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        "author": "Shunta Saito",
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        "description": "PyNVVL\n======\n[![pypi-pynvvl-cuda80](https://img.shields.io/pypi/v/pynvvl-cuda80.svg)](https://pypi.org/project/pynvvl-cuda80)\n[![pypi-pynvvl-cuda90](https://img.shields.io/pypi/v/pynvvl-cuda90.svg)](https://pypi.org/project/pynvvl-cuda90)\n[![pypi-pynvvl-cuda91](https://img.shields.io/pypi/v/pynvvl-cuda91.svg)](https://pypi.org/project/pynvvl-cuda91)\n[![pypi-pynvvl-cuda92](https://img.shields.io/pypi/v/pynvvl-cuda92.svg)](https://pypi.org/project/pynvvl-cuda92)\n[![GitHub license](https://img.shields.io/github/license/mitmul/pynvvl.svg)](https://github.com/mitmul/pynvvl)\n\nPyNVVL 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.\n\n## Requirements\n\n- CUDA 8.0, 9.0, 9.1, or 9.2\n- Python 2.7.6+, 3.4.7+, 3.5.1+, or 3.6.0+\n- [CuPy](https://github.com/cupy/cupy) v4.5.0\n\n## Tested Environment\n\n- Ubuntu 16.04\n- Python 2.7.6+, 3.4.7+, 3.5.1+, and 3.6.0+\n- CUDA 8.0, 9.0, 9.1, and 9.2\n\n## Install the pre-built binary\n\nPlease choose a right package depending on your CUDA version.\n\n```bash\n# [For CUDA 8.0]\npip install pynvvl-cuda80\n\n# [For CUDA 9.0]\npip install pynvvl-cuda90\n\n# [For CUDA 9.1]\npip install pynvvl-cuda91\n\n# [For CUDA 9.2]\npip install pynvvl-cuda92\n```\n\n## Usage\n\n```python\nimport pynvvl\nimport matplotlib.pyplot as plt\n\n# Create NVVLVideoLoader object\nloader = pynvvl.NVVLVideoLoader(device_id=0, log_level='error')\n\n# Show the number of frames in the video\nn_frames = loader.frame_count('examples/sample.mp4')\nprint('Number of frames:', n_frames)\n\n# Load a video and return it as a CuPy array\nvideo = loader.read_sequence(\n    'examples/sample.mp4',\n    horiz_flip=True,\n    scale_height=512,\n    scale_width=512,\n    crop_y=60,\n    crop_height=385,\n    crop_width=512,\n    scale_method='Linear',\n    normalized=True\n)\n\nprint(video.shape)  # => (91, 3, 385, 512): (n_frames, channels, height, width)\nprint(video.dtype)  # => float32\n\n# Get the first frame as numpy array\nframe = video[0].get()\nframe = frame.transpose(1, 2, 0)\n\nplt.imshow(frame)\nplt.savefig('examples/sample.png')\n```\n\n![](https://github.com/mitmul/pynvvl/raw/master/examples/sample.png)\n\nThis video is `flickr-2-6-3-3-5-2-7-6-5626335276_4.mp4` from the Moments-In-Time dataset.\n\nNote 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.\n\n## VideoLoader options\n\nPlease specify the GPU device id when you create a `NVVLVideoLoader` object.\nYou can also specify the logging level with a argument `log_level` for the constructor of `NVVLVideoLoader`.\n\n```\nWrapper of NVVL VideoLoader\n\n    Args:\n        device_id (int): Specify the device id used to load a video.\n        log_level (str): Logging level which should be either 'debug',\n            'info', 'warn', 'error', or 'none'.\n            Logs with levels >= log_level is shown. The default is 'warn'.\n```\n\n## Transformation Options\n\n`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.\n\n```\nLoads the video from disk and returns it as a CuPy ndarray.\n\n    Args:\n        filename (str): The path to the video.\n        frame (int): The initial frame number of the returned sequence.\n            Default is 0.\n        count (int): The number of frames of the returned sequence.\n            If it is None, whole frames of the video are loaded.\n        channels (int): The number of color channels of the video.\n            Default is 3.\n        scale_height (int): The height of the scaled video.\n            Note that scaling is performed before cropping.\n            If it is 0 no scaling is performed. Default is 0.\n        scale_width (int): The width of the scaled video.\n            Note that scaling is performed before cropping.\n            If it is 0, no scaling is performed. Default is 0.\n        crop_x (int): Location of the crop within the scaled frame.\n            Must be set such that crop_y + height <= original height.\n            Default is 0.\n        crop_y (int): Location of the crop within the scaled frame.\n            Must be set such that crop_x + width <= original height.\n            Default is 0.\n        crop_height (int): The height of cropped region of the video.\n            If it is None, no cropping is performed. Default is None.\n        crop_width (int): The width of cropped region of the video.\n            If it is None, no cropping is performed. Default is None.\n        scale_method (str): Scaling method. It should be either of\n            'Nearest' or 'Lienar'. Default is 'Linear'.\n        horiz_flip (bool): Whether horizontal flipping is performed or not.\n            Default is False.\n        normalized (bool): If it is True, the values of returned video is\n            normalized into [0, 1], otherwise the value range is [0, 255].\n            Default is False.\n        color_space (str): The color space of the values of returned video.\n            It should be either 'RGB' or 'YCbCr'. Default is 'RGB'.\n        chroma_up_method (str): How the chroma channels are upscaled from\n            yuv 4:2:0 to 4:4:4. It should be 'Linear' currently.\n        out (cupy.ndarray): Alternate output array where place the result.\n            It must have the same shape and the dtype as the expected\n            output, and its order must be C-contiguous.\n```\n\n## How to build\n\n### Build wheels using Docker: \n\nRequirements:\n\n- Docker\n- nvidia-docker (v1/v2)\n\n```\nbash docker/build_wheels.sh\n```\n\n### Setup development environment without Docker:\n\nThe `setup.py` script searches for necessary libraries.\n\nRequirements: the following libraries are available in `LIBRARY_PATH`.\n\n- libnvvl.so\n- libavformat.so.57\n- libavfilter.so.6\n- libavcodec.so.57\n- libavutil.so.55\n\nYou can build `libnvvl.so` in the `nvvl` repository. Follow the instructions\nof `nvvl` library. The `build` directory must be in `LIBRARY_PATH`.\n\nOther three libraries are available as packages in Ubuntu 16.04.\nThey are installed under `/usr/lib/x86_64-linux-gnu`, so they must be in `LIBRARY_PATH` as well.\n\n```\npython setup.py develop\npython setup.py bdist_wheel\n```\n\n\n",
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