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