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| import numpy as np | |
| from scipy import signal | |
| import math | |
| import matplotlib.pyplot as plt | |
| from huggingface_hub import from_pretrained_keras | |
| import streamlit as st | |
| # Needed in requirements.txt for importing to use in the transformers model | |
| import tensorflow | |
| # HELLO HUGGING FACE | |
| ######################################################################################################################## | |
| # Define the piecewise functions to create each of the possible shapes | |
| 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 | |
| ######################################################################################################################## | |
| # Create the desired shape using the density and thickness | |
| 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 | |
| ######################################################################################################################## | |
| # The function to add thickness to struts in an array | |
| def add_thickness(array_original, thickness: int) -> np.ndarray: | |
| """ | |
| :param array_original: [ndarray] - an array with thickness 1 of any shape type | |
| :param thickness: [int] - the number of pixels to be activated surrounding the base shape | |
| :return: [ndarray] - the output is a unit cell that has been convolved to expand the number of pixels activated | |
| based on the desired thickness. The activated pixels are 1 (white) and the deactivated pixels are 0 (black) | |
| """ | |
| A = array_original | |
| if thickness == 0: # want an array of all 0's for thickness = 0 | |
| A[A > 0] = 0 | |
| else: | |
| filter_size = 2*thickness - 1 # the size of the filter needs to extend far enough to reach the base shape | |
| filter = np.zeros((filter_size, filter_size)) | |
| filter[np.floor((filter_size - 1) / 2).astype(int), :] = filter[:, np.floor((filter_size - 1) / 2).astype(int)] =1 | |
| filter[np.ceil((filter_size - 1) / 2).astype(int), :] = filter[:, np.ceil((filter_size - 1) / 2).astype(int)] = 1 | |
| # The filter is made into a '+' shape using these functions | |
| convolution = signal.convolve2d(A, filter, mode='same') | |
| A = np.where(convolution <= 1, convolution, 1) | |
| return A | |
| # The function to efficiently combine arrays in a list | |
| def combine_arrays(arrays): | |
| output_array = np.sum(arrays, axis=0) # Add the list of arrays | |
| output_array = np.array(output_array > 0, dtype=int) # Convert all values in array to 1 | |
| return output_array | |
| ######################################################################################################################## | |
| # Explain the App | |
| st.header("Multi-Lattice Generator Through a VAE Model") | |
| st.write("Shape: the type of shape the lattice will have") | |
| st.write("Density: the pixel intensity of each activated pixel") | |
| st.write("Thickness: the additional pixels added to the base shape") | |
| st.write("Interpolation Length: the number of internal interpolation points that will exist in the interpolation") | |
| ######################################################################################################################## | |
| # Provide the Options for users to select from | |
| shape_options = ("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") | |
| density_options = ["{:.2f}".format(x) for x in np.linspace(0.1, 1, 10)] | |
| thickness_options = [str(int(x)) for x in np.linspace(0, 10, 11)] | |
| interpolation_options = [str(int(x)) for x in [3, 5, 10, 20]] | |
| # Provide User Options | |
| st.header("Option 1: Perform a Linear Interpolation") | |
| # Select Shapes | |
| shape_1 = st.selectbox("Shape 1", shape_options) | |
| shape_2 = st.selectbox("Shape 2", shape_options) | |
| # Select Density | |
| density_1 = st.selectbox("Density 1:", density_options, index=len(density_options)-1) | |
| density_2 = st.selectbox("Density 2:", density_options, index=len(density_options)-1) | |
| # Select Thickness | |
| thickness_1 = st.selectbox("Thickness 1", thickness_options) | |
| thickness_2 = st.selectbox("Thickness 2", thickness_options) | |
| # Select Interpolation Length | |
| interp_length = st.selectbox("Interpolation Length", interpolation_options, index=2) | |
| # Define the function to generate unit cells based on user inputs | |
| def generate_unit_cell(shape, density, thickness): | |
| return globals()[shape](int(thickness), float(density), 28) | |
| # Generate the endpoints | |
| number_1 = generate_unit_cell(shape_1, density_1, thickness_1) | |
| number_2 = generate_unit_cell(shape_2, density_2, thickness_2) | |
| # Display the endpoints to the user | |
| if st.button("Generate Endpoint Images"): | |
| plt.figure(1) | |
| st.header("Endpoints to be generated:") | |
| plt.subplot(1, 2, 1), plt.imshow(number_1, cmap='gray', vmin=0, vmax=1) | |
| plt.subplot(1, 2, 2), plt.imshow(number_2, cmap='gray', vmin=0, vmax=1) | |
| plt.figure(1) | |
| st.pyplot(plt.figure(1)) | |
| ######################################################################################################################## | |
| # Load the models from existing huggingface model | |
| # Load the encoder model | |
| encoder_model_boxes = from_pretrained_keras("cmudrc/2d-lattice-encoder") | |
| # Load the decoder model | |
| decoder_model_boxes = from_pretrained_keras("cmudrc/2d-lattice-decoder") | |
| ######################################################################################################################## | |
| # Encode the Desired Endpoints | |
| # 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 | |
| ######################################################################################################################## | |
| # Establish the Framework for a LINEAR Interpolation | |
| number_internal = int(interp_length) # the number of interpolations that the model will find between two points | |
| num_interp = number_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 | |
| ######################################################################################################################## | |
| # Plotting the Interpolation in 2D Using Chosen Points | |
| if st.button("Generate Linear Interpolation"): | |
| # plt.figure(2) | |
| linear_interp_latent = np.linspace(latent_point_1, latent_point_2, num_interp) | |
| print(len(linear_interp_latent)) | |
| linear_predicted_interps = [] | |
| figure_2 = np.zeros((28, 28 * num_interp)) | |
| for i in range(num_interp): | |
| generated_image = decoder_model_boxes.predict(np.array([linear_interp_latent[i]]))[0] | |
| figure_2[0:28, i * 28:(i + 1) * 28, ] = generated_image[:, :, -1] | |
| linear_predicted_interps.append(generated_image[:, :, -1]) | |
| # plt.figure_2(figsize=(15, 15)) | |
| # plt.imshow(figure, cmap='gray') | |
| # plt.figure(2) | |
| # st.pyplot(figure_2) | |
| st.image(figure_2) | |
| ######################################################################################################################## | |
| # Provide User Options | |
| st.header("Option 2: Perform a Mesh Interpolation") | |
| st.write("The four corners of this mesh are defined using the shapes in both Option 1 and Option 2") | |
| # Select Shapes | |
| shape_3 = st.selectbox("Shape 3", shape_options) | |
| shape_4 = st.selectbox("Shape 4", shape_options) | |
| # Select Density | |
| density_3 = st.selectbox("Density 3:", density_options, index=len(density_options)-1) | |
| density_4 = st.selectbox("Density 4:", density_options, index=len(density_options)-1) | |
| # Select Thickness | |
| thickness_3 = st.selectbox("Thickness 3", thickness_options) | |
| thickness_4 = st.selectbox("Thickness 4", thickness_options) | |
| # Generate the endpoints | |
| number_3 = generate_unit_cell(shape_3, density_3, thickness_3) | |
| number_4 = generate_unit_cell(shape_4, density_4, thickness_4) | |
| # Display the endpoints to the user | |
| if st.button("Generate Endpoint Images for Mesh"): | |
| plt.figure(1) | |
| st.header("Endpoints to be generated:") | |
| plt.subplot(2, 2, 1), plt.imshow(number_1, cmap='gray', vmin=0, vmax=1) | |
| plt.subplot(2, 2, 2), plt.imshow(number_2, cmap='gray', vmin=0, vmax=1) | |
| plt.subplot(2, 2, 3), plt.imshow(number_3, cmap='gray', vmin=0, vmax=1) | |
| plt.subplot(2, 2, 4), plt.imshow(number_4, cmap='gray', vmin=0, vmax=1) | |
| plt.figure(1) | |
| st.pyplot(plt.figure(1)) | |
| ######################################################################################################################## | |
| # Encode the Desired Endpoints | |
| # resize the array to match the prediction size requirement | |
| number_3_expand = np.expand_dims(np.expand_dims(number_3, axis=2), axis=0) | |
| number_4_expand = np.expand_dims(np.expand_dims(number_4, axis=2), axis=0) | |
| # Determine the latent point that will represent our desired number | |
| latent_point_3 = encoder_model_boxes.predict(number_3_expand)[0] | |
| latent_point_4 = encoder_model_boxes.predict(number_4_expand)[0] | |
| latent_dimensionality = len(latent_point_1) # define the dimensionality of the latent space | |
| ######################################################################################################################## | |
| # Plot a Mesh Gridded Interpolation | |
| if st.button("Generate Mesh Interpolation"): | |
| latent_matrix_2 = [] # This will contain the latent points of the interpolation | |
| for column in range(latent_dimensionality): | |
| new_column = np.linspace(latent_point_3[column], latent_point_4[column], num_interp) | |
| latent_matrix_2.append(new_column) | |
| latent_matrix_2 = np.array(latent_matrix_2).T # Transposes the matrix so that each row can be easily indexed | |
| mesh = [] # This will create a mesh by interpolating between the two interpolations | |
| for column in range(num_interp): | |
| row = np.linspace(latent_matrix[column], latent_matrix_2[column], num_interp) | |
| mesh.append(row) | |
| mesh = np.transpose(mesh, axes=(1, 0, 2)) # Transpose the array so it matches the original interpolation | |
| generator_model = decoder_model_boxes | |
| figure_3 = np.zeros((28 * num_interp, 28 * num_interp)) | |
| mesh_predicted_interps = [] | |
| for i in range(num_interp): | |
| for j in range(num_interp): | |
| generated_image = generator_model.predict(np.array([mesh[i][j]]))[0] | |
| figure_3[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28, ] = generated_image[:, :, -1] | |
| mesh_predicted_interps.append(generated_image[:, :, -1]) | |
| st.image(figure_3) | |