291 lines
10 KiB
Python
291 lines
10 KiB
Python
import time
|
||
from sqlalchemy.orm import Session
|
||
from typing import List
|
||
from fastapi import UploadFile
|
||
import subprocess
|
||
import torch
|
||
from app.util.yolov5.models.common import DetectMultiBackend
|
||
from app.util.yolov5.utils.torch_utils import select_device
|
||
from app.util.yolov5.utils.dataloaders import LoadStreams
|
||
from app.util.yolov5.utils.general import check_img_size, Profile, non_max_suppression, cv2, scale_boxes
|
||
from ultralytics.utils.plotting import Annotator, colors
|
||
|
||
from app.model.crud import project_detect_crud as pdc
|
||
from app.model.schemas.project_detect_schemas import ProjectDetectIn, ProjectDetectOut, ProjectDetectLogIn
|
||
from app.model.bussiness_model import ProjectDetect, ProjectDetectImg, ProjectTrain, ProjectDetectLog, ProjectDetectLogImg
|
||
from app.util.random_utils import random_str
|
||
from app.config.config_reader import detect_url
|
||
from app.util import os_utils as os
|
||
from app.util import random_utils as ru
|
||
from app.config.config_reader import yolo_url
|
||
from app.websocket.web_socket_server import room_manager
|
||
from app.common.redis_cli import redis_conn
|
||
|
||
|
||
def add_detect(detect_in: ProjectDetectIn, session: Session):
|
||
"""
|
||
新增训练集合信息,并创建文件夹
|
||
:param detect_in:
|
||
:param session:
|
||
:return:
|
||
"""
|
||
detect = ProjectDetect(**detect_in.dict())
|
||
detect.detect_no = random_str(6)
|
||
detect.detect_version = 0
|
||
detect.detect_status = '0'
|
||
url = os.create_folder(detect_url, detect.detect_no, 'images')
|
||
detect.folder_url = url
|
||
detect = pdc.add_detect(detect, session)
|
||
return detect
|
||
|
||
|
||
def del_detect(detect_id: int, session: Session):
|
||
"""
|
||
删除推理集合和推理记录
|
||
:param detect_id:
|
||
:param session:
|
||
:return:
|
||
"""
|
||
detect = pdc.get_detect_by_id(detect_id, session)
|
||
session.delete(detect)
|
||
if detect.file_type != 'rtsp':
|
||
folder_url = [detect.folder_url]
|
||
detect_logs = pdc.get_logs(detect_id, session)
|
||
for log in detect_logs:
|
||
folder_url.append(log.detect_folder_url)
|
||
os.create_folder(folder_url)
|
||
session.commit()
|
||
|
||
|
||
def check_image_name(detect_id: int, files: List[UploadFile], session: Session):
|
||
"""
|
||
校验上传的文件名称是否重复
|
||
:param detect_id:
|
||
:param files:
|
||
:param session:
|
||
:return:
|
||
"""
|
||
for file in files:
|
||
if not pdc.check_img_name(detect_id, file.filename, session):
|
||
return False, file.filename
|
||
return True, None
|
||
|
||
|
||
def upload_detect_imgs(detect: ProjectDetectOut, files: List[UploadFile], session: Session):
|
||
"""
|
||
上传推理集合的照片，保存原图，并生成缩略图
|
||
:param detect:
|
||
:param files:
|
||
:param session:
|
||
:return:
|
||
"""
|
||
images = []
|
||
for file in files:
|
||
image = ProjectDetectImg()
|
||
image.detect_id = detect.id
|
||
image.file_name = file.filename
|
||
# 保存原图
|
||
path = os.save_images(detect.folder_url, file=file)
|
||
image.image_url = path
|
||
# 生成缩略图
|
||
thumb_image_url = os.file_path(detect.folder_url, 'thumb', ru.random_str(10) + ".jpg")
|
||
os.create_thumbnail(path, thumb_image_url)
|
||
image.thumb_image_url = thumb_image_url
|
||
images.append(image)
|
||
pdc.add_detect_imgs(images, session)
|
||
|
||
|
||
def del_detect_img(detect_img_id: int, session: Session):
|
||
"""
|
||
删除训练集合图片
|
||
:param detect_img_id:
|
||
:param session:
|
||
:return:
|
||
"""
|
||
detect_img = session.query(ProjectDetectImg).filter_by(id=detect_img_id).first()
|
||
if detect_img is None:
|
||
return 0
|
||
os.delete_file_if_exists(detect_img.image_url, detect_img.thumb_image_url)
|
||
session.delete(detect_img)
|
||
session.commit()
|
||
return 1
|
||
|
||
|
||
def run_detect_yolo(detect_in: ProjectDetectLogIn, detect: ProjectDetect, train: ProjectTrain, session: Session):
|
||
"""
|
||
开始推理
|
||
:param detect:
|
||
:param detect_in:
|
||
:param train:
|
||
:param session:
|
||
: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 = 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 = pdc.add_detect_log(detect_log, session)
|
||
return detect_log
|
||
|
||
|
||
async def run_detect_img(weights: str, source: str, project: str, name: str, log_id: int, detect_id: int, session: Session):
|
||
"""
|
||
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 session:
|
||
: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", "0.4"]
|
||
is_gpu = redis_conn.get('is_gpu')
|
||
# 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':
|
||
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:
|
||
pdc.update_detect_status(detect_id, -1, session)
|
||
else:
|
||
await room_manager.send_to_room(room, 'success')
|
||
pdc.update_detect_status(detect_id, 2, session)
|
||
detect_imgs = pdc.get_img_list(detect_id, session)
|
||
detect_log_imgs = []
|
||
for detect_img in detect_imgs:
|
||
detect_log_img = ProjectDetectLogImg()
|
||
detect_log_img.log_id = log_id
|
||
image_url = os.file_path(project, name, detect_img.file_name)
|
||
detect_log_img.image_url = image_url
|
||
detect_log_img.file_name = detect_img.file_name
|
||
detect_log_imgs.append(detect_log_img)
|
||
pdc.add_detect_imgs(detect_log_imgs, session)
|
||
|
||
|
||
async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id: int):
|
||
"""
|
||
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
|
||
:return:
|
||
"""
|
||
room = 'detect_rtsp_' + str(detect_id)
|
||
# 选择设备（CPU 或 GPU）
|
||
device = select_device('cpu')
|
||
is_gpu = redis_conn.get('is_gpu')
|
||
# 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
|
||
imgsz = check_img_size((640, 640), s=stride) # check image size
|
||
|
||
dataset = LoadStreams(rtsp_url, img_size=imgsz, stride=stride, auto=pt, vid_stride=1)
|
||
bs = len(dataset)
|
||
|
||
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))
|
||
|
||
seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
|
||
|
||
time.sleep(3)# 等待3s，等待websocket进入
|
||
|
||
for path, im, im0s, vid_cap, s in dataset:
|
||
if room_manager.rooms.get(room):
|
||
with dt[0]:
|
||
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
|
||
if model.xml and im.shape[0] > 1:
|
||
ims = torch.chunk(im, im.shape[0], 0)
|
||
|
||
# Inference
|
||
with dt[1]:
|
||
if model.xml and im.shape[0] > 1:
|
||
pred = None
|
||
for image in ims:
|
||
if pred is None:
|
||
pred = model(image, augment=False, visualize=False).unsqueeze(0)
|
||
else:
|
||
pred = torch.cat((pred, model(image, augment=False, visualize=False).unsqueeze(0)),
|
||
dim=0)
|
||
pred = [pred, None]
|
||
else:
|
||
pred = model(im, augment=False, visualize=False)
|
||
# NMS
|
||
with dt[2]:
|
||
pred = non_max_suppression(pred, 0.45, 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:
|
||
break
|
||
|
||
|
||
|
||
|