diff --git a/Bank_second_part/detect_process/video_process.py b/Bank_second_part/detect_process/video_process.py
new file mode 100644
index 0000000..ee83010
--- /dev/null
+++ b/Bank_second_part/detect_process/video_process.py
@@ -0,0 +1,312 @@
+import numpy as np
+import cv2
+import os
+import time
+from tqdm import tqdm
+from ultralytics import YOLO
+from ultralytics.yolo.utils.plotting import Annotator
+import queue
+
+from yolov8_det import analysis_yolov8
+import threading
+from config import Q_SZ
+
+
+class DealVideo():
+
+    def __init__(self,video_file,video_save_file):
+
+        self.video_file = video_file
+        self.video_save_file = video_save_file
+
+
+
+        # 图片检测后队列
+        self.videoQueue = queue.Queue(maxsize=Q_SZ)
+        self.frameQueue = queue.Queue(maxsize=0)
+
+        #线程
+        self.get_video_listThread = threading.Thread(target=self.get_video_list)
+        self.get_video_frameThread = threading.Thread(target=self.get_video_frame)
+        self.write_videoThread = threading.Thread(target=self.write_video)
+        
+    # 图像文件夹
+    def get_video_list(self):
+
+        if os.path.isdir(self.video_file):
+
+            video_ext = [".mp4", ".avi",".MP4"]
+            for maindir, subdir, file_name_list in os.walk(self.video_file):
+                for filename in file_name_list:
+                    apath = os.path.join(maindir, filename)
+                    ext = os.path.splitext(apath)[1]
+                    if ext in video_ext:
+                        self.videoQueue.put(apath)
+
+        else:
+            self.videoQueue.put(self.video_file)
+
+        
+
+    def get_video_frame(self):
+
+        while True:
+            if ~self.videoQueue.empty():
+                video_path = self.videoQueue.get()  
+
+                video_basename = os.path.basename(video_path).split('.')[0]
+
+                # print('video_path:',video_path)
+
+                cap = cv2.VideoCapture(video_path)
+
+                frame_list = []
+                count_fps = 0
+
+                while cap.isOpened():
+                    success, frame = cap.read()
+                    if not success:
+                        print("Ignoring empty camera frame.")
+                        break
+                    count_fps  += 1
+
+                    frame_dict = {'fps':count_fps,'frame':frame}
+                    frame_list.append(frame_dict)
+
+                video_dict = {'video_path':video_path,'frame_list':frame_list,'cap':cap}
+ 
+                self.frameQueue.put(video_dict)
+                
+    def write_video(self):
+
+        while True:
+            if ~self.frameQueue.empty():
+                video_frame_dict = self.frameQueue.get()
+
+                video_basename = os.path.basename(video_frame_dict['video_path'])
+
+                video_name_save = os.path.join(self.video_save_file, video_basename)
+
+                # 原视频帧率和尺寸
+                cap = video_frame_dict['cap']
+                fps = cap.get(cv2.CAP_PROP_FPS)
+                size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+
+                # 保存视频
+                videoWriter =cv2.VideoWriter(video_name_save,cv2.VideoWriter_fourcc('X','V','I','D'),fps,size) 
+
+                frame_list = video_frame_dict['frame_list']
+
+                for i in range(len(frame_list)):
+
+                    if frame_list[i]["fps"] == i + 1:
+
+                        videoWriter.write(frame_list[i]["frame"])
+
+                    else:
+                        break
+
+
+
+
+    def run(self):
+
+        self.get_video_listThread.start()
+        self.get_video_frameThread.start()
+        self.write_videoThread.start()
+
+
+class Process_tools():
+
+    # 图像文件夹
+    def get_video_list(path):
+        video_ext = [".mp4", ".avi",".MP4"]
+        video_names = []
+        for maindir, subdir, file_name_list in os.walk(path):
+            for filename in file_name_list:
+                apath = os.path.join(maindir, filename)
+                ext = os.path.splitext(apath)[1]
+                if ext in video_ext:
+                    video_names.append(apath)
+        return video_names
+
+
+    # 截取裁剪需要的视频帧
+    def save_seg_video(video_name,frameToStart,frametoStop,videoWriter,bbox):
+
+        cap = cv2.VideoCapture(video_name)
+        count = 0
+        while True:
+
+            success, frame = cap.read()
+
+            if success:
+
+                count += 1
+                if count <= frametoStop and count > frameToStart:  # 选取起始帧
+                    print('correct= ', count)
+                    
+                    #裁剪视频画面
+                    frame_target = frame[int(bbox[1]):int(bbox[3]), int(bbox[0]):int(bbox[2])]  # (split_height, split_width)
+
+                    videoWriter.write(frame_target)
+                    
+            if not success or count >= frametoStop:
+                break
+
+            print('end')  
+
+
+
+    # 获得字典中所有values值（这个值是列表）
+    def get_dict_values(lst):
+        """
+        获取列表中所有字典的 values 值（如果值是列表）
+        
+        参数:
+        lst: 包含字典的列表
+        
+        返回值:
+        values: 包含所有字典的 values 值的列表（如果值是列表）
+        """
+        return [value for dictionary in lst for value in dictionary.values() if isinstance(value, list)]
+
+
+
+    # 解析检测后的结果，为检测后的结果排序
+    def analysis_sort_list(result_dict):
+
+        # print('result_dict:',result_dict)
+
+        # 获得检测列表
+        re_list = result_dict['start_bbox']
+        # print('re_list:',re_list)
+
+        # 获得列表中所有字典的values值
+        re_bbox_list = get_dict_values(re_list)
+
+        # 为检测出来的标注框排序
+        sorted_lst = sorted(re_bbox_list, key=lambda x: x[0])
+
+        return sorted_lst
+
+
+    #对比重叠率高的两个部分，并结合标注框，保存最大的标注框
+    def contrast_bbox(e_bbox,r_bbox):
+
+        e_bbox_min = e_bbox[:2]
+        r_bbox_min = r_bbox[:2]
+
+        bbox_min = [min(x, y) for x, y in zip(e_bbox_min, r_bbox_min)]
+
+        e_bbox_max = e_bbox[-2:]
+        r_bbox_max = r_bbox[-2:]
+
+        bbox_max = [max(x, y) for x, y in zip(e_bbox_max, r_bbox_max)]
+
+        bbox = bbox_min + bbox_max
+
+        return bbox
+
+
+    # 解析result_list列表
+    def analysis_re01_list(example_dict,result_dict):
+
+        # 第一次检测到目标的帧率和信息
+        example_dict_fps = list(example_dict.keys())[0]
+        example_sorted_lst = Process_tools.analysis_sort_list(example_dict)
+
+        # 当前帧检测结果中所有的检测结果数值
+        re_dict_fps = list(result_dict.keys())[0]
+        re_dict_sorted_lst = Process_tools.analysis_sort_list(result_dict)
+
+        # 保存前后帧率连续的范围、筛选出相同的部分
+        cut_list = []
+        example_temp = []
+        re_temp = []
+
+        for i,ex_bbox in enumerate(example_sorted_lst):
+
+            for j,re_bbox in enumerate(re_dict_sorted_lst):
+
+                iou = calculate_iou(box1=ex_bbox, box2=re_bbox)
+
+                # print(iou)
+                
+                if iou > 0:
+
+                    bbox = contrast_bbox(e_bbox=ex_bbox,r_bbox=re_bbox)
+
+                    cut_list.append({i:bbox})
+                    example_temp.append(ex_bbox)
+                    re_temp.append(re_bbox)
+
+                    break
+
+                else:
+                    continue
+
+        example_sorted_lst = [item for item in example_sorted_lst if item not in example_temp]
+        re_dict_sorted_lst = [item for item in re_dict_sorted_lst if item not in re_temp]
+
+        return cut_list,example_sorted_lst,re_dict_sorted_lst
+
+
+    # 计算前后帧率重叠范围
+    def calculate_iou(box1, box2):
+        """
+        计算两个边界框之间的IoU值
+        
+        参数:
+        box1: 边界框1的坐标（x1, y1, x2, y2）
+        box2: 边界框2的坐标（x1, y1, x2, y2）
+        
+        返回值:
+        iou: 两个边界框之间的IoU值
+        """
+        x1 = max(box1[0], box2[0])
+        y1 = max(box1[1], box2[1])
+        x2 = min(box1[2], box2[2])
+        y2 = min(box1[3], box2[3])
+        
+        # 计算交集区域面积
+        intersection_area = max(0, x2 - x1 + 1) * max(0, y2 - y1 + 1)
+        
+        # 计算边界框1和边界框2的面积
+        box1_area = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1)
+        box2_area = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1)
+        
+        # 计算并集区域面积
+        union_area = box1_area + box2_area - intersection_area
+        
+        # 计算IoU值
+        iou = intersection_area / union_area
+        
+        return iou
+
+
+
+
+if __name__ == '__main__':  
+     
+
+    # 每个视频的时长（单位秒）
+    dertTime = 5
+
+    video = "E:/Bank_files/Bank_02/dataset/video_person/after_1/"
+    video_save = 'videos_codes_2'
+
+    # get_seg_video(video_file=video,video_save_path=video_save,dertTime=dertTime)
+    deal = DealVideo(video_file=video,video_save_file=video_save)
+    deal.run()
+
+
+    
+
+
+
+
+
+
+
+
diff --git a/xznsh_flow/cfg.json b/xznsh_flow/cfg.json
index db8eb37..14e9b64 100644
--- a/xznsh_flow/cfg.json
+++ b/xznsh_flow/cfg.json
@@ -1 +1,16 @@
-{"log_path": "xznsh.log", "frame": 0.05, "camera": {"camera_01": "rtsp://admin:@192.168.10.18", "camera_02": "rtsp://admin:@192.168.10.12"}, "video_path": {"person": "video/person/", "head": "video/head/"}, "username": "eWF4aW4=", "password": "eWF4aW5AMTIz", "url": "", "model_path": {"person": "person.pt", "head": "person_cut_hh.pt", "phone": "xxx", "action": "xxx"}, "confidence": {"person": 0.5, "head": 0.5, "phone": 0.5}, "video_encoding": "MP42", "video_length": 90, "error_x": 200, "error_y": 200, "fps": 30, "test_video_path": "0711-4.mp4"}
\ No newline at end of file
+{"log_path": "xznsh.log", 
+"frame": 0.05,
+ "camera": {"camera_01": "rtsp://admin:@192.168.10.18", 
+ "camera_02": "rtsp://admin:@192.168.10.12"},
+  "video_path": {"person": "video/person/", "head": "video/head/"},
+   "username": "eWF4aW4=", 
+   "password": "eWF4aW5AMTIz",
+   "url": "", 
+   "model_path": {"person": "person.pt", "head": "person_cut_hh.pt", "phone": "xxx", "action": "xxx"}, 
+   "confidence": {"person": 0.5, "head": 0.5, "phone": 0.5},
+    "video_encoding": "MP42",
+     "video_length": 90,
+      "error_x": 200,
+      "error_y": 200, 
+      "fps": 30, 
+      "test_video_path": "0711-4.mp4"}
\ No newline at end of file