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| 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, shown below for reference: 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. | |
| """, | |
| article = """ | |
| <table> | |
| <tr> | |
| <td style="text-align:center">van Gogh</td> | |
| <td style="text-align:center">Monet</td> | |
| <td style="text-align:center">da Vinci</td> | |
| <td style="text-align:center">Rembrandt</td> | |
| <td style="text-align:center">Picasso</td> | |
| <td style="text-align:center">Dali</td> | |
| </tr> | |
| <tr> | |
| <td><img src="https://cdn.britannica.com/78/43678-050-F4DC8D93/Starry-Night-canvas-Vincent-van-Gogh-New-1889.jpg" width="200"></td> | |
| <td><img src="https://upload.wikimedia.org/wikipedia/commons/a/af/Claude_Monet_-_Water_Lilies_-_Google_Art_Project_%28462013%29.jpg" width="200"></td> | |
| <td><img src="https://upload.wikimedia.org/wikipedia/commons/f/f2/Leonardo_da_Vinci_-_Mona_Lisa_%28La_Gioconda%29_-_WGA12711.jpg" width="200"></td> | |
| <td><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/94/The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg/1259px-The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg" width="200"></td> | |
| <td><img src="https://upload.wikimedia.org/wikipedia/en/1/14/Picasso_The_Weeping_Woman_Tate_identifier_T05010_10.jpg" width="200"></td> | |
| <td><img src="https://upload.wikimedia.org/wikipedia/en/d/dd/The_Persistence_of_Memory.jpg" width="200"></td> | |
| </tr> | |
| </table> | |
| This app uses [Arbitrary Style Transfer with Magenta](https://arxiv.org/abs/1705.06830). | |
| """ | |
| ) | |
| app.launch() |