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| import random | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from matplotlib.offsetbox import OffsetImage, AnnotationBbox | |
| import tensorflow | |
| from tensorflow.python.framework.ops import disable_eager_execution | |
| import pandas as pd | |
| import math | |
| disable_eager_execution() | |
| load_data = np.load('data/train_test_split_data.npz') # Data saved by the VAE | |
| # Convert Data to Tuples and Assign to respective variables | |
| box_matrix_train, box_density_train, additional_pixels_train, box_shape_train = tuple(load_data['box_matrix_train']), tuple(load_data['box_density_train']), tuple(load_data['additional_pixels_train']), tuple(load_data['box_shape_train']) | |
| box_matrix_test, box_density_test, additional_pixels_test, box_shape_test = tuple(load_data['box_matrix_test']), tuple(load_data['box_density_test']), tuple(load_data['additional_pixels_test']), tuple(load_data['box_shape_test']) | |
| testX = box_matrix_test # Shows the relationship to the MNIST Dataset vs the Shape Dataset | |
| image_size = np.shape(testX)[-1] # Determines the size of the images | |
| test_data = np.reshape(testX, (len(testX), image_size, image_size, 1)) | |
| # Creates tuples that contain all of the data generated | |
| allX = np.append(box_matrix_train,box_matrix_test, axis=0) | |
| all_box_density = np.append(box_density_train, box_density_test, axis=0) | |
| all_additional_pixels = np.append(additional_pixels_train, additional_pixels_test,axis=0) | |
| all_box_shape = np.append(box_shape_train, box_shape_test,axis=0) | |
| all_data = np.reshape(allX, (len(allX), image_size, image_size, 1)) | |
| # train_latent_points = [] | |
| # train_data = np.reshape(box_matrix_train, (len(box_matrix_train), image_size, image_size, 1)) | |
| # for i in range(len(box_shape_train)): | |
| # predicted_train = encoder_model_boxes.predict(np.array([train_data[i]])) | |
| # train_latent_points.append(predicted_train[0]) | |
| # train_latent_points = np.array(train_latent_points) | |
| 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") | |
| import math | |
| def basic_box_array(image_size): | |
| A = np.zeros((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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| return A | |
| def center_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 == math.floor((image_size-1)/2) and j == math.ceil((image_size-1)/2): | |
| A[i][j] = 1 | |
| if i == math.floor((image_size-1)/2) and j == math.floor((image_size-1)/2): | |
| A[i][j] = 1 | |
| if j == math.ceil((image_size-1)/2) and i == math.ceil((image_size-1)/2): | |
| A[i][j] = 1 | |
| if j == math.floor((image_size-1)/2) and i == math.ceil((image_size-1)/2): | |
| A[i][j] = 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 add_pixels(array_original, additional_pixels, image_size): | |
| # Adds pixels to the thickness of each component of the box | |
| A = array_original | |
| A_updated = np.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values | |
| for dens in range(additional_pixels): | |
| for i in range(1, image_size - 1): | |
| for j in range(1, image_size - 1): | |
| if A[i - 1][j] + A[i + 1][j] + A[i][j - 1] + A[i][j + 1] > 0: | |
| A_updated[i][j] = 1 | |
| A = update_array(A, A_updated,image_size) | |
| return A | |
| def basic_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Creates the outside edges of the box | |
| # Increase the thickness of each part of the box | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A*density | |
| def horizontal_vertical_box_split(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Creates the outside edges of the box | |
| # Place pixels across the horizontal and vertical axes to split the box | |
| A = update_array(A, hot_dog_array(image_size), image_size) | |
| A = update_array(A, hamburger_array(image_size), image_size) | |
| # Increase the thickness of each part of the box | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A*density | |
| def diagonal_box_split(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Creates the outside edges of the box | |
| # Add pixels along the diagonals of the box | |
| A = update_array(A, back_slash_array(image_size), image_size) | |
| A = update_array(A, forward_slash_array(image_size), image_size) | |
| # Adds pixels to the thickness of each component of the box | |
| # Increase the thickness of each part of the box | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A*density | |
| def back_slash_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, back_slash_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def forward_slash_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, forward_slash_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def hot_dog_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, hot_dog_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def hamburger_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, hamburger_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def x_plus_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, hot_dog_array(image_size), image_size) | |
| A = update_array(A, hamburger_array(image_size), image_size) | |
| A = update_array(A, forward_slash_array(image_size), image_size) | |
| A = update_array(A, back_slash_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def forward_slash_plus_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, hot_dog_array(image_size), image_size) | |
| A = update_array(A, hamburger_array(image_size), image_size) | |
| A = update_array(A, forward_slash_array(image_size), image_size) | |
| # A = update_array(A, back_slash_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def back_slash_plus_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, hot_dog_array(image_size), image_size) | |
| A = update_array(A, hamburger_array(image_size), image_size) | |
| # A = update_array(A, forward_slash_array(image_size), image_size) | |
| A = update_array(A, back_slash_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def x_hot_dog_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, hot_dog_array(image_size), image_size) | |
| # A = update_array(A, hamburger_array(image_size), image_size) | |
| A = update_array(A, forward_slash_array(image_size), image_size) | |
| A = update_array(A, back_slash_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def x_hamburger_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| # A = update_array(A, hot_dog_array(image_size), image_size) | |
| A = update_array(A, hamburger_array(image_size), image_size) | |
| A = update_array(A, forward_slash_array(image_size), image_size) | |
| A = update_array(A, back_slash_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| def center_box(additional_pixels, density, image_size): | |
| A = basic_box_array(image_size) # Initializes A matrix with 0 values | |
| A = update_array(A, center_array(image_size), image_size) | |
| A = add_pixels(A, additional_pixels, image_size) | |
| return A * density | |
| import tensorflow as tf | |
| sess = tf.compat.v1.Session() | |
| from keras import backend as K | |
| K.set_session(sess) | |
| # Gradio Interface | |
| import gradio | |
| import numpy | |
| endpoint_types = shapes | |
| density_options = ["{:.2f}".format(x) for x in numpy.linspace(0.1, 1, 10)] | |
| thickness_options = [str(int(x)) for x in numpy.linspace(0, 10, 11)] | |
| interpolation_options = [str(int(x)) for x in [3, 5, 10, 20]] | |
| def interpolate(t1, t2, d1, d2, th1, th2, steps): | |
| # Load the decoder model | |
| decoder_model_boxes = tensorflow.keras.models.load_model('data/decoder_model_boxes', compile=False) | |
| # # import the encoder model architecture | |
| json_file_loaded = open('data/model.json', 'r') | |
| loaded_model_json = json_file_loaded.read() | |
| # load model using the saved json file | |
| encoder_model_boxes = tensorflow.keras.models.model_from_json(loaded_model_json) | |
| # load weights into newly loaded_model | |
| encoder_model_boxes.load_weights('data/model_tf') | |
| num_internal = int(steps) | |
| number_1 = globals()[t1](int(th1), float(d1), 28) | |
| number_2 = globals()[t2](int(th2), float(d2), 28) | |
| # resize the array to match the prediction size requirement | |
| number_1_expand = np.expand_dims(np.expand_dims(number_1, axis=2), axis=0) | |
| number_2_expand = np.expand_dims(np.expand_dims(number_2, axis=2), axis=0) | |
| # Determine the latent point that will represent our desired number | |
| latent_point_1 = encoder_model_boxes.predict(number_1_expand)[0] | |
| latent_point_2 = encoder_model_boxes.predict(number_2_expand)[0] | |
| latent_dimensionality = len(latent_point_1) # define the dimensionality of the latent space | |
| num_interp = num_internal # the number of images to be pictured | |
| latent_matrix = [] # This will contain the latent points of the interpolation | |
| for column in range(latent_dimensionality): | |
| new_column = np.linspace(latent_point_1[column], latent_point_2[column], num_interp) | |
| latent_matrix.append(new_column) | |
| latent_matrix = np.array(latent_matrix).T # Transposes the matrix so that each row can be easily indexed | |
| plot_rows = 2 | |
| plot_columns = num_interp + 2 | |
| predicted_interps = [number_1_expand[0, :, :, 0]] | |
| for latent_point in range(2, num_interp + 2): # cycles the latent points through the decoder model to create images | |
| generated_image = decoder_model_boxes.predict(np.array([latent_matrix[latent_point - 2]]))[0] # generates an interpolated image based on the latent point | |
| predicted_interps.append(generated_image[:, :, -1]) | |
| predicted_interps.append(number_2_expand[0, :, :, 0]) | |
| transition_region = predicted_interps[0] | |
| for i in range(len(predicted_interps)-1): | |
| transition_region = numpy.hstack((transition_region, predicted_interps[i+1])) | |
| return transition_region | |
| def generate_unit_cell(t, d, th): | |
| number_1 = globals()[t](int(th), float(d), 28) | |
| # resize the array to match the prediction size requirement | |
| number_1_expand = np.expand_dims(np.expand_dims(number_1, axis=2), axis=0) | |
| return number_1_expand[0, :, :, 0] | |
| with gradio.Blocks() as demo: | |
| with gradio.Row(): | |
| with gradio.Column(): | |
| t1 = gradio.Dropdown(endpoint_types, label="Type 1", value=random.choice(endpoint_types)) | |
| d1 = gradio.Dropdown(density_options, label="Density 1", value=random.choice(density_options)) | |
| th1 = gradio.Dropdown(thickness_options, label="Thickness 1", value=random.choice(thickness_options)) | |
| img1 = gradio.Image(label="Endpoint 1") | |
| t1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1]) | |
| d1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1]) | |
| th1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1]) | |
| with gradio.Column(): | |
| t2 = gradio.Dropdown(endpoint_types, label="Type 2", value=random.choice(endpoint_types)) | |
| d2 = gradio.Dropdown(density_options, label="Density 2", value=random.choice(density_options)) | |
| th2 = gradio.Dropdown(thickness_options, label="Thickness 2", value=random.choice(thickness_options)) | |
| img2 = gradio.Image(label="Endpoint 2") | |
| t2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1]) | |
| d2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1]) | |
| th2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1]) | |
| steps = gradio.Dropdown(interpolation_options, label="Interpolation Length", value=random.choice(interpolation_options)) | |
| btn = gradio.Button("Run") | |
| img = gradio.Image(label="Transition") | |
| btn.click(fn=interpolate, inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img]) | |
| examples = gradio.Examples(examples=[["hamburger_box", "hot_dog_box", "1.00", "1.00", "2", "2", "20"], ["hamburger_box", "hot_dog_box", "0.10", "1.00", "10", "10", "5"]], fn=interpolate, inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img], cache_examples = True) | |
| demo.launch() |