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import tensorflow as tf | |
import pathlib | |
import gradio as gradio | |
import matplotlib.pyplot as plt | |
from huggingface_hub import from_pretrained_keras | |
import numpy as np | |
# Normalizing the images to [-1, 1] | |
def normalize_test(input_image): | |
input_image = tf.cast(input_image, tf.float32) | |
input_image = (input_image / 127.5) - 1 | |
return input_image | |
def resize(input_image, height, width): | |
input_image = tf.image.resize(input_image, [height, width], | |
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) | |
return input_image | |
def load_image_infer(image_file): | |
input_image = resize(image_file, 256, 256) | |
input_image = normalize_test(input_image) | |
return input_image | |
def generate_images(test_input): | |
test_input = load_image_infer(test_input) | |
prediction = generator(np.expand_dims(test_input, axis=0), training=True) | |
fig = plt.figure(figsize=(128, 128)) | |
title = ['Predicted Image'] | |
plt.title('Predicted Image') | |
# Getting the pixel values in the [0, 1] range to plot. | |
plt.imshow(prediction[0,:,:,:] * 0.5 + 0.5) | |
plt.axis('off') | |
return fig | |
generator = from_pretrained_keras("keras-io/pix2pix-generator") | |
img = gr.inputs.Image(shape=(256,256)) | |
plot = gr.outputs.Image(type="plot") | |
description = "Pix2Pix Facade Reconstructor" | |
gr.Interface(generate_images, inputs = img, outputs = plot, | |
title = "Pix2Pix Facade Reconstructor", description = description, examples = [["./img.png"]]).launch() | |