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import spaces |
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import gradio as gr |
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import cv2 |
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import tempfile |
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from ultralytics import YOLOv10 |
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model = YOLOv10.from_pretrained(f'jameslahm/yolov10s') |
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@spaces.GPU |
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def yolov10_inference(image, conf_threshold): |
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width, _ = image.size |
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results = model.predict(source=image, imgsz=width, conf=conf_threshold) |
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annotated_image = results[0].plot() |
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return annotated_image[:, :, ::-1] |
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def app(): |
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with gr.Blocks(): |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(type="pil", label="Image", visible=True) |
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conf_threshold = gr.Slider( |
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label="Confidence Threshold", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.05, |
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value=0.25, |
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) |
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with gr.Column(): |
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output_image = gr.Image(type="numpy", label="Annotated Image", visible=True) |
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image.stream( |
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fn=yolov10_inference, |
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inputs=[image, conf_threshold], |
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outputs=[image], |
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stream_every=0.1, |
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time_limit=30 |
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) |
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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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<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a> |
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</h3> |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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app() |
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if __name__ == '__main__': |
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gradio_app.launch() |
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