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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 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): | |
| content_img = unsharp_mask(content_img,amount=1) | |
| content_img = tf.image.resize(tf.convert_to_tensor(content_img,tf.float32)[tf.newaxis,...] / 255.,(512,512),preserve_aspect_ratio=True) | |
| style_img = tf.convert_to_tensor(style_image,tf.float32)[tf.newaxis,...] / 255. | |
| if style_blur: | |
| style_img= tf.nn.avg_pool(style_img, [3,3], [1,1], "VALID") | |
| style_img = tf.image.adjust_contrast(style_img, style_weight) | |
| 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) | |
| stylized_img = model(content_img, style_img)[0] | |
| return Image.fromarray(np.uint8(stylized_img[0]*255)) | |
| 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> " | |
| content_input = gr.inputs.Image(label="Upload an image to which you want the style to be applied.", ) | |
| style_input = gr.inputs.Image( label="Upload Style Image ",shape= (256,256), ) | |
| style_slider = gr.inputs.Slider(0,2,label="Adjust Style Density" ,default=1,) | |
| content_slider = gr.inputs.Slider(1,5,label="Content Sharpness" ,default=1,) | |
| style_checkbox = gr.Checkbox(value=False,label="Tune Style(experimental)") | |
| 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"] | |
| ] | |
| interface = gr.Interface(fn=style_transfer, | |
| inputs=[content_input, | |
| style_input, | |
| style_slider , | |
| content_slider, | |
| style_checkbox], | |
| outputs=gr.outputs.Image(), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| enable_queue=True | |
| ) | |
| interface.launch() |