Testing how to improve piecewise functions

Data_Generation/Piecewise_Box_Functions.py

@@ -1,8 +1,8 @@
 import numpy as np
 from scipy import signal
 import math
-
-
+import matplotlib.pyplot as plt
+import itertools
 def basic_box_array(image_size):
     A = np.ones((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     # Creates the outside edges of the box
@@ -69,20 +69,75 @@ def hamburger_array(image_size):
 #             if array_new[i][j] == 1:
 #                 A[i][j] = 1
 #     return A
-def update_array(array_original, array_new, image_size):
+def update_array(array_original, array_new):
     A = array_original
     A[array_new == 1] = 1
     return A
 
 
-def add_pixels(array_original, additional_pixels):
-    # Adds pixels to the thickness of each component of the box
+# def add_pixels(array_original, additional_pixels):
+#     # Adds pixels to the thickness of each component of the box
+#     A = array_original
+#     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
+
+
+def add_pixels(array_original, thickness):
     A = array_original
-    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
+    # if thickness !=0:
+
+    # filter = np.array(([0, 1, 0], [1, 1, 1], [0, 1, 0]))
+    # filter = np.stack([filter] * additional_pixels, axis=-1)
+    filter_size = 2*thickness+1
+    filter = np.zeros((filter_size,filter_size))
+    filter[np.floor((filter_size - 1) / 2).astype(int), :] = filter[:, np.floor((filter_size - 1) / 2).astype(int)] =1
+    filter[np.ceil((filter_size - 1) / 2).astype(int), :] = filter[:, np.ceil((filter_size - 1) / 2).astype(int)] = 1
+
+    # filter[0,0] = filter[-1,0] = filter[0,-1] = filter[-1,-1] = 0
+    print(filter)
+    convolution = signal.convolve2d(A, filter, mode='same')
+    A = np.where(convolution <= 1, convolution, 1)
+    return A
+
+
+# def create_array(basic_box_thickness, forward_slash_thickness, back_slash_thickness, hamburger_thickness, hot_dog_thickness):
+#
+
+# TESTING
+image_size = 9
+# test = forward_slash_array(image_size)
+test = hamburger_array((image_size))
+back = back_slash_array((image_size))
+hot = hot_dog_array(image_size)
+forward = forward_slash_array(image_size)
+basic = basic_box_array((image_size))
+
+# test = update_array(test, back)
+# test = update_array(test, hot)
+# test = update_array(test, forward)
+test = test + back + forward + hot + basic
+test = np.array(test > 0, dtype=int)
+
+# test = add_pixels(test, 1)
+print(test)
+plt.imshow(test)
+plt.show()
+
+
+# basic_box_thickness = np.linspace(0,14, num=15)
+# print(basic_box_thickness)
+# forward_slash_thickness = np.linspace(0,14, num=15)
+# back_slash_thickness = np.linspace(0,14, num=15)
+# hamburger_thickness = np.linspace(0,14, num=15)
+# hot_dog_thickness =np.linspace(0,14, num=15)
+# print(np.meshgrid((basic_box_thickness, forward_slash_thickness, back_slash_thickness, hamburger_thickness, hot_dog_thickness)))
+
+# all_thicknesses = list(itertools.product(basic_box_thickness, repeat=5))
+# print(all_thicknesses)
+# print(np.shape(all_thicknesses))