diff --git a/app/yolov5/detect_server.py b/app/yolov5/detect_server.py
index f6a14c0..d96e104 100644
--- a/app/yolov5/detect_server.py
+++ b/app/yolov5/detect_server.py
@@ -29,8 +29,10 @@ import os
 import platform
 import sys
 from pathlib import Path
-
+from app.schemas.TrainResult import DetectReport, DetectProcessValueDice
+from app.controller.AlgorithmController import algorithm_process_value_websocket
 import torch
+from datetime import datetime
 
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[0]  # YOLOv5 root directory
@@ -113,14 +115,28 @@ def run(id,
     # Run inference
     model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
     seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    #回调函数参数定义
+    report = DetectReport(rate_of_progess=0, precision=[], id=id)
+
+    @algorithm_process_value_websocket()
+    def report_cellback(i, num_epochs, ori_img, res_img):
+        report.rate_of_progess = ((i + 1) / num_epochs) * 100
+        #report.progress = (i + 1)
+        report.end_time = datetime.now()
+        #report.precision[0].value.append(reportAccu)
+        process_value_list = DetectProcessValueDice(ori_img=ori_img, res_img=res_img)
+        report.precision.append(process_value_list)
+        return report.dict()
+    #######定义声明完成##################
+    count = 0
     for path, im, im0s, vid_cap, s in dataset:
+        count = count + 1
         with dt[0]:
             im = torch.from_numpy(im).to(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
         with dt[1]:
             visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
@@ -206,9 +222,9 @@ def run(id,
                         vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                     vid_writer[i].write(im0)
 
-
                 # 原始图像路径/结果图像路径 传参
                 #online_img_tools.get_res_img(res_path=save_path, img_path=path, proj_no=pro)
+                report_cellback(count,len(os.listdir(source)),path,save_path)
         # Print time (inference-only)
         LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
     #######统计检测结果：图片总数量，成功数量，失败数量