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Video Summarization Tool

Description

This tool generates video summaries using four state-of-the-art summarization models:

  • PGL-SUM
  • CA-SUM
  • DSNet anchor based
  • DSNet anchor free

The models are pretrained on the TVSum and SumMe datasets.

Installation

  1. Set up a virtual environment:

    python -m venv .summarization

  2. Activate the virtual environment:

    source .summarization/bin/activate

  3. Install required packages:

    pip install -r requirements.txt

Usage

Summary Generation

  1. Navigate to the source folder:

    cd src

  2. Generate summary for a single video:

    python inference.py pglsum --source ../custom_data/videos/source_video_name.mp4 --save-path ./output/summary_video_name.mp4 --sample-rate 30 --final-frame-length 30

  3. Generate summaries for a folder of videos:

    python inference.py pglsum --source ../custom_data/videos/source_video_folder --save-path ./output/summary_videos_folder --sample-rate 30 --final-frame-length 30

Model Name References

  • pglsum - PGL-SUM
  • casum - CA-SUM
  • dsnet_ab - DSNet anchor based
  • dsnet_af - DSNet anchor free

Parameters Explanation

  • --sample-rate 30: The model analyzes every 30th frame.
  • --final-frame-length 30: The resulting video summary will contain around 30 frames, roughly equivalent to 27 seconds. This duration can vary between 23-31 seconds depending on the frames per second of the original video.
  • --max-shot-length 8: A single shot in the summary won't exceed 8 frames.
  • --min-penalty-shot-length 5: Shots that are 5 frames or shorter will incur a length penalty, thus making them less likely to appear in the final summary.