Merge branch 'master' of https://gitea.star-rising.cn/xkrs_manan/RODY

app/controller/AlgorithmController.py

@@ -387,7 +387,7 @@ def returnTrainParams():
         {"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
          'show': True},
         {"index": 3, "name": "device", "value": f'{_kernel[0]}', "description": '训练核心', "default":  f'{_kernel[0]}', "type": "E",
-         "items": _kernel, 'show': True},  # _kernel
+         "items": _kernel, 'show': False},  # _kernel
         {"index": 4, "name": "saveModDir", "value": "E:/alg_demo-master/alg_demo/app/yolov5/best.pt",
          "description": '保存模型路径',
          "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},

app/file_tool.py

@@ -107,6 +107,8 @@ def mv_file(train_files: List[str], test_files: List[str], r_v_rate: Optional[fl
     train_len = ceil(train_len_all * r_v_rate)
     # t_files: 训练集， val_files:验证集
     t_files = train_img_files[0:train_len]
+    if train_len==train_len_all:
+        train_len = 0
     val_files = train_img_files[train_len:train_len_all]
 
     # 判断目标文件夹是否存在， 存在则删除目录下文件
@@ -179,18 +181,15 @@ def get_file(ori_path: str, type_list: Union[object,str]):
     test_files = []
     # 训练、测试比例强制9：1
     for img in imgs[0:1]:
-        path = ori_path + '/images/' +img #'/images/'
+        path = ori_path + '/images/' +img #'./ori/images/xxx.jpg'
-        # print(os.path.exists(path))
         print('图像路径',path)
         if os.path.exists(path):
             test_files.append(path)
             print('1111')
-            #label = ori_path + 'labels/' + os.path.split(path)[1]
             (filename1, extension) = os.path.splitext(img)  # 文件名与后缀名分开
             label = ori_path + '/labels/' + filename1 + '.json' #'/labels/'
             print('标签',label)
             if label is not None:
-                #train_files.append(label)
                 test_files.append(label)
                 print('2222')
     for img in imgs[1:]:
@@ -209,10 +208,6 @@ def get_file(ori_path: str, type_list: Union[object,str]):
     # proj = plc.get_proj_by_proj_no(proj_no=proj_no, db=db)
     target_path = mv_file(train_files=train_files, test_files=test_files)
     # 生成标签
-    # img_types = ilc.get_label_by_proj_no(proj_no=proj_no, db=db)
-    # type_list = []
-    # for img_type in img_types:
-    #     type_list.append(img_type.lebel_type)
     type_dict = {'classes': type_list}
     str_json = json.dumps(type_dict)
     path = os.path.dirname(ori_path) + '/img_label_type'

app/yolov5/train_server.py

@@ -326,6 +326,8 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
         report.progress = (i + 1)
         report.end_time = datetime.now()
         report.precision[0].value.append(reportAccu)
+        if (i+1) == num_epochs:
+            report.isfinish = 2
         return report.dict()
     ###################结束#######################
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------