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import os
import tensorflow as tf
os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
import numpy as np
import PIL.Image
import gradio as gr
import tensorflow_hub as hub
import matplotlib.pyplot as plt
from real_esrgan_app import *
'''
inference(img,mode)
'''
hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
def tensor_to_image(tensor):
tensor = tensor*255
tensor = np.array(tensor, dtype=np.uint8)
if np.ndim(tensor)>3:
assert tensor.shape[0] == 1
tensor = tensor[0]
return PIL.Image.fromarray(tensor)
style_urls = {
'Kanagawa great wave': 'The_Great_Wave_off_Kanagawa.jpg',
'Kandinsky composition 7': 'Kandinsky_Composition_7.jpg',
'Hubble pillars of creation': 'Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg',
'Van gogh starry night': 'Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg',
'Turner nantes': 'JMW_Turner_-_Nantes_from_the_Ile_Feydeau.jpg',
'Munch scream': 'Edvard_Munch.jpg',
'Picasso demoiselles avignon': 'Les_Demoiselles.jpg',
'Picasso violin': 'picaso_violin.jpg',
'Picasso bottle of rum': 'picaso_rum.jpg',
'Fire': 'Large_bonfire.jpg',
'Derkovits woman head': 'Derkovits_Gyula_Woman_head_1922.jpg',
'Amadeo style life': 'Amadeo_Souza_Cardoso.jpg',
'Derkovtis talig': 'Derkovits_Gyula_Talig.jpg',
'Kadishman': 'kadishman.jpeg'
}
style_images = [k for k, v in style_urls.items()]
def image_click(images, evt: gr.SelectData,
):
img_selected = images[evt.index]["name"]
#print(img_selected)
return img_selected
#radio_style = gr.Radio(style_images, label="Choose Style")
def perform_neural_transfer(content_image_input, style_image_input, super_resolution_type, hub_module = hub_module):
content_image = content_image_input.astype(np.float32)[np.newaxis, ...] / 255.
content_image = tf.image.resize(content_image, (400, 600))
#style_image_input = style_urls[style_image_input]
#style_image_input = plt.imread(style_image_input)
style_image = style_image_input.astype(np.float32)[np.newaxis, ...] / 255.
style_image = tf.image.resize(style_image, (256, 256))
outputs = hub_module(tf.constant(content_image), tf.constant(style_image))
stylized_image = outputs[0]
stylized_image = tensor_to_image(stylized_image)
content_image_input = tensor_to_image(content_image_input)
stylized_image = stylized_image.resize(content_image_input.size)
print("super_resolution_type :")
print(super_resolution_type)
#print(super_resolution_type.value)
if super_resolution_type not in ["base", "anime"]:
return stylized_image
else:
print("call else :")
stylized_image = inference(stylized_image, super_resolution_type)
return stylized_image
with gr.Blocks() as demo:
gr.HTML("<h1><center> 🐑 Art Generation with Neural Style Transfer Fixed by Real-ESRGAN </center></h1>")
with gr.Row():
style_reference_input_gallery = gr.Gallery(list(style_urls.values()),
#width = 512,
height = 768 + 128,
label = "Style Image gallery (click to use)")
with gr.Column():
#super_resolution_type = gr.Radio(["base", "anime", "none"], type="value", default="base", label="choose Real-ESRGAN model type used to super resolution the Image Transformed")
super_resolution_type = gr.Radio(choices = ["base", "anime", "none"],
value="base", label="choose Real-ESRGAN model type used to super resolution the Image Transformed",
interactive = True)
style_reference_input_image = gr.Image(
label = "Style Image (you can upload yourself or click from left gallery)",
#width = 512,
interactive = True, value = style_urls["Kanagawa great wave"]
)
content_image_input = gr.Image(label="Content Image", interactive = True,
#width = 512
)
trans_image_output = gr.Image(label="Image Transformed", interactive = True,
#width = 512
)
trans_button = gr.Button(label = "transform Content image style from Style Image")
style_reference_input_gallery.select(
image_click, style_reference_input_gallery, style_reference_input_image
)
trans_button.click(perform_neural_transfer, [content_image_input, style_reference_input_image, super_resolution_type], trans_image_output)
gr.Examples(
[
[style_urls["Kanagawa great wave"], style_urls["Kadishman"], "none"],
[style_urls["Derkovits woman head"], style_urls["Kadishman"], "base"],
[style_urls["Kadishman"], style_urls["Kadishman"], "anime"],
],
inputs = [style_reference_input_image, content_image_input, super_resolution_type],
label = "Transform Examples"
)
demo.launch() |