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import numpy as np
# from Data_Generation.Shape_Generation_Functions import basic_box, diagonal_box_split, horizontal_vertical_box_split, \
# back_slash_box, forward_slash_box, back_slash_plus_box, forward_slash_plus_box, hot_dog_box, hamburger_box, \
# x_hamburger_box, x_hot_dog_box, x_plus_box
from Data_Generation.Piecewise_Box_Functions import basic_box_array, back_slash_array, forward_slash_array, hamburger_array, hot_dog_array
# from Piecewise_Box_Functions import basic_box_array, back_slash_array, forward_slash_array, hamburger_array, hot_dog_array
import matplotlib.pyplot as plt
########################################################################################################################
# Make the data using all the code in Shape_Generation_Functions.py
def make_boxes(image_size, densities):
"""
:param image_size: [int] - the pixel height and width of the generated arrays
:param densities: [list] - of the values of each of the active pixels in each shape
:return: [list[tuple]] - [Array, Density, Thickness of each strut type]
"""
matrix = []
# Establish the maximum thickness for each type of strut
max_vert = int(np.ceil(1 / 2 * image_size) - 2)
max_diag = int(image_size - 3)
max_basic = int(np.ceil(1 / 2 * image_size) - 1)
# Adds different density values
for i in range(len(densities)):
for j in range(1, max_basic): # basic box loop, always want a border
basic_box_thickness = j
array_1 = basic_box_array(image_size, basic_box_thickness)
if np.unique([array_1]).all() > 0: # Checks if there is a solid figure
break
for k in range(0, max_vert):
hamburger_box_thickness = k
array_2 = hamburger_array(image_size, hamburger_box_thickness) + array_1
array_2 = np.array(array_2 > 0, dtype=int) # Keep all values 0/1
if np.unique([array_2]).all() > 0:
break
for l in range(0, max_vert):
hot_dog_box_thickness = l
array_3 = hot_dog_array(image_size, hot_dog_box_thickness) + array_2
array_3 = np.array(array_3 > 0, dtype=int)
if np.unique([array_3]).all() > 0:
break
for m in range(0, max_diag):
forward_slash_box_thickness = m
array_4 = forward_slash_array(image_size, forward_slash_box_thickness) + array_3
array_4 = np.array(array_4 > 0, dtype=int)
if np.unique([array_4]).all() > 0:
break
for n in range(0, max_diag):
back_slash_box_thickness = n
array_5 = back_slash_array(image_size, back_slash_box_thickness) + array_4
array_5 = np.array(array_5 > 0, dtype=int)
if np.unique([array_5]).all() > 0:
break
the_tuple = (array_5*densities[i], densities[i], basic_box_thickness,
forward_slash_box_thickness, back_slash_box_thickness,
hot_dog_box_thickness, hamburger_box_thickness)
matrix.append(the_tuple)
return matrix
########################################################################################################################
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