├── .github └── workflows │ └── build.yml ├── .gitignore ├── CMakeLists.txt ├── Dockerfile ├── LICENSE ├── README.md ├── classifier.cpp ├── classifier.hpp ├── color.cpp ├── color.hpp ├── constants.hpp ├── features.cpp ├── features.hpp ├── gbm.cpp ├── gbm.hpp ├── labels.cpp ├── labels.hpp ├── pcclassify.cpp ├── pctrain.cpp ├── point_io.cpp ├── point_io.hpp ├── randomforest.cpp ├── randomforest.hpp ├── scale.cpp ├── scale.hpp ├── statistics.hpp └── vendor ├── cxxopts.hpp ├── ethz ├── LICENSE ├── README ├── dataview.h └── random-forest │ ├── common-libraries.hpp │ ├── forest.hpp │ ├── node-gini.hpp │ ├── node.hpp │ └── tree.hpp ├── json └── json.hpp └── nanoflann └── nanoflann.hpp /.github/workflows/build.yml: -------------------------------------------------------------------------------- 1 | name: Build 2 | 3 | on: 4 | push: 5 | branches: [main] 6 | tags: 7 | - "v*" 8 | pull_request: 9 | branches: [main] 10 | 11 | # We dont have tests yet, so we can just build 12 | jobs: 13 | build: 14 | runs-on: ubuntu-latest 15 | steps: 16 | - uses: actions/checkout@v2 17 | - name: Install dependencies 18 | run: sudo apt-get update && sudo apt install -y --fix-missing --no-install-recommends git build-essential software-properties-common cmake libtbb-dev libboost-system-dev libboost-serialization-dev libpdal-dev libeigen3-dev 19 | - name: Build 20 | run: mkdir build && cd build && cmake -DWITH_GBT=ON -DPORTABLE_BUILD=ON .. && make -j$(nproc) 21 | - name: Archive compiled binaries 22 | run: cd build && tar -czvf opc.tar.gz pcclassify pctrain 23 | - name: Upload Distribution Files 24 | uses: actions/upload-artifact@v2 25 | with: 26 | name: Compiled Binaries 27 | path: build/opc.tar.gz 28 | - name: Upload Binaries to Release 29 | uses: svenstaro/upload-release-action@v2 30 | if: startsWith(github.ref, 'refs/tags/') 31 | with: 32 | repo_token: ${{ secrets.GITHUB_TOKEN }} 33 | file: build/opc.tar.gz 34 | tag: ${{ github.ref }} 35 | overwrite: true 36 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Prerequisites 2 | *.d 3 | 4 | # Compiled Object files 5 | *.slo 6 | *.lo 7 | *.o 8 | *.obj 9 | 10 | # Precompiled Headers 11 | *.gch 12 | *.pch 13 | 14 | # Compiled Dynamic libraries 15 | *.so 16 | *.dylib 17 | *.dll 18 | 19 | # Fortran module files 20 | *.mod 21 | *.smod 22 | 23 | # Compiled Static libraries 24 | *.lai 25 | *.la 26 | *.a 27 | *.lib 28 | 29 | # Executables 30 | *.exe 31 | *.out 32 | *.app 33 | 34 | build/ -------------------------------------------------------------------------------- /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.10) 2 | project(opc VERSION 1.0 LANGUAGES CXX) 3 | 4 | SET(WITH_GBT OFF CACHE BOOL "Build GBT support") 5 | SET(WITH_PDAL ON CACHE BOOL "Build PDAL readers support") 6 | SET(BUILD_PCTRAIN ON CACHE BOOL "Build pctrain") 7 | SET(BUILD_PCCLASSIFY ON CACHE BOOL "Build pcclassify") 8 | SET(PORTABLE_BUILD OFF CACHE BOOL "Build portable binaries") 9 | 10 | if(NOT CMAKE_BUILD_TYPE) 11 | set(CMAKE_BUILD_TYPE "Release" CACHE STRING 12 | "Choose the type of build, options are: Debug Release 13 | RelWithDebInfo MinSizeRel." 14 | FORCE) 15 | endif() 16 | 17 | # specify the C++ standard 18 | set(CMAKE_CXX_STANDARD 17) 19 | set(CMAKE_CXX_STANDARD_REQUIRED True) 20 | 21 | if (NOT WIN32 AND NOT APPLE) 22 | set(STDPPFS_LIBRARY stdc++fs) 23 | endif() 24 | 25 | if((CMAKE_CXX_COMPILER_ID MATCHES "Clang") OR (CMAKE_CXX_COMPILER_ID MATCHES "GNU")) 26 | if(NOT ((${CMAKE_SYSTEM_PROCESSOR} STREQUAL "aarch64") OR (${CMAKE_SYSTEM_PROCESSOR} STREQUAL "arm64"))) 27 | if(NOT PORTABLE_BUILD) 28 | message("Building with native optimizations") 29 | add_compile_options(-march=native) 30 | else() 31 | message("Building portable binaries") 32 | add_compile_options(-march=nehalem) 33 | endif() 34 | endif() 35 | endif() 36 | 37 | find_package(OpenMP REQUIRED) 38 | find_package(Eigen3 REQUIRED) 39 | 40 | if (WITH_PDAL) 41 | find_package(PDAL) 42 | if (PDAL_FOUND) 43 | include_directories(${PDAL_INCLUDE_DIRS}) 44 | else() 45 | message(WARNING "PDAL not found, LAS/LAZ support will not be built") 46 | set(WITH_PDAL OFF) 47 | endif() 48 | endif() 49 | 50 | if (WITH_GBT) 51 | message("Building with GBT support") 52 | 53 | set(LIGHTGBM_PREFIX "${CMAKE_CURRENT_BINARY_DIR}/local") 54 | include(ExternalProject) 55 | ExternalProject_Add(lightgbm 56 | GIT_REPOSITORY https://github.com/microsoft/LightGBM 57 | GIT_TAG e4231205a3bac13662a81db9433ddaea8924fbce 58 | CMAKE_ARGS -DBUILD_STATIC_LIB=ON 59 | -DCMAKE_INSTALL_PREFIX=${LIGHTGBM_PREFIX} 60 | ) 61 | add_library(lib_lightgbm SHARED IMPORTED) 62 | 63 | set(LIGHTGBM_INCLUDE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lightgbm-prefix/src/lightgbm/include") 64 | 65 | # TODO: test on Windows, macOS 66 | set(LIGHTGBM_LIB_PATH "${LIGHTGBM_PREFIX}/lib/lib_lightgbm.a") 67 | if (WIN32) 68 | set(LIGHTGBM_LIB_PATH "${LIGHTGBM_PREFIX}/lib/lib_lightgbm.lib") 69 | endif() 70 | 71 | if( NOT ((${CMAKE_SYSTEM_PROCESSOR} STREQUAL "aarch64") OR (${CMAKE_SYSTEM_PROCESSOR} STREQUAL "arm64")) ) 72 | add_definitions(-DMM_MALLOC) 73 | endif() 74 | 75 | if (NOT EXISTS ${LIGHTGBM_INCLUDE_DIRECTORY}) 76 | file(MAKE_DIRECTORY ${LIGHTGBM_INCLUDE_DIRECTORY}) 77 | endif() 78 | if (NOT EXISTS ${LIGHTGBM_LIB_PATH}) 79 | file(WRITE ${LIGHTGBM_LIB_PATH} "") 80 | endif() 81 | 82 | set_target_properties(lib_lightgbm PROPERTIES 83 | IMPORTED_LOCATION ${LIGHTGBM_LIB_PATH} 84 | INTERFACE_INCLUDE_DIRECTORIES ${LIGHTGBM_INCLUDE_DIRECTORY} 85 | ) 86 | 87 | include_directories(${LIGHTGBM_INCLUDE_DIRECTORY}) 88 | endif() 89 | 90 | include_directories(${EIGEN3_INCLUDE_DIR}) 91 | include_directories(vendor/ethz) 92 | 93 | set(SOURCES classifier.cpp scale.cpp point_io.cpp randomforest.cpp features.cpp color.cpp labels.cpp) 94 | set(HEADERS classifier.hpp scale.hpp point_io.hpp randomforest.hpp features.hpp color.hpp labels.hpp statistics.hpp) 95 | if (WITH_GBT) 96 | list(APPEND SOURCES gbm.cpp) 97 | list(APPEND HEADERS gbm.hpp) 98 | add_definitions(-DWITH_GBT) 99 | endif() 100 | 101 | if (WITH_PDAL) 102 | add_definitions(-DWITH_PDAL) 103 | set(PDAL_LIB ${PDAL_LIBRARIES}) 104 | endif() 105 | 106 | add_library(libopc OBJECT ${SOURCES} ${HEADERS}) 107 | 108 | if (WITH_GBT) 109 | add_dependencies(libopc lightgbm) 110 | set(GBM_LIB ${LIGHTGBM_LIB_PATH}) 111 | endif() 112 | 113 | if (BUILD_PCTRAIN) 114 | add_executable(pctrain pctrain.cpp) 115 | endif() 116 | 117 | if (BUILD_PCCLASSIFY) 118 | add_executable(pcclassify pcclassify.cpp) 119 | endif() 120 | 121 | target_link_libraries(libopc ${STDPPFS_LIBRARY} Eigen3::Eigen OpenMP::OpenMP_CXX ${GBM_LIB} ${PDAL_LIB}) 122 | 123 | if (BUILD_PCTRAIN) 124 | target_link_libraries(pctrain libopc) 125 | install(TARGETS pctrain RUNTIME DESTINATION bin) 126 | endif() 127 | 128 | if (BUILD_PCCLASSIFY) 129 | target_link_libraries(pcclassify libopc) 130 | install(TARGETS pcclassify RUNTIME DESTINATION bin) 131 | endif() -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM ubuntu:22.04 as builder 2 | 3 | # Prerequisites 4 | ENV TZ=Europe/Rome 5 | RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone 6 | RUN apt-get update && apt install -y --fix-missing --no-install-recommends \ 7 | git \ 8 | build-essential \ 9 | software-properties-common \ 10 | cmake \ 11 | libtbb-dev \ 12 | libboost-system-dev \ 13 | libboost-serialization-dev \ 14 | libpdal-dev \ 15 | libeigen3-dev 16 | 17 | COPY . /opc 18 | 19 | # Run build 20 | RUN cd /opc && mkdir build && cd build && cmake -DWITH_GBT=ON .. && make -j$(nproc) 21 | 22 | FROM ubuntu:22.04 as runner 23 | 24 | LABEL Author="Luca Di Leo " \ 25 | Description="Fast and memory efficient semantic segmentation of 3D point clouds" 26 | 27 | # Prerequisites 28 | ENV TZ=Europe/Rome 29 | RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone 30 | RUN apt-get update && apt-get install -y --fix-missing --no-install-recommends \ 31 | libtbb2 \ 32 | libboost-iostreams1.74.0 \ 33 | libboost-system1.74.0 \ 34 | libboost-serialization1.74.0 \ 35 | libpdal-base13 \ 36 | libgomp1 && \ 37 | rm -rf /var/lib/apt/lists/* && \ 38 | apt-get clean && \ 39 | apt-get autoremove 40 | 41 | COPY --from=builder /opc/build ./build 42 | 43 | # 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If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # OpenPointClass - Fast Semantic Segmentation of 3D Point Clouds 2 | 3 | A fast, memory efficient free and open source point cloud classifier. It generates an AI model from a set of input point clouds that have been labeled and can subsequently use that model to classify new datasets. 4 | 5 | ![image](https://user-images.githubusercontent.com/1951843/226082918-296e183a-964f-4564-86ad-c56764e2e090.png) 6 | 7 | ![image](https://user-images.githubusercontent.com/1951843/222988854-afd47307-8ded-4c23-a322-f3c718ce70b8.png) 8 | 9 | On the default parameters it can classify 15 million points in less than 2 minutes on a 4-core Intel i5, which is faster than any other freely available software we've tried. 10 | 11 | It generalizes well to point clouds of varying density and includes local smoothing regularization methods. 12 | 13 | It supports all point cloud formats supported by [PDAL](https://pdal.io/en/latest/stages/readers.html). When built without PDAL, it supports a subset of the PLY format only, which is optimized for speed. 14 | 15 | ## Install 16 | 17 | If you're on Windows, you have two choices: 18 | * [Purchase](http://sites.fastspring.com/masseranolabs/product/openpointclassforwindows) the program for Windows and support the project ❤️ 19 | * Build the program from sources (see instructions below) 20 | 21 | If you're on macOS/Linux you currently need to build from sources (see instructions below). 22 | 23 | ## Build 24 | 25 | Dependencies: 26 | * Intel TBB 27 | * Eigen 28 | * PDAL (optional for LAS/LAZ support) 29 | 30 | ### Linux 31 | 32 | ``` 33 | mkdir build 34 | cd build 35 | cmake .. [-DWITH_GBT=ON] 36 | make -j$(nproc) 37 | ``` 38 | 39 | ### Windows 40 | 41 | You will need [Visual Studio](https://visualstudio.microsoft.com/it/downloads/), [CMake](https://cmake.org/download/) and [VCPKG](https://vcpkg.io/en/getting-started.html). 42 | 43 | Install required packages with VCPKG: 44 | 45 | ``` 46 | vcpkg install eigen3 tbb pdal 47 | ``` 48 | Replace `` with the path to your VCPKG installation in the following commands: 49 | ``` 50 | mkdir build 51 | cd build 52 | cmake [-DWITH_GBT=ON] -DCMAKE_TOOLCHAIN_FILE=/scripts/buildsystems/vcpkg.cmake .. 53 | cmake --build . --config Release --target ALL_BUILD -- /maxcpucount:14 54 | ``` 55 | 56 | ## Usage 57 | 58 | ``` 59 | ./pctrain ./ground_truth.ply 60 | ./pcclassify ./dataset.ply ./classified.ply [model.bin] 61 | ``` 62 | 63 | We provide access to a pre-trained model if you don't have access to labeled data. Please note the model was generated using a limited number of samples and it might not work well with all datasets. 64 | 65 | * [model.bin](https://github.com/uav4geo/OpenPointClass/releases/download/v1.1.3/vehicles-vegetation-buildings.zip) 66 | 67 | Training classes are assumed to follow the [ASPRS 1.4 Classification](https://www.asprs.org/wp-content/uploads/2019/03/LAS_1_4_r14.pdf) and to be stored in either a `label`, `class` or `classification` property. 68 | 69 | You can re-map classification codes by creating a `.json` in the same directory as `.ply`: 70 | 71 | ``` 72 | { 73 | "source": "https://url-to-your-data", 74 | "classification": { 75 | "0": "ground", 76 | "1": "building", 77 | "2": "low_vegetation", 78 | "3": "medium_vegetation", 79 | "4": "high_vegetation", 80 | "17": "ground" 81 | } 82 | } 83 | ``` 84 | 85 | You can also use the `--classes` flag to limit training to a subset of the ASPRS classes: 86 | 87 | ``` 88 | ./pctrain ./ground_truth.laz --classes 2,5,6 89 | ``` 90 | 91 | ### Supported Classes 92 | 93 | | Class | Number | 94 | --------|--------- 95 | | unclassified | 1 | 96 | | ground | 2 | 97 | | low_vegetation | 3 | 98 | | medium_vegetation | 4 | 99 | | high_vegetation | 5 | 100 | | building | 6 | 101 | | low_point | 7 | 102 | | water | 9 | 103 | | rail | 10 | 104 | | road_surface | 11 | 105 | | wire_guard | 13 | 106 | | wire_conductor | 14 | 107 | | transmission_tower | 15 | 108 | | wire_structure_connector | 16 | 109 | | bridge_deck | 17 | 110 | | high_noise | 18 | 111 | | overhead_structure | 19 | 112 | | ignored_ground | 20 | 113 | | snow | 21 | 114 | | temporal_exclusion | 22 | 115 | | human_made_object | 64 | 116 | 117 | 118 | ### Evaluation 119 | 120 | You can check a model accuracy by using the `--eval` argument: 121 | 122 | `./pctrain ./ground_truth.ply --eval test.ply` 123 | 124 | You can use [PDAL](https://pdal.io) to conveniently split a dataset into two (one for training, one for evaluation): 125 | 126 | `pdal split [--capacity numpoints] input.ply input_split.ply` 127 | 128 | ### Color Output 129 | 130 | You can output the results of classification as a colored point cloud by using the `--color` option: 131 | 132 | `./pcclassify ./dataset.ply ./classified.ply --color` 133 | 134 | ### Classifier Types 135 | 136 | `pctrain` can generate AI models using either random forests (default) or gradient boosted trees: 137 | 138 | `./pctrain -c gbt [...]` 139 | 140 | ### Advanced Options 141 | 142 | See `./pctrain --help`. 143 | 144 | ### Docker 145 | 146 | You can build a Docker image with the following command: 147 | 148 | ```bash 149 | docker build -t uav4geo/openpointclass:latest . 150 | ``` 151 | 152 | Run the image with the following command: 153 | 154 | ```bash 155 | docker run -it --rm -v /dataset-path:/data uav4geo/openpointclass:latest bash 156 | ``` 157 | Where `/dataset-path` is the path to the directory containing the dataset files and the `model.bin` file. 158 | 159 | You will be presented with a bash prompt inside the container. You can then run the `pctrain` and `pcclassify` as described above. 160 | 161 | Otherwise, you can use the commands directly with the following syntax: 162 | 163 | ```bash 164 | docker run -it --rm -v /dataset-path:/data uav4geo/openpointclass:latest pctrain /data/ground_truth.ply 165 | docker run -it --rm -v /dataset-path:/data uav4geo/openpointclass:latest pcclassify /data/dataset.ply /data/classified.ply /data/model.bin 166 | ``` 167 | 168 | ## Known Issues 169 | 170 | * We only support a subset of the PLY format (for performance reasons) and certain less common variations of the format might give trouble. Most importantly, the X/Y/Z coordinates must be `float` values when using binary PLY, not `double` or `float64`. We recommend to use LAS/LAZ if higher precision coordinates are needed. 171 | 172 | ## License 173 | 174 | The software is released under the terms of the AGPLv3 175 | 176 | Made with ❤️ by [UAV4GEO](https://uav4geo.com) 177 | -------------------------------------------------------------------------------- /classifier.cpp: -------------------------------------------------------------------------------- 1 | #include "classifier.hpp" 2 | 3 | Regularization parseRegularization(const std::string ®ularization) { 4 | if (regularization == "none") return None; 5 | if (regularization == "local_smooth") return LocalSmooth; 6 | throw std::runtime_error("Invalid regularization value: " + regularization); 7 | } 8 | 9 | ClassifierType fingerprint(const std::string &modelFile) { 10 | std::ifstream ifs(modelFile.c_str(), std::ios::binary); 11 | if (!ifs.is_open()) throw std::runtime_error("Cannot open " + modelFile); 12 | 13 | // Read first 4 bytes 14 | char buf[4] = { 0x00, 0x00, 0x00, 0x00 }; 15 | ifs.read(reinterpret_cast(&buf), sizeof(char) * 4); 16 | 17 | ifs.close(); 18 | 19 | return buf[0] == 0x74 && buf[1] == 0x72 && buf[2] == 0x65 && buf[3] == 0x65 ? 20 | GradientBoostedTrees : 21 | RandomForest; 22 | } 23 | 24 | -------------------------------------------------------------------------------- /classifier.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CLASSIFIER_H 2 | #define CLASSIFIER_H 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | #include "features.hpp" 9 | #include "labels.hpp" 10 | #include "constants.hpp" 11 | #include "point_io.hpp" 12 | #include "statistics.hpp" 13 | 14 | enum Regularization { None, LocalSmooth }; 15 | Regularization parseRegularization(const std::string ®ularization); 16 | 17 | enum ClassifierType { RandomForest, GradientBoostedTrees }; 18 | ClassifierType fingerprint(const std::string &modelFile); 19 | 20 | 21 | template 22 | void getTrainingData(const std::vector &filenames, 23 | double *startResolution, 24 | const int numScales, 25 | const double radius, 26 | const int maxSamples, 27 | const std::vector &asprsClasses, 28 | F storeFeatures, 29 | I init) { 30 | auto labels = getTrainingLabels(); 31 | 32 | bool trainSubset = asprsClasses.size() > 0; 33 | std::array trainClass; 34 | 35 | if (trainSubset) { 36 | trainClass.fill(false); 37 | 38 | auto asprsToTrain = getAsprs2TrainCodes(); 39 | for (auto &c : asprsClasses) { 40 | trainClass[asprsToTrain[c]] = true; 41 | } 42 | } 43 | 44 | for (size_t i = 0; i < filenames.size(); i++) { 45 | std::cout << "Processing " << filenames[i] << std::endl; 46 | auto pointSet = readPointSet(filenames[i]); 47 | if (!pointSet->hasLabels()) { 48 | std::cout << filenames[i] << " has no labels, skipping..." << std::endl; 49 | continue; 50 | } 51 | 52 | if (*startResolution == -1.0) { 53 | *startResolution = pointSet->spacing(); // meters 54 | std::cout << "Starting resolution: " << *startResolution << std::endl; 55 | } 56 | 57 | auto scales = computeScales(numScales, pointSet, *startResolution, radius); 58 | auto features = getFeatures(scales); 59 | std::cout << "Features: " << features.size() << std::endl; 60 | 61 | if (i == 0) init(features.size(), labels.size()); 62 | 63 | std::vector count(labels.size(), 0); 64 | std::vector sampled(pointSet->count(), false); 65 | std::vector > idxes; 66 | 67 | for (size_t i = 0; i < pointSet->count(); i++) { 68 | int g = pointSet->labels[i]; 69 | if (g != LABEL_UNASSIGNED) { 70 | if (trainSubset && !trainClass[g]) continue; 71 | 72 | size_t idx = pointSet->pointMap[i]; 73 | if (!sampled[idx]) { 74 | idxes.push_back(std::make_pair(idx, g)); 75 | count[std::size_t(g)]++; 76 | sampled[idx] = true; 77 | } 78 | } 79 | } 80 | 81 | size_t samplesPerLabel = std::numeric_limits::max(); 82 | for (std::size_t i = 0; i < labels.size(); i++) { 83 | if (count[i] > 0) samplesPerLabel = std::min(count[i], samplesPerLabel); 84 | } 85 | samplesPerLabel = std::min(samplesPerLabel, maxSamples); 86 | std::vector added(labels.size(), 0); 87 | 88 | std::cout << "Samples per label: " << samplesPerLabel << std::endl; 89 | 90 | std::random_device rd; 91 | std::mt19937 ranGen(rd()); 92 | std::shuffle(idxes.begin(), idxes.end(), ranGen); 93 | 94 | for (const auto &p : idxes) { 95 | size_t idx = p.first; 96 | int g = p.second; 97 | if (added[std::size_t(g)] < samplesPerLabel) { 98 | storeFeatures(features, idx, g); 99 | added[std::size_t(g)]++; 100 | } 101 | } 102 | 103 | for (std::size_t i = 0; i < labels.size(); i++) 104 | std::cout << " * " << labels[i].getName() << ": " << added[i] << " / " << count[i] << std::endl; 105 | 106 | // Free up memory for next 107 | for (size_t i = 0; i < scales.size(); i++) delete scales[i]; 108 | for (size_t i = 0; i < features.size(); i++) delete features[i]; 109 | RELEASE_POINTSET(pointSet); 110 | } 111 | } 112 | 113 | template 114 | void classifyData(PointSet &pointSet, 115 | F evaluateFunc, 116 | const std::vector &features, 117 | const std::vector