Search Results for "cmake"

...Unlike traditional 2D geometry libraries, S2 ensures accuracy over large scales by modeling the globe directly, avoiding distortions caused by map projections. The project includes core C++ support, optional Python bindings, and integrations with Bazel and CMake for flexible builds. It relies on Abseil, OpenSSL, and googletest, and is tested across major platforms.

LaMa is a C++11 software library for robotic localization and mapping developed at the Intelligent Robotics and Systems (IRIS) Laboratory at the University of Aveiro - Portugal. It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching, and two SLAM solutions (an Online SLAM and a Particle Filter SLAM). The main feature is efficiency. Low computational effort and low memory usage whenever possible. The minimum viable computer to run our...

...A C++ library that powers customizable vector maps in native applications on multiple platforms by taking stylesheets that conform to the Mapbox Style Specification, applying them to vector tiles that conform to the Mapbox Vector Tile Specification, and rendering them using OpenGL or Metal. To embed interactive maps into a native application using a platform-specific language, install the Mapbox Maps SDK. We use CMake to build Mapbox GL Native for various platforms, including Linux, Android, iOS, macOS and Windows. The provided command, executed from the root of this repository tree, will build Mapbox GL Native targeting your host architecture given that you have all the dependencies installed and run the example app.

.../fast_count_multi num_threads gtf_file bam_file(s) > output Requires bamtools API library at run time, and c++0x for compile. git clone https://github.com/pezmaster31/bamtools cd bamtools mkdir build cd build cmake .. make export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:path to/lib g++ -I bamtools/include/ -L bamtools/lib/ -o fast_count_multi fast_count_multi.cpp -lz -lbamtools -fpermissive -pthread -std=c++0x

...The main features are: - Based on sba-1.6, one of the most popular and robust bundle adjustment implementation, which is extensively used and tested by the community - sba installation is not needed since it is included in cvsba - New CMake structure which makes the library compilation, installation and linkage easier - Similar interface than Bundle Adjustment implementation on cv::LevMarqSparse::bundleAdjust() - Include examples to test the library on synthetically generated data - GPL licence

LuaCV is OpenCV library wrapper for Lua language. Main goal is to access fast image processing library to light-weight, non-type language. Thanks to CMake project is a much easier to compile it under many OS platforms which are supported by OpenCV. LuaCV is being developed at faculty of Electrical Engineering and Communication of Brno University of Technology in Czech republic. Working with: Lua 5.2, OpenCV 2.2-2.4 LuaCV-0.2.0 (svn rev. 62) is last version compatible with Lua 5.1 but working only with OpenCV 2.2 and below.

Welcome to the project homepage of ViennaX, a high-performance scientific computing C++ framework for executing plugins. A plugin system allows to wrap already available functionality into reusable components, which can have data input and output dependencies via a socket system. Different scheduler kernels are available, supporting serial and MPI-based distributed executions, supporting task and data parallelism.

This is a C++ library to create and simulate standard and hybrid bond graph. The library support XML input and output of the bond graph structure. Graphic representation of the bond graph in SVG are generated using the Graphviz library.

Glass Brain is a tool specifically designed for displaying neural connections within the human brain given multidimensional scan data and a connectivity graph. This project benefits from Qt, VTK, ITK, and CMake.

QRasMol is a reimplementation of RasMol in C++ with updated CMake build systems.

...Clone the repository: git clone git@github.com:auralius/libkine.git 3. Go to the directory of the cloned project, use git-lfs to get the binaries and the robot 3D models: git lfs checkout 4. Build the source using CMake 5. Create directory: mkdir build 6. Go inside build, type: cmake .. 7. Type: make 8. Run: ./demo1 or ./demo2 ../model/puma/puma.csv ../model/puma/puma.csv --- Binaries and robot 3D models are stored with git-lfs (https://git-lfs.github.com).