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graph-training

Generation of training data for the graph extraction of endoscopic images.

Workflow

Video Workflow

  1. Append new video data to end of video_data.py
  2. Run before_filter.py
  3. In MATLAB, run filtering/Bladder_vessels.m (filepath can be input manually)
  4. Run after_filter.py

Image Folder Workflow

  1. In video_data.py:
    1. Update/Set variable video_filepath = "C:/My/FullPath/ImageFolder" to folder containing the .png images
    2. Set variable use_images = True
    3. If you want to use the FFT filter, set variable fft_filter = True
  2. Run before_filter.py
  3. In MATLAB, filtering/Bladder_vessels.m:
    1. Update/Set variable line 30 VIDEO_FILEPATH = 'C:/My/FullPath/ImageFolder'
    2. Run filtering/Bladder_vessels.m
  4. Run after_filter.py
Functions Description
before_filter.py Extracts and crops video frames
filtering/Bladder_vessels.m Applies B-COSFIRE filter to cropped images
after_filter.py Applies: mask, thresholding, skeletonising, graph generation

Folders

Folder Description
raw Raw video stills
cropped Cropped images, 256x256px
filtered Filtered images
masked Filtered images masked with a circular mask
threshed Thresholded images
skeleton Skeletonised images
graphs Graphs saved as .json files
overlay Graph overlaid on cropped image