pip install -r requirements.txt- If you do not want to use deep learning version, delete
torch>=1.8.1+cu111 torchvision>=0.9.1+cu111inrequirements.txt - Run
app.pyorapp_without_dl.py(no deep learning utils), drag folder with pictures in then label on them
========= Instruction ==========
| When not editing: | |
|---|---|
| key: A | Switch to last pic, and save track |
| key: D | Switch to next pic, and save track |
| key: space | Begin editing |
| When editing: | |
|---|---|
| key: space | Exit editing and confirm (AUTO close polygon) |
| mouse: Left button | Begin/Add a key point |
| mouse: Move | Auto fit a curve |
| key: Backspace | Undo a key point (and the red curve) |
| key: esc | Exit editing |
| key: C | Clear curves on canvas |
| Other | You can also use matplotlib fit and drag buttons |
The project in deep-learning-version is an expanded version of original one. Just run app.py. Specifically,
- A new interactive GUI written by pyplot, with similar opeartion logic in PS.
- Add deep learning model to provide extra gradient information, can be more accurate and be customized.
- Optimized cost function and support 8-connected graph.
- Useful for deep-learning segmentation labelling work.
- Save path coordinates to txt files. (Will complete functions to be consistent with VOC or other formats)
This repository contains a working implementation of Livewire algorithm for image segmentation. The general idea of the algorithm is to use image information for segmentation and avoid crossing object boundaries. A gradient image highlights the boundaries, and Dijkstra's shrotest path algorithm computes a path using gradient differences as segment costs. Thus the line avoids strong gradients in the gradient image, which corresponds to following object boundaries in the original image.
The notebook includes a script that loads a demo DICOM image (MR head scan), and allows the user to segment a structure in the brain by setting seed points and letting Livewire algorithm compute the "best" path in regard to the underlying objects to the next seed point. The algorithm suggestions are displayed as user moves the mouse over the image.
To run this script, you will need:
- ipython (for Python 2.7)
- ipython notebooks, aka Jupyter notebooks
- scikit-image
- matplotlib You can get them all out-of-the-box in an Anaconda install.
