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import gradio as gr |
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from ultralytics.yolo.data import utils |
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import ultralytics |
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from pathlib import Path |
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from torchkeras import plots |
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model = ultralytics.YOLO('yolov8n.pt') |
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yaml_path = str('coco128.yaml') |
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class_names = utils.yaml_load(yaml_path)['names'] |
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def detect(img): |
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if isinstance(img,str): |
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img = get_url_img(img) if img.startswith('http') else Image.open(img).convert('RGB') |
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result = model.predict(source=img) |
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if len(result[0].boxes.data)>0: |
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vis = plots.plot_detection(img,boxes=result[0].boxes.data, |
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class_names=class_names, min_score=0.2) |
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else: |
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vis = img |
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return vis |
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with gr.Blocks() as demo: |
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gr.Markdown("# yolov8目标检测演示") |
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with gr.Row(): |
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in_img = gr.Image(source='webcam',type='pil') |
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out_img = gr.Image(type='pil') |
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with gr.Row(): |
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button = gr.Button("执行检测",variant="primary") |
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button.click(detect, |
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inputs=in_img, |
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outputs=out_img) |
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gr.close_all() |
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demo.queue(concurrency_count=5) |
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demo.launch() |
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