# --------------------------------------------------------
# Based on yolov10
# https://github.com/THU-MIG/yolov10/app.py
# --------------------------------------------------------'
import gradio as gr
import cv2
import tempfile
from ultralytics import YOLO
import threading
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
import logging
from fastapi.responses import JSONResponse
from fastapi.exception_handlers import RequestValidationError
from fastapi.exceptions import RequestValidationError
from fastapi import status
# 设置日志格式和级别
logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s')
def yolov12_inference(image, video, model_id, image_size, conf_threshold):
model = YOLO(model_id)
if image:
results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
annotated_image = results[0].plot()
return annotated_image[:, :, ::-1], None
else:
video_path = tempfile.mktemp(suffix=".webm")
with open(video_path, "wb") as f:
with open(video, "rb") as g:
f.write(g.read())
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
output_video_path = tempfile.mktemp(suffix=".webm")
out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)
annotated_frame = results[0].plot()
out.write(annotated_frame)
cap.release()
out.release()
return None, output_video_path
def yolov12_inference_for_examples(image, model_path, image_size, conf_threshold):
annotated_image, _ = yolov12_inference(image, None, model_path, image_size, conf_threshold)
return annotated_image
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image", visible=True)
video = gr.Video(label="Video", visible=False)
input_type = gr.Radio(
choices=["Image", "Video"],
value="Image",
label="Input Type",
)
model_id = gr.Dropdown(
label="Model",
choices=[
"yolov12n.pt",
"yolov12s.pt",
"yolov12m.pt",
"yolov12l.pt",
"yolov12x.pt",
],
value="yolov12m.pt",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.25,
)
yolov12_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)
output_video = gr.Video(label="Annotated Video", visible=False)
def update_visibility(input_type):
image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
return image, video, output_image, output_video
input_type.change(
fn=update_visibility,
inputs=[input_type],
outputs=[image, video, output_image, output_video],
)
def run_inference(image, video, model_id, image_size, conf_threshold, input_type):
if input_type == "Image":
return yolov12_inference(image, None, model_id, image_size, conf_threshold)
else:
return yolov12_inference(None, video, model_id, image_size, conf_threshold)
yolov12_infer.click(
fn=run_inference,
inputs=[image, video, model_id, image_size, conf_threshold, input_type],
outputs=[output_image, output_video],
)
gr.Examples(
examples=[
[
"ultralytics/assets/bus.jpg",
"yolov12s.pt",
640,
0.25,
],
[
"ultralytics/assets/zidane.jpg",
"yolov12x.pt",
640,
0.25,
],
],
fn=yolov12_inference_for_examples,
inputs=[
image,
model_id,
image_size,
conf_threshold,
],
outputs=[output_image],
cache_examples='lazy',
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
YOLOv12: Attention-Centric Real-Time Object Detectors
""")
gr.HTML(
"""
""")
with gr.Row():
with gr.Column():
app()
def start_gradio():
gradio_app.launch(server_name="0.0.0.0", server_port=7860)
# FastAPI部分
app_fastapi = FastAPI()
class TrainParams(BaseModel):
"""
用于接收/yolov12/train接口的训练参数，所有参数均需前端传入。
"""
data: str # 数据集配置文件路径
epochs: int # 训练轮数
batch: int # 批次大小
imgsz: int # 输入图片尺寸
scale: float # 随机缩放增强比例
mosaic: float # mosaic数据增强概率
mixup: float # mixup数据增强概率
copy_paste: float # copy-paste数据增强概率
device: str # 训练设备
project: str # 工程名
name: str # 实验名
exist_ok: bool # 是否允许覆盖同名目录
@app_fastapi.post("/yolov12/train")
def yolov12_train(params: TrainParams):
"""
RESTful POST接口：/yolov12/train
接收训练参数，调用YOLO模型训练，并返回训练结果。
返回格式：{"code": 0/1, "msg": "success/错误原因", "result": 训练结果或None}
"""
logging.info("收到/yolov12/train训练请求")
logging.info(f"请求参数: {params}")
try:
model = YOLO("yolov12.yaml") # 如有yolov12n.yaml可替换
logging.info("开始模型训练...")
results = model.train(
data=params.data,
epochs=params.epochs,
batch=params.batch,
imgsz=params.imgsz,
scale=params.scale,
mosaic=params.mosaic,
mixup=params.mixup,
copy_paste=params.copy_paste,
device=params.device,
project=params.project,
name=params.name,
exist_ok=params.exist_ok,
)
logging.info("模型训练完成")
# logging.info(f"训练结果: {str(results)}")
return {
"code": 0,
"msg": "success",
"result": str(results.save_dir)
}
except Exception as e:
logging.error(f"训练过程发生异常: {e}")
return {
"code": 1,
"msg": str(e),
"result": None
}
# 全局异常处理器：参数校验失败时统一返回格式
@app_fastapi.exception_handler(RequestValidationError)
async def validation_exception_handler(request, exc):
err_msg = f"参数校验失败: 路径={request.url.path}, 错误={exc.errors()}"
logging.error(err_msg)
return JSONResponse(
status_code=status.HTTP_200_OK,
content={
"code": 422,
"msg": err_msg,
"result": None
}
)
if __name__ == "__main__":
threading.Thread(target=start_gradio, daemon=True).start()
uvicorn.run(app_fastapi, host="0.0.0.0", port=8000)