Data_Generation/Piecewise_Box_Functions.py

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