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| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision.transforms.functional import normalize | |
| import gradio as gr | |
| from gradio_imageslider import ImageSlider | |
| from briarmbg import BriaRMBG | |
| import PIL | |
| from PIL import Image | |
| from typing import Tuple | |
| net = BriaRMBG.from_pretrained("briaai/RMBG-1.4") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| net.to(device) | |
| net.eval() | |
| def resize_image(image): | |
| image = image.convert('RGB') | |
| model_input_size = (1024, 1024) | |
| image = image.resize(model_input_size, Image.BILINEAR) | |
| return image | |
| def process(image): | |
| # prepare input | |
| orig_image = Image.fromarray(image) | |
| w,h = orig_im_size = orig_image.size | |
| image = resize_image(orig_image) | |
| im_np = np.array(image) | |
| im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1) | |
| im_tensor = torch.unsqueeze(im_tensor,0) | |
| im_tensor = torch.divide(im_tensor,255.0) | |
| im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0]) | |
| if torch.cuda.is_available(): | |
| im_tensor=im_tensor.cuda() | |
| #inference | |
| result=net(im_tensor) | |
| # post process | |
| result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0) | |
| ma = torch.max(result) | |
| mi = torch.min(result) | |
| result = (result-mi)/(ma-mi) | |
| # image to pil | |
| result_array = (result*255).cpu().data.numpy().astype(np.uint8) | |
| pil_mask = Image.fromarray(np.squeeze(result_array)) | |
| # add the mask on the original image as alpha channel | |
| new_im = orig_image.copy() | |
| new_im.putalpha(pil_mask) | |
| return new_im | |
| # return [new_orig_image, new_im] | |
| gr.Markdown("## BRIA RMBG 1.4") | |
| gr.HTML(''' | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| This is a demo for BRIA RMBG 1.4 that using | |
| <a href="https://huggingface.co/briaai/RMBG-1.4" target="_blank">BRIA RMBG-1.4 image matting model</a> as backbone. | |
| </p> | |
| ''') | |
| title = "Background Removal" | |
| description = r"""Background removal model developed by <a href='https://BRIA.AI' target='_blank'><b>BRIA.AI</b></a>, trained on a carefully selected dataset and is available as an open-source model for non-commercial use.<br> | |
| For test upload your image and wait. Read more at model card <a href='https://huggingface.co/briaai/RMBG-1.4' target='_blank'><b>briaai/RMBG-1.4</b></a>. To purchase a commercial license, simply click <a href='https://go.bria.ai/3ZCBTLH' target='_blank'><b>Here</b></a>. <br> | |
| """ | |
| examples = [['./input.jpg'],] | |
| demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description) | |
| if __name__ == "__main__": | |
| demo.launch(share=False) |