共享目录：灾害数据输出格式化文件

predict/predict_yolo11seg.py

@@ -692,10 +692,14 @@ class YOLOSegmentationInference:
                 results.append(result)
 
             # 推送识别数据到共享目录
-            pile_dict = get_pile_dict(image_path, user_name, pwd)
+            tmpConfig = get_conf(input_dir, user_name, pwd)
-            process_dir(pile_dict, output_dir)
+            pile_dict = get_pile_dict(input_dir, user_name, pwd)
+            road_dict = get_road_dict(f"{tmpConfig['ip']}/{tmpConfig['share']}", user_name, pwd)
+            process_dir(road_dict, pile_dict, output_dir)
             current_time = datetime.now().strftime("%Y%m%d%H%M%S")
-            scanner.upload_directory(output_dir, config['share'], remote_dir=input_dir+f"_识别/{task_id}/{current_time}")
+            
+            remote_dir = f"{tmpConfig['dir']}_识别/{task_id}/{current_time}"
+            # scanner.upload_directory(output_dir, config['share'], remote_dir=remote_dir)
 
             return results
 
@@ -894,7 +898,8 @@ def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_d
     scanner = get_scanner(zip_url, user_name, pwd)
     found_paths = scanner.find_folders_by_name(
         share_path=config['share'],
-        folder_name='图像类'
+        folder_name='图像类',
+        start_dir=config['dir']
     )
 
     target_path = ""    # 识别图片目录

util/smb.py

@@ -86,13 +86,13 @@ class SMBScanner:
         results = []
         
         for row_number, (index, row) in enumerate(df.iterrows(), 1):
-            print(f"\n处理第 {row_number} 行:")
+            # print(f"\n处理第 {row_number} 行:")
-            print("-" * 40)
+            # print("-" * 40)
             
             # 显示行数据
             for col_name in df.columns:
                 value = row[col_name]
-                print(f"  {col_name}: {value}")
+                # print(f"  {col_name}: {value}")
             
             # 处理逻辑（根据实际需求修改）
             processed_row = {
@@ -108,7 +108,7 @@ class SMBScanner:
             if row_number % 10 == 0 or row_number == len(df):
                 print(f"\n 进度: {row_number}/{len(df)} ({row_number/len(df)*100:.1f}%)")
         
-        print("\n" + "=" * 60)
+        # print("\n" + "=" * 60)
         print(f"处理完成！共处理 {len(results)} 行数据")
         
         return results
@@ -751,27 +751,76 @@ def get_scanner(zip_url, user_name, pwd) :
     )
     return scanner
 
+# 路线编码 -> 路线信息
+def get_road_dict(dir,user_name,pwd) :
+    config = get_conf(dir, user_name, pwd)
+    scanner = get_scanner(dir, user_name=user_name, pwd=pwd)
+    found_paths = scanner.find_files_by_name(
+            share_path=config['share'],
+            file_name='每公里指标明细表*.xls',
+            start_dir=config['dir']
+        )
+    print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
+    for i, path in enumerate(found_paths, 1):
+        print(f"{i}. {path}")
+
+    road_dict = {}
+    if len(found_paths) > 0 :
+        df = scanner.read_excel(found_paths[0])
+        rows = scanner.process_all_rows(df)
+        for i, row in enumerate(rows, 1):
+            data = row['data']
+            if pd.notna(data['线路编码']) :
+                up_or_down = 'A'
+                if data['方向(上行/下行)'] == '下行' : 
+                    up_or_down = 'B'
+                key = f"{data['线路编码']}{str(int(data['区划代码']))}{up_or_down}"
+                if road_dict.get(key) : 
+                    road_dict[key].append(row)
+                else : 
+                    road_dict[key] = [row]  # 路线编码 -> 路线信息
+    
+    return road_dict
+
 # filename -> 桩号
 def get_pile_dict(dir,user_name,pwd) :
     config = get_conf(dir, user_name, pwd)
     scanner = get_scanner(dir, user_name=user_name, pwd=pwd)
     found_paths = scanner.find_files_by_name(
             share_path=config['share'],
-            file_name='fileindex.txt'
+            file_name='fileindex.txt',
+            start_dir=config['dir']
         )
     print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
     for i, path in enumerate(found_paths, 1):
         print(f"{i}. {path}")
-    
-    lines = scanner.read_txt_by_line(full_path=found_paths[0])
 
     pile_dict = {}
-    for i, line in enumerate(lines, 1):
+    if len(found_paths) > 0 :
-        parts = line.strip().split("->")
+        lines = scanner.read_txt_by_line(full_path=found_paths[0])
-        if len(parts)>=4:
+        for i, line in enumerate(lines, 1):
-            pile_dict[parts[3]]=parts[1]  # filename -> 桩号
+            parts = line.strip().split("->")
+            if len(parts)>=4:
+                pile_dict[parts[3]]=parts  # filename -> 桩号
     
     return pile_dict
+        
+
+def write_to_excel_pandas(data, filename, sheet_name='Sheet1'):
+    """
+    使用 pandas 将数据写入 Excel
+    
+    Args:
+        data: 数据列表，每个元素是一行
+        filename: 输出文件名
+        sheet_name: 工作表名称
+    """
+    # 创建 DataFrame
+    df = pd.DataFrame(data)
+    
+    # 写入 Excel
+    df.to_excel(filename, sheet_name=sheet_name, index=False, header=False)
+    print(f"数据已写入 {filename}")
 
 def main():
     # 配置信息
@@ -825,6 +874,15 @@ def main():
     # for i, path in enumerate(found_paths, 1):
     #     print(f"{i}. {path}")
 
+    # # 查找指定文件
+    # found_paths = scanner.find_files_by_name(
+    #     share_path=config['share'],
+    #     file_name='每公里指标明细表*.xls'
+    # )
+    # print(f"\n找到 {len(found_paths)} 个")
+    # for i, path in enumerate(found_paths, 1):
+    #     print(f"{i}. {path}")
+
 
     # # 查找指定目录中的所有图片
     # full_path = scanner.build_full_path(share_path=config['share'], file_path='西南计算机\\AA县\\报送数据')
@@ -833,9 +891,21 @@ def main():
     #     print(f"{i}. {path}")
     
     # # 读取excel
-    # full_path = scanner.build_full_path(share_path=config['share'], file_path='西南计算机\\AA县\\24年年报.xlsx')
+    # full_path = scanner.build_full_path(share_path=config['share'], file_path='西南计算机\\AA县\\每公里指标明细表（北碚）.xls')
     # df = scanner.read_excel(full_path)
-    # scanner.process_all_rows(df)
+    # rows = scanner.process_all_rows(df)
+    # road_dict = {}
+    # for i, row in enumerate(rows, 1):
+    #     data = row['data']
+    #     if pd.notna(data['线路编码']) :
+    #         up_or_down = 'A'
+    #         if data['方向(上行/下行)'] == '下行' : 
+    #             up_or_down = 'B'
+    #         key = f"{data['线路编码']}{str(int(data['区划代码']))}{up_or_down}"
+    #         if road_dict.get(key) : 
+    #             road_dict[key].append(row)
+    #         else : 
+    #             road_dict[key] = [row]  # 路线编码 -> 路线信息
 
 
     # 读取txt
@@ -847,13 +917,19 @@ def main():
     # for i, path in enumerate(found_paths, 1):
     #     print(f"{i}. {path}")
     
+    # 读取txt
     # lines = scanner.read_txt_by_line(full_path=found_paths[0])
     # for i, line in enumerate(lines, 1):
     #         print(f"{i}. {line}")
 
-    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions"
+    # 上传目录
-    scanner.upload_directory(output_dir, config['share'], remote_dir="西南计算机/AA县/报送数据_识别")
+    # output_dir = "D:/devForBdzlWork/ai-train_platform/predictions"
+    # scanner.upload_directory(output_dir, config['share'], remote_dir="西南计算机/AA县/报送数据_识别")
 
+    # get_pile_dict
+    # input_dir = "192.168.110.114/share_File/西南计算机/AA县/报送数据/图像类/CD45500155A/Images"
+    # pile_dict = get_pile_dict(input_dir, config['username'], config['password'])
+    # print("-------------------------------------------")
 
 if __name__ == "__main__":
     main()

util/yolo2pix_new.py

@@ -5,6 +5,9 @@ import cv2
 import numpy as np
 from collections import defaultdict
 import smb
+import pandas as pd
+import glob
+from datetime import datetime
 
 # ---------------- 常量 ----------------
 CELL_AREA = 0.0036       # 每格面积 (平方米)
@@ -23,6 +26,12 @@ CLASS_MAP_GRAVEL = {
     "坑槽":0,"沉陷":1,"车辙":2,"波浪搓板":3
 }
 
+ROAD_TYPE_EN_TO_CN = {
+    "asphalt":"沥青",
+    "cement":"水泥",
+    "gravel":"砾石"
+}
+
 # ---------------- 工具函数 ----------------
 def num_to_coord(num, cols, cell_w, cell_h):
     n = num - 1
@@ -66,6 +75,107 @@ def detect_road_type_from_content(label_file):
         if kw in content: return "gravel"
     return "gravel"
 
+def get_road_info(road_dict, pile_dict, img_file_name):
+    """获取路线信息"""
+    parts = pile_dict.get(img_file_name)
+    if parts :
+        road_code = parts[0]
+        road_info = road_dict.get(road_code)
+        if road_info :
+            data = road_info['data']
+            return data
+    return {}
+
+def detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name):
+    """根据读取的excel内容内容判断路面类型"""
+    road_code = 'xxxxxxxxxxx'
+    pile_no = "xxxxx"
+    road_type = "asphalt"
+    parts = pile_dict.get(img_file_name)
+    if parts :
+        road_code = parts[0]
+        pile_no = parts[1]
+        road_info = road_dict.get(road_code)
+        if road_info :
+            data = road_info['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）']
+            if road_type_cn == '沥青' :
+                road_type = "asphalt"
+            elif road_type_cn == '水泥' :
+                road_type = "cement"
+            elif road_type_cn == '砾石' :
+                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):
+    """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)
+    if road_type=="asphalt": class_map = CLASS_MAP_ASPHALT
+    elif road_type=="cement": class_map = CLASS_MAP_CEMENT
+    else: class_map = CLASS_MAP_GRAVEL
+    class_names = list(class_map.keys())
+
+    img = cv2.imread(image_path)
+    if img is None: return "", {}
+    h, w = img.shape[:2]
+    cols = max(1, w//GRID_WIDTH)
+    rows = max(1, h//GRID_HEIGHT)
+
+    result_lines = []
+    all_class_cells = {}
+    with open(label_file,'r',encoding='utf-8') as f:
+        for line in f:
+            parts = line.strip().split()
+            if len(parts)<5: continue
+            cls_id = int(parts[0])
+            coords = [float(x) for x in parts[1:]]
+            if len(coords)%2!=0: coords=coords[:-1]
+            if len(coords)<6: continue
+            poly = np.array(coords,dtype=np.float32).reshape(-1,2)
+            poly[:,0]*=w
+            poly[:,1]*=h
+            mask = np.zeros((h,w),dtype=np.uint8)
+            cv2.fillPoly(mask,[poly.astype(np.int32)],255)
+            cell_info = []
+            covered_cells=[]
+            min_x = cols
+            min_y = rows
+            max_x = 0
+            max_y = 0
+            for r in range(rows):
+                for c in range(cols):
+                    x1,y1 = c*GRID_WIDTH, r*GRID_HEIGHT
+                    x2,y2 = min(w,x1+GRID_WIDTH), min(h,y1+GRID_HEIGHT)
+                    region = mask[y1:y2, x1:x2]
+                    if np.count_nonzero(region)/region.size>cover_ratio:
+                        covered_cells.append(r*cols+c+1)
+                        # 最小x坐标，y坐标
+                        if min_x > c :
+                            min_x = c
+                        if min_y > r :
+                            min_y = r
+                        # 最大x坐标，y坐标
+                        if max_x < c + 1 :
+                            max_x = c + 1
+                        if max_y < r + 1 :
+                            max_y = r + 1
+
+            if not covered_cells: continue
+            # min_cell = covered_cells[0]
+            # max_cell = covered_cells[len(covered_cells)-1]
+
+            cname = class_names[cls_id] if cls_id<len(class_names) else str(cls_id)
+            ids_str = '-'.join(map(str,sorted(covered_cells)))+'-'
+            result_lines.append(f"{cname}   {f"桩号:K000{pile_no}"}   {ROAD_TYPE_EN_TO_CN.get(road_type, 'xx')}   {ids_str}")
+            if cname not in all_class_cells: all_class_cells[cname]=set()
+            cell_info.append(covered_cells)
+            cell_info.append([max_x - min_x, max_y - min_y])
+            all_class_cells[cname] = cell_info
+    return '\n'.join(result_lines), all_class_cells, road_type, rows * cols
+
 def yoloseg_to_grid(image_path,label_file,cover_ratio=COVER_RATIO):
     """将YOLO-Seg标签转换成格子编号和类别"""
     road_type = detect_road_type_from_content(label_file)
@@ -117,9 +227,55 @@ def generate_header(road_type):
     if road_type=="gravel": return "起点桩号(km),识别宽度(m),破损率DR(%),坑槽,沉陷,车辙,波浪搓板"
     return ""
 
+def get_min_max_pile(group) :
+    min_pile = float(99.000)
+    max_pile = float(0.000)
+    for g in group :
+        tmp_pile = convert_special_format(g[0])
+        if min_pile > tmp_pile :
+            min_pile = tmp_pile
+        if max_pile < tmp_pile :
+            max_pile = tmp_pile
+    
+    return min_pile, max_pile
+
+def convert_special_format(input_str):
+    """
+    将特殊格式字符串转换为浮点数
+    支持格式: "0+022" -> 0.022, "1+234" -> 1.234
+    """
+    if '+' in input_str:
+        # 分割整数部分和小数部分
+        parts = input_str.split('+')
+        if len(parts) == 2:
+            integer_part = parts[0]
+            decimal_part = parts[1]
+            
+            # 构建标准小数格式
+            standard_format = f"{integer_part}.{decimal_part}"
+            return float(standard_format)
+        else:
+            raise ValueError(f"无效的格式: {input_str}")
+    else:
+        # 如果没有 '+'，直接转换
+        return float(input_str)
+
+# 是否在区间内
+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
+        else :
+            return False
+    else :
+        if cur_pile_no > tmp_end and cur_pile_no <= tmp_start :
+            return True
+        else :
+            return False
+
 
 # ---------------- 主函数-共享目录 ----------------
-def process_dir(pile_dict,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
+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)
     # 解压
     # 读取桩号映射
@@ -133,26 +289,43 @@ def process_dir(pile_dict,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH
                 if not os.path.exists(label_file):
                     print(f"⚠️ 找不到标签: {label_file}")
                     continue
-                out_txt, class_cells, road_type = yoloseg_to_grid(image_path,label_file)
+                out_txt, class_cells, road_type, all_cell_num = yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file)
                 # 写每张图独立 _grid.txt
                 grid_txt_path = os.path.splitext(image_path)[0]+"_grid.txt"
                 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,save_path=os.path.splitext(image_path)[0]+"_grid.jpg")
                 # 统计各类面积
-                counts = {k:len(v)*cell_area for k,v in class_cells.items()}
+                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())
+                # total_area = sum(counts.values())
-                # 桩号
+                # 灾害总面积比例
-                pile_no = pile_dict.get(file,"未知")
+                merged_set = set([])
+                for k,v in class_cells.items() :
+                    merged_set = merged_set.union(v[0])
+                total_area = len(merged_set)
+                
+                # 桩号 路线编号
+                parts = pile_dict.get(file)
+                pile_no = "0+000"
+                if parts :
+                    pile_no = parts[1]
+                
                 # 破损率 DR (%) = total_area / 总面积
-                DR = total_area/ (total_area if total_area>0 else 1) *100  # 简化为100%或者0
+                # DR = total_area/ (total_area if total_area>0 else 1) *100  # 简化为100%或者0
+                DR= total_area / all_cell_num * 100
                 summary_data.append((pile_no, DR, counts, road_type))
 
+    current_time = datetime.now().strftime("%Y%m%d%H%M%S")
     # 写桩号问题列表.txt
     if summary_data:
+        img_file_name = os.path.basename(image_path)
+        road_data = get_road_info(road_dict, pile_dict, img_file_name)
+        road_code = pile_dict.get(img_file_name)[0]
         road_type = summary_data[0][3]
-        out_file = os.path.join(dir,"桩号问题列表.txt")
+
+        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")
         header = generate_header(road_type)
         with open(out_file,'w',encoding='utf-8') as f:
             f.write(header+'\n')
@@ -165,9 +338,121 @@ def process_dir(pile_dict,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH
                 else:
                     keys = list(CLASS_MAP_GRAVEL.keys())
                 for k in keys:
-                    row.append(f"{counts.get(k,0):.2f}")
+                    row.append(f"{counts.get(k,[0,0,0])[0]:.2f}")
                 f.write(','.join(row)+'\n')
-        print(f"✅ 输出完成: {out_file}")
+        print(f"输出完成: {out_file}")
+
+    # 写灾害数据.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]
+        group_by_road_type = {}
+        for data in summary_data:
+            group_by_road_type.setdefault(data[3], []).append(data)
+
+
+        for road_type, group in group_by_road_type.items():
+            min_pile, max_pile = get_min_max_pile(group)
+            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
+            # 上行/下行
+            if up_or_down == '下行' :
+                group_list = list(group)[::-1]
+                increment = float(-0.010)
+                tmp_pile_no = convert_special_format(group[len(group)-1][1])
+                pile_no_start = tmp_pile_no
+                if tmp_pile_no % increment != 0 :
+                    pile_no_end = tmp_pile_no + (tmp_pile_no % increment)
+                else :
+                    pile_no_end = tmp_pile_no + increment
+
+            with open(out_file,'w',encoding='utf-8') as f:
+                f.write(header+'\n')
+                index = 0
+                tmp_start = pile_no_start
+                tmp_end = pile_no_end
+                while True :
+                    # 每10m一个区间，在区间内进行灾害计算
+                    pile_no, DR, counts, road_type = summary_data[index]
+                    cur_pile_no = convert_special_format(pile_no)
+                    if not in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
+                        # 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}"]
+                        if road_type=="asphalt":
+                            keys = list(CLASS_MAP_ASPHALT.keys())
+                        elif road_type=="cement":
+                            keys = list(CLASS_MAP_CEMENT.keys())
+                        else:
+                            keys = list(CLASS_MAP_GRAVEL.keys())
+                        for k in keys:
+                            row.append(f"{0:.2f}")
+                        f.write(','.join(row)+'\n')
+                    else :
+                        row = [f"{tmp_start:0.3f}", identify_width]
+                        subRows = []
+                        while index < len(group_list):
+                            pile_no, DR, counts, road_type = summary_data[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}"
+                                tmp_row = [DR]
+                                if road_type=="asphalt":
+                                    keys = list(CLASS_MAP_ASPHALT.keys())
+                                elif road_type=="cement":
+                                    keys = list(CLASS_MAP_CEMENT.keys())
+                                else:
+                                    keys = list(CLASS_MAP_GRAVEL.keys())
+                                for k in keys:
+                                    tmp_row.append(counts.get(k, [0,0,0])[0])
+                                subRows.append(tmp_row)
+                                index = index + 1
+                            else :
+                                break
+                        
+                        # 同列汇总 10m一个区间--对应5张图
+                        column_sums = [f"{(sum(column)/5):0.2f}" for column in zip(*subRows)]
+                        row += column_sums
+                        f.write(','.join(row)+'\n')
+
+                    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
+        
+        print(f"输出完成: {out_file}")
+
+    # 病害明显列表.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:
+        img_file_path = os.path.dirname(image_path)
+        img_file_name = os.path.basename(image_path)
+        road_data = get_road_info(road_dict, pile_dict, img_file_name)
+        road_code, pile_no, road_type = detect_road_type_from_road_dict(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')
+        excel_index = 1
+        for data in summary_data:
+            damage_data = data[2]
+            for attr_name, attr_value in damage_data.items():
+                excel_data = [excel_index, road_code, up_or_down, f"K000{data[0]}", ROAD_TYPE_EN_TO_CN.get(road_type), attr_name, '', attr_value[1]*cell_area, attr_value[2]*cell_area, attr_value[0], '', '']
+                data_list.append(excel_data)
+
+    all_data = [headers] + data_list
+    smb.write_to_excel_pandas(all_data, img_file_path + '/病害明显列表.xlsx')
 
 # ---------------- 主函数 ----------------
 def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
@@ -184,7 +469,7 @@ def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,g
         for line in f:
             parts = line.strip().split("->")
             if len(parts)>=4:
-                pile_dict[parts[3]]=parts[1]  # filename -> 桩号
+                pile_dict[parts[3]]=parts  # filename -> 桩号
 
     # 遍历图片
     summary_data = []
@@ -196,7 +481,7 @@ def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,g
                 if not os.path.exists(label_file):
                     print(f"⚠️ 找不到标签: {label_file}")
                     continue
-                out_txt, class_cells, road_type = yoloseg_to_grid(image_path,label_file)
+                out_txt, class_cells, road_type, = yoloseg_to_grid(image_path,label_file)
                 # 写每张图独立 _grid.txt
                 grid_txt_path = os.path.splitext(image_path)[0]+"_grid.txt"
                 with open(grid_txt_path,'w',encoding='utf-8') as f:
@@ -207,9 +492,9 @@ def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,g
                 counts = {k:len(v)*cell_area for k,v in class_cells.items()}
                 total_area = sum(counts.values())
                 # 桩号
-                pile_no = pile_dict.get(file,"未知")
+                pile_no = pile_dict.get(file,"0+000")
                 # 破损率 DR (%) = total_area / 总面积
-                DR = total_area/ (total_area if total_area>0 else 1) *100  # 简化为100%或者0
+                DR = total_area / (total_area if total_area > 0 else 1) * 100  # 简化为100%或者0
                 summary_data.append((pile_no, DR, counts, road_type))
 
     # 写桩号问题列表.txt
@@ -232,12 +517,99 @@ def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,g
                 f.write(','.join(row)+'\n')
         print(f"✅ 输出完成: {out_file}")
 
+
+# 路线编码 -> 路线信息
+def get_road_dict(local_dir):
+    """
+    从本地目录读取Excel文件，构建路线字典
+    
+    Args:
+        local_dir: 本地目录路径
+        
+    Returns:
+        dict: 路线编码到路线信息的映射字典
+    """
+    # 查找匹配的Excel文件
+    pattern = os.path.join(local_dir, '每公里指标明细表*.xls')
+    found_paths = glob.glob(pattern)
+    
+    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}")
+
+    road_dict = {}
+    if len(found_paths) > 0:
+        # 读取第一个匹配的Excel文件
+        df = pd.read_excel(found_paths[0])
+        
+        # 处理所有行（这里需要根据实际情况调整处理逻辑）
+        for index, row in df.iterrows():
+            data = row.to_dict()
+            if pd.notna(data.get('线路编码', None)):
+                up_or_down = 'A'
+                if data.get('方向(上行/下行)', '') == '下行':
+                    up_or_down = 'B'
+                
+                # 构建key，确保区划代码为整数
+                area_code = data.get('区划代码', '')
+                if pd.notna(area_code):
+                    area_code = str(int(float(area_code))) if str(area_code).replace('.', '').isdigit() else str(area_code)
+                else:
+                    area_code = ''
+                
+                key = f"{data['线路编码']}{area_code}{up_or_down}"
+                
+                if key in road_dict:
+                    road_dict[key].append({'index': index, 'data': data})
+                else:
+                    road_dict[key] = [{'index': index, 'data': data}]
+    
+    return road_dict
+
+# filename -> 桩号
+def get_pile_dict(local_dir):
+    """
+    从本地目录读取fileindex.txt文件，构建桩号字典
+    
+    Args:
+        local_dir: 本地目录路径
+        
+    Returns:
+        dict: 文件名到桩号信息的映射字典
+    """
+    # 查找fileindex.txt文件
+    pattern = os.path.join(local_dir, 'fileindex.txt')
+    found_paths = glob.glob(pattern)
+    
+    print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
+    for i, path in enumerate(found_paths, 1):
+        print(f"{i}. {path}")
+
+    pile_dict = {}
+    if len(found_paths) > 0:
+        # 读取第一个匹配的txt文件
+        with open(found_paths[0], 'r', encoding='utf-8') as file:
+            lines = file.readlines()
+        
+        for i, line in enumerate(lines, 1):
+            parts = line.strip().split("->")
+            if len(parts) >= 4:
+                pile_dict[parts[3]] = parts  # filename -> 桩号
+    
+    return pile_dict
+
 # ---------------- 示例调用 ----------------
 if __name__=="__main__":
     # zip_path = "D:/devForBdzlWork/ai-train_platform/predict/inferenceResult.zip"          # 输入 ZIP 文件
     # pile_map_file = "D:/devForBdzlWork/ai-train_platform/predict/pile_map.txt"    # 图片名 -> 桩号
     # process_zip(zip_path=zip_path,pile_map_file=pile_map_file,output_dir="output")
 
-    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/1"
+    # output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/7"
-    pile_dict = smb.get_pile_dict("192.168.110.114/share_File/西南计算机", "administrator", "abc@1234")
+    # pile_dict = smb.get_pile_dict("192.168.110.114/share_File/西南计算机", "administrator", "abc@1234")
-    process_dir(pile_dict, output_dir)
+    # 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"
+    pile_dict = get_pile_dict(output_dir)
+    road_dict = get_road_dict(output_dir)
+    process_dir(road_dict, pile_dict, output_dir)