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api handle for data transform

Project description

Auchor

Mtripix.Mutils.Mtrans(usage)

安装方式

pip install Mtripix

使用示例

import Mtripix.Mutils.Mtrans as Mt

idxs = [1,2,3]
labels = ["dog", "cat", "person"]
d = Mt.labels2dict(labels, idxs)
path_xml = "C:\Users\18829\Desktop\MTP\A.xml" path_txt = "C:\Users\18829\Desktop\MTP\A1.txt"
Mt.xml2yoloobb(path_xml, path_txt, d)

updata

  • 0.0.7
  • 增加了语义分割的标签图像转换
  • 增加了文件夹中标签的统计
  • 0.0.8
  • 增加了文件夹图片计算均值和方差的代码
  • 修复了yolo2voc中空目标转换的bug,修复了未引用math的bug

Environment

  • import json
  • import os.path
  • import PIL
  • import imgviz
  • import numpy as np
  • import cv2
  • import lxml.etree as ET
  • import math
  • from lxml.etree import Element, SubElement, tostring, ElementTree

Mtripix Function

Mtripix.Mutils(lists)

Mtripix.Mutils.Mtrans(lists)

Mtripix.Mutils.Mtrans(intrudction)

Mtripix.Mutils.Mtrans.labels2dict(labels, idxs)

@input:

  • labels(list/tuple): 标签名 里边都是str
  • idxs(list/tuple): 类别名 里边都是str

@return:

  • namedict(dict) 标签与类别对应的字典

Mtripix.Mutils.Mtrans.voc2yolo(path_xml, path_txt, namedict)

@input:

  • path_xml(str): xml文件地址
  • path_txt(str): 生成的yolo的txt地址
  • namedict(dict): 标签和类别对应的字典

@return:

  • 0

Mtripix.Mutils.Mtrans.json2yolo(path_json, path_txt, namedict, mode)

@input:

  • path_json(str): json文件地址
  • path_txt(str): 生成的yolo的txt地址
  • namedict(dict): 标签和类别对应的字典
  • mode(str): 一共可填两种模式“detection”和“segmentation”, 分别对应yolo检测和分割的文件,默认是“detection”

@return:

  • 0

Mtripix.Mutils.Mtrans.voc2dotaobb(path_xml, path_txt)

@input:

  • pathxml(str): xml文件地址
  • path_txt(str): 生成的DOTA的txt地址

@return:

  • 0

Mtripix.Mutils.Mtrans.voc2yoloobb(path_xml, path_txt, namedict)

@input:

  • path_xml(str): xml文件地址
  • path_txt(str): 生成的yoloobb的txt地址
  • namedict(dict): 标签和类别对应的字典

@return:

  • 0

Mtripix.Mutils.Mtrans.yolo2voc(path_img, path_txt, path_xml, namedict)

@input:

  • path_img(str): 图像地址
  • path_txt(str): 图像的yolo格式txt地址
  • path_xml(str): 生成的xml地址
  • namedict(dict): 类别和标签对应的字典

@return:

  • 0

Mtripix.Mutils.Mtrans.labelsCheck(path, format)

@input:

  • path(str): 当前的标签的文件夹地址
  • format: 需要统计的数据格式,有"voc","yolo"和”data“三种格式

@return:

  • count()输出字典, keys是当前的类别, value是当前文件夹中各类别出现的次数

Mtripix.Mutils.Mtrans.json2mask_sem(path_json, path_mask, namedict)

@input:

  • path_json(str): labelme标注的语义分割json文件
  • path_txt(str): 需要保存的8位标签伪图像,png格式
  • namedict(dict): 类别和标签对应的字典

@return:

  • img(array): 原图灰度图大小的分类特征图,类别为像素值,背景为0

Mtripix.Mutils.Mtrans.compute_mean_and_std(path, format=1)

@input:

  • path(str): 图像存放的文件夹路径
  • format(int): 图片的类型 1 代表RGB, 2代表灰度图

@return:

  • 返回两个参数,文件夹下图片的均值,方差(量化后)
  • mean: 文件夹下图片的均值(量化后)
  • std: 文件夹下图片的方差(量化后)

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