Chaldene

Notebook-Embedded Visual Programming for Authoring Interactive Image-Processing Workflows

Key Features

• Drag-and-drop node-based programming for image processing
• Interactive workflow creation in JupyterLab
• Parameter adjustment and image visualization

Requirements

• JupyterLab ≥ 4.0.0
• OpenJDK 11 (required for PyImageJ)

For example, with Conda:

conda install -c conda-forge "openjdk=11"

Quick Start

1. Install Chaldene

   pip install chaldene

2. Launch JupyterLab

   jupyter lab

3. Create a new notebook

   • Click "+" to create a new notebook
   • Add a Visual Code cell from the cell toolbar

4. Start building workflows

   • Drag and drop nodes to create your image processing workflows
   • Connect nodes to build workflows
   • Adjust parameters and inspect the outputs to refine the workflows

New to Chaldene? Watch our tutorial video.

Examples

📂 Examples are available in the use_cases/ folder

Below are two representative workflows created by users, demonstrating Chaldene's capabilities for interactive image processing:

Development

• 📖 Developer Guide - Setup and development instructions
• 🚀 Release Guide - Package Build and Releae

Cite

If you use this package in your research, please cite our paper:

For the visual programming environment:

@INPROCEEDINGS{chen2022Chaldene,
  author={Chen, Fei and Slusallek, Philipp and Müller, Martin and Dahmen, Tim},
  booktitle={2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)}, 
  title={Chaldene: Towards Visual Programming Image Processing in Jupyter Notebooks}, 
  year={2022},
  volume={},
  number={},
  pages={1-3},
  doi={10.1109/VL/HCC53370.2022.9832910}}

For the underlying image conversion systems:

@article{chen2025im2im,
  author    = {Fei Chen and Sunita Saha and Manuela Schuler and Philipp Slusallek and Tim Dahmen},
  title     = {im2im: Automatically Converting In-Memory Image Representations using A Knowledge Graph Approach},
  journal   = {Proc. ACM Program. Lang.},
  volume    = {9},
  number    = {OOPSLA2},
  pages     = {281:1--281:26},
  year      = {2025},
  month     = oct,
  doi       = {10.1145/3763059}
}