| Name | avhardware JSON |
| Version |
0.1.6
JSON |
| download |
| home_page | None |
| Summary | PyAV extension with hardware decoding support. |
| upload_time | 2024-09-01 10:12:46 |
| maintainer | None |
| docs_url | None |
| author | Matteo Destro |
| requires_python | None |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# PyAV-Hardware
[](https://pypi.org/project/avhardware/)
**PyAV-Hardware** is an extension of [PyAV](https://github.com/PyAV-Org/PyAV) that adds support for hardware-accelerated video decoding using Nvidia GPUs. It integrates with FFmpeg and PyTorch, providing CUDA-accelerated kernels for efficient color space conversion.
## Installation
1. Build and install FFmpeg with [hardware acceleration support](https://pytorch.org/audio/stable/build.ffmpeg.html).
2. To enable hardware acceleration in PyAV, it needs to be reinstalled from source. Assuming FFmpeg is installed in `/opt/ffmpeg`, run:
```bash
pip uninstall av
PKG_CONFIG_LIBDIR="/opt/ffmpeg/lib/pkgconfig" pip install av --no-binary av --no-cache
```
If the installation was successful, `h264_cuvid` should appear between the available codecs:
```python
import av
print(av.codecs_available)
```
3. Install PyAV-Hardware:
```bash
PKG_CONFIG_LIBDIR="/opt/ffmpeg/lib/pkgconfig" CUDA_HOME="/usr/local/cuda" pip install avhwardware
```
4. Test the installation by running `python examples/benchmark.py`. The output should show something like:
```
Running CPU decoding... took 34.99s
Running GPU decoding... took 8.30s
```
## Usage
To use hardware decoding, instantiate an `HWDeviceContext` and attach it to a `VideoStream`. Note that an `HWDeviceContext` can be shared by multiple `VideoStream` instances to save memory.
```python
import av
import avhardware
CUDA_DEVICE = 0
with (
av.open("video.mp4") as container,
avhardware.HWDeviceContext(CUDA_DEVICE) as hwdevice_ctx,
):
stream = container.streams.video[0]
hwdevice_ctx.attach(stream.codec_context)
# Convert frames into RGB PyTorch tensors on the same device
for frame in container.decode(stream):
frame_tensor = hwdevice_ctx.to_tensor(frame)
```
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"description": "# PyAV-Hardware\n[](https://pypi.org/project/avhardware/)\n\n**PyAV-Hardware** is an extension of [PyAV](https://github.com/PyAV-Org/PyAV) that adds support for hardware-accelerated video decoding using Nvidia GPUs. It integrates with FFmpeg and PyTorch, providing CUDA-accelerated kernels for efficient color space conversion.\n\n## Installation\n\n1. Build and install FFmpeg with [hardware acceleration support](https://pytorch.org/audio/stable/build.ffmpeg.html).\n\n2. To enable hardware acceleration in PyAV, it needs to be reinstalled from source. Assuming FFmpeg is installed in `/opt/ffmpeg`, run:\n ```bash\n pip uninstall av\n PKG_CONFIG_LIBDIR=\"/opt/ffmpeg/lib/pkgconfig\" pip install av --no-binary av --no-cache\n ```\n If the installation was successful, `h264_cuvid` should appear between the available codecs:\n ```python\n import av\n print(av.codecs_available)\n ```\n\n3. Install PyAV-Hardware:\n ```bash\n PKG_CONFIG_LIBDIR=\"/opt/ffmpeg/lib/pkgconfig\" CUDA_HOME=\"/usr/local/cuda\" pip install avhwardware\n ```\n\n4. Test the installation by running `python examples/benchmark.py`. The output should show something like:\n ```\n Running CPU decoding... took 34.99s\n Running GPU decoding... took 8.30s\n ```\n\n\n## Usage\n\nTo use hardware decoding, instantiate an `HWDeviceContext` and attach it to a `VideoStream`. Note that an `HWDeviceContext` can be shared by multiple `VideoStream` instances to save memory.\n\n```python\nimport av\nimport avhardware\n\nCUDA_DEVICE = 0\n\nwith (\n av.open(\"video.mp4\") as container,\n avhardware.HWDeviceContext(CUDA_DEVICE) as hwdevice_ctx,\n):\n stream = container.streams.video[0]\n hwdevice_ctx.attach(stream.codec_context)\n\n # Convert frames into RGB PyTorch tensors on the same device\n for frame in container.decode(stream):\n frame_tensor = hwdevice_ctx.to_tensor(frame)\n```\n",
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