diff --git a/process_addWrite.py b/process_addWrite.py
new file mode 100644
index 0000000..53449f1
--- /dev/null
+++ b/process_addWrite.py
@@ -0,0 +1,542 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Sep 29 08:59:21 2024
+
+@author: ym
+"""
+import os
+# import sys
+import cv2
+import pickle
+import numpy as np
+from pathlib import Path
+from scipy.spatial.distance import cdist
+from track_reid import yolo_resnet_tracker, yolov10_resnet_tracker
+
+from tracking.dotrack.dotracks_back import doBackTracks
+from tracking.dotrack.dotracks_front import doFrontTracks 
+from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
+from utils.getsource import get_image_pairs, get_video_pairs
+from tracking.utils.read_data import read_similar, get_process_csv_data
+
+from openpyxl import Workbook, load_workbook
+
+
+def save_subimgs(imgdict, boxes, spath, ctype, featdict = None):
+    '''
+    当前 box 特征和该轨迹前一个 box 特征的相似度，可用于和跟踪序列中的相似度进行比较
+    '''
+    boxes = boxes[np.argsort(boxes[:, 7])]
+    for i in range(len(boxes)):
+        simi = None
+        tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
+        
+        if i>0:
+            _, fid0, bid0 = int(boxes[i-1, 4]), int(boxes[i-1, 7]), int(boxes[i-1, 8])
+            if f"{fid0}_{bid0}" in featdict.keys() and f"{fid}_{bid}" in featdict.keys():
+                feat0 = featdict[f"{fid0}_{bid0}"]
+                feat1 = featdict[f"{fid}_{bid}"]
+                simi = 1 - np.maximum(0.0, cdist(feat0[None, :], feat1[None, :], "cosine"))[0][0]
+            
+        img = imgdict[f"{fid}_{bid}"]
+        imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
+        if simi is not None:
+            imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simi:.2f}.png"
+        
+        cv2.imwrite(imgpath, img)
+        
+        
+def save_subimgs_1(imgdict, boxes, spath, ctype, simidict = None):
+    '''
+    当前 box 特征和该轨迹 smooth_feat 特征的相似度, yolo_resnet_tracker 函数中，
+    采用该方式记录特征相似度
+    '''
+    for i in range(len(boxes)):
+        tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
+        
+        key = f"{fid}_{bid}"
+        img = imgdict[key]
+        imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
+        if simidict is not None and key in simidict.keys():
+            imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simidict[key]:.2f}.png"
+        
+        cv2.imwrite(imgpath, img)
+        
+def show_result(eventpath, event_tracks, yrtDict, savepath_pipe):
+    '''保存 Tracking 输出的运动轨迹子图，并记录相似度'''
+    
+    savepath_pipe_subimgs = savepath_pipe / Path("subimgs") 
+    if not savepath_pipe_subimgs.exists():
+        savepath_pipe_subimgs.mkdir(parents=True, exist_ok=True)
+
+    
+    for CamerType, vts in event_tracks:
+        if len(vts.tracks)==0: continue
+        if CamerType == 'front':
+            # yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"]
+            
+            yolos = yrtDict["frontyrt"]
+            ctype = 1
+        if CamerType == 'back':
+            # yolos = ShoppingDict["backCamera"]["yoloResnetTracker"]
+            
+            yolos = yrtDict["backyrt"]
+            ctype = 0
+            
+        imgdict, featdict, simidict = {}, {}, {}
+        for y in yolos:
+            imgdict.update(y["imgs"])
+            featdict.update(y["feats"])
+            simidict.update(y["featsimi"])
+
+        for track in vts.Residual:
+            if isinstance(track, np.ndarray):
+                save_subimgs(imgdict, track, savepath_pipe_subimgs, ctype, featdict)
+            else:
+                save_subimgs(imgdict, track.slt_boxes, savepath_pipe_subimgs,  ctype, featdict)
+
+    '''(3) 轨迹显示与保存'''
+    illus = [None, None]
+    for CamerType, vts in event_tracks:
+        if len(vts.tracks)==0: continue
+        
+        if CamerType == 'front':
+            edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
+            
+            h, w = edgeline.shape[:2]
+            # nh, nw = h//2, w//2
+            # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
+            
+            img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True)
+            illus[0] = img_tracking
+
+            plt = plot_frameID_y2(vts)
+            '''==========yj  callbackdata========='''
+            plt.savefig(os.path.join(eventpath, "front_y2.png"))
+            '''========================================'''
+            
+        if CamerType == 'back':
+            edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
+            
+            h, w = edgeline.shape[:2]
+            # nh, nw = h//2, w//2
+            # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
+            
+            img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True)
+            illus[1] = img_tracking
+    
+    illus = [im for im in illus if im is not None]
+    if len(illus):
+        img_cat = np.concatenate(illus, axis = 1)
+        if len(illus)==2:
+            H, W = img_cat.shape[:2]
+            cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3)
+
+        '''==========yj  callbackdata========='''
+        trajpath = os.path.join(eventpath, "trajectory.png")
+        # trajpath = os.path.join(savepath_pipe, "trajectory.png")
+        '''======================================'''
+        cv2.imwrite(trajpath, img_cat)
+        
+    
+        
+
+def pipeline(dict_data,
+             pickle_exist,
+             eventpath,
+             SourceType,
+             weights,
+             DataType = "raw",    #raw, pkl: images or videos, pkl, pickle file
+             YoloVersion="V5",
+             savepath = None,
+             saveimages = True,
+
+            ):
+
+    ## 构造购物事件字典
+    evtname = Path(eventpath).stem
+    barcode = evtname.split('_')[-1] if  len(evtname.split('_'))>=2 \
+                                     and len(evtname.split('_')[-1])>=8 \
+                                     and evtname.split('_')[-1].isdigit() else ''
+    
+    '''事件结果存储文件夹: savepath_pipe, savepath_pkl'''
+    if not savepath:
+        savepath = Path(__file__).resolve().parents[0] / "events_result"
+    savepath_pipe = Path(savepath) / Path("yolos_tracking") / evtname
+    
+
+    savepath_pkl = Path(savepath) / "shopping_pkl"
+    if not savepath_pkl.exists():
+        savepath_pkl.mkdir(parents=True, exist_ok=True)
+    pklpath = Path(savepath_pkl) / Path(str(evtname)+".pickle")
+    
+    
+    
+    yrt_out = []
+    if DataType == "raw":
+        if not pickle_exist:
+            ### 不重复执行已经过yolo-resnet-tracker
+            if pklpath.exists():
+                print(f"Pickle file have saved: {evtname}.pickle")
+                return
+
+        if SourceType == "video":
+            vpaths = get_video_pairs(eventpath)
+        elif SourceType == "image":
+            vpaths = get_image_pairs(eventpath)
+            
+
+        
+        for vpath in vpaths:
+            '''================= 2. 事件结果存储文件夹 ================='''
+            
+            
+            if isinstance(vpath, list):
+                savepath_pipe_imgs = savepath_pipe / Path("images") 
+            else:
+                savepath_pipe_imgs = savepath_pipe / Path(str(Path(vpath).stem))
+            
+            if not savepath_pipe_imgs.exists():
+                savepath_pipe_imgs.mkdir(parents=True, exist_ok=True) 
+            
+            optdict = {}
+            optdict["weights"] = weights
+            optdict["source"] = vpath
+            optdict["save_dir"] = savepath_pipe_imgs
+
+            optdict["is_save_img"] = saveimages
+            optdict["is_save_video"] = True
+    
+            
+            if YoloVersion == "V5":
+                yrtOut = yolo_resnet_tracker(**optdict)
+            elif YoloVersion == "V10":
+                yrtOut = yolov10_resnet_tracker(**optdict)
+                
+            yrt_out.append((vpath, yrtOut))
+    
+    elif DataType == "pkl":
+        pass
+
+    else:
+        return
+    
+    
+
+    '''====================== 构造 ShoppingDict 模块 ======================='''
+    ShoppingDict = {"eventPath": eventpath, 
+                    "eventName": evtname,
+                    "barcode": barcode,
+                    "eventType": '',   # "input", "output", "other"
+                    "frontCamera": {}, 
+                    "backCamera": {},
+                    "one2n": []        # 
+                    }
+    procpath = Path(eventpath).joinpath('process.data')
+    if procpath.is_file():
+        SimiDict = read_similar(procpath)
+        ShoppingDict["one2n"] = SimiDict['one2n']
+
+    yrtDict = {}
+    event_tracks = []
+    for vpath, yrtOut in yrt_out:
+        '''================= 1. 构造相机事件字典 ================='''
+        CameraEvent = {"cameraType": '',   # "front", "back"
+                       "videoPath": '',
+                       "imagePaths": [],
+                       "yoloResnetTracker": [],
+                       "tracking": [],
+                       }
+        
+        if isinstance(vpath, list):
+            CameraEvent["imagePaths"] = vpath
+            bname = os.path.basename(vpath[0])
+        if not isinstance(vpath, list):
+            CameraEvent["videoPath"] = vpath
+            bname = os.path.basename(vpath).split('.')[0]
+        if bname.split('_')[0] == "0" or bname.find('back')>=0:
+            CameraEvent["cameraType"] = "back"
+        if  bname.split('_')[0] == "1" or bname.find('front')>=0:
+            CameraEvent["cameraType"] = "front"
+        
+        
+        '''2种保存方式: (1) no save subimg, (2) save img'''
+        ###(1) save images
+        yrtOut_save = []
+        for frdict in yrtOut:
+            fr_dict = {}
+            for k, v in frdict.items():
+                if k != "imgs":
+                    fr_dict[k]=v
+            yrtOut_save.append(fr_dict) 
+        CameraEvent["yoloResnetTracker"] = yrtOut_save
+        
+        ###(2) no save images
+        # CameraEvent["yoloResnetTracker"] = yrtOut
+        
+        '''================= 4. tracking ================='''
+        '''(1) 生成用于 tracking 模块的 boxes、feats'''  
+        bboxes = np.empty((0, 6), dtype=np.float64)
+        trackerboxes = np.empty((0, 9), dtype=np.float64)
+        trackefeats = {}
+        for frameDict in yrtOut:            
+            tboxes = frameDict["tboxes"]
+            ffeats = frameDict["feats"]
+            
+            boxes = frameDict["bboxes"]
+            bboxes = np.concatenate((bboxes, np.array(boxes)), axis=0)
+            trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0)
+            for i in range(len(tboxes)):
+                fid, bid = int(tboxes[i, 7]), int(tboxes[i, 8])
+                trackefeats.update({f"{fid}_{bid}": ffeats[f"{fid}_{bid}"]})
+            
+
+        '''(2) tracking, 后摄'''
+        if CameraEvent["cameraType"] == "back":
+            vts = doBackTracks(trackerboxes, trackefeats)
+            vts.classify()
+            event_tracks.append(("back", vts))
+            
+            CameraEvent["tracking"] = vts
+            ShoppingDict["backCamera"] = CameraEvent
+
+            '''====yj  callbackdata======='''
+            back_cnts = len(vts.Residual)
+            dict_data['后摄轨迹数'] = back_cnts
+            print(f"back_cnts: {back_cnts}")
+            '''=============================='''
+            
+            yrtDict["backyrt"] = yrtOut
+        
+        '''(2) tracking, 前摄'''    
+        if  CameraEvent["cameraType"] == "front":
+            vts = doFrontTracks(trackerboxes, trackefeats)
+            vts.classify()
+            event_tracks.append(("front", vts))
+            
+            CameraEvent["tracking"] = vts
+            ShoppingDict["frontCamera"] = CameraEvent
+
+            '''====yj  callbackdata======='''
+            front_cnts = len(vts.Residual)
+            dict_data['前摄轨迹数'] = front_cnts
+            print(f"front_cnts: {front_cnts}")
+            '''=============================='''
+            
+            yrtDict["frontyrt"] = yrtOut
+    
+    '''========================== 保存模块 ================================='''
+
+    # 保存 ShoppingDict
+    with open(str(pklpath), 'wb') as f:
+        pickle.dump(ShoppingDict, f)
+    
+    # 绘制并保存轨迹图    
+    show_result(eventpath, event_tracks, yrtDict, savepath_pipe)
+    return dict_data
+
+class WriteExcel:
+
+    def is_excel(self, input_excel):
+
+        # 若文件存在，加载工作簿
+        wb = load_workbook(input_excel)
+        sheet_name = wb.sheetnames[0]  ##默认回传分析表只有一个sheet
+        # 获取活动工作表
+        ws = wb.active
+        sheet = wb[sheet_name]
+        ##确定新增列的位置
+        # new_col_index = sheet.max_column
+        return wb, ws, sheet
+
+    def init_excel(self, input_excel, output_excel, headers, max_col=13):
+        if os.path.exists(output_excel):
+            wb, ws, sheet = self.is_excel(output_excel)
+            return wb, ws, sheet
+        elif os.path.exists(input_excel):
+            wb, ws, sheet = self.is_excel(input_excel)
+            self.add_header(wb, sheet, max_col, headers, output_excel)
+            return wb, ws, sheet
+        else:
+            raise FileNotFoundError(f"文件 '{input_excel}' 不存在")
+
+    '''在已有excel文件中新增列标题'''
+    def add_header(self, wb, sheet, column, headers, file_name):
+
+        write_data = {}
+        sub_data = {}
+        for i, head in enumerate(headers):
+            k = column + i + 1
+            sub_data[k] = head
+        write_data[1] = sub_data
+        self.add_data_to_excel(wb, sheet, write_data, file_name)
+
+    def add_data_to_excel(self, wb, sheet, write_data, file_name):
+        '''
+         示例写入数据，格式为 {行号: {列号: 值}}
+            write_data = {
+            5: {11: 1, 12: 2, 13: 3}
+        }
+        '''
+        # 写入指定行列的数据
+        for row_num, col_data in write_data.items():
+            for col_num, value in col_data.items():
+                # print('row_num', row_num, 'col_num', col_num, 'value', value)
+                sheet.cell(row=row_num, column=col_num, value=str(value))
+        wb.save(file_name)
+
+    def get_simiTitle_index(self, ws, title_name='事件名'):
+        '''获取excel文件中标题名称与追加内容相同部分的列索引
+        例如：默认以"事件名"为基准，追加统一事件名下不同组成信息'''
+        # 获取列标题
+        headers = [cell.value for cell in ws[1]]
+        # # 找到“事件名”所在的列索引
+        event_name_index = headers.index(title_name) if title_name in headers else None
+        if event_name_index is None:
+            print("未找到标题为'事件名'的列")
+            return
+        else:
+            return event_name_index
+
+    def get_event_row(self, sheet, event_name_index, event_name):
+        '''获得当前事件名event_name在excel文件中所在的行索引'''
+        row_index = 0
+        for row, content in enumerate(sheet.iter_rows(min_row=2, values_only=True)):
+            # print('row', row, content[event_name_index])
+            if content[event_name_index] == event_name:
+                row_index = row + 2  ### 默认只有一行标题，若有两行标题则需改为+3
+                # print('row_index', row_index)
+                break
+        return row_index
+    def write_simi_add(self, wb, ws, sheet, max_col, evtname, dict_data, headers, file_name):
+        '''
+            在已有excel文件中追加内容
+        找出事件名所在行索引和原excel最大列索引，在原excel最大列索引后指定行写入新内容，内容顺序与新增headers顺序一致
+        '''
+        event_index = self.get_simiTitle_index(ws)
+        if event_index is not None:
+            print('evtname', evtname)
+            row_index = self.get_event_row(sheet, event_index, evtname)
+            if row_index > 0:
+                sub_dict = {}
+                print('headers', headers)
+                for i, header in enumerate(headers):
+                    col_index = max_col + i + 1
+                    # print('list(dict_data.keys())', list(dict_data.keys()))
+                    if header in list(dict_data.keys()):
+                        sub_dict[col_index] = dict_data[header]
+                    else:
+                        sub_dict[col_index] = ''
+                write_data = {row_index: sub_dict}
+                self.add_data_to_excel(wb, sheet, write_data, file_name)
+
+                print("=========save  excel===========")
+            else:
+                raise Exception(f"未找到事件名:{evtname}")
+        else:
+            raise Exception("未找到标题为'事件名'的列")
+
+    
+def execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip", 
+                     DataType = "raw",   # raw, pkl
+                     save_path = r"D:\work\result_pipeline", 
+                     kk=1,
+                     source_type = "video",   # video, image,                      
+                     yolo_ver = "V10",   # V10, V5
+                     weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' ,
+                     weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt',
+                     saveimages = True,
+                     max_col = 12,
+                     track_txt = ''
+                     ):
+    '''
+    运行函数 pipeline()，遍历事件文件夹，每个文件夹是一个事件
+    '''
+    parmDict = {}
+    parmDict["DataType"] = DataType
+    parmDict["savepath"] = save_path
+    parmDict["SourceType"] = source_type
+    
+    parmDict["YoloVersion"] = yolo_ver
+    if parmDict["YoloVersion"] == "V5":
+        parmDict["weights"] = weight_yolo_v5
+    elif parmDict["YoloVersion"] == "V10":
+        parmDict["weights"] = weight_yolo_v10
+        
+    parmDict["saveimages"] = saveimages   
+        
+
+    evtdir = Path(evtdir) 
+    errEvents = []
+    # k = 0
+    '''=========change callbackdata=============='''
+    csv_name = 'data.csv'
+    xlsx_name = '现场回传数据分析表.xlsx'
+    output_name = '现场回传数据分析表_all.xlsx'
+    # headers = ['algroStartToEnd', 'one2one', 'one2SN', 'one2n', '前摄轨迹数', '后摄轨迹数']
+    headers = ['algroStartToEnd', 'one2one', 'one2SN', 'one2n']
+
+
+    excelWriter = WriteExcel()  ## 实例化excel对象
+
+    for name in evtdir.iterdir():  ##人名
+        for date_file in name.iterdir():  ##2025-01-13
+            # try:
+            xlsx_data = os.path.join(date_file, xlsx_name)
+            csv_data = os.path.join(date_file, csv_name)
+            excel_name = os.path.join(date_file, output_name)
+            wb, ws, sheet = excelWriter.init_excel(xlsx_data, excel_name, headers, max_col)
+            if csv_data == '':
+                with open('no_datacsv.txt', 'a') as f:
+                    f.write(str(date_file) + '\n')
+
+
+            for item in date_file.iterdir():
+                # dict_data = {}
+                if item.is_dir():
+                    # item = evtdir/Path("20241212-171505-f0afe929-fdfe-4efa-94d0-2fa748d65fbb_6907992518930")
+                    parmDict["eventpath"] = item
+                    event_name = str(item.name)
+                    dict_data = get_process_csv_data(csv_data, item)
+                    print('dict_data', dict_data)
+                    # dict_data_all = pipeline(dict_data, pickle_exist, **parmDict)
+                    if dict_data is not None:      #已保存pickle文件的事件返回为None
+                        # print('dict_data_all', dict_data_all)
+                        excelWriter.write_simi_add(wb, ws, sheet, max_col, event_name, dict_data, headers, excel_name)
+
+            # except Exception as e:
+            #     with open('process_error.txt', 'a') as f:
+            #         f.write(str(date_file) + ':' + str(e) + '\n')
+
+
+
+if __name__ == "__main__": 
+    # datapath = '/home/yujia/yj/gpu_code/callback_data_test/'
+    # datapath = '/home/yujia/yj/gpu_code/callback_data_test_0417/'
+    # savepath = '/home/yujia/yj/gpu_code/result_0417_v10/'
+
+    datapath = '/shareData/data/temp_data/tengXunCloud_data/code_test_0427/'
+    # savepath = '/shareData/data/temp_data/tengXunCloud_data/code_test/pipline_result/'  ##保存pipline结果路径
+
+    max_col = 12  ##excel表格列索引从0开始，从这列开始写入代码解析内容
+    # track_txt = '轨迹数为空.txt'
+    track_txt = ''      ##第一次跑pipline
+
+    execute_pipeline(evtdir=datapath,
+                      DataType = "raw",   # raw, pkl
+                      kk=1,
+                      source_type = "video",   # video, image,
+                      save_path = '',
+                      yolo_ver = "V5",   # V10, V5  ##20250401之前使用V5 ressnet使用resv10
+                      weight_yolo_v5 = './ckpts/best_cls10_0906.pt' ,
+                      weight_yolo_v10 = './ckpts/best_v10s_width0375_1205.pt',
+                      saveimages = False,
+                      max_col = max_col,
+                      track_txt = track_txt
+                    )
+    
+    
+    
+    
+    
+    
\ No newline at end of file