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| import numpy as np | |
| import gradio as gr | |
| from segmentation import get_mask,replace_sofa | |
| from styleTransfer import resize_sofa,resize_style,create_styledSofa | |
| from PIL import Image | |
| import cv2 | |
| def style_sofa(input_img: np.ndarray, style_img: np.ndarray): | |
| """ | |
| Styles (all) the sofas in the image to the given style. | |
| This function uses a transformer to combine the image with the desired style according | |
| to a generated mask of the sofas in the image. | |
| Input: | |
| input_img = image containing a sofa | |
| style_img = image containing a style | |
| Return: | |
| new_sofa = image containing the styled sofa | |
| """ | |
| # preprocess input images to be (640,640) squares to fit requirements of the segmentation model | |
| resized_img,box = resize_sofa(input_img) | |
| resized_style = resize_style(style_img) | |
| # generate mask for image | |
| print('starting mask') | |
| mask = get_mask(resized_img) | |
| print('got mask') | |
| styled_sofa = create_styledSofa(resized_img,resized_style) | |
| print('resizing') | |
| new_sofa = replace_sofa(resized_img,mask,styled_sofa) | |
| new_sofa = Image.fromarray(new_sofa).crop(box) | |
| return new_sofa | |
| image = gr.inputs.Image() | |
| style = gr.inputs.Image() | |
| demo = gr.Interface( | |
| style_sofa, | |
| [image,style], | |
| 'image', | |
| examples=[ | |
| ['sofa_example1.jpg','style_example1.jpg'], | |
| ['sofa_example1.jpg','style_example2.jpg'], | |
| ['sofa_example1.jpg','style_example3.jpg'], | |
| ['sofa_example1.jpg','style_example4.jpg'], | |
| ['sofa_example1.jpg','style_example5.jpg'], | |
| ], | |
| title="Style your sofa", | |
| description="🛋 Customize your sofa to your wildest dreams! 🛋", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |
| #https://github.com/dhawan98/Post-Processing-of-Image-Segmentation-using-CRF |