python:批量统计xml中各类目标的数量案例

之前写了一个matlab的，越用越觉得麻烦，如果不同数据集要改类别数目，而且运行速度慢。所以重新写了一个Python的，直接读取xml文件夹路径就可以，不用预先知道类别，直接能够检测出所有类别的目标名称及其对应的数量。

分享出来给大家。

代码如下：

# -*- coding:utf-8 -*-
import os
import xml.etree.ElementTree as ET
import numpy as np
np.set_printoptions(suppress=True, threshold=np.nan)
import matplotlib
from 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='C:/Users/nansbas/Desktop/hebin/03/'
 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。')

补充知识：Python对目标检测数据集xml文件操作（统计目标种类、数量、面积、比例等&修改目标名字）

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 os
import xml.etree.ElementTree as ET
import numpy as np
np.set_printoptions(suppress=True, threshold=np.nan)
import matplotlib
from 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 os
import xml.etree.ElementTree as ET
import numpy as np

#np.set_printoptions(suppress=True, threshold=np.nan) #10,000,000
np.set_printoptions(suppress=True, threshold=10000000) #10,000,000
import matplotlib
from 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
    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 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__':
  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：*******\n'.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={}\n'.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, sys
import glob
from xml.etree import ElementTree as ET

# 批量读取Annotations下的xml文件
# per=ET.parse(r'C:\Users\rockhuang\Desktop\Annotations\000003.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)

以上这篇python:批量统计xml中各类目标的数量案例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。