cvcuda-cu12 0.16.0

CUDA-accelerated Computer Vision algorithms

CV-CUDA Python Package

CV-CUDA is an open-source library of GPU-accelerated computer vision algorithms designed for speed and scalability. It delivers high-throughput, low-latency image/video processing for AI pipelines across NVIDIA cloud, desktop, and edge platforms. CV-CUDA is built for performance and works seamlessly with C/C++ and Python frameworks.

For more information on available operators, API documentation, and getting started guides, refer to our online documentation.

Installation

pip install cvcuda-cu12

CV-CUDA in action

Fully GPU-accelerated image resizing with nvImageCodec and CV-CUDA.

import cvcuda
from nvidia import nvimgcodec

# Decode image directly to GPU
decoder = nvimgcodec.Decoder()
image = decoder.read("input.jpg")

# Convert to CV-CUDA tensor and process
cvcuda_tensor = cvcuda.as_tensor(image, "HWC")
resized = cvcuda.resize(cvcuda_tensor, (224, 224, 3), cvcuda.Interp.LINEAR)

Documentation

• Official Documentation
• Samples

Requirements

• CUDA Toolkit 12 or later
• Python 3.9 or later
• NumPy 1.23.5 or later
• Linux x86_64 (Ubuntu 20.04, 22.04, or later)

License

CV-CUDA is licensed under the Apache License 2.0.

Support

• GitHub Issues
• GitHub Discussions

Citation

If you use CV-CUDA in your research, please cite:

@software{cvcuda,
  title = {CV-CUDA},
  author = {NVIDIA Corporation},
  year = {2023},
  url = {https://github.com/CVCUDA/CV-CUDA}
}

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Note: This is version 0.16.0 for CUDA 12.