YOLOV5 采用的資料集和以前的yolo模型不一樣，資料結構如下圖：
images檔案夾存放train和val的圖檔
labels裡面存放train和val的物體資料，裡面的每個txt檔案和images裡面的圖檔是一一對應的。
txt檔案的内容如下：
格式：物體類别 x y w h
坐标是不是真實的坐标，是将坐标除以寬高後的計算出來的，是相對于寬和高的比例。
資料介紹完了，下面講如何将voc資料轉為yolov5使用的資料集。
本次采用的資料集是PASCAL VOC 2007。
位址：
訓練集和驗證集：
http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
測試集：
http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar下載下傳後解壓，将測試集和訓練集合并在一起。在YOLOV5工程下面建立tmp檔案夾，然後将voc資料集放到tmp檔案夾下面，如圖：
在tmp檔案夾下面新家voc2txt.py檔案，将voc的資料轉為txt資料。
講解voc2txt.py代碼：
導入包:
import xml.etree.ElementTree as ET
import os
from os import getcwd
列出資料集的類别：
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"]
對box進行轉換，轉換後的坐标就是相對長寬的小數:
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)
下面這個方法是擷取單個xml的内容，将其轉換。
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')
整體代碼如下：
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() 運作完成後會生成test.txt train.txt val.txt。如圖：
在tmp檔案夾建立makedata.py，将生成的中間結果轉為YOLOV5所使用的最終代碼。
代碼如下：
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) 執行完成後，會在yolov5工程下生成最終的資料集。