Spaces:
Running
on
Zero
Running
on
Zero
πwπ
Browse files- README.md +1 -1
- app.py +47 -0
- requirements.txt +16 -0
README.md
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---
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title: Background Removal
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-
emoji:
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colorFrom: purple
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colorTo: indigo
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sdk: gradio
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---
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title: Background Removal
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emoji: πwπ
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colorFrom: purple
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colorTo: indigo
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sdk: gradio
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app.py
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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('zhengpeng7/BiRefNet', trust_remote_code=True,device="auto",torch_dtype=torch.float16)
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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 fn(image):
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im = load_img(image)
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im = im.convert('RGB')
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image = load_img(im)
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input_images = transform_image(image).unsqueeze(0).to('cuda')
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# Prediction
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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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out = (pred_pil , im)
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return out
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slider1 = ImageSlider(label="birefnet", type="pil")
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slider2 = ImageSlider(label="RMBG", 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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tab1 = gr.Interface(fn,inputs= image, outputs= slider1, api_name="image")
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tab2 = gr.Interface(fn,inputs= text, outputs= slider2, api_name="text")
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demo = gr.TabbedInterface([tab1,tab2],["image","text"],title="RMBG with image slider")
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if __name__ == "__main__":
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demo.launch()
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requirements.txt
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torch
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accelerate
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opencv-python
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spaces
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pillow
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numpy
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timm
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kornia
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prettytable
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typing
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scikit-image
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huggingface_hub
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transformers>=4.39.1
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gradio
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gradio_imageslider
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loadimg>=0.1.1
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