修改一些小问题

app/application/app.py

@@ -1,7 +1,6 @@
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 import asyncio
-import torch
 
 from app.application.token_middleware import TokenMiddleware
 from app.application.logger_middleware import LoggerMiddleware

app/service/project_detect_service.py

@@ -54,7 +54,7 @@ def del_detect(detect_id: int, session: Session):
         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)
+        os.delete_paths(folder_url)
     session.commit()
 
 
@@ -168,7 +168,7 @@ async def run_detect_img(weights: str, source: str, project: str, name: str, log
     is_gpu = redis_conn.get('is_gpu')
     # 判断是否存在cuda版本
     if is_gpu == 'True':
-        commend.append("--device", "0")
+        commend.append("--device=0")
     # 启动子进程
     with subprocess.Popen(
             commend,
@@ -225,12 +225,12 @@ async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id:
     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
+    img_sz = check_img_size((640, 640), s=stride)  # check image size
 
-    dataset = LoadStreams(rtsp_url, img_size=imgsz, stride=stride, auto=pt, vid_stride=1)
+    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, *imgsz))
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *img_sz))
 
     seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
 
@@ -244,22 +244,11 @@ async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id:
                 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)
@@ -286,5 +275,5 @@ async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id:
                     frame_data = jpeg.tobytes()
                     await room_manager.send_stream_to_room(room, frame_data)
         else:
-            print(room, '结束推理');
+            print(room, '结束推理')
             break

app/service/project_train_service.py

@@ -88,7 +88,6 @@ def del_img(image_id: int, session: Session):
     session.commit()
 
 
-
 def save_img_label(img_leafer_label: ProjectImgLeaferLabel, session: Session):
     """
     保存图片的标签框选信息，每次保存都会针对图片的信息全部删除，然后重新保存
@@ -223,7 +222,7 @@ async def run_commend(data: str, project: str, name: str, epochs: int, patience:
         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 '0%' not in line:
+            if line != '\n' and '0%' not in line and 'yolo' not in line 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

app/websocket/web_socket_server.py

@@ -15,8 +15,6 @@ class SocketManager:
             self.rooms[room].remove(websocket)
             if len(self.rooms[room]) == 0:
                 del self.rooms[room]
-                if room.startswith('detect_rtsp_'):
-                    print()
 
     async def broadcast_to_room(self, room: str, message: str, exclude_websocket: WebSocket = None):
         if room in self.rooms:

requirements_deepsort.txt

@@ -1,38 +0,0 @@
-# deep_sort ----------------------这个暂时还不需要
-atomicwrites==1.3.0
-attrs==19.3.0
-colorama==0.4.3
-easydict~=1.13
-entrypoints==0.3
-et-xmlfile==1.0.1
-flake8==3.7.9
-flake8-import-order==0.18.1
-importlib-metadata==1.6.0
-jdcal==1.4.1
-joblib==1.2.0
-lap==0.4.0
-mccabe==0.6.1
-more-itertools==8.2.0
-motmetrics==1.2.0
-openpyxl==3.0.3
-packaging==20.3
-pluggy==0.13.1
-py==1.10.0
-py-cpuinfo==5.0.0
-pycodestyle==2.5.0
-pyflakes==2.1.1
-pyparsing==2.4.7
-pytest==5.4.1
-pytest-benchmark==3.2.3
-python-dateutil==2.8.1
-pytz==2019.3
-scikit-learn==1.6.1
-six==1.14.0
-sklearn==0.0
-Vizer==0.1.5
-wcwidth==0.1.9
-xmltodict==0.12.0
-zipp==3.1.0
-mmdet~=3.0.0
-pycocotools~=2.0.6
-python-dotenv~=0.21.0

requirements_gpu.txt

@@ -32,9 +32,9 @@ PyYAML>=5.3.1
 requests>=2.32.2
 scipy==1.13.1
 thop>=0.1.1  # FLOPs computation
-# torch==2.6.0+cu124 # 本地安装
-# torchvision==0.21.0+cu124 # 本地安装
+# torch==2.6.0+cu124
+# torchvision==0.21.0+cu124
 tqdm>=4.66.3
-ultralytics==8.3.75  # https://ultralytics.com
+ultralytics==8.3.75
 pandas==2.2.3
 seaborn==0.11.2 # 对应的这个依赖也需要降

yolov5/train.py

@@ -485,7 +485,7 @@ def train(hyp, opt, device, callbacks):
                     "ema": deepcopy(ema.ema).half(),
                     "updates": ema.updates,
                     "optimizer": optimizer.state_dict(),
-                    "opt": vars(opt),
+                    # "opt": vars(opt),
                     "git": GIT_INFO,  # {remote, branch, commit} if a git repo
                     "date": datetime.now().isoformat(),
                 }