Optimizing functions to produce dataset with large image sizes

Data_Generation/Piecewise_Box_Functions.py

@@ -1,4 +1,5 @@
 import numpy as np
+from scipy import signal
 import math
 
 
@@ -77,12 +78,11 @@ def update_array(array_original, array_new, image_size):
 def add_pixels(array_original, additional_pixels, image_size):
     # Adds pixels to the thickness of each component of the box
     A = array_original
-    A_updated = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
-    for dens in range(additional_pixels):
-        for i in range(1, image_size - 1):
-            for j in range(1, image_size - 1):
-                if A[i - 1][j] + A[i + 1][j] + A[i][j - 1] + A[i][j + 1] > 0:
-                    A_updated[i][j] = 1
-        A = update_array(A, A_updated,image_size)
-    return A
-
+    filter = np.array(([0, 1, 0], [1, 1, 1], [0, 1, 0]))  # This filter will only add value where there are pixels on
+    # the top, bottom, left or right of a pixel
+
+    # This filter adds thickness based on the desired number of additional pixels
+    for item in range(additional_pixels):
+        convolution = signal.convolve2d(A, filter, mode='same')
+        A = np.where(convolution <= 1, convolution, 1)
+    return A