--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/slice_image.py	Sat Feb 09 14:46:50 2019 -0500
@@ -0,0 +1,73 @@
+import argparse
+import sys
+import warnings
+import numpy as np
+import random
+import os.path
+import skimage.io
+import skimage.util
+import skimage.feature
+from scipy.stats import entropy as scipy_entropy
+
+def slice_image(input_file, out_folder, label=None, label_out_folder=None, window_size=64, 
+                stride=1, bg_thresh=1, limit_slices=False, n_thresh=5000, seed=None):
+    #TODO NOT Implemented:process labels
+    # --> label and label_out_folder useless so far
+
+    # primarily for testing purposes:
+    if seed is not None:
+        random.seed(seed)
+
+    img_raw = skimage.io.imread(input_file)
+    if len(img_raw.shape) == 2:
+        img_raw = np.expand_dims(img_raw, 3)
+
+    with warnings.catch_warnings(): # ignore FutureWarning
+        warnings.simplefilter("ignore")
+        patches_raw = skimage.util.view_as_windows(img_raw, (window_size, window_size, img_raw.shape[2]), step=stride)
+        patches_raw = patches_raw.reshape([-1, window_size, window_size, img_raw.shape[2]])
+
+        new_path = os.path.join(out_folder, "%d.tiff") 
+        
+        #samples for thresholding the amount of slices
+        sample = random.sample(range(patches_raw.shape[0]), n_thresh)
+
+        for i in range(0, patches_raw.shape[0]):
+            # TODO improve
+            sum_image = np.sum(patches_raw[i], 2)/img_raw.shape[2]
+            total_entr = np.var(sum_image.reshape([-1]))
+
+            if bg_thresh > 0:
+                sum_image = skimage.util.img_as_uint(sum_image)
+                g = skimage.feature.greycomatrix(sum_image, [1,2], [0, np.pi/2], nnormed=True, symmetric=True)
+                hom = np.var(skimage.feature.greycoprops(g, prop='homogeneity'))
+                if hom > bg_thresh: #0.0005
+                    continue
+        
+            if limit_slices == True:
+                if i in sample:
+                    res = skimage.util.img_as_uint(patches_raw[i]) #Attention: precision loss
+                    skimage.io.imsave(new_path % i, res, plugin='tifffile')
+            else:
+                res = skimage.util.img_as_uint(patches_raw[i]) #Attention: precision loss
+                skimage.io.imsave(new_path % i, res, plugin='tifffile') 
+                    
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('input_file', type=argparse.FileType('r'), help='input file')
+    parser.add_argument('out_folder', help='out folder')
+    parser.add_argument('--label', dest='label_file', default=None, help='auxiliary label file to split in the same way')
+    parser.add_argument('--label_out_folder', dest='label_out_folder', default=None, help='label out folder')
+    parser.add_argument('--stride', dest='stride', type=int, default=1, help='applied stride')
+    parser.add_argument('--window_size', dest='window_size', type=int, default=64, help='size of resulting patches')
+    parser.add_argument('--bg_thresh', dest='bg_thresh', type=float, default=0, help='skip patches without information using a treshold')
+    parser.add_argument('--limit_slices', dest='limit_slices', type=bool, default=False, help='limit amount of slices')
+    parser.add_argument('--n_thresh', dest='n_thresh', type=int, default=5000, help='amount of slices')
+    parser.add_argument('--seed', dest='seed', type=int, default=None, help='seed for random choice of limited slices')
+    args = parser.parse_args()
+
+    slice_image(args.input_file.name, args.out_folder,
+                label=args.label_file, label_out_folder=args.label_out_folder,
+                stride=args.stride, window_size=args.window_size, bg_thresh=args.bg_thresh, 
+                limit_slices=args.limit_slices, n_thresh=args.n_thresh, seed=args.seed)