226 lines
8.6 KiB
Python
226 lines
8.6 KiB
Python
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from application.settings import yolo_url, detect_url
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from utils.websocket_server import room_manager
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from utils import os_utils as os
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from . import models, crud, schemas
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from apps.business.train import models as train_models
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from utils.yolov5.models.common import DetectMultiBackend
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from utils.yolov5.utils.torch_utils import select_device
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from utils.yolov5.utils.dataloaders import LoadStreams
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from utils.yolov5.utils.general import check_img_size, Profile, non_max_suppression, cv2, scale_boxes
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from ultralytics.utils.plotting import Annotator, colors
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import time
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import torch
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import asyncio
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import subprocess
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from redis.asyncio import Redis
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from sqlalchemy.ext.asyncio import AsyncSession
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async def before_detect(
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detect_in: schemas.ProjectDetectLogIn,
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detect: models.ProjectDetect,
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train: train_models.ProjectTrain,
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db: AsyncSession):
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"""
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开始推理
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:param detect:
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:param detect_in:
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:param train:
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:param db:
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:return:
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"""
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# 推理版本
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version_path = 'v' + str(detect.detect_version + 1)
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# 权重文件
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pt_url = train.best_pt if detect_in.pt_type == 'best' else train.last_pt
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# 推理集合文件路径
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img_url = detect.folder_url
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out_url = os.file_path(detect_url, detect.detect_no, 'detect')
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# 构建推理记录数据
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detect_log = models.ProjectDetectLog()
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detect_log.detect_name = detect.detect_name
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detect_log.detect_id = detect.id
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detect_log.detect_version = version_path
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detect_log.train_id = train.id
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detect_log.train_version = train.train_version
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detect_log.pt_type = detect_in.pt_type
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detect_log.pt_url = pt_url
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detect_log.folder_url = img_url
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detect_log.detect_folder_url = out_url
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await crud.ProjectDetectLogDal(db).create_data(detect_log)
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return detect_log
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async def run_detect_img(
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weights: str,
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source: str,
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project: str,
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name: str,
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log_id: int,
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detect_id: int,
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db: AsyncSession,
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rd: Redis):
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"""
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执行yolov5的推理
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:param weights: 权重文件
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:param source: 图片所在文件
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:param project: 推理完成的文件位置
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:param name: 版本名称
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:param log_id: 日志id
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:param detect_id: 推理集合id
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:param db: 数据库session
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:param rd: Redis
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:return:
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"""
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yolo_path = os.file_path(yolo_url, 'detect.py')
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room = 'detect_' + str(detect_id)
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await room_manager.send_to_room(room, f"AiCheck: 模型训练开始，请稍等。。。\n")
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commend = ["python", '-u', yolo_path, "--weights", weights, "--source", source, "--name", name, "--project",
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project, "--save-txt", "--conf-thres", "0.4"]
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is_gpu = rd.get('is_gpu')
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# 判断是否存在cuda版本
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if is_gpu == 'True':
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commend.append("--device", "0")
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# 启动子进程
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with subprocess.Popen(
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commend,
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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
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shell=False,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT, # 这里可以显示yolov5训练过程中出现的进度条等信息
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text=True, # 缓存内容为文本，避免后续编码显示问题
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encoding='utf-8',
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) as process:
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while process.poll() is None:
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line = process.stdout.readline()
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process.stdout.flush() # 刷新缓存，防止缓存过多造成卡死
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if line != '\n':
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await room_manager.send_to_room(room, line + '\n')
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# 等待进程结束并获取返回码
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return_code = process.wait()
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if return_code != 0:
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await room_manager.send_to_room(room, 'error')
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else:
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await room_manager.send_to_room(room, 'success')
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detect_files = crud.ProjectDetectFileDal(db).get_data(
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v_where=[models.ProjectDetectFile.detect_id == detect_id])
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detect_log_files = []
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for detect_file in detect_files:
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detect_log_img = models.ProjectDetectLogFile()
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detect_log_img.log_id = log_id
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image_url = os.file_path(project, name, detect_file.file_name)
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detect_log_img.image_url = image_url
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detect_log_img.file_name = detect_file.file_name
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detect_log_files.append(detect_log_img)
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await crud.ProjectDetectLogFileDal(db).create_datas(detect_log_files)
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async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id: int, rd: Redis):
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"""
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rtsp 视频流推理
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:param detect_id: 训练集的id
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:param weights_pt: 权重文件
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:param rtsp_url: 视频流地址
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:param data: yaml文件
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:param rd: Redis :redis
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:return:
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"""
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room = 'detect_rtsp_' + str(detect_id)
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# 选择设备（CPU 或 GPU）
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device = select_device('cpu')
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is_gpu = rd.get('is_gpu')
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# 判断是否存在cuda版本
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if is_gpu == 'True':
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device = select_device('cuda:0')
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# 加载模型
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model = DetectMultiBackend(weights_pt, device=device, dnn=False, data=data, fp16=False)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size((640, 640), s=stride) # check image size
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dataset = LoadStreams(rtsp_url, img_size=imgsz, stride=stride, auto=pt, vid_stride=1)
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bs = len(dataset)
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))
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seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
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time.sleep(3) # 等待3s，等待websocket进入
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for path, im, im0s, vid_cap, s in dataset:
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if room_manager.rooms.get(room):
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with dt[0]:
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im = torch.from_numpy(im).to(model.device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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if model.xml and im.shape[0] > 1:
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ims = torch.chunk(im, im.shape[0], 0)
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# Inference
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with dt[1]:
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if model.xml and im.shape[0] > 1:
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pred = None
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for image in ims:
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if pred is None:
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pred = model(image, augment=False, visualize=False).unsqueeze(0)
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else:
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pred = torch.cat((pred, model(image, augment=False, visualize=False).unsqueeze(0)),
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dim=0)
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pred = [pred, None]
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else:
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pred = model(im, augment=False, visualize=False)
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# NMS
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with dt[2]:
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pred = non_max_suppression(pred, 0.45, 0.45, None, False, max_det=1000)
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# Process predictions
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for i, det in enumerate(pred): # per image
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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annotator = Annotator(im0, line_width=3, example=str(names))
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
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# Write results
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for *xyxy, conf, cls in reversed(det):
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c = int(cls) # integer class
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label = None if False else (names[c] if False else f"{names[c]} {conf:.2f}")
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annotator.box_label(xyxy, label, color=colors(c, True))
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# Stream results
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im0 = annotator.result()
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# 将帧编码为 JPEG
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ret, jpeg = cv2.imencode('.jpg', im0)
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if ret:
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frame_data = jpeg.tobytes()
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await room_manager.send_stream_to_room(room, frame_data)
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else:
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print(room, '结束推理')
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break
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def run_img_loop(weights: str, source: str, project: str, name: str, log_id: int, detect_id: int, db: AsyncSession):
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# 运行异步函数
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loop.run_until_complete(run_detect_img(weights, source, project, name, log_id, detect_id, db))
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# 可选: 关闭循环
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loop.close()
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def run_rtsp_loop(weights_pt: str, rtsp_url: str, data: str, detect_id: int, rd: Redis):
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# 运行异步函数
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loop.run_until_complete(run_detect_rtsp(weights_pt, rtsp_url, data, detect_id, rd))
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# 可选: 关闭循环
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loop.close()
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