import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
#Imports
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import tensorflow_hub as hub
from PIL import Image
import gradio as gr
from helper_functions import *
def style_transfer(input_image, artist):
style_path_van_gogh = keras.utils.get_file('Starry-Night-canvas-Vincent-van-Gogh-New-1889.jpg',
'https://cdn.britannica.com/78/43678-050-F4DC8D93/Starry-Night-canvas-Vincent-van-Gogh-New-1889.jpg')
style_path_davinci = keras.utils.get_file('Leonardo_da_Vinci_-_Mona_Lisa_%28La_Gioconda%29_-_WGA12711.jpg',
'https://upload.wikimedia.org/wikipedia/commons/f/f2/Leonardo_da_Vinci_-_Mona_Lisa_%28La_Gioconda%29_-_WGA12711.jpg')
style_path_dali = keras.utils.get_file('The_Persistence_of_Memory.jpg',
'https://upload.wikimedia.org/wikipedia/en/d/dd/The_Persistence_of_Memory.jpg')
style_path_monet = keras.utils.get_file('Claude_Monet_-_Water_Lilies_-_Google_Art_Project_%28462013%29.jpg',
'https://upload.wikimedia.org/wikipedia/commons/a/af/Claude_Monet_-_Water_Lilies_-_Google_Art_Project_%28462013%29.jpg')
style_path_picasso = keras.utils.get_file('Picasso_The_Weeping_Woman_Tate_identifier_T05010_10.jpg',
'https://upload.wikimedia.org/wikipedia/en/1/14/Picasso_The_Weeping_Woman_Tate_identifier_T05010_10.jpg')
style_path_rembrandt = keras.utils.get_file('1259px-The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg',
'https://upload.wikimedia.org/wikipedia/commons/thumb/9/94/The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg/1259px-The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg')
#set dimensions of input image
oc_max_dim = 1080
#set parameters for each choice of artist
if artist == "Vincent van Gogh":
style_max_dim = 442
style_path = style_path_van_gogh
elif artist == "Claude Monet":
style_max_dim = 256
style_path = style_path_monet
elif artist == "Leonardo da Vinci":
style_max_dim = 442
style_path = style_path_davinci
elif artist == "Rembrandt":
style_max_dim = 256
style_path = style_path_rembrandt
elif artist == "Pablo Picasso":
style_max_dim = 256
style_path = style_path_picasso
elif artist == "Salvador Dali":
style_max_dim = 512
style_path = style_path_dali
#load content and style images
content_image = load_img(input_image, content=True, max_dim=oc_max_dim)
style_image = load_img(style_path, content=False, max_dim=style_max_dim)
#Load Magenta Arbitrary Image Stylization network
hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
#Pass content and style images as arguments in TensorFlow Constant object format
stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]
print("stylized_image:")
print(stylized_image[0])
print(stylized_image)
return tf.keras.preprocessing.image.img_to_array(stylized_image[0])
app = gr.Interface(
style_transfer,
[gr.Image(type='pil'), gr.Radio(["Vincent van Gogh", "Claude Monet", "Leonardo da Vinci", "Rembrandt", "Pablo Picasso", "Salvador Dali"])],
gr.Image(type='pil'),
title="Artist Style Transfer Tool",
description="""Fast style transfer using the Magenta model lets you make your own art in the style of six famous artists using a pretrained neural network and deep learning! Simply upload an image and select an artist's style to have transferred to your picture. Each artist's styles are based on a single one of their most famous paintings: Starry Night (van Gogh), Water Lilies (Monet), Mona Lisa (da Vinci), The Night Watch (Rembrandt), The Weeping Woman (Picasso), and The Persistence of Memory (Dali). Note that some input images may be rotated 90 degrees in the output to facilitate the style transfer.
| van Gogh |
Monet |
da Vinci |
Rembrandt |
Picasso |
Dali |
 |
 |
 |
 |
|
 |
""",
article = "This app uses [Arbitrary Style Transfer with Magenta](https://arxiv.org/abs/1705.06830)."
)
app.launch()
"""
"""