updated .py file

compress_start.py

@@ -12,7 +12,7 @@ images and extract important statistics from them.
 
 import numpy as np
 from matplotlib import pyplot as plt
-
+from itertools import product
 import os
 import sys
 from PIL import Image
@@ -41,7 +41,31 @@ def image_extractor(scenes):
     return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor
 
 if __name__ == '__main__':
-    scene_names = file_extractor()
+    
+    
+    image = Image.open("practice_tiff.tiff")
+    image2 = Image.open("practice2.tiff")
+    imarray2 = np.array(image2)
+    imarray = np.array(image)
+    work = imarray2[:,:,0]
+    ind1, ind2 = np.random.randint(0,434), np.random.randint(0,650)
+    surrounding = []
+    for i,j in product(np.arange(-1,2), repeat=2):
+        if i == 0 and j == 0:
+            continue
+        else:
+            surrounding.append(work[ind1+i, ind1+j])
+    diff = []
+    diff.append(np.max(surrounding)-np.min(surrounding))
+    print(surrounding)
+    print(diff)
+    
+    """For boundary cases: Start by grabbing the shape of the images and saving those
+    as variables. Then, if statements for if row == 0 or row == maximum and if
+    col == 0 or col == maximum. Then grab corresponding open pixels. Then proceed to do
+    an and statement that handles the corners"""
+    
+    """scene_names = file_extractor()
     images = image_extractor(scene_names)
     im = Image.open(images[0])
     imarray = np.array(im)
@@ -50,8 +74,6 @@ if __name__ == '__main__':
     image_array = []
     for r in randos:
         image_array.append(np.array(Image.open(images[r])))
-    indices = [val[i][j] for val in image_array]
-    
-
+    indices = [val[i][j] for val in image_array]"""
 
     
\ No newline at end of file