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'''
Neural Style Transfer using TensorFlow's Pretrained Style Transfer Model
https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2
'''
import gradio as gr
import tensorflow as tf
import tensorflow_hub as hub
from PIL import Image
import numpy as np
import functools
import cv2
import os
model = hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2")
# source: https://stackoverflow.com/questions/4993082/how-can-i-sharpen-an-image-in-opencv
def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0):
"""Return a sharpened version of the image, using an unsharp mask."""
blurred = cv2.GaussianBlur(image, kernel_size, sigma)
sharpened = float(amount + 1) * image - float(amount) * blurred
sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
sharpened = sharpened.round().astype(np.uint8)
if threshold > 0:
low_contrast_mask = np.absolute(image - blurred) < threshold
np.copyto(sharpened, image, where=low_contrast_mask)
return sharpened
def style_transfer(content_img, style_image, style_weight=1, content_weight=1, style_blur=False):
# Resize and preprocess the content image
content_img = unsharp_mask(content_img, amount=1)
content_img = tf.image.resize(
tf.convert_to_tensor(content_img, dtype=tf.float32)[tf.newaxis, ...] / 255.0,
(512, 512),
preserve_aspect_ratio=True
)
# Resize and preprocess the style image
style_image = Image.fromarray(style_image).resize((256, 256))
style_img = tf.convert_to_tensor(np.array(style_image), dtype=tf.float32)[tf.newaxis, ...] / 255.0
if style_blur:
style_img = tf.nn.avg_pool(style_img, ksize=[3, 3], strides=[1, 1], padding="VALID")
# Apply style weight to the style image
style_img = tf.image.adjust_contrast(style_img, style_weight)
# Apply content weight and other adjustments to the content image
content_img = tf.image.adjust_contrast(content_img, content_weight)
content_img = tf.image.adjust_saturation(content_img, 2)
content_img = tf.image.adjust_contrast(content_img, 1.5)
# Stylize the content image using the style image
stylized_img = model(content_img, style_img)[0]
# Convert the stylized image tensor to a NumPy array
stylized_img = tf.squeeze(stylized_img).numpy()
# Convert the NumPy array to an image
stylized_img = np.clip(stylized_img * 255.0, 0, 255).astype(np.uint8)
return Image.fromarray(stylized_img)
title = "Artistic Neural Style Transfer Demo 🖼️"
description = "Gradio Demo for Artistic Neural Style Transfer. To use it, simply upload a content image and a style image. [Learn More](https://www.tensorflow.org/tutorials/generative/style_transfer)."
article = "</br><p style='text-align: center'><a href='https://github.com/Mr-Hexi' target='_blank'>GitHub</a></p> "
# Define inputs
content_input = gr.Image(label="Upload an image to which you want the style to be applied.")
style_input = gr.Image(label="Upload Style Image") # Removed the shape parameter
style_slider = gr.Slider(0, 2, label="Adjust Style Density", value=1)
content_slider = gr.Slider(1, 5, label="Content Sharpness", value=1)
style_checkbox = gr.Checkbox(value=False, label="Tune Style (experimental)")
# Define examples
examples = [
["Content/content_2.jpg", "Styles/style_15.jpg", 1.20, 1.70, ""],
["Content/content_4.jpg", "Styles/style_10.jpg", 0.91, 2.54, "style_checkbox"]
]
# Define the interface
interface = gr.Interface(
fn=style_transfer,
inputs=[content_input, style_input, style_slider, content_slider, style_checkbox],
outputs=gr.Image(),
title=title,
description=description,
article=article,
examples=examples,
allow_flagging="never",
)
# Launch the interface
interface.launch(debug=True)
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