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| from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation | |
| import gradio as gr | |
| import torch | |
| import matplotlib.pyplot as plt | |
| import cv2 | |
| processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") | |
| model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") | |
| def process_image(image, prompts): | |
| inputs = processor(text=prompts, images=[image] * len(prompts), padding="max_length", return_tensors="pt") | |
| # predict | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| preds = outputs.logits.unsqueeze(1) | |
| filename = f"mask.png" | |
| plt.imsave(filename,torch.sigmoid(preds[1][0])) | |
| img2 = cv2.imread(filename) | |
| gray_image = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) | |
| (thresh, bw_image) = cv2.threshold(gray_image, 100, 255, cv2.THRESH_BINARY) | |
| # fix color format | |
| cv2.cvtColor(bw_image, cv2.COLOR_BGR2RGB) | |
| return Image.fromarray(bw_image) | |
| 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. 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." | |
| 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>" | |
| examples = [["a glass", "something to fill", "wood", "a jar"]] | |
| interface = gr.Interface(fn=process_image, | |
| inputs=[gr.Image(type="pil"), gr.Textbox(label="What do you want to identify (separated by comma)?")], | |
| outputs=gr.Image(type="pil"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples) | |
| interface.launch(debug=True) |