Upload 3 files
Browse files- .gitattributes +1 -0
- app.py +57 -0
- chameleon.jpg +3 -0
- requirements.txt +16 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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chameleon.jpg filter=lfs diff=lfs merge=lfs -text
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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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device = "cuda" if torch.cuda.is_available() else "cpu"
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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(device)
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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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image = load_img(im)
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input_images = transform_image(image).unsqueeze(0).to(device)
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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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mask = pred_pil.resize(image_size)
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image.putalpha(mask)
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return image
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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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demo = gr.Interface(
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fn,
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inputs=gr.Image(label="Upload an image"),
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outputs=gr.Image(label="birefnet", format="png"),
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examples=[chameleon],
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api_name="image",
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flagging_mode="never",
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cache_mode="lazy",
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)
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demo.queue(default_concurrency_limit=1).launch(show_error=True)
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chameleon.jpg
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Git LFS Details
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requirements.txt
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@@ -0,0 +1,16 @@
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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_imageslider
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loadimg>=0.1.1
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einops
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