cls_object.py

# coding:utf-8
# 在切割后图片中，进行统计各个类别的目标数量

# -*- coding: utf-8 -*-
# -*- 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='D:/datasets/DOTA_clip/val/labeltxt/'
  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。')