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| from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation | |
| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") | |
| model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") | |
| def process_image(image, prompt): | |
| # Prepare inputs with the processor | |
| inputs = processor(text=prompt, images=image, return_tensors="pt") | |
| # Predict | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| preds = outputs.logits.squeeze() # Assuming the output logits is of shape [1, H, W] | |
| # Apply sigmoid to convert logits to probabilities | |
| preds = torch.sigmoid(preds) | |
| # Convert to numpy array | |
| mask = preds.numpy() | |
| # Save the image correctly handling dimensions | |
| filename = "mask.png" | |
| plt.imsave(filename, mask, cmap='gray') # Use cmap='gray' for grayscale image saving | |
| # Convert to PIL Image and return | |
| return Image.open(filename).convert("RGB") | |
| title = "Interactive demo: zero-shot image segmentation with CLIPSeg" | |
| description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a></p>" | |
| examples = [["example_image.png", "a description of what to segment"]] | |
| interface = gr.Interface(fn=process_image, | |
| inputs=[gr.Image(type="pil"), gr.Textbox(label="Please describe what you want to identify")], | |
| outputs=gr.Image(type="pil"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples) | |
| interface.launch(debug=True) | |