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import cv2 |
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import gradio as gr |
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from ultralytics import YOLO |
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from PIL import Image |
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model = YOLO('hotspot_detector.pt') |
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def detect_hotspots(image): |
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result = model(image) |
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for r in result: |
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im_array = r.plot() |
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return Image.fromarray(im_array[..., ::-1]) |
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description = """ |
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<center><img src="https://huggingface.co/spaces/intelliarts/hotspot-anomaly-detection-for-solar-panels/resolve/main/images/ia_logo.png" width=270px> </center><br> |
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<center>This is a demo of a computer vision model designed to detect anomalies in solar panels. It operates on infrared images of solar panels. The model indicates the overheated area and the accuracy of anomaly detection. You can use your own infrared images for testing or utilize samples from our dataset</center> |
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""" |
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demo = gr.Interface(fn=detect_hotspots, inputs=gr.Image(type='pil'), outputs="image", |
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examples=[['images/test_image_1.jpg'], ['images/test_image_2.jpg'], |
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['images/test_image_3.jpg'], ['images/test_image_4.jpg']], |
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examples_per_page=4, |
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cache_examples= False, |
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description=description |
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) |
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demo.launch() |
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