• This project works on extracting corneal boundaries with deep learning, precisely, based on the U-Net networks.
• Better usability thanks to the handsome web GUI.
• Accelerated by GPU (CUDA).

• Python 3

• PyTorch, see offical instructions.

1. Clone this repository

   Note: Currently only support Python 3+

   cd /path/to/anywhere/you/like
   git clone https://github.com/zengyu714/corneal-gui

2. Download the pre-trained weights

   cd corneal-gui/unet
   wget -c --referer=https://pan.baidu.com/s/1qYn3JbQ -O corneal-weights.zip "https://www.baidupcs.com/rest/2.0/pcs/file?method=batchdownload&app_id=250528&zipcontent=%7B%22fs_id%22%3A%5B%22747895932828831%22%5D%7D&sign=DCb740ccc5511e5e8fedcff06b081203:ZFmUVd4WUFSnu7qfoVgD0wczdaY%3D&uid=2265930192&time=1513187178&dp-logid=8026429103670053409&dp-callid=0&vuk=2265930192&from_uk=2265930192"
   unzip corneal-weights.zip 
   mv corneal-gui-weights weights
   rm -rf corneal-*

3. Install requirements

   # may activate your virtual environment first
   sudo pip install -r /path/to/corneal-gui/requirements.txt

4. Fire the web server

   cd /path/to/corneal-gui
   python gui.py runserver
   
   # or specify the host and port, default is http://localhost:5001
   # python gui.py runserver --host X.X.X.X -p XXXX

5. Navigate to http://localhost:5001 and you would see:

   

1. When you see the videos listed in the Repository, click the button Run all.

   Note: Make sure the button is actually responseded by checking the browser status:

   

2. Wait a minute (around 8 seconds for a video consisting 139 frames). Alternatively, you could watch the detailed process in the console.

   TODO: display the console output in a real-time way, maybe Response.

   Anyway, the page would show the output and errors (if any) in the end:

   

3. Then click and select the video you want to inspect, this checked video name would be shown in the Checked panel:

   

4. Click Inspect button and a new tab would be opened:

   

• Biomechanical Parameters

  

  Further explanation of these parameters could be found here.

• Charts

  1. line chart: curvature and thickness of the central corneal area through the whole video.

     

  2. 3D scatter: depicts the upper corneal surface.

     

  Thanks to the WONDERFUL ECharts, we could manipulate these drawings, say, zoom-in/out, view original data, save figures by toolbox:

  .

• Videos

  

  

  Quickly inspect the detecting result.

• Frames

  

• Upload file limit is 12 files or 200MB

• BETTER to Refresh the page after uploading

• Clear the Repository after inspection

  

• Use browser search (^ + F) when tend to locate a specific frame promptly

  

• PyTorch - deep learning framework
• CUDA - GPU acceleration
• U-Net - neural net
• Flask - web framework
• Bootstrap - front-end component library
• ECharts - visualization of charts

• Zeng Yu - Homepage

This project is licensed under the Apache License

• If you want to train the dataset from the beginning, see this repository.
• Recommend Anaconda to create envs and manage python packages.
• I am verrrrry excited to release my first true, complete project. 😘 😘