灾害数据输出到DR目录；excel数据输出到excel目录

predict/predict_yolo11seg.py

@@ -18,7 +18,7 @@ from ultralytics import YOLO
 
 from middleware.minio_util import upload_file, downFile, check_zip_size, upload_folder
 from util.yolo2pix_new import *
-from util.smb import *
+from util.smb_tool import *
 import threading
 
 # 定义红白蓝颜色 (BGR格式)
@@ -635,8 +635,17 @@ class YOLOSegmentationInference:
         except Exception as e:
             print(f"处理目录失败: {e}")
             return results
+        
+    def process_image_directory_share_dir_circle(self, task_id, current_time, input_dir_list, user_name, pwd, output_dir: Optional[str] = None,
+                                conf_threshold: float = 0.25, iou_threshold: float = 0.5,
+                                save_mask: bool = False, save_label: bool = False, show: bool = False,
+                                result_save: [] = None) -> List[
+        InferenceResult]:
+        for input_dir in input_dir_list :
+            self.process_image_directory_share_dir(task_id,current_time,input_dir,user_name,pwd,output_dir,conf_threshold,iou_threshold,save_mask,save_label,show,result_save)
 
-    def process_image_directory_share_dir(self, task_id, input_dir, user_name, pwd, output_dir: Optional[str] = None,
+
+    def process_image_directory_share_dir(self, task_id, current_time, input_dir, user_name, pwd, output_dir: Optional[str] = None,
                                 conf_threshold: float = 0.25, iou_threshold: float = 0.5,
                                 save_mask: bool = False, save_label: bool = False, show: bool = False,
                                 result_save: [] = None) -> List[
@@ -694,12 +703,13 @@ class YOLOSegmentationInference:
             # 推送识别数据到共享目录
             tmpConfig = get_conf(input_dir, user_name, pwd)
             pile_dict = get_pile_dict(input_dir, user_name, pwd)
-            road_dict = get_road_dict(f"{tmpConfig['ip']}/{tmpConfig['share']}", user_name, pwd)
+            road_dict = get_road_dict(f"{tmpConfig['ip']}/{tmpConfig['share']}/{tmpConfig['excel_dir']}", user_name, pwd)
             process_dir(road_dict, pile_dict, output_dir)
-            current_time = datetime.now().strftime("%Y%m%d%H%M%S")
-            
+            # 识别生成的文件压缩成zip
+            zip_folder_shutil(output_dir)
+            move_file_shutil(output_dir+".zip", output_dir)
             remote_dir = f"{tmpConfig['dir']}_识别/{task_id}/{current_time}"
-            # scanner.upload_directory(output_dir, config['share'], remote_dir=remote_dir)
+            scanner.upload_directory(output_dir, config['share'], remote_dir=remote_dir)
 
             return results
 
@@ -827,7 +837,7 @@ def predict_images(pt_name, zip_url, output_dir="predictions", conf_threshold=0.
             new_mask_label = os.path.join(pix_result_dir, label_name)
             shutil.rmtree(result_dir)  # 删除所有源文件
 
-            output_str, class_cells = yoloseg_to_grid_cells_fixed_v5(
+            output_str, class_cells = yoloseg_to_grid(
                 new_origin_img, new_mask_label, class_names=classes
             )
 
@@ -900,9 +910,10 @@ def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_d
         share_path=config['share'],
         folder_name='图像类',
         start_dir=config['dir'],
-        max_depth=2
+        max_depth=5
     )
 
+    current_time = datetime.now().strftime("%Y%m%d%H%M%S")
     target_path = ""    # 识别图片目录
     flag_dir_path = "" # 标识目录
     if len(found_paths) > 0:
@@ -931,11 +942,11 @@ def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_d
             # )
 
             # 创建并启动线程
-            thread1 = threading.Thread(target=inference.process_image_directory_share_dir, args=(task_id, target_path,user_name,pwd,output_dir,conf_threshold,iou_threshold,save_mask,save_label,show,result_save))
+            thread1 = threading.Thread(target=inference.process_image_directory_share_dir_circle, args=(task_id,current_time,found_paths,user_name,pwd,output_dir,conf_threshold,iou_threshold,save_mask,save_label,show,result_save))
 
             # 启动线程
             thread1.start()
     else:
         print(f"错误: 输入 {zip_url} 不是有效的文件或目录")
 
-    return f"{target_path}_识别/{task_id}", "success"
+    return standardized_path(f"{target_path}_识别/{task_id}/{current_time}/{task_id}.zip"), "success"

util/smb_tool.py

@@ -12,6 +12,8 @@ import numpy as np
 import cv2
 import pandas as pd
 import io
+import shutil
+
 
 class SMBScanner:
     def __init__(self, ip, username, password, domain=''):
@@ -614,13 +616,13 @@ class SMBScanner:
                         # print(f"{indent}文件夹：{entry.name}/")
                         result.append(item)
                         if recursive and current_depth < max_depth:
-                            sub_items = self._walk_directory(
+                            self._walk_directory(
                                 entry.path, 
                                 recursive, 
                                 max_depth, 
-                                current_depth + 1
+                                current_depth + 1,
+                                result
                             )
-                            result.extend(sub_items)
                     else:
                         file_size = self._format_size(file_stat.st_size)
                         mod_time = datetime.fromtimestamp(
@@ -635,8 +637,6 @@ class SMBScanner:
                     
         except Exception as e:
             print(f"无法读取目录 {path}: {e}")
-        
-        return result
     
     def _format_size(self, size_bytes):
         """格式化文件大小"""
@@ -709,12 +709,17 @@ class SMBScanner:
         cv2.destroyAllWindows()
         print("窗口已关闭")
 
+def standardized_path(path) :
+    path = path.replace('\\\\', '/')
+    path = path.replace('\\', '/')
+    if path.startswith("/"):
+        path = path.replace('/', '', 1)
+
+    return path
+
 # 从传入的路径中提取ip，共享目录，目标访问目录
 def get_conf(zip_url, user_name, pwd) :
-    zip_url = zip_url.replace('\\\\', '/')
-    zip_url = zip_url.replace('\\', '/')
-    if zip_url.startswith("/"):
-        zip_url = zip_url.replace('/', '', 1)
+    zip_url = standardized_path(zip_url)
 
     parts = zip_url.split('/')
     if len(parts) < 2 :
@@ -725,6 +730,12 @@ def get_conf(zip_url, user_name, pwd) :
     if len(parts) > 2:
         new_parts = parts[2:]
         dir = '/'.join(new_parts)
+    
+    excel_dir = ''
+    for index, part in enumerate(parts, 0) :
+        if part == 'Images' and index - 3 > 2 :
+            excel_dir = '/'.join(parts[2:index-3])
+        
 
     # 配置信息
     config = {
@@ -733,7 +744,8 @@ def get_conf(zip_url, user_name, pwd) :
         'password': pwd,
         'domain': '',  # 工作组留空
         'share': parts[1],
-        'dir': dir
+        'dir': dir,
+        'excel_dir': excel_dir
     }
 
     return config
@@ -759,9 +771,9 @@ def get_road_dict(dir,user_name,pwd) :
             share_path=config['share'],
             file_name='每公里指标明细表*.xls',
             start_dir=config['dir'],
-            max_depth=1
+            max_depth=4
         )
-    print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
+    print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls' 文件:")
     for i, path in enumerate(found_paths, 1):
         print(f"{i}. {path}")
 
@@ -791,7 +803,7 @@ def get_pile_dict(dir,user_name,pwd) :
             share_path=config['share'],
             file_name='fileindex.txt',
             start_dir=config['dir'],
-            max_depth=8
+            max_depth=2
         )
     print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
     for i, path in enumerate(found_paths, 1):
@@ -824,6 +836,61 @@ def write_to_excel_pandas(data, filename, sheet_name='Sheet1'):
     df.to_excel(filename, sheet_name=sheet_name, index=False, header=False)
     print(f"数据已写入 {filename}")
 
+def zip_folder_shutil(folder_path, output_path=None):
+    """
+    使用shutil.make_archive压缩文件夹
+    
+    Args:
+        folder_path: 要压缩的文件夹路径
+        output_path: 输出的zip文件路径（可选）
+    """
+    # 如果未指定输出路径，使用文件夹名作为zip文件名
+    if output_path is None:
+        output_path = folder_path
+    
+    # 确保文件夹路径是绝对路径
+    folder_path = os.path.abspath(folder_path)
+    
+    # 获取文件夹的父目录和文件夹名
+    parent_dir, folder_name = os.path.split(folder_path)
+    
+    try:
+        # 创建zip文件
+        shutil.make_archive(
+            base_name=output_path,  # 输出文件的基本名（不包含扩展名）
+            format='zip',           # 压缩格式
+            root_dir=parent_dir,    # 根目录
+            base_dir=folder_name    # 要压缩的目录（相对于根目录）
+        )
+        print(f"成功压缩文件夹: {folder_path} -> {output_path}.zip")
+        return f"{output_path}.zip"
+    except Exception as e:
+        print(f"压缩失败: {e}")
+        return None
+
+def move_file_shutil(source_path, destination_path):
+    """
+    使用shutil.move移动文件或文件夹
+    
+    Args:
+        source_path: 源文件/文件夹路径
+        destination_path: 目标路径
+    """
+    try:
+        # 检查源文件是否存在
+        if not os.path.exists(source_path):
+            print(f"错误: 源文件不存在 - {source_path}")
+            return False
+        
+        # 执行移动操作
+        shutil.move(source_path, destination_path)
+        print(f"成功移动: {source_path} -> {destination_path}")
+        return True
+        
+    except Exception as e:
+        print(f"移动失败: {e}")
+        return False
+
 def main():
     # 配置信息
     config = {
@@ -834,6 +901,9 @@ def main():
         'share': 'share_File',
         'dir': '西南计算机'
     }
+
+    # 配置数据提取
+    tmp_conf = get_conf("192.168.110.114/share_File/北碚报送数据/3.25/图像类/C071500109B/Images",'administrator','abc@1234')
     
     # 创建扫描器
     scanner = SMBScanner(
@@ -844,9 +914,9 @@ def main():
     )
     
     # 遍历共享目录
-    # scanner.list_directory(
+    # result = scanner.list_directory(
     #     share_path=config['share'],
-    #     dir=config['dir'],
+    #     dir= '/西南计算机/北碚报送数据/3.25/图像类/C071500109B/Images_识别/77',
     #     recursive=True,      # 递归遍历
     #     max_depth=9         # 最大深度
     # )
@@ -933,5 +1003,13 @@ def main():
     # pile_dict = get_pile_dict(input_dir, config['username'], config['password'])
     # print("-------------------------------------------")
 
+    # 检查文件是否存在
+    # path = '192.168.110.114/share_File/西南计算机/北碚报送数据/3.25/图像类/C071500109B/Images_识别/77/20251120115639/77.zip'
+    # flag = scanner.directory_exists(path)
+    # print(f"flag={flag}")
+
+    str = '\\\\192.168.110.114\\share_File\\西南计算机/北碚报送数据/3.25/图像类\\C071500109B\\Images_识别/77/20251120172316/77.zip'
+    print(f"standardized_path(str)={standardized_path(str)}")
+
 if __name__ == "__main__":
     main()

util/yolo2pix_new.py

@@ -1,19 +1,19 @@
 import os
 import zipfile
-import shutil
 import cv2
 import numpy as np
-from collections import defaultdict
-import smb
 import pandas as pd
 from openpyxl import Workbook
 from openpyxl.styles import Font, Alignment
 from openpyxl.utils import get_column_letter
 import glob
 from datetime import datetime
+from .smb_tool import *
 
 # ---------------- 常量 ----------------
-CELL_AREA = 0.0036       # 每格面积 (平方米)
+CELL_WIDTH = 0.1         # 每格宽 (米)
+CELL_HEIGHT = 0.1        # 每格高 (米)
+CELL_AREA = 0.01         # 每格面积 (平方米)
 GRID_WIDTH = 108         # 网格像素宽
 GRID_HEIGHT = 102        # 网格像素高
 COVER_RATIO = 0.01       # mask 覆盖比例阈值
@@ -43,6 +43,18 @@ def num_to_coord(num, cols, cell_w, cell_h):
     x2, y2 = x1 + cell_w, y1 + cell_h
     return x1, y1, x2, y2
 
+def calc_grid_param(pic_width, pic_height, actual_width, actual_height, grid_x_count=38, grid_y_count=20) :
+    calc_cell_area = CELL_WIDTH * CELL_HEIGHT       # 每格面积 (平方米)
+    calc_grid_width = 108                           # 网格像素宽
+    calc_grid_height = 102                          # 网格像素高
+
+    grid_x_count = actual_width / CELL_WIDTH
+    grid_y_count = actual_height / CELL_HEIGHT
+    calc_grid_width = round(pic_width / grid_x_count)
+    calc_grid_height = round(pic_height / grid_y_count)
+    return calc_cell_area, calc_grid_width, calc_grid_height
+
+
 def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGHT), save_path=None):
     image = cv2.imread(image_path)
     if image is None: return
@@ -52,7 +64,7 @@ def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGH
     overlay = image.copy()
     for cname, nums in grid_cells.items():
         color = (np.random.randint(64,255),np.random.randint(64,255),np.random.randint(64,255))
-        for num in nums:
+        for num in nums[0]:
             x1,y1,x2,y2 = num_to_coord(num, cols, cell_w, cell_h)
             cv2.rectangle(overlay,(x1,y1),(x2,y2),color,-1)
     cv2.addWeighted(overlay,0.4,image,0.6,0,image)
@@ -85,8 +97,12 @@ def get_road_info(road_dict, pile_dict, img_file_name):
         road_code = parts[0]
         road_info = road_dict.get(road_code)
         if road_info :
-            data = road_info['data']
-            return data
+            pile_no = convert_special_format(parts[1]) * 1000
+            for road in road_info :
+                data = road['data']
+                if data.get('起桩号（米）',0) > pile_no and pile_no > data.get('止桩号（米）', 0) or data.get('起桩号（米）', 0) < pile_no and pile_no < data.get('止桩号（米）', 0) :
+                    return data
+
     return {}
 
 def detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name):
@@ -100,7 +116,7 @@ def detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name):
         pile_no = parts[1]
         road_info = road_dict.get(road_code)
         if road_info :
-            data = road_info['data']
+            data = road_info[0]['data']
             pile_no = parts[1]
             road_type_cn = data['路面类型（沥青/水泥/砂石）']
             identify_width = data['识别宽度（米）']
@@ -112,7 +128,7 @@ def detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name):
                 road_type = "gravel"
     return road_code, pile_no, road_type
 
-def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,cover_ratio=COVER_RATIO):
+def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT,cover_ratio=COVER_RATIO):
     """将YOLO-Seg标签转换成格子编号和类别"""
     img_file_name = os.path.basename(image_path)
     road_code, pile_no, road_type = detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name)
@@ -268,14 +284,18 @@ def convert_special_format(input_str):
 def in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
     if increment > 0 :
         if cur_pile_no >= tmp_start and cur_pile_no < tmp_end :
-            return True
+            return 1
+        elif cur_pile_no < tmp_start :
+            return -1 # 会导致死循环
         else :
-            return False
+            return 0
     else :
         if cur_pile_no > tmp_end and cur_pile_no <= tmp_start :
-            return True
+            return 1
+        elif cur_pile_no > tmp_start :
+            return -1 # 会导致死循环
         else :
-            return False
+            return 0
         
 # 指定间隔区间，输出基本信息+灾害数据
 def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interval=10,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT) :
@@ -288,34 +308,46 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
         road_code = pile_dict.get(img_file_name)[0]
         road_level = road_data.get('技术等级', '')
         road_type_cn = road_data.get('路面类型（沥青/水泥/砂石）', '沥青')
+        first_pile_no = round(road_data.get('起桩号（米）',0) / 1000, 3)
+        last_pile_no = round(road_data.get('止桩号（米）', 0) / 1000, 3)
 
-        group_list = summary_data
-        increment = float(0.001 * interval)
-        pile_no_start = float(0.000)
-        pile_no_end = float(0.000) + increment
+        group_list = []
         # 上行/下行
         if up_or_down == '下行' :
-            group_list = list(summary_data)[::-1]
-            increment = float(-0.001 * interval)
-            tmp_pile_no = convert_special_format(group_list[len(group_list)-1][1])
+            group_list = summary_data
+            list(reversed(group_list))
+            increment = float(-0.001) * interval
+            tmp_pile_no = first_pile_no
             pile_no_start = tmp_pile_no
             if tmp_pile_no % increment != 0 :
-                pile_no_end = tmp_pile_no + (tmp_pile_no % increment)
+                pile_no_end = tmp_pile_no - (tmp_pile_no % (-increment))
+                pile_no_end = round(pile_no_end, 3) #处理精度问题
             else :
                 pile_no_end = tmp_pile_no + increment
-
+                pile_no_end = round(pile_no_end, 3) #处理精度问题
+        else :
+            group_list = summary_data
+            increment = float(0.001) * interval
+            pile_no_start = first_pile_no
+            pile_no_end = pile_no_start + increment
         
         index = 0
         tmp_start = pile_no_start
         tmp_end = pile_no_end
         while True :
+            if tmp_end < last_pile_no :
+                tmp_end = last_pile_no
+            print(f"process_info:tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
+            if index >= len(group_list) :
+                break
             # 每10m一个区间，在区间内进行灾害计算
             pile_no, DR, counts, road_type = group_list[index]
             cur_pile_no = convert_special_format(pile_no)
-            pile_no_s = f"{tmp_start:0.3f}"
-            pile_no_e = f"{tmp_end:0.3f}"
+            pile_no_s = format_number_to_k_code(tmp_start)
+            pile_no_e = format_number_to_k_code(tmp_end)
             up_or_down_code = "B" if up_or_down == '下行' else "A"
-            if not in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
+            in_interval_val = in_interval(increment, cur_pile_no, tmp_start, tmp_end)
+            if in_interval_val == 0 :
                 # 没在刻度内直接输出无病害数据
                 row = [road_code, pile_no_s,pile_no_e,up_or_down_code,up_or_down,road_level,f"{road_type_cn}路面",'','',f"{0:.2f}",'','','','','','','','','','','','','','','']
                 if road_type=="asphalt":
@@ -328,7 +360,7 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
                 #     row.append(f"{0:.2f}")
                 process_info_data.append(row)
                 # f.write(','.join(row)+'\n')
-            else :
+            elif in_interval_val == 1 :
                 row = [road_code, pile_no_s,pile_no_e,up_or_down_code,up_or_down,road_level,f"{road_type_cn}路面",'','']
                 subRows = []
                 while index < len(group_list):
@@ -336,8 +368,8 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
                     cur_pile_no = convert_special_format(pile_no)
                     
                     tmp_row = []
-                    if in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
-                        pile_no = f"{tmp_start:0.3f}"
+                    in_interval_val = in_interval(increment, cur_pile_no, tmp_start, tmp_end)
+                    if in_interval_val == 1 :
                         tmp_row = [DR]
                         if road_type=="asphalt":
                             keys = list(CLASS_MAP_ASPHALT.keys())
@@ -358,13 +390,12 @@ def process_info(road_dict,pile_dict,summary_data,image_path,current_time,interv
                 # f.write(','.join(row)+'\n')
                 row += ['','','','','','','','','','','','','','','']
                 process_info_data.append(row)
-
+            else :
+                break;
             tmp_start = tmp_end
             tmp_end = tmp_start + increment
+            tmp_end = round(tmp_end, 3)
 
-            print(f"tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
-            if index >= len(group_list) :
-                break
     return process_info_data
 
 def adjust_column_width(worksheet):
@@ -435,6 +466,23 @@ def create_multiple_sheets_with_multiple_headers(file_path, excel_data):
     wb.save(file_path)
     print(f"文件已保存为 {file_path}")
 
+def format_number_to_k_code(number):
+    """
+    将数字转换为 K0000+000 格式
+    """
+    num_str = str(number)
+    
+    # 处理整数情况（如 1.0 -> 1）
+    if '.' not in num_str:
+        integer_part = int(num_str)
+        decimal_part = "000"
+    else:
+        integer_part, decimal_part = num_str.split('.')
+        integer_part = int(integer_part)
+        # 确保小数部分有3位，不足补0
+        decimal_part = decimal_part.ljust(3, '0')[:3]
+    return f"K{integer_part:04d}+{decimal_part}"
+
 # ---------------- 主函数-共享目录 ----------------
 def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
     os.makedirs(dir,exist_ok=True)
@@ -456,7 +504,7 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 with open(grid_txt_path,'w',encoding='utf-8') as f:
                     f.write(out_txt)
                 # 生成网格可视化
-                # draw_grid_on_image(image_path,class_cells,save_path=os.path.splitext(image_path)[0]+"_grid.jpg")
+                draw_grid_on_image(image_path,class_cells,cell_size=(GRID_WIDTH, GRID_HEIGHT),save_path=os.path.splitext(image_path)[0]+"_grid.jpg")
                 # 统计各类面积
                 counts = {k:[len(v[0])*cell_area, v[1][0], v[1][1]] for k,v in class_cells.items()}
                 # total_area = sum(counts.values())
@@ -486,7 +534,8 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
         road_type = summary_data[0][3]
 
         min_pile, max_pile = get_min_max_pile(summary_data)
-        out_file = os.path.join(dir,f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-detail-{current_time}.txt")
+        print(f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-detail-{current_time}.txt")
+        out_file = os.path.join(f"{dir}/DR",f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-detail-{current_time}.txt")
         header = generate_header(road_type)
         with open(out_file,'w',encoding='utf-8') as f:
             f.write(header+'\n')
@@ -503,13 +552,15 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 f.write(','.join(row)+'\n')
         print(f"输出完成: {out_file}")
 
-    # 写灾害数据.txt
+    # 灾害数据.txt
     if summary_data:
         img_file_name = os.path.basename(image_path)
         road_data = get_road_info(road_dict, pile_dict, img_file_name)
         identify_width = road_data.get('识别宽度（米）', '3.6')
         up_or_down = road_data.get('方向(上行/下行)', '上行')
         road_code = pile_dict.get(img_file_name)[0]
+        first_pile_no = round(road_data.get('起桩号（米）',0) / 1000, 3)
+        last_pile_no = round(road_data.get('止桩号（米）', 0) / 1000, 3)
         group_by_road_type = {}
         for data in summary_data:
             group_by_road_type.setdefault(data[3], []).append(data)
@@ -517,23 +568,29 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
 
         for road_type, group in group_by_road_type.items():
             min_pile, max_pile = get_min_max_pile(group)
+            print(f"DR/{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-{current_time}.txt")
             out_file = os.path.join(dir,f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-{current_time}.txt")
             header = generate_header(road_type)
 
-            group_list = group
-            increment = float(0.010)
-            pile_no_start = float(0.000)
-            pile_no_end = float(0.000) + increment
+            group_list = []
             # 上行/下行
             if up_or_down == '下行' :
-                group_list = list(group)[::-1]
+                group_list = group
+                list(reversed(group_list))
                 increment = float(-0.010)
-                tmp_pile_no = convert_special_format(group_list[len(group_list)-1][1])
+                tmp_pile_no = first_pile_no
                 pile_no_start = tmp_pile_no
                 if tmp_pile_no % increment != 0 :
-                    pile_no_end = tmp_pile_no + (tmp_pile_no % increment)
+                    pile_no_end = tmp_pile_no - (tmp_pile_no % (-increment))
+                    pile_no_end = round(pile_no_end, 3) #处理精度问题
                 else :
                     pile_no_end = tmp_pile_no + increment
+                    pile_no_end = round(pile_no_end, 3) #处理精度问题
+            else :
+                group_list = group
+                increment = float(0.010)
+                pile_no_start = first_pile_no
+                pile_no_end = pile_no_start + increment
 
             with open(out_file,'w',encoding='utf-8') as f:
                 f.write(header+'\n')
@@ -541,13 +598,19 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 tmp_start = pile_no_start
                 tmp_end = pile_no_end
                 while True :
+                    if tmp_end < last_pile_no :
+                        tmp_end = last_pile_no
+                    print(f"process_dir:tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
+                    if index >= len(group_list) :
+                        break
                     # 每10m一个区间，在区间内进行灾害计算
                     pile_no, DR, counts, road_type = group_list[index]
                     cur_pile_no = convert_special_format(pile_no)
-                    if not in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
+                    in_interval_val = in_interval(increment, cur_pile_no, tmp_start, tmp_end)
+                    if in_interval_val == 0 :
                         # 没在刻度内直接输出无病害数据
-                        pile_no = f"{tmp_start:0.3f}"
-                        row = [pile_no,identify_width,f"{0:.2f}"]
+                        pile_no = format_number_to_k_code(tmp_start)
+                        row = [pile_no,f"{identify_width}",f"{0:.2f}"]
                         if road_type=="asphalt":
                             keys = list(CLASS_MAP_ASPHALT.keys())
                         elif road_type=="cement":
@@ -557,16 +620,16 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                         for k in keys:
                             row.append(f"{0:.2f}")
                         f.write(','.join(row)+'\n')
-                    else :
-                        row = [f"{tmp_start:0.3f}", identify_width]
+                    elif in_interval_val == 1 :
+                        row = [format_number_to_k_code(tmp_start), f"{identify_width}"]
                         subRows = []
                         while index < len(group_list):
                             pile_no, DR, counts, road_type = group_list[index]
                             cur_pile_no = convert_special_format(pile_no)
                             
                             tmp_row = []
-                            if in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
-                                pile_no = f"{tmp_start:0.3f}"
+                            in_interval_val = in_interval(increment, cur_pile_no, tmp_start, tmp_end)
+                            if in_interval_val == 1 :
                                 tmp_row = [DR]
                                 if road_type=="asphalt":
                                     keys = list(CLASS_MAP_ASPHALT.keys())
@@ -585,17 +648,16 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                         column_sums = [f"{(sum(column)/5):0.2f}" for column in zip(*subRows)]
                         row += column_sums
                         f.write(','.join(row)+'\n')
-
+                    else :
+                        break
                     tmp_start = tmp_end
                     tmp_end = tmp_start + increment
-
-                    print(f"tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
-                    if index >= len(group_list) :
-                        break
+                    tmp_end = round(tmp_end, 3)
         
         print(f"输出完成: {out_file}")
 
-    # 病害明显列表.xlsx
+    # 病害明细列表.xlsx
+    print("输出:病害明细列表.xlsx")
     headers = ['序号','路线编码','方向','桩号','路面类型','病害名称','程度','长度(m)',' 宽度(m)',' 面积(㎡)',' 横向位置','备注']
     data_list = []
     if summary_data:
@@ -613,9 +675,10 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
                 data_list.append(excel_data)
 
     all_data = [headers] + data_list
-    smb.write_to_excel_pandas(all_data, img_file_path + '/病害明细列表.xlsx')
+    write_to_excel_pandas(all_data, img_file_path + '/excel/病害明细列表.xlsx')
 
     # 综合明细表.xlsx
+    print("输出:综合明细表.xlsx")
     heads = ['路线编码','起点','终点','车道编码','上下行','公路等级','路面类型','PQI','PQI等级','DR(%)','PCI','PCI等级','IRI','RQI','RQI等级','RD','RDI','RDI等级','SMTD','PBI','PBI等级','WR','PWI','PWI等级','备注']
     data1 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,10)
     data2 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,100)
@@ -641,7 +704,8 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
             'data': data3
         }
     ]
-    create_multiple_sheets_with_multiple_headers(img_file_path + '/综合明细表.xlsx', excel_data)
+    create_multiple_sheets_with_multiple_headers(img_file_path + '/excel/综合明细表.xlsx', excel_data)
+    
 
 
 
@@ -666,6 +730,8 @@ def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,g
     summary_data = []
     for root,_,files in os.walk(output_dir):
         for file in files:
+            if file.lower().endswith(("_grid.jpg","_grid.png","_grid.jpeg","_grid.bmp")) :
+                continue
             if file.lower().endswith((".jpg",".png",".jpeg",".bmp")) :
                 image_path = os.path.join(root,file)
                 label_file = os.path.splitext(image_path)[0]+".txt"
@@ -800,7 +866,13 @@ if __name__=="__main__":
     # road_dict = smb.get_road_dict("192.168.110.114/share_File/西南计算机", "administrator", "abc@1234")
     # process_dir(road_dict, pile_dict, output_dir)
 
-    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/7"
+    # calc_cell_area, calc_grid_width, calc_grid_height = calc_grid_param(2048, 4096, 3.6, 2)
+    # print(f"calc_cell_area={calc_cell_area}, calc_grid_width={calc_grid_width}, calc_grid_height={calc_grid_height}")
+
+    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/77"
     pile_dict = get_pile_dict(output_dir)
     road_dict = get_road_dict(output_dir)
     process_dir(road_dict, pile_dict, output_dir)
+
+
+