videosum 0.0.4

Description

Code for the videosum Python package. Given a video file, this package produces a single-image storyboard that summarises the video.

Install dependencies

• Ubuntu/Debian:

$ sudo apt install ffmpeg

Install with pip

$ python3 -m pip install videosum --user

Install from source

$ python3 setup.py install --user

Run video summarisation on a single video

$ python3 -m videosum.run --input video.mp4 --output collage.jpg --nframes 100 --height 1080 -width 1920 --algo time

Options:

• --input: path to the input video file.
• --output: path where the output collage will be saved.
• --nframes: number of frames that you want to see in the collage image.
• --height: height of the collage image.
• --width: width of the collage image.
• --time-segmentation: set it to either 0 or 1. If 1, the clustering results are displayed in a bar underneath the collage (i.e. the columns of the bar represent the frames of the video, and the colours represent the clustering label).
• --fps: number of frames you want to read per second of video, used to downsample the input video and have less frames to describe and cluster.
• --algo: algorithm used to select the key frames of the video.
  • time: evenly spaced frames are selected.
  • inception: k-medoids clustering (l2-norm metric) on InceptionV3 latent space vectors.
  • fid : k-medoids clustering (Frechet Inception Distance metric) on InceptionV3 latent space vectors.
  • scda: k-medoids clustering (l2-norm metric) on SCDA image descriptors (Wei et al. 2017 Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval). InceptionV3 was trained on ImageNet as opposed to Wei et al. where authors used VGG-16.

Exemplary code snippet

import cv2
import videosum

# Choose the number of frames you want in the summary
nframes = 100

# Choose the dimensions of the collage
widtth = 1920
height = 1080

# Choose the algotrithm that selects the key frames
algo = 'inception'  # The options are: 'time', 'inception', 'fid', 'scda'

# Create video summariser object
vs = videosum.VideoSummariser(algo, nframes, width, height)

# Create collage image
im = vs.summarise('video.mp4')

# Save collage to file
cv2.imwrite('collage.jpg', im)

# Retrieve a list of Numpy/OpenCV BGR images corresponding to the key frames of the video
key_frames = vs.get_key_frames('video.mp4')       

# Print the video frame indices of the key frames, available after calling summarise() or get_key_frames()
print(vs.indices_)

# Print the video frame cluster labels, available after calling summarise() or get_key_frames()
print(vs.labels_)

Exemplary result

• Exemplary video: here

• Summary based on time algorithm:

$ python3 -m videosum.run --input test/data/video.mp4 --output test/data/time.jpg --nframes 16 --height 1080 --width 1920 --algo time --time-segmentation 1

• Summary based on inception algorithm:

$ python3 -m videosum.run --input test/data/video.mp4 --output test/data/inception.jpg --nframes 16 --height 1080 --width 1920 --algo inception --time-segmentation 1

• Summary based on fid algorithm:

$ python3 -m videosum.run --input test/data/video.mp4 --output test/data/fid.jpg --nframes 16 --height 1080 --width 1920 --algo fid --time-segmentation 1

• Summary based on scda algorithm:

$ python3 -m videosum.run --input test/data/video.mp4 --output test/data/scda.jpg --nframes 16 --height 1080 --width 1920 --algo scda --time-segmentation 1

Run unit testing

$ python3 setup.py test

Run timing script

$ python3 -m videosum.timing 

Author

Luis Carlos Garcia Peraza Herrera (luiscarlos.gph@gmail.com), 2022.

License

This code repository is shared under an MIT license.