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A simple and efficient wrapper for reading videos as NumPy tensors.

Project description

Mydia

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Reading videos as NumPy arrays was never more simple. This library provides an entire range of additional functionalities such as custom frame selection, frame resizing, pixel normalization, grayscale conversion and much more.

READ THE DOCUMENTATION

Getting started

1. Read a video, given its path

# Import
from mydia import Videos

# Initialize video path
video_path = r".docs/examples/sample_video/bigbuckbunny.mp4"

# Create a reader object
reader = Videos()

# Call the 'read()' function to get the video tensor
# which will be of shape (1, 132, 720, 1280, 3)
video = reader.read(video_path)

The tensor can be interpreted as:

  • 1 video
  • Having 132 frames,
  • Dimension (width x height) of each frame: 1280x720 pixels
  • 3 denotes that the video is in RGB format

2. You can even use multiple workers for reading the videos in parallel

from mydia import Videos

video_paths = [
    "path/to/video_1", 
    "path/to/video_2", 
    "path/to/video_3",
    ...,
]

reader = Videos()
video = reader.read(video_path, workers=4)

3. View detailed examples here

Requirements

  • Python 3.x (preferably from the Anaconda Distribution)

  • FFmpeg: The backend for reading and processing the videos.

    The recommended (and probably the easiest) way of installing FFmpeg is via the conda package manager.

        conda install -c mrinaljain17 ffmpeg
    

    However, if you are not using conda, then

    For Linux users -

        $ sudo apt-get update
        $ sudo apt-get install ffmpeg
    

    For Windows or MAC/OSX users -

    Download the required binaries from here. Extract the zip file and add the location of binaries to the PATH variable.

Installation

  1. Using the conda package manager (recommended):

        conda install -c mrinaljain17 mydia
    
  2. Using pip:

        pip install mydia
    

The following python packages that mydia depends on, will also be installed, along with their dependencies.

License

Copyright 2018 Mrinal Jain.

Released under the MIT License.

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