From 8dff98158349bc774e9b517f8f80113132f1f5cd Mon Sep 17 00:00:00 2001 From: liyubo Date: Thu, 9 Apr 2026 09:35:39 +0800 Subject: [PATCH] =?UTF-8?q?=E7=94=9F=E6=88=90=E6=B1=87=E6=80=BB=E7=9B=AE?= =?UTF-8?q?=E5=BD=95sum=EF=BC=8C=E8=BE=93=E5=87=BA=E6=96=87=E4=BB=B6?= =?UTF-8?q?=E5=90=8D=E4=BB=A5=E8=B7=AF=E7=BA=BF=E7=BC=96=E7=A0=81=E6=89=93?= =?UTF-8?q?=E5=A4=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- predict/predict_yolo11seg.py | 18 +++- util/smb_tool.py | 189 +++++++++++++++++++++++++++++++++-- util/yolo2pix_new.py | 98 ++++++++++++++++-- 3 files changed, 282 insertions(+), 23 deletions(-) diff --git a/predict/predict_yolo11seg.py b/predict/predict_yolo11seg.py index b4c5247..1775445 100644 --- a/predict/predict_yolo11seg.py +++ b/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) - move_file_shutil(tmp_output_dir+".zip", tmp_output_dir + f"/{task_id}.zip") + # zip_folder_shutil(tmp_output_dir) + # 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 = { diff --git a/util/smb_tool.py b/util/smb_tool.py index 53bf828..0c91219 100644 --- a/util/smb_tool.py +++ b/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'], - max_depth=4 + start_dir=config['excel_dir'], + 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'], - max_depth=4 + start_dir=config['excel_dir'], + 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'], - max_depth=2 + start_dir=config['fileindex_dir'], + max_depth=4 ) print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:") for i, path in enumerate(found_paths, 1): diff --git a/util/yolo2pix_new.py b/util/yolo2pix_new.py index 72a71d7..582d8fb 100644 --- a/util/yolo2pix_new.py +++ b/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) - if image is None: return + # 使用公共方法读取图片 + 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) - if img is None: return "", {} + img = read_image(image_path) + 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 :