【经验分享】目标检测 VOC 格式数据集制作

  本教程详细介绍了 VOC 格式数据集的制作方法。

1、目录结构

   其中 makeTXT.py 用于生成 VOCdevkit/VOC/ImageSets/Main/*.txt，voc_label.py 根据 VOCdevkit/VOC/Annotations/* 、VOCdevkit/VOC/images/* 和 VOCdevkit/VOC/ImageSets/Main/*.txt 生成 VOCdevkit/labels/*.txt、VOCdevkit/VOC/test.txt(train.txt、val.txt)

2、Annotations

  可以用 LabelImg 对训练图片进行标注，会得到 *.xml，看起来像这样：

3、images

 这个没啥好说的，就是训练的图片。

4、ImageSets/Main

  由 makeTXT.py 生成 VOCdevkit/VOC/ImageSets/Main/* .txt 文件，包括 test.txt、train.txt、trainval.txt、val.txt。各文件里面的内容看起来差不多，像这样：

5、labels

  由 voc_label.py 生成，来看一下 labels/*.txt 里的文件内容，像这样：

6、makeTXT.py

  这个脚本用于生成 VOCdevkit/voc/ImageSets/Main 下的 *.txt。

  来看一下 makeTXT.py 脚本的内容：

import os
import random
 
trainval_percent = 0.1
train_percent = 0.9
xmlfilepath = 'VOCdevkit/VOC/Annotations'
txtsavepath = 'VOCdevkit/VOC/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('VOCdevkit/VOC/ImageSets/Main/trainval.txt', 'w')
ftest = open('VOCdevkit/VOC/ImageSets/Main/test.txt', 'w')
ftrain = open('VOCdevkit/VOC/ImageSets/Main/train.txt', 'w')
fval = open('VOCdevkit/VOC/ImageSets/Main/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()

7、voc_label.py

  这个脚本主要用于生成 VOCdevkit/VOC/labels/*.txt 以及 最终训练要用的 VOCdevkit/VOC/train.txt、VOCdevkit/VOC/test.txt 和 VOCdevkit/VOC/val.txt。

  来看一下 voc_label.py 脚本的内容：

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join

sets = ['train', 'test','val']

classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", 
    "car", "cat", "chair", "cow", "diningtable", "dog", "horse", 
    "motorbike", "person", "pottedplant", "sheep", "sofa", "train", 
    "tvmonitor"]

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('VOCdevkit/VOC/Annotations/%s.xml' % (image_id))
    out_file = open('VOCdevkit/VOC/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'):
        if obj.find('difficult'):
            difficult = obj.find('difficult').text
        else:
            difficult = 0
        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('VOCdevkit/VOC/labels/'):
        os.makedirs('VOCdevkit/VOC/labels/')
    image_ids = open('VOCdevkit/VOC/ImageSets/Main/%s.txt' % (image_set)).read().strip().split()
    list_file = open('VOCdevkit/VOC/%s.txt' % (image_set), 'w')
    for image_id in image_ids:
        list_file.write('VOCdevkit/VOC/images/%s.jpg\n' % (image_id))
        convert_annotation(image_id)
    list_file.close()

  收工~

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