Computer vision software for the interpretation of diffraction images
Project description
darfix is a Python library for the analysis of dark-field microscopy data. It provides a series of computer vision techniques, together with a graphical user interface and an Orange3 (https://github.com/biolab/orange3) add-on to define the workflow.
Installation
If you are on Linux:
It is recommended to create a virtual environment to avoid conflicts between dependencies (https://docs.python.org/3/library/venv.html).
python3 -m venv /path/to/new/virtual/environment
source /path/to/new/virtual/environment/bin/activate
Note: To deactivate the environment call: deactivate
Then, you can install darfix with all its dependencies:
pip install darfix[full]
To install darfix with a minimal set of dependencies run instead:
pip install darfix
Start the GUI and make sure darfix appears as an add-on:
orange-canvas
If you are on Windows:
The easiest way is to install Miniconda: https://docs.conda.io/en/latest/miniconda.html
After installed, open Anaconda Prompt and install the following packages:
conda config --add channels conda-forge
conda install orange3 silx scikit-image opencv
And install darfix and ewoks:
pip install ewoks[orange] darfix
Start the GUI and make sure darfix appears as an add-on:
orange-canvas
To install from sources:
git clone https://gitlab.esrf.fr/XRD/darfix.git
cd darfix
pip install .
Or with all its dependencies:
pip install .[full]
To test the orange workflow (only from sources) just run
orange-canvas orangecontrib/darfix/tutorials/darfix_example2.ows
To test a workflow execution without the canvas (only from sources) just run
darfix -wf orangecontrib/darfix/tutorials/darfix_example2.ows -fd orangecontrib/darfix/tutorials/ -td /tmp/darfix
Documentation
The documentation of the latest release is available at http://www.edna-site.org/pub/doc/darfix/latest
User guide
A user guide can be downloaded at http://www.edna-site.org/pub/doc/darfix/latest/user_guide.html
Project details
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