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import os |
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import gradio as gr |
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from gradio_imageslider import ImageSlider |
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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", "highest"][0]) |
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birefnet = AutoModelForImageSegmentation.from_pretrained( |
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"briaai/RMBG-2.0", trust_remote_code=True |
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) |
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birefnet.to("cpu") |
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transform_image = transforms.Compose( |
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[ |
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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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) |
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output_folder = 'output_images' |
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if not os.path.exists(output_folder): |
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os.makedirs(output_folder) |
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def fn(image): |
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im = load_img(image, output_type="pil") |
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im = im.convert("RGB") |
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origin = im.copy() |
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image = process(im) |
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image_path = os.path.join(output_folder, "no_bg_image.png") |
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image.save(image_path) |
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return (image, origin), image_path |
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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("cpu") |
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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 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") |
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im = im.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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slider1 = ImageSlider(label="RMBG-2.0", type="pil") |
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slider2 = ImageSlider(label="RMBG-2.0", type="pil") |
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image = gr.Image(label="Upload an image") |
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image2 = gr.Image(label="Upload an image",type="filepath") |
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text = gr.Textbox(label="Paste an image URL") |
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png_file = gr.File(label="output png file") |
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tab1 = gr.Interface( |
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fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], api_name="image", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding." |
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) |
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tab2 = gr.Interface( |
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fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], api_name="text", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding." |
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) |
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tab3 = gr.Interface( |
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process_file, inputs=image2, outputs=png_file, api_name="png", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding." |
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) |
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demo = gr.TabbedInterface( |
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[tab1, tab2], ["Using Image", "Usling URL"], title="✂️ RMBG Image Background Remover ✂️", theme="Yntec/HaleyCH_Theme_Orange" |
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) |
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if __name__ == "__main__": |
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demo.launch(show_error=True, debug=True) |
