Create model.py
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model.py
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from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
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from PIL import Image
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import torch
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import numpy as np
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# Load model
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processor = AutoImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
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model = AutoModelForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
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def predict_defect(image: Image.Image):
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inputs = processor(images=image, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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logits = outputs.logits
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segmentation = torch.argmax(logits.squeeze(), dim=0).detach().cpu().numpy()
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# Convert to RGB overlay
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overlay = np.zeros((segmentation.shape[0], segmentation.shape[1], 3), dtype=np.uint8)
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overlay[segmentation == 12] = [255, 0, 0] # example label index for defects (adjust accordingly)
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return Image.fromarray(overlay)
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