生成汇总目录sum，输出文件名以路线编码打头
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liyubo
parent
5b18b592d0
commit
8dff981583
@ -703,7 +703,7 @@ class YOLOSegmentationInference:
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推理结果列表
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"""
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tmp_output_dir = output_dir + "\\" + datetime.now().strftime("%Y%m%d%H%M%S")
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tmp_output_dir = output_dir + "\\" + current_time
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print(f"正在处理共享目录: {input_dir} - {tmp_output_dir}")
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try:
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@ -770,14 +770,22 @@ class YOLOSegmentationInference:
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print(f"清理后，内存使用: {mem_usage:.2f} MB")
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# 处理输出目录的文件格式
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process_dir(road_dict, pile_dict, tmp_output_dir)
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process_dir(road_dict, pile_dict, current_time, tmp_output_dir)
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# 识别生成的文件压缩成zip
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zip_folder_shutil(tmp_output_dir)
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move_file_shutil(tmp_output_dir+".zip", tmp_output_dir + f"/{task_id}.zip")
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# zip_folder_shutil(tmp_output_dir)
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# move_file_shutil(tmp_output_dir+".zip", tmp_output_dir + f"/{task_id}.zip")
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# 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
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image_class_dir = smb_tool.extract_upto_image_class(tmpConfig['dir'])
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remote_image_class_dir = f"{image_class_dir}/sum/{task_id}/{current_time}"
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scanner.upload_directory(f"{tmp_output_dir}/sum", config['share'], remote_dir=remote_image_class_dir)
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del_file_shutil(f"{tmp_output_dir}/sum")
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# 上传识别完整结果
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remote_dir = f"{tmpConfig['dir']}_识别/{task_id}/{current_time}"
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scanner.upload_directory(tmp_output_dir, config['share'], remote_dir=remote_dir)
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# del_file_shutil(tmp_output_dir)
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del_file_shutil(tmp_output_dir)
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# 更新识别任务状态为已结束
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DB_CONFIG = {
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189
util/smb_tool.py
189
util/smb_tool.py
@ -13,6 +13,7 @@ import cv2
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import pandas as pd
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import io
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import shutil
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from pathlib import Path
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class SMBScanner:
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@ -500,6 +501,158 @@ class SMBScanner:
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except Exception as e:
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print(f"上传文件失败: {e}")
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return False
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def download_directory(self, share_name, remote_dir, local_dir, overwrite=True, exclude_patterns=None):
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"""
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从远程共享目录下载到本地目录
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Args:
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share_name: 共享名称
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remote_dir: 远程目录路径
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local_dir: 本地目录路径
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overwrite: 是否覆盖已存在的文件
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exclude_patterns: 要排除的文件名模式列表，默认为 None
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Returns:
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bool: 下载是否成功
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"""
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if not self.connect():
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return False
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# 默认排除模式
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if exclude_patterns is None:
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exclude_patterns = ['_grid.jpg', '_grid.txt']
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print(f"开始下载目录: {share_name}/{remote_dir} -> {local_dir}")
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print(f"排除文件模式: {exclude_patterns}")
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# 确保本地目录存在
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if not os.path.exists(local_dir):
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try:
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os.makedirs(local_dir, exist_ok=True)
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print(f"创建本地目录: {local_dir}")
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except Exception as e:
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print(f"创建本地目录失败: {e}")
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return False
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try:
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# 递归下载目录内容
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success = self._download_directory_recursive(share_name, remote_dir, local_dir, overwrite, exclude_patterns)
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if success:
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print("目录下载完成")
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else:
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print("目录下载过程中出现错误")
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return success
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except Exception as e:
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print(f"下载目录失败: {e}")
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return False
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def _download_directory_recursive(self, share_name, remote_path, local_path, overwrite, exclude_patterns):
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"""
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递归下载目录内容
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Args:
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share_name: 共享名称
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remote_path: 远程目录路径
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local_path: 本地目录路径
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overwrite: 是否覆盖已存在的文件
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exclude_patterns: 要排除的文件名模式列表
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Returns:
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bool: 下载是否成功
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"""
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try:
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success = True
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# 构建远程完整路径
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full_remote_path = self.build_full_path(share_name, remote_path)
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# 遍历远程目录
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for entry in scandir(full_remote_path):
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remote_item_path = f"{remote_path}/{entry.name}" if remote_path else entry.name
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local_item_path = os.path.join(local_path, entry.name)
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if entry.is_dir():
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# 处理子目录
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print(f"下载子目录: {entry.name}")
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# 确保本地子目录存在
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if not os.path.exists(local_item_path):
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try:
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os.makedirs(local_item_path, exist_ok=True)
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except Exception as e:
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print(f"创建本地子目录失败 {local_item_path}: {e}")
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success = False
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continue
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# 递归下载子目录
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sub_success = self._download_directory_recursive(share_name, remote_item_path, local_item_path, overwrite, exclude_patterns)
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if not sub_success:
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success = False
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else:
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# 下载文件
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file_success = self._download_single_file(share_name, remote_item_path, local_item_path, overwrite, exclude_patterns)
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if not file_success:
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success = False
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return success
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except Exception as e:
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print(f"下载目录内容失败 {remote_path}: {e}")
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return False
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def _download_single_file(self, share_name, remote_file_path, local_file_path, overwrite, exclude_patterns):
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"""
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下载单个文件
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Args:
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share_name: 共享名称
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remote_file_path: 远程文件路径
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local_file_path: 本地文件路径
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overwrite: 是否覆盖已存在的文件
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exclude_patterns: 要排除的文件名模式列表
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Returns:
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bool: 下载是否成功
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"""
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try:
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# 检查文件是否已存在
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if not overwrite and os.path.exists(local_file_path):
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print(f"文件已存在，跳过: {local_file_path}")
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return True
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# 检查文件名是否匹配排除模式，如果是则跳过
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file_name = os.path.basename(remote_file_path)
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for pattern in exclude_patterns:
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if file_name.endswith(pattern):
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print(f"文件被过滤，跳过: {file_name}")
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return True
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# 构建远程完整路径
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full_remote_path = self.build_full_path(share_name, remote_file_path)
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# 下载文件
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print(f"下载文件: {file_name}")
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# 读取远程文件
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with open_file(full_remote_path, mode='rb') as remote_file:
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remote_content = remote_file.read()
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# 写入本地文件
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with open(local_file_path, 'wb') as local_file:
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local_file.write(remote_content)
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file_size = len(remote_content)
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print(f"文件下载成功: {local_file_path} ({file_size} 字节)")
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return True
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except Exception as e:
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print(f"下载文件失败 {remote_file_path}: {e}")
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return False
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def find_folders_by_name(self, share_path, folder_name, start_dir="", max_depth=10):
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"""专门查找文件夹"""
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@ -754,6 +907,23 @@ def standardized_path(path) :
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return path
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def extract_upto_image_class(path_str):
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"""
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提取路径中直到 '图像类' 目录的部分（包括 '图像类' 本身）
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如果路径中没有 '图像类'，返回原始路径。
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"""
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p = Path(path_str)
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parts = p.parts # 获取所有路径部分（元组）
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try:
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idx = parts.index('图像类') # 找到 '图像类' 的位置
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except ValueError:
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# 没找到，返回原始路径
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return path_str
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# 取到 '图像类' 为止的部分，再组合成路径
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return Path(*parts[:idx+1])
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# 从传入的路径中提取ip，共享目录，目标访问目录
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def get_conf(zip_url, user_name, pwd) :
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zip_url = standardized_path(zip_url)
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@ -769,12 +939,16 @@ def get_conf(zip_url, user_name, pwd) :
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dir = '/'.join(new_parts)
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excel_dir = ''
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fileindex_dir = ''
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excel_dir = '/'.join(parts[2:])
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fileindex_dir = '/'.join(parts[2:])
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for index, part in enumerate(parts, 0) :
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if part == 'Images' and index - 2 > 2 :
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excel_dir = '/'.join(parts[2:index-2])
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fileindex_dir = '/'.join(parts[2:index])
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if part == '图像类' and index > 2 :
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excel_dir = '/'.join(parts[2:index])
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fileindex_dir = '/'.join(parts[2:index+2])
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# 配置信息
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@ -785,7 +959,8 @@ def get_conf(zip_url, user_name, pwd) :
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'domain': '', # 工作组留空
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'share': parts[1],
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'dir': dir,
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'excel_dir': excel_dir
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'excel_dir': excel_dir,
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'fileindex_dir': fileindex_dir,
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}
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return config
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@ -813,8 +988,8 @@ 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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max_depth=4
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start_dir=config['excel_dir'],
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max_depth=2
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)
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print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls*' 文件:")
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for i, path in enumerate(found_paths, 1):
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@ -845,8 +1020,8 @@ def get_road_dict_for_width(config, scanner):
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found_paths_for_width = scanner.find_files_by_name(
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share_path=config['share'],
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file_name='nl_yy_glzb*.xls',
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start_dir=config['dir'],
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max_depth=4
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start_dir=config['excel_dir'],
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max_depth=2
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)
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road_dict_for_width = {}
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if len(found_paths_for_width) > 0 :
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@ -871,8 +1046,8 @@ 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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max_depth=2
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start_dir=config['fileindex_dir'],
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max_depth=4
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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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@ -7,6 +7,7 @@ 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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import shutil
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from datetime import datetime
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try:
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# 相对导入
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@ -15,6 +16,33 @@ except ImportError:
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# 直接运行时回退到绝对导入
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import smb_tool
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# ---------------- 图片操作 ----------------
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def read_image(image_path):
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"""读取图片，处理中文路径"""
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try:
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image_path = smb_tool.standardized_path(image_path)
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img_array = np.fromfile(image_path, dtype=np.uint8)
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img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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return img
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except Exception as e:
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print(f"读取图片失败: {image_path}, 错误: {e}")
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return None
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def save_image(image, save_path):
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"""保存图片，处理中文路径"""
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try:
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save_path = smb_tool.standardized_path(save_path)
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success, img_encoded = cv2.imencode('.jpg', image)
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if success:
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img_encoded.tofile(save_path)
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return True
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else:
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print(f"编码图片失败: {save_path}")
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return False
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except Exception as e:
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print(f"保存图片失败: {save_path}, 错误: {e}")
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return False
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# ---------------- 常量 ----------------
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CELL_WIDTH = 0.1 # 每格宽 (米)
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CELL_HEIGHT = 0.1 # 每格高 (米)
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@ -63,8 +91,10 @@ def calc_grid_param(pic_width, pic_height, actual_width, actual_height, grid_x_c
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def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGHT), save_path=None):
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image = cv2.imread(image_path)
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if image is None: return
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# 使用公共方法读取图片
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image = read_image(image_path)
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if image is None: return None
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h, w = image.shape[:2]
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cell_w, cell_h = cell_size
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cols = w // cell_w
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@ -84,7 +114,9 @@ def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGH
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cv2.line(image,(i,0),(i,h),(100,100,100),1)
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for j in range(0, h, cell_h):
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cv2.line(image,(0,j),(w,j),(100,100,100),1)
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if save_path: cv2.imwrite(save_path,image)
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if save_path:
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# 使用公共方法保存图片
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save_image(image, save_path)
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return image
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def detect_road_type_from_content(label_file):
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@ -102,6 +134,14 @@ def detect_road_type_from_content(label_file):
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if kw in content: return "gravel"
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return "gravel"
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def get_road_code(pile_dict) :
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"""获取路线编码"""
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img_file_name = list(pile_dict.keys())[0]
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parts = pile_dict.get(img_file_name)
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if parts :
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road_code = parts[0]
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return road_code
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def get_road_info(road_dict, pile_dict, img_file_name):
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"""获取路线信息"""
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parts = pile_dict.get(img_file_name)
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@ -172,8 +212,8 @@ def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,cell_ind
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else: class_map = CLASS_MAP_GRAVEL
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class_names = list(class_map.keys())
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img = cv2.imread(image_path)
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if img is None: return "", {}
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img = read_image(image_path)
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if img is None: return "", {}, road_type, 20 * RESERVED_CELL_NUM
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h, w = img.shape[:2]
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cols = max(1, w//grid_width)
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rows = max(1, h//grid_height)
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@ -286,6 +326,12 @@ def yoloseg_to_grid(image_path,label_file,cover_ratio=COVER_RATIO):
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all_class_cells[cname].update(covered_cells)
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return '\n'.join(result_lines), all_class_cells, road_type
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def generate_classes_txt_content(road_type) :
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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"
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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"
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if road_type=="gravel": return "坑槽\n沉陷\n车辙\n波浪搓板"
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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"
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def generate_header(road_type):
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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"
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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 :
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user