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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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import zipfile |
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import os |
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torch.set_float32_matmul_precision(["high", "highest"][0]) |
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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("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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@spaces.GPU |
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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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image_size = im.size |
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origin = im.copy() |
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input_images = transform_image(im).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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im.putalpha(mask) |
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output_file_path = os.path.join("output_images", "output_image_single.png") |
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im.save(output_file_path) |
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return (im, origin) |
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@spaces.GPU |
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def fn_url(url): |
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im = load_img(url, output_type="pil") |
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im = im.convert("RGB") |
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origin = im.copy() |
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image_size = im.size |
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input_images = transform_image(im).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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im.putalpha(mask) |
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output_file_path = os.path.join("output_images", "output_image_url.png") |
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im.save(output_file_path) |
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return [im, origin] |
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@spaces.GPU |
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def batch_fn(images): |
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output_paths = [] |
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for idx, image_path in enumerate(images): |
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im = load_img(image_path, output_type="pil") |
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im = im.convert("RGB") |
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image_size = im.size |
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input_images = transform_image(im).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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im.putalpha(mask) |
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output_file_path = os.path.join("output_images", f"output_image_batch_{idx + 1}.png") |
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im.save(output_file_path) |
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output_paths.append(output_file_path) |
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zip_file_path = os.path.join("output_images", "processed_images.zip") |
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with zipfile.ZipFile(zip_file_path, 'w') as zipf: |
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for file in output_paths: |
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zipf.write(file, os.path.basename(file)) |
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return zip_file_path |
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batch_image = gr.File(label="Upload multiple images", type="filepath", file_count="multiple") |
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slider1 = ImageSlider(label="Processed Image", type="pil") |
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slider2 = ImageSlider(label="Processed Image from URL", type="pil") |
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image = gr.Image(label="Upload an image") |
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text = gr.Textbox(label="Paste an image URL") |
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chameleon = load_img("chameleon.jpg", output_type="pil") |
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url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg" |
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tab1 = gr.Interface( |
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fn, inputs=image, outputs=slider1, examples=[chameleon], api_name="image" |
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) |
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tab2 = gr.Interface(fn_url, inputs=text, outputs=slider2, examples=[url], api_name="text") |
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tab3 = gr.Interface( |
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batch_fn, |
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inputs=batch_image, |
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outputs=gr.File(label="Download Processed Files"), |
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api_name="batch", |
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css=""" |
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#component-37 { |
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display: none; |
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} |
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""" |
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) |
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demo = gr.TabbedInterface( |
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[tab1, tab2, tab3], ["image", "text", "batch"], title="Multi Birefnet for Background Removal" |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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