python实现的批量分析xml标签中各个类别个数功能示例
本文实例讲述了python实现的批量分析xml标签中各个类别个数功能。分享给大家供大家参考,具体如下:
文章目录
需要个脚本分析下各个目标的数目 顺带练习下多进程,自用,直接上代码:
# -*- coding: utf-8 -*- # @Time : 2019/06/10 18:56 # @Author : TuanZhangSama import os import xml.etree.ElementTree as ET from multiprocessing import Pool,freeze_support,cpu_count import imghdr import logging def get_all_xml_path(xml_dir:str,filter=['.xml']): #遍历文件夹下所有xml result=[] #maindir是当前搜索的目录 subdir是当前目录下的文件夹名 file是目录下文件名 for maindir,subdir,file_name_list in os.walk(xml_dir): for filename in file_name_list: ext=os.path.splitext(filename)[1]#返回扩展名 if ext in filter: result.append(os.path.join(maindir,filename)) return result def analysis_xml(xml_path:str): tree=ET.parse(xml_path) root=tree.getroot() result_dict={} for obj in root.findall('object'): obj_name = obj.find('name').text obj_num=result_dict.get(obj_name,0)+1 result_dict[obj_name]=obj_num if imghdr.what(xml_path.replace('.xml','.jpg')) != 'jpeg': print(xml_path.replace('.xml','.jpg'),'is worng') # logging.info(xml_path.replace('.xml','.jpg')) if is_valid_jpg(xml_path.replace('.xml','.jpg')): pass return result_dict def analysis_xmls_batch(xmls_path_list:list): result_list=[] for i in xmls_path_list: result_list.append(analysis_xml(i)) return result_list def collect_result(result_list:list): all_result_dict={} for result_dict in result_list: for key,values in result_dict.items(): obj_num=all_result_dict.get(key,0)+values all_result_dict[key]=obj_num return all_result_dict def main(xml_dir:str,result_save_path:str =None): r'''根据xml文件统计所有样本的数目.对于文件不完整的图片和有xml但无图片的样本,直接进行删除.默认跑满所有的cpu核心 Parameters ---------- xml_dir : str xml所在的文件夹.用的递归形式,因此只需保证xml在此目录的子目录下即可.对应的图片和其xml要在同一目录 result_save_path : str 分析结果的日志保存路径.默认 None 无日志 ''' if result_save_path is not None: assert isinstance(result_save_path,str),'{} is illegal path'.format(result_save_path) else: logging.basicConfig(filename=result_save_path,filemode='w',level=logging.INFO) freeze_support()#windows 上用 xmls_path=get_all_xml_path(xml_dir) worker_num=cpu_count() print('your CPU num is',cpu_count()) length=float(len(xmls_path))/float(worker_num) #计算下标,尽可能均匀地划分输入文件的列表 indices=[int(round(i*length)) for i in range(worker_num+1)] #生成每个进程要处理的子文件列表 sublists=[xmls_path[indices[i]:indices[i+1]] for i in range(worker_num)] pool=Pool(processes=worker_num) all_process_result_list=[] for i in range(worker_num): all_process_result_list.append(pool.apply_async(analysis_xmls_batch,args=(sublists[i],))) pool.close() pool.join() print('analysis done!') _temp_list=[] for i in all_process_result_list: _temp_list=_temp_list+i.get() result=collect_result(_temp_list) logging.info(result) print(result) def is_valid_jpg(jpg_file): """判断JPG文件下载是否完整 """ if not os.path.exists(jpg_file): print(jpg_file,'is not existes') os.remove(jpg_file.replace('.jpg','.xml')) with open(jpg_file, 'rb') as fr: fr.seek(-2, 2) if fr.read() == b'\xff\xd9': return True else: os.remove(jpg_file) os.remove(jpg_file.replace('.jpg','.xml')) print(jpg_file) logging.error(jpg_file,'is imperfect img') return False if __name__=='__main__': test_dir='/home/chiebotgpuhq/Share/winshare/origin' save_path='/home/chiebotgpuhq/MyCode/python/pytorch/mmdetection-master/result.log' main(test_dir,save_path)
PS:这里再为大家提供几款关于xml操作的在线工具供大家参考使用:
在线XML/JSON互相转换工具:
http://tools.jb51.net/code/xmljson
在线格式化XML/在线压缩XML:
http://tools.jb51.net/code/xmlformat
XML在线压缩/格式化工具:
http://tools.jb51.net/code/xml_format_compress
XML代码在线格式化美化工具:
http://tools.jb51.net/code/xmlcodeformat
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希望本文所述对大家Python程序设计有所帮助。