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import gradio as gr
import pandas as pd
from PIL import Image
from torchkeras import plots
from torchkeras.data import get_url_img
from pathlib import Path
from ultralytics import YOLO
import ultralytics
from ultralytics.yolo.data import utils
model = 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:
with gr.Tab("Webcam"):
input_img = gr.Image(source='webcam',type='pil')
button = gr.Button("Detect",variant="primary")
gr.Markdown("## Output")
out_img = gr.Image(type='pil')
button.click(detect,
inputs=input_img,
outputs=out_img)
with gr.Tab("Url"):
default_url = 'https://t7.baidu.com/it/u=3601447414,1764260638&fm=193&f=GIF'
url = gr.Textbox(value=default_url)
button = gr.Button("Detect",variant="primary")
gr.Markdown("## Output")
out_img = gr.Image(type='pil')
button.click(detect,
inputs=url,
outputs=out_img)
with gr.Tab("Upload"):
input_img = gr.Image(type='pil')
button = gr.Button("Detect",variant="primary")
gr.Markdown("## Output")
out_img = gr.Image(type='pil')
button.click(detect,
inputs=input_img,
outputs=out_img)
gr.close_all()
demo.queue()
demo.launch() |