This is the code for our bachelor's thesis at KTH (course code SA114X/EF112X). The process of performing the inverse diffusion with options of plotting and source localization and counting is done as following: first the InverseDiffusion.py script is run, then the Plot.py script is run if you want the plots and then finally the Detection.py script is run if you want the source count.
Python: Copyright © 2001-2018 Python Software Foundation; All Rights Reserved
TensorFlow: Copyright 2017, The TensorFlow Authors
The Python Imaging Library (PIL): Copyright © 1997-2011 by Secret Labs AB Copyright © 1995-2011 by Fredrik Lundh
Pillow: Copyright © 2010-2018 by Alex Clark and contributors
NumPy Copyright © 2005-2017, NumPy Developers. All rights reserved.
matplotlib: Copyright (c) 2012-2013 Matplotlib Development Team; All Rights Reserved
scikit-image: Much of detection code taken from: http://scikit-image.org/docs/dev/auto\_examples/segmentation/plot\_peak\_local\_max.html