Video processing improved

 If you are reading this post, most probably, you have seen my last post about the video processing pipeline. Actually, this approach has few issues.

  1. Segmentation classes are hard do distinguish
  2. It is slow
For the last two weeks I was working towards fixing these issues.

Improving the processing speed

To fix the processing speed I only had to adjust few parameters of OpenCV. Initially the FPS of resulting video did not match original (it was less than the original), but after I have fixed it, all processing went faster.


Making segmentation easier to read

I have adjusted the project so that now two modes are available. Now there are two possible modes: coloured segmentation and segmentation of a single class.

Coloured segmentation

Example of coloured segmentation

On the image above it is easy to differentiate segmentation classes, but here is a single issue. You cannot identify classes if their indexes are not knows (so you have to read the documentation :D)

Single class segmentation

The three images above show how surgical tools can be segmented on video. The masks are precise, so we can use this approach for segmentation of educational videos / monitoring of trainings.


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