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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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import numpy as np |
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import spaces |
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from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation |
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processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") |
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model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined").cuda() |
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@spaces.GPU |
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def process_image(image, prompt, threshold, alpha_value, draw_rectangles): |
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inputs = processor( |
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text=prompt, images=image, return_tensors="pt" |
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) |
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inputs = {k: v.cuda() for k, v in inputs.items()} |
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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.squeeze().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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bmask = mask > threshold |
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mask[mask < threshold] = 0 |
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fig, ax = plt.subplots() |
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ax.imshow(image) |
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ax.imshow(mask, alpha=alpha_value, cmap="jet") |
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if draw_rectangles: |
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contours, hierarchy = cv2.findContours( |
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bmask.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE |
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) |
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for contour in contours: |
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x, y, w, h = cv2.boundingRect(contour) |
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rect = plt.Rectangle( |
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(x, y), w, h, fill=False, edgecolor="yellow", linewidth=2 |
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) |
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ax.add_patch(rect) |
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ax.axis("off") |
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plt.tight_layout() |
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bmask = Image.fromarray(bmask.astype(np.uint8) * 255, "L") |
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output_image = Image.new("RGBA", image.size, (0, 0, 0, 0)) |
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output_image.paste(image, mask=bmask) |
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return fig, mask, output_image |
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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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gr.Markdown( |
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"*Example images are taken from the [ImageNet-A](https://paperswithcode.com/dataset/imagenet-a) dataset*" |
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) |
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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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input_prompt = gr.Textbox(label="Please describe what you want to identify") |
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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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input_slider_A = gr.Slider(minimum=0, maximum=1, value=0.5, label="Alpha") |
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draw_rectangles = gr.Checkbox(label="Draw rectangles") |
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btn_process = gr.Button("Process") |
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with gr.Column(): |
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output_plot = gr.Plot(label="Segmentation Result") |
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output_mask = gr.Image(label="Mask") |
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output_image = gr.Image(label="Output Image") |
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btn_process.click( |
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process_image, |
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inputs=[ |
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input_image, |
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input_prompt, |
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input_slider_T, |
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input_slider_A, |
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draw_rectangles, |
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], |
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outputs=[output_plot, output_mask, output_image], |
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) |
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gr.Examples( |
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[ |
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["0.003473_cliff _ cliff_0.51112.jpg", "dog", 0.5, 0.5, True], |
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["0.001861_submarine _ submarine_0.9862991.jpg", "beacon", 0.55, 0.4, True], |
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["0.004658_spatula _ spatula_0.35416836.jpg", "banana", 0.4, 0.5, True], |
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], |
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inputs=[ |
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input_image, |
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input_prompt, |
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input_slider_T, |
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input_slider_A, |
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draw_rectangles, |
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], |
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) |
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demo.launch(share=True) |
