mirror of
https://github.com/THU-MIG/yolov10.git
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100 lines
2.8 KiB
Python
100 lines
2.8 KiB
Python
# Ackownledgement: https://huggingface.co/spaces/kadirnar/Yolov10/blob/main/app.py
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# Thanks to @kadirnar
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import gradio as gr
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from ultralytics import YOLOv10
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def yolov10_inference(image, model_path, image_size, conf_threshold):
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model = YOLOv10(model_path)
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model.predict(source=image, imgsz=image_size, conf=conf_threshold, save=True)
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return model.predictor.plotted_img[:, :, ::-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")
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model_id = gr.Dropdown(
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label="Model",
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choices=[
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"yolov10n.pt",
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"yolov10s.pt",
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"yolov10m.pt",
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"yolov10b.pt",
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"yolov10l.pt",
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"yolov10x.pt",
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],
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value="yolov10s.pt",
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)
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image_size = gr.Slider(
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label="Image Size",
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minimum=320,
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maximum=1280,
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step=32,
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value=640,
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)
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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=0.1,
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value=0.25,
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)
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yolov10_infer = gr.Button(value="Detect Objects")
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with gr.Column():
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output_image = gr.Image(type="numpy", label="Annotated Image")
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yolov10_infer.click(
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fn=yolov10_inference,
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inputs=[
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image,
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model_id,
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image_size,
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conf_threshold,
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],
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outputs=[output_image],
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)
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gr.Examples(
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examples=[
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[
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"ultralytics/assets/bus.jpg",
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"yolov10s.pt",
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640,
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0.25,
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],
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[
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"ultralytics/assets/zidane.jpg",
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"yolov10s.pt",
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640,
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0.25,
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],
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],
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fn=yolov10_inference,
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inputs=[
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image,
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model_id,
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image_size,
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conf_threshold,
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],
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outputs=[output_image],
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cache_examples=True,
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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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with gr.Row():
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with gr.Column():
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app()
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gradio_app.launch(debug=True) |