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import numpy as np |
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from Data_Generation.Piecewise_Box_Functions import basic_box_array, back_slash_array, forward_slash_array, hamburger_array, hot_dog_array |
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import pandas as pd |
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import json |
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import matplotlib.pyplot as plt |
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from json import JSONEncoder |
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def make_boxes(image_size: int, densities: list) -> list: |
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""" |
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:param image_size: [int] - the pixel height and width of the generated arrays |
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:param densities: [list[float]] - of the desired pixel values to apply to active pixels - Recommend values (0,1] |
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:return: list[tuple] - [Array, Density, Thickness of each strut type] this is all the defining information for |
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all the generated data. |
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""" |
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matrix = [] |
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max_vert = int(np.ceil(1 / 2 * image_size) - 2) |
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max_diag = int(image_size - 3) |
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max_basic = int(np.ceil(1 / 2 * image_size) - 1) |
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for i in range(len(densities)): |
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for j in range(1, max_basic): |
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basic_box_thickness = j |
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array_1 = basic_box_array(image_size, basic_box_thickness) |
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if np.unique([array_1]).all() > 0: |
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break |
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for k in range(0, max_vert): |
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hamburger_box_thickness = k |
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array_2 = hamburger_array(image_size, hamburger_box_thickness) + array_1 |
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array_2 = np.array(array_2 > 0, dtype=int) |
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if np.unique([array_2]).all() > 0: |
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break |
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for l in range(0, max_vert): |
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hot_dog_box_thickness = l |
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array_3 = hot_dog_array(image_size, hot_dog_box_thickness) + array_2 |
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array_3 = np.array(array_3 > 0, dtype=int) |
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if np.unique([array_3]).all() > 0: |
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break |
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for m in range(0, max_diag): |
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forward_slash_box_thickness = m |
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array_4 = forward_slash_array(image_size, forward_slash_box_thickness) + array_3 |
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array_4 = np.array(array_4 > 0, dtype=int) |
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if np.unique([array_4]).all() > 0: |
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break |
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for n in range(0, max_diag): |
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back_slash_box_thickness = n |
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array_5 = back_slash_array(image_size, back_slash_box_thickness) + array_4 |
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array_5 = np.array(array_5 > 0, dtype=int) |
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if np.unique([array_5]).all() > 0: |
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break |
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the_tuple = (array_5*densities[i], densities[i], basic_box_thickness, |
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forward_slash_box_thickness, back_slash_box_thickness, |
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hot_dog_box_thickness, hamburger_box_thickness) |
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matrix.append(the_tuple) |
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return matrix |
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''' |
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df = pd.read_csv('2D_Lattice.csv') |
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print(np.shape(df)) |
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row = 1 |
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box = df.iloc[row, 1] |
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array = np.array(json.loads(box)) |
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plt.imshow(array, vmin=0, vmax=1) |
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plt.show() |
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''' |
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