Lets give this app a go

app.py

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+import random
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.offsetbox import OffsetImage, AnnotationBbox
+import tensorflow
+import warnings
+from tensorflow.python.framework.ops import disable_eager_execution
+import pandas as pd
+
+warnings.filterwarnings('ignore')
+disable_eager_execution()
+
+load_data = np.load('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 numpy as np
+import matplotlib.pyplot as plt
+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
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+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, 11)]
+thickness_options = [str(int(x)) for x in numpy.linspace(0, 10, 11)]
+interpolation_options = [str(int(x)) for x in [3, 5, 10]]
+
+def interpolate(t1, t2, d1, d2, th1, th2, steps):
+    decoder_model_boxes = tensorflow.keras.models.load_model('decoder_model_boxes', compile=False)
+    # # compile=False ignores a warning from tensorflow, can be removed to see warning
+
+    # # import the encoder model architecture
+    json_file_loaded = open('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('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 + 2  # 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
+
+with gradio.Blocks() as demo:
+    with gradio.Row():
+        with gradio.Column():
+            t1 = gradio.Dropdown(endpoint_types, label="Type 1", value="basic_box")
+            d1 = gradio.Dropdown(density_options, label="Density 1", value="0.10")
+            th1 = gradio.Dropdown(thickness_options, label="Thickness 1", value="5")
+        with gradio.Column():
+            t2 = gradio.Dropdown(endpoint_types, label="Type 2", value="basic_box")
+            d2 = gradio.Dropdown(density_options, label="Density 2", value="0.10")
+            th2 = gradio.Dropdown(thickness_options, label="Thickness 2", value="5")
+    steps = gradio.Dropdown(interpolation_options, label="Interpolation Length", value="5")
+    btn = gradio.Button("Run")
+    img = gradio.Image(label="Transition")
+    btn.click(fn=interpolate, inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img])
+
+demo.launch(debug=True)