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import os
import random
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow import keras
from tensorflow.keras import layers
from glob import glob
from PIL import Image
import gradio as gr
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras("RobotJelly/GauGAN-Image-generation")
def predict(image_file):
# print(image_file)
# img = Image.open(image_file)
# image_file = str(img)
print("image_file-->", image_file)
image_list = []
segmentation_map = image_file.replace("images", "segmentation_map").replace("jpg", "png")
labels = image_file.replace("images", "segmentation_labels").replace("jpg", "bmp")
print("labels", labels)
image_list = [segmentation_map, image_file, labels]
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.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 = tf.squeeze(model.predict([latent_vector, final_img_list[2]]), axis=0)
fake_image = model.predict([latent_vector, final_img_list[2]])
real_images = final_img_list
# return tf.squeeze(real_images[1], axis=0), fake_image
return [(real_images[0][0]+1)/2, (fake_image[0]+1)/2]
# input
input = [gr.inputs.Image(type="filepath", label="Ground Truth - Real Image")]
facades_data = []
data_dir = 'examples/'
for idx, images in enumerate(os.listdir(data_dir)):
image = os.path.join(data_dir, images)
if os.path.isfile(image) and idx < 6:
facades_data.append(image)
# output
output = [gr.outputs.Image(type="numpy", label="Mask/Segmentation used"), gr.outputs.Image(type="numpy", label="Generated - Conditioned Images")]
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"
gr.Interface(fn=predict, inputs = input, outputs = output, examples=facades_data, allow_flagging=False, 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>").launch(enable_queue=True, debug=True) |