├── .gitattributes ├── .gitignore ├── Dockerfile ├── LICENCE ├── README.md ├── imgs └── 3d_interion_model.png ├── ipcp ├── cpp_modules │ ├── efficient_ransac │ ├── polyfit │ ├── polyfit_ransac │ └── src │ │ ├── efficient_ransac │ │ ├── CMakeLists.txt │ │ └── efficient_ransac.cpp │ │ ├── polyfit │ │ ├── CMakeLists.txt │ │ └── polyfit.cpp │ │ └── polyfit_ransac │ │ ├── CMakeLists.txt │ │ └── polyfit_ransac.cpp ├── datasets │ ├── apt_subsampled.ply │ └── output.city.json ├── modules │ ├── __init__.py │ ├── floor_split.py │ ├── mesh_stats.py │ ├── primitive_detection.py │ ├── room_detection.py │ └── room_reconstruct.py ├── notebooks │ └── Complete Pipeline.ipynb ├── preprocessors │ ├── __intit__.py │ ├── sor.py │ └── spatial_subsample.py ├── script.py └── src │ ├── __init__.py │ ├── interpolation.py │ ├── pipeline.py │ ├── region_growing.py │ └── utils │ ├── __init__.py │ ├── cityjson_utils.py │ ├── clip_utils.py │ ├── log_utils.py │ ├── math_utils.py │ ├── pcd_utils.py │ └── plot_utils.py └── requirements.txt /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 105 | __pypackages__/ 106 | 107 | # Celery stuff 108 | celerybeat-schedule 109 | celerybeat.pid 110 | 111 | # SageMath parsed files 112 | *.sage.py 113 | 114 | # Environments 115 | .env 116 | .venv 117 | env/ 118 | venv/ 119 | ENV/ 120 | env.bak/ 121 | venv.bak/ 122 | 123 | # Spyder project settings 124 | .spyderproject 125 | .spyproject 126 | 127 | # Rope project settings 128 | .ropeproject 129 | 130 | # mkdocs documentation 131 | /site 132 | 133 | # mypy 134 | .mypy_cache/ 135 | .dmypy.json 136 | dmypy.json 137 | 138 | # Pyre type checker 139 | .pyre/ 140 | 141 | # pytype static type analyzer 142 | .pytype/ 143 | 144 | # Cython debug symbols 145 | cython_debug/ 146 | 147 | # PyCharm 148 | # JetBrains specific template is maintainted in a separate JetBrains.gitignore that can 149 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 150 | # and can be added to the global gitignore or merged into this file. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # T3D PointCloud Processing 2 | 3 | This repository contains methods for the **automatic detection of rooms in interior PointClouds**. The methods can serve as inspiration, or can be applied as-is. 4 | 5 | ![3D-interior-model](./imgs/3d_interion_model.png) 6 | 7 | --- 8 | 9 | ## Project Goal 10 | 11 | The goal of this project is to automatically detect rooms in interior point clouds. One of the main challenges in working with interior 3D point cloud data is detecting walls and defining a room boundaries. 12 | 13 | This repository contains a five-staged pipeline that preprocesses, split into floors, detects rooms, reconstructs the surface, and computes volume and area 14 | 15 | For a quick dive into this repository take a look at our [Complete Pipeline](ipcp/notebooks/Complete%20Pipeline.ipynb). 16 | 17 | --- 18 | 19 | ## Folder Structure 20 | 21 | * [`ipcp`](./ipcp) 22 | * [`datasets`](./ipcp/datasets) _Example datasets of interior pointclouds_ 23 | * [`notebooks`](./ipcp/notebooks) _Jupyter notebook tutorials_ 24 | * [`src`](./ipcp/src) _Python source code_ 25 | * [`utils`](./ipcp/src/utils) _Utility functions_ 26 | * [`modules`](./ipcp/modules) _Pipeline modules_ 27 | * [`cpp_modules`](./ipcp/cpp_modules) _Pipeline C++ modules_ 28 | * [`preprocessors`](./ipcp/preprocessors) _Pre-processor modules_ 29 | 30 | --- 31 | 32 | ## Dataset 33 | 34 | The sample pointcloud provided is a subsampled version of the "Apartment, merged & resampled" from the [Redwood Indoor Lidar-RGBD Scan Dataset](http://redwood-data.org/indoor_lidar_rgbd/download.html). When using this dataset, please cite the [original source](http://redwood-data.org/indoor_lidar_rgbd/license.html): 35 | > Jaesik Park, Qian-Yi Zhou and Vladlen Koltun, _Colored Point Cloud Registration Revisited_. ICCV, 2017. 36 | 37 | --- 38 | 39 | ## Installation 40 | 41 | There are two ways for using this repository. The easiest and recommended way is to build and use the provided docker image (see instructions below). Option 2 is to build to pipeline from scratch for OS of preference. 42 | 43 | ### Option 1: Docker-image 44 | 45 | 1. Clone this repository 46 | 47 | 2. Build docker image (the building can take a couple of minutes): 48 | ```bash 49 | docker build -f Dockerfile . -t t3d_docker:latest 50 | ``` 51 | 52 | 3. Run docker container (as jupyter server on port 8888): 53 | ```bash 54 | docker run -v `pwd`/ipcp:/usr/app/ipcp -it -p 8888:8888 t3d_docker:latest 55 | ``` 56 | 57 | 4. Check out the [notebooks](notebooks) in jupter on port 8888 for a demonstration. 58 | 59 | ### Option 2: Build from scratch 60 | 61 | 1. Clone this repository 62 | 63 | 2. Install [CGAL](https://doc.cgal.org/latest/Manual/installation.html) and [GLPK](https://www.gnu.org/software/glpk/#downloading) by following the instructions on their pages. 64 | 65 | 3. Build the C++ modules located in the [cpp_modules](./cpp_modules/src) folder using CMake. 66 | 67 | 4. Install the python dependencies (requires Python >=3.8): 68 | ```bash 69 | python -m pip install -r requirements.txt 70 | ``` 71 | 72 | --- 73 | 74 | ## Running 75 | 76 | 1. Using the notebook [Complete Pipeline](notebooks/Complete%20Pipeline.ipynb) 77 | 78 | 2. Using command line `python script.py [-h] --in_file path [--out_folder path]`, e.g.: 79 | ```bash 80 | python script.py --in_file './datasets/apt_subsampled.ply' 81 | ``` 82 | 83 | --- 84 | 85 | ## Acknowledgements 86 | 87 | This repository was created by _Falke Boskaljon_ for the City of Amsterdam. 88 | -------------------------------------------------------------------------------- /imgs/3d_interion_model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/imgs/3d_interion_model.png -------------------------------------------------------------------------------- /ipcp/cpp_modules/efficient_ransac: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/ipcp/cpp_modules/efficient_ransac -------------------------------------------------------------------------------- /ipcp/cpp_modules/polyfit: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/ipcp/cpp_modules/polyfit -------------------------------------------------------------------------------- /ipcp/cpp_modules/polyfit_ransac: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/ipcp/cpp_modules/polyfit_ransac -------------------------------------------------------------------------------- /ipcp/cpp_modules/src/efficient_ransac/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # Created by the script cgal_create_CMakeLists 2 | # This is the CMake script for compiling a set of CGAL applications. 3 | 4 | project(efficient_ransac) 5 | 6 | cmake_minimum_required(VERSION 3.1...3.23) 7 | 8 | # CGAL and its components 9 | find_package(CGAL REQUIRED) 10 | 11 | # Boost and its components 12 | find_package(Boost REQUIRED) 13 | 14 | if(NOT Boost_FOUND) 15 | 16 | message( 17 | STATUS 18 | "NOTICE: This project requires the Boost library, and will not be compiled." 19 | ) 20 | 21 | return() 22 | 23 | endif() 24 | 25 | # Creating entries for all C++ files with "main" routine 26 | # ########################################################## 27 | 28 | find_package(Eigen3 3.1.0) #(requires 3.1.0 or greater) 29 | include(CGAL_Eigen3_support) 30 | if(NOT TARGET CGAL::Eigen3_support) 31 | message( 32 | STATUS 33 | "NOTICE: This project requires Eigen 3.1 (or greater) and will not be compiled." 34 | ) 35 | return() 36 | endif() 37 | 38 | create_single_source_cgal_program("efficient_ransac.cpp") 39 | target_link_libraries(efficient_ransac PUBLIC CGAL::Eigen3_support) 40 | -------------------------------------------------------------------------------- /ipcp/cpp_modules/src/efficient_ransac/efficient_ransac.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include 6 | #include 7 | #include 8 | 9 | #include 10 | 11 | #include 12 | #include 13 | #include 14 | 15 | #define MIN_PARAMETERS 8 16 | 17 | typedef CGAL::Exact_predicates_inexact_constructions_kernel Kernel; 18 | typedef Kernel::FT FT; 19 | typedef Kernel::Point_3 Point; 20 | typedef Kernel::Vector_3 Vector; 21 | // Point with normal, and plane index. 22 | typedef boost::tuple PNI; 23 | typedef std::vector Point_vector; 24 | typedef CGAL::Nth_of_tuple_property_map<0, PNI> Point_map; 25 | typedef CGAL::Nth_of_tuple_property_map<1, PNI> Normal_map; 26 | typedef CGAL::Nth_of_tuple_property_map<2, PNI> Plane_index_map; 27 | 28 | typedef CGAL::Shape_detection::Efficient_RANSAC_traits Traits; 29 | typedef CGAL::Shape_detection::Efficient_RANSAC Efficient_ransac; 30 | typedef CGAL::Shape_detection::Plane Plane; 31 | typedef CGAL::Shape_detection::Point_to_shape_index_map Point_to_shape_index_map; 32 | 33 | // Concurrency 34 | typedef CGAL::Parallel_if_available_tag Concurrency_tag; 35 | 36 | class Index_map 37 | { 38 | public: 39 | using key_type = std::size_t; 40 | using value_type = int; 41 | using reference = value_type; 42 | using category = boost::readable_property_map_tag; 43 | 44 | Index_map() { } 45 | template Index_map(const PointRange& points, 46 | const std::vector< std::vector >& regions) 47 | : m_indices(new std::vector(points.size(), -1)) { 48 | for (std::size_t i = 0; i < regions.size(); ++i) 49 | for (const std::size_t idx : regions[i]) 50 | (*m_indices)[idx] = static_cast(i); 51 | } 52 | 53 | inline friend value_type get(const Index_map& index_map, 54 | const key_type key) { 55 | const auto& indices = *(index_map.m_indices); 56 | return indices[key]; 57 | } 58 | 59 | private: 60 | std::shared_ptr< std::vector > m_indices; 61 | }; 62 | 63 | int main(int argc, char *argv[]) 64 | { 65 | if (argc < MIN_PARAMETERS) 66 | { 67 | std::cout << "no valid input found" << std::endl; 68 | return (-1); 69 | } 70 | else 71 | { 72 | std::cout << "Succes" << std::endl; 73 | } 74 | std::string input_file_name = argv[1]; 75 | std::string output_file_name = argv[2]; 76 | float probability = std::stof(argv[3]); 77 | float min_points = std::stof(argv[4]); 78 | float epsilon = std::stof(argv[5]); 79 | float cluster_epsilon = std::stof(argv[6]); 80 | float normal_threshold = std::stof(argv[7]); 81 | 82 | Point_vector points; 83 | 84 | // Load point set from a file. 85 | const std::string& input_file(input_file_name); 86 | std::cout << "Loading point cloud: " << input_file << "..."; 87 | CGAL::Timer t; 88 | t.start(); 89 | 90 | // VERSION: given normals 91 | std::ifstream input_stream(argv[1], std::ios_base::binary); 92 | if (!CGAL::IO::read_PLY(input_stream, 93 | std::back_inserter(points), 94 | CGAL::parameters::point_map(Point_map()) 95 | .normal_map(Normal_map()))) { 96 | std::cerr << "Error: cannot read file " << input_file << std::endl; 97 | return EXIT_FAILURE; 98 | } 99 | else 100 | std::cout << " Done. " << points.size() << " points. Time: " 101 | << t.time() << " sec." << std::endl; 102 | 103 | 104 | // VERSION: not given normals 105 | // std::ifstream input_stream(argv[1], std::ios_base::binary); 106 | // if (!CGAL::IO::read_PLY(input_stream, 107 | // std::back_inserter(points), 108 | // // CGAL::make_ply_point_reader(Point_map()) 109 | // CGAL::parameters::point_map(Point_map()))) { 110 | // std::cerr << "Error: cannot read file " << input_file << std::endl; 111 | // return EXIT_FAILURE; 112 | // } 113 | // else 114 | // std::cout << " Done. " << points.size() << " points. Time: " 115 | // << t.time() << " sec." << std::endl; 116 | 117 | // std::cerr << "Estimating normals..."; 118 | // t.reset(); 119 | 120 | // // Radius 121 | // CGAL::pca_estimate_normals(points, 122 | // 40, // no limit on the number of neighbors returns 123 | // CGAL::parameters::point_map(Point_map()) 124 | // .normal_map(Normal_map()) 125 | // .neighbor_radius(0.12)); 126 | 127 | // // neighbours 128 | // // const int nb_neighbors = 16; 129 | // // CGAL::pca_estimate_normals(points, 130 | // nb_neighbors, 131 | // CGAL::parameters::point_map(Point_map()) 132 | // .normal_map(Normal_map())); 133 | 134 | // std::cout << " Done. Time: " << t.time() << " sec." << std::endl; 135 | 136 | ////////////////////////////////////////////////////////////////////////// 137 | 138 | // Shape detection. 139 | 140 | // Set parameters for shape detection. 141 | Efficient_ransac::Parameters parameters; 142 | parameters.probability = probability; // Probability to miss the largest primitive at each iteration. 143 | parameters.min_points = min_points; // Detect shapes with at least number points. 144 | parameters.epsilon = epsilon; // Max distance between a point and a shape. 145 | parameters.cluster_epsilon = cluster_epsilon; // Maximum distance between points to be clustered. 146 | parameters.normal_threshold = normal_threshold; // Mximum normal deviation. 0.9 < dot(surface_normal, point_normal); 147 | 148 | Efficient_ransac ransac; 149 | ransac.set_input(points); 150 | ransac.add_shape_factory(); 151 | 152 | std::cout << "Extracting planes..."; 153 | t.reset(); 154 | ransac.detect(parameters); 155 | 156 | Efficient_ransac::Plane_range planes = ransac.planes(); 157 | std::size_t num_planes = planes.size(); 158 | 159 | std::cout << " Done. " << planes.size() << " planes extracted. Time: " 160 | << t.time() << " sec." << std::endl; 161 | 162 | // Print number of detected shapes and unassigned points. 163 | std::cout << ransac.shapes().end() - ransac.shapes().begin() 164 | << " detected shapes, " 165 | << ransac.number_of_unassigned_points() 166 | << " unassigned points." << std::endl; 167 | 168 | // Stores the plane index of each point as the third element of the tuple. 169 | Point_to_shape_index_map shape_index_map(points, planes); 170 | for (std::size_t i = 0; i < points.size(); ++i) { 171 | // Uses the get function from the property map that accesses the 3rd element of the tuple. 172 | int plane_index = get(shape_index_map, i); 173 | points[i].get<2>() = plane_index; 174 | } 175 | 176 | ////////////////////////////////////////////////////////////////////////// 177 | 178 | // Write point set 179 | const std::string& output_file(output_file_name); 180 | std::ofstream output_stream(output_file, std::ios_base::binary); 181 | CGAL::IO::set_binary_mode(output_stream); // The PLY file will be written in the binary format 182 | if (CGAL::IO::write_PLY_with_properties(output_stream, 183 | points, 184 | CGAL::make_ply_point_writer (Point_map()), 185 | CGAL::make_ply_normal_writer (Normal_map()), 186 | // CGAL::parameters::point_map (Point_map()) 187 | // .normal_map (Normal_map()), 188 | std::make_pair(Plane_index_map(), CGAL::IO::PLY_property("plane_index")))) { 189 | std::cout << " Done. Saved to " << output_file << std::endl; 190 | } 191 | else { 192 | std::cerr << " Failed saving file." << std::endl; 193 | return EXIT_FAILURE; 194 | } 195 | return EXIT_SUCCESS; 196 | } -------------------------------------------------------------------------------- /ipcp/cpp_modules/src/polyfit/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # Created by the script cgal_create_CMakeLists 2 | # This is the CMake script for compiling a set of CGAL applications. 3 | 4 | project(polyfit) 5 | 6 | cmake_minimum_required(VERSION 3.1...3.23) 7 | 8 | # CGAL and its components 9 | find_package(CGAL REQUIRED) 10 | 11 | # Boost and its components 12 | find_package(Boost REQUIRED) 13 | 14 | if(NOT Boost_FOUND) 15 | 16 | message( 17 | STATUS 18 | "NOTICE: This project requires the Boost library, and will not be compiled." 19 | ) 20 | 21 | return() 22 | 23 | endif() 24 | 25 | # Creating entries for all C++ files with "main" routine 26 | # ########################################################## 27 | 28 | find_package(Eigen3 3.1.0) #(requires 3.1.0 or greater) 29 | include(CGAL_Eigen3_support) 30 | if(NOT TARGET CGAL::Eigen3_support) 31 | message( 32 | STATUS 33 | "NOTICE: This project requires Eigen 3.1 (or greater) and will not be compiled." 34 | ) 35 | return() 36 | endif() 37 | 38 | find_package(GLPK QUIET) 39 | include(CGAL_GLPK_support) 40 | if(NOT TARGET CGAL::GLPK_support) 41 | message( 42 | STATUS 43 | "NOTICE: This project requires either GLPK, and will not be compiled." 44 | ) 45 | return() 46 | endif() 47 | 48 | create_single_source_cgal_program("polyfit.cpp") 49 | target_link_libraries(polyfit PUBLIC CGAL::Eigen3_support) 50 | target_link_libraries(polyfit PUBLIC CGAL::GLPK_support) -------------------------------------------------------------------------------- /ipcp/cpp_modules/src/polyfit/polyfit.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | 7 | #ifdef CGAL_USE_SCIP // defined (or not) by CMake scripts, do not define by hand 8 | #include 9 | typedef CGAL::SCIP_mixed_integer_program_traits MIP_Solver; 10 | #elif defined(CGAL_USE_GLPK) // defined (or not) by CMake scripts, do not define by hand 11 | #include 12 | typedef CGAL::GLPK_mixed_integer_program_traits MIP_Solver; 13 | #endif 14 | 15 | #if defined(CGAL_USE_GLPK) || defined(CGAL_USE_SCIP) 16 | 17 | #include 18 | 19 | #include 20 | 21 | #define MIN_PARAMETERS 6 22 | 23 | typedef CGAL::Exact_predicates_inexact_constructions_kernel Kernel; 24 | typedef Kernel::Point_3 Point; 25 | typedef Kernel::Vector_3 Vector; 26 | typedef CGAL::Polygonal_surface_reconstruction Polygonal_surface_reconstruction; 27 | typedef CGAL::Surface_mesh Surface_mesh; 28 | 29 | // Point with normal, and plane index 30 | typedef boost::tuple PNI; 31 | typedef CGAL::Nth_of_tuple_property_map<0, PNI> Point_map; 32 | typedef CGAL::Nth_of_tuple_property_map<1, PNI> Normal_map; 33 | typedef CGAL::Nth_of_tuple_property_map<2, PNI> Plane_index_map; 34 | 35 | /* 36 | * The following example shows the reconstruction using user-provided 37 | * planar segments stored in PLY format. In the PLY format, a property 38 | * named "segment_index" stores the plane index for each point (-1 if 39 | * the point is not assigned to a plane). 40 | */ 41 | 42 | int main(int argc, char *argv[]) 43 | { 44 | 45 | if (argc < MIN_PARAMETERS) 46 | { 47 | std::cout << "no valid input found" << std::endl; 48 | return (-1); 49 | } 50 | else 51 | { 52 | std::cout << "Succes" << std::endl; 53 | } 54 | std::string input_file_name = argv[1]; 55 | std::string output_path = argv[2]; 56 | float fitting = std::stof(argv[3]); 57 | float coverage = std::stof(argv[4]); 58 | float complexity = std::stof(argv[5]); 59 | if ((fitting + coverage + complexity) > 1.0f) { 60 | std::cout << fitting << " " << coverage << " " << complexity << std::endl; 61 | std::cerr << "Parameters sum to greater than 1" << std::endl; 62 | return EXIT_FAILURE; 63 | }; 64 | 65 | const std::string& input_file(input_file_name); 66 | std::ifstream input_stream(input_file.c_str(), std::ios::binary); 67 | 68 | std::vector points; // store points 69 | 70 | std::cout << "Loading point cloud: " << input_file << "..."; 71 | CGAL::Timer t; 72 | t.start(); 73 | 74 | if (!CGAL::IO::read_PLY_with_properties(input_stream, 75 | std::back_inserter(points), 76 | CGAL::make_ply_point_reader(Point_map()), 77 | CGAL::make_ply_normal_reader(Normal_map()), 78 | std::make_pair(Plane_index_map(), CGAL::PLY_property("segment_index")))) 79 | { 80 | std::cerr << "Error: cannot read file " << input_file << std::endl; 81 | return EXIT_FAILURE; 82 | } 83 | else 84 | std::cout << " Done. " << points.size() << " points. Time: " << t.time() << " sec." << std::endl; 85 | 86 | ////////////////////////////////////////////////////////////////////////// 87 | 88 | t.reset(); 89 | 90 | Polygonal_surface_reconstruction algo( 91 | points, 92 | Point_map(), 93 | Normal_map(), 94 | Plane_index_map() 95 | ); 96 | 97 | std::cout << " Done. Time: " << t.time() << " sec." << std::endl; 98 | 99 | ////////////////////////////////////////////////////////////////////////// 100 | 101 | Surface_mesh model; 102 | 103 | std::cout << "Reconstructing..."; 104 | t.reset(); 105 | 106 | if (!algo.reconstruct(model, fitting, coverage, complexity)) { 107 | std::cerr << " Failed: " << algo.error_message() << std::endl; 108 | return EXIT_FAILURE; 109 | } 110 | 111 | if (model.is_empty()) { 112 | std::cerr << " Failed: no vertices" << std::endl; 113 | return EXIT_FAILURE; 114 | } 115 | 116 | // Saves the mesh model 117 | const std::string& output_file(output_path + "/polyfit_result.obj"); 118 | std::ofstream output_stream(output_file.c_str()); 119 | if (output_stream && CGAL::IO::write_OBJ(output_stream, model)) 120 | std::cout << " Done. Saved to " << output_file << ". Time: " << t.time() << " sec." << std::endl; 121 | else { 122 | std::cerr << " Failed saving file." << std::endl; 123 | return EXIT_FAILURE; 124 | } 125 | 126 | return EXIT_SUCCESS; 127 | } 128 | 129 | #else 130 | 131 | int main(int, char**) 132 | { 133 | std::cerr << "This test requires either GLPK or SCIP.\n"; 134 | return EXIT_SUCCESS; 135 | } 136 | 137 | #endif // defined(CGAL_USE_GLPK) || defined(CGAL_USE_SCIP) -------------------------------------------------------------------------------- /ipcp/cpp_modules/src/polyfit_ransac/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | # Created by the script cgal_create_CMakeLists 2 | # This is the CMake script for compiling a set of CGAL applications. 3 | 4 | project(polyfit_ransac) 5 | 6 | cmake_minimum_required(VERSION 3.1...3.23) 7 | 8 | # CGAL and its components 9 | find_package(CGAL REQUIRED) 10 | 11 | # Boost and its components 12 | find_package(Boost REQUIRED) 13 | 14 | if(NOT Boost_FOUND) 15 | 16 | message( 17 | STATUS 18 | "NOTICE: This project requires the Boost library, and will not be compiled." 19 | ) 20 | 21 | return() 22 | 23 | endif() 24 | 25 | # Creating entries for all C++ files with "main" routine 26 | # ########################################################## 27 | 28 | find_package(Eigen3 3.1.0) #(requires 3.1.0 or greater) 29 | include(CGAL_Eigen3_support) 30 | if(NOT TARGET CGAL::Eigen3_support) 31 | message( 32 | STATUS 33 | "NOTICE: This project requires Eigen 3.1 (or greater) and will not be compiled." 34 | ) 35 | return() 36 | endif() 37 | 38 | find_package(GLPK QUIET) 39 | include(CGAL_GLPK_support) 40 | if(NOT TARGET CGAL::GLPK_support) 41 | message( 42 | STATUS 43 | "NOTICE: This project requires either GLPK, and will not be compiled." 44 | ) 45 | return() 46 | endif() 47 | 48 | create_single_source_cgal_program("polyfit_ransac.cpp") 49 | target_link_libraries(polyfit_ransac PUBLIC CGAL::Eigen3_support) 50 | target_link_libraries(polyfit_ransac PUBLIC CGAL::GLPK_support) -------------------------------------------------------------------------------- /ipcp/cpp_modules/src/polyfit_ransac/polyfit_ransac.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include 6 | #include 7 | #include 8 | 9 | #include 10 | #include 11 | #include 12 | 13 | #include 14 | #include 15 | #include 16 | 17 | #ifdef CGAL_USE_SCIP // defined (or not) by CMake scripts, do not define by hand 18 | #include 19 | typedef CGAL::SCIP_mixed_integer_program_traits MIP_Solver; 20 | #elif defined(CGAL_USE_GLPK) // defined (or not) by CMake scripts, do not define by hand 21 | #include 22 | typedef CGAL::GLPK_mixed_integer_program_traits MIP_Solver; 23 | #endif 24 | 25 | #if defined(CGAL_USE_GLPK) || defined(CGAL_USE_SCIP) 26 | 27 | #define MIN_PARAMETERS 11 28 | 29 | typedef CGAL::Exact_predicates_inexact_constructions_kernel Kernel; 30 | typedef Kernel::FT FT; 31 | typedef Kernel::Point_3 Point; 32 | typedef Kernel::Vector_3 Vector; 33 | // Point with normal, and plane index 34 | typedef boost::tuple PNI; 35 | typedef std::vector Point_vector; 36 | typedef CGAL::Nth_of_tuple_property_map<0, PNI> Point_map; 37 | typedef CGAL::Nth_of_tuple_property_map<1, PNI> Normal_map; 38 | typedef CGAL::Nth_of_tuple_property_map<2, PNI> Plane_index_map; 39 | typedef CGAL::Polygonal_surface_reconstruction Polygonal_surface_reconstruction; 40 | typedef CGAL::Surface_mesh Surface_mesh; 41 | typedef CGAL::Shape_detection::Efficient_RANSAC_traits Traits; 42 | typedef CGAL::Shape_detection::Efficient_RANSAC Efficient_ransac; 43 | typedef CGAL::Shape_detection::Plane Plane; 44 | typedef CGAL::Shape_detection::Point_to_shape_index_map Point_to_shape_index_map; 45 | 46 | // Concurrency 47 | typedef CGAL::Parallel_if_available_tag Concurrency_tag; 48 | 49 | class Index_map 50 | { 51 | public: 52 | using key_type = std::size_t; 53 | using value_type = int; 54 | using reference = value_type; 55 | using category = boost::readable_property_map_tag; 56 | 57 | Index_map() { } 58 | template Index_map(const PointRange& points, 59 | const std::vector< std::vector >& regions) 60 | : m_indices(new std::vector(points.size(), -1)) { 61 | for (std::size_t i = 0; i < regions.size(); ++i) 62 | for (const std::size_t idx : regions[i]) 63 | (*m_indices)[idx] = static_cast(i); 64 | } 65 | 66 | inline friend value_type get(const Index_map& index_map, 67 | const key_type key) { 68 | const auto& indices = *(index_map.m_indices); 69 | return indices[key]; 70 | } 71 | 72 | private: 73 | std::shared_ptr< std::vector > m_indices; 74 | }; 75 | 76 | /* 77 | * The following example shows the reconstruction using user-provided 78 | * planar segments stored in PLY format. In the PLY format, a property 79 | * named "segment_index" stores the plane index for each point (-1 if 80 | * the point is not assigned to a plane). 81 | */ 82 | 83 | int main(int argc, char *argv[]) 84 | { 85 | 86 | if (argc < MIN_PARAMETERS) 87 | { 88 | std::cout << "no valid input found" << std::endl; 89 | return (-1); 90 | } 91 | else 92 | { 93 | std::cout << "Succes" << std::endl; 94 | } 95 | std::string input_file_name = argv[1]; 96 | std::string output_path = argv[2]; 97 | float probability = std::stof(argv[3]); 98 | float min_points = std::stof(argv[4]); 99 | float epsilon = std::stof(argv[5]); 100 | float cluster_epsilon = std::stof(argv[6]); 101 | float normal_threshold = std::stof(argv[7]); 102 | float fitting = std::stof(argv[8]); 103 | float coverage = std::stof(argv[9]); 104 | float complexity = std::stof(argv[10]); 105 | 106 | if ((fitting + coverage + complexity) > 1.0f) { 107 | std::cout << fitting << " " << coverage << " " << complexity << std::endl; 108 | std::cerr << "Parameters sum to greater than 1" << std::endl; 109 | return EXIT_FAILURE; 110 | }; 111 | 112 | Point_vector points; 113 | 114 | // Load point set from a file. 115 | const std::string& input_file(input_file_name); 116 | std::ifstream input_stream(input_file.c_str(), std::ios::binary); 117 | std::cout << "Loading point cloud: " << input_file << "..."; 118 | CGAL::Timer t; 119 | t.start(); 120 | 121 | if (!CGAL::IO::read_PLY(input_stream, 122 | std::back_inserter(points), 123 | CGAL::parameters::point_map(Point_map()))) { 124 | std::cerr << "Error: cannot read file " << input_file << std::endl; 125 | return EXIT_FAILURE; 126 | } 127 | else 128 | std::cout << " Done. " << points.size() << " points. Time: " 129 | << t.time() << " sec." << std::endl; 130 | 131 | std::cout << "Estimating normals..."; 132 | t.reset(); 133 | 134 | // // Radius 135 | // CGAL::pca_estimate_normals(points, 136 | // 16, // limit on the number of neighbors 137 | // CGAL::parameters::point_map(Point_map()) 138 | // .normal_map(Normal_map()) 139 | // .neighbor_radius(0.10)); 140 | 141 | // Radius 142 | CGAL::pca_estimate_normals(points, 143 | 16, // limit on the number of neighbors 144 | CGAL::parameters::point_map(Point_map()) 145 | .normal_map(Normal_map())); 146 | 147 | std::cout << " Done. Time: " << t.time() << " sec." << std::endl; 148 | 149 | ////////////////////////////////////////////////////////////////////////// 150 | 151 | // Shape detection. 152 | 153 | // Set parameters for shape detection. 154 | Efficient_ransac::Parameters parameters; 155 | parameters.probability = probability; // Probability to miss the largest primitive at each iteration. 156 | parameters.min_points = min_points; // Detect shapes with at least number points. 157 | parameters.epsilon = epsilon; // Max distance between a point and a shape. 158 | parameters.cluster_epsilon = cluster_epsilon; // Maximum distance between points to be clustered. 159 | parameters.normal_threshold = normal_threshold; // Mximum normal deviation. 0.9 < dot(surface_normal, point_normal); 160 | 161 | Efficient_ransac ransac; 162 | ransac.set_input(points); 163 | ransac.add_shape_factory(); 164 | 165 | std::cout << "Extracting planes..."; 166 | t.reset(); 167 | ransac.detect(parameters); 168 | 169 | Efficient_ransac::Plane_range planes = ransac.planes(); 170 | std::size_t num_planes = planes.size(); 171 | 172 | std::cout << " Done. " << planes.size() << " planes extracted. Time: " 173 | << t.time() << " sec." << std::endl; 174 | 175 | // Print number of detected shapes and unassigned points. 176 | std::cout << ransac.shapes().end() - ransac.shapes().begin() 177 | << " detected shapes, " 178 | << ransac.number_of_unassigned_points() 179 | << " unassigned points." << std::endl; 180 | 181 | // Stores the plane index of each point as the third element of the tuple. 182 | Point_to_shape_index_map shape_index_map(points, planes); 183 | for (std::size_t i = 0; i < points.size(); ++i) { 184 | // Uses the get function from the property map that accesses the 3rd element of the tuple. 185 | int plane_index = get(shape_index_map, i); 186 | points[i].get<2>() = plane_index; 187 | } 188 | 189 | ////////////////////////////////////////////////////////////////////////// 190 | 191 | // Write point set 192 | const std::string& output_file_ply(output_path + "/ransac_result.ply"); 193 | std::ofstream output_stream_ply(output_file_ply.c_str(), std::ios_base::binary); 194 | CGAL::IO::set_binary_mode(output_stream_ply); // The PLY file will be written in the binary format 195 | if (CGAL::IO::write_PLY_with_properties(output_stream_ply, 196 | points, 197 | CGAL::make_ply_point_writer (Point_map()), 198 | CGAL::make_ply_normal_writer (Normal_map()), 199 | std::make_pair(Plane_index_map(), CGAL::IO::PLY_property("plane_index")))) { 200 | std::cout << " Done. Saved to " << output_file_ply << std::endl; 201 | } 202 | else { 203 | std::cerr << " Failed saving file." << std::endl; 204 | return EXIT_FAILURE; 205 | } 206 | 207 | ////////////////////////////////////////////////////////////////////////// 208 | 209 | // Polygonal Surface Reconstruction 210 | 211 | Polygonal_surface_reconstruction algo( 212 | points, 213 | Point_map(), 214 | Normal_map(), 215 | Plane_index_map() 216 | ); 217 | 218 | Surface_mesh model; 219 | 220 | std::cout << "Polyfit..."; 221 | t.reset(); 222 | 223 | if (!algo.reconstruct(model, fitting, coverage, complexity)) { 224 | std::cerr << " Failed: " << algo.error_message() << std::endl; 225 | return EXIT_FAILURE; 226 | } 227 | 228 | std::cout << " Done. Time: " << t.time() << " sec." << std::endl; 229 | 230 | if (model.is_empty()) { 231 | std::cerr << " Failed: no vertices" << std::endl; 232 | return EXIT_FAILURE; 233 | } 234 | 235 | // Saves the mesh model 236 | const std::string& output_file_mesh(output_path + "/polyfit_result.obj"); 237 | std::ofstream output_stream_mesh(output_file_mesh.c_str()); 238 | if (output_stream_mesh && CGAL::IO::write_OBJ(output_stream_mesh, model)) 239 | std::cout << " Done. Saved to " << output_file_mesh << ". Time: " << t.time() << " sec." << std::endl; 240 | else { 241 | std::cerr << " Failed saving file." << std::endl; 242 | return EXIT_FAILURE; 243 | } 244 | 245 | return EXIT_SUCCESS; 246 | } 247 | 248 | #else 249 | 250 | int main(int, char**) 251 | { 252 | std::cerr << "This test requires either GLPK or SCIP.\n"; 253 | return EXIT_SUCCESS; 254 | } 255 | 256 | #endif // defined(CGAL_USE_GLPK) || defined(CGAL_USE_SCIP) -------------------------------------------------------------------------------- /ipcp/datasets/apt_subsampled.ply: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/ipcp/datasets/apt_subsampled.ply -------------------------------------------------------------------------------- /ipcp/datasets/output.city.json: -------------------------------------------------------------------------------- 1 | {"type": "CityJSON", 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import logging 4 | import time 5 | 6 | 7 | from scipy.ndimage import label, binary_closing, binary_dilation, binary_erosion 8 | from scipy.interpolate import griddata 9 | from scipy.stats import binned_statistic_2d 10 | from src.interpolation import FastGridInterpolator 11 | from src.utils.clip_utils import poly_box_clip 12 | from rasterio import features, Affine 13 | from shapely import geometry 14 | from shapely.geometry import Polygon, Point 15 | from shapely.ops import unary_union, transform 16 | import matplotlib.pyplot as plt 17 | 18 | NOISE = 0 19 | SLANTED = 1 20 | ALMOST_VERTICAL = 2 21 | ALMOST_HORIZONTAL = 3 22 | 23 | logger = logging.getLogger(__name__) 24 | 25 | class FloorSplitter: 26 | """ 27 | FloorSplitter class for the division of pointcloud data into floors. 28 | The class splits the pointcloud into floors using density analysis along the z-axis. 29 | 30 | Parameters 31 | ---------- 32 | 33 | """ 34 | 35 | def __init__(self, subsample_size=0.03): 36 | self.subsample_size = subsample_size 37 | 38 | def _func(self, values): 39 | """ 40 | Converts detected peaks into top and bottom split values 41 | 42 | Parameters 43 | ---------- 44 | values : array 45 | The indices of the peaks 46 | 47 | Returns 48 | ------- 49 | An array of split values for each floor 50 | """ 51 | 52 | def _process_poly(self, poly: Polygon) -> Polygon: 53 | """ 54 | Close polygon holes by limitation to the exterior ring. 55 | Args: 56 | poly: Input shapely Polygon 57 | Example: 58 | df.geometry.apply(lambda p: close_holes(p)) 59 | """ 60 | if poly.interiors: 61 | return Polygon(list(poly.exterior.coords)).buffer(3).buffer(-2).simplify(1) 62 | else: 63 | return poly.buffer(3).buffer(-2).simplify(1) 64 | 65 | 66 | 67 | def _create_3d_grid(self, points, bin_size=.1): 68 | min_x, max_x = min(points[:, 0])-2*bin_size, max(points[:, 0])+2*bin_size 69 | min_y, max_y = min(points[:, 1])-2*bin_size, max(points[:, 1])+2*bin_size 70 | min_z, max_z = min(points[:, 2])-2*bin_size, max(points[:, 2])+2*bin_size 71 | dimx = max_x - min_x 72 | dimy = max_y - min_y 73 | dimz = max_z - min_z 74 | bins = [np.uint(dimx/bin_size), np.uint(dimy/bin_size), np.uint(dimz/bin_size)] 75 | hist_range = [[min_x, max_x], [min_y, max_y], [min_z, max_z]] 76 | 77 | counts, edges = np.histogramdd(points, range=hist_range, bins=bins) 78 | origin = (hist_range[0][0]+bin_size/2,hist_range[1][0]+bin_size/2, hist_range[2][0]+bin_size/2) 79 | grid = counts > 0 80 | 81 | return grid, edges, bins, hist_range, origin 82 | 83 | def _check_multistory(self, primitives, labels, min_floor_height=1.8, min_slab_size=4): 84 | slab_z = [] 85 | for i,r in primitives.items(): 86 | if r['type'] != ALMOST_VERTICAL: 87 | if np.sum(labels==i) * (self.subsample_size**2) > min_slab_size: 88 | slab_z.append(r['bbox'][:,2].max()) 89 | if len(slab_z) > 1: 90 | slab_z = np.sort(slab_z) 91 | ceil_slabs = slab_z[slab_z > slab_z[0] + min_floor_height] 92 | if len(ceil_slabs) > 0: 93 | if np.sum(ceil_slabs > ceil_slabs[0] + min_floor_height) > 0: 94 | return 2 95 | else: 96 | return 1 97 | return 0 98 | 99 | def _extract_floor(self, points, labels, primitives, bin_size, min_floor_size, min_cluster_size): 100 | 101 | # create grid 102 | grid, edges, bins, hist_range, origin = self._create_3d_grid(points, bin_size) 103 | 104 | # Define floor primitives 105 | floor_primitives = [] 106 | for i,r in primitives.items(): 107 | mask = labels==i 108 | if r['type'] == ALMOST_HORIZONTAL and np.sum(mask) > min_floor_size/(self.subsample_size**2): 109 | floor_primitives.append([i,points[mask][:,2].min(), points[mask][:,2].max()]) 110 | if len(floor_primitives) == 0: 111 | return np.ones(len(points), dtype=bool) 112 | floor_primitives = np.asarray(floor_primitives) 113 | floor_idx = np.argmin(floor_primitives[:,1]) 114 | floor_point_mask = labels==floor_primitives[floor_idx,0] 115 | 116 | # floor grid bounds 117 | l, t = np.digitize(floor_primitives[floor_idx,1:], edges[2])-1 118 | r = int(t+np.ceil(1.5/bin_size)) 119 | floor_proj = binary_dilation(np.histogram2d(*points[floor_point_mask,:2].T, range=hist_range[:2], bins=bins[:2])[0]>0) 120 | # plt.imshow(floor_proj) 121 | # plt.show() 122 | 123 | # Project space above floor 124 | space_proj = binary_dilation(np.sum(grid[:,:,l:r],axis=2)>0) 125 | lcc_space_proj = label(space_proj)[0] 126 | lcc_space_mask = np.where(np.unique(lcc_space_proj, return_counts=True)[1] < min_cluster_size/(bin_size**2)) 127 | lcc_space_proj[np.isin(lcc_space_proj, lcc_space_mask)] = 0 # remove small blobs in space projection 128 | space_proj = np.isin(lcc_space_proj, [l for l in np.unique(lcc_space_proj[floor_proj]) if l > 0]) 129 | 130 | # Merge floor & space 131 | floor_proj = space_proj | floor_proj 132 | ceil_proj = np.zeros(floor_proj.shape, dtype=bool) 133 | ceiling_map = np.full(floor_proj.shape, np.nan) 134 | cnf_proj = np.copy(floor_proj) 135 | # plt.imshow(floor_proj) 136 | # plt.show() 137 | 138 | # list candidate ceilings 139 | ceiling_candidates = [] 140 | for i,r in primitives.items(): 141 | if r['type'] != ALMOST_VERTICAL and np.sum(labels==i) > min_cluster_size/(self.subsample_size**2): 142 | if points[labels==i][:,2].max() > floor_primitives[floor_idx,1]+1.8: 143 | ceiling_candidates.append((i,points[labels==i][:,2].min(), points[labels==i][:,2].max())) 144 | ceiling_candidates = np.asarray(ceiling_candidates) 145 | ceiling_candidates = ceiling_candidates[np.argsort(ceiling_candidates[:,1])] 146 | 147 | # Loop through ceiling candidates 148 | for l, _, ceil_max_z in ceiling_candidates: 149 | cand_z_proj = binned_statistic_2d(*points[labels==l].T, statistic='max', range=hist_range[:2], bins=bins[:2])[0] 150 | cand_proj = binary_closing(~np.isnan(cand_z_proj)) 151 | 152 | # fig, ax = plt.subplots(1, 3, sharey=True) 153 | # ax[0].imshow(floor_proj) 154 | # ax[1].imshow(cand_proj) 155 | # ax[2].imshow(ceil_proj) 156 | # plt.show() 157 | # print(l, np.sum(labels==l), np.sum(floor_proj[cand_proj]), np.sum(cand_proj)) 158 | 159 | floor_overlap = (np.sum(floor_proj[cand_proj]) / np.sum(cand_proj)).round(2) 160 | ceiling_overlap = (np.sum(ceil_proj[cand_proj]) / np.sum(cand_proj)).round(2) 161 | # print(l, ceiling_overlap, floor_overlap) 162 | 163 | # Check if ceiling is valid 164 | if floor_overlap > .5 and ceiling_overlap < .1: 165 | floor_proj[cand_proj] = False 166 | cnf_proj[cand_proj] = True 167 | ceiling_map[cand_proj] = cand_z_proj[cand_proj] + .1 # some nan ? interpolation 168 | 169 | # Tracking purposes.. 170 | ceil_proj[cand_proj] = True 171 | keep_searching = np.any(np.unique(label(binary_erosion(floor_proj))[0], return_counts=True)[1][1:] > 0.8*min_cluster_size/(bin_size**2)) 172 | if not keep_searching: 173 | break 174 | 175 | # Interpolate ceiling 176 | ceil_mask = ~np.isnan(ceiling_map) 177 | ceiling_map[~ceil_mask] = griddata(np.vstack(np.where(ceil_mask)).T, ceiling_map[ceil_mask], np.where(~ceil_mask), method='nearest') 178 | 179 | # Floor polygon 180 | floor_complete = binary_dilation(cnf_proj).astype(np.uint8) 181 | generator = features.shapes(floor_complete, mask=floor_complete>0) 182 | floor_polygons = [self._process_poly(geometry.shape(shape)) for shape, _ in generator] 183 | floor_polygon = transform(lambda x, y, z=None: (y*bin_size+origin[0], x*bin_size+origin[1]), unary_union(floor_polygons)) 184 | 185 | # Create mask 186 | max_z_interpolator = FastGridInterpolator(bin_x=edges[0], bin_y=edges[1], values=ceiling_map) 187 | min_z = floor_primitives[floor_idx,1]-.1 188 | floor_mask = poly_box_clip(points, floor_polygon, bottom=min_z, top=np.max(ceiling_map)+.15) 189 | height_mask = points[floor_mask, 2] <= max_z_interpolator(points[floor_mask]) 190 | floor_mask[floor_mask] = height_mask 191 | 192 | return floor_mask 193 | 194 | 195 | 196 | def process(self, pcd, labels, primitives): 197 | """ 198 | Parameters 199 | ---------- 200 | points : array of shape (n_points, 3) 201 | The point cloud . 202 | 203 | Returns 204 | ------- 205 | An array of masks, where each mask represents a floor in the pointcloud. 206 | """ 207 | 208 | logger.debug('Analysing pointcloud for floors...') 209 | points = np.asarray(pcd.points) 210 | un_mask = np.ones(len(points), dtype=bool) 211 | floors = [] 212 | start = time.time() 213 | while self._check_multistory(primitives, labels[un_mask]) > 0: 214 | # TODO: error test 215 | mask = self._extract_floor(points[un_mask], labels[un_mask], primitives, bin_size=0.1, min_floor_size=5, min_cluster_size=.5) 216 | floor_mask = np.zeros(len(points), dtype=bool) 217 | floor_mask[un_mask] = mask 218 | floors.append(floor_mask) 219 | un_mask[floor_mask] = False 220 | 221 | logger.debug(f"Done. Number of floor extracted {len(floors)} floors. {round(time.time()-start,2)}s\n") 222 | 223 | return floors 224 | -------------------------------------------------------------------------------- /ipcp/modules/mesh_stats.py: -------------------------------------------------------------------------------- 1 | """Mesh Area Volume Module""" 2 | import numpy as np 3 | import logging 4 | 5 | import pymeshlab 6 | import json 7 | import argparse 8 | import os 9 | 10 | logger = logging.getLogger(__name__) 11 | 12 | class MeshAnalyser: 13 | """ 14 | MeshAnalyser class for the volume and area computation of a mesh. 15 | The class compute both the volume and area for a given mesh. 16 | 17 | Parameters 18 | ---------- 19 | face_condition : float (default=-0.95) 20 | The condition for face selection. 21 | """ 22 | 23 | def __init__(self, face_condition=-0.95): 24 | self.face_condition = face_condition 25 | 26 | def _get_area_volume(self, meshset): 27 | meshset.meshing_remove_duplicate_vertices() 28 | meshset.meshing_merge_close_vertices() 29 | meshset.meshing_re_orient_faces_coherentely() 30 | 31 | geom = meshset.get_geometric_measures() 32 | if 'mesh_volume' in geom.keys(): 33 | volume = geom['mesh_volume'] 34 | else: 35 | volume = 0 36 | logger.debug(f'volume is {volume}') 37 | 38 | if volume<0: 39 | meshset.meshing_invert_face_orientation() 40 | geom = meshset.get_geometric_measures() 41 | volume = geom['mesh_volume'] 42 | logger.debug(f'after inverting, volume is now {volume}') 43 | 44 | statement = ('(fnz < ' + str(self.face_condition) + ')') 45 | meshset.compute_selection_by_condition_per_face(condselect=statement) 46 | meshset.apply_selection_inverse(invfaces = True) 47 | meshset.meshing_remove_selected_faces() 48 | 49 | geom2 = meshset.get_geometric_measures() 50 | logger.debug(f'floor area is {geom2["surface_area"]}') 51 | 52 | floorarea = geom2['surface_area'] 53 | 54 | return volume, floorarea 55 | 56 | 57 | def process(self, mesh): 58 | """ 59 | Parameters 60 | ---------- 61 | mesh : pmeshlab.Mesh 62 | The pymeshlab Meshset. 63 | 64 | Returns 65 | ------- 66 | volume : float 67 | The volume of the input mesh. 68 | floorarea : float 69 | The floorarea of the input mesh. 70 | """ 71 | 72 | logger.debug(f'Analysing mesh...') 73 | 74 | try: 75 | volume, floorarea = self._get_area_volume(mesh) 76 | logger.debug(f'The volume of room is {volume:.2f} and the floorarea is {floorarea:.2f}') 77 | except: 78 | volume, floorarea = None, None 79 | logger.debug(f'metrics cannot be calculated') 80 | 81 | return volume, floorarea 82 | -------------------------------------------------------------------------------- /ipcp/modules/primitive_detection.py: -------------------------------------------------------------------------------- 1 | """Primitive Detection Module""" 2 | 3 | import os 4 | import logging 5 | import subprocess 6 | import numpy as np 7 | import open3d as o3d 8 | from scipy.spatial import KDTree 9 | 10 | import src.utils.math_utils as math_utils 11 | from src.region_growing import RegionGrowing 12 | from src.utils.pcd_utils import merge_point_clouds 13 | 14 | logger = logging.getLogger(__name__) 15 | 16 | NOISE = 0 17 | SLANTED = 1 18 | ALMOST_VERTICAL = 2 19 | ALMOST_HORIZONTAL = 3 20 | 21 | class PrimitiveDetector: 22 | """ 23 | PrimitiveDetector class for the geometric primitives in the pointcloud. 24 | The class labels primitives as horizontal, vertical or slanted. 25 | 26 | Parameters 27 | ---------- 28 | min_peak_height : int (default=2500) 29 | The required height of a peak in the vertical density profile. 30 | threshold : int (default=250) 31 | The required threshold of peaks, the vertical distance to its neighboring samples. 32 | distance : int (default=20) 33 | The required minimal horizontal distance (>= 1) in samples between neighbouring peaks. 34 | prominence : int (default=2) 35 | The required prominence of peaks. 36 | min_floor_height : float (default=2.1) 37 | The minimal height a floor must be. 38 | floor_buffer : float (default=0.1) 39 | The buffer used to include the whole floor/ceiling. 40 | """ 41 | 42 | def __init__(self, excecutable_path, subsample_size=0.03): 43 | self.excecutable_path = excecutable_path 44 | self.subsample_size = subsample_size 45 | 46 | def _plane_normal(self, pcd): 47 | a,b,c,_ = pcd.segment_plane(distance_threshold=0.03, 48 | ransac_n=5, 49 | num_iterations=10)[0] 50 | normal = np.array([a,b,c]) 51 | normal /= np.linalg.norm(normal) 52 | return normal 53 | 54 | def _merge_parallel(self, pcd, labels): 55 | un_labels_ = np.unique(labels[labels>-1]) 56 | normals = {} 57 | for label in un_labels_: 58 | pcd_label = pcd.select_by_index(np.where(labels==label)[0]) 59 | normal = self._plane_normal(pcd_label) 60 | normals[label] = normal 61 | 62 | points = np.asarray(pcd.points) 63 | labels_iter = list(un_labels_) 64 | merge_count = 0 65 | pairs = [] 66 | while len(labels_iter) > 0: 67 | label = labels_iter.pop(0) 68 | label_mask = np.where(labels==label)[0] 69 | kd_i = KDTree(points[label_mask]) 70 | label_normal = normals[label] 71 | 72 | for i in labels_iter: 73 | angle = np.rad2deg(angle_between(label_normal[:2], normals[i][:2])) 74 | if angle < 3: 75 | num_pairs = np.sum(kd_i.query(points[labels==i], k=1, distance_upper_bound=.12)[1] 10: 77 | pairs.append((label,i)) 78 | merge_count += 1 79 | 80 | lookup = {} 81 | for i,j in pairs: 82 | if j in lookup: 83 | lookup[i] = lookup[j] 84 | elif i in lookup: 85 | lookup[j] = lookup[i] 86 | else: 87 | lookup[j] = i 88 | 89 | for k,v in lookup.items(): 90 | labels[labels==k] = v 91 | 92 | labels = consecutive_labels(labels) 93 | 94 | logger.debug(f'merged: {merge_count}') 95 | return labels 96 | 97 | def _detect_vertical(self, pcd): 98 | tmp_file = './tmp_pr/cloud.ply' 99 | if not os.path.isdir('./tmp_pr'): 100 | os.mkdir('./tmp_pr') 101 | 102 | # point selection 103 | labels = np.full(len(pcd.points),-1) 104 | verticality = compute_verticality(pcd, radius=.2) 105 | mask = np.where(verticality > 0.75)[0] 106 | pcd_ = pcd.select_by_index(mask) 107 | planarity = compute_planarity(pcd_, radius=.15) 108 | mask = mask[planarity > .3] 109 | 110 | # detect primtives 111 | pcd_ = pcd.select_by_index(mask) 112 | pcd_, ransac_lables = efficient_ransac(pcd_, self.excecutable_path, tmp_file) 113 | labels[-len(mask):] = ransac_lables 114 | pcd = merge_point_clouds(pcd.select_by_index(mask, invert=True), pcd_) 115 | 116 | # region grow 117 | region_growing = RegionGrowing() 118 | labels = region_growing.process(pcd, labels) 119 | 120 | # merge primitives 121 | labels = self._merge_parallel(pcd, labels) 122 | 123 | return pcd, labels 124 | 125 | def _detect_non_vertical(self, pcd): 126 | tmp_file = './tmp_pr/cloud.ply' 127 | if not os.path.isdir('./tmp_pr'): 128 | os.mkdir('./tmp_pr') 129 | 130 | # point selection 131 | labels = np.full(len(pcd.points), -1) 132 | planarity = compute_planarity(pcd, radius=.15) 133 | mask = np.where(planarity > .5)[0] 134 | 135 | # detect primitives 136 | pcd_ = pcd.select_by_index(mask) 137 | pcd_, ransac_lables = efficient_ransac(pcd_, 138 | self.excecutable_path, tmp_file, prob='0.005', 139 | eps='0.06', cluster_thres='0.15') 140 | labels[-len(mask):] = ransac_lables 141 | pcd = merge_point_clouds(pcd.select_by_index(mask, invert=True), pcd_) 142 | 143 | # clean verticals 144 | un_labels_ = np.unique(labels[labels>-1]) 145 | for label in un_labels_: 146 | pcd_ = pcd.select_by_index(np.where(labels==label)[0]) 147 | normal = self._plane_normal(pcd_) 148 | angle = np.rad2deg(np.arccos(np.abs(normal[2] / 1))) 149 | if angle > 85: 150 | labels[labels==label] = -1 151 | 152 | # region grow 153 | region_growing = RegionGrowing() 154 | labels = region_growing.process(pcd, labels) 155 | 156 | return pcd, labels 157 | 158 | def _labels_to_primitives(self, pcd, labels, exclude_labels=[-1], min_points=200): 159 | planes = {} 160 | rectangle_labels = [r for r in np.unique(labels) if r not in exclude_labels and np.sum(labels==r) > min_points] 161 | for i in rectangle_labels: 162 | region_cloud = pcd.select_by_index(np.where(labels==i)[0]) 163 | region_pts = np.asarray(region_cloud.points) 164 | pts_surface = len(region_pts)*(self.subsample_size**2) 165 | 166 | # TODO: filter outliers out! 167 | a, b, c, d = region_cloud.segment_plane(distance_threshold=0.01, 168 | ransac_n=5, 169 | num_iterations=100)[0] 170 | normal = np.array([a,b,c]) 171 | normal /= np.linalg.norm(normal) 172 | center = np.mean(region_pts,axis=0) 173 | slope = np.rad2deg(np.arccos(np.abs(normal[2] / 1))) 174 | 175 | # Compute bounding box 176 | if slope < 5: 177 | plane_type = ALMOST_HORIZONTAL 178 | min_bbox = math_utils.minimum_bounding_rectangle(region_pts[:,:2]) 179 | if min_bbox[2] < .2: 180 | continue 181 | bbox_points = min_bbox[0] 182 | bbox_points = np.concatenate((bbox_points, np.full((4,1), center[2])),axis=1) 183 | else: 184 | xaxis = np.cross(normal, [0, 0, 1]) 185 | yaxis = np.cross(normal, xaxis) 186 | xaxis /= np.linalg.norm(xaxis) 187 | yaxis /= np.linalg.norm(yaxis) 188 | 189 | new_x = np.dot(region_pts-center, xaxis) 190 | new_y = np.dot(region_pts-center, yaxis) 191 | 192 | xmin, ymin, xmax, ymax = math_utils.compute_bounding_box(np.vstack([new_x, new_y]).T) 193 | bbox_points = np.array([[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]]) 194 | bbox_points = center + bbox_points[:,0][:, None]*xaxis + bbox_points[:,1][:, None]*yaxis 195 | if slope > 80: 196 | plane_type = ALMOST_VERTICAL 197 | else: 198 | plane_type = SLANTED 199 | 200 | surface = bbox_area(bbox_points) 201 | coverage = pts_surface/surface 202 | if coverage < 0.05: # minimal coverage 203 | continue 204 | 205 | line_set = o3d.geometry.LineSet( 206 | points=o3d.utility.Vector3dVector(bbox_points), 207 | lines=o3d.utility.Vector2iVector([[0, 1],[1, 2],[2, 3],[3, 0]]), 208 | ) 209 | 210 | plane_object = { 211 | 'region': i, 212 | 'bbox': bbox_points, 213 | 'lineset': line_set, 214 | 'surface': surface, 215 | 'coverage': coverage, 216 | 'center': center, 217 | 'normal': normal, 218 | 'D': d, 219 | 'slope': slope, 220 | 'type': plane_type 221 | } 222 | 223 | planes[i] = plane_object 224 | 225 | return planes 226 | 227 | def process(self, pcd): 228 | """ 229 | Parameters 230 | ---------- 231 | points : array of shape (n_points, 3) 232 | The point cloud . 233 | 234 | Returns 235 | ------- 236 | An array of masks, where each mask represents a floor in the pointcloud. 237 | """ 238 | 239 | logger.debug('Detecting primitives in pointcloud...') 240 | labels = np.full(len(pcd.points), SLANTED, dtype=np.uint8) 241 | 242 | logger.debug('Searching vertical primitives...') 243 | pcd, labels = self._detect_vertical(pcd) 244 | logger.debug(f'Done. Found {len(np.unique(labels))-1} primitives') 245 | 246 | logger.debug('Searching other primitives...') 247 | idx = np.where(labels==-1)[0] 248 | pcd_ = pcd.select_by_index(idx) 249 | pcd_, labels_ = self._detect_non_vertical(pcd_) 250 | logger.debug(f'Done. Found {len(np.unique(labels))-1} primitives') 251 | 252 | # Merge 253 | pcd = merge_point_clouds(pcd.select_by_index(idx, invert=True), pcd_) 254 | labels_[labels_>-1] += labels.max()+1 255 | labels = np.concatenate((labels[labels!=-1], labels_)) 256 | primitives = self._labels_to_primitives(pcd, labels) 257 | 258 | return pcd, primitives, labels 259 | 260 | def compute_verticality(pcd, radius): 261 | '''Bla''' 262 | pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamRadius(radius=radius)) 263 | verticality = 1 - np.abs(np.asarray(pcd.normals)[:,2]) 264 | return verticality 265 | 266 | def compute_planarity(pcd, radius): 267 | '''Bla''' 268 | pcd.estimate_covariances( 269 | search_param=o3d.geometry.KDTreeSearchParamRadius(radius=radius)) 270 | eig_val, _ = np.linalg.eig(np.asarray(pcd.covariances)) 271 | eig_val = np.sort(eig_val, axis=1) 272 | planarity = (eig_val[:,1]-eig_val[:,0])/eig_val[:,2] 273 | return planarity 274 | 275 | def efficient_ransac(pcd, excecutable_path, file_path, normals_radius=.12, prob='0.001', 276 | min_pts='200', eps='0.03', cluster_thres='0.12', normal_thres='0.5'): 277 | '''Bla''' 278 | 279 | labels = np.full(len(pcd.points),-1) 280 | 281 | try: 282 | # Compute normals 283 | if not pcd.has_normals(): 284 | pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamRadius(radius=normals_radius)) 285 | 286 | # Write point cloud 287 | o3d.io.write_point_cloud(file_path, pcd) 288 | 289 | # RANSAC 290 | subprocess.run([excecutable_path, file_path, file_path, prob, min_pts, eps, cluster_thres, normal_thres], 291 | timeout=20, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 292 | 293 | # Read point cloud 294 | pcd_ = o3d.t.io.read_point_cloud(file_path) 295 | pcd = pcd_.to_legacy() 296 | labels = np.hstack(pcd_.point['plane_index'].numpy()) 297 | 298 | except subprocess.TimeoutExpired: 299 | logger.error('RANSAC timeout') 300 | except subprocess.CalledProcessError as CPE: 301 | logger.error(f'Error in RANSAC with returncode {CPE.returncode}.') 302 | except Exception as e: 303 | logger.error(str(e)) 304 | 305 | if os.path.isfile(file_path): 306 | os.remove(file_path) 307 | 308 | return pcd, labels 309 | 310 | def unit_vector(vector): 311 | """ Returns the unit vector of the vector. """ 312 | return vector / np.linalg.norm(vector) 313 | 314 | def angle_between(v1, v2): 315 | """ Returns the angle in radians between vectors 'v1' and 'v2':: 316 | 317 | >>> angle_between((1, 0, 0), (0, 1, 0)) 318 | 1.5707963267948966 319 | >>> angle_between((1, 0, 0), (1, 0, 0)) 320 | 0.0 321 | >>> angle_between((1, 0, 0), (-1, 0, 0)) 322 | 3.141592653589793 323 | """ 324 | v1_u = unit_vector(v1) 325 | v2_u = unit_vector(v2) 326 | return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) 327 | 328 | def consecutive_labels(labels): 329 | labels[labels>-1] = np.unique(labels[labels>-1], return_inverse=True)[1] 330 | return labels 331 | 332 | def bbox_area(bbox): 333 | return np.linalg.norm(bbox[0,:]-bbox[1,:]) * np.linalg.norm(bbox[2,:]-bbox[1,:]) 334 | -------------------------------------------------------------------------------- /ipcp/modules/room_detection.py: -------------------------------------------------------------------------------- 1 | """Room Detection Module""" 2 | import os 3 | import logging 4 | 5 | import cv2 6 | import open3d as o3d 7 | import numpy as np 8 | import random as rng 9 | import open3d as o3d 10 | from scipy import signal 11 | import matplotlib.pyplot as plt 12 | from shapely.geometry import Polygon 13 | import src.utils.clip_utils as clip_utils 14 | import src.utils.math_utils as math_utils 15 | from skspatial.objects import Plane 16 | import networkx as nx 17 | from scipy.stats import binned_statistic_2d 18 | from scipy.ndimage import binary_closing, binary_dilation, binary_erosion, generate_binary_structure, binary_fill_holes 19 | import rasterio 20 | from rasterio import features 21 | import shapely 22 | from shapely.geometry import Point, Polygon 23 | from shapely.ops import transform 24 | from skimage import measure 25 | from src.interpolation import FastGridInterpolator 26 | 27 | logger = logging.getLogger(__name__) 28 | 29 | # Point labels 30 | NOISE = 0 31 | SLANTED = 1 32 | ALMOST_VERTICAL = 2 33 | ALMOST_HORIZONTAL = 3 34 | 35 | # Primitive Connections 36 | WALL_WALL = 1 37 | WALL_CEILING = 2 38 | WALL_FLOOR = 3 39 | CEILING_CEILING = 4 40 | WALL_SLANTEDWALL = 5 41 | 42 | # Primitive Classes 43 | WALL = 1 44 | CEILING = 2 45 | FLOOR = 3 46 | SLANTED_WALL = 4 47 | CLUTTER = 5 48 | 49 | class RoomDetector: 50 | """ 51 | RoomDetector class for the detection of rooms in a pointcloud floor. 52 | The class ..... 53 | 54 | Parameters 55 | ---------- 56 | min_peak_height : int (default=1500) 57 | The required height of a peak in the vertical density profile. 58 | threshold : int (default=250) 59 | The required threshold of peaks, the vertical distance to its neighboring samples. 60 | distance : int (default=20) 61 | The required minimal horizontal distance (>= 1) in samples between neighbouring peaks. 62 | prominence : int (default=2) 63 | The required prominence of peaks. 64 | thickness : int (default=20) 65 | The description of the parameter. 66 | """ 67 | 68 | def __init__(self, subsample_size=0.03, plot=False): 69 | self.subsample_size = subsample_size 70 | self.plot = plot 71 | 72 | def _get_edge_type(self, primitive_i, primitive_j): 73 | if primitive_i['type'] == ALMOST_VERTICAL and primitive_j['type'] == ALMOST_VERTICAL: 74 | return WALL_WALL 75 | elif primitive_i['type'] == ALMOST_VERTICAL: 76 | if primitive_i['bbox'][:,2].mean() > primitive_j['bbox'][:,2].max(): 77 | return WALL_FLOOR 78 | else: 79 | return WALL_CEILING 80 | elif primitive_j['type'] == ALMOST_VERTICAL: 81 | if primitive_i['bbox'][:,2].max() < primitive_j['bbox'][:,2].mean(): 82 | return WALL_FLOOR 83 | else: 84 | return WALL_CEILING 85 | else: 86 | return CEILING_CEILING 87 | 88 | def _find_neighbouring_primitives(self, points, labels, primitives): 89 | neighbours = set() 90 | buffer = .5 91 | 92 | for _,r in primitives.items(): 93 | bbox = r['bbox'] 94 | rp = Polygon(bbox[:,:2]).buffer(buffer) 95 | c_mask = clip_utils.poly_box_clip(points, rp, bbox[:,2].min()-buffer, bbox[:,2].max()+buffer) 96 | for rn in np.unique(labels[c_mask]): 97 | if rn != r['region'] and rn in primitives.keys(): 98 | neighbours.add(tuple(sorted((int(rn),r['region'])))) 99 | 100 | logger.debug(f'Found {len(neighbours)} neighbouring planes.') 101 | return neighbours 102 | 103 | def _adjacency_graph(self, points, labels, primitives, neighbours): 104 | 105 | G = nx.Graph() 106 | G.add_nodes_from([(i,{'type':r['type']}) for i,r in primitives.items()]) 107 | 108 | d_adj = .25 109 | l_intersect = .2 110 | 111 | # Create edges 112 | valid_pairs = 0 113 | for (i,j) in neighbours: 114 | angle_ij = math_utils.vector_angle(primitives[i]['normal'], primitives[j]['normal']) 115 | angle_ij = 90-abs(90-angle_ij) 116 | if angle_ij > 5: 117 | plane_i = Plane(primitives[i]['bbox'].mean(axis=0), primitives[i]['normal']) 118 | plane_j = Plane(primitives[j]['bbox'].mean(axis=0), primitives[j]['normal']) 119 | ij_intersect = plane_i.intersect_plane(plane_j) 120 | dist_i = math_utils.line_dist(points[labels==i], ij_intersect.point, ij_intersect.vector) 121 | dist_j = math_utils.line_dist(points[labels==j], ij_intersect.point, ij_intersect.vector) 122 | if np.sum(dist_i10 and np.sum(dist_j 10: 123 | proj_i = np.dot((points[labels==i][dist_il_intersect: 128 | edge_type = self._get_edge_type(primitives[i],primitives[j]) 129 | G.add_edges_from([(i, j, {'type':edge_type})]) 130 | valid_pairs+=1 131 | 132 | logger.debug(f'Number of valid pairs: {valid_pairs}') 133 | return G 134 | 135 | def _classify_graph(self, G): 136 | for V in nx.nodes(G): 137 | if G.nodes[V]['type'] == ALMOST_VERTICAL: 138 | if np.sum([G.edges[V,j]['type'] == WALL_CEILING for j in G[V]]) >= 1: 139 | G.nodes[V]['label'] = WALL 140 | elif np.sum([G.edges[V,j]['type'] == WALL_WALL for j in G[V]]) > 0 and \ 141 | np.sum([G.edges[V,j]['type'] == WALL_SLANTEDWALL for j in G[V]]) > 0: 142 | G.nodes[V]['label'] = SLANTED_WALL 143 | else: 144 | G.nodes[V]['label'] = CLUTTER 145 | elif G.nodes[V]['type'] == ALMOST_HORIZONTAL: # MISSING wall-wall edge count 146 | if np.sum([G.edges[V,j]['type'] == WALL_CEILING for j in G[V]]) >= 2: 147 | G.nodes[V]['label'] = CEILING 148 | elif np.sum([G.edges[V,j]['type'] == WALL_FLOOR for j in G[V]]) >= 2: 149 | G.nodes[V]['label'] = FLOOR 150 | else: 151 | G.nodes[V]['label'] = CLUTTER 152 | elif G.nodes[V]['type'] == SLANTED: # MISSING wall-wall edge count 153 | if np.sum([G.edges[V,j]['type'] == WALL_CEILING for j in G[V]]) >= 2: 154 | G.nodes[V]['label'] = CEILING 155 | else: 156 | G.nodes[V]['label'] = CLUTTER 157 | else: 158 | G.nodes[V]['label'] = CLUTTER 159 | 160 | return G 161 | 162 | def _get_rooms(self, projection, origin, bin_size): 163 | room_labels = measure.label(~projection, background=0) 164 | min_surface = 0.6 165 | min_width = 0.65 166 | 167 | # Removes pillars and inner walls in top down projection 168 | for i in np.unique(room_labels): 169 | if i > 0: 170 | lcc_pts = np.vstack(np.where(room_labels==i)).T 171 | if len(lcc_pts) < min_surface/(bin_size**2): # min. sqr meters 172 | room_labels[np.where(room_labels==i)] = 0 173 | else: 174 | min_dims, max_dims = math_utils.minimum_bounding_rectangle(lcc_pts)[2:4] 175 | if min_dims < min_width / bin_size: # min. room dimension 176 | room_labels[np.where(room_labels==i)] = 0 177 | else: 178 | room_labels[binary_closing(room_labels==i, structure=generate_binary_structure(2, 2), iterations=2)] = i 179 | 180 | if self.plot: 181 | plt.figure(figsize=(6, 6)) 182 | plt.subplot(111) 183 | plt.imshow(room_labels.T, cmap='nipy_spectral') 184 | plt.axis('off') 185 | 186 | # Convert to polygons 187 | rooms = [] 188 | for shape, value in features.shapes(room_labels.astype(np.int16), mask=(room_labels>0), transform=rasterio.Affine(1.0, 0, 0, 0, 1.0, 0)): 189 | room_polygon = shapely.geometry.shape(shape).simplify(0.75) 190 | room_polygon = transform(lambda x, y, z=None: (y*bin_size+origin[0], x*bin_size+origin[1]), room_polygon) 191 | shape_proj = binary_fill_holes(room_labels==value) 192 | rooms.append((room_polygon, shape_proj)) 193 | 194 | return rooms 195 | 196 | def _clean_mid_horizontal(self, pcd, labels, primitives): 197 | 198 | horizontal_labels = [i for i,k in primitives.items() if k['type'] != ALMOST_VERTICAL] 199 | horizontal_mask = np.isin(labels, horizontal_labels) 200 | points = np.asarray(pcd.points)[horizontal_mask] 201 | 202 | x_bins = np.arange(points[:,0].min(), points[:,0].max()+.125, 0.25) 203 | y_bins = np.arange(points[:,1].min(), points[:,1].max()+.125, 0.25) 204 | 205 | stat_min = binned_statistic_2d(points[:,0], points[:,1], points[:,2], 'min', bins=[x_bins, y_bins])[0] 206 | min_z = FastGridInterpolator(x_bins, y_bins, stat_min) 207 | floor_offset = points[:,2] - min_z(points) 208 | 209 | stat_max = binned_statistic_2d(points[:,0], points[:,1], points[:,2], 'max', bins=[x_bins, y_bins])[0] 210 | max_z = FastGridInterpolator(x_bins, y_bins, stat_max) 211 | ceil_offset = max_z(points) - points[:,2] 212 | 213 | for label in horizontal_labels: 214 | label_mask = labels[horizontal_mask] == label 215 | if floor_offset[label_mask].mean() > .4 and ceil_offset[label_mask].mean() > .4: 216 | labels[labels==label] = -1 217 | if label in primitives: 218 | del primitives[label] 219 | 220 | def _labels_to_primitives(self, pcd, labels, exclude_labels=[-1]): 221 | planes = {} 222 | # TODO: change to box_area 223 | rectangle_labels = [r for r in np.unique(labels) if r not in exclude_labels and np.sum(labels==r) > .3/(self.subsample_size**2)] 224 | for i in rectangle_labels: 225 | region_cloud = pcd.select_by_index(np.where(labels==i)[0]) 226 | region_pts = np.asarray(region_cloud.points) 227 | 228 | # TODO: filter outliers out! 229 | a, b, c, d = region_cloud.segment_plane(distance_threshold=0.01, 230 | ransac_n=5, 231 | num_iterations=100)[0] 232 | normal = np.array([a,b,c]) 233 | normal /= np.linalg.norm(normal) 234 | center = np.mean(region_pts,axis=0) 235 | slope = np.rad2deg(np.arccos(np.abs(normal[2] / 1))) 236 | 237 | # Compute bounding box 238 | if slope < 5: 239 | plane_type = ALMOST_HORIZONTAL 240 | bbox_points = math_utils.minimum_bounding_rectangle(region_pts[:,:2])[0] 241 | bbox_points = np.concatenate((bbox_points, np.full((4,1), center[2])),axis=1) 242 | else: 243 | xaxis = np.cross(normal, [0, 0, 1]) 244 | yaxis = np.cross(normal, xaxis) 245 | xaxis /= np.linalg.norm(xaxis) 246 | yaxis /= np.linalg.norm(yaxis) 247 | 248 | new_x = np.dot(region_pts-center, xaxis) 249 | new_y = np.dot(region_pts-center, yaxis) 250 | 251 | xmin, ymin, xmax, ymax = math_utils.compute_bounding_box(np.vstack([new_x, new_y]).T) 252 | bbox_points = np.array([[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]]) 253 | bbox_points = center + bbox_points[:,0][:, None]*xaxis + bbox_points[:,1][:, None]*yaxis 254 | if slope > 80: 255 | plane_type = ALMOST_VERTICAL 256 | else: 257 | plane_type = SLANTED 258 | 259 | surface = bbox_area(bbox_points) 260 | line_set = o3d.geometry.LineSet( 261 | points=o3d.utility.Vector3dVector(bbox_points), 262 | lines=o3d.utility.Vector2iVector([[0, 1],[1, 2],[2, 3],[3, 0]]), 263 | ) 264 | 265 | plane_object = { 266 | 'region': i, 267 | 'bbox': bbox_points, 268 | 'lineset': line_set, 269 | 'surface': surface, 270 | 'center': center, 271 | 'normal': normal, 272 | 'D': d, 273 | 'slope': slope, 274 | 'type': plane_type 275 | } 276 | planes[i] = plane_object 277 | 278 | return planes 279 | 280 | def process(self, pcd, labels): 281 | """ 282 | Parameters 283 | ---------- 284 | points : array of shape (n_points, 3) 285 | The point cloud of a floor. 286 | 287 | Returns 288 | ------- 289 | An array of masks, where each mask represents a floor in the pointcloud. 290 | """ 291 | 292 | points = np.asarray(pcd.points) 293 | logger.debug(f'detecting rooms in floor of {len(points)} points') 294 | 295 | # Classify primitives 296 | logger.debug(f'Classify primitives...') 297 | primitives = self._labels_to_primitives(pcd, labels) 298 | 299 | # Clear middle planes 300 | self._clean_mid_horizontal(pcd, labels, primitives) 301 | 302 | # Primitive adjacency classification 303 | neighbours = self._find_neighbouring_primitives(points, labels, primitives) 304 | primitive_graph = self._adjacency_graph(points, labels, primitives, neighbours) 305 | primitive_graph = self._classify_graph(primitive_graph) 306 | ceiling_nodes = [v for v in primitive_graph.nodes() if primitive_graph.nodes[v]['label']==CEILING] 307 | floor_nodes = [v for v in primitive_graph.nodes() if primitive_graph.nodes[v]['label']==FLOOR] 308 | wall_nodes = [v for v in primitive_graph.nodes() if primitive_graph.nodes[v]['label']==WALL] 309 | slantedwall_nodes = [v for v in primitive_graph.nodes() if primitive_graph.nodes[v]['label']==SLANTED_WALL] 310 | clutter_nodes = [v for v in primitive_graph.nodes() if primitive_graph.nodes[v]['label']==CLUTTER] 311 | prim_regions = wall_nodes+floor_nodes+ceiling_nodes 312 | logger.debug(f'{(len(ceiling_nodes),len(floor_nodes),len(wall_nodes),len(slantedwall_nodes),len(clutter_nodes))}') 313 | 314 | # plot 315 | pcd_group_1 = pcd.select_by_index(np.where(np.isin(labels, floor_nodes))[0]) 316 | pcd_group_1 = pcd_group_1.paint_uniform_color([1.0,0.3,0.3]) 317 | pcd_group_2 = pcd.select_by_index(np.where(np.isin(labels, ceiling_nodes))[0]) 318 | pcd_group_2 = pcd_group_2.paint_uniform_color([0.2,0.2,1.0]) 319 | pcd_group_3 = pcd.select_by_index(np.where(np.isin(labels, wall_nodes))[0]) 320 | pcd_group_3 = pcd_group_3.paint_uniform_color([0.2,1.0,0.2]) 321 | pcd_group_4 = pcd.select_by_index(np.where(np.isin(labels, clutter_nodes))[0]) 322 | pcd_group_4 = pcd_group_4.paint_uniform_color([0,0,0]) 323 | pcd_group_5 = pcd.select_by_index(np.where(np.isin(labels, slantedwall_nodes))[0]) 324 | pcd_group_5 = pcd_group_5.paint_uniform_color([0,1,1]) 325 | if self.plot: 326 | o3d.visualization.draw_geometries([pcd_group_1, pcd_group_2, pcd_group_3, pcd_group_4]) 327 | 328 | # Detect Rooms 329 | logger.debug(f'Detect rooms...') 330 | 331 | # create grid 332 | bin_size = 0.05 333 | x_bins = np.arange(points[:,0].min()-bin_size/2,points[:,0].max()+bin_size,bin_size) 334 | y_bins = np.arange(points[:,1].min()-bin_size/2,points[:,1].max()+bin_size,bin_size) 335 | origin = [x_bins[0]+bin_size/2,y_bins[0]-bin_size/2] 336 | 337 | # Create projection 338 | walls = np.zeros((len(x_bins)-1,len(y_bins)-1)) 339 | for i,r in primitives.items(): 340 | if i in wall_nodes: 341 | length = np.linalg.norm(r['bbox'][0,:2]-r['bbox'][1,:2]) 342 | line = np.linspace(r['bbox'][0,:2],r['bbox'][1,:2], int(length*20)) 343 | walls += np.histogram2d(line[:,0],line[:,1],[x_bins, y_bins])[0] 344 | walls = walls>0 345 | walls = binary_dilation(walls, structure=generate_binary_structure(2, 2), iterations=3, border_value=1) 346 | walls = binary_erosion(walls, structure=generate_binary_structure(2, 2), iterations=2) 347 | ceiling_mask = np.isin(labels, ceiling_nodes + floor_nodes) 348 | projection, xedges, yedges = np.histogram2d(points[ceiling_mask,0], points[ceiling_mask,1], bins=[x_bins,y_bins]) 349 | projection = binary_closing(projection!=0, np.ones((3,3)), iterations=2) 350 | projection = np.logical_or(~projection,walls) 351 | if self.plot: 352 | plt.figure(figsize=(6, 6)) 353 | plt.subplot(111) 354 | plt.imshow(projection.T) 355 | plt.axis('off') 356 | 357 | # Get room polygons 358 | room_polys = self._get_rooms(projection, origin, bin_size) 359 | 360 | room_mask = np.zeros((len(points),len(room_polys)), dtype=bool) 361 | for i, item in enumerate(room_polys): 362 | room_poly = item[0] 363 | clip_mask = clip_utils.poly_clip(points, room_poly.buffer(0.2)) 364 | 365 | search = binary_dilation(binary_erosion(item[1]) ^ binary_dilation(item[1]), iterations=4) 366 | wall_mask = clip_mask & np.isin(labels, wall_nodes) 367 | walls = np.histogram2d(points[wall_mask,0],points[wall_mask,1],[x_bins, y_bins])[0]>0 368 | walls_dil = binary_dilation(walls, iterations=6) 369 | search[walls_dil] = False 370 | 371 | # Add missing walls 372 | outside_primitives = [i for i in np.unique(labels[clip_mask]) if i in clutter_nodes] 373 | track = walls.astype(int) 374 | news = [] 375 | for label in outside_primitives: 376 | label_mask = (labels==label) & clip_mask 377 | label_grid = np.histogram2d(points[label_mask,0],points[label_mask,1],[x_bins, y_bins])[0]>0 378 | if search[label_grid].sum() > 10: 379 | track[label_grid] = 2 380 | news.append(label) 381 | wall_nodes.append(label) 382 | clutter_nodes.remove(label) 383 | 384 | # Clean inside walls 385 | # TODO: delete all points? 386 | room_inside = binary_erosion(item[1], iterations=6) 387 | room_primitives = [i for i in np.unique(labels[clip_mask]) if i in wall_nodes] 388 | track = room_inside.astype(int) 389 | inside = [] 390 | for label in room_primitives: 391 | label_mask = (labels==label) & clip_mask 392 | label_grid = np.histogram2d(points[label_mask,0],points[label_mask,1],[x_bins, y_bins])[0]>0 393 | if label_grid[~room_inside].sum() == 0: 394 | track[label_grid] = 2 395 | inside.append(label) 396 | clutter_nodes.append(label) 397 | wall_nodes.remove(label) 398 | 399 | prim_regions = wall_nodes+floor_nodes+ceiling_nodes 400 | room_mask[:,i] = clip_mask & np.isin(labels, prim_regions) 401 | 402 | return room_mask, labels #, room_polys, (wall_nodes,floor_nodes,ceiling_nodes) 403 | 404 | def bbox_area(bbox): 405 | return np.linalg.norm(bbox[0,:]-bbox[1,:]) * np.linalg.norm(bbox[2,:]-bbox[1,:]) 406 | -------------------------------------------------------------------------------- /ipcp/modules/room_reconstruct.py: -------------------------------------------------------------------------------- 1 | """room_reconstruct Module""" 2 | import numpy as np 3 | import logging 4 | import os 5 | 6 | import subprocess 7 | import pymeshlab 8 | from pathlib import Path 9 | import src.utils.pcd_utils as pcd_utils 10 | import open3d as o3d 11 | import time 12 | import shutil 13 | 14 | logger = logging.getLogger(__name__) 15 | 16 | ALMOST_HORIZONTAL = 3 17 | 18 | class RoomReconstructor: 19 | """ 20 | PlaneReconstruct class for the reconstruction of a point cloud 21 | to a mesh. 22 | 23 | Parameters 24 | ---------- 25 | fitting : float (default=0.25) 26 | The fitting parameter for PolyFit. 27 | coverage : float (default=0.45) 28 | The coverage parameter for PolyFit. 29 | complexity : float (default=0.60) 30 | The complexity parameter for PolyFit. 31 | """ 32 | 33 | def __init__(self, exe_ransac_polyfiy, exe_polyfit, fitting=0.5, coverage=0.27, 34 | complexity=0.23): 35 | self.exe_ransac_polyfiy = exe_ransac_polyfiy 36 | self.exe_polyfit = exe_polyfit 37 | self.fitting = fitting 38 | self.coverage = coverage 39 | self.complexity = complexity 40 | 41 | def _clean_mesh(self, meshset): 42 | meshset.meshing_remove_duplicate_vertices() 43 | meshset.meshing_merge_close_vertices() 44 | meshset.meshing_re_orient_faces_coherentely() 45 | volume = meshset.get_geometric_measures()['mesh_volume'] 46 | if volume < 0: 47 | meshset.meshing_invert_face_orientation() 48 | volume = meshset.get_geometric_measures()['mesh_volume'] 49 | 50 | return meshset 51 | 52 | def _add_missing(self, points, labels, primitives): 53 | horizontal_z = [points[labels==l,2].min() for l in np.unique(labels) if l in primitives.keys() and primitives[l]['type'] == ALMOST_HORIZONTAL] 54 | if len(horizontal_z) == 0 or np.min(horizontal_z) - points[:,2].min() > 0.75: 55 | logger.info("No floor, create") 56 | bin_size = 0.15 57 | x_bins = np.arange(points[:,0].min(),points[:,0].max(),bin_size) 58 | y_bins = np.arange(points[:,1].min(),points[:,1].max(),bin_size) 59 | projection, xedges, yedges = np.histogram2d(points[:,0], points[:,1], bins=(x_bins, y_bins)) 60 | coords = np.where(projection>0) 61 | coords = np.vstack([xedges[coords[0]], yedges[coords[1]], np.full(len(coords[0]), points[:,2].min())]).T 62 | points = np.append(points, coords, axis=0) 63 | labels = np.append(labels, np.full(len(coords), labels.max()+1)) 64 | 65 | return points, labels 66 | 67 | def _ransac_polyfit(self, pcd): 68 | 69 | meshset = None 70 | try: 71 | # Write file 72 | logger.debug(f'Write temp file to {self.ply_infile}') 73 | pcd = o3d.t.geometry.PointCloud(pcd.point['positions']) 74 | o3d.t.io.write_point_cloud(self.ply_infile, pcd, write_ascii=True) 75 | 76 | logger.debug(f'Run PolyFit') 77 | subprocess.run([self.exe_ransac_polyfiy, self.ply_infile, self.dir_path,'0.01','100','0.1','1.5','0.6','0.5','0.27','0.23'], 78 | timeout=10, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 79 | 80 | meshset = pymeshlab.MeshSet() 81 | meshset.load_new_mesh(self.mesh_outfile) 82 | meshset = self._clean_mesh(meshset) 83 | 84 | logger.debug('Succes.') 85 | 86 | except subprocess.TimeoutExpired: 87 | logger.debug('Ployfit timeout') 88 | except subprocess.CalledProcessError as CPE: 89 | logger.debug(f'Error in Ployfit with returncode {CPE.returncode}.') 90 | except Exception as e: 91 | logger.debug('Hai', str(e)) 92 | 93 | return meshset 94 | 95 | def _user_polyfit(self, pcd): 96 | 97 | meshset = None 98 | 99 | try: 100 | # Write file 101 | logger.debug(f'Write temp file to {self.ply_infile}') 102 | o3d.t.io.write_point_cloud(self.ply_infile, pcd, write_ascii=True) 103 | 104 | logger.debug(f'Run PolyFit') 105 | 106 | subprocess.run([self.exe_polyfit, self.ply_infile, self.dir_path, 107 | str(self.fitting), str(self.coverage), 108 | str(self.complexity)], timeout=15, 109 | check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) 110 | 111 | meshset = pymeshlab.MeshSet() 112 | meshset.load_new_mesh(self.mesh_outfile) 113 | meshset = self._clean_mesh(meshset) 114 | 115 | logger.debug('Succes.') 116 | 117 | except: 118 | meshset = self._ransac_polyfit(pcd) 119 | 120 | return meshset 121 | 122 | def process(self, points, primitive_index, primitives): 123 | """ 124 | Parameters 125 | ---------- 126 | points : array of shape (n_points, 3) 127 | The point cloud of a room to reconsturct. 128 | 129 | Returns 130 | ------- 131 | mesh : MeshSet 132 | The volume of the input mesh. 133 | """ 134 | logger.debug(f'Room reconstruction for pointcloud of {len(points)} points and {len(np.unique(primitive_index))} faces') 135 | 136 | meshset = None 137 | 138 | self.dir_path = './tmp_pr' 139 | self.ply_infile = self.dir_path + '/polyfit_input.ply' 140 | self.mesh_outfile = self.dir_path + '/polyfit_result.obj' 141 | if not os.path.isdir(self.dir_path): 142 | os.mkdir(self.dir_path) 143 | 144 | # points, primitive_index = self._add_missing(points, primitive_index, primitives) 145 | 146 | pcd = o3d.t.geometry.PointCloud(o3d.core.Tensor(points, o3d.core.float32)) 147 | segment_index = np.unique(primitive_index, return_inverse=True)[1][:,np.newaxis] 148 | pcd.point['segment_index'] = o3d.core.Tensor(segment_index, o3d.core.int32) 149 | 150 | if len(points) > 20000: 151 | pcd = pcd.voxel_down_sample(0.1) 152 | 153 | meshset = self._user_polyfit(pcd) 154 | 155 | shutil.rmtree('./tmp_pr') 156 | 157 | return meshset 158 | -------------------------------------------------------------------------------- /ipcp/notebooks/Complete Pipeline.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import sys\n", 10 | "sys.path.append('../')" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "import numpy as np\n", 20 | "import open3d as o3d\n", 21 | "import time\n", 22 | "import logging\n", 23 | "from tqdm import tqdm\n", 24 | "import pymeshlab\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "\n", 27 | "from src.utils import pcd_utils\n", 28 | "from src.utils import cityjson_utils\n", 29 | "from src.utils import log_utils\n", 30 | "from src.utils import plot_utils\n", 31 | "\n", 32 | "from preprocessors.sor import SOR\n", 33 | "from preprocessors.spatial_subsample import SpatialSubsample\n", 34 | "from modules.floor_split import FloorSplitter\n", 35 | "from modules.room_detection import RoomDetector\n", 36 | "from modules.primitive_detection import PrimitiveDetector\n", 37 | "from modules.room_reconstruct import RoomReconstructor\n", 38 | "from modules.mesh_stats import MeshAnalyser" 39 | ] 40 | }, 41 | { 42 | "cell_type": "markdown", 43 | "metadata": {}, 44 | "source": [ 45 | "#### Load the interior PointCloud" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": null, 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [ 54 | "in_file = '../datasets/apt_subsampled.ply'" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": null, 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "pcd = pcd_utils.read_pointcloud(in_file)\n", 64 | "len(pcd.points)" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "metadata": {}, 70 | "source": [ 71 | "#### 1. Preprocessing" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": null, 77 | "metadata": {}, 78 | "outputs": [], 79 | "source": [ 80 | "ss = SpatialSubsample(min_distance=0.03)\n", 81 | "sor = SOR(knn=6, n_sigma=2)\n", 82 | "\n", 83 | "preprocessors = [ss, sor]\n", 84 | "\n", 85 | "for obj in preprocessors:\n", 86 | " start = time.time()\n", 87 | " pcd = obj.process(pcd)\n", 88 | " duration = time.time() - start\n", 89 | " print(f'Processor finished in {duration:.2f}s, {len(pcd.points)} points.') " 90 | ] 91 | }, 92 | { 93 | "cell_type": "markdown", 94 | "metadata": {}, 95 | "source": [ 96 | "#### 2. Primitive Detection" 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": null, 102 | "metadata": {}, 103 | "outputs": [], 104 | "source": [ 105 | "ransac_exe_path = '../cpp_modules/efficient_ransac'\n", 106 | "primitive_detector = PrimitiveDetector(ransac_exe_path)\n", 107 | "\n", 108 | "pcd, primitives, primitive_labels = primitive_detector.process(pcd)" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": null, 114 | "metadata": {}, 115 | "outputs": [], 116 | "source": [ 117 | "## Comment line when running as docker\n", 118 | "# plot_utils.show_pcd(pcd, primitive_labels)" 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": {}, 124 | "source": [ 125 | "#### 3. Detect Floors" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": null, 131 | "metadata": {}, 132 | "outputs": [], 133 | "source": [ 134 | "floor_splitter = FloorSplitter()\n", 135 | "floors = floor_splitter.process(pcd, primitive_labels, primitives)\n", 136 | "\n", 137 | "print(f'Done. Detected {len(floors)} floors.')" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": null, 143 | "metadata": {}, 144 | "outputs": [], 145 | "source": [ 146 | "## Comment line when running as docker\n", 147 | "# plot_utils.show_pcd_floors(pcd, floors)" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "#### 4. Room Detection" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": null, 160 | "metadata": {}, 161 | "outputs": [], 162 | "source": [ 163 | "room_detector = RoomDetector(plot=False)\n", 164 | "\n", 165 | "rooms = []\n", 166 | "for floor_mask in floors:\n", 167 | " floor_pcd = pcd.select_by_index(np.where(floor_mask)[0])\n", 168 | " floor_labels = primitive_labels[floor_mask]\n", 169 | " floor_rooms, floor_labels = room_detector.process(floor_pcd, floor_labels)\n", 170 | " primitive_labels[floor_mask] = floor_labels\n", 171 | " for room_i in range(floor_rooms.shape[1]):\n", 172 | " room_mask = np.zeros(len(pcd.points), dtype=bool)\n", 173 | " room_mask[floor_mask] = floor_rooms[:,room_i]\n", 174 | " rooms.append(room_mask)\n", 175 | " \n", 176 | "print(f'Done. Detected {len(rooms)} rooms.')" 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "metadata": {}, 182 | "source": [ 183 | "#### 5. Room Reconstruction" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": null, 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "room_reconstructor = RoomReconstructor('../cpp_modules/polyfit_ransac', '../cpp_modules/polyfit')\n", 193 | "\n", 194 | "room_meshes = []\n", 195 | "failed = []\n", 196 | "for i in tqdm(np.arange(len(rooms))):\n", 197 | " room_mask = rooms[i]\n", 198 | " room_pts = np.asarray(pcd.points)[room_mask]\n", 199 | "\n", 200 | " meshset = room_reconstructor.process(room_pts, primitive_labels[room_mask], primitives)\n", 201 | " if meshset is None:\n", 202 | " failed.append(i)\n", 203 | " else:\n", 204 | " room_meshes.append(meshset)\n", 205 | "\n", 206 | "if len(failed) > 0: \n", 207 | " print(f'Failed to reconstruct rooms {failed}')" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "#### 6. CityJSON Export" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": null, 220 | "metadata": {}, 221 | "outputs": [], 222 | "source": [ 223 | "out_path = '../datasets/output.city.json'\n", 224 | "\n", 225 | "cityjson = cityjson_utils.to_cityjson_v1(room_meshes)\n", 226 | "cityjson_utils.save_to_file(cityjson, out_path)" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": {}, 232 | "source": [ 233 | "#### 7. Room Statistics" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": null, 239 | "metadata": {}, 240 | "outputs": [], 241 | "source": [ 242 | "mesh_analyser = MeshAnalyser() \n", 243 | "\n", 244 | "for i in range(len(room_meshes)):\n", 245 | " room_mesh = room_meshes[i]\n", 246 | " volume, floorarea = mesh_analyser.process(room_mesh)\n", 247 | " try: \n", 248 | " print(f'Room {i}: {volume:.2f}, {floorarea:.2f}')\n", 249 | " except:\n", 250 | " print(f'Room {i} analysis failed.')" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": null, 256 | "metadata": {}, 257 | "outputs": [], 258 | "source": [] 259 | } 260 | ], 261 | "metadata": { 262 | "kernelspec": { 263 | "display_name": "Python 3 (ipykernel)", 264 | "language": "python", 265 | "name": "python3" 266 | }, 267 | "language_info": { 268 | "codemirror_mode": { 269 | "name": "ipython", 270 | "version": 3 271 | }, 272 | "file_extension": ".py", 273 | "mimetype": "text/x-python", 274 | "name": "python", 275 | "nbconvert_exporter": "python", 276 | "pygments_lexer": "ipython3", 277 | "version": "3.8.10" 278 | }, 279 | "vscode": { 280 | "interpreter": { 281 | "hash": "f57f7d9456f5b7dc730d015ec7d854ad65788762049c2fb9123860f88d8941f7" 282 | } 283 | } 284 | }, 285 | "nbformat": 4, 286 | "nbformat_minor": 2 287 | } 288 | -------------------------------------------------------------------------------- /ipcp/preprocessors/__intit__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/ipcp/preprocessors/__intit__.py -------------------------------------------------------------------------------- /ipcp/preprocessors/sor.py: -------------------------------------------------------------------------------- 1 | """Statistical Outlier Removal Preprocessor""" 2 | import numpy as np 3 | import logging 4 | import open3d as o3d 5 | 6 | logger = logging.getLogger(__name__) 7 | 8 | class SOR: 9 | """ 10 | Preprocessor class for the statistical outlier removal of pointcloud data. 11 | It computes first the average distance of each point to its neighbors. 12 | Then it rejects the points that are farther than the average distance plus 13 | a number of times the standard deviation (the max distance will be: 14 | average distance + n * standard deviation). 15 | 16 | Parameters 17 | ---------- 18 | knn : int (default=6) 19 | The number of neighbours that will be used to compute 20 | the 'mean distance to neighbors' for each point. 21 | n_sigma : float (default=1.0) 22 | The standard deviation. 23 | """ 24 | 25 | def __init__(self, knn=6, n_sigma=1.0): 26 | """ Init variables """ 27 | self.knn = knn 28 | self.n_sigma = n_sigma 29 | 30 | def process(self, pcd): 31 | """ 32 | Parameters 33 | ---------- 34 | points : array of shape (n_points, 3) 35 | The point cloud . 36 | """ 37 | 38 | logger.info(f'Removing outliers...') 39 | 40 | refCloud, _ = pcd.remove_statistical_outlier(nb_neighbors=self.knn, std_ratio=self.n_sigma) 41 | return refCloud 42 | -------------------------------------------------------------------------------- /ipcp/preprocessors/spatial_subsample.py: -------------------------------------------------------------------------------- 1 | """Spatial Subsample Preprocessor""" 2 | import numpy as np 3 | import logging 4 | import open3d as o3d 5 | 6 | logger = logging.getLogger(__name__) 7 | 8 | class SpatialSubsample: 9 | """ 10 | Preprocessor class for the subsampling of pointcloud data. 11 | The class reduce the number of points in the pointcloud using spatial. 12 | 13 | Parameters 14 | ---------- 15 | min_distance : float (default=0.05) 16 | The minimal space between points. 17 | """ 18 | 19 | def __init__(self, min_distance=0.05): 20 | """ Init variables """ 21 | self.min_distance = min_distance 22 | 23 | def process(self, pcd): 24 | """ 25 | Parameters 26 | ---------- 27 | points : array of shape (n_points, 3) 28 | The point cloud . 29 | """ 30 | 31 | logger.info(f'Reducing pointcloud...') 32 | 33 | refCloud = pcd.voxel_down_sample(self.min_distance) 34 | 35 | return refCloud 36 | -------------------------------------------------------------------------------- /ipcp/script.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import sys 4 | 5 | from src.pipeline import Pipeline 6 | from preprocessors.sor import SOR 7 | from preprocessors.spatial_subsample import SpatialSubsample 8 | from modules.primitive_detection import PrimitiveDetector 9 | from modules.floor_split import FloorSplitter 10 | from modules.room_detection import RoomDetector 11 | from modules.room_reconstruct import RoomReconstructor 12 | from modules.mesh_stats import MeshAnalyser 13 | 14 | ransac_exe = './cpp_modules/efficient_ransac' 15 | reconstruct_exe = './cpp_modules/polyfit' 16 | reconstruct_ransac_exe = './cpp_modules/polyfit_ransac' 17 | 18 | def main(in_file, out_folder): 19 | sor = SOR() 20 | ss = SpatialSubsample() 21 | primitive_detector = PrimitiveDetector(ransac_exe) 22 | floor_splitter = FloorSplitter() 23 | room_detector = RoomDetector() 24 | room_reconstructor = RoomReconstructor(reconstruct_ransac_exe, reconstruct_exe) 25 | mesh_analyser = MeshAnalyser() 26 | 27 | pipeline = Pipeline(primitive_detector, floor_splitter, room_detector, room_reconstructor, mesh_analyser, preprocessors=[ss,sor]) 28 | 29 | pipeline.process_file(in_file, out_folder) 30 | 31 | if __name__ == "__main__": 32 | global args 33 | 34 | desc_str = '''This script provides room reconstruction for indoor point clouds.''' 35 | parser = argparse.ArgumentParser(description=desc_str) 36 | 37 | parser.add_argument('--in_file', metavar='path', action='store', 38 | type=str, required=True) 39 | parser.add_argument('--out_folder', metavar='path', action='store', 40 | type=str, required=False) 41 | args = parser.parse_args() 42 | 43 | main(args.in_file, args.out_folder) -------------------------------------------------------------------------------- /ipcp/src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/ipcp/src/__init__.py -------------------------------------------------------------------------------- /ipcp/src/interpolation.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module provides tools for interpolating data. 3 | """ 4 | 5 | import numpy as np 6 | 7 | class FastGridInterpolator: 8 | """ 9 | Class to perform fast interpolation using gridded data. The interpolator 10 | simply returns the values of the grid cells in which the queried points 11 | fall. Grid coordinates are assumed to be the centroids of each grid cell. 12 | 13 | Parameters 14 | ---------- 15 | bin_x : list or array-like 16 | The x-coordinates of the grid (ascending). 17 | 18 | bin_y : list or array-like 19 | The y-coordinates of the grid (decsending). 20 | 21 | values : array of shape (Ny, Nx) 22 | The values of the gridded data. 23 | """ 24 | 25 | def __init__(self, bin_x, bin_y, values): 26 | self.bin_x = bin_x[:-1] 27 | self.bin_y = bin_y[:-1] 28 | self.values = values 29 | 30 | def __call__(self, positions): 31 | """ 32 | Evaluate the interpolator at the given positions. 33 | 34 | Parameters 35 | ---------- 36 | positions : array of shape (Np, 2) 37 | Array of points to query. The first column contains the x-values, 38 | the second column contains the y-values. 39 | """ 40 | x_idx = np.digitize(positions[:, 0], self.bin_x) - 1 41 | y_idx = np.digitize(positions[:, 1], self.bin_y) - 1 42 | return self.values[x_idx, y_idx] -------------------------------------------------------------------------------- /ipcp/src/pipeline.py: -------------------------------------------------------------------------------- 1 | """Room Reconstruction Pipeline""" 2 | 3 | import os 4 | import pathlib 5 | import time 6 | import logging 7 | import gc 8 | import pymeshlab 9 | import numpy as np 10 | import open3d as o3d 11 | from tqdm import tqdm 12 | 13 | from .utils import cityjson_utils 14 | from .utils import pcd_utils 15 | 16 | logger = logging.getLogger(__name__) 17 | 18 | 19 | class Pipeline: 20 | """ 21 | Pipeline for room reconstruction. The class processes a single point cloud 22 | or a folder of pointclouds by applying the given modules consecutively. 23 | 24 | Parameters 25 | ---------- 26 | floor_splitter : FloorSplitter 27 | The module that splits the point cloud into separate floors 28 | room_detector : RoomDetector 29 | The module that detects rooms within a single floor. 30 | surface_reconstructor : SurfaceReconstructor 31 | The module that reconstructs the surface to a mesh. 32 | mesh_volume : MeshVolume 33 | The module that computes the area and volume of a mesh. 34 | preprocessors : iterable of type PreProcessor 35 | The preprocessors to apply, in order. 36 | """ 37 | 38 | FILE_TYPES = ('.LAS', '.las', '.LAZ', '.laz', '.ply') 39 | 40 | def __init__(self, primitive_detector, floor_splitter, room_detector, 41 | room_reconstructor, mesh_analyser, preprocessors=[]): 42 | if len(preprocessors) == 0: 43 | logger.info('No preprocessors specified.') 44 | self.preprocessors = preprocessors 45 | self.primitive_detector = primitive_detector 46 | self.floor_splitter = floor_splitter 47 | self.room_detector = room_detector 48 | self.room_reconstructor = room_reconstructor 49 | self.mesh_analyser = mesh_analyser 50 | 51 | def _process_cloud(self, pcd): 52 | """ 53 | Process a single point cloud. 54 | 55 | Parameters 56 | ---------- 57 | points : array of shape (n_points, 3) 58 | The point cloud . 59 | 60 | Returns 61 | ------- 62 | An cityJSON object representing the indoor of a building. 63 | """ 64 | 65 | 66 | 67 | # 1. Preprocess pointcloud 68 | logger.info(f'Preprocessing...') 69 | for obj in self.preprocessors: 70 | start = time.time() 71 | pcd = obj.process(pcd) 72 | duration = time.time() - start 73 | logger.info(f'Processor finished in {duration:.2f}s, ' + 74 | f'{len(pcd.points)} points.') 75 | gc.collect() 76 | 77 | 78 | # 2. Primitive Detection 79 | start = time.time() 80 | logger.info(f'Detecting primitives...') 81 | pcd, primitives, primitive_labels = self.primitive_detector.process(pcd) 82 | duration = time.time() - start 83 | logger.info(f'Done. Detected {len(primitives.keys())}. {duration:.2f}s') 84 | gc.collect() 85 | 86 | # 3. Detect floors 87 | start = time.time() 88 | logger.info(f'Detecting floors...') 89 | floors = self.floor_splitter.process(pcd, primitive_labels, primitives) 90 | duration = time.time() - start 91 | logger.info(f'Done. Detected {len(floors)} floors. {duration:.2f}s') 92 | gc.collect() 93 | 94 | # 4. Detect Rooms 95 | start = time.time() 96 | logger.info(f'Detecting rooms...') 97 | rooms = [] 98 | for floor_mask in floors: 99 | floor_pcd = pcd.select_by_index(np.where(floor_mask)[0]) 100 | floor_labels = primitive_labels[floor_mask] 101 | floor_rooms, _ = self.room_detector.process(floor_pcd, floor_labels) 102 | for room_i in range(floor_rooms.shape[1]): 103 | room_mask = np.zeros(len(pcd.points), dtype=bool) 104 | room_mask[floor_mask] = floor_rooms[:,room_i] 105 | rooms.append(room_mask) 106 | gc.collect() 107 | duration = time.time() - start 108 | logger.info(f'Done. Detected {len(rooms)} rooms. {duration:.2f}s') 109 | 110 | # 5. Reconstruct rooms 111 | start = time.time() 112 | logger.info(f'Reconstructing rooms into meshes...') 113 | room_meshes = [] 114 | for room_mask in tqdm(rooms): 115 | meshset = self.room_reconstructor.process(np.asarray(pcd.points)[room_mask], primitive_labels[room_mask], primitives) 116 | if meshset is not None: 117 | room_meshes.append(meshset) 118 | gc.collect() 119 | duration = time.time() - start 120 | logger.info(f'Done. Succesfully reconstructed {len(room_meshes)}/{len(rooms)} rooms. {duration:.2f}s') 121 | 122 | # 6. Convert CityJSON 123 | cityjson = cityjson_utils.to_cityjson_v1(room_meshes) 124 | 125 | # 7. Compute Area and Volume 126 | start = time.time() 127 | logger.info(f'Computing mesh metrics') 128 | room_stats = [] 129 | for i, room_mesh in enumerate(room_meshes): 130 | volume, floorarea = self.mesh_analyser.process(room_mesh) 131 | room_stats.append((volume, floorarea)) 132 | logger.debug(f'volume room {i}: {volume}, floorarea: {floorarea}') 133 | duration = time.time() - start 134 | logger.info(f'Done. {duration:.2f}s') 135 | 136 | return cityjson, room_stats 137 | 138 | def process_file(self, in_file, out_folder=None, out_prefix=''): 139 | """ 140 | Process a single LAS file and save the result as .laz file. 141 | 142 | Parameters 143 | ---------- 144 | in_file : str 145 | The file to process. 146 | out_file : str (default: None) 147 | The name of the output file. If None, the input will be 148 | overwritten. 149 | """ 150 | logger.info(f'Processing file {in_file}.') 151 | start = time.time() 152 | if not os.path.isfile(in_file): 153 | logger.error('The input file specified does not exist') 154 | return None 155 | elif not in_file.endswith(self.FILE_TYPES): 156 | logger.error('The input file specified has the wrong format') 157 | return None 158 | 159 | filename = pathlib.Path(in_file).stem 160 | outputname = out_prefix + filename 161 | in_folder = os.path.dirname(in_file) 162 | if out_folder is None: 163 | out_folder = in_folder 164 | else: 165 | pathlib.Path(out_folder).mkdir(parents=True, exist_ok=True) 166 | out_path = out_folder + '/' + outputname + '.city.json' 167 | 168 | pcd = pcd_utils.read_pointcloud(in_file) 169 | 170 | citysjon, room_stats = self._process_cloud(pcd) 171 | cityjson_utils.save_to_file(citysjon, out_path) 172 | 173 | # write stats 174 | lines = [] 175 | for i, stats in enumerate(room_stats): 176 | line = 'Room ' + str(i) + ': volume='+str(stats[0])+', surface='+str(stats[1])+'\n' 177 | lines.append(line) 178 | stats_path = out_folder + '/' + outputname + '_stats.txt' 179 | with open(stats_path, 'w') as f: 180 | f.writelines(lines) 181 | 182 | duration = time.time() - start 183 | # stats = analysis_tools.get_label_stats(labels) 184 | # logger.info('STATISTICS\n' + stats) 185 | logger.info(f'File processed in {duration:.2f}s, ' + 186 | f'output written to {out_path}.\n' + '='*20) 187 | -------------------------------------------------------------------------------- /ipcp/src/region_growing.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import open3d as o3d 3 | import copy 4 | import logging 5 | import time 6 | from tqdm import tqdm 7 | from tqdm.contrib.logging import logging_redirect_tqdm 8 | 9 | from scipy.spatial import KDTree 10 | 11 | import warnings 12 | 13 | warnings.filterwarnings('error') 14 | 15 | from scipy import spatial 16 | 17 | logger = logging.getLogger(__name__) 18 | 19 | 20 | class RegionGrowing: 21 | """ 22 | Region growing implementation based on: 23 | https://pcl.readthedocs.io/projects/tutorials/en/latest/region_growing_segmentation.html 24 | """ 25 | def __init__(self): 26 | """ Init variables. """ 27 | 28 | def _ransac_plane_fit(self, pcd): 29 | plane_model, _ = pcd.segment_plane(distance_threshold=0.01, 30 | ransac_n=5, 31 | num_iterations=100) 32 | [a, b, c, d] = plane_model 33 | normal = np.array([a,b,c]) 34 | normal /= np.linalg.norm(normal) 35 | center, _ = pcd.compute_mean_and_covariance() 36 | return normal, center 37 | 38 | def _growing_neighbours(self, tree, points, radius=.2): 39 | return flatten(tree.query_ball_point(points, r=radius)) 40 | 41 | def _region_growing(self, pcd, regions, exclude=[-1], max_iter=2): 42 | """ 43 | The work of this region growing algorithm is based on the comparison 44 | of the angles between the points normals. 45 | 46 | The same can also be performed in Python using scipy.spatial.cKDTree 47 | with query_ball_tree or query. 48 | """ 49 | 50 | regions_to_grow = np.unique(regions) 51 | regions_to_grow = regions_to_grow[~np.isin(regions_to_grow, exclude)] 52 | regions_to_grow = np.sort(regions_to_grow) 53 | 54 | unassigned_mask = regions == -1 55 | conv = np.where(unassigned_mask)[0] 56 | 57 | P = np.asarray(pcd.points) 58 | N = np.asarray(pcd.normals) 59 | tree = KDTree(P[unassigned_mask]) 60 | 61 | for region_label in tqdm(regions_to_grow): 62 | R = np.where(regions==region_label)[0] # Region 63 | F = R.tolist() # Front 64 | if len(F) < 5: 65 | continue 66 | 67 | pcd_ = pcd.select_by_index(np.where(regions==region_label)[0]) 68 | n, c = self._ransac_plane_fit(pcd_) # Normal & Center 69 | 70 | i = 0 71 | while len(F) > 0 and i < max_iter: 72 | i += 1 73 | try: 74 | k_idx = conv[self._growing_neighbours(tree, P[F], .2)] 75 | F = k_idx[~np.isin(k_idx, R)] # remove points in R and grown 76 | F = F[np.rad2deg(np.arccos(np.abs(np.clip(np.dot(N[F], n),-1.0,1.0)))) < 25] # normal criterium 77 | F = F[np.abs(np.dot(P[F] - c, n)) < 0.06] # distance criterium 78 | R = np.append(R, F) 79 | except: 80 | F = [] 81 | regions[R] = region_label 82 | 83 | logger.debug(f'Done. Added {np.sum(regions[conv]!=-1)} points.') 84 | 85 | return regions 86 | 87 | 88 | def _edge_refinement(self, pcd, regions): 89 | logger.debug('Refine edges ...') 90 | P = np.asarray(pcd.points) 91 | start = time.time() 92 | assigned_mask = regions >= 0 93 | assigned_tree = KDTree(P[assigned_mask]) 94 | 95 | d, idx = assigned_tree.query(P[~assigned_mask], k=1, distance_upper_bound=0.25) 96 | unassigned_regions = np.full(np.sum(~assigned_mask),-1) 97 | 98 | idx_unassigned = np.where(idx0)} points. {round(time.time()-start,2)}\n") 110 | 111 | return regions 112 | 113 | def process(self, pcd, labels): 114 | """ 115 | Returns the label mask for the given pointcloud. 116 | 117 | Parameters 118 | ---------- 119 | points : array of shape (n_points, 3) 120 | The point cloud . 121 | 122 | Returns 123 | ------- 124 | An array of shape (n_points,) with dtype=bool indicating which points 125 | should be labelled according to this fuser. 126 | """ 127 | logger.debug('KDTree based Region Growing.') 128 | 129 | grown_labels = np.copy(labels) 130 | 131 | grown_labels = self._region_growing(pcd, grown_labels) 132 | grown_labels = self._edge_refinement(pcd, grown_labels) 133 | 134 | return grown_labels 135 | 136 | def flatten(l): 137 | return np.unique([j for i in l for j in i]) 138 | 139 | def detection_prob(n, s, N, k=1): 140 | return 1 - np.power(1 - np.power((n/N),k),s) 141 | -------------------------------------------------------------------------------- /ipcp/src/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Amsterdam-AI-Team/Indoor-PointCloud-to-CityJSON/fefcbbc201721e34d00f9fb432671028c64c2e3f/ipcp/src/utils/__init__.py -------------------------------------------------------------------------------- /ipcp/src/utils/cityjson_utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | CityJSON tools for polygon meshes. 3 | 4 | The documentation of CityJSON can be found here: 5 | https://www.cityjson.org/ 6 | """ 7 | import os 8 | import json 9 | import logging 10 | import numpy as np 11 | 12 | 13 | logger = logging.getLogger(__name__) 14 | 15 | def add_point_set(): 16 | # # room coordinates to vertices 17 | # coords = [point for room in rooms for primitive in room for point in primitive] 18 | 19 | # # create vertices 20 | # vertices, inv_vertices = np.unique(coords, axis=0, return_inverse=True) 21 | # cityjson['vertices'] = vertices.tolist() 22 | 23 | # # convert to wavefront type format 24 | # v_index = inv_vertices.tolist() 25 | # for i in range(len(rooms)): 26 | # outer_shell = [[[v_index.pop(0) for x in primitive]] for primitive in rooms[i]] 27 | # building_part = { 28 | # "type": "BuildingPart", 29 | # "parents": [parent_id], 30 | # "geometry": [{ 31 | # "type": "Solid", 32 | # "lod": 2, 33 | # "boundaries": [outer_shell] 34 | # }] 35 | # } 36 | # cityjson['CityObjects']['room_'+str(i+1)] = building_part 37 | return False 38 | 39 | def add_buildingpart_to_cjson(cityjson, parent_id, meshset, part_name): 40 | 41 | num_vertices = len(cityjson['vertices']) 42 | 43 | # Load building part 44 | meshset.meshing_merge_close_vertices() 45 | mesh = meshset.mesh(0) 46 | faces = mesh.face_matrix() 47 | vertices = mesh.vertex_matrix().astype(np.float16) 48 | 49 | # Create building part 50 | boundaries = (np.uint32(faces[np.newaxis,:,np.newaxis,:]+num_vertices)).tolist() 51 | building_part = { 52 | "type": "BuildingPart", 53 | "parents": [parent_id], 54 | "geometry": [{ 55 | "type": "Solid", 56 | "lod": 2, 57 | "boundaries": boundaries 58 | }] 59 | } 60 | 61 | # Add building part & update vertices 62 | cityjson['CityObjects'][part_name] = building_part 63 | cityjson['vertices'].extend(vertices.tolist()) 64 | 65 | return cityjson 66 | 67 | def add_buildingroom_to_cjson(cityjson, parent_id, meshset, part_name): 68 | 69 | num_vertices = len(cityjson['vertices']) 70 | 71 | # Load building part 72 | meshset.meshing_merge_close_vertices() 73 | mesh = meshset.mesh(0) 74 | faces = mesh.face_matrix() 75 | vertices = mesh.vertex_matrix().astype(np.float16) 76 | 77 | # Create building part 78 | boundaries = (np.uint32(faces[np.newaxis,:,np.newaxis,:]+num_vertices)).tolist() 79 | building_part = { 80 | "type": "BuildingRoom", 81 | "parents": [parent_id], 82 | "geometry": [{ 83 | "type": "Solid", 84 | "lod": 2, 85 | "boundaries": boundaries 86 | }] 87 | } 88 | 89 | # Add building part & update vertices 90 | cityjson['CityObjects'][part_name] = building_part 91 | cityjson['vertices'].extend(vertices.tolist()) 92 | 93 | return cityjson 94 | 95 | def save_to_file(cityjson, outfile): 96 | try: 97 | if os.path.isfile(outfile): 98 | os.remove(outfile) 99 | with open(outfile, "w", encoding="utf8") as file: 100 | json.dump(cityjson,file) 101 | logger.info(f'Succesfully saved output to {str(outfile)}') 102 | except: 103 | logger.error('Failed to save file.') 104 | 105 | def to_cityjson_v1(rooms): 106 | """ 107 | A function that converts a list of geometrical defined rooms to CityJSON v1.0 format. 108 | 109 | Parameters 110 | ---------- 111 | rooms : list 112 | A list containing the rooms that should be converted. A room is a list of primitives/surfaces. 113 | Each primitive is a list of vertices (x,y,z), only linear and planar primitives are allowed. 114 | 115 | Returns 116 | ------- 117 | A CityJSON object 118 | """ 119 | 120 | # Assertions 121 | assert isinstance(rooms, list), "Argument rooms is not of type List." 122 | 123 | cityjson = { 124 | "type": "CityJSON", 125 | "version": "1.0", 126 | "CityObjects": {}, 127 | "vertices": [], 128 | "transform": { 129 | "scale":[1.0,1.0,1.0], 130 | "translate":[0.0,0.0,0.0] 131 | } 132 | } 133 | 134 | # add parent 135 | parent_id = "id-1" 136 | cityjson['CityObjects'][parent_id] = {"type":"Building", "geometry":[]} 137 | 138 | for i, meshset in enumerate(rooms): 139 | try: 140 | part_name = "room_" + str(i) 141 | add_buildingpart_to_cjson(cityjson, parent_id, meshset, part_name) 142 | except Exception as e: 143 | logger.error(f'Failed {part_name}') 144 | 145 | return cityjson 146 | 147 | def to_cityjson_v1_1(rooms): 148 | """ 149 | A function that converts a list of geometrical defined rooms to CityJSON v1.1.2 format. 150 | 151 | Parameters 152 | ---------- 153 | rooms : list 154 | A list containing the rooms that should be converted. A room is a list of primitives/surfaces. 155 | Each primitive is a list of vertices (x,y,z), only linear and planar primitives are allowed. 156 | outfile : str 157 | The output path to be used for saving the CityJSON file. 158 | 159 | Returns 160 | ------- 161 | A CityJSON object 162 | """ 163 | 164 | # Assertions 165 | assert isinstance(rooms, list), "Argument rooms is not of type List." 166 | 167 | cityjson = { 168 | "type": "CityJSON", 169 | "version": "1.1", 170 | "CityObjects": {}, 171 | "vertices": [], 172 | "transform": { 173 | "scale":[1.0,1.0,1.0], 174 | "translate":[0.0,0.0,0.0] 175 | } 176 | } 177 | 178 | # add parent 179 | parent_id = "id-1" 180 | cityjson['CityObjects'][parent_id] = {"type":"Building"} 181 | 182 | # add parent 183 | parent_id = "id-1" 184 | cityjson['CityObjects'][parent_id] = {"type":"Building"} 185 | 186 | i = 1 187 | for meshset in rooms: 188 | try: 189 | part_name = "room_" + str(i) 190 | add_buildingroom_to_cjson(cityjson, parent_id, meshset, part_name) 191 | except Exception as e: 192 | logger.error(f'Failed {part_name}') 193 | i += 1 194 | 195 | return cityjson -------------------------------------------------------------------------------- /ipcp/src/utils/clip_utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | Clipping tools for point clouds and polygons. 3 | 4 | The method poly_clip() is adapted from: 5 | https://github.com/brycefrank/pyfor/blob/master/pyfor/clip.py 6 | 7 | The method _point_inside_poly is adapted from: 8 | https://github.com/sasamil/PointInPolygon_Py 9 | """ 10 | import numpy as np 11 | from numba import jit 12 | import numba 13 | import logging 14 | 15 | from ..utils import math_utils 16 | 17 | logger = logging.getLogger(__name__) 18 | 19 | 20 | @jit(nopython=True, cache=True) 21 | def rectangle_clip(points, rect): 22 | """ 23 | Clip all points within a rectangle. 24 | 25 | Parameters 26 | ---------- 27 | points : array of shape (n_points, 2) 28 | The points. 29 | rect : tuple of floats 30 | (x_min, y_min, x_max, y_max) 31 | 32 | Returns 33 | ------- 34 | A boolean mask with True entries for all points within the rectangle. 35 | """ 36 | clip_mask = ((points[:, 0] >= rect[0]) & (points[:, 0] <= rect[2]) 37 | & (points[:, 1] >= rect[1]) & (points[:, 1] <= rect[3])) 38 | return clip_mask 39 | 40 | 41 | @jit(nopython=True, cache=True) 42 | def box_clip(points, rect, bottom=-np.inf, top=np.inf): 43 | """ 44 | Clip all points within a 3D box. 45 | 46 | Parameters 47 | ---------- 48 | points : array of shape (n_points, 2) 49 | The points. 50 | rect : tuple of floats 51 | (x_min, y_min, x_max, y_max) 52 | bottom : float (default: -inf) 53 | Bottom of the box. 54 | top : float (default: inf) 55 | Top of the box. 56 | 57 | Returns 58 | ------- 59 | A boolean mask with True entries for all points within the 3D box. 60 | """ 61 | box_mask = rectangle_clip(points, rect) 62 | box_mask = box_mask & ((points[:, 2] <= top) & (points[:, 2] >= bottom)) 63 | return box_mask 64 | 65 | 66 | @jit(nopython=True, cache=True) 67 | def circle_clip(points, center, radius): 68 | """ 69 | Clip all points within a circle (or unbounded cylinder). 70 | 71 | Parameters 72 | ---------- 73 | points : array of shape (n_points, 2) 74 | The points. 75 | center : tuple of floats (x, y) 76 | Center point of the circle. 77 | radius : float 78 | Radius of the circle. 79 | 80 | Returns 81 | ------- 82 | A boolean mask with True entries for all points within the circle. 83 | """ 84 | clip_mask = (np.power((points[:, 0] - center[0]), 2) 85 | + np.power((points[:, 1] - center[1]), 2) 86 | <= np.power(radius, 2)) 87 | return clip_mask 88 | 89 | 90 | @jit(nopython=True, cache=True) 91 | def cylinder_clip(points, center, radius, bottom=-np.inf, top=np.inf): 92 | """ 93 | Clip all points within a cylinder. 94 | 95 | Parameters 96 | ---------- 97 | points : array of shape (n_points, 2) 98 | The points. 99 | center : tuple of floats (x, y) 100 | Center point of the circle. 101 | radius : float 102 | Radius of the circle. 103 | bottom : float (default: -inf) 104 | Bottom of the cylinder. 105 | top : float (default: inf) 106 | Top of the cylinder. 107 | 108 | Returns 109 | ------- 110 | A boolean mask with True entries for all points within the circle. 111 | """ 112 | clip_mask = circle_clip(points, center, radius) 113 | clip_mask = clip_mask & ((points[:, 2] <= top) & (points[:, 2] >= bottom)) 114 | return clip_mask 115 | 116 | 117 | @jit(nopython=True, cache=True) 118 | def _point_inside_poly(polygon, point): 119 | """ 120 | Improved version of the Crossing Number algorithm that checks if a point is 121 | inside a polygon. 122 | Implementation taken from https://github.com/sasamil/PointInPolygon_Py 123 | """ 124 | length = len(polygon) - 1 125 | dy2 = point[1] - polygon[0][1] 126 | intersections = 0 127 | ii = 0 128 | jj = 1 129 | 130 | while ii < length: 131 | dy = dy2 132 | dy2 = point[1] - polygon[jj][1] 133 | 134 | # consider only lines which are not completely above/below/right from 135 | # the point 136 | if dy*dy2 <= 0.0 and (point[0] >= polygon[ii][0] 137 | or point[0] >= polygon[jj][0]): 138 | 139 | # non-horizontal line 140 | if dy < 0 or dy2 < 0: 141 | F = (dy * (polygon[jj][0] - polygon[ii][0]) 142 | / (dy-dy2) + polygon[ii][0]) 143 | 144 | if point[0] > F: 145 | # if line is left from the point - the ray moving towards 146 | # left, will intersect it 147 | intersections += 1 148 | elif point[0] == F: # point on line 149 | return 2 150 | 151 | # point on upper peak (dy2=dx2=0) or horizontal line (dy=dy2=0 and 152 | # dx*dx2<=0) 153 | elif (dy2 == 0 154 | and (point[0] == polygon[jj][0] 155 | or (dy == 0 and (point[0] - polygon[ii][0]) 156 | * (point[0] - polygon[jj][0]) <= 0))): 157 | return 2 158 | 159 | ii = jj 160 | jj += 1 161 | 162 | return intersections & 1 163 | 164 | 165 | @jit(nopython=True, cache=True, parallel=True) 166 | def is_inside(x, y, polygon): 167 | """ 168 | Checks for each point in a list whether that point is inside a polygon. 169 | 170 | Parameters 171 | ---------- 172 | x : list 173 | X-coordinates. 174 | y : list 175 | Y-coordinates. 176 | polygon : list of tuples 177 | Polygon as linear ring. 178 | 179 | Returns 180 | ------- 181 | An array of shape (len(x),) with dtype bool, where each entry indicates 182 | whether the corresponding point is inside the polygon. 183 | """ 184 | n = len(x) 185 | mask = np.empty((n,), dtype=numba.boolean) 186 | for i in numba.prange(n): 187 | mask[i] = _point_inside_poly(polygon, (x[i], y[i])) 188 | return mask 189 | 190 | 191 | def poly_clip(points, poly): 192 | """ 193 | Clip all points within a polygon. 194 | 195 | Parameters 196 | ---------- 197 | points : array of shape (n_points, 2) 198 | The points. 199 | poly : shapely.geometry Polygon object 200 | Polygon to clip. Can have interior gaps. 201 | 202 | Returns 203 | ------- 204 | A boolean mask with True entries for all points within the polygon. 205 | """ 206 | # Convert to numpy to work with numba jit in nopython mode. 207 | exterior = np.array(poly.exterior.coords) 208 | interiors = [np.array(interior.coords) for interior in poly.interiors] 209 | 210 | clip_mask = np.zeros((len(points),), dtype=bool) 211 | 212 | # Clip exterior to include points. 213 | bbox_mask = rectangle_clip( 214 | points, math_utils.compute_bounding_box(exterior)) 215 | exterior_mask = is_inside(points[bbox_mask, 0], points[bbox_mask, 1], 216 | exterior) 217 | bbox_inds = np.where(bbox_mask)[0] 218 | clip_mask[bbox_inds[exterior_mask]] = True 219 | 220 | # Clip interior(s) to exclude points. 221 | for interior in interiors: 222 | bbox_mask = rectangle_clip( 223 | points, math_utils.compute_bounding_box(interior)) 224 | interior_mask = is_inside(points[bbox_mask, 0], points[bbox_mask, 1], 225 | interior) 226 | bbox_inds = np.where(bbox_mask)[0] 227 | clip_mask[bbox_inds[interior_mask]] = False 228 | 229 | return clip_mask 230 | 231 | 232 | def poly_box_clip(points, poly, bottom=-np.inf, top=np.inf): 233 | """ 234 | Clip all points within a 3D polygon with fixed height. 235 | 236 | Parameters 237 | ---------- 238 | points : array of shape (n_points, 2) 239 | The points. 240 | poly : shapely.geometry Polygon object 241 | Polygon to clip. Can have interior gaps. 242 | bottom : float (default: -inf) 243 | Bottom height of the 3D polygon. 244 | top : float (default: inf) 245 | Top height of the 3D polygon. 246 | 247 | Returns 248 | ------- 249 | A boolean mask with True entries for all points within the 3D polygon. 250 | """ 251 | clip_mask = poly_clip(points, poly) 252 | clip_mask = clip_mask & ((points[:, 2] <= top) & (points[:, 2] >= bottom)) 253 | return clip_mask 254 | -------------------------------------------------------------------------------- /ipcp/src/utils/log_utils.py: -------------------------------------------------------------------------------- 1 | # Urban_PointCloud_Processing by Amsterdam Intelligence, GPL-3.0 license 2 | 3 | import logging 4 | import pathlib 5 | import sys 6 | 7 | BASE_NAME = 'upcp' 8 | BASE_LEVEL = logging.DEBUG 9 | 10 | 11 | class LastPartFilter(logging.Filter): 12 | def filter(self, record): 13 | record.name_last = record.name.rsplit('.', 1)[-1] 14 | return True 15 | 16 | 17 | def reset_logger(base_level=BASE_LEVEL): 18 | logger = logging.getLogger(BASE_NAME) 19 | logger.setLevel(base_level) 20 | logger.handlers = [] 21 | 22 | 23 | def add_console_logger(level=logging.INFO): 24 | logger = logging.getLogger(BASE_NAME) 25 | ch = logging.StreamHandler(sys.stdout) 26 | ch.set_name('UPCP Console Logger') 27 | ch.setLevel(level) 28 | formatter = logging.Formatter( 29 | '%(levelname)s - %(message)s') 30 | ch.setFormatter(formatter) 31 | logger.addHandler(ch) 32 | 33 | 34 | def add_file_logger(logfile, level=logging.DEBUG, clear_log=False): 35 | log_path = pathlib.Path(logfile) 36 | if log_path.is_file(): 37 | if clear_log: 38 | open(log_path, "w").close() 39 | else: 40 | pathlib.Path(log_path.parent).mkdir(parents=True, exist_ok=True) 41 | logger = logging.getLogger(BASE_NAME) 42 | fh = logging.FileHandler(log_path) 43 | fh.set_name('UPCP File Logger') 44 | fh.setLevel(level) 45 | formatter = logging.Formatter( 46 | '%(asctime)s - %(name)s - %(levelname)s - %(message)s', 47 | datefmt="%Y-%m-%d %H:%M:%S") 48 | fh.setFormatter(formatter) 49 | fh.addFilter(LastPartFilter()) 50 | logger.addHandler(fh) 51 | 52 | 53 | def set_console_level(level=logging.INFO): 54 | logger = logging.getLogger(BASE_NAME) 55 | for hl in logger.handlers: 56 | if hl.get_name() == 'UPCP Console Logger': 57 | hl.setLevel(level) 58 | -------------------------------------------------------------------------------- /ipcp/src/utils/math_utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from numba import jit 3 | from scipy.spatial import ConvexHull 4 | from shapely.geometry import Polygon 5 | 6 | 7 | def vector_angle(u, v=np.array([0., 0., 1.])): 8 | """ 9 | Returns the angle in degrees between vectors 'u' and 'v'. If only 'u' is 10 | provided, the angle between 'u' and the vertical axis is returned. 11 | """ 12 | # see https://stackoverflow.com/a/2827466/425458 13 | c = np.dot(u/np.linalg.norm(u), v/np.linalg.norm(v)) 14 | clip = np.minimum(1, np.maximum(c, -1)) 15 | return np.rad2deg(np.arccos(clip)) 16 | 17 | def lineseg_dist(p, a, b): 18 | """ 19 | Returns the distance between a set of points and a linesegment. 20 | It takes into account the ending of the line. 21 | """ 22 | # see https://stackoverflow.com/a/56467661 23 | 24 | d = np.divide(b - a, np.linalg.norm(b - a)) 25 | s = np.dot(a - p, d) 26 | t = np.dot(p - b, d) 27 | h = np.maximum.reduce([s, t, 0]) 28 | c = np.cross(p - a, d) 29 | return np.hypot(h, np.linalg.norm(c)) 30 | 31 | #@jit(nopython=True, cache=True, parallel=True) 32 | def line_dist(p, a, d): 33 | """ 34 | Returns the distance between a set of points and a linesegment. 35 | It takes into account the ending of the line. 36 | """ 37 | return np.linalg.norm(np.cross(p - a, d), axis=1) 38 | 39 | @jit(nopython=True, cache=True, parallel=True) 40 | def compute_bounding_box(points): 41 | """ 42 | Get the min/max values of a point list. 43 | 44 | Parameters 45 | ---------- 46 | points : array of shape (n_points, 2) 47 | The (x, y) coordinates of the points. Any further dimensions will be 48 | ignored. 49 | 50 | Returns 51 | ------- 52 | tuple 53 | (x_min, y_min, x_max, y_max) 54 | """ 55 | x_min = np.min(points[:, 0]) 56 | x_max = np.max(points[:, 0]) 57 | y_min = np.min(points[:, 1]) 58 | y_max = np.max(points[:, 1]) 59 | 60 | return (x_min, y_min, x_max, y_max) 61 | 62 | 63 | def convex_hull_poly(points): 64 | """Return convex hull as a shapely Polygon.""" 65 | hull = points[ConvexHull(points).vertices] 66 | return Polygon(np.vstack((hull, hull[0]))) 67 | 68 | 69 | def minimum_bounding_rectangle(points): 70 | """ 71 | Find the smallest bounding rectangle for a set of points. 72 | Returns a set of points representing the corners of the bounding box. 73 | 74 | :param points: an nx2 matrix of coordinates 75 | :rval: an nx2 matrix of coordinates 76 | """ 77 | pi2 = np.pi/2. 78 | 79 | # get the convex hull for the points 80 | hull_points = points[ConvexHull(points).vertices] 81 | 82 | # calculate edge angles 83 | edges = np.zeros((len(hull_points)-1, 2)) 84 | edges = hull_points[1:] - hull_points[:-1] 85 | 86 | angles = np.zeros((len(edges))) 87 | angles = np.arctan2(edges[:, 1], edges[:, 0]) 88 | 89 | angles = np.abs(np.mod(angles, pi2)) 90 | angles = np.unique(angles) 91 | 92 | # find rotation matrices 93 | rotations = np.vstack([ 94 | np.cos(angles), 95 | np.cos(angles-pi2), 96 | np.cos(angles+pi2), 97 | np.cos(angles)]).T 98 | rotations = rotations.reshape((-1, 2, 2)) 99 | 100 | # apply rotations to the hull 101 | rot_points = np.dot(rotations, hull_points.T) 102 | 103 | # find the bounding points 104 | min_x = np.nanmin(rot_points[:, 0], axis=1) 105 | max_x = np.nanmax(rot_points[:, 0], axis=1) 106 | min_y = np.nanmin(rot_points[:, 1], axis=1) 107 | max_y = np.nanmax(rot_points[:, 1], axis=1) 108 | 109 | # find the box with the best area 110 | areas = (max_x - min_x) * (max_y - min_y) 111 | best_idx = np.argmin(areas) 112 | 113 | # return the best box 114 | x1 = max_x[best_idx] 115 | x2 = min_x[best_idx] 116 | y1 = max_y[best_idx] 117 | y2 = min_y[best_idx] 118 | r = rotations[best_idx] 119 | 120 | # Calculate center point and project onto rotated frame 121 | center_x = (x1 + x2) / 2 122 | center_y = (y1 + y2) / 2 123 | center_point = np.dot([center_x, center_y], r) 124 | 125 | min_bounding_rect = np.zeros((4, 2)) 126 | min_bounding_rect[0] = np.dot([x1, y2], r) 127 | min_bounding_rect[1] = np.dot([x2, y2], r) 128 | min_bounding_rect[2] = np.dot([x2, y1], r) 129 | min_bounding_rect[3] = np.dot([x1, y1], r) 130 | 131 | # Compute the dims of the min bounding rectangle 132 | dims = [(x1 - x2), (y1 - y2)] 133 | 134 | return min_bounding_rect, hull_points, min(dims), max(dims), center_point 135 | -------------------------------------------------------------------------------- /ipcp/src/utils/pcd_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | from laspy import read as read_las 3 | import numpy as np 4 | import open3d as o3d 5 | 6 | def read_pointcloud(infile): 7 | """Read a file and return the pointcloud object.""" 8 | filename, file_extension = os.path.splitext(infile) 9 | 10 | if file_extension == '.ply': 11 | return o3d.io.read_point_cloud(infile) 12 | else: 13 | las = read_las(infile) 14 | points = np.vstack([las.x,las.y,las.z]).T 15 | return o3d.geometry.PointCloud(o3d.utility.Vector3dVector(points)) 16 | 17 | def write_pointcloud(points, filename): 18 | pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(points)) 19 | o3d.io.write_point_cloud(filename, pcd) 20 | return True 21 | 22 | def merge_point_clouds(pcd_1, pcd_2): 23 | points = np.concatenate((pcd_1.points, pcd_2.points)) 24 | normals = np.concatenate((pcd_1.normals, pcd_2.normals)) 25 | 26 | pcd = o3d.geometry.PointCloud() 27 | pcd.points = o3d.utility.Vector3dVector(points) 28 | pcd.normals = o3d.utility.Vector3dVector(normals) 29 | 30 | return pcd 31 | 32 | -------------------------------------------------------------------------------- /ipcp/src/utils/plot_utils.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import open3d as o3d 3 | import numpy as np 4 | 5 | def show_pcd(pcd, labels, exclude_labels=[]): 6 | 7 | mask = np.where(~np.isin(labels, exclude_labels))[0] 8 | pcd_ = pcd.select_by_index(mask) 9 | labels_ = labels[mask] 10 | 11 | # mix 12 | mask_noise = labels_ != -1 13 | un_labels = np.unique(labels_[mask_noise]) 14 | shuffle_ = np.random.choice(len(un_labels), len(un_labels),replace=False) 15 | inv = np.unique(labels_[mask_noise], return_inverse=True)[1] 16 | labels_[mask_noise] = shuffle_[inv] 17 | 18 | max_label = labels_.max() 19 | colors = plt.get_cmap("gist_rainbow")(labels_ / (max_label if max_label > 0 else 1)) 20 | colors[labels_ < 0] = 0 21 | pcd_.colors = o3d.utility.Vector3dVector(colors[:, :3]) 22 | o3d.visualization.draw_geometries([pcd_]) 23 | 24 | def show_pcd_floors(pcd, floors): 25 | pcds_ = [] 26 | for i in range(len(floors)): 27 | mask = floors[i] 28 | pcd_ = pcd.select_by_index(np.where(mask)[0]) 29 | color = plt.get_cmap("tab20")(i / len(floors)) 30 | pcd_.paint_uniform_color(color[:3]) 31 | pcds_.append(pcd_) 32 | 33 | o3d.visualization.draw_geometries(pcds_) -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | laspy==2.0.3 2 | matplotlib==3.5.3 3 | networkx==2.8.6 4 | numba==0.56.2 5 | numpy==1.23.2 6 | open3d==0.15.2 7 | opencv_python==4.6.0.66 8 | pymeshlab==2022.2.post2 9 | rasterio==1.3.2 10 | scikit_image==0.19.3 11 | scikit_spatial==6.6.0 12 | scipy==1.9.0 13 | Shapely==1.8.4 14 | tqdm==4.64.0 15 | --------------------------------------------------------------------------------