Replaced update array with addition of arrays, use convolution filter to streamline thickness calculation. All for loops were removed from these main functions
5b65c9e
| import numpy as np | |
| from scipy import signal | |
| def basic_box_array(image_size, thickness): | |
| A = np.ones((int(image_size), int(image_size))) # Initializes A matrix with 0 values | |
| A[1:-1, 1:-1] = 0 # replaces all internal rows/columns with 0's | |
| A = add_thickness(A, thickness) | |
| return A | |
| def back_slash_array(image_size, thickness): | |
| A = np.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values | |
| np.fill_diagonal(A, 1) # fills the diagonal with 1 values | |
| A = add_thickness(A, thickness) | |
| return A | |
| def forward_slash_array(image_size, thickness): | |
| A = np.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values | |
| np.fill_diagonal(np.fliplr(A), 1) # Flips the array to then fill the diagonal the opposite direction | |
| A = add_thickness(A, thickness) | |
| return A | |
| def hot_dog_array(image_size, thickness): | |
| # 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 | |
| A[:, np.floor((image_size - 1) / 2).astype(int)] = 1 # accounts for even and odd values of image_size | |
| A[:, np.ceil((image_size - 1) / 2).astype(int)] = 1 | |
| A = add_thickness(A, thickness) | |
| return A | |
| def hamburger_array(image_size, thickness): | |
| # 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 | |
| A[np.floor((image_size - 1) / 2).astype(int), :] = 1 # accounts for even and odd values of image_size | |
| A[np.ceil((image_size - 1) / 2).astype(int), :] = 1 | |
| A = add_thickness(A, thickness) | |
| return A | |
| def add_thickness(array_original, thickness): | |
| A = array_original | |
| if thickness == 0: # want an array of all 0's for thickness = 0 | |
| A[A > 0] = 0 | |
| else: | |
| filter_size = 2*thickness - 1 # the size of the filter needs to extend far enough to reach the base shape | |
| 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 | |
| convolution = signal.convolve2d(A, filter, mode='same') | |
| A = np.where(convolution <= 1, convolution, 1) | |
| return A | |