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app.py
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import gradio as gr
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from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
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import torch
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from PIL import Image
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import numpy as np
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def process(input_image, prompt):
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inputs = processor(text=prompt, images=input_image, padding="max_length", return_tensors="pt")
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# predict
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with torch.no_grad():
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outputs = model(**inputs)
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preds = torch.sigmoid(outputs.logits).squeeze().detach().cpu().numpy()
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preds = np.where(preds > 0.5, 255, 0).astype(np.uint8)
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preds = Image.fromarray(preds.astype(np.uint8))
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preds = np.array(preds.resize((input_image.width, input_image.height)))
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print(preds)
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return preds
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if __name__ == '__main__':
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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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input_image = gr.inputs.Image(label='image', type='pil')
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prompt = gr.Textbox(label='Prompt')
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ips = [
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input_image, prompt
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]
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outputs = "image"
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input_size = (256, 256)
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output_size = (256, 256)
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iface = gr.Interface(fn=process,
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inputs=ips,
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outputs=outputs,
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input_size=input_size,
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output_size=output_size)
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iface.launch()
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