| 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 | |
| # 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): | |
| 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 | |
| def add_pixels(array_original, thickness): | |
| A = array_original | |
| # 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)) |