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
Requirements
- CUDA Toolkit 12 or later
- Python 3.10 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
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}
}
Note: This is version 0.17.0 for CUDA 12.
2 maintainers
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