celux-cuda 0.3.5

HW accelerated video reading for ML Inference (CUDA version).

CeLux

CeLux is a high-performance Python library for video processing, leveraging the power of FFmpeg. It delivers some of the fastest decode times for full HD videos globally, enabling efficient and seamless video decoding directly into PyTorch tensors.

The name CeLux is derived from the Latin words celer (speed) and lux (light), reflecting its commitment to speed and efficiency.

🚀 Features

• ⚡ Ultra-Fast Video Decoding: Achieve lightning-fast decode times for full HD videos using hardware acceleration.
• 🔗 Direct Decoding to Tensors: Decode video frames directly into PyTorch tensors for immediate processing.
• 🖥️ Hardware Acceleration Support: Utilize CUDA for GPU-accelerated decoding, significantly improving performance.
• 🔄 Easy Integration: Seamlessly integrates with existing Python workflows, making it easy to incorporate into your projects.
• 🗂️ Supports Multiple Data Types: Handle video frames in uint8, float32, or float16 data types.

📦 Installation

CeLux offers two installation options tailored to your system's capabilities:

1. CPU-Only Version: For systems without CUDA-capable GPUs.
2. CUDA (GPU) Version: For systems with NVIDIA GPUs supporting CUDA.

🖥️ CPU-Only Installation

Install the CPU version of CeLux using pip:

pip install celux

Note: The CPU version only supports CPU operations. Attempting to use GPU features with this version will result in an error.

🖥️ CUDA (GPU) Installation

Install the CUDA version of CeLux using pip:

pip install celux-cuda

Note: The CUDA version requires a CUDA-capable GPU and the corresponding Torch-Cuda installation.

🔄 Both Packages Import as celux

Regardless of the installation choice, both packages are imported using the same module name:

import celux #as cx

This design ensures a seamless transition between CPU and CUDA versions without changing your import statements.

📚 Getting Started

🎉 Quick Start

Here's a simple example demonstrating how to use CeLux to read video frames and process them:

import celux as cx

def process_frame(frame):
    # Implement your frame processing logic here
    pass

# Choose device based on your installation
device = "cuda" if torch.cuda.is_available() else "cpu"

with cx.VideoReader(
    "path/to/input/video.mp4",
    device=device,      # "cpu" or "cuda"
    dtype="uint8"       # Options: "uint8", "float32", "float16"
) as reader:
    for frame in reader:
        # Frame is a PyTorch tensor in HWC format
        process_frame(frame)

Parameters:

• device (str): Device to use. Can be "cpu" or "cuda".
• dtype (str): Data type of the output frames ("uint8", "float32", or "float16").

Note: If you set dtype to "float" or "half", the frame values will be normalized between 0.0 and 1.0.

📜 Detailed Usage

CeLux allows you to efficiently decode and process video frames with ease. Below are some common operations:

Initialize VideoReader

reader = cx.VideoReader(
    "path/to/video.mp4",
    device="cuda",        # Use "cpu" or "cuda"
    dtype="float32",      # Data type: "uint8", "float32", "float16"
    frame_range=[10, 20]  # Optional: Read frames 10 to 20
)

Iterate Through Frames

for frame in reader:
    # Your processing logic
    pass

Access Video Properties

properties = reader.get_properties()
print(properties)

🛠️ Building from Source

While CeLux is easily installable via pip, you might want to build it from source for customization or contributing purposes.

1. Clone the Repository:

   git clone https://github.com/Trentonom0r3/celux.git
   cd celux
   

2. Install Dependencies:

   Ensure all prerequisites are installed. You can use vcpkg for managing dependencies on Windows.

3. Configure the Project with CMake:

   cmake -B build -S . -DCMAKE_BUILD_TYPE=Release
   

   Windows Users: If using Vcpkg, include the toolchain file:

   cmake -B build -S . -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=<path_to_vcpkg>/scripts/buildsystems/vcpkg.cmake
   

4. Build the Project:

   cmake --build build --config Release
   

5. Install the Package:

   cmake --install build
   

6. Set Up Environment Variables:

   Ensure FFmpeg binaries and other dependencies are in your system's PATH. On Unix systems, you might need to set LD_LIBRARY_PATH or DYLD_LIBRARY_PATH.

🤝 Contributing

We welcome contributions! Follow these steps to contribute:

1. Fork the Repository:

   Click the "Fork" button at the top right of the repository page.

2. Clone Your Fork:

   git clone https://github.com/your-username/celux.git
   cd celux
   

3. Create a New Branch:

   git checkout -b feature/your-feature-name
   

4. Make Your Changes:

   Implement your feature or bugfix.

5. Commit Your Changes:

   git commit -am "Add your commit message here"
   

6. Push to Your Fork:

   git push origin feature/your-feature-name
   

7. Submit a Pull Request:

   Go to the original repository and click on "Pull Requests," then "New Pull Request."

📈 Changelog

Version 0.3.5 (2024-10-19)

• Pre-Release Update:
  • (somewhat) Fixed VideoWriter class. Working on cuda for now, but cpu still has incorrect output.
  • Added VideoWriter, and LogLevel definitions to .pyi stub file.
  • Adjusted github actions to publish to pypi.

Version 0.3.4.1 (2024-10-19)

• Pre-Release Update:
  • Added logging utility for debugging purposes.

    import celux
    celux.set_log_level(celux.LogLevel.debug)
    

Version 0.3.3 (2024-10-19)

• Pre-Release Update:
  • Added buffer_size and stream arguments.
    • Choose Pre-Decoded Frame buffer size, and pass your own cuda stream.
  • Some random cleanup and small refactorings.

Version 0.3.1 (2024-10-17)

• Pre-Release Update:
  • Adjusted Frame Range End in VideoReader to be exclusive to match cv2 behavior.
  • Removed unnecessary error throws.
  • Encoder DOES NOT work currenty. WiP.

Version 0.3.0 (2024-10-17)

• Pre-Release Update:
  • Renamed from ffmpy to CeLux.
  • Created official pypi release.
  • Refactored to split cpu and cuda backends.

Version 0.2.6 (2024-10-15)

• Pre-Release Update:
  • Removed Numpy support in favor of PyTorch tensors with GPU/CPU support.
  • Added NV12ToBGR, BGRToNV12, and NV12ToNV12 conversion modules.
  • Fixed several minor issues.
  • Updated documentation and examples.

Version 0.2.2 (2024-10-14)

• Pre-Release Update:

  • Fixed several minor issues.

  • Made VideoReader and VideoWriter callable.

  • Created BGR conversion modules.

  • Added frame range (in/out) arguments.

    with VideoReader('input.mp4')([10, 20]) as reader:
        for frame in reader:
            print(f"Processing frame {frame}")
    

Version 0.2.1 (2024-10-13)

• Pre-Release Update:
  • Adjusted Python bindings to use snake_case.
  • Added .pyi stub files to .whl.
  • Adjusted dtype arguments to (uint8, float32, float16).
  • Added GitHub Actions for new releases.
  • Added HW Accel Encoder support, direct encoding from numpy/tensors.
  • Added has_audio property to VideoReader.get_properties().

Version 0.1.1 (2024-10-06)

• Pre-Release Update:
  • Implemented support for multiple data types (uint8, float, half).
  • Provided example usage and basic documentation.

📄 License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). See the LICENSE file for details.

🙏 Acknowledgments

• FFmpeg: The backbone of video processing in CeLux.
• PyTorch: For tensor operations and CUDA support.
• Vcpkg: Simplifies cross-platform dependency management.
• @NevermindNilas: For assistance with testing, API suggestions, and more.

❓ FAQ

Q: Can I use CeLux without CUDA or GPU acceleration?

A: Yes, you can set device="cpu" when initializing VideoReader. However, decoding performance will be significantly slower compared to GPU-accelerated decoding.

Q: What video formats are supported?

A: CeLux aims to support all video formats and codecs supported by FFmpeg. However, hardware-accelerated decoding is currently available for specific codecs like H.264 and HEVC. These are the only codecs tested so far.

Q: How do I report a bug or request a feature?

A: Please open an issue on the GitHub Issues page with detailed information about the bug or feature request.

🚤 Roadmap

• Audio Processing:

  • Introduce capabilities for audio extraction and processing.

• Performance Enhancements:

  • Further optimize decoding performance and memory usage.

• Cross-Platform Support:

  • Improve compatibility with different operating systems and hardware configurations.

• Support for Additional Codecs:

  • Expand the range of supported video codecs.