|
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') |
|
|
|
|
|
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.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() |
|
|