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import numpy as np
from scipy import signal
import math
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
# for i in range(image_size):
# for j in range(image_size):
# if i == 0 or j == 0 or i == image_size - 1 or j == image_size - 1:
# A[i][j] = 1
# A[1:-1, 1:-1] = 1
# np.pad(A[1:-1,1:-1], pad_width=((1, 1), (1, 1)), mode='constant', constant_values=1)
A[1:-1, 1:-1] = 0
return A
def back_slash_array(image_size):
A = np.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values
# for i in range(image_size):
# for j in range(image_size):
# if i == j:
# A[i][j] = 1
np.fill_diagonal(A, 1)
return A
def forward_slash_array(image_size):
A = np.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values
# for i in range(image_size):
# for j in range(image_size):
# if i == (image_size-1)-j:
# A[i][j] = 1
np.fill_diagonal(np.fliplr(A), 1)
return A
def hot_dog_array(image_size):
# Places pixels down the vertical axis to split the box
A = np.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values
# for i in range(image_size):
# for j in range(image_size):
# if j == math.floor((image_size - 1) / 2) or j == math.ceil((image_size - 1) / 2):
# A[i][j] = 1
A[:, np.floor((image_size - 1) / 2).astype(int)] = 1
A[:, np.ceil((image_size - 1) / 2).astype(int)] = 1
return A
def hamburger_array(image_size):
# Places pixels across the horizontal axis to split the box
A = np.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values
# for i in range(image_size):
# for j in range(image_size):
# if i == math.floor((image_size - 1) / 2) or i == math.ceil((image_size - 1) / 2):
# A[i][j] = 1
A[np.floor((image_size - 1) / 2).astype(int), :] = 1
A[np.ceil((image_size - 1) / 2).astype(int), :] = 1
return A
# def update_array(array_original, array_new, image_size):
# A = array_original
# for i in range(image_size):
# for j in range(image_size):
# if array_new[i][j] == 1:
# A[i][j] = 1
# return A
def update_array(array_original, array_new, image_size):
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
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 |