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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
import matplotlib.pyplot as plt
from Data_Generation.Piecewise_Box_Functions import add_pixels
########################################################################################################################
# Make the data using all the code in Shape_Generation_Functions.py
def make_boxes(image_size, densities, shapes):
"""
: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
:param shapes: [list] - of the various shapes desired for the dataset
:return: [list[tuple]] - [Array, Density, Thickness, Shape]
"""
# matrix = []
#
# for function in shapes: # Adds different types of shapes
#
# # Adds different density values
# for i in range(len(densities)):
# # Loops through the possible thickness values
# for j in range(image_size): # Adds additional Pixels
# thickness = j
# Array = (function(thickness, densities[i], image_size))
#
# # Checks if there are any 0's left in the array to append
# if (np.where((Array == float(0)))[0] > 0).any():
# the_tuple = (Array, str(function.__name__), densities[i], thickness)
# matrix.append(the_tuple)
#
# # Prevents solids shapes from being appended to the array
# else:
# break
matrix = []
base_shapes = []
density_1 = []
for function in shapes: # Create an array of the base shapes
thickness = 0
Array = function(thickness, 1, image_size)
# density_1_tuple = np.array([Array, str(function.__name__), 1, thickness]) # Array, Shape, Density, Thickness
# base_shapes.append(density_1_tuple)
density_1 = np.append(density_1,(np.array([Array, str(function.__name__), 1, thickness])), axis=1) # Array, Shape, Density, Thickness
# Add one to the thickness of the previous array
# for j in range(image_size):
while (np.where((Array == float(0)))[0] > 0).any():
# Checks if there are any 0's left in the array to append
# if (np.where((Array == float(0)))[0] > 0).any():
# density_1.append(density_1_tuple, axis=0)
thickness += 1
if np.shape(density_1) == (4,):
Array = add_pixels(density_1[0], 1) # will add 1 pixel to each previous array, rather than adding multiple and having to loop
else:
print(np.shape(density_1))
print(density_1[-1][0])
Array = add_pixels(density_1[-1][0], 1)
# print(np.shape(Array))
density_1_tuple = np.array([Array, str(function.__name__), 1, thickness])
# else: # Prevents solids shapes from being appended to the array
# break
density_1 = np.vstack((density_1, density_1_tuple))
matrix = []
# print(np.shape(density_1[0]))
# print(density_1[:][0])
for i in range(len(densities)):
some = np.multiply(density_1[:][0],densities[i]) #,density_1[:1])
# print(np.shape(some))
matrix.append(tuple(some))
# # Adds different density values
# for i in range(len(densities)):
# # Loops through the possible thickness values
# for j in range(image_size): # Adds additional Pixels
# thickness = j
# Array = (function(thickness, densities[i], image_size))
#
# # Checks if there are any 0's left in the array to append
# if (np.where((Array == float(0)))[0] > 0).any():
# the_tuple = (Array, str(function.__name__), densities[i], thickness)
# matrix.append(the_tuple)
#
# # Prevents solids shapes from being appended to the array
# else:
# break
return matrix
########################################################################################################################
# Testing
image_size = 9
densities = [1]
shapes = [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]
boxes = make_boxes(image_size, densities, shapes)
# print(np.shape(boxes))
desired_label = 'basic_box'
desired_density = 1
desired_thickness = 0
box_arrays, box_shape, box_density, box_thickness, = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3]
# print(np.shape(box_arrays))
# print(np.shape(box_shape))
# print(np.shape(box_density))
indices = [i for i in range(len(box_arrays)) if box_shape[i] == desired_label and box_density[i] == desired_density and box_thickness[i] == desired_thickness]
plt.imshow(box_arrays[indices[0]])
plt.show()
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