# --------------------------------------------------------
# Based on yolov10
# https://github.com/THU-MIG/yolov10/app.py
# --------------------------------------------------------'
import logging
import tempfile
import threading
import cv2
import gradio as gr
import uvicorn
from fastapi import FastAPI
from fastapi import status
from fastapi.exceptions import RequestValidationError
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from ultralytics import YOLO
# 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。
"""
model: str # 训练底模
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:
# 根据params.model动态确定配置文件
if params.model.endswith('.pt'):
# 如果是.pt文件，将后缀替换为.yaml
config_file = params.model.replace('.pt', '.yaml')
else:
# 如果不是.pt文件，使用默认配置
config_file = "yolov12.yaml"
model = YOLO(config_file)
model.load(params.model)
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
}
class PredictParams(BaseModel):
"""
用于接收/yolov12/predict接口的预测参数，与YOLO predict方法保持一致。
"""
model: str = "yolov12m.pt" # 模型路径
source: str = None # 输入源（图片/视频路径、URL等）
stream: bool = False # 是否流式处理
conf: float = 0.25 # 置信度阈值
iou: float = 0.7 # IoU阈值
max_det: int = 300 # 最大检测数量
imgsz: int = 640 # 输入图片尺寸
batch: int = 1 # 批次大小
device: str = "" # 设备
show: bool = False # 是否显示结果
save: bool = False # 是否保存结果
save_txt: bool = False # 是否保存txt文件
save_conf: bool = False # 是否保存置信度
save_crop: bool = False # 是否保存裁剪图片
show_labels: bool = True # 是否显示标签
show_conf: bool = True # 是否显示置信度
show_boxes: bool = True # 是否显示边界框
line_width: int = None # 线条宽度
vid_stride: int = 1 # 视频帧步长
stream_buffer: bool = False # 流缓冲区
visualize: bool = False # 可视化特征
augment: bool = False # 数据增强
agnostic_nms: bool = False # 类别无关NMS
classes: list = None # 指定类别
retina_masks: bool = False # 高分辨率分割掩码
embed: list = None # 特征向量层
half: bool = False # 半精度
dnn: bool = False # OpenCV DNN
project: str = "" # 项目名
name: str = "" # 实验名
exist_ok: bool = False # 是否覆盖现有目录
verbose: bool = True # 详细输出
@app_fastapi.post("/yolov12/predict")
def yolov12_predict(params: PredictParams):
"""
RESTful POST接口：/yolov12/predict
接收预测参数，调用YOLO模型进行预测，并返回预测结果。
返回格式：{"code": 0/1, "msg": "success/错误原因", "result": {"save_dir": "保存目录", "filename": "文件名"}}
"""
logging.info("收到/yolov12/predict预测请求")
logging.info(f"请求参数: {params}")
try:
model = YOLO(params.model)
logging.info("开始模型预测...")
# 构建预测参数
predict_kwargs = {}
for field, value in params.dict().items():
if field not in ['model'] and value is not None:
predict_kwargs[field] = value
# 确保保存结果
predict_kwargs['save'] = True
results = model.predict(**predict_kwargs)
logging.info("模型预测完成")
# 获取保存目录和最终文件名
result = results[0]
save_dir = result.save_dir if hasattr(result, 'save_dir') else None
# 获取最终生成的文件名
final_filename = None
if save_dir:
import os
import glob
if os.path.exists(save_dir):
# 检查输入源类型
source = params.source
if source:
source_ext = os.path.splitext(source)[1].lower()
video_extensions = ['.mp4', '.avi', '.mov', '.mkv', '.webm', '.flv', '.wmv']
# 如果输入是图片，返回图片文件
if source_ext not in video_extensions:
image_files = []
for ext in ['*.jpg', '*.jpeg', '*.png']:
image_files.extend(glob.glob(os.path.join(save_dir, ext)))
if image_files:
latest_image = max(image_files, key=os.path.getmtime)
final_filename = os.path.basename(latest_image)
logging.info(f"输入为图片，返回图片文件: {final_filename}")
# 如果输入是视频，检查并转换为MP4
else:
# 查找所有视频文件
video_files = []
for ext in ['*.avi', '*.webm', '*.mov']:
video_files.extend(glob.glob(os.path.join(save_dir, ext)))
# 如果找到非MP4视频文件，转换为MP4
for video_file in video_files:
output_mp4 = video_file.rsplit('.', 1)[0] + '.mp4'
try:
import cv2
cap = cv2.VideoCapture(video_file)
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# 尝试不同的MP4编码器（按兼容性排序）
fourcc_options = ['avc1', 'H264', 'mp4v']
out = None
for fourcc in fourcc_options:
try:
fourcc_code = cv2.VideoWriter_fourcc(*fourcc)
out = cv2.VideoWriter(output_mp4, fourcc_code, fps, (width, height))
if out.isOpened():
logging.info(f"使用编码器 {fourcc} 创建MP4文件")
break
except:
continue
if out and out.isOpened():
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
out.write(frame)
cap.release()
out.release()
# 使用ffmpeg进一步优化MP4文件（如果可用）
try:
import subprocess
temp_mp4 = output_mp4 + '.temp.mp4'
os.rename(output_mp4, temp_mp4)
# 使用ffmpeg重新编码为H.264格式
cmd = [
'ffmpeg', '-i', temp_mp4,
'-c:v', 'libx264',
'-preset', 'fast',
'-crf', '23',
'-y', output_mp4
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode == 0:
os.remove(temp_mp4)
logging.info(f"使用ffmpeg优化MP4文件: {output_mp4}")
else:
# ffmpeg失败，恢复原文件
os.rename(temp_mp4, output_mp4)
logging.warning(f"ffmpeg优化失败，使用OpenCV生成的MP4: {output_mp4}")
except (FileNotFoundError, subprocess.SubprocessError) as e:
# ffmpeg不可用，使用OpenCV生成的MP4
logging.warning(f"ffmpeg不可用，使用OpenCV生成的MP4: {output_mp4}")
# 删除原文件
os.remove(video_file)
logging.info(f"视频已转换为MP4格式: {output_mp4}")
else:
# OpenCV编码器失败，尝试使用ffmpeg直接转换
logging.warning(f"OpenCV编码器失败，尝试使用ffmpeg转换")
try:
import subprocess
cmd = [
'ffmpeg', '-i', video_file,
'-c:v', 'libx264',
'-preset', 'fast',
'-crf', '23',
'-y', output_mp4
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode == 0:
os.remove(video_file)
logging.info(f"使用ffmpeg直接转换MP4文件: {output_mp4}")
else:
logging.error(f"ffmpeg转换失败: {result.stderr}")
except (FileNotFoundError, subprocess.SubprocessError) as e:
logging.error(f"ffmpeg不可用，保持原格式: {e}")
except Exception as e:
logging.error(f"转换视频格式时出错: {e}")
# 获取MP4文件
mp4_files = glob.glob(os.path.join(save_dir, "*.mp4"))
if mp4_files:
latest_mp4 = max(mp4_files, key=os.path.getmtime)
final_filename = os.path.basename(latest_mp4)
logging.info(f"输入为视频，返回MP4文件: {final_filename}")
# 如果无法确定输入类型或未找到文件，返回最新文件
if not final_filename:
all_files = []
for ext in ['*.jpg', '*.jpeg', '*.png', '*.mp4']:
all_files.extend(glob.glob(os.path.join(save_dir, ext)))
if all_files:
latest_file = max(all_files, key=os.path.getmtime)
final_filename = os.path.basename(latest_file)
logging.info(f"返回最新文件: {final_filename}")
return {
"code": 0,
"msg": "success",
"result": save_dir+"/"+final_filename
}
except Exception as e:
logging.error(f"预测过程发生异常: {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)