app.py

import gradio as gr
from ultralytics import YOLO


def yolov10_inference(image, model_id, image_size, conf_threshold):
    model = YOLO(f'{model_id}.engine')
    results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
    annotated_image = results[0].plot()
    return annotated_image[:, :, ::-1], None


def yolov10_inference_for_examples(image, model_path, image_size, conf_threshold):
    annotated_image, _ = yolov10_inference(image, model_path, image_size, conf_threshold)
    return annotated_image


def app():
    with gr.Blocks():
        with gr.Row():
            with gr.Column():
                image = gr.Image(type="pil", label="Image", visible=True)
                model_id = gr.Dropdown(
                    label="Model",
                    choices=[
                        "yolov10s",
                        "yolov10m",
                        "yolov10m_ssvd_0.4",
                        "yolov10m_wsvd_0.4",
                    ],
                    value="yolov10s",
                )
                image_size = gr.Slider(
                    label="Image Size",
                    minimum=320,
                    maximum=1280,
                    step=32,
                    value=640,
                )
                conf_threshold = gr.Slider(
                    label="Confidence Threshold",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.25,
                )
                yolov10_infer = gr.Button(value="Detect Smoke")

            with gr.Column():
                output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)

        yolov10_infer.click(
            fn=yolov10_inference,
            inputs=[image, model_id, image_size, conf_threshold],
            outputs=[output_image],
        )

        gr.Examples(
            examples=[
                [
                    "examples/smoke1.jpg",
                    "yolov10s",
                    1280,
                    0.25,
                ],
                [
                    "examples/smoke2.jpg",
                    "yolov10s",
                    1280,
                    0.25,
                ],
                [
                    "examples/smoke3.jpg",
                    "yolov10s",
                    1280,
                    0.25,
                ],
                [
                    "examples/smoke4.jpg",
                    "yolov10s",
                    1280,
                    0.25,
                ],
                [
                    "examples/smoke5.jpg",
                    "yolov10s",
                    1280,
                    0.25,
                ],
            ],
            fn=yolov10_inference_for_examples,
            inputs=[
                image,
                model_id,
                image_size,
                conf_threshold,
            ],
            outputs=[output_image],
            cache_examples='lazy',
        )

if __name__ == '__main__':
    gradio_app = gr.Blocks()
    with gradio_app:
        gr.HTML(
            """
        <h1 style='text-align: center'>
        YOLOv10 for early fire detection with low resource consumption
        </h1>
        """)
        gr.HTML(
            """
            <h3 style='text-align: center'>
            Original paper and code:
            <a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
            </h3>
            """)
        with gr.Row():
            with gr.Column():
                app()
    gradio_app.launch()