获取labelimg标注真实框的宽高、归一化数据再存入列表当中

问题：目标检测往往需要聚类anchors，借助标注的真实框信息，怎么才能将可视化呢？
解决思路：获取labelimg标注真实框的宽高、归一化数据再存入列表当中即可。
方法：读取解析xml文件，比如Annotations文件夹下的xml文件(本博客以该方法讲解)；txt文本数据挖掘，比如（2007_train.txt）
话不多说，直接上菜：

import csv
import numpy as np
import glob
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
import pandas as pd
import math
datas = []
# 对于每一个xml都寻找box
#获取labelimg标注真实框的宽高并归一化存入列表代码
path = r'VOCdevkit/VOC2007/Annotations'
for xml_file in glob.glob('{}/*xml'.format(path)):
    tree = ET.parse(xml_file)
    height = int(tree.findtext('./size/height'))
    width = int(tree.findtext('./size/width'))
    if height <= 0 or width <= 0:
        continue

    # 对于每一个目标都获得它的宽高
    for obj in tree.iter('object'):
        xmin = int(float(obj.findtext('bndbox/xmin'))) / width
        ymin = int(float(obj.findtext('bndbox/ymin'))) / height
        xmax = int(float(obj.findtext('bndbox/xmax'))) / width
        ymax = int(float(obj.findtext('bndbox/ymax'))) / height

        xmin = np.float64(xmin)
        ymin = np.float64(ymin)
        xmax = np.float64(xmax)
        ymax = np.float64(ymax)
        # 得到宽高
        #取小数点后两位
        a=xmax - xmin
        b=ymax - ymin
        c=2
        a=math.floor(a * 10 ** c) / (10 ** c)
        b=math.floor(b * 10 ** c) / (10 ** c)

        #datas.append([xmax - xmin, ymax - ymin])
        datas.append([a, b]
        #print(datas)
 """    
#获取labelimg标注真实框的宽高后直接存入列表代码
import numpy as np
from PIL import Image
import os
import glob
import random
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
import pandas as pd

datas = []
# 对于每一个xml都寻找box
path = r'VOCdevkit/VOC2007/Annotations'
for xml_file in glob.glob('{}/*xml'.format(path)):
    tree = ET.parse(xml_file)
    height = int(tree.findtext('./size/height'))
    width = int(tree.findtext('./size/width'))
    if height <= 0 or width <= 0:
        continue

    # 对于每一个目标都获得它的宽高
    for obj in tree.iter('object'):
        xmin = int(float(obj.findtext('bndbox/xmin')))
        ymin = int(float(obj.findtext('bndbox/ymin')))
        xmax = int(float(obj.findtext('bndbox/xmax')))
        ymax = int(float(obj.findtext('bndbox/ymax')))

        xmin = np.int32(xmin)
        ymin = np.int32(ymin)
        xmax = np.int32(xmax)
        ymax = np.int32(ymax)
        # 得到宽高
        datas.append([xmax - xmin, ymax - ymin])
        #print(datas)
"""   

归一化后结果：

未归一化结果：

后期将介绍采用二维列表转csv格式，将真实框信息可视化。

标签：xml,glob,归一化,height,labelimg,import,标注
来源： https://blog.csdn.net/wuzhihuaw/article/details/123639061