GEPARD - Gepard-Enabled PARticle Detection for Raman microscopes.
Copyright (C) 2018 Lars Bittrich and Josef Brandt, Leibniz-Institut für Polymerforschung Dresden e. V. firstname.lastname@example.org
- python >=3.6, PyQt5, OpenCV >=3.4.1, numpy >=1.14, scikit-image >=0.13.1, scipy >=1.1.0, win32com, pythoncom, cython 0.28
- We advise the use of Anaconda (python 3.8): https://www.anaconda.com/download as it contains most of the python libraries. However, following packages need to be installed manually: opencv-python, scikit-image, dill
- Alternatively, the provided
ymlfiles can be used to set up a well-defined and tested environment with
conda env create -f xxx.yml: GepardEnv-windows.yml (Windows), GepardEnv.yml (Linux)
- We recommend working with a 64bit OS and also a python interpreter compiled for 64bit as many use cases require a lot of memory (32 GB recommended).
- The tsp module in externalmodules can be built with:
Please note: for this step a valid compiler needs to be installed in the system; otherwise use a pre-compiled tsp-module.
If you plan on using one of the interfaces to control your device, please note: You use this interface at your OWN RISK! Make sure, that no obstacles block the objective and that you UNDERSTAND and VALIDATE the code that controls the microscope! Start with "witectesting.py" or "renishawtesting.py" which should read and move within small margins.
At the moment the program is an executable python module. Copy the folder with all its content to some place and run just outside the path (e.g. using anaconda prompt):
python -m gepard
It is possible to create a windows link file, that executes python with the gepard script as an argument and the working directory pointing to the folder containing the gepard module for convenience.
A step by step installation guide can be found here.
The working principle is explained here