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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 torch |
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import numpy as np |
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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( |
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text=prompt, images=image, padding="max_length", return_tensors="pt" |
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) |
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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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pred = torch.sigmoid(preds) |
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mat = pred.cpu().numpy() |
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mask = Image.fromarray(np.uint8(mat * 255), "L") |
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mask = mask.convert("RGB") |
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mask = mask.resize(image.size) |
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mask = np.array(mask)[:, :, 0] |
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mask_min = mask.min() |
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mask_max = mask.max() |
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mask = (mask - mask_min) / (mask_max - mask_min) |
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ax = plt.subplots() |
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ax.imshow(image) |
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ax.imshow(mask, alpha=0.5, cmap="jet") |
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ax.axis("off") |
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plt.tight_layout() |
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return mask |
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def get_masks(prompts, img, threhsold): |
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prompts = prompts.split(",") |
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masks = [] |
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for prompt in prompts: |
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mask = process_image(img, prompt) |
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mask = mask > threhsold |
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masks.append(mask) |
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return masks |
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def extract_image(img, pos_prompts, neg_prompts, threshold): |
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positive_masks = get_masks(pos_prompts, img, threshold) |
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negative_masks = get_masks(neg_prompts, img, threshold) |
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pos_mask = np.any(np.stack(positive_masks), axis=0) |
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neg_mask = np.any(np.stack(negative_masks), axis=0) |
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final_mask = pos_mask & ~neg_mask |
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final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L") |
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inverse_mask = np.invert(final_mask) |
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output_image = Image.new("RGBA", img.size, (0, 0, 0, 0)) |
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output_image.paste(img, mask=final_mask) |
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return output_image, final_mask, inverse_mask |
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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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with gr.Blocks() as demo: |
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gr.Markdown("# CLIPSeg: Image Segmentation Using Text and Image Prompts") |
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gr.Markdown(article) |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(type="pil") |
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positive_prompts = gr.Textbox( |
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label="Please describe what you want to identify (comma separated)" |
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) |
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negative_prompts = gr.Textbox( |
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label="Please describe what you want to ignore (comma separated)" |
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) |
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input_slider_T = gr.Slider( |
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minimum=0, maximum=1, value=0.4, label="Threshold" |
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) |
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btn_process = gr.Button(label="Process") |
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with gr.Column(): |
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output_image = gr.Image(label="Result") |
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output_mask = gr.Image(label="Mask") |
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inverse_mask = gr.Image(label="Inverse") |
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btn_process.click( |
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extract_image, |
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inputs=[ |
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input_image, |
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positive_prompts, |
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negative_prompts, |
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input_slider_T, |
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], |
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outputs=[output_image, output_mask, inverse_mask], |
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api_name="mask" |
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) |
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demo.launch() |
