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| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision.transforms.functional import normalize | |
| from huggingface_hub import hf_hub_download | |
| 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() | |
| # model_path = "./model1.pth" | |
| #model_path = hf_hub_download("briaai/RMBG-1.4", 'model.pth') | |
| model_path = hf_hub_download("cocktailpeanut/gbmr", 'model.pth') | |
| if torch.cuda.is_available(): | |
| net.load_state_dict(torch.load(model_path)) | |
| net=net.cuda() | |
| device = "cuda" | |
| elif torch.backends.mps.is_available(): | |
| net.load_state_dict(torch.load(model_path,map_location="mps")) | |
| net=net.to("mps") | |
| device = "mps" | |
| else: | |
| net.load_state_dict(torch.load(model_path,map_location="cpu")) | |
| 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 device == "cuda": | |
| im_tensor=im_tensor.cuda() | |
| elif device == "mps": | |
| im_tensor=im_tensor.to("mps") | |
| #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 | |
| im_array = (result*255).cpu().data.numpy().astype(np.uint8) | |
| pil_im = Image.fromarray(np.squeeze(im_array)) | |
| # paste the mask on the original image | |
| new_im = Image.new("RGBA", pil_im.size, (0,0,0,0)) | |
| new_im.paste(orig_image, mask=pil_im) | |
| # new_orig_image = orig_image.convert('RGBA') | |
| return new_im | |
| # return [new_orig_image, new_im] | |
| # block = gr.Blocks().queue() | |
| # with block: | |
| # 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> | |
| # ''') | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam | |
| # # input_image = gr.Image(sources=None, type="numpy") # None for upload, ctrl+v and webcam | |
| # run_button = gr.Button(value="Run") | |
| # with gr.Column(): | |
| # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[1], height='auto') | |
| # ips = [input_image] | |
| # run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
| # block.launch(debug = True) | |
| # block = gr.Blocks().queue() | |
| 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>.<br> | |
| """ | |
| examples = [['./input.jpg'],] | |
| # output = ImageSlider(position=0.5,label='Image without background', type="pil", show_download_button=True) | |
| # demo = gr.Interface(fn=process,inputs="image", outputs=output, examples=examples, title=title, description=description) | |
| demo = gr.Interface(fn=process,inputs="image", outputs="image", examples=examples, title=title, description=description) | |
| if __name__ == "__main__": | |
| demo.launch(share=False) | |