A library to create kaleidoscope effect on images. You can build on all platforms using CMake. ![GitHub](https://img.shields.io/github/license/egecetin/libKaleidoscope?style=for-the-badge) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/egecetin/libKaleidoscope/pre-commit.yml?branch=master&label=pre-commit&logo=precommit&logoColor=white&style=for-the-badge) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/egecetin/libKaleidoscope/codeql-analysis.yml?branch=master&label=CodeQL&logo=github&style=for-the-badge) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/egecetin/libKaleidoscope/os-builds.yml?branch=master&label=Build&logo=github&logoColor=white&style=for-the-badge) ![Codecov](https://img.shields.io/codecov/c/github/egecetin/libkaleidoscope?logo=codecov&logoColor=white&style=for-the-badge&token=70EJQJRRBH) ![Codacy grade](https://img.shields.io/codacy/grade/b6c3a6abeeb34c2e8aa67aaeb8bd2982?logo=codacy&style=for-the-badge) ![C Badge](https://img.shields.io/badge/C-%23555555?style=for-the-badge&logo=c&logoColor=white) ![C++ Badge](https://img.shields.io/badge/C%2B%2B-%23f34b7d?style=for-the-badge&logo=cplusplus&logoColor=white) ![Python Badge](https://img.shields.io/badge/Python-%233572A5?style=for-the-badge&logo=python&logoColor=white) ![CUDA Badge](https://img.shields.io/badge/CUDA-%233A4E3A?style=for-the-badge&logo=nvidia&logoColor=white)
The library is written in C language so you can use Foreign Function Interface (FFI) to call functions from your favorite programming language. You can download from python package from PyPI. It also has C++ header only library to provide easier interface for C++ users and CUDA support for people who have doubts about performance. Check for mathematical explanation of the kaleidoscope effect from my [webpage](https://egecetin.github.io/Projects/kaleidoscope) ## Supported Languages - C : Main programming language - C++ : Header only binding for easier usage - Python : Bindings using Cython - CUDA : For GPU computing ## Install for Python ``` pip install LibKaleidoscope ``` Python users check python/python-test.py in GitHub for basic usage ## Building Use the following commands, ``` mkdir build && cd build cmake -DCMAKE_BUILD_TYPE=Release .. cmake --build . --parallel ``` If you want to enable CUDA backend, ``` mkdir build && cd build cmake -DCMAKE_BUILD_TYPE=Release -DKALEIDOSCOPE_ENABLE_CUDA=ON .. cmake --build . --parallel ``` There is no direct dependency for libjpeg-turbo inside from the library. It is just for test and demonstration purposes. If you don't want to install/compile just disable command line tool compilation with ``-DKALEIDOSCOPE_ENABLE_CMD_TOOL=OFF`` ## Usage The library has a simple usage and you need only three functions to use it. Check the sample usage at ``src/kaleidoscope-cmd.c`` - Initialization of the transformation matrix: ``int initKaleidoscope(KaleidoscopeHandle *handler, int n, int width, int height, double scaleDown)`` - Processing image (Can be used multiple times if the input images have same dimensions): ``void processKaleidoscope(KaleidoscopeHandle *handler, double k, unsigned char *imgIn, unsigned char *imgOut)`` - Deinitialization of the transformation matrix: ``void deInitKaleidoscope(KaleidoscopeHandle *handler)`` Alternatively you can directly use the command line program to create kaleidoscope effect with ``./kaleidoscope-cmd ``. You can see an example below for ``N=8``

Image source: AC Valhalla
For C++ and CUDA usage check the unit tests at ``tests/processingTest.cpp`` and ``tests/processingTest.cu``. It is very easy! Just include the header and construct the ``Kaleidoscope`` class from ``kalos`` namespace. - For C++ header only binding, ``` #include int main() { kalos::Kaleidoscope handler(n, width, height, nComponents, scaleDown, k); /* ... */ handler.processImage(inData, outData, nPixel); /* ... */ return 0; } ``` - For CUDA backend, ``` #include int main() { kalos::cuda::Kaleidoscope handler(n, width, height, nComponents, scaleDown, k); /* ... */ // Make sure inData and outData is device allocated! handler.processImage(inData, outData, nPixel); /* ... */ return 0; } ``` ## Benchmark It is really fast! On a Intel i7-11800H CPU it achieves, - ~65 FPS for 4K UHD (3840 x 2160) - ~265 FPS for Full HD (1920 x 1080) - ~640 FPS for 720p (1280 x 720) - ~1350 FPS for 576p (720 x 576) resolution images. The performance estimation can be seen at the below
$$ FPS = a\text{ }e^{b\text{ }nPixels}+c\text{ }e^{d\text{ }nPixels} $$ $$ a = 2492 \text{, } b = -2.165\text{ }10^{-6} \text{, } c = 364.9 \text{, } d = -2.08\text{ }10^{-7} $$ If you want to benchmark code on your system make sure you configured with ```-DCMAKE_BUILD_TYPE=Release``` and use this command, ```./kaleidoscope-cmd ```