yolo、voc格式数据集

我的理解是：
yolo格式的数据集：每个图片对应一个txt文件的标签
voc格式的数据集：每张图片对应一个xml文件的标签
都有相应的标注工具。

1、准备工作

1.1、200类的，分类存放的数据集，每个文件夹内是具体的类别
1.2、边框信息

1.3、类别信息
1.4、图片信息

2、需要这样的TXT

2.1、只含类别的TXT
一个脚本其实可以实现，通过手动也可以实现，手动实现的方法：
可以将1.3的TXT复制到Excel中，选中，按.分列，然后将类别那列复制出来放到TXT里，这样可能会遇到别的编码的问题，具体遇到再说
最后得到：命名为voc_classes.txt

2.2 图片的位置即图片上目标位置的TXT
将1.2的边框信息和1.4的图片信息拼接到一起，还是用2.1中的那种Excel的方法进行
最后得到：命名为2010_train.txt

3、txt转xml，即yolo格式转voc格式

import os
import sys
from xml.etree import ElementTree
from xml.etree.ElementTree import Element, SubElement
from lxml import etree # 我用lxml
import copy
import codecs
import random
import cv2
from tqdm import tqdm
from PIL import Image


XML_EXT = '.xml'
ENCODE_METHOD = 'utf-8'


class PascalVocWriter:

    def __init__(self, annotationDict):  # annotationDict
        self.annotationDict = annotationDict

    def save(self, path=None):  # 保存
        """
        return:
          生成一个VOC格式的xml，返回一个xml的根标签，以开始
        """
        root = self.genXML()  # 生成xml-----------top = Element('annotation')
        if path is None:  # 写进文件夹
            out_file = codecs.open(self.annotationDict['filename'] + XML_EXT, 'w', encoding=ENCODE_METHOD)
            # codecs.open(filepath, method, encoding)
        else:
            out_file = codecs.open(path, 'w', encoding=ENCODE_METHOD)

        prettifyResult = self.prettify(root)  # 转化格式
        out_file.write(prettifyResult.decode('utf-8'))
        out_file.close()

    def genXML(self):
        # 生成xml
        top = Element('annotation')

        folder = SubElement(top, 'folder')
        folder.text = self.annotationDict['folder']

        filename = SubElement(top, 'filename')
        filename.text = self.annotationDict['filename']

        path = SubElement(top, 'path')
        path.text = self.annotationDict['path']

        source = SubElement(top, 'source')
        database = SubElement(source, 'database')
        database.text = self.annotationDict['source']['database']

        size_part = SubElement(top, 'size')
        width = SubElement(size_part, 'width')
        height = SubElement(size_part, 'height')
        depth = SubElement(size_part, 'depth')

        width.text = str(self.annotationDict['size']['width'])
        height.text = str(self.annotationDict['size']['height'])
        depth.text = str(self.annotationDict['size']['depth'])

        segmented = SubElement(top, 'segmented')
        segmented.text = str(self.annotationDict['segmented'])

        for obj in self.annotationDict['objects']:
            object_item = SubElement(top, 'object')

            name = SubElement(object_item, 'name')
            name.text = obj['name']

            pose = SubElement(object_item, 'pose')
            pose.text = obj['pose']

            truncated = SubElement(object_item, 'truncated')
            truncated.text = str(obj['truncated'])

            difficult = SubElement(object_item, 'difficult')
            difficult.text = str(obj['difficult'])

            bndbox = SubElement(object_item, 'bndbox')
            xmin = SubElement(bndbox, 'xmin')
            xmin.text = str(obj['bndbox']['xmin'])
            ymin = SubElement(bndbox, 'ymin')
            ymin.text = str(obj['bndbox']['ymin'])
            xmax = SubElement(bndbox, 'xmax')
            xmax.text = str(obj['bndbox']['xmax'])
            ymax = SubElement(bndbox, 'ymax')
            ymax.text = str(obj['bndbox']['ymax'])
        return top

    def prettify(self, elem):
        rough_string = ElementTree.tostring(elem, 'utf-8')# 改
        # rough_string = etree.tostring(elem, 'utf8')
        root = etree.fromstring(rough_string)
        return etree.tostring(root, pretty_print=True, encoding=ENCODE_METHOD).replace("  ".encode(), "\t".encode())
        # element, encoding=None, method=None, *,
        #              short_empty_elements=True

def loadTxt(txt_p):
    with open(txt_p, 'r',encoding="utf-8") as f: # encoding="utf-8"加在这里有效
        plist = [line[:-1] for line in f.readlines()]
    return plist


def getGtBoxInfoDict(boxInfoTxt):
    gtBoxInfoDict = {}
    lines = loadTxt(boxInfoTxt)

    for line in lines:
        imgFilename, xmin, ymin, xmax, ymax, name = line.split(',')
        boxInfoDict = {
            'name': name,
            'xmin': int(float(xmin)),
            'ymin': int(float(ymin)),
            'xmax': int(float(xmax)),
            'ymax': int(float(ymax))
        }
        if gtBoxInfoDict.get(imgFilename, None) is None:
            gtBoxInfoDict[imgFilename] = []
        gtBoxInfoDict[imgFilename].append(boxInfoDict)
    return gtBoxInfoDict


def createAnnotation(imgname, boxInfoList):
    path_dir = './VOC2007/JPEGImages/' + imgname
    img = Image.open(path_dir) # 1读图片
    annotation = {
        'folder': 'JPEGImages', 'filename': imgname,
        'path': path_dir,
        'source': {'database': 'Unknow'},
        'size': {'width':img.size[0], 'height':img.size[1], 'depth': 3},
        # 这里原代码有自己的赋值机制，对我的无效，我就改成了我的，用1读的图片进行取长宽操作，
        # 深度都是彩色图，直接赋值3，如果不确定是否都为彩色可以用img.size[2]代替
        'segmented': 0,
        'objects': []
    }
    for boxInfo in boxInfoList:
        obj = {
            'name': boxInfo['name'],
            'pose': 'Unspecified',
            'truncated': 0,
            'difficult': 0,
            'bndbox': {
                'xmin': boxInfo['xmin'],
                'ymin': boxInfo['ymin'],
                'xmax': boxInfo['xmax'],
                'ymax': boxInfo['ymax']
            }
        }
        annotation['objects'].append(obj)
        return annotation


def main():
    """添加的utf8"""
    # open("label/my.txt",encoding="gbk")
    gt_txt = r'2012_train.txt'  ##另一个.py生成的图片信息和位置信息放在一起的一个TXT##############################改了反斜杠#########
    annotation_dir = r'./VOC2007/Annotation'

    gtBoxInfoDict = getGtBoxInfoDict(gt_txt) #返回一个嵌套字典

    for imgname, boxInfoList in gtBoxInfoDict.items():
        # annotation = createAnnotation(imgname, boxInfoList)
        # PascalVocWriter(annotation).save(os.path.join(annotation_dir, os.path.splitext(imgname)[0]+ XML_EXT))
        annotation = createAnnotation(imgname, boxInfoList) #返回一个xml的东西
        #            createAnnotation(imgname, boxInfoList)
        # my_path=r"os.path.splitext(imgname)[0]"
        ####################################################
        data_list = imgname.split('/')  # 我的图片里名称带路径，我要切开，如果你有更好的办法，哈哈求推荐
        one = data_list[0]
        two = data_list[1]
        ####################################################
        PascalVocWriter(annotation).save(os.path.join(annotation_dir, one + os.path.splitext(two)[0] + XML_EXT))
        # 切开路径为这里做准备
        print(os.path.join(annotation_dir, one , os.path.splitext(two)[0] + XML_EXT))
#       top = Element('annotation')

if __name__ == '__main__':
    main()

生成的xml

JPEGImages和Annotations中对应的文件名是一致的

4、利用生成的xml生成4个txt，即拆分训练集和测试集

import os

import random

trainval_percent = 0.1

train_percent = 0.9

xmlfilepath = 'D:/py_project/yolo2/VOCdevkit/VOC2007/Annotations'

txtsavepath = 'D:/py_project/yolo2/VOCdevkit/VOC2007/ImageSets'

total_xml = os.listdir(xmlfilepath)

num = len(total_xml)

list = range(num)

tv = int(num * trainval_percent)

tr = int(tv * train_percent)

trainval = random.sample(list, tv)

train = random.sample(trainval, tr)

ftrainval = open('D:/py_project/yolo2/VOCdevkit/VOC2007/ImageSets/trainval.txt', 'w')

ftest = open('D:/py_project/yolo2/VOCdevkit/VOC2007/ImageSets/test.txt', 'w')

ftrain = open('D:/py_project/yolo2/VOCdevkit/VOC2007/ImageSets/train.txt', 'w')

fval = open('D:/py_project/yolo2/VOCdevkit/VOC2007/ImageSets/val.txt', 'w')

for i in list:

    name = total_xml[i][:-4] + '\n'

    if i in trainval:

        ftrainval.write(name)

        if i in train:

            ftest.write(name)

        else:

            fval.write(name)

    else:

        ftrain.write(name)

ftrainval.close()

ftrain.close()

fval.close()

ftest.close()

5、voc变为txt（yolo格式），生成每个类别对应的TXT，即数据集格式变为yolo格式

import xml.etree.ElementTree as ET
from read_class import csv_list ##生成类别列表1
import pickle

import os

from os import listdir, getcwd

from os.path import join

sets = ['train', 'test', 'val']
##########生成的类别列表
class_path = "C:\\Users\\sy\\Desktop\\online\\my71000_665_1044839747790380683\\class.csv"##生成类别列表2
classes = csv_list(class_path)  ##生成类别列表3


def convert(size, box):
    dw = 1. / size[0]

    dh = 1. / size[1]

    x = (box[0] + box[1]) / 2.0

    y = (box[2] + box[3]) / 2.0

    w = box[1] - box[0]

    h = box[3] - box[2]

    x = x * dw

    w = w * dw

    y = y * dh

    h = h * dh

    return (x, y, w, h)


def convert_annotation(image_id):
    in_file = open('D:/py_project/yolo2/VOCdevkit/VOC2007/Annotations/%s.xml' % (image_id))

    out_file = open('D:/py_project/yolo2/VOCdevkit/VOC2007/labels/%s.txt' % (image_id), 'w')

    tree = ET.parse(in_file)

    root = tree.getroot()

    size = root.find('size')

    w = int(size.find('width').text)

    h = int(size.find('height').text)

    for obj in root.iter('object'):

        difficult = obj.find('difficult').text

        cls = obj.find('name').text

        if cls not in classes or int(difficult) == 1:
            continue

        cls_id = classes.index(cls)

        xmlbox = obj.find('bndbox')

        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),

             float(xmlbox.find('ymax').text))

        bb = convert((w, h), b)

        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


wd = getcwd()

print(wd)

for image_set in sets:

    if not os.path.exists('D:/py_project/yolo2/VOCdevkit/VOC2007/labels/'):
        os.makedirs('D:/py_project/yolo2/VOCdevkit/VOC2007/labels/')

    image_ids = open('D:/py_project/yolo2/VOCdevkit/VOC2007/ImageSets/%s.txt' % (image_set)).read().strip().split()

    list_file = open('D:/py_project/yolo2/VOCdevkit/VOC2007/%s.txt' % (image_set), 'w')

    for image_id in image_ids:
        list_file.write('D:/py_project/yolo2/VOCdevkit/VOC2007/images/%s.jpg\n' % (image_id))

        convert_annotation(image_id)

    list_file.close()