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https://github.com/tychota/oklab-cpp
https://github.com/tychota/oklab-cpp
Last synced: 2 days ago
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- Host: GitHub
- URL: https://github.com/tychota/oklab-cpp
- Owner: tychota
- License: mit
- Created: 2024-06-03T15:44:51.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-07-05T14:38:47.000Z (4 months ago)
- Last Synced: 2024-07-05T19:33:45.742Z (4 months ago)
- Language: C++
- Size: 35.2 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://conan.io/) - C/C++ package manager
- [CMake](https://cmake.org/) - Build system
- [Google Test](https://github.com/google/googletest) - Unit testing framework
- [Google Benchmark](https://github.com/google/benchmark) - Benchmarking library
- [Poetry](https://python-poetry.org/) - Python dependency management and packaging
### Building the Project
1. **Install Poetry:**
Follow the instructions on the [Poetry website](https://python-poetry.org/docs/#installation) to install Poetry.
2. Clone the repository:
```bash
git clone
cd
```
3. **Install Python Dependencies:**
```bash
poetry install
```
4. Create a build directory and install dependencies using Conan:
```bash
mkdir build
cd build
conan install .. --build=missing
```
5. Configure and build the project using CMake:
```bash
cmake ..
make
```
### Running Tests and Benchmarks
- To run the unit tests:
```bash
./build/Release/oklabTests
```
- To run the benchmarks:
```bash
./build/Release/oklabBenchmark
```
## Example Usage
Here's a simple example of how to use the Oklab interpolation library:
```cpp
#include "Oklab.h"
#include
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:
```cpp
#include
#include "MatrixOperations.h"
Vector3 neon_multiply(const Matrix3x3& matrix, const Vector3& vec) {
// Convert the matrix and vector to float
float32x4_t row0 = {static_cast(matrix[0][0]), static_cast(matrix[0][1]), static_cast(matrix[0][2]), 0};
float32x4_t row1 = {static_cast(matrix[1][0]), static_cast(matrix[1][1]), static_cast(matrix[1][2]), 0};
float32x4_t row2 = {static_cast(matrix[2][0]), static_cast(matrix[2][1]), static_cast(matrix[2][2]), 0};
float32x4_t v = {static_cast(vec[0]), static_cast(vec[1]), static_cast(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.