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| import streamlit as st | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| st.title("Fast Neural image style transfer") | |
| st.write("Streamlit demo for Fast arbitrary image style transfer using a pretrained Image Stylization model from TensorFlow Hub. To use it, simply upload a content image and style image. To learn more about the project, please find the references listed below.") | |
| # Load image stylization module. | |
| def load_model(): | |
| return hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2") | |
| style_transfer_model = load_model() | |
| def perform_style_transfer(content_image, style_image): | |
| # Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1] | |
| content_image = tf.convert_to_tensor(content_image, np.float32)[tf.newaxis, ...] / 255. | |
| style_image = tf.convert_to_tensor(style_image, np.float32)[tf.newaxis, ...] / 255. | |
| output = style_transfer_model(content_image, style_image) | |
| stylized_image = output[0] | |
| return Image.fromarray(np.uint8(stylized_image[0] * 255)) | |
| # Upload content and style images. | |
| content_image = st.file_uploader("Upload a content image") | |
| style_image = st.file_uploader("Upload a style image") | |
| # default images | |
| st.write("Or you can choose from the following examples") | |
| col1, col2, col3,col4 = st.columns(4) | |
| if col1.button("Couple on bench"): | |
| content_image = "examples/couple_on_bench.jpeg" | |
| style_image = "examples/starry_night.jpeg" | |
| if col2.button("Couple Walking"): | |
| content_image = "examples/couple_walking.jpeg" | |
| style_image = "examples/couple_watercolor.jpeg" | |
| if col3.button("Golden Gate Bridge"): | |
| content_image = "examples/golden_gate_bridge.jpeg" | |
| style_image = "examples/couple_watercolor.jpeg" | |
| if col4.button("Joshua Tree"): | |
| content_image = "examples/joshua_tree.jpeg" | |
| style_image = "examples/starry_night.jpeg" | |
| if style_image and content_image is not None: | |
| col1, col2 = st.columns(2) | |
| content_image = Image.open(content_image) | |
| # It is recommended that the style image is about 256 pixels (this size was used when training the style transfer network). | |
| style_image = Image.open(style_image).resize((256, 256)) | |
| col1.header("Content Image") | |
| col1.image(content_image, use_column_width=True) | |
| col2.header("Style Image") | |
| col2.image(style_image, use_column_width=True) | |
| output_image=perform_style_transfer(content_image, style_image) | |
| st.header("Output: Style transfer Image") | |
| st.image(output_image, use_column_width=True) | |
| # scroll down to see the references | |
| st.markdown("**References**") | |
| st.markdown("<a href='https://arxiv.org/abs/1705.06830' target='_blank'>1. Exploring the structure of a real-time, arbitrary neural artistic stylization network</a>", unsafe_allow_html=True) | |
| st.markdown("<a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization' target='_blank'>2. Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub</a> \n", unsafe_allow_html=True) | |
| st.markdown("<a href='https://huggingface.co/spaces/luca-martial/neural-style-transfer' target='_blank'>3. The idea to build a neural style transfer application was inspired from this Hugging Face Space </a>", unsafe_allow_html=True) |