YOLOV5 采用的数据集和以前的yolo模型不一样，数据结构如下图：
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：
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
测试集：
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：
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：
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：
1.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。