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import os | |
import random | |
import numpy as np | |
import tensorflow as tf | |
from PIL import Image | |
import gradio as gr | |
from huggingface_hub import from_pretrained_keras | |
model = from_pretrained_keras("keras-io/GauGAN-Image-generation") | |
def predict(image_file, segmentation_png, bitmap_img): | |
image_list = [segmentation_png, image_file, bitmap_img] | |
image = tf.image.decode_png(tf.io.read_file(image_list[1]), channels=3) | |
image = tf.cast(image, tf.float32) / 127.5 - 1 | |
segmentation_file = tf.image.decode_png(tf.io.read_file(image_list[0]), channels=3) | |
segmentation_file = tf.cast(segmentation_file, tf.float32) / 127.5 - 1 | |
label_file = tf.image.decode_bmp(tf.io.read_file(image_list[2]), channels=0) | |
# label_file = tf.image.rgb_to_grayscale(tf.image.decode_bmp(tf.io.read_file(image_list[2]), channels=3)) | |
# print("after decode_bmp --> ", label_file.shape, type(label_file)) | |
label_file = tf.squeeze(label_file) | |
image_list = [segmentation_file, image, label_file] | |
crop_size = tf.convert_to_tensor((256, 256)) | |
image_shape = tf.shape(image_list[1])[:2] | |
margins = image_shape - crop_size | |
y1 = tf.random.uniform(shape=(), maxval=margins[0], dtype=tf.int32) | |
x1 = tf.random.uniform(shape=(), maxval=margins[1], dtype=tf.int32) | |
y2 = y1 + crop_size[0] | |
x2 = x1 + crop_size[1] | |
cropped_images = [] | |
for img in image_list: | |
cropped_images.append(img[y1:y2, x1:x2]) | |
final_img_list = [ | |
tf.expand_dims(cropped_images[0], axis=0), | |
tf.expand_dims(cropped_images[1], axis=0), | |
tf.expand_dims(tf.one_hot(cropped_images[2], 12), axis=0), | |
] | |
# print(final_img_list[0].shape) | |
# print(final_img_list[1].shape) | |
# print(final_img_list[2].shape) | |
latent_vector = tf.random.normal(shape=(1, 256), mean=0.0, stddev=2.0) | |
# Generate fake images | |
fake_image = model.predict([latent_vector, final_img_list[2]]) | |
fake_img = tf.squeeze(fake_image, axis=0) | |
return np.array((fake_img + 1) / 2) | |
# Define inputs with modern Gradio syntax | |
ground_truth = gr.Image(type="filepath", label="Ground Truth - Real Image (jpg)") | |
segmentation = gr.Image(type="filepath", label="Corresponding Segmentation (png)") | |
bitmap = gr.Image( | |
type="filepath", label="Corresponding bitmap image (bmp)", image_mode="L" | |
) | |
examples = [ | |
[ | |
"facades_data/cmp_b0010.jpg", | |
"facades_data/cmp_b0010.png", | |
"facades_data/cmp_b0010.bmp", | |
], | |
[ | |
"facades_data/cmp_b0020.jpg", | |
"facades_data/cmp_b0020.png", | |
"facades_data/cmp_b0020.bmp", | |
], | |
[ | |
"facades_data/cmp_b0030.jpg", | |
"facades_data/cmp_b0030.png", | |
"facades_data/cmp_b0030.bmp", | |
], | |
[ | |
"facades_data/cmp_b0040.jpg", | |
"facades_data/cmp_b0040.png", | |
"facades_data/cmp_b0040.bmp", | |
], | |
[ | |
"facades_data/cmp_b0050.jpg", | |
"facades_data/cmp_b0050.png", | |
"facades_data/cmp_b0050.bmp", | |
], | |
] | |
title = "GauGAN For Conditional Image Generation" | |
description = "Upload an Image or take one from examples to generate realistic images that are conditioned on cue images and segmentation maps" | |
# Create interface with modern Gradio syntax | |
demo = gr.Interface( | |
fn=predict, | |
inputs=[ground_truth, segmentation, bitmap], | |
outputs=gr.Image(type="numpy", label="Generated - Conditioned Images"), | |
examples=examples, | |
flagging_mode="never", | |
analytics_enabled=False, | |
title=title, | |
description=description, | |
article="<center>Space By: <u><a href='https://github.com/robotjellyzone'><b>Kavya Bisht</b></a></u> \n Based on <a href='https://keras.io/examples/generative/gaugan/'><b>this notebook</b></a></center>", | |
) | |
if __name__ == "__main__": | |
demo.queue() | |
demo.launch(debug=True) | |