🌈✨ Transform Images into Mesmerizing Kaleidoscope Art ✨🌈

A blazingly fast, cross-platform library to create stunning kaleidoscope effects on images
Built with ❤️ using C, C++, Python, and CUDA

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📊 Project Status

🛠️ Technology Stack

LibKaleidoscope is a high-performance, cross-platform library that transforms ordinary images into breathtaking kaleidoscope patterns. Written in C with FFI support, it offers seamless integration with multiple programming languages and includes GPU acceleration for ultimate performance.

🔗 Learn More: Check out the mathematical explanation of the kaleidoscope effect!

# 🎉 One-liner installation from PyPI
pip install LibKaleidoscope

💡 Pro Tip: Check python/python-test.py for example usage!

# 🚀 Quick build commands
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
cmake --build . --parallel

# 🔥 GPU-accelerated build
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DKALEIDOSCOPE_ENABLE_CUDA=ON ..
cmake --build . --parallel

# 🎛️ Disable command line tool (reduces dependencies)
cmake -DCMAKE_BUILD_TYPE=Release -DKALEIDOSCOPE_ENABLE_CMD_TOOL=OFF ..

LibKaleidoscope makes image transformation incredibly simple with just 3 functions:

flowchart LR
    A[🎯 Initialize] --> B[🎨 Process] --> C[🧹 Cleanup]
    B --> B

// 🎯 Step 1: Initialize
int initKaleidoscope(KaleidoscopeHandle *handler, int n, int width, int height, double scaleDown);

// 🎨 Step 2: Process (use multiple times)
void processKaleidoscope(KaleidoscopeHandle *handler, double k, unsigned char *imgIn, unsigned char *imgOut);

// 🧹 Step 3: Cleanup
void deInitKaleidoscope(KaleidoscopeHandle *handler);

Transform images instantly with the command line tool:

# ✨ Create kaleidoscope effect (N=8 segments)
./kaleidoscope-cmd <Input_Image> <Output_Image> <N>

➡️
🎮 Original → Kaleidoscope (N=8)
Image source: AC Valhalla

#include <kaleidoscope.hpp>

int main() {
    // 🎯 One-line initialization with all parameters
    kalos::Kaleidoscope handler(n, width, height, nComponents, scaleDown, k);

    // 🎨 Process your image data
    handler.processImage(inData, outData, nPixel);

    // 🧹 Automatic cleanup when handler goes out of scope
    return 0;
}

#include <cuda/kaleidoscope.cuh>

int main() {
    // 🚀 GPU-accelerated kaleidoscope
    kalos::cuda::Kaleidoscope handler(n, width, height, nComponents, scaleDown, k);

    // ⚡ Ultra-fast GPU processing
    // ⚠️ Important: inData and outData must be device-allocated!
    handler.processImage(inData, outData, nPixel);

    return 0;
}

🧪 Examples: See tests/processingTest.cpp and tests/processingTest.cu for complete implementations

Hardware: Intel i7-11800H CPU

# 🎯 Test performance on your hardware
./kaleidoscope-cmd <Input_Image> <Output_Image> <N> <Number_of_loops>

⚠️ Important: Use -DCMAKE_BUILD_TYPE=Release for accurate benchmarks

We welcome contributions! Please feel free to submit issues, feature requests, or pull requests.

This project is licensed under the terms of MIT License.