Metadata-Version: 2.1
Name: pwsAI
Version: 0.1.1
Summary: A GUI for AI segmentation of cell imagery.
Home-page: https://github.com/nanthony21/pws_AI
Author: Nico Acosta
Author-email: nicolasacosta2026@u.northwestern.edu
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: PySimpleGUI
Requires-Dist: pwspy
Requires-Dist: pillow
Requires-Dist: scikit-image
Requires-Dist: scikit-learn
Requires-Dist: tifffile
Requires-Dist: opencv-python
Requires-Dist: patchify
Requires-Dist: h5py
Requires-Dist: rasterio
Requires-Dist: tensorflow

Welcome to PWS_AI
-------------------
This code is incomplete in that the gui is not completely finished.
Coming up on the gui is the ability to check the results for the threshold so
the segmentation is optimized by the user before going to check it on 
pwspy analysis software. 

As it stands, you will go through 5 main steps. 

Step 0) Run analysis on dataset of interest, that means running it on the pws 
	 analysis software 

Step 1) Select folder with cell data where you just ran your analysis (same way its
	done on the pwspy software) 

Step 2) Access rms images from analysis. This is done automatically when you click
	the button "Get RMS Images" 

Step 3) Use PWS_AI to generate the ROIs automatically and save them as TIF File in
        Corresponding Folder 

Step 4) Conver the PWS_AI output into the pwspy compatible format. Done by clicking 
	"Push ROI TO PWSPY" 


Once this is done you can go to the pws analysis software where you can now use the 
AI generated ROIs. This is a beta version and not ready for release but thank you for 
testing it out! 

- Nico 6/15/23
