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https://github.com/mitmul/pynvvl
A Python wrapper of NVIDIA Video Loader (NVVL) with CuPy for fast video loading with Python
https://github.com/mitmul/pynvvl
cuda cupy gpu numpy nvidia-video-loader nvvl python video video-processing
Last synced: 3 months ago
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A Python wrapper of NVIDIA Video Loader (NVVL) with CuPy for fast video loading with Python
- Host: GitHub
- URL: https://github.com/mitmul/pynvvl
- Owner: mitmul
- License: mit
- Archived: true
- Created: 2018-05-03T00:17:55.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-04T12:40:12.000Z (over 4 years ago)
- Last Synced: 2024-04-24T20:03:09.995Z (8 months ago)
- Topics: cuda, cupy, gpu, numpy, nvidia-video-loader, nvvl, python, video, video-processing
- Language: Python
- Homepage:
- Size: 1.3 MB
- Stars: 101
- Watchers: 10
- Forks: 12
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
PyNVVL
======
[](https://pypi.org/project/pynvvl-cuda80)
[](https://pypi.org/project/pynvvl-cuda90)
[](https://pypi.org/project/pynvvl-cuda91)
[](https://pypi.org/project/pynvvl-cuda92)
[](https://github.com/mitmul/pynvvl)
PyNVVL is a thin wrapper of [NVIDIA Video Loader (NVVL)](https://github.com/NVIDIA/nvvl). This package enables you to load videos directly to GPU memory and access them as [CuPy](https://github.com/cupy/cupy) ndarrays with zero copy. The pre-built binaries of PyNVVL include NVVL itself, so you do not need to install NVVL.
## Requirements
- CUDA 8.0, 9.0, 9.1, or 9.2
- Python 2.7.6+, 3.4.7+, 3.5.1+, or 3.6.0+
- [CuPy](https://github.com/cupy/cupy) v4.5.0
## Tested Environment
- Ubuntu 16.04
- Python 2.7.6+, 3.4.7+, 3.5.1+, and 3.6.0+
- CUDA 8.0, 9.0, 9.1, and 9.2
## Install the pre-built binary
Please choose a right package depending on your CUDA version.
```bash
# [For CUDA 8.0]
pip install pynvvl-cuda80
# [For CUDA 9.0]
pip install pynvvl-cuda90
# [For CUDA 9.1]
pip install pynvvl-cuda91
# [For CUDA 9.2]
pip install pynvvl-cuda92
```
## Usage
```python
import pynvvl
import matplotlib.pyplot as plt
# Create NVVLVideoLoader object
loader = pynvvl.NVVLVideoLoader(device_id=0, log_level='error')
# Show the number of frames in the video
n_frames = loader.frame_count('examples/sample.mp4')
print('Number of frames:', n_frames)
# Load a video and return it as a CuPy array
video = loader.read_sequence(
'examples/sample.mp4',
horiz_flip=True,
scale_height=512,
scale_width=512,
crop_y=60,
crop_height=385,
crop_width=512,
scale_method='Linear',
normalized=True
)
print(video.shape) # => (91, 3, 385, 512): (n_frames, channels, height, width)
print(video.dtype) # => float32
# Get the first frame as numpy array
frame = video[0].get()
frame = frame.transpose(1, 2, 0)
plt.imshow(frame)
plt.savefig('examples/sample.png')
```
This video is `flickr-2-6-3-3-5-2-7-6-5626335276_4.mp4` from the Moments-In-Time dataset.
Note that cropping is performed after scaling. In the above example, NVVL performs scaling up from 256 x 256 to 512 x 512 first, then cropping the region [60:60 + 385, 0:512]. See the following section to know more about the transformation options.
## VideoLoader options
Please specify the GPU device id when you create a `NVVLVideoLoader` object.
You can also specify the logging level with the argument `log_level` for the constructor of `NVVLVideoLoader`.
```
Wrapper of NVVL VideoLoader
Args:
device_id (int): Specify the device id used to load a video.
log_level (str): Logging level which should be either 'debug',
'info', 'warn', 'error', or 'none'.
Logs with levels >= log_level is shown. The default is 'warn'.
```
## Transformation Options
`pynvvl.NVVLVideoLoader.read_sequence` can take some options to specify the color space, the value range, and what transformations you want to perform to the video.
```
Loads the video from disk and returns it as a CuPy ndarray.
Args:
filename (str): The path to the video.
frame (int): The initial frame number of the returned sequence.
Default is 0.
count (int): The number of frames of the returned sequence.
If it is None, whole frames of the video are loaded.
channels (int): The number of color channels of the video.
Default is 3.
scale_height (int): The height of the scaled video.
Note that scaling is performed before cropping.
If it is 0 no scaling is performed. Default is 0.
scale_width (int): The width of the scaled video.
Note that scaling is performed before cropping.
If it is 0, no scaling is performed. Default is 0.
crop_x (int): Location of the crop within the scaled frame.
Must be set such that crop_y + height <= original height.
Default is 0.
crop_y (int): Location of the crop within the scaled frame.
Must be set such that crop_x + width <= original height.
Default is 0.
crop_height (int): The height of cropped region of the video.
If it is None, no cropping is performed. Default is None.
crop_width (int): The width of cropped region of the video.
If it is None, no cropping is performed. Default is None.
scale_method (str): Scaling method. It should be either of
'Nearest' or 'Lienar'. Default is 'Linear'.
horiz_flip (bool): Whether horizontal flipping is performed or not.
Default is False.
normalized (bool): If it is True, the values of returned video is
normalized into [0, 1], otherwise the value range is [0, 255].
Default is False.
color_space (str): The color space of the values of returned video.
It should be either 'RGB' or 'YCbCr'. Default is 'RGB'.
chroma_up_method (str): How the chroma channels are upscaled from
yuv 4:2:0 to 4:4:4. It should be 'Linear' currently.
out (cupy.ndarray): Alternate output array where place the result.
It must have the same shape and the dtype as the expected
output, and its order must be C-contiguous.
```
## How to build
### Build wheels using Docker:
Requirements:
- Docker
- nvidia-docker (v1/v2)
```
bash docker/build_wheels.sh
```
### Setup development environment without Docker:
The `setup.py` script searches for necessary libraries.
Requirements: the following libraries should be available in `LIBRARY_PATH`.
- libnvvl.so
- libavformat.so.57
- libavfilter.so.6
- libavcodec.so.57
- libavutil.so.55
You can build `libnvvl.so` in the `nvvl` repository. Follow the instructions
of `nvvl` library. The `build` directory must be in `LIBRARY_PATH`.
The other three libraries are available as packages in Ubuntu 16.04.
They are installed under `/usr/lib/x86_64-linux-gnu`, so they must be in `LIBRARY_PATH` as well.
```
python setup.py develop
python setup.py bdist_wheel
```