https://github.com/ryanfox/retread

Detect reused frames in video
https://github.com/ryanfox/retread

Last synced: about 1 year ago
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Detect reused frames in video

• Host: GitHub
• URL: https://github.com/ryanfox/retread
• Owner: ryanfox
• Archived: true
• Created: 2016-05-26T02:09:01.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2022-05-03T03:53:59.000Z (about 4 years ago)
• Last Synced: 2024-08-03T20:12:13.043Z (almost 2 years ago)
• Language: HTML
• Size: 6.84 KB
• Stars: 29
• Watchers: 4
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

          
# retread

Detect reused frames in video with Python.

Retread works by computing a hash for each frame in a video (dhash, [described here](http://www.hackerfactor.com/blog/?/archives/529-Kind-of-Like-That.html) if you're interested).

It then counts how many times in the video that frame is reused.  Once the duplicates are calculated, the data is output in JSON format, suitable for use in e.g. [D3.js](https://d3js.org/).

A video with low reuse across the board doesn't repeat many frames - not many duplicated shots, and not very long static images either.  For example, _Mad Max Fury Road_, a fast-moving film with short shots and quick cuts:

![mad max fury road](https://i.imgur.com/ZUIVTWh.png)

A video with high spikes reuses a lot of short shots or single images.  For example, _Memento_, a film notable for its nonlinear story, cutting forward and backward across the same scenes:

![memento](https://i.imgur.com/YZ6vORD.png)

## Installation

Clone the repository:

    $ git clone git@github.com:ryanfox/retread.git

Install dependencies:

    $ cd retread

    $ pip install -r requirements.txt

## Usage

### Quickstart

    $ python retread.py my_video.mp4 > scores.json

Open `bar.html` in a browser to see the data plotted.

### Advanced usage

Process a video file:

    $ python retread.py my_video.mp4 > scores.json

`scores.json` will contain a list of integers, where each element is the number of duplicates that frame had in the original.

Processing a video can take a while - several minutes for a feature-length file.  There are a lot of frames to analyze!  If you want to reprocess the same video without waiting, you can use the flag `-s` (for "save") to save the hashes for later:

    $ python retread.py -s my_video.mp4 > scores.json

The hashes will be saved in `my_video.mp4.json`.  retread will overwrite the file if it already exists.

To reuse saved hashes, use the flag `-u` (for "use"):

    $ python retread.py -u my_video.mp4.json > scores.json

This will read the saved hashes (much faster than re-processing the whole video), do the duplicate analysis, and write the duplicate counts to standard out as usual.

To see what the most-duplicated frames are, you can save a jpeg image of the N most common frames.  Use the flag `-c` (for "common"):

    $ python retread.py -c 5 my_video.mp4 > scores.json

    $ ls

    0.jpg

    1.jpg

    2.jpg

    3.jpg

    4.jpg

    my_video.mp4

    [etc...]

This can be combined with `-u`, but you still need to specify the video file):

    $ python retread.py -u my_video.mp4.json -c 5 my_video.mp4 > scores.json

    $ ls

    0.jpg

    1.jpg

    2.jpg

    3.jpg

    4.jpg

    my_video.mp4

    my_video.mp4.json

    [etc...]