v0.0.3a2

• Improved setup.py to allow users to do custom installations (thanks, @keisukefukuda!)
• Released a new wheel for CUDA 9.2.

v0.0.2a4

Bug fix: #4

v0.0.2a3

Update: Add log_level option to control the logging level of NVVL

PyNVVL

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

v0.0.2a2

Update: Use stream to create CuPy ndarray and wait for the NVVL processing finishes.

PyNVVL

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.

Requirements

• Python 2.7.6+, 3.4.7+, 3.5.1+, 3.6.0+
• CuPy v4.0.0

Tested Environment

• Ubuntu 16.04
• Python 2.7.6+, 3.4.7+, 3.5.1+, 3.6.0+

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)

# 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,
    crop_y=30,
    crop_height=190,
    scale_method='Nearest',
    normalized=True
)

print(video.shape)  # => (91, 3, 256, 256): (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 from the Moments-In-Time dataset.

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

v0.0.2a1

PyNVVL

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.

Requirements

• Python 2.7.6+, 3.4.7+, 3.5.1+, 3.6.0+
• CuPy v4.0.0

Tested Environment

• Ubuntu 16.04
• Python 2.7.6+, 3.4.7+, 3.5.1+, 3.6.0+

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)

# 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,
    crop_y=30,
    crop_height=190,
    scale_method='Nearest',
    normalized=True
)

print(video.shape)  # => (91, 3, 256, 256): (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 from the Moments-In-Time dataset.

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_docker.sh
sudo rm -rf docker/lib
bash docker/build_nvvl.sh
sudo rm -rf build dist *.egg-info
python docker/build_wheels.py

v0.0.1

PyNVVL

PyNVVL is a thin wrapper of NVIDIA Video Loader (NVVL). This packages enables you to load videos directoly to GPU memory and access them as CuPy ndarrays with zero copy.

Requirements

• CuPy v4.0.0

Install

pip install [WHEEL URL]

Replace [WHEEL URL] with one of the wheels below:

Python 3.5

• CUDA 8.0: https://github.com/mitmul/pynvvl/releases/download/v0.0.1/pynvvl_cuda80-0.0.1-cp35-cp35m-linux_x86_64.whl
• CUDA 9.0: https://github.com/mitmul/pynvvl/releases/download/v0.0.1/pynvvl_cuda90-0.0.1-cp35-cp35m-linux_x86_64.whl
• CUDA 9.1: https://github.com/mitmul/pynvvl/releases/download/v0.0.1/pynvvl_cuda91-0.0.1-cp35-cp35m-linux_x86_64.whl

Usage

import pynvvl
import matplotlib.pyplot as plt

# Create NVVLVideoLoader object
loader = pynvvl.NVVLVideoLoader(device_id=0)

# 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,
    crop_y=30,
    crop_height=190,
    scale_method='Nearest',
    normalized=True
)

print(video.shape)  # => (91, 3, 256, 256): (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')

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.

Build wheels

Requirements for build

• Docker
• nvidia-docker (v1/v2)

bash docker/build_docker.sh
sudo rm -rf docker/lib
bash docker/build_nvvl.sh
bash docker/build_wheels.sh