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2nd-year

This repository contains codes and pre-trained checkpoints of a place recognition model for Friends video.

Requirements

  • Ubuntu (tested on v14.04)
  • Python (tested on v2.7)
  • Pytorch (tested on v0.4.1)
  • Numpy
  • PIL
  • opencv-python
  • matplotlib
  • jsonl

Download pre-trained checkpoint

Pre-trained checkpoint should be placed in the root directory.

You can download the pre-trained checkpoint from this link

How to use

Input: video file (e.g. *.avi, *.mkv)

Output: jsonl file (predicted class of video frames for every second, 1 fps)

Below is an example of output jsonl flle.

{"second": 0.0, "type": "location", "class": "none"}
{"second": 0.0, "type": "location", "class": "none"}
...
{"second": 52.0, "type": "location", "class": "cafe"}
{"second": 53.0, "type": "location", "class": "cafe"}
...
{"second": 314.0, "type": "location", "class": "home-livingroom-Monica"}
{"second": 315.0, "type": "location", "class": "home-livingroom-Monica"}
...

You can run following command on a terminal,

python demo.py <video-file> <output-file-name>

For example

python demo.py input.avi output

Then output.jsonl file will be saved in the root directory

Acknowledgements

This project was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2017-0-01780, The technology development for event recognition/relational reasoning and learning knowledge based system for video understanding)