--- a
+++ b/class_runner.py
@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep  2 15:54:42 2020
+
+@author: Billy
+"""
+import CellDetector
+import DataPreparation
+import unet
+import os
+
+#Example Script using all of the created classes:
+
+#assemble all svs into a folder and save the name of the folder as svs_loc
+svs_loc = "C:\\Users\\Billy\\Downloads\\Data"
+
+#save the model checkpoint folder, so that it can be loaded when the unet is initialised
+model_loc = 'N:\\8_(384, 384, 3)\\cp.ckpt'
+
+#initialise your data prep object with the svs_loc
+cd = DataPreparation.DataPreparation(svs_loc)
+
+#initialise your unet model with the location of your .cpkt file, 
+#which will be on the same level as all of the wieghts and model parameters
+unet = unet.uNet_segmentor(model_loc,window = 384, first_conv=8)
+
+#this method automatically crops and covnerts all svs into png images
+cd.AutocropAll()
+
+#this will create subfolders, with converted, cropped version of the svs files.
+#These subfolders will contain png files corresponding to images from each svs file.
+#loop through these subfolders and analyse them, like so:
+
+#firstly the pngs were saved in a sibling folder to svs_loc.
+#this process saves the locations of the pngs as png_loc:
+dir_ = os.path.split(svs_loc)[0]
+png_loc = os.path.join(dir_, "Prepared_SVS")
+
+#using this location, create a list of subfolders:
+subfolders = [x[0] for x in os.walk(png_loc)][1:]
+
+
+
+# The next part makes predictions for every image that was generated by cd.AutocropAll(),
+#and generates a dataset for each subfolder.
+#
+#It is useful at this stage to go into each subfolder and check the cropping is correct on all photos. Ideally,
+#the png images would be cropped such that only the epithelium is contained. Using your operating system's
+#photo manipulation app, rotate/straighten the photo and crop, to cut as much stroma and background out as possible.
+for subfolder in subfolders:
+    subfolder_loc = os.path.join(dir_,subfolder)
+    for file in os.listdir(subfolder):
+        file_loc = os.path.join(subfolder_loc,file)
+        unet.predict(file_loc)
+        
+    cd = CellDetector.CellDetector(subfolder_loc, cell_only = True, isOpt = True)
+    cd.predictAll(subfolder_loc, isOpt=True, cell_only=False)
+    data = cd.dataCleaner(subfolder_loc, cell_only=True)
+    
+    
+    
+# if you wish to train the model, you need to provide the location of the training images 
+# and their corresponding ground truths, like so:
+
+image_loc = "N:\\Bill_Mcgough\\Correct Labelling\\Labels\\ImageData"
+label_loc = "N:\\Bill_Mcgough\\Correct Labelling\\Labels\\MatlabLabelData"
+unet.learn_from_matlab(image_loc, label_loc, photo_per_mask=65, batch = 10, epochs = 5000, colour = True)
+