This project aims at providing a “batteries included” toolkit for digital image correlation in Python. The functionality you need to perform digital image correlation on experimental data as well as for doing virtual experiments are included.
Typical usage is demonstrated in the examples located in the /Examples folder.
This toolkit includes the following:
- Image reader tools
- Virtual lab
- Speckle image generators
- Image deformation tools
- Noise injection
- Image down-sampling
- B-spline finite elements
- Arbitrary polynomial order
- Knot vectors can be manipulated
- Meshing tools:
- A light weight GUI for structured meshing
- Image correlation routines:
- Non linear least squares solver
- Post processor
- Calculates most popular strain measures
- Light weight visualization
- Logging
The following changes were done in version 0.2.0:
- Added Q4 element support
- Q4 elements are now the default
- Removed uneccessary scaling when images are deformed using displacement functions
- Added quiver plots for displacements
- Python 2.7 is no longer supported
- Removed Perlin noise support as the package is only available for Python 2.7
- Various bug fixes (See commit history)
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
This toolkit is tested on Python 3.7 and need all dependencies listen in requirements.txt
Make sure you have Python 3 installed with pip and virtualenv
Make new folder and use a terminal to make a virtual environment:
$ python -m venv env
$ source env/bin/activate #On Linux and Mac OS
$ env\Scripts\activate.bat #On Windows
We can now install µDIC inside this environment using pip
$ pip install muDIC
Now, lets run all the tests included by using nosetests
$ nosetests muDIC
Start to clone this repo to your preferred location:
$ cd /path/to/project/
$ git init
$ git clone https://github.com/PolymerGuy/muDIC.git
We recommend that you always use virtual environments, either by virtualenv or by Conda env
Virtual env:
$ cd /path/to/muDIC
$ python -m venv env
$ source ./env/bin/activate #On Linux and Mac OS
$ env\Scripts\activate.bat #On Windows
$ pip install -r requirements.txt
The tests should always be launched to check your installation.
If you installed by a package manager:
$ nosetests muDIC #Note capital cases
If you cloned the repo:
$ cd /path/to/muDIC/
$ nosetests
Documentation is found here: Read the docs
The motivation for this work was the need for a transparent code which could be modified and extended easily, without digging deep into C or C++ source code. The implementation is pure python with the exception of third-party packages such as Scipy, Numy etc.
Clone the repository, add your changes, add new tests and you are ready for a pull request
- Sindre Olufsen - Implementation - PolymerGuy
- Marius Endre Andersen - Wrote the Matlab code which was the starting point for this project
This project is licensed under the MIT License - see the LICENSE.md file for details
This project is described in the following paper and citation is highly appreciated µDIC: An open-source toolkit for digital image correlation