TensorFlow 制作自己数据集时,xml转csv
TensorFlow 制作自己数据集时,xml转csv千篇一律,把我拐入坑里了。
如果训练自己的数据集只有一个类别,用网络上的xml_to_csv,完全没有问题,源码如下:
# -*- coding: utf-8 -*- import os import glob import pandas as pd import xml.etree.ElementTree as ET def xml_to_csv(path): xml_list = [] # 读取注释文件 for xml_file in glob.glob(path + '/*.xml'): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): value = (root.find('filename').text + '.jpg', int(root.find('size')[0].text), int(root.find('size')[1].text), member[0].text, int(member[4][0].text), int(member[4][1].text), int(member[4][2].text), int(member[4][3].text) ) xml_list.append(value) column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] # 将所有数据分为样本集和验证集,一般按照3:1的比例 train_list = xml_list[0: int(len(xml_list) * 0.67)] eval_list = xml_list[int(len(xml_list) * 0.67) + 1: ] # 保存为CSV格式 train_df = pd.DataFrame(train_list, columns=column_name) eval_df = pd.DataFrame(eval_list, columns=column_name) train_df.to_csv('data/train.csv', index=None) eval_df.to_csv('data/eval.csv', index=None) def main(): path = './xml' xml_to_csv(path) print('Successfully converted xml to csv.') main()
如果你的类别数据集,超过2类以上,再用上述源码,觉得把所有的数据集3:1的分割,而非一个类别的3:1分割 。
对上述源码略作调整,完美把每一类数据集按照9:1分割为训练数据集和测试数据集,源代码如下:
# coding: utf-8 import glob import pandas as pd import xml.etree.ElementTree as ET classes = ["20Km_h", "no_passing_35", "no_passing", "keep_left", "keep_right", "mandatory", "straight_or_left", "passing_limits", "bicycles", "pedestrians", "stop", "dangerous"] def xml_to_csv(path): train_list = [] eval_list = [] for cls in classes: xml_list = [] # 读取注释文件 for xml_file in glob.glob(path + '/*.xml'): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): if cls == member[0].text: value = (root.find('filename').text, int(root.find('size')[0].text), int(root.find('size')[1].text), member[0].text, int(member[4][0].text), int(member[4][1].text), int(member[4][2].text), int(member[4][3].text) ) xml_list.append(value) for i in range(0,int(len(xml_list) * 0.9)): train_list.append(xml_list[i]) for j in range(int(len(xml_list) * 0.9) + 1,int(len(xml_list))): eval_list.append(xml_list[j]) column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] # 保存为CSV格式 train_df = pd.DataFrame(train_list, columns=column_name) eval_df = pd.DataFrame(eval_list, columns=column_name) train_df.to_csv('data/train.csv', index=None) eval_df.to_csv('data/eval.csv', index=None) def main(): # path = 'E:\\\data\\\Images' path = r'D:\work\PycharmPro\trafficsign\SSD_NET\data\xml_data' # path参数更具自己xml文件所在的文件夹路径修改 xml_to_csv(path) print('Successfully converted xml to csv.') main()
classes = ["20Km_h", "no_passing_35", "no_passing", "keep_left", "keep_right", "mandatory", "straight_or_left", "passing_limits", "bicycles", "pedestrians", "stop", "dangerous"]
该处需要改为自己数据集类别标签名。
原文:https://blog.csdn.net/miao0967020148/article/details/90208139