灾害数据输出：综合明细表.xlsx
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liyubo
parent
d3d2d23f18
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333238c7b1
@ -899,7 +899,8 @@ def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_d
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found_paths = scanner.find_folders_by_name(
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share_path=config['share'],
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folder_name='图像类',
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start_dir=config['dir']
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start_dir=config['dir'],
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max_depth=2
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)
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target_path = "" # 识别图片目录
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@ -911,7 +912,8 @@ def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_d
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found_paths = scanner.find_folders_by_name(
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share_path=config['share'],
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folder_name='Images',
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start_dir=tmpConfig['dir']
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start_dir=tmpConfig['dir'],
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max_depth=3
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)
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if len(found_paths) > 0:
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target_path = found_paths[0]
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@ -758,7 +758,8 @@ def get_road_dict(dir,user_name,pwd) :
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found_paths = scanner.find_files_by_name(
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share_path=config['share'],
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file_name='每公里指标明细表*.xls',
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start_dir=config['dir']
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start_dir=config['dir'],
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max_depth=1
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)
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print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
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for i, path in enumerate(found_paths, 1):
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@ -789,7 +790,8 @@ def get_pile_dict(dir,user_name,pwd) :
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found_paths = scanner.find_files_by_name(
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share_path=config['share'],
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file_name='fileindex.txt',
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start_dir=config['dir']
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start_dir=config['dir'],
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max_depth=8
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)
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print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
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for i, path in enumerate(found_paths, 1):
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@ -6,6 +6,9 @@ import numpy as np
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from collections import defaultdict
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import smb
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import pandas as pd
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from openpyxl import Workbook
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from openpyxl.styles import Font, Alignment
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from openpyxl.utils import get_column_letter
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import glob
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from datetime import datetime
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@ -169,7 +172,8 @@ def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,cover_ra
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cname = class_names[cls_id] if cls_id<len(class_names) else str(cls_id)
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ids_str = '-'.join(map(str,sorted(covered_cells)))+'-'
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result_lines.append(f"{cname} {f"桩号:K000{pile_no}"} {ROAD_TYPE_EN_TO_CN.get(road_type, 'xx')} {ids_str}")
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pile_no_tmp = f"桩号:K000{pile_no}"
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result_lines.append(f"{cname} {pile_no_tmp} {ROAD_TYPE_EN_TO_CN.get(road_type, 'xx')} {ids_str}")
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if cname not in all_class_cells: all_class_cells[cname]=set()
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cell_info.append(covered_cells)
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cell_info.append([max_x - min_x, max_y - min_y])
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@ -273,6 +277,163 @@ def in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
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else :
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return False
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# 指定间隔区间，输出基本信息+灾害数据
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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) :
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process_info_data = []
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if summary_data:
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img_file_name = os.path.basename(image_path)
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road_data = get_road_info(road_dict, pile_dict, img_file_name)
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identify_width = road_data.get('识别宽度（米）', '3.6')
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up_or_down = road_data.get('方向(上行/下行)', '上行')
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road_code = pile_dict.get(img_file_name)[0]
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road_level = road_data.get('技术等级', '')
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road_type_cn = road_data.get('路面类型（沥青/水泥/砂石）', '沥青')
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group_list = summary_data
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increment = float(0.001 * interval)
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pile_no_start = float(0.000)
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pile_no_end = float(0.000) + increment
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# 上行/下行
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if up_or_down == '下行' :
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group_list = list(summary_data)[::-1]
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increment = float(-0.001 * interval)
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tmp_pile_no = convert_special_format(group_list[len(group_list)-1][1])
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pile_no_start = tmp_pile_no
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if tmp_pile_no % increment != 0 :
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pile_no_end = tmp_pile_no + (tmp_pile_no % increment)
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else :
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pile_no_end = tmp_pile_no + increment
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index = 0
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tmp_start = pile_no_start
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tmp_end = pile_no_end
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while True :
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# 每10m一个区间，在区间内进行灾害计算
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pile_no, DR, counts, road_type = group_list[index]
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cur_pile_no = convert_special_format(pile_no)
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pile_no_s = f"{tmp_start:0.3f}"
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pile_no_e = f"{tmp_end:0.3f}"
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up_or_down_code = "B" if up_or_down == '下行' else "A"
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if not in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
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# 没在刻度内直接输出无病害数据
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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}",'','','','','','','','','','','','','','','']
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if road_type=="asphalt":
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keys = list(CLASS_MAP_ASPHALT.keys())
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elif road_type=="cement":
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keys = list(CLASS_MAP_CEMENT.keys())
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else:
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keys = list(CLASS_MAP_GRAVEL.keys())
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# for k in keys:
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# row.append(f"{0:.2f}")
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process_info_data.append(row)
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# f.write(','.join(row)+'\n')
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else :
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row = [road_code, pile_no_s,pile_no_e,up_or_down_code,up_or_down,road_level,f"{road_type_cn}路面",'','']
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subRows = []
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while index < len(group_list):
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pile_no, DR, counts, road_type = group_list[index]
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cur_pile_no = convert_special_format(pile_no)
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tmp_row = []
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if in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
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pile_no = f"{tmp_start:0.3f}"
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tmp_row = [DR]
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if road_type=="asphalt":
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keys = list(CLASS_MAP_ASPHALT.keys())
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elif road_type=="cement":
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keys = list(CLASS_MAP_CEMENT.keys())
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else:
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keys = list(CLASS_MAP_GRAVEL.keys())
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# for k in keys:
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# tmp_row.append(counts.get(k, [0,0,0])[0])
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subRows.append(tmp_row)
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index = index + 1
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else :
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break
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# 同列汇总 10m一个区间--对应5张图
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column_sums = [f"{(sum(column)/(interval / 2)):0.2f}" for column in zip(*subRows)]
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row += column_sums
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# f.write(','.join(row)+'\n')
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row += ['','','','','','','','','','','','','','','']
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process_info_data.append(row)
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tmp_start = tmp_end
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tmp_end = tmp_start + increment
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print(f"tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
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if index >= len(group_list) :
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break
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return process_info_data
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def adjust_column_width(worksheet):
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"""
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调整列宽函数
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"""
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for col_idx in range(1, worksheet.max_column + 1):
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max_length = 0
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column_letter = get_column_letter(col_idx)
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for row_idx in range(1, worksheet.max_row + 1):
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try:
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cell = worksheet.cell(row=row_idx, column=col_idx)
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# 检查单元格是否在合并单元格范围内
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if any(cell.coordinate in merged_range for merged_range in worksheet.merged_cells.ranges):
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continue # 跳过合并单元格
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if cell.value is not None:
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cell_length = len(str(cell.value))
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if cell_length > max_length:
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max_length = cell_length
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except:
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continue
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if max_length > 0:
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worksheet.column_dimensions[column_letter].width = max_length + 2
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def create_multiple_sheets_with_multiple_headers(file_path, excel_data):
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# 创建工作簿
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wb = Workbook()
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# 删除默认创建的sheet
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wb.remove(wb.active)
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for sheet_data in excel_data:
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# 创建工作表
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ws = wb.create_sheet(title=sheet_data['name'])
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# 设置多表头
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current_row = 1
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# 多级表头
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for heads in sheet_data['headers'] :
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for head in heads :
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ws.merge_cells(f'{head[1]}{current_row}:{head[2]}{current_row}')
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ws[f'{head[1]}{current_row}'] = head[0]
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ws[f'{head[1]}{current_row}'].font = Font(bold=True, size=14)
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ws[f'{head[1]}{current_row}'].alignment = Alignment(horizontal='center')
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current_row += 1
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# 列标题
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for col_idx, column_name in enumerate(sheet_data['columns'], 1):
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cell = ws.cell(row=current_row, column=col_idx, value=column_name)
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cell.font = Font(bold=True)
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current_row += 1
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# 写入数据
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for row_data in sheet_data['data']:
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for col_idx, value in enumerate(row_data, 1):
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ws.cell(row=current_row, column=col_idx, value=value)
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current_row += 1
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# 自动调整列宽
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adjust_column_width(ws)
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# 保存文件
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wb.save(file_path)
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print(f"文件已保存为 {file_path}")
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# ---------------- 主函数-共享目录 ----------------
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def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
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@ -367,7 +528,7 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
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if up_or_down == '下行' :
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group_list = list(group)[::-1]
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increment = float(-0.010)
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tmp_pile_no = convert_special_format(group[len(group)-1][1])
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tmp_pile_no = convert_special_format(group_list[len(group_list)-1][1])
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pile_no_start = tmp_pile_no
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if tmp_pile_no % increment != 0 :
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pile_no_end = tmp_pile_no + (tmp_pile_no % increment)
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@ -381,7 +542,7 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
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tmp_end = pile_no_end
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while True :
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# 每10m一个区间，在区间内进行灾害计算
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pile_no, DR, counts, road_type = summary_data[index]
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pile_no, DR, counts, road_type = group_list[index]
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cur_pile_no = convert_special_format(pile_no)
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if not in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
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# 没在刻度内直接输出无病害数据
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@ -400,7 +561,7 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
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row = [f"{tmp_start:0.3f}", identify_width]
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subRows = []
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while index < len(group_list):
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pile_no, DR, counts, road_type = summary_data[index]
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pile_no, DR, counts, road_type = group_list[index]
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cur_pile_no = convert_special_format(pile_no)
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tmp_row = []
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@ -452,7 +613,37 @@ def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=
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data_list.append(excel_data)
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all_data = [headers] + data_list
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smb.write_to_excel_pandas(all_data, img_file_path + '/病害明显列表.xlsx')
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smb.write_to_excel_pandas(all_data, img_file_path + '/病害明细列表.xlsx')
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# 综合明细表.xlsx
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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']
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data1 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,10)
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data2 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,100)
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data3 = process_info(road_dict,pile_dict,summary_data,image_path,current_time,1000)
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excel_data = [
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{
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'name': '综合明细十米',
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'headers': [[['综合明细十米','A','Y']]],
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'columns': heads,
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'data': data1
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},
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{
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'name': '综合明细百米',
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'headers': [[['综合明细百米','A','Y']]],
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'columns': heads,
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'data': data2
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},
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{
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'name': '综合明细千米',
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'headers': [[['综合明细千米','A','Y']]],
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'columns': heads,
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'data': data3
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}
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]
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create_multiple_sheets_with_multiple_headers(img_file_path + '/综合明细表.xlsx', excel_data)
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# ---------------- 主函数 ----------------
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def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
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