pynvvl-cuda80 0.0.3a2
PyNVVL: A Python wrapper for NVIDIA Video Loader (NVVL) with CuPy
License: MIT License

PyNVVL
======
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PyNVVL is a thin wrapper of [NVIDIA Video Loader (NVVL)](https://github.com/NVIDIA/nvvl). This package enables you to load videos directoly 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')
```

![](https://github.com/mitmul/pynvvl/raw/master/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 a 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 are 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`.

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
```