app.py

import random
import gradio
import numpy
import tensorflow
import math
from tensorflow.python.framework.ops import disable_eager_execution
import huggingface_hub # for loading model
import matplotlib.pyplot

# Because important
# disable_eager_execution()

def basic_box_array(image_size):
    A = numpy.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 = numpy.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 = numpy.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 = numpy.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 = numpy.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 = numpy.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 = numpy.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)



endpoint_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 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 generate_unit_cell(t, d, th):
    return globals()[t](int(th), float(d), 28)


def interpolate(t1, t2, d1, d2, th1, th2, steps):
    # Load the decoder model
    decoder_model_boxes = huggingface_hub.from_pretrained_keras("cmudrc/2d-lattice-decoder")
    # 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')
    encoder_model_boxes = huggingface_hub.from_pretrained_keras("cmudrc/2d-lattice-encoder")
    
    num_internal = int(steps)
    number_1 = generate_unit_cell(t1, d1, th1)
    number_2 = generate_unit_cell(t2, d2, th2)

    # resize the array to match the prediction size requirement
    number_1_expand = numpy.expand_dims(numpy.expand_dims(number_1, axis=2), axis=0)
    number_2_expand = numpy.expand_dims(numpy.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 = numpy.linspace(latent_point_1[column], latent_point_2[column], num_interp)
        latent_matrix.append(new_column)
    latent_matrix = numpy.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(numpy.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 numpy.round(transition_region)


t1 = gradio.Dropdown(endpoint_options, label="Type 1", value="hamburger_box")
d1 = gradio.Dropdown(density_options, label="Density 1", value="1.00")
th1 = gradio.Dropdown(thickness_options, label="Thickness 1", value="2")

t2 = gradio.Dropdown(endpoint_options, label="Type 2", value="hot_dog_box")
d2 = gradio.Dropdown(density_options, label="Density 2", value="1.00")
th2 = gradio.Dropdown(thickness_options, label="Thickness 2", value="2")

steps = gradio.Dropdown(interpolation_options, label="Interpolation Length", value=random.choice(interpolation_options))
img = gradio.Image(label="Transition")
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])

lattice_interpolation_inetrface = gradio.Interface(
    fn = interpolate, inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img]
)

# Loadin the model
model1 = huggingface_hub.from_pretrained_keras("cmudrc/microstructure-colorization")

# Get the color map by name:
cm = matplotlib.pyplot.get_cmap('RdBu')

#simple image scaling to (nR x nC) size
def scale(im, nR, nC):
  nR0 = len(im)     # source number of rows 
  nC0 = len(im[0])  # source number of columns 
  return numpy.array([[ im[int(nR0 * r / nR)][int(nC0 * c / nC)]  
             for c in range(nC)] for r in range(nR)])


# Prediction function
def predict(mask):
    print(mask)
    scaled_mask = numpy.ones((101, 636)) if mask is None else numpy.round(scale(mask, 101, 636)/255.0)
    print(scaled_mask)
    X = scaled_mask[numpy.newaxis, :, :, numpy.newaxis]    
    print(X.shape)
    v = model1.predict(X)
    print(v)
    measure = max(v.max(), -v.min())
    output = (v / measure)
    legend = "<h2>Strain</h2><table style=\"width:100%\"><tr>"
    for i in range(11):
        color = cm(i/10.0)[:3]
        value = -measure + i*2*measure/10
        print(sum(list(color)))
        hex = matplotlib.colors.to_hex(list(color))
        text_color = "black" if sum(list(color)) > 2.0 else "white"
        legend = legend + f"<td style=\"background-color: {hex}; color: {text_color}\">{value:+.2e}</td>"
    legend = legend + "</tr></table>"
    return cm((numpy.multiply(output[0, :, :, 0], scaled_mask)+1.0)/2.0), cm((numpy.multiply(output[0, :, :, 1], scaled_mask)+1.0)/2.0), cm((numpy.multiply(output[0, :, :, 2], scaled_mask)+1.0)/2.0), legend

mask = gradio.Image(image_mode="L", source="canvas", label="microstructure")    

exx = gradio.Image(label="ε-xx") 
eyy = gradio.Image(label="ε-yy") 
exy = gradio.Image(label="ε-xy")
legend = gradio.HTML(label="", value="")

microstructure_interface = gradio.Interface(
    fn=predict,
    inputs=[mask],
    outputs=[exx, eyy, exy, legend]
)

gradio.Series(
    lattice_interpolation_inetrface, 
    microstructure_interface
).launch(debug=True)