SUN-DIC

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Stellenbosch University Digital Image Correlation (DIC) Code

SUN-DIC is an open-source Python package for 2D digital image correlation (DIC) developed at Stellenbosch University. It provides both a graphical user interface (GUI) and a Python API for displacement and strain analysis from image sets, making it suitable for both interactive use and research workflows.

Early release notice: SUN-DIC is currently in an early public release phase. Core functionality is available and documented, but the interface and documentation will continue to evolve. Bug reports, suggestions, and feedback are very welcome.

Quick Start

Install with pip

Note: Please see detailed installation instructions for both pip and conda further down in this README file.

python3.11 -m venv sundic
source sundic/bin/activate
pip install SUN-DIC
copy-examples
sundic

Documentation

SUN-DIC documentation is currently provided through the following resources:

• User manual: installation, GUI workflow, and API overview SUN-DIC User Manual (PDF)

• Example configuration: a fully documented settings.ini file included with the provided example problem

• Example notebook: test_sundic.ipynb, copied into your current directory with copy-examples

• GUI tooltips: GUI options include tooltip descriptions

• API documentation: online reference documentation
  

Publications

1. Venter, Gerhard and Neaves, Melody, SUN-DIC: A Python-Based Open-Source Software Tool for Digital Image Correlation, Advances in Engineering Software, Volume 211, 2025.

Key Features

• Fully open-source, using standard Python libraries wherever possible
• Provides both a user-friendly GUI and a programmable API
• Implements the Zero-Mean Normalized Sum of Squared Differences (ZNSSD) correlation criterion
• Uses an advanced starting strategy based on the AKAZE feature detection algorithm for initial guess generation
• Supports both linear (affine) and quadratic shape functions
• Includes Inverse Compositional Gauss-Newton (IC-GN) and Inverse Compositional Levenberg-Marquardt (IC-LM) solvers
• Provides both absolute and relative update strategies for handling multiple image pairs
• Supports rectangular regions of interest (ROI) and custom ROIs defined by a black/white mask
• Automatically ignores subsets with an all-black background, allowing irregularly shaped domains to be handled naturally
• Computes displacements and strains, with multiple plotting and visualization options
• Uses Savitzky-Golay smoothing for strain calculations, with optional displacement smoothing using the same algorithm
• Supports parallel computing for improved performance
• Easy installation via PyPI

Limitations

• Currently supports 2D planar DIC problems only
• A stereo / 3D version is under development

Installation

Although SUN-DIC can be installed without creating a virtual environment, using one is strongly recommended for easier dependency management.

Note The ray library providing the parallel computing functionality is typically not supported for the latest Python releases on Windows. If you run into a ray dependency issue during installation, please try an older version of Python.

Note If you are installing on a Mac equipped with an Apple Silicon processor (Mac M1/M2/M3), using the standard Anaconda distribution may cause version conflicts or C++ compilation errors (eg, with llvmlite or ray). This is typically due to Anaconda defaulting to x86_64 emulation. To ensure a seamless, native ARM64 installation without needing to modify the requirements.txt file, it is recommended to use Miniforge instead of Anaconda as outlined below.

General Steps

1. Create a virtual environment

2. Activate the environment

3. Install the package from PyPI

4. Optionally install Jupyter dependencies

5. Copy the example problem into your current directory using copy-examples

   Optionally you can issue the copy-examples --manual command to also copy the user manual to your current directory

   The example problem includes:

   • test_sundic.ipynb
   • settings.ini
   • planar_images/

   These files provide a practical starting point for both the GUI and API workflows.

Using pip

1. Create a virtual environment (e.g., sundic):

   python3.11 -m venv sundic

2. Activate the virtual environment:

   Linux / macOS

   source sundic/bin/activate

   Windows (Command Prompt)

   sundic\Scripts\activate

3. Install the base package:

   pip install SUN-DIC

4. Optional -- install Jupyter notebook support:

   pip install "SUN-DIC[jupyter]"

5. Copy the example problem:

   copy-examples

Using conda

1. Create a virtual environment with Python 3.11:

   conda create -n sundic python=3.11

2. Activate the environment:

   conda activate sundic

3. Install the base package:

   pip install SUN-DIC

4. Optional -- install Jupyter notebook support:

   pip install "SUN-DIC[jupyter]"

5. Copy the example problem:

   copy-examples

Instructions for Apple Silicon

1. Install the macOS arm64 version of Miniforge.
2. Open a new terminal to ensure Miniforge is active (you should see the base environment).
3. Proceed with the standard installation using conda as described above.

Installing Directly from GitHub (Advanced Users)

1. Create and activate a virtual environment using either pip or conda as described above

2. Clone the repository and install the package:

   git clone https://github.com/gventer/SUN-DIC.git
   pip install ./SUN-DIC

3. Optional: install Jupyter notebook support:

   pip install "./SUN-DIC[jupyter]"

4. The example problem is available in:

   SUN-DIC/sundic/examples
   

Usage

Make sure the virtual environment where SUN-DIC is installed is active before proceeding.

Running the GUI

1. Launch the GUI from a terminal:

   sundic

2. Use the copy-examples command to copy a complete example to your current directory

3. In the GUI, use File → Import Settings File to import the example settings.ini

4. Run the example problem from the Analysis panel

5. Perform post-processing using the Results panel

6. Follow the workflow shown on the left-hand side of the GUI

GUI entries include tooltips describing the available options.

Using the API

1. Use the copy-examples command to copy a complete example to your current directory

2. Open test_sundic.ipynb for a fully worked example

3. If needed, install the optional Jupyter dependencies:

   pip install "SUN-DIC[jupyter]"

A typical API workflow involves:

• modifying the settings.ini file
• running the DIC analysis
• post-processing the results

Although the provided example uses a Jupyter notebook, the API can also be used in standard Python scripts.

Support and Feedback

If you encounter a bug, have a feature suggestion, or would like to provide feedback, please open an issue on the GitHub repository.

Citation

@article{sun-dic,
title = {SUN-DIC: A Python-based open-source software tool for Digital Image Correlation},
author = {Venter, Gerhard and Neaves, Melody},
year = {2025},
journal = {Advances in Engineering Software},
volume = {211},
pages = {104043},
doi = {https://doi.org/10.1016/j.advengsoft.2025.104043},
url = {https://www.sciencedirect.com/science/article/pii/S0965997825001814},
}

Presentations

1. 2025-04-17 -- MOD Research Group Meeting - Overview of SUN-DIC

Acknowledgments

• SUN-DIC analysis code: based on work by Ed Brisley as part of his MEng degree at Stellenbosch University. His thesis is available through the Stellenbosch University Library.
• Interpolator: uses fast_interp by David Stein, licensed under Apache 2.0. Repository: fast_interp
• Smoothing algorithm: implements the 2D Savitzky-Golay algorithm from the SciPy Cookbook
• GUI development: initial development by Elijah Stockhall
• Graphical design: Dr. Melody Neaves

License

This project is licensed under the MIT License. See the LICENSE file for details.

Author

Developed by Gerhard Venter.