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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')
#load class_names
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()
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