import gradio as gr import pandas as pd from skimage import data from ultralytics.yolo.data import utils import ultralytics from pathlib import Path from torchkeras import plots model = ultralytics.YOLO('yolov8n.pt') #load class_names yaml_path = str(Path(ultralytics.__file__).parent/'datasets/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.boxes)>0: vis = plots.plot_detection(img,boxes=result[0].boxes.boxes, 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.Tab("捕捉摄像头喔"): in_img = gr.Image(source='webcam',type='pil') button = gr.Button("执行检测",variant="primary") gr.Markdown("## 预测输出") out_img = gr.Image(type='pil') button.click(detect, inputs=in_img, outputs=out_img) gr.close_all() demo.queue(concurrency_count=5) demo.launch()