Skip to main content

PyNVVL: A Python wrapper for NVIDIA Video Loader (NVVL) with CuPy

Project description

PyNVVL

pypi-pynvvl-cuda80 pypi-pynvvl-cuda90 pypi-pynvvl-cuda91 GitHub license

PyNVVL is a thin wrapper of NVIDIA Video Loader (NVVL). This package enables you to load videos directoly to GPU memory and access them as 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, or 9.1
  • Python 2.7.6+, 3.4.7+, 3.5.1+, or 3.6.0+
  • CuPy v4.0.0

Tested Environment

  • Ubuntu 16.04
  • Python 2.7.6+, 3.4.7+, 3.5.1+, and 3.6.0+
  • CUDA 8.0, 9.0, and 9.1

Install the pre-built binary

Please choose a right package depending on your CUDA version.

# [For CUDA 8.0]
pip install pynvvl-cuda80

# [For CUDA 9.0]
pip install pynvvl-cuda90

# [For CUDA 9.1]
pip install pynvvl-cuda91

Usage

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 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.

How to build wheels

Requirements for build

  • Docker
  • nvidia-docker (v1/v2)
bash docker/build_wheels.sh

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

File details

Details for the file pynvvl_cuda80-0.0.2a3-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for pynvvl_cuda80-0.0.2a3-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 952410737725d8e05c4c2e7fac689e9e8545e978773ad022d4ed462b262a710d
MD5 37bde76b7e7f91ae63ee28d66982717a
BLAKE2b-256 c7fa7becf7a474ce22aad7a82e1e41807c01282023f4136588391f7d30ac450f

See more details on using hashes here.

File details

Details for the file pynvvl_cuda80-0.0.2a3-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for pynvvl_cuda80-0.0.2a3-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b3f65cef979ce5da4e60db506c0beaf4be1582e2ddad94caa420e86f211ffb9b
MD5 74018a45b8bb990dee1d787601f4287d
BLAKE2b-256 9bada4a124783014a1b69f4c35a55210ba8f5d48d254222672b8e9095af52a9d

See more details on using hashes here.

File details

Details for the file pynvvl_cuda80-0.0.2a3-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for pynvvl_cuda80-0.0.2a3-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 a426bdee141d3ae62aa071f22d744dc8f055793590e7d52a71ba68384eb1813f
MD5 e9706308ba33914e09c3d74cc236635f
BLAKE2b-256 2fd8232d2ea5a6d5166211d69d0d945353a11b18e88435fd698eb8478cc42e59

See more details on using hashes here.

File details

Details for the file pynvvl_cuda80-0.0.2a3-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for pynvvl_cuda80-0.0.2a3-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 40372fe5a7a9cb31921a9e0f5f9b0d0daf9bef2ca168bedd580423fbe56deb3e
MD5 dc874189f3a72dcd459e655c375d3b9b
BLAKE2b-256 1fca573f9cf884608fa211c9e6008c3f31698600b14691c4148e63e0d6c04c99

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page