command() +@click.option( + "--config", + "-c", + type=click.Path(exists=True), + default="config/puma.yml", + help="Path to the config file", +) +@click.option( + "--dataset", + "-d", + type=click.Path(exists=True), + default=os.environ["HOME"] + "/data/kitti-odometry/ply/", + help="Location of the KITTI-like dataset", +) +@click.option( + "--n_scans", + "-n", + type=int, + default=-1, + required=False, + help="Number of scans to integrate", +) +@click.option( + "--sequence", + "-s", + type=str, + default=None, + required=False, + help="Sequence number", +) +@click.option( + "--odometry_only", + is_flag=True, + default=False, + help="Run odometry only pipeline", +) +def main(config, dataset, n_scans, sequence, odometry_only): + """This script to run the full puma pipeline as described in the paper. It + assumes you have the data in the kitti-like format and all the scans where + already pre-converted to '.ply', for example: + + \b + kitti/ply + ├── poses + │   └── 00.txt + └── sequences + └── 00 + ├── calib.txt + ├── poses.txt + ├── times.txt + └── velodyne + ├── 000000.ply + ├── 000001.ply + └── ... + + How to run it and check a quick example: + + \b + $ ./slam/puma_pipeline.py -d ./data/ -s 00 -n 40 + """ + config = load_config_from_yaml(config) + if config.debug: + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) + dataset = os.path.join(dataset, "") + os.makedirs(config.out_dir, exist_ok=True) + + map_name = Path(dataset).parent.name + map_name += "_" + sequence + map_name += "_depth_" + str(config.depth) + map_name += "_cropped" if config.min_density else "" + map_name += "_" + config.method + map_name += "_" + config.strategy + + # Save config + config_file = map_name + ".yml" + config_file = os.path.join(config.out_dir, config_file) + save_config_yaml(config_file, dict(config)) + + poses_file = map_name + ".txt" + poses_file = os.path.join(config.out_dir, poses_file) + print("Results will be saved to", poses_file) + + scans = os.path.join(dataset, "sequences", sequence, "velodyne", "") + scan_names = sorted(glob.glob(scans + "*.ply")) + + # Use the whole sequence if -1 is specified + n_scans = len(scan_names) if n_scans == -1 else n_scans + + # Create data containers to store the map + mesh = o3d.geometry.TriangleMesh() + + # Create a circular buffer, the same way we do in the C++ implementation + local_map = deque(maxlen=config.acc_frame_count) + + # Mapping facilities + global_mesh = o3d.geometry.TriangleMesh() + mapping_enabled = not odometry_only + + poses = [np.eye(4, 4, dtype=np.float64)] + deltas = [np.eye(4, 4, dtype=np.float64)] + last_scan = preprocess(o3d.io.read_point_cloud(scan_names[0]), config) + + # Start the Odometry and Mapping pipeline + scan_count = 0 + map_count = 0 + pbar = get_progress_bar(1, n_scans) + for idx in pbar: + str_size = print_progress(pbar, idx, n_scans) + scan = preprocess(o3d.io.read_point_cloud(scan_names[idx]), config) + initial_guess = deltas[-1].copy() if config.warm_start else np.eye(4) + if mesh.has_vertices(): + msg = "[scan #{}] Registering scan to mesh model".format(idx) + pbar.set_description(msg.rjust(str_size)) + mesh.transform(np.linalg.inv(poses[-1])) + pose = register_scan_to_mesh( + scan, mesh, initial_guess, deltas, last_scan, config + ) + else: + pose = run_icp(scan, last_scan, initial_guess, config) + deltas.append(pose) + poses.append(poses[-1] @ pose) + last_scan = copy.deepcopy(scan) + scan.transform(poses[-1]) + local_map.append(scan) + + scan_count += 1 + if scan_count >= config.acc_frame_count or idx == n_scans - 1: + save_poses(poses_file, vel2cam(poses)) + msg = "[scan #{}] Running PSR over local_map".format(idx) + pbar.set_description(msg.rjust(str_size)) + mesh, _ = create_mesh_from_map( + local_map, config.depth, config.n_threads, config.min_density + ) + + if mapping_enabled: + map_count += 1 + if map_count >= config.acc_map_count or idx == n_scans - 1: + map_count = 0 + global_mesh += mesh + global_mesh = global_mesh.remove_duplicated_triangles() + global_mesh = global_mesh.remove_duplicated_vertices() + + if mapping_enabled: + # Save map to file + mesh_map_file = os.path.join(config.out_dir, map_name + ".ply") + print("Saving Map to", mesh_map_file) + o3d.io.write_triangle_mesh(mesh_map_file, global_mesh) + + +if __name__ == "__main__": + main() diff --git a/apps/run_poisson.py b/apps/run_poisson.py new file mode 100755 index 0000000..f69abce --- /dev/null +++ b/apps/run_poisson.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 + +import os + +import click +import open3d as o3d + +from puma.mesh import plot_density_mesh, run_poisson + + +@click.command() +@click.argument("filename", type=click.Path(exists=True)) +@click.option( + "--depth", + "-d", + type=int, + default=8, + help="Depth of the tree that will be used for reconstruction", +) +@click.option( + "--min_density", + "-md", + type=float, + default=0.1, + help="The minimun vertex density of the final mesh", +) +@click.option( + "--visualize", + is_flag=True, + default=False, + help="Visualize the reconstructed surface", +) +@click.option( + "--plot_densities", + is_flag=True, + default=False, + help="Plot the densities of the reconstructed mesh", +) +def main(filename, depth, min_density, visualize, plot_densities): + """Util script to run the Poisson Surface Reconstruction over a + determined input PointCloud, usually a pre-build PointCloud map, for + example like the ones you could build with + apps/pipelines/mapping/build_gt_cloud.py.""" + print("Reading " + filename + "...") + pcd = o3d.io.read_point_cloud(filename, print_progress=True) + + print("Running Poisson Surface Reconstruction, go grab a coffe") + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) + mesh, densities = run_poisson(pcd, depth, -1, min_density) + + if plot_densities: + plot_density_mesh(mesh, densities, min_density) + + print(mesh) + mesh.compute_vertex_normals() + mesh_name = os.path.splitext(filename)[0] + "_mesh.ply" + print("Saving mesh to " + mesh_name) + o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Error) + o3d.io.write_triangle_mesh(mesh_name, mesh) + if visualize: + o3d.visualization.draw_geometries([mesh]) + + +if __name__ == "__main__": + main() diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..9a2589a --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,19 @@ +version: "3.4" +services: + builder: + image: ignaciovizzo/puma:latest + build: + context: . + dockerfile: docker/builder/Dockerfile + target: builder + apps: + build: + context: . + dockerfile: docker/apps/Dockerfile + args: + USER_ID: ${USER_ID:-0} + GROUP_ID: ${GROUP_ID:-0} + working_dir: /apps + volumes: + - ./apps/:/apps:rw + - ${DATASETS}:/data:ro diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000..0c99918 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,56 @@ +# Docker containers + +After trying multiple scenarios of the [docker multistage +builds](https://docs.docker.com/develop/develop-images/multistage-build) and +tired because nothing worked. I've decided to split the containers into 2 +separate ones. One will be mainly used to build the package(`builder`) and the +other one is the one you will most likely run locally, and thus you need to +build it by your own(`apps`). + +## `builder` container + +To actually build this container, in case you need to modify the base image or +because you don't have access to the registry provided, just run this command +from the `puma` root directory: + +```sh +docker-compose up --build builder +``` + +That will build the base image and tag it with the same tag that the `apps` +container is using it. Ideally you should never do this unless you need to +change something from the dependencies. + +If you plan to update the registry, you can do so by just running: + +```sh +docker-compose push builder +``` + +## `apps` container + +This container needs to be built locally since it will use information of your +current user. This looks tricky and complicated but it's the easiest way to +share data between the container and the host-machine without needing to run +unnecessary long commands. + +Your user information is encoded in the [.env](../.env) file. If your user id +and group id are not `1000` (defaults in debian-based sytem) then you need to +change this file. + +Using this container is trivial, plus it also comes packaged with nice tools +such as zsh, vim, etc: + +```sh +docker-compose run --rm apps +``` + +If the base image(`builder`) is not there it will pull it from the registry. If +you can't access it, as said above, then you need to build it by hand. + +Be aware that this will only build the `apps` container the very first time. +**Only** if you need to re-build it then you need to run: + +```sh +docker-compose up --build apss +``` diff --git a/docker/apps/Dockerfile b/docker/apps/Dockerfile new file mode 100644 index 0000000..20d5c86 --- /dev/null +++ b/docker/apps/Dockerfile @@ -0,0 +1,27 @@ +FROM ignaciovizzo/puma + +ENV DATASETS=/data/ +RUN mkdir -p $DATASETS + +# Install some utils in case you run the container interactively +RUN apt-get update && apt-get install --no-install-recommends -y \ + curl \ + git \ + silversearcher-ag \ + tree \ + vim \ + zsh \ + && rm -rf /var/lib/apt/lists/* + +# Add user to share files between container and host system +ARG USER_ID +ARG GROUP_ID + +RUN addgroup --gid $GROUP_ID user +RUN adduser --disabled-password --gecos '' --uid $USER_ID --gid $GROUP_ID user +USER user + +# Install zsh, I hate bash +RUN sh -c "$(wget -O- https://github.com/deluan/zsh-in-docker/releases/download/v1.1.1/zsh-in-docker.sh)" -- \ + -t gentoo +CMD ["zsh"] diff --git a/docker/builder/Dockerfile b/docker/builder/Dockerfile new file mode 100644 index 0000000..41cf2fd --- /dev/null +++ b/docker/builder/Dockerfile @@ -0,0 +1,51 @@ +# ==== Builder ================================================================# +FROM ubuntu:20.04 AS builder +LABEL maintainer="Ignacio Vizzo " + +# setup environment +ENV TERM xterm +ENV DEBIAN_FRONTEND=noninteractive + +# install essentials +RUN apt-get update && apt-get install --no-install-recommends -y \ + build-essential \ + git \ + python3 \ + python3-dev \ + python3-pip \ + wget \ + && rm -rf /var/lib/apt/lists/* + +# Add python3 and pip aliases +RUN update-alternatives \ + --install /usr/bin/python python $(which python3) 20 \ + --slave /usr/bin/pip pip $(which pip3) + +# Install 3rdparty dependencies, split dependencies for separate docker overlays +COPY 3rdparty/embree.sh 3rdparty/ +RUN 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+int wrap(int x, int dim) { + int value = x; + if (value >= dim) { + value = value - dim; + } + if (value < 0) { + value = value + dim; + } + return value; +} + +py::array_t gen_normal_map(const py::array_t &range, + const py::array_t &vertex, + const int W, + const int H) { + pybind11::buffer_info buf1 = range.request(); + pybind11::buffer_info buf2 = vertex.request(); + + /* allocate the buffer */ + py::array_t normal_map_buffer = py::array_t(buf2.size); + + pybind11::buffer_info buf3 = normal_map_buffer.request(); + + auto *depth_buffer = (float *)buf1.ptr; + auto *vertex_map = (float *)buf2.ptr; + auto *normal_map = (float *)buf3.ptr; + +#pragma omp parallel for + for (int i = 0; i < H * W * 3; ++i) { + normal_map[i] = 0; + } + +#pragma omp parallel + { +#pragma omp for nowait collapse(2) + for (int x = 0; x < W; ++x) { + for (int y = 0; y < H - 1; ++y) { + const float &px = vertex_map[y * W * 3 + x * 3]; + const float &py = vertex_map[y * W * 3 + x * 3 + 1]; + const float &pz = vertex_map[y * W * 3 + x * 3 + 2]; + const float &depth = depth_buffer[y * W + x]; + + if (depth > 0) { + int wrap_x = wrap(x + 1, W); + const float &ux = vertex_map[y * W * 3 + wrap_x * 3]; + const float &uy = vertex_map[y * W * 3 + wrap_x * 3 + 1]; + const float &uz = vertex_map[y * W * 3 + wrap_x * 3 + 2]; + const float &u_depth = depth_buffer[y * W + wrap_x]; + if (u_depth <= 0) { + continue; + } + + const float &vx = vertex_map[(y + 1) * W * 3 + x * 3]; + const float &vy = vertex_map[(y + 1) * W * 3 + x * 3 + 1]; + const float &vz = vertex_map[(y + 1) * W * 3 + x * 3 + 2]; + const float &v_depth = depth_buffer[(y + 1) * W + x]; + if (v_depth <= 0) { + continue; + } + + float l = 0.0; + float u_normx = ux - px; + float u_normy = uy - py; + float u_normz = uz - pz; + l = sqrt(u_normx * u_normx + u_normy * u_normy + + u_normz * u_normz); + u_normx /= l; + u_normy /= l; + u_normz /= l; + + float v_normx = vx - px; + float v_normy = vy - py; + float v_normz = vz - pz; + l = sqrt(v_normx * v_normx + v_normy * v_normy + + v_normz * v_normz); + v_normx /= l; + v_normy /= l; + v_normz /= l; + + const float crossx = u_normz * v_normy - u_normy * v_normz; + const float crossy = u_normx * v_normz - u_normz * v_normx; + const float crossz = u_normy * v_normx - u_normx * v_normy; + float norm = sqrt(crossx * crossx + crossy * crossy + + crossz * crossz); + +#pragma omp critical + if (norm > 0) { + const float normalx = crossx / norm; + const float normaly = crossy / norm; + const float normalz = crossz / norm; + normal_map[y * W * 3 + x * 3] = normalx; + normal_map[y * W * 3 + x * 3 + 1] = normaly; + normal_map[y * W * 3 + x * 3 + 2] = normalz; + } + } + } + } + } + + // reshape array to match input shape + normal_map_buffer.resize({H, W, 3}); + return normal_map_buffer; +} + +PYBIND11_MODULE(normal_map, m) { + m.doc() = "generate normal map using pybind11"; + m.def("gen_normal_map", &gen_normal_map, "generate normal map"); +} diff --git a/puma/mesh/__init__.py b/puma/mesh/__init__.py new file mode 100644 index 0000000..10a50f8 --- /dev/null +++ b/puma/mesh/__init__.py @@ -0,0 +1,2 @@ +from .poisson import * +from .size import * diff --git a/puma/mesh/poisson.py b/puma/mesh/poisson.py new file mode 100644 index 0000000..bb461df --- /dev/null +++ b/puma/mesh/poisson.py @@ -0,0 +1,46 @@ +import matplotlib.pyplot as plt +import numpy as np +import open3d as o3d + +from ..utils import buffer_to_pointcloud + + +def plot_density_mesh(mesh, densities, min_density): + """Inspired in the original open3d implementation.""" + densities = np.asarray(densities) + density_colors = plt.get_cmap("jet")( + (densities - densities.min()) / (densities.max() - densities.min()) + ) + density_colors = density_colors[:, :3] + density_mesh = o3d.geometry.TriangleMesh() + density_mesh.vertices = mesh.vertices + density_mesh.triangles = mesh.triangles + density_mesh.triangle_normals = mesh.triangle_normals + density_mesh.vertex_colors = o3d.utility.Vector3dVector(density_colors) + + print("Visualize densities") + o3d.visualization.draw_geometries([density_mesh]) + + print("Remove low density vertices") + vertices_to_remove = densities < np.quantile(densities, min_density) + density_mesh.remove_vertices_by_mask(vertices_to_remove) + o3d.visualization.draw_geometries([density_mesh]) + + +def run_poisson(pcd, depth, n_threads, min_density=None): + mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson( + pcd, depth=depth, n_threads=n_threads + ) + + # Post-process the mesh + if min_density: + vertices_to_remove = densities < np.quantile(densities, min_density) + mesh.remove_vertices_by_mask(vertices_to_remove) + mesh.compute_vertex_normals() + + return mesh, densities + + +def create_mesh_from_map(buffer, depth, n_threads, min_density=None): + pcd = buffer_to_pointcloud(buffer) + return run_poisson(pcd, depth, n_threads, min_density) diff --git a/puma/mesh/size.py b/puma/mesh/size.py new file mode 100644 index 0000000..a2fb23b --- /dev/null +++ b/puma/mesh/size.py @@ -0,0 +1,20 @@ +from math import floor + +import numpy as np + + +def get_mesh_size(mesh): + size = -1 + triangles = np.array(mesh.triangles) + vertices = np.array(mesh.vertices) + size += triangles.size * triangles.itemsize + size += vertices.size * vertices.itemsize + return size + + +def get_mesh_size_kb(mesh): + return floor(get_mesh_size(mesh) / 1024.0) + + +def get_mesh_size_mb(mesh): + return floor(get_mesh_size_kb(mesh) / 1024.0) diff --git a/puma/preprocessing/__init__.py b/puma/preprocessing/__init__.py new file mode 100644 index 0000000..f05db4a --- /dev/null +++ b/puma/preprocessing/__init__.py @@ -0,0 +1 @@ +from .preprocess_cloud import * diff --git a/puma/preprocessing/preprocess_cloud.py b/puma/preprocessing/preprocess_cloud.py new file mode 100755 index 0000000..4156b8f --- /dev/null +++ b/puma/preprocessing/preprocess_cloud.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python3 +from copy import deepcopy + +import open3d as o3d + +from .range_image_normal import compute_normals as ri_normal + + +def preprocess_cloud( + pcd, + voxel_size=0.25, + max_nn=20, + normals=None, + downsample=False, + W=1024, + H=64, +): + if downsample: + cloud = pcd.voxel_down_sample(voxel_size) + else: + cloud = deepcopy(pcd) + + if normals: + if normals == "range_image": + ri_normal(cloud, W, H) + else: + params = o3d.geometry.KDTreeSearchParamKNN(max_nn) + cloud.estimate_normals(params) + cloud.orient_normals_towards_camera_location() + + return cloud + + +def preprocess(pcd, config): + return preprocess_cloud( + pcd, + config.voxel_size, + config.max_nn, + config.normals, + config.downsample, + config.W, + config.H, + ) diff --git a/puma/preprocessing/range_image_normal.py b/puma/preprocessing/range_image_normal.py new file mode 100755 index 0000000..f9f1de5 --- /dev/null +++ b/puma/preprocessing/range_image_normal.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python3 +import click +import open3d as o3d + +from ..cpp.normal_map import gen_normal_map +from ..projections.range_image import project_to_range_image + + +def compute_normals(cloud, w, h): + range_image, vertex_map = project_to_range_image(cloud, w, h) + normal_map = gen_normal_map(range_image, vertex_map, w, h) + cloud.points = o3d.utility.Vector3dVector(vertex_map.reshape(h * w, 3)) + cloud.normals = o3d.utility.Vector3dVector(normal_map.reshape(h * w, 3)) + cloud.remove_non_finite_points() + return cloud + + +@click.command() +@click.argument("file", type=click.Path(exists=True)) +@click.option("-w", default=1024, type=int) +@click.option("-h", default=64, type=int) +def main(file, w, h): + cloud = compute_normals(o3d.io.read_point_cloud(file), w, h) + o3d.visualization.draw_geometries([cloud]) + + +if __name__ == "__main__": + main() diff --git a/puma/projections/__init__.py b/puma/projections/__init__.py new file mode 100644 index 0000000..541040c --- /dev/null +++ b/puma/projections/__init__.py @@ -0,0 +1,2 @@ +from .range_image import * +from .scan2mesh import * diff --git a/puma/projections/range_image.py b/puma/projections/range_image.py new file mode 100755 index 0000000..ed0f695 --- /dev/null +++ b/puma/projections/range_image.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 + +import click +import matplotlib.pyplot as plt +import numpy as np +import open3d as o3d + + +def project_to_range_image(cloud, W=1024, H=64, max_range=50): + """Project a pointcloud into a spherical projection image.projection. + Function takes no arguments because it can be also called externally if the + value of the constructor was not set (in case you change your mind about + wanting the projection) + """ + current_vertex = np.asarray(cloud.points) + + # laser parameters, TODO: make this configurable + fov_up = 3.0 / 180.0 * np.pi # field of view up in radians + fov_down = -25.0 / 180.0 * np.pi # field of view down in radians + fov = abs(fov_down) + abs(fov_up) # get field of view total in radians + + # get depth of all points + depth = np.linalg.norm(current_vertex, axis=1) + current_vertex = current_vertex[(depth > 0) & (depth < max_range)] + depth = depth[(depth > 0) & (depth < max_range)] + + # get scan components + scan_x = current_vertex[:, 0] + scan_y = current_vertex[:, 1] + scan_z = current_vertex[:, 2] + + # get angles of all points + yaw = -np.arctan2(scan_y, scan_x) + pitch = np.arcsin(scan_z / depth) + + # get projections in image coords + proj_x = 0.5 * (yaw / np.pi + 1.0) # in [0.0, 1.0] + proj_y = 1.0 - (pitch + abs(fov_down)) / fov # in [0.0, 1.0] + + # scale to image size using angular resolution + proj_x *= W # in [0.0, W] + proj_y *= H # in [0.0, H] + + # round and clamp for use as index + proj_x = np.floor(proj_x) + proj_x = np.minimum(W - 1, proj_x) + proj_x = np.maximum(0, proj_x).astype(np.int32) + + proj_y = np.floor(proj_y) + proj_y = np.minimum(H - 1, proj_y) + proj_y = np.maximum(0, proj_y).astype(np.int32) + + # order in decreasing depth + order = np.argsort(depth)[::-1] + depth = depth[order] + proj_y = proj_y[order] + proj_x = proj_x[order] + + scan_x = scan_x[order] + scan_y = scan_y[order] + scan_z = scan_z[order] + + proj_range = np.full((H, W), np.nan, dtype=np.float32) + proj_vertex = np.full((H, W, 3), np.nan, dtype=np.float32) + + proj_range[proj_y, proj_x] = depth + proj_vertex[proj_y, proj_x] = np.array([scan_x, scan_y, scan_z]).T + + return proj_range, proj_vertex + + +@click.command() +@click.argument("file", type=click.Path(exists=True)) +@click.option("-w", default=1024, type=int) +@click.option("-h", default=64, type=int) +def main(file, w, h): + cloud = o3d.io.read_point_cloud(file) + print( + "Projecting {cloud} to range image w={w}, h={h}".format( + cloud=file, w=w, h=h + ) + ) + range_image, _ = project_to_range_image(cloud, w, h) + dpi = 96 + fig = plt.figure(frameon=False, figsize=(w / dpi, h / dpi), dpi=dpi) + ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0]) + ax.set_axis_off() + fig.add_axes(ax) + + # Then draw your image on it : + ax.imshow(range_image, aspect="auto", cmap="jet_r") + plt.show() + fig.savefig("range_image.png", dpi=dpi) + + +if __name__ == "__main__": + main() diff --git a/puma/projections/scan2mesh.py b/puma/projections/scan2mesh.py new file mode 100644 index 0000000..1ea3575 --- /dev/null +++ b/puma/projections/scan2mesh.py @@ -0,0 +1,69 @@ +import copy + +import numpy as np +import open3d as o3d +import trimesh + +if not trimesh.ray.has_embree: + raise "PyEmbree engine not installed, this experiment will never end" + + +def outlier_rejection( + source_points, source_normals, target_points, target_normals, max_dist=2.0 +): + # Prune long rays, max_distance threshold + ray_distances = np.linalg.norm(target_points - source_points, axis=1) + + # Get distance inliers + source_points = source_points[ray_distances < max_dist] + target_points = target_points[ray_distances < max_dist] + source_normals = source_normals[ray_distances < max_dist] + target_normals = target_normals[ray_distances < max_dist] + + return source_points, source_normals, target_points, target_normals + + +def project_scan_to_mesh(tmesh, source, max_dist=2.0): + """Project a PointCloud to the given mesh using ray to triangle + intersections.""" + + # Create the rays we will shoot + source_points = np.asarray(source.points) + source_normals = np.asarray(source.normals) + ray_directions = copy.deepcopy(source_points) + ray_origins = np.zeros_like(ray_directions) + + # run the mesh- ray query + target_points, index_ray, index_tri = tmesh.ray.intersects_location( + ray_origins=ray_origins, + ray_directions=ray_directions, + multiple_hits=False, + ) + + # Manually pick all the points and normals that did hit any of the triangles + source_points = source_points[index_ray] + source_normals = source_normals[index_ray] + target_normals = np.asarray(tmesh.face_normals[index_tri]) + + ( + source_points, + source_normals, + target_points, + target_normals, + ) = outlier_rejection( + source_points, source_normals, target_points, target_normals, max_dist + ) + + # Create the new source and target PointClouds + source_cloud = o3d.geometry.PointCloud() + source_points = o3d.utility.Vector3dVector(np.asarray(source_points)) + source_normals = o3d.utility.Vector3dVector(np.asarray(source_normals)) + source_cloud.points = source_points + source_cloud.normals = source_normals + + target_cloud = o3d.geometry.PointCloud() + target_points = o3d.utility.Vector3dVector(np.asarray(target_points)) + target_normals = o3d.utility.Vector3dVector(np.asarray(target_normals)) + target_cloud.points = target_points + target_cloud.normals = target_normals + return source_cloud, target_cloud diff --git a/puma/registration/__init__.py b/puma/registration/__init__.py new file mode 100644 index 0000000..e7f0e5b --- /dev/null +++ b/puma/registration/__init__.py @@ -0,0 +1,5 @@ +from .method_selector import * +from .o3d_aliases import * +from .run_icp import * +from .scan2mesh import * +from .scan2mesh_icp import * diff --git a/puma/registration/method_selector.py b/puma/registration/method_selector.py new file mode 100644 index 0000000..3f6ff58 --- /dev/null +++ b/puma/registration/method_selector.py @@ -0,0 +1,14 @@ +import open3d as o3d + +from .o3d_aliases import GICP, PointToPlane, PointToPoint + + +def get_te_method(str_method): + if str_method == "gicp": + return GICP() + if str_method == "p2p": + return PointToPoint() + if str_method == "p2l": + loss = o3d.pipelines.registration.HuberLoss(0.5) + return PointToPlane(loss) + return None diff --git a/puma/registration/o3d_aliases.py b/puma/registration/o3d_aliases.py new file mode 100644 index 0000000..4fed513 --- /dev/null +++ b/puma/registration/o3d_aliases.py @@ -0,0 +1,10 @@ +import open3d as o3d + +PointToPoint = o3d.pipelines.registration.TransformationEstimationPointToPoint +PointToPlane = o3d.pipelines.registration.TransformationEstimationPointToPlane +try: + GICP = o3d.pipelines.registration.TransformationEstimationForGeneralizedICP +except AttributeError as error: + raise SystemExit( + "Open3D with GeneralizedICP support not installed, check INSTALL.md" + ) diff --git a/puma/registration/run_icp.py b/puma/registration/run_icp.py new file mode 100644 index 0000000..bed63b5 --- /dev/null +++ b/puma/registration/run_icp.py @@ -0,0 +1,15 @@ +import open3d as o3d + +from .method_selector import get_te_method + + +def run_icp(src, tgt, trans_init, config): + te = get_te_method(config.method) + if config.method == "gicp": + return o3d.pipelines.registration.registration_generalized_icp( + src, tgt, config.threshold, trans_init, te + ).transformation + + return o3d.pipelines.registration.registration_icp( + src, tgt, config.threshold, trans_init, te + ).transformation diff --git a/puma/registration/scan2mesh.py b/puma/registration/scan2mesh.py new file mode 100644 index 0000000..3360ba4 --- /dev/null +++ b/puma/registration/scan2mesh.py @@ -0,0 +1,31 @@ +import numpy as np +import open3d as o3d + +from .run_icp import run_icp +from .scan2mesh_icp import scan2mesh_icp + + +def lost_track(delta, max_t_err=0.4): + """Return True if the estimation from the mesh is to big.""" + t_err = np.linalg.norm(delta[:3, 3]) + return t_err > max_t_err + + +def register_scan_to_mesh( + source, mesh, initial_guess, deltas, last_scan, config +): + te = config.method + th = config.threshold + if config.strategy == "sample": + tgt = o3d.geometry.PointCloud() + tgt.points = o3d.utility.Vector3dVector(mesh.vertices) + tgt.normals = o3d.utility.Vector3dVector(mesh.vertex_normals) + pose = run_icp(source, tgt, initial_guess, config) + else: + success, pose = scan2mesh_icp(mesh, source, initial_guess, th, te) + if not success: + return run_icp(source, last_scan, initial_guess, config) + + if lost_track(np.linalg.inv(deltas[-1]) @ pose): + pose = run_icp(source, last_scan, initial_guess, config) + return pose diff --git a/puma/registration/scan2mesh_icp.py b/puma/registration/scan2mesh_icp.py new file mode 100644 index 0000000..84e35a1 --- /dev/null +++ b/puma/registration/scan2mesh_icp.py @@ -0,0 +1,76 @@ +import copy + +import numpy as np +import open3d as o3d +import trimesh + +from ..projections import project_scan_to_mesh +from .method_selector import get_te_method + + +def align_clouds(source, target, method): + """Align 2 PointCloud objects assuming 1-1 correspondences.""" + assert len(source.points) == len(target.points), "N of points must match!" + corr = np.zeros((len(source.points), 2)) + corr[:, 0] = np.arange(len(source.points)) + corr[:, 1] = np.arange(len(target.points)) + te = get_te_method(method) + return te.compute_transformation( + source, target, o3d.utility.Vector2iVector(corr) + ) + + +def scan2mesh_icp( + mesh, + pcd, + trans_init=np.eye(4, 4), + max_dist=0.1, + method="gicp", + max_iterations=30, + tolerance=0.00001, + debug=False, +): + source = copy.deepcopy(pcd) + prev_error = 0 + distances = 0 + transformation = trans_init + source.transform(trans_init) + + # Convert from Open3D mesh object to a trimesh one + tmesh = trimesh.Trimesh( + vertices=np.asarray(mesh.vertices), faces=np.asarray(mesh.triangles) + ) + for i in range(max_iterations): + # Project the input cloud to the mesh and obtain the projected cloud + source, target = project_scan_to_mesh(tmesh, source, max_dist) + if ( + not target.has_points() + or not target.has_normals() + or len(target.points) < 1000 + ): + return False, None + + # compute the transformation between clouds + T = align_clouds(source, target, method) + + # update the current source + source.transform(T) + transformation = T @ transformation + + # check error + distances = source.compute_point_cloud_distance(target) + mean_error = np.mean(distances) + if np.abs(prev_error - mean_error) < tolerance: + break + + if debug: + print("Iteration {} completed".format(i)) + print("Number of inliers :", len(source.points)) + print( + "mean_error = {err}, prev_error = {prev}".format( + err=mean_error, prev=prev_error + ) + ) + prev_error = mean_error + + return True, transformation diff --git a/puma/utils/__init__.py b/puma/utils/__init__.py new file mode 100644 index 0000000..6d532d5 --- /dev/null +++ b/puma/utils/__init__.py @@ -0,0 +1,7 @@ +from .calibration import * +from .circular_buffer import * +from .config import * +from .kitti_poses import * +from .progress_bar import * +from .save_geoms import * +from .timeit import * diff --git a/puma/utils/calibration.py b/puma/utils/calibration.py new file mode 100644 index 0000000..8f0c287 --- /dev/null +++ b/puma/utils/calibration.py @@ -0,0 +1,34 @@ +import numpy as np +import pykitti + + +T_cam_velo = np.array( + [ + [4.27680239e-04, -9.99967248e-01, -8.08449168e-03, -1.19845993e-02], + [-7.21062651e-03, 8.08119847e-03, -9.99941316e-01, -5.40398473e-02], + [9.99973865e-01, 4.85948581e-04, -7.20693369e-03, -2.92196865e-01], + [0.00000000e00, 0.00000000e00, 0.00000000e00, 1.00000000e00], + ] +) + +T_velo_cam = np.array( + [ + [4.27679428e-04, -7.21062673e-03, 9.99973959e-01, 2.91804720e-01], + [-9.99967209e-01, 8.08119861e-03, 4.85949420e-04, -1.14055066e-02], + [-8.08449174e-03, -9.99941381e-01, -7.20693461e-03, -5.6239412e-02], + [0.00000000e00, 0.00000000e00, 0.00000000e00, 1.00000000e00], + ] +) + + +def cam2vel(poses): + return T_velo_cam @ poses @ T_cam_velo + + +def vel2cam(poses): + return T_cam_velo @ poses @ T_velo_cam + + +def load_kitti_gt_poses(dataset, sequence): + data = pykitti.odometry(dataset, sequence) + return T_velo_cam @ data.poses @ T_cam_velo diff --git a/puma/utils/circular_buffer.py b/puma/utils/circular_buffer.py new file mode 100644 index 0000000..fc5e39a --- /dev/null +++ b/puma/utils/circular_buffer.py @@ -0,0 +1,11 @@ +import open3d as o3d + + +def buffer_to_pointcloud(buffer, compute_normals=False): + pcd = o3d.geometry.PointCloud() + for cloud in buffer: + pcd += cloud + if compute_normals: + pcd.estimate_normals() + + return pcd diff --git a/puma/utils/config.py b/puma/utils/config.py new file mode 100644 index 0000000..f353b95 --- /dev/null +++ b/puma/utils/config.py @@ -0,0 +1,28 @@ +import os + +import yaml +from easydict import EasyDict + + +def load_config_from_yaml(path): + """Returns an EasyDict from the given path to a config.yml file. + + The path to the config file can be specified with an absolute + path(e.g. ../../path_to_config/config.yml) or using a relative path + to the current git directory(e.g config/config.yml). This function + will try to load first from the absolute path and fallback to the + git repo. + """ + try: + config_file = open(path) + except FileNotFoundError: + raise FileNotFoundError("{} file doesn't exist".format(path)) + + # Returns any of the two possible config_file + return EasyDict(yaml.safe_load(config_file)) + + +def save_config_yaml(path, config): + """Stores the given config dictionary to the specified path.""" + with open(path, "w") as outfile: + yaml.dump(config, outfile, default_flow_style=False) diff --git a/puma/utils/kitti_poses.py b/puma/utils/kitti_poses.py new file mode 100644 index 0000000..b80a3d2 --- /dev/null +++ b/puma/utils/kitti_poses.py @@ -0,0 +1,28 @@ +import numpy as np + + +def save_poses(filename, poses): + rows = [] + for pose in poses: + rows.append(np.concatenate((pose[0], pose[1], pose[2]))) + + np.savetxt(filename, np.array(rows)) + + +def load_poses(pose_file): + """Load ground truth poses (T_w_cam0) from file.""" + + # Read and parse the poses + poses = [] + try: + with open(pose_file, "r") as f: + for line in f.readlines(): + T_w_cam0 = np.fromstring(line, dtype=float, sep=" ") + T_w_cam0 = T_w_cam0.reshape(3, 4) + T_w_cam0 = np.vstack((T_w_cam0, [0, 0, 0, 1])) + poses.append(T_w_cam0) + + except FileNotFoundError: + print("GT Poses file not found: " + pose_file) + + return poses diff --git a/puma/utils/progress_bar.py b/puma/utils/progress_bar.py new file mode 100644 index 0000000..a291078 --- /dev/null +++ b/puma/utils/progress_bar.py @@ -0,0 +1,17 @@ +from tqdm import trange + + +def print_progress(pbar, idx, n_scans): + msg = "[scan #{0}] Integrating scan #{0} of {1}".format(idx, n_scans) + pbar.set_description(msg) + return len(msg) + + +def get_progress_bar(first_scan, last_scan): + return trange( + first_scan, + last_scan, + unit=" scans", + dynamic_ncols=True, + bar_format="{l_bar}{bar}[{elapsed}<{remaining}, {rate_fmt}{postfix}]", + ) diff --git a/puma/utils/save_geoms.py b/puma/utils/save_geoms.py new file mode 100644 index 0000000..35a5f4d --- /dev/null +++ b/puma/utils/save_geoms.py @@ -0,0 +1,18 @@ +import os + +import open3d as o3d + + +def save_cloud(cloud, name, out_dir): + filename = os.path.join(out_dir, "gt_map_" + name + ".ply") + print("saving cloud_map to " + filename) + o3d.io.write_point_cloud(filename, cloud) + + +def save_mesh(mesh, name, out_dir): + mesh.compute_vertex_normals() + mesh.compute_triangle_normals() + + filename = os.path.join(out_dir, name + ".ply") + print("saving mesh_map to " + filename) + o3d.io.write_triangle_mesh(filename, mesh, print_progress=True) diff --git a/puma/utils/timeit.py b/puma/utils/timeit.py new file mode 100644 index 0000000..d29431e --- /dev/null +++ b/puma/utils/timeit.py @@ -0,0 +1,17 @@ +import time + + +def timeit(f): + def wrap(*args, **kargs): + time1 = time.time() + ret = f(*args, **kargs) + time2 = time.time() + print( + "{:s} function took {:.3f} ms".format( + f.__name__, (time2 - time1) * 1000.0 + ) + ) + + return ret + + return wrap diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..aefb1a5 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,16 @@ +[tool.black] +line-length = 80 + +[tool.isort] +multi_line_output = 3 +include_trailing_comma = true +force_grid_wrap = 0 +use_parentheses = true +ensure_newline_before_comments = true +line_length = 80 + +[tool.pylint.messages_control] +disable = "C0330, C0326, C0114, E1120, C0116, C0103, E1101, R0914, R0915, R0913" + +[tool.pylint.format] +max-line-length = "80" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..57e7e69 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,9 @@ +Click +GitPython +PyYAML +easydict +matplotlib +numpy +pykitti +tqdm +trimesh diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..8baa71c --- /dev/null +++ b/setup.cfg @@ -0,0 +1,33 @@ +[metadata] +name = puma +author =Ignacio Martin Vizzo (aka Nacho) and Cyrill Stachniss +author_email =ignaciovizzo@gmail.com +license_files = LICENSE.txt +description =Poisson Surface Reconstruction for LiDAR Odometry and Mapping +long_description = file:README.md +long_description_content_type = text/markdown +version = attr: puma.__version__ +url = https://github.com/PRBonn/puma +classifiers= + Programming Language :: Python :: 3 + License :: OSI Approved :: MIT License + Development Status :: 3 - Alpha + Intended Audience :: Developers + Topic :: Software Development :: Libraries +keywords = SLAM, LiDAR, Odometry, Localization, Mapping, + +[options] +packages = find: +install_requires = + Click + PyYAML + easydict + matplotlib + numpy + pykitti + tqdm + trimesh + +[flake8] +max-line-length = 80 +extend-ignore = E203 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..05bc575 --- /dev/null +++ b/setup.py @@ -0,0 +1,18 @@ +from glob import glob + +from pybind11.setup_helpers import Pybind11Extension +from setuptools import setup + +ext_modules = [ + Pybind11Extension( + "puma.cpp.normal_map", + sorted(glob("puma/cpp/*.cpp")), + extra_compile_args=["-fopenmp"], + extra_link_args=["-fopenmp"], + ), +] + + +setup( + ext_modules=ext_modules, +)

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