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from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation |
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import gradio as gr |
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from PIL import Image |
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import torch |
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import matplotlib.pyplot as plt |
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import cv2 |
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processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") |
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model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") |
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def process_image(image, prompt): |
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inputs = processor(text=prompt, images=image, padding="max_length", return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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preds = outputs.logits |
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filename = f"mask.png" |
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plt.imsave(filename, torch.sigmoid(preds)) |
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return Image.open("mask.png").convert("RGB") |
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title = "Interactive demo: zero-shot image segmentation with CLIPSeg" |
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description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. To use it, simply upload an image and add a text to mask (identify in the image), or use one of the examples below and click 'submit'. Results will show up in a few seconds." |
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>" |
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examples = [["example_image.png", "wood"]] |
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interface = gr.Interface(fn=process_image, |
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inputs=[gr.Image(type="pil"), gr.Textbox(label="Please describe what you want to identify")], |
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outputs=gr.Image(type="pil"), |
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title=title, |
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description=description, |
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article=article, |
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examples=examples) |
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interface.launch(debug=True) |