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import gradio as gr |
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from loadimg import load_img |
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import spaces |
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from transformers import AutoModelForImageSegmentation |
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import torch |
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from torchvision import transforms |
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torch.set_float32_matmul_precision("high") |
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birefnet = AutoModelForImageSegmentation.from_pretrained( |
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"ZhengPeng7/BiRefNet", trust_remote_code=True |
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) |
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birefnet.to("cuda") |
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transform_image = transforms.Compose([ |
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transforms.Resize((1024, 1024)), |
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transforms.ToTensor(), |
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
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]) |
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@spaces.GPU |
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def process(image): |
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image_size = image.size |
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input_images = transform_image(image).unsqueeze(0).to("cuda") |
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with torch.no_grad(): |
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preds = birefnet(input_images)[-1].sigmoid().cpu() |
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pred = preds[0].squeeze() |
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pred_pil = transforms.ToPILImage()(pred) |
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mask = pred_pil.resize(image_size) |
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image.putalpha(mask) |
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return image |
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def from_upload(image): |
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im = load_img(image, output_type="pil").convert("RGB") |
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origin = im.copy() |
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processed = process(im) |
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return (processed, origin) |
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def from_url(url): |
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im = load_img(url, output_type="pil").convert("RGB") |
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origin = im.copy() |
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processed = process(im) |
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return (processed, origin) |
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def process_file(f): |
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name_path = f.rsplit(".", 1)[0] + ".png" |
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im = load_img(f, output_type="pil").convert("RGB") |
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transparent = process(im) |
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transparent.save(name_path) |
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return name_path |
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tab1 = gr.Interface(from_upload, inputs=gr.Image(), outputs=[gr.Image(label="Processed"), gr.Image(label="Original")], title="Upload Image") |
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tab2 = gr.Interface(from_url, inputs=gr.Textbox(label="Paste Image URL"), outputs=[gr.Image(label="Processed"), gr.Image(label="Original")], title="From URL") |
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tab3 = gr.Interface(process_file, inputs=gr.Image(type="filepath"), outputs=gr.File(), title="Save Transparent PNG") |
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demo = gr.TabbedInterface([tab1, tab2, tab3], ["Upload", "URL", "Save PNG"], title="Background Removal with BiRefNet") |
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if __name__ == "__main__": |
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demo.launch(show_error=True) |
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