from utils import os_utils as os
from . import models, crud, schemas
from utils.websocket_server import room_manager
from application.settings import yolo_url, detect_url
from apps.business.train import models as train_models
from utils.yolov5.utils.dataloaders import LoadStreams
from utils.yolov5.utils.torch_utils import select_device
from ultralytics.utils.plotting import Annotator, colors
from utils.yolov5.models.common import DetectMultiBackend
from apps.business.deepsort import service as deepsort_service
from utils.yolov5.utils.general import check_img_size, non_max_suppression, cv2, scale_boxes
import time
import torch
import asyncio
import subprocess
from sqlalchemy.ext.asyncio import AsyncSession
async def before_detect(
detect_in: schemas.ProjectDetectLogIn,
detect: models.ProjectDetect,
train: train_models.ProjectTrain,
db: AsyncSession,
user_id: int):
"""
开始推理
:param detect:
:param detect_in:
:param train:
:param db:
:param user_id:
:return:
"""
# 推理版本
version_path = 'v' + str(detect.detect_version + 1)
# 权重文件
pt_url = train.best_pt if detect_in.pt_type == 'best' else train.last_pt
# 推理集合文件路径
img_url = detect.folder_url
out_url = os.file_path(detect_url, detect.detect_no, 'detect')
# 构建推理记录数据
detect_log = models.ProjectDetectLog()
detect_log.detect_name = detect.detect_name
detect_log.detect_id = detect.id
detect_log.detect_version = version_path
detect_log.train_id = train.id
detect_log.train_version = train.train_version
detect_log.pt_type = detect_in.pt_type
detect_log.pt_url = pt_url
detect_log.folder_url = img_url
detect_log.detect_folder_url = out_url
detect_log.user_id = user_id
await crud.ProjectDetectLogDal(db).create_model(detect_log)
return detect_log
def run_img_loop(
weights: str,
source: str,
project: str,
name: str,
detect_id: int,
is_gpu: str):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# 运行异步函数
loop.run_until_complete(run_detect_img(weights, source, project, name, detect_id, is_gpu))
# 可选: 关闭循环
loop.close()
async def run_detect_img(
weights: str,
source: str,
project: str,
name: str,
detect_id: int,
is_gpu: str):
"""
执行yolov5的推理
:param weights: 权重文件
:param source: 图片所在文件
:param project: 推理完成的文件位置
:param name: 版本名称
:param log_id: 日志id
:param detect_id: 推理集合id
:param db: 数据库session
:param is_gpu: 是否gpu加速
:return:
"""
yolo_path = os.file_path(yolo_url, 'detect.py')
room = 'detect_' + str(detect_id)
await room_manager.send_to_room(room, f"AiCheck: 模型训练开始，请稍等。。。\n")
commend = ["python", '-u', yolo_path, "--weights", weights, "--source", source, "--name", name, "--project",
project, "--save-txt", "--conf-thres", "0.6"]
# 判断是否存在cuda版本
if is_gpu == 'True':
commend.append("--device=0")
# 启动子进程
with subprocess.Popen(
commend,
bufsize=1, # bufsize=0时，为不缓存；bufsize=1时，按行缓存；bufsize为其他正整数时，为按照近似该正整数的字节数缓存
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, # 这里可以显示yolov5训练过程中出现的进度条等信息
text=True, # 缓存内容为文本，避免后续编码显示问题
encoding='utf-8',
) as process:
while process.poll() is None:
line = process.stdout.readline()
process.stdout.flush() # 刷新缓存，防止缓存过多造成卡死
if line != '\n' and 'yolo' not in line:
await room_manager.send_to_room(room, line + '\n')
# 等待进程结束并获取返回码
return_code = process.wait()
if return_code != 0:
await room_manager.send_to_room(room, 'error')
else:
await room_manager.send_to_room(room, 'success')
async def update_sql(db: AsyncSession, detect_id: int, log_id: int, project, name):
"""
更新推理集合的状态
"""
detect_dal = crud.ProjectDetectDal(db)
detect = await detect_dal.get_data(detect_id)
detect.detect_version = detect.detect_version + 1
await detect_dal.put_data(data_id=detect_id, data=detect)
detect_files = await crud.ProjectDetectFileDal(db).get_datas(
limit=0,
v_where=[models.ProjectDetectFile.detect_id == detect_id],
v_return_objs=True,
v_return_count=False)
detect_log_files = []
for detect_file in detect_files:
detect_log_img = models.ProjectDetectLogFile()
detect_log_img.log_id = log_id
image_url = os.file_path(project, name, detect_file.file_name)
detect_log_img.file_url = image_url
detect_log_img.file_name = detect_file.file_name
detect_log_files.append(detect_log_img)
await crud.ProjectDetectLogFileDal(db).create_models(detect_log_files)
async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id: int, is_gpu: str):
"""
rtsp 视频流推理
:param detect_id: 训练集的id
:param weights_pt: 权重文件
:param rtsp_url: 视频流地址
:param data: yaml文件
:param is_gpu: 是否启用加速
:return:
"""
room = 'detect_rtsp_' + str(detect_id)
# 选择设备（CPU 或 GPU）
device = select_device('cpu')
# 判断是否存在cuda版本
if is_gpu == 'True':
device = select_device('cuda:0')
# 加载模型
model = DetectMultiBackend(weights_pt, device=device, dnn=False, data=data, fp16=False)
stride, names, pt = model.stride, model.names, model.pt
img_sz = check_img_size((640, 640), s=stride) # check image size
dataset = LoadStreams(rtsp_url, img_size=img_sz, stride=stride, auto=pt, vid_stride=1)
bs = len(dataset)
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *img_sz))
time.sleep(3) # 等待3s，等待websocket进入
start_time = time.time()
for path, im, im0s, vid_cap, s in dataset:
# 检查是否已经超过10分钟（600秒）
elapsed_time = time.time() - start_time
if elapsed_time > 600: # 600 seconds = 10 minutes
print(room, "已达到最大执行时间，结束推理。")
break
if room_manager.rooms.get(room):
im = torch.from_numpy(im).to(model.device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
pred = model(im, augment=False, visualize=False)
# NMS
pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000)
# Process predictions
for i, det in enumerate(pred): # per image
p, im0, frame = path[i], im0s[i].copy(), dataset.count
annotator = Annotator(im0, line_width=3, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
c = int(cls) # integer class
label = None if False else (names[c] if False else f"{names[c]} {conf:.2f}")
annotator.box_label(xyxy, label, color=colors(c, True))
# Stream results
im0 = annotator.result()
# 将帧编码为 JPEG
ret, jpeg = cv2.imencode('.jpg', im0)
if ret:
frame_data = jpeg.tobytes()
await room_manager.send_stream_to_room(room, frame_data)
else:
print(room, '结束推理')
break
def run_rtsp_loop(weights_pt: str, rtsp_url: str, data: str, detect_id: int, is_gpu: str):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# 运行异步函数
loop.run_until_complete(
run_detect_rtsp(
weights_pt,
rtsp_url,
data,
detect_id,
is_gpu
)
)
# 可选: 关闭循环
loop.close()
def run_deepsort_loop(
detect_id: int,
weights_pt: str,
data: str,
idx_to_class: {},
sort_type: str = 'video',
video_path: str = None,
rtsp_url: str = None
):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# 运行异步函数
loop.run_until_complete(
deepsort_service.run_deepsort(
detect_id,
weights_pt,
data,
idx_to_class,
sort_type,
video_path,
rtsp_url
)
)
# 可选: 关闭循环
loop.close()