A simple and efficient wrapper for reading videos as NumPy tensors.
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.
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
3denotes 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
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
FFmpegis 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
Using the conda package manager (recommended):
conda install -c mrinaljain17 mydia
pip install mydia
The following python packages that
mydia depends on, will also be
installed, along with their dependencies.
Copyright 2018 Mrinal Jain.
Released under the MIT License.
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