Win10环境下，将VOC数据集转为YOLOV5使用的数据集。

小咪咪 2022-12-10 07:28 228阅读 0赞

YOLOV5 采用的数据集和以前的yolo模型不一样，数据结构如下图：

![20200924200709826.png][]

images文件夹存放train和val的图片

labels里面存放train和val的物体数据，里面的每个txt文件和images里面的图片是一一对应的。

txt文件的内容如下：

![2020092420072036.png][]

格式：物体类别 x y w h

坐标是不是真实的坐标，是将坐标除以宽高后的计算出来的，是相对于宽和高的比例。

数据介绍完了，下面讲如何将voc数据转为yolov5使用的数据集。

本次采用的数据集是PASCAL VOC 2007。

地址：

训练集和验证集：

[http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval\_06-Nov-2007.tar][http_host.robots.ox.ac.uk_pascal_VOC_voc2007_VOCtrainval_06-Nov-2007.tar]

测试集：

[http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest\_06-Nov-2007.tar][http_host.robots.ox.ac.uk_pascal_VOC_voc2007_VOCtest_06-Nov-2007.tar]

下载后解压，将测试集和训练集合并在一起。在YOLOV5工程下面新建tmp文件夹，然后将voc数据集放到tmp文件夹下面，如图：

![20200924200730264.png][]

在tmp文件夹下面新家voc2txt.py文件，将voc的数据转为txt数据。

讲解voc2txt.py代码：

导入包:

import xml.etree.ElementTree as ET

import os

from os import getcwd

列出数据集的类别：

sets=\[('2007', 'train'), ('2007', 'val'), ('2007', 'test')\]

classes = \["aeroplane", "bicycle", "bird", "boat", "bottle",

"bus", "car", "cat", "chair", "cow", "diningtable", "dog",

"horse", "motorbike", "person", "pottedplant", "sheep",

"sofa", "train", "tvmonitor"\]

对box进行转换，转换后的坐标就是相对长宽的小数:

def convert(size, box):

dw = 1./(size\[0\])

dh = 1./(size\[1\])

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

y = (box\[2\] + box\[3\])/2.0 - 1

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)

下面这个方法是获取单个xml的内容，将其转换。

def convert\_annotation(year, image\_id):

in\_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image\_id))

out\_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, 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')

整体代码如下：

import xml.etree.ElementTree as ET
    import os
    from os import getcwd
    
    sets = [('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
    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 - 1
        y = (box[2] + box[3]) / 2.0 - 1
        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(year, image_id):
        in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
        out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, 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()
    for year, image_set in sets:
        if not os.path.exists('VOCdevkit/VOC%s/labels/' % year):
            os.makedirs('VOCdevkit/VOC%s/labels/' % year)
        image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()
        list_file = open('%s.txt' % image_set, 'w')
        for image_id in image_ids:
            list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (wd, year, image_id))
            convert_annotation(year, image_id)
        list_file.close()

运行完成后会生成test.txt  train.txt  val.txt。如图：

![20200929182010797.png][]

在tmp文件夹新建makedata.py，将生成的中间结果转为YOLOV5所使用的最终代码。

代码如下：

import shutil
    import os
    
    file_List = ["train", "val", "test"]
    for file in file_List:
        if not os.path.exists('../VOC/images/%s' % file):
            os.makedirs('../VOC/images/%s' % file)
        if not os.path.exists('../VOC/labels/%s' % file):
            os.makedirs('../VOC/labels/%s' % file)
        print(os.path.exists('../tmp/%s.txt' % file))
        f = open('../tmp/%s.txt' % file, 'r')
        lines = f.readlines()
        for line in lines:
            print(line)
            line = "/".join(line.split('/')[-5:]).strip()
            shutil.copy(line, "../VOC/images/%s" % file)
            line = line.replace('JPEGImages', 'labels')
            line = line.replace('jpg', 'txt')
            shutil.copy(line, "../VOC/labels/%s/" % file)

执行完成后，会在yolov5工程下生成最终的数据集。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70][]

[20200924200709826.png]: /images/20221123/3917989b891144408459cc2ba75ef417.png
[2020092420072036.png]: /images/20221123/bfbdb999f8f2433582979078b719455c.png
[http_host.robots.ox.ac.uk_pascal_VOC_voc2007_VOCtrainval_06-Nov-2007.tar]: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
[http_host.robots.ox.ac.uk_pascal_VOC_voc2007_VOCtest_06-Nov-2007.tar]: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
[20200924200730264.png]: /images/20221123/27deec16022b4c8e8f7cc3455ccc8410.png
[20200929182010797.png]: /images/20221123/f1294a97cd0d4694a7b6119f6dc07a06.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70]: /images/20221123/6017ceb0e9c34adda96a8d213d26587c.png