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import gradio as gr |
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from ultralytics import YOLOv10 |
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import supervision as sv |
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import spaces |
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MODEL_PATH = 'yolov10n.pt' |
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model = YOLOv10(MODEL_PATH) |
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box_annotator = sv.BoxAnnotator() |
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@spaces.GPU(duration=200) |
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def detect(image): |
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results = model(source=image, conf=0.25, verbose=False)[0] |
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detections = sv.Detections.from_ultralytics(results) |
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labels = [ |
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f"{model.model.names[class_id]} {confidence:.2f}" |
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for class_id, confidence in zip(detections.class_id, detections.confidence) |
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] |
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annotated_image = box_annotator.annotate(image, detections=detections, labels=labels) |
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return annotated_image |
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gradio_app = gr.Blocks() |
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with gradio_app: |
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gr.HTML( |
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""" |
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<h1 style='text-align: center'> |
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YOLOv10: Real-Time End-to-End Object Detection |
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</h1> |
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""") |
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gr.HTML( |
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""" |
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<h3 style='text-align: center'> |
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Follow me for more! |
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<a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a> |
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</h3> |
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""") |
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input_image = gr.Image(type="numpy") |
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output_image = gr.Image(type="numpy", label="Annotated Image") |
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gr.Interface( |
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fn=detect, |
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inputs=input_image, |
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outputs=output_image, |
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) |
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gradio_app.launch() |
