Oklab Color Interpolation Library

This project provides a library for color interpolation using the Oklab color space. It includes unit tests and benchmarks to ensure correctness and performance. Future optimizations may leverage NEON-specific instructions for improved performance on ARM architectures.

Note: the code is not proof. Don't use in production.
I adapted it from a working (yet unproven) javascript file we wrote earlier on.
I used GPT4 and GPT4o extensively to ease the conversion (and test conversational programming).
I repeat, don't use it in production.

Features

Interpolation between colors in the Oklab color space
Gamut mapping to ensure colors stay within valid RGB ranges
Unit tests for verifying functionality
Benchmarks for performance profiling

Getting Started

Prerequisites

Conan - C/C++ package manager
CMake - Build system
Google Test - Unit testing framework
Google Benchmark - Benchmarking library
Poetry - Python dependency management and packaging

Building the Project

Install Poetry:

Follow the instructions on the Poetry website to install Poetry.

Clone the repository:

git clone <repository-url>
cd <repository-directory>

Install Python Dependencies:
```
poetry install
```
Create a build directory and install dependencies using Conan:
```
mkdir build
cd build
conan install .. --build=missing
```
Configure and build the project using CMake:
```
cmake ..
make
```

Running Tests and Benchmarks

To run the unit tests:
```
./build/Release/oklabTests
```
To run the benchmarks:
```
./build/Release/oklabBenchmark
```

Example Usage

Here's a simple example of how to use the Oklab interpolation library:

#include "Oklab.h"
#include <iostream>

int main() {
    RGB color1 = { 255, 0, 0 }; // Red
    RGB color2 = { 0, 0, 255 }; // Blue

    float t = 0.5;
    RGB result = interpolateColor(color1, color2, t);

    std::cout << "Interpolated Color: ("
              << result[0] << ", "
              << result[1] << ", "
              << result[2] << ")" << std::endl;

    return 0;
}

Future Optimizations with NEON

To enhance the performance of matrix operations, we may introduce NEON-specific optimizations.

Below is an example of how such an optimization might look for matrix-vector multiplication:

#include <arm_neon.h>
#include "MatrixOperations.h"

Vector3 neon_multiply(const Matrix3x3& matrix, const Vector3& vec) {
    // Convert the matrix and vector to float
    float32x4_t row0 = {static_cast<float>(matrix[0][0]), static_cast<float>(matrix[0][1]), static_cast<float>(matrix[0][2]), 0};
    float32x4_t row1 = {static_cast<float>(matrix[1][0]), static_cast<float>(matrix[1][1]), static_cast<float>(matrix[1][2]), 0};
    float32x4_t row2 = {static_cast<float>(matrix[2][0]), static_cast<float>(matrix[2][1]), static_cast<float>(matrix[2][2]), 0};

    float32x4_t v = {static_cast<float>(vec[0]), static_cast<float>(vec[1]), static_cast<float>(vec[2]), 0};

    float32x4_t result0 = vmulq_f32(row0, v);
    float32x4_t result1 = vmulq_f32(row1, v);
    float32x4_t result2 = vmulq_f32(row2, v);

    Vector3 result;
    result[0] = vaddvq_f32(result0); // Sum of all elements
    result[1] = vaddvq_f32(result1);
    result[2] = vaddvq_f32(result2);

    return result;

}

However, for small matrix operation like this, it doesn't seems to be worth.

micro benchmark without neon.

2024-06-03T17:19:02+02:00
Running ./build/Release/oklabBenchmark
Run on (12 X 24 MHz CPU s)
CPU Caches:
  L1 Data 64 KiB
  L1 Instruction 128 KiB
  L2 Unified 4096 KiB (x12)
Load Average: 5.50, 5.43, 4.95
---------------------------------------------------------------------
Benchmark                           Time             CPU   Iterations
---------------------------------------------------------------------
BM_InterpolateColor               162 ns          161 ns      4377325
BM_InterpolateRandomColors        289 ns          288 ns      2434131

micro benchmark with NEON

2024-06-03T17:33:53+02:00
Running ./build/Release/oklabBenchmark
Run on (12 X 24 MHz CPU s)
CPU Caches:
  L1 Data 64 KiB
  L1 Instruction 128 KiB
  L2 Unified 4096 KiB (x12)
Load Average: 6.17, 5.63, 5.22
---------------------------------------------------------------------
Benchmark                           Time             CPU   Iterations
---------------------------------------------------------------------
BM_InterpolateColor               173 ns          173 ns      4046149
BM_InterpolateRandomColors        303 ns          302 ns      2313293

License

This project is licensed under the MIT License - see the LICENSE file for details.

tychota/oklab-cpp