Python如何操作目标检测数据集xml

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1. 根据xml文件统计目标种类以及数量

# -*- coding:utf-8 -*-#根据xml文件统计目标种类以及数量import osimport xml.etree.ElementTree as ETimport numpy as npnp.set_printoptions(suppress=True, threshold=np.nan)import matplotlibfrom PIL import Image def parse_obj(xml_path, filename):  tree=ET.parse(xml_path+filename)  objects=[]  for obj in tree.findall('object'):    obj_struct={}    obj_struct['name']=obj.find('name').text    objects.append(obj_struct)  return objects  def read_image(image_path, filename):  im=Image.open(image_path+filename)  W=im.size[0]  H=im.size[1]  area=W*H  im_info=[W,H,area]  return im_info  if __name__ == '__main__':  xml_path='/home/dlut/网络/make_database/数据集--合集/VOCdevkit/VOC2018/Annotations/'  filenamess=os.listdir(xml_path)  filenames=[]  for name in filenamess:    name=name.replace('.xml','')    filenames.append(name)  recs={}  obs_shape={}  classnames=[]  num_objs={}  obj_avg={}  for i,name in enumerate(filenames):    recs[name]=parse_obj(xml_path, name+ '.xml' )  for name in filenames:    for object in recs[name]:      if object['name'] not in num_objs.keys():         num_objs[object['name']]=1      else:         num_objs[object['name']]+=1      if object['name'] not in classnames:         classnames.append(object['name'])  for name in classnames:    print('{}:{}个'.format(name,num_objs[name]))  print('信息统计算完毕。')

2.根据xml文件统计目标的平均长度、宽度、面积以及每一个目标在原图中的占比

# -*- coding:utf-8 -*-#统计# 计算每一个目标在原图中的占比# 计算目标的平均长度、# 计算平均宽度，# 计算平均面积、# 计算目标平均占比import osimport xml.etree.ElementTree as ETimport numpy as np#np.set_printoptions(suppress=True, threshold=np.nan)  #10,000,000np.set_printoptions(suppress=True, threshold=10000000)  #10,000,000import matplotlibfrom PIL import Imagedef parse_obj(xml_path, filename):    tree = ET.parse(xml_path + filename)    objects = []    for obj in tree.findall('object'):        obj_struct = {}        obj_struct['name'] = obj.find('name').text        bbox = obj.find('bndbox')        obj_struct['bbox'] = [int(bbox.find('xmin').text),                              int(bbox.find('ymin').text),                              int(bbox.find('xmax').text),                              int(bbox.find('ymax').text)]        objects.append(obj_struct)    return objectsdef read_image(image_path, filename):    im = Image.open(image_path + filename)    W = im.size[0]    H = im.size[1]    area = W * H    im_info = [W, H, area]    return im_infoif __name__ == '__main__':    image_path = '/home/dlut/网络/make_database/数据集--合集/VOCdevkit/VOC2018/JPEGImages/'    xml_path = '/home/dlut/网络/make_database/数据集--合集/VOCdevkit/VOC2018/Annotations/'    filenamess = os.listdir(xml_path)    filenames = []    for name in filenamess:        name = name.replace('.xml', '')        filenames.append(name)    print(filenames)    recs = {}    ims_info = {}    obs_shape = {}    classnames = []    num_objs={}    obj_avg = {}    for i, name in enumerate(filenames):        print('正在处理 {}.xml '.format(name))        recs[name] = parse_obj(xml_path, name + '.xml')        print('正在处理 {}.jpg '.format(name))        ims_info[name] = read_image(image_path, name + '.jpg')    print('所有信息收集完毕。')    print('正在处理信息......')    for name in filenames:        im_w = ims_info[name][0]        im_h = ims_info[name][1]        im_area = ims_info[name][2]        for object in recs[name]:            if object['name'] not in num_objs.keys():                num_objs[object['name']] = 1            else:                num_objs[object['name']] += 1            #num_objs += 1            ob_w = object['bbox'][2] - object['bbox'][0]            ob_h = object['bbox'][3] - object['bbox'][1]            ob_area = ob_w * ob_h            w_rate = ob_w / im_w            h_rate = ob_h / im_h            area_rate = ob_area / im_area            if not object['name'] in obs_shape.keys():                obs_shape[object['name']] = ([[ob_w,                                               ob_h,                                               ob_area,                                               w_rate,                                               h_rate,                                               area_rate]])            else:                obs_shape[object['name']].append([ob_w,                                                  ob_h,                                                  ob_area,                                                  w_rate,                                                  h_rate,                                                  area_rate])        if object['name'] not in classnames:            classnames.append(object['name'])  # 求平均    for name in classnames:        obj_avg[name] = (np.array(obs_shape[name]).sum(axis=0)) / num_objs[name]        print('{}的情况如下：*******'.format(name))        print('  目标平均W={}'.format(obj_avg[name][0]))        print('  目标平均H={}'.format(obj_avg[name][1]))        print('  目标平均area={}'.format(obj_avg[name][2]))        print('  目标平均与原图的W比例={}'.format(obj_avg[name][3]))        print('  目标平均与原图的H比例={}'.format(obj_avg[name][4]))        print('  目标平均原图面积占比={}'.format(obj_avg[name][5]))    print('信息统计计算完毕。')

3.修改xml文件中某个目标的名字为另一个名字

#修改xml文件中的目标的名字，import os, sysimport globfrom xml.etree import ElementTree as ET# 批量读取Annotations下的xml文件# per=ET.parse(r'C:UsersockhuangDesktopAnnotations00003.xml')xml_dir = r'/home/dlut/网络/make_database/数据集--合集/VOCdevkit/VOC2018/Annotations'xml_list = glob.glob(xml_dir + '/*.xml')for xml in xml_list:    print(xml)    per = ET.parse(xml)    p = per.findall('/object')    for oneper in p:  # 找出person节点        child = oneper.getchildren()[0]  # 找出person节点的子节点        if child.text == 'PinNormal':   #需要修改的名字            child.text = 'normal bolt'    #修改成什么名字        if child.text == 'PinDefect':    #需要修改的名字            child.text = 'defect bolt-1'   #修改成什么名字    per.write(xml)    print(child.tag, ':', child.text)

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