生成汇总目录sum，输出文件名以路线编码打头

predict/predict_yolo11seg.py

@@ -703,7 +703,7 @@ class YOLOSegmentationInference:
             推理结果列表
         """
         
-        tmp_output_dir = output_dir + "\\" + datetime.now().strftime("%Y%m%d%H%M%S")
+        tmp_output_dir = output_dir + "\\" + current_time
         print(f"正在处理共享目录: {input_dir} - {tmp_output_dir}")
 
         try:
@@ -770,14 +770,22 @@ class YOLOSegmentationInference:
                     print(f"清理后，内存使用: {mem_usage:.2f} MB")
             
             # 处理输出目录的文件格式
-            process_dir(road_dict, pile_dict, tmp_output_dir)
+            process_dir(road_dict, pile_dict, current_time, tmp_output_dir)
 
             # 识别生成的文件压缩成zip
-            zip_folder_shutil(tmp_output_dir)
+            # zip_folder_shutil(tmp_output_dir)
-            move_file_shutil(tmp_output_dir+".zip", tmp_output_dir + f"/{task_id}.zip")
+            # move_file_shutil(tmp_output_dir+".zip", tmp_output_dir + f"/{task_id}.zip")
+
+            # 生成汇总目录:图像类/sum/，包含灾害数据.txt+病害明细列表.xlsx+综合明细表.xlsx
+            image_class_dir = smb_tool.extract_upto_image_class(tmpConfig['dir'])
+            remote_image_class_dir = f"{image_class_dir}/sum/{task_id}/{current_time}"
+            scanner.upload_directory(f"{tmp_output_dir}/sum", config['share'], remote_dir=remote_image_class_dir)
+            del_file_shutil(f"{tmp_output_dir}/sum")
+
+            # 上传识别完整结果
             remote_dir = f"{tmpConfig['dir']}_识别/{task_id}/{current_time}"
             scanner.upload_directory(tmp_output_dir, config['share'], remote_dir=remote_dir)
-            # del_file_shutil(tmp_output_dir)
+            del_file_shutil(tmp_output_dir)
 
             # 更新识别任务状态为已结束
             DB_CONFIG = {

util/smb_tool.py

@@ -13,6 +13,7 @@ import cv2
 import pandas as pd
 import io
 import shutil
+from pathlib import Path
 
 
 class SMBScanner:
@@ -500,6 +501,158 @@ class SMBScanner:
         except Exception as e:
             print(f"上传文件失败: {e}")
             return False
+    
+    def download_directory(self, share_name, remote_dir, local_dir, overwrite=True, exclude_patterns=None):
+        """
+        从远程共享目录下载到本地目录
+        
+        Args:
+            share_name: 共享名称
+            remote_dir: 远程目录路径
+            local_dir: 本地目录路径
+            overwrite: 是否覆盖已存在的文件
+            exclude_patterns: 要排除的文件名模式列表，默认为 None
+            
+        Returns:
+            bool: 下载是否成功
+        """
+        if not self.connect():
+            return False
+        
+        # 默认排除模式
+        if exclude_patterns is None:
+            exclude_patterns = ['_grid.jpg', '_grid.txt']
+        
+        print(f"开始下载目录: {share_name}/{remote_dir} -> {local_dir}")
+        print(f"排除文件模式: {exclude_patterns}")
+        
+        # 确保本地目录存在
+        if not os.path.exists(local_dir):
+            try:
+                os.makedirs(local_dir, exist_ok=True)
+                print(f"创建本地目录: {local_dir}")
+            except Exception as e:
+                print(f"创建本地目录失败: {e}")
+                return False
+        
+        try:
+            # 递归下载目录内容
+            success = self._download_directory_recursive(share_name, remote_dir, local_dir, overwrite, exclude_patterns)
+            
+            if success:
+                print("目录下载完成")
+            else:
+                print("目录下载过程中出现错误")
+                
+            return success
+            
+        except Exception as e:
+            print(f"下载目录失败: {e}")
+            return False
+    
+    def _download_directory_recursive(self, share_name, remote_path, local_path, overwrite, exclude_patterns):
+        """
+        递归下载目录内容
+        
+        Args:
+            share_name: 共享名称
+            remote_path: 远程目录路径
+            local_path: 本地目录路径
+            overwrite: 是否覆盖已存在的文件
+            exclude_patterns: 要排除的文件名模式列表
+            
+        Returns:
+            bool: 下载是否成功
+        """
+        try:
+            success = True
+            
+            # 构建远程完整路径
+            full_remote_path = self.build_full_path(share_name, remote_path)
+            
+            # 遍历远程目录
+            for entry in scandir(full_remote_path):
+                remote_item_path = f"{remote_path}/{entry.name}" if remote_path else entry.name
+                local_item_path = os.path.join(local_path, entry.name)
+                
+                if entry.is_dir():
+                    # 处理子目录
+                    print(f"下载子目录: {entry.name}")
+                    
+                    # 确保本地子目录存在
+                    if not os.path.exists(local_item_path):
+                        try:
+                            os.makedirs(local_item_path, exist_ok=True)
+                        except Exception as e:
+                            print(f"创建本地子目录失败 {local_item_path}: {e}")
+                            success = False
+                            continue
+                    
+                    # 递归下载子目录
+                    sub_success = self._download_directory_recursive(share_name, remote_item_path, local_item_path, overwrite, exclude_patterns)
+                    if not sub_success:
+                        success = False
+                        
+                else:
+                    # 下载文件
+                    file_success = self._download_single_file(share_name, remote_item_path, local_item_path, overwrite, exclude_patterns)
+                    if not file_success:
+                        success = False
+            
+            return success
+            
+        except Exception as e:
+            print(f"下载目录内容失败 {remote_path}: {e}")
+            return False
+    
+    def _download_single_file(self, share_name, remote_file_path, local_file_path, overwrite, exclude_patterns):
+        """
+        下载单个文件
+        
+        Args:
+            share_name: 共享名称
+            remote_file_path: 远程文件路径
+            local_file_path: 本地文件路径
+            overwrite: 是否覆盖已存在的文件
+            exclude_patterns: 要排除的文件名模式列表
+            
+        Returns:
+            bool: 下载是否成功
+        """
+        try:
+            # 检查文件是否已存在
+            if not overwrite and os.path.exists(local_file_path):
+                print(f"文件已存在，跳过: {local_file_path}")
+                return True
+            
+            # 检查文件名是否匹配排除模式，如果是则跳过
+            file_name = os.path.basename(remote_file_path)
+            for pattern in exclude_patterns:
+                if file_name.endswith(pattern):
+                    print(f"文件被过滤，跳过: {file_name}")
+                    return True
+            
+            # 构建远程完整路径
+            full_remote_path = self.build_full_path(share_name, remote_file_path)
+            
+            # 下载文件
+            print(f"下载文件: {file_name}")
+            
+            # 读取远程文件
+            with open_file(full_remote_path, mode='rb') as remote_file:
+                remote_content = remote_file.read()
+            
+            # 写入本地文件
+            with open(local_file_path, 'wb') as local_file:
+                local_file.write(remote_content)
+            
+            file_size = len(remote_content)
+            print(f"文件下载成功: {local_file_path} ({file_size} 字节)")
+            return True
+            
+        except Exception as e:
+            print(f"下载文件失败 {remote_file_path}: {e}")
+            return False
 
     def find_folders_by_name(self, share_path, folder_name, start_dir="", max_depth=10):
         """专门查找文件夹"""
@@ -754,6 +907,23 @@ def standardized_path(path) :
 
     return path
 
+def extract_upto_image_class(path_str):
+    """
+    提取路径中直到 '图像类' 目录的部分（包括 '图像类' 本身）
+    如果路径中没有 '图像类'，返回原始路径。
+    """
+    p = Path(path_str)
+    parts = p.parts  # 获取所有路径部分（元组）
+
+    try:
+        idx = parts.index('图像类')  # 找到 '图像类' 的位置
+    except ValueError:
+        # 没找到，返回原始路径
+        return path_str
+
+    # 取到 '图像类' 为止的部分，再组合成路径
+    return Path(*parts[:idx+1])
+
 # 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 = standardized_path(zip_url)
@@ -769,12 +939,16 @@ def get_conf(zip_url, user_name, pwd) :
         dir = '/'.join(new_parts)
     
     excel_dir = ''
+    fileindex_dir = ''
     excel_dir = '/'.join(parts[2:])
+    fileindex_dir = '/'.join(parts[2:])
     for index, part in enumerate(parts, 0) :
         if part == 'Images' and index - 2 > 2 :
             excel_dir = '/'.join(parts[2:index-2])
+            fileindex_dir = '/'.join(parts[2:index])
         if part == '图像类' and index > 2 :
             excel_dir = '/'.join(parts[2:index])
+            fileindex_dir = '/'.join(parts[2:index+2])
         
 
     # 配置信息
@@ -785,7 +959,8 @@ def get_conf(zip_url, user_name, pwd) :
         'domain': '',  # 工作组留空
         'share': parts[1],
         'dir': dir,
-        'excel_dir': excel_dir
+        'excel_dir': excel_dir,
+        'fileindex_dir': fileindex_dir,
     }
 
     return config
@@ -813,8 +988,8 @@ def get_road_dict(dir,user_name,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'],
+            start_dir=config['excel_dir'],
-            max_depth=4
+            max_depth=2
         )
     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):
@@ -845,8 +1020,8 @@ def get_road_dict_for_width(config, scanner):
     found_paths_for_width = scanner.find_files_by_name(
             share_path=config['share'],
             file_name='nl_yy_glzb*.xls',
-            start_dir=config['dir'],
+            start_dir=config['excel_dir'],
-            max_depth=4
+            max_depth=2
         )
     road_dict_for_width = {}
     if len(found_paths_for_width) > 0 :
@@ -871,8 +1046,8 @@ def get_pile_dict(dir,user_name,pwd) :
     found_paths = scanner.find_files_by_name(
             share_path=config['share'],
             file_name='fileindex.txt',
-            start_dir=config['dir'],
+            start_dir=config['fileindex_dir'],
-            max_depth=2
+            max_depth=4
         )
     print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
     for i, path in enumerate(found_paths, 1):

util/yolo2pix_new.py

@@ -7,6 +7,7 @@ from openpyxl import Workbook
 from openpyxl.styles import Font, Alignment
 from openpyxl.utils import get_column_letter
 import glob
+import shutil
 from datetime import datetime
 try:
     # 相对导入
@@ -15,6 +16,33 @@ except ImportError:
     # 直接运行时回退到绝对导入
     import smb_tool
 
+# ---------------- 图片操作 ----------------
+def read_image(image_path):
+    """读取图片，处理中文路径"""
+    try:
+        image_path = smb_tool.standardized_path(image_path)
+        img_array = np.fromfile(image_path, dtype=np.uint8)
+        img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+        return img
+    except Exception as e:
+        print(f"读取图片失败: {image_path}, 错误: {e}")
+        return None
+
+def save_image(image, save_path):
+    """保存图片，处理中文路径"""
+    try:
+        save_path = smb_tool.standardized_path(save_path)
+        success, img_encoded = cv2.imencode('.jpg', image)
+        if success:
+            img_encoded.tofile(save_path)
+            return True
+        else:
+            print(f"编码图片失败: {save_path}")
+            return False
+    except Exception as e:
+        print(f"保存图片失败: {save_path}, 错误: {e}")
+        return False
+
 # ---------------- 常量 ----------------
 CELL_WIDTH = 0.1         # 每格宽 (米)
 CELL_HEIGHT = 0.1        # 每格高 (米)
@@ -63,8 +91,10 @@ def calc_grid_param(pic_width, pic_height, actual_width, actual_height, grid_x_c
 
 
 def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGHT), save_path=None):
-    image = cv2.imread(image_path)
+    # 使用公共方法读取图片
-    if image is None: return
+    image = read_image(image_path)
+    
+    if image is None: return None
     h, w = image.shape[:2]
     cell_w, cell_h = cell_size
     cols = w // cell_w
@@ -84,7 +114,9 @@ def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGH
         cv2.line(image,(i,0),(i,h),(100,100,100),1)
     for j in range(0, h, cell_h):
         cv2.line(image,(0,j),(w,j),(100,100,100),1)
-    if save_path: cv2.imwrite(save_path,image)
+    if save_path:
+        # 使用公共方法保存图片
+        save_image(image, save_path)
     return image
 
 def detect_road_type_from_content(label_file):
@@ -102,6 +134,14 @@ def detect_road_type_from_content(label_file):
         if kw in content: return "gravel"
     return "gravel"
 
+def get_road_code(pile_dict) :
+    """获取路线编码"""
+    img_file_name = list(pile_dict.keys())[0]
+    parts = pile_dict.get(img_file_name)
+    if parts :
+        road_code = parts[0]
+    return road_code
+
 def get_road_info(road_dict, pile_dict, img_file_name):
     """获取路线信息"""
     parts = pile_dict.get(img_file_name)
@@ -172,8 +212,8 @@ def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,cell_ind
     else: class_map = CLASS_MAP_GRAVEL
     class_names = list(class_map.keys())
 
-    img = cv2.imread(image_path)
+    img = read_image(image_path)
-    if img is None: return "", {}
+    if img is None: return "", {}, road_type, 20 * RESERVED_CELL_NUM
     h, w = img.shape[:2]
     cols = max(1, w//grid_width)
     rows = max(1, h//grid_height)
@@ -286,6 +326,12 @@ def yoloseg_to_grid(image_path,label_file,cover_ratio=COVER_RATIO):
             all_class_cells[cname].update(covered_cells)
     return '\n'.join(result_lines), all_class_cells, road_type
 
+def generate_classes_txt_content(road_type) :
+    if road_type=="asphalt": return "龟裂\n块状裂缝\n纵向裂缝\n横向裂缝\n沉陷\n车辙\n波浪拥包\n坑槽\n松散\n泛油\n修补"
+    if road_type=="cement": return "破碎板\n裂缝\n板角断裂\n错台\n拱起\n边角剥落\n接缝料损坏\n坑洞\n唧泥\n露骨\n修补"
+    if road_type=="gravel": return "坑槽\n沉陷\n车辙\n波浪搓板"
+    return "龟裂\n块状裂缝\n纵向裂缝\n横向裂缝\n沉陷\n车辙\n波浪拥包\n坑槽\n松散\n泛油\n修补"
+
 def generate_header(road_type):
     if road_type=="asphalt": return "起点桩号(km),识别宽度(m),破损率DR(%),龟裂,块状裂缝,纵向裂缝,横向裂缝,沉陷,车辙,波浪拥包,坑槽,松散,泛油,修补"
     if road_type=="cement": return "起点桩号(km),识别宽度(m),破损率DR(%),破碎板,裂缝,板角断裂,错台,拱起,边角剥落,接缝料损坏,坑洞,唧泥,露骨,修补"
@@ -557,8 +603,11 @@ def format_number_to_k_code(number):
     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,cell_width=CELL_WIDTH,cell_height=CELL_HEIGHT,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
+def process_dir(road_dict,pile_dict,current_time,dir="output",cell_area=CELL_AREA,cell_width=CELL_WIDTH,cell_height=CELL_HEIGHT,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
     os.makedirs(dir,exist_ok=True)
+    os.makedirs(f"{dir}/DR",exist_ok=True)
+    os.makedirs(f"{dir}/excel",exist_ok=True)
+    os.makedirs(f"{dir}/sum",exist_ok=True)
     # 识别宽度（米）
     road_recognize_width = road_dict.get('识别宽度（米）', 3.6)
     road_recognize_height = ROAD_RECOGNIZE_HEIGHT
@@ -583,7 +632,9 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width=
                 
                 # 读取图片并计算宽高像素点
                 if grid_width is None or grid_height is None :
-                    img = cv2.imread(image_path)
+                    # 使用公共方法读取图片
+                    img = read_image(image_path)
+                    
                     if img is not None : 
                         h, w = img.shape[:2]
                         # 网格像素宽高
@@ -625,7 +676,8 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width=
                 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")
+    # classes.txt
+    process_classes_txt(road_dict, pile_dict, dir, image_path)
 
     # 写桩号问题列表.txt
     process_damage_detail_txt(road_dict, pile_dict, dir, summary_data, current_time)
@@ -639,6 +691,13 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,cell_width=
     # 综合明细表.xlsx
     process_damage_composite_excel(road_dict, pile_dict, dir, summary_data, current_time)
 
+def process_classes_txt(road_dict, pile_dict, dir, image_path):
+    img_file_name = os.path.basename(image_path)
+    _, _, road_type = detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name)
+    classes_txt_content = generate_classes_txt_content(road_type)
+    with open(f"{dir}/classes.txt", "w") as file:
+        file.write(classes_txt_content)
+
 
 def process_damage_composite_excel(road_dict, pile_dict, dir, summary_data, current_time):
     print("输出:综合明细表.xlsx")
@@ -646,6 +705,7 @@ def process_damage_composite_excel(road_dict, pile_dict, dir, summary_data, curr
     data1 = process_info(road_dict,pile_dict,summary_data,current_time,10)
     data2 = process_info(road_dict,pile_dict,summary_data,current_time,100)
     data3 = process_info(road_dict,pile_dict,summary_data,current_time,1000)
+    road_code = get_road_code(pile_dict)
 
     excel_data = [
         {
@@ -667,7 +727,12 @@ def process_damage_composite_excel(road_dict, pile_dict, dir, summary_data, curr
             'data': data3
         }
     ]
-    create_multiple_sheets_with_multiple_headers(f"{dir}/excel/综合明细表.xlsx", excel_data)
+    out_file = f"{dir}/excel/{road_code}-综合明细表.xlsx"
+    out_file_sum = f"{dir}/sum/{road_code}-综合明细表.xlsx"
+    create_multiple_sheets_with_multiple_headers(out_file, excel_data)
+    
+    # 将文件复制到汇总目录
+    shutil.copy(out_file, out_file_sum)
 
 
 def process_damage_detail_txt(road_dict, pile_dict, dir, summary_data, current_time):
@@ -721,7 +786,14 @@ def process_damage_detail_excel(road_dict, pile_dict, dir, cell_area, cell_width
                     data_list.append(excel_data)
 
     all_data = [headers] + data_list
-    smb_tool.write_to_excel_pandas(all_data, f"{dir}/excel/病害明细列表.xlsx")
+
+    road_code = get_road_code(pile_dict)
+    out_file = f"{dir}/excel/{road_code}-病害明细列表.xlsx"
+    out_file_sum = f"{dir}/sum/{road_code}-病害明细列表.xlsx"
+    smb_tool.write_to_excel_pandas(all_data, out_file)
+
+    # 将文件复制到汇总目录
+    shutil.copy(out_file, out_file_sum)
 
 
 def process_damage_txt(road_dict, pile_dict, dir, summary_data, current_time):
@@ -742,6 +814,7 @@ def process_damage_txt(road_dict, pile_dict, dir, summary_data, current_time):
             min_pile, max_pile = get_min_max_pile(group)
             print(f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-{current_time}.txt")
             out_file = os.path.join(f"{dir}/DR",f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-{current_time}.txt")
+            out_file_sum = os.path.join(f"{dir}/sum",f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-{current_time}.txt")
             header = generate_header(road_type)
 
             group_list = []
@@ -849,6 +922,8 @@ def process_damage_txt(road_dict, pile_dict, dir, summary_data, current_time):
                     tmp_end = tmp_start + increment
                     tmp_end = round(tmp_end, 3)
         
+        # 将文件复制到汇总目录
+        shutil.copy(out_file, out_file_sum)
         print(f"输出完成: {out_file}")
     
 
@@ -1037,7 +1112,8 @@ if __name__=="__main__":
     output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/jlp/C006500107A"
     pile_dict = get_pile_dict(output_dir)
     road_dict = get_road_dict(output_dir)
-    process_dir(road_dict, pile_dict, output_dir)
+    current_time = datetime.now().strftime("%Y%m%d%H%M%S")
+    process_dir(road_dict, pile_dict, current_time, output_dir)
 
     # arr = [44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 68, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]
     # for a in arr :