如何利用python将NWPU VHR-10目标检测遥感数据集的格式转换成VOC目标检测数据集的格式

VOC目标检测数据集的格式

其中图片存放在JPEGImages文件夹中，标注是xml文件，存储在Annotations文件中

关于train集和val集的txt划分存放在ImageSets文件夹下面的Main文件夹下

如下是VOC2012某张图片的标注信息xml文件

<annotation><folder>VOC2012</folder><filename>2008_006604.jpg</filename><source><database>The VOC2008 Database</database><annotation>PASCAL VOC2008</annotation><image>flickr</image></source><size><width>500</width><height>375</height><depth>3</depth></size><segmented>0</segmented><object><name>aeroplane</name><pose>Frontal</pose><truncated>1</truncated><occluded>0</occluded><bndbox><xmin>1</xmin><ymin>97</ymin><xmax>500</xmax><ymax>375</ymax></bndbox><difficult>0</difficult></object>
</annotation>

关于xml文件，python中有一条语句可以直接读取xml文件转换成python内置的字典格式。
对于运行测试，非常友好

这里是一篇关于VOC数据集标注格式的介绍

https://blog.csdn.net/m0_37970224/article/details/89212906

NWPU VHR-10数据集的格式

打开NWPU数据集的格式，只有简单的几个文件夹，标注信息是txt文件，存放在ground truth中。

采用的是字符串的标注信息。每个目标的标注信息为(x1,y1),(x2,y2),class_number五个数字表示。

只有positive image set中的图片有标注信息txt文件，每个txt文件的行数不固定，取决于该正样本图片中存在的目标个数。

negative image set中的图片没有标注信息txt文件。

在negative image set文件夹中有150张图片，这些图片中没有检测目标。

在positive image set文件夹中有650张图片，某一张正样本图片，有很多飞机模板目标

所以说NWPU数据集的格式非常不规范，不利于数据预处理和运行，格式划分的非常随意，标注信息也不利于读取。将NWPU数据集的格式转换成VOC数据集的格式尤为重要。

总结一些坑

NWPU数据集负样本图片在negative image set中，也是从001.jpg开始的，非常不友好。需要将正样本图片重命名为000001 - 000650.jpg，需要将负样本图片重命名为000651 - 000800.jpg。同时输出图片的路径为/JPEGImages/
NWPU数据集负样本没有标注信息txt文件，这里也需要对负样本图片生成VOC标注格式的xml文件，里面包括图片的size信息，不含object对象的包围框信息。负样本输出的标注xml文件命名范围为000651 - 000800.xml，输出路径为/Annotations/
正样本输出标注信息xml文件命名范围为000001 - 000650.xml，输出路径为/Annotations/

转换代码

"""
code  by lyf0801 in 2021.03.14
"""
import shutil
from lxml.etree import Element,SubElement,tostring
from xml.dom.minidom import parseString
import xml.dom.minidom
import os
import sys
from PIL import Image# 处理NWPU VHR-10数据集中的txt标注信息转换成 xml文件
# 此处的path应该传入的是NWPU VHR-10数据集文件夹下面的ground truth文件夹的目录
# 即 path = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/ground truth"
def deal(path):files=os.listdir(path)  # files获取所有标注txt文件的文件名# 此处可以自行设置输出路径  按照VOC数据集的格式，xml文件应该输出在数据集文件下面的Annotations文件夹下面outpath = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/Annotations/"# 如果输出文件夹不存在，就创建它if os.path.exists(outpath) == False:os.mkdir(outpath)# 遍历所有的txt标注文件，一共650个txt文件for file in files:filename=os.path.splitext(file)[0] # 获取ground truth文件夹中标注txt文件的文件名，比如如果文件名为001.txt，那么filename = '001'print(filename)sufix=os.path.splitext(file)[1]#  获取标注txt文件的后缀名 判断是否为txtif sufix=='.txt':   # 标注txt文件中每一行代表一个目标，(x1,y1)，(x2,y2)，class_number来表示xmins=[]    ymins=[]xmaxs=[]ymaxs=[]names=[]# num,xmins,ymins,xmaxs,ymaxs,names=readtxt(path + '/' + file)    # 调用readtxt文件获取信息，转到readtxt函数path_txt = path + '/' + file     # 获取txt标注文件的路径信息# 打开txt标注文件with open(path_txt, 'r') as f:contents = f.read()    # 将txt文件的信息按行读取到contents列表中print("contents:")print(contents)"""一个输出例子：contents:(563,478),(630,573),1"""objects=contents.split('\n') # 以换行划分每一个目标的标注信息，因为每一个目标的标注信息在txt文件中为一行print("objects:")print(objects)"""objects:['(563,478),(630,573),1 ', '']"""for i in range(objects.count('')): objects.remove('')    # 将objects中的空格移除print("objects:")print(objects)"""objects:['(563,478),(630,573),1 ']"""num=len(objects) # 获取一个标注文件的目标个数，objects中一个元素代表的信息就是一个检测目标      #print(num)# 遍历 objects列表，获取每一个检测目标的五维信息for objecto in objects:    print("objecto:")print(objecto)   xmin=objecto.split(',')[0]   # xmin = '(563'xmin=xmin.split('(')[1]   # xmin = '563' 可能存在空格xmin=xmin.strip()  # strip函数去掉字符串开头结尾的空格符ymin=objecto.split(',')[1]  # ymin = '478)'print("ymin:")print(ymin)ymin=ymin.split(')')[0]   # ymin = '478'  可能存在空格ymin=ymin.strip()    # strip函数去掉字符串开头结尾的空格符xmax=objecto.split(',')[2]  # xmax同理xmax=xmax.split('(')[1]xmax=xmax.strip()ymax=objecto.split(',')[3]   # ymax同理ymax=ymax.split(')')[0]ymax=ymax.strip()name=objecto.split(',')[4]  # 与上 同理name=name.strip()if name=="1 " or name=="1":  # 将数字信息转换成label字符串信息name='airplane'elif name=="2 "or name=="2":name='ship'elif name== "3 "or name=="3":name='storage tank'elif name=="4 "or name=="4":name='baseball diamond'elif name=="5 "or name=="5":name='tennis court'elif name=="6 "or name=="6":name='basketball court'elif name=="7 "or name=="7":name='ground track field'elif name=="8 "or name=="8":name='harbor'elif name=="9 "or name=="9":name='bridge'elif name=="10 "or name=="10":name='vehicle' else:print(path)            # print(xmin,ymin,xmax,ymax,name)xmins.append(xmin)ymins.append(ymin)xmaxs.append(xmax)ymaxs.append(ymax)names.append(name)print("num,xmins,ymins,xmaxs,ymaxs,names")print(num,xmins,ymins,xmaxs,ymaxs,names)"""num,xmins,ymins,xmaxs,ymaxs,names1 ['563'] ['478'] ['630'] ['573'] ['airplane']"""print("num,xmins,ymins,xmaxs,ymaxs,names")print(num,xmins,ymins,xmaxs,ymaxs,names)filename_fill = str(int(filename)).zfill(6)   # 将xml的文件名填充为6位数。比如1.xml就改为00001.xmlfilename_jpg = filename_fill + ".jpg"    # 由于xml中存储的文件名为000001.jpg 所以还得对所有的NWPU数据集中的图片进行重命名dealpath = outpath + filename_fill +".xml"# 注意，经过重命名转换之后，图片都存放在D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/JPEGImages/中imagepath = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/JPEGImages/" + filename_fill + ".jpg"with open(dealpath, 'w') as f:img=Image.open(imagepath) # 根据图片的地址打开图片并获取图片的宽 和 高width=img.size[0]height=img.size[1]# 将图片的宽和高以及其他和VOC数据集向对应的信息writexml(dealpath,filename_jpg,num,xmins,ymins,xmaxs,ymaxs,names, height, width)#  同时也得给negatiive image set文件夹下面的所有负样本图片生成xml标注negative_path = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/negative image set/"negative_images = os.listdir(negative_path)for file in negative_images:filename = file.split('.')[0]  # 获取文件名，不包括后缀名filename_fill = str(int(filename) + 650).zfill(6)   # 将xml的文件名填充为6位数。同时加上650，比如1.xml就改为00001.xmlfilename_jpg = filename_fill + '.jpg'   # 比如第一个负样本001.jpg的filename_jpg 为000651.jpg## 重命名为6位数print(filename_fill)## 生成不含目标的xml文件dealpath = outpath + filename_fill +".xml"# 注意，经过重命名转换之后，图片都存放在D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/JPEGImages/中imagepath = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/JPEGImages/" + filename_fill + ".jpg"with open(dealpath, 'w') as f:img = Image.open(imagepath)width = img.size[0]height = img.size[1]# 将宽高和空的目标标注信息写入xml标注writexml(dealpath,filename_jpg,num = 0,xmins = [],ymins = [],xmaxs = [],ymaxs = [],names = [],width=width,height=height)# with open()# NWPU数据集中标注的五维信息 (x1,y1) denotes the top-left coordinate of the bounding box,
#  (x2,y2) denotes the right-bottom coordinate of the bounding box
# 所以 xmin = x1  ymin = y1,  xmax = x2, ymax = y2  同时要注意这里的相对坐标是以图片左上角为坐标原点计算的
# VOC数据集对于包围框标注的格式是bounding-box（包含左下角和右上角xy坐标# 将从txt读取的标注信息写入到xml文件中
def writexml(path,filename,num,xmins,ymins,xmaxs,ymaxs,names,height, width):# Nwpu-vhr-10 < 1000*600node_root=Element('annotation')node_folder=SubElement(node_root,'folder')node_folder.text="VOC2012"node_filename=SubElement(node_root,'filename')node_filename.text="%s" % filenamenode_size=SubElement(node_root,"size")node_width = SubElement(node_size, 'width')node_width.text = '%s' % widthnode_height = SubElement(node_size, 'height')node_height.text = '%s' % heightnode_depth = SubElement(node_size, 'depth')node_depth.text = '3'for i in range(num):node_object = SubElement(node_root, 'object')node_name = SubElement(node_object, 'name')node_name.text = '%s' % names[i]node_name = SubElement(node_object, 'pose')node_name.text = '%s' % "unspecified"node_name = SubElement(node_object, 'truncated')node_name.text = '%s' % "0"node_difficult = SubElement(node_object, 'difficult')node_difficult.text = '0'node_bndbox = SubElement(node_object, 'bndbox')node_xmin = SubElement(node_bndbox, 'xmin')node_xmin.text = '%s'% xmins[i]node_ymin = SubElement(node_bndbox, 'ymin')node_ymin.text = '%s' % ymins[i]node_xmax = SubElement(node_bndbox, 'xmax')node_xmax.text = '%s' % xmaxs[i]node_ymax = SubElement(node_bndbox, 'ymax')node_ymax.text = '%s' % ymaxs[i]xml = tostring(node_root, pretty_print=True)  dom = parseString(xml)with open(path, 'wb') as f:f.write(xml)return
# 该代码主要解决的是图片的重命名问题，因为voc的图片是从000001.jpg开始，而且是6位数
def imag_rename(old_path, new_path,start_number = 0):filelist = os.listdir(old_path)  # 该文件夹下所有的文件（包括文件夹）if os.path.exists(new_path) == False:os.mkdir(new_path)for file in filelist:  # 遍历所有文件Olddir = os.path.join(old_path, file)  # 原来的文件路径if os.path.isdir(Olddir):  # 如果是文件夹则跳过continuefilename = os.path.splitext(file)[0]  # 文件名filetype = os.path.splitext(file)[1]  # 文件扩展名if filetype == '.jpg':Newdir = os.path.join(new_path, str(int(filename) + start_number).zfill(6) + filetype)  # 用字符串函数zfill 以0补全所需位数shutil.copyfile(Olddir, Newdir)if __name__ == "__main__":# # 由于xml中存储的文件名为000001.jpg 所以还得对所有的NWPU数据集中的图片进行重命名处理# 解决positive image set文件夹中的重命名问题，start_number = 0old_path = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/positive image set/"new_path = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/JPEGImages/"imag_rename(old_path, new_path)# 解决negative image set文件夹中的重命名问题，start_number = 650old_path = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/negative image set/"new_path = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/JPEGImages/"imag_rename(old_path,new_path,start_number = 650)# path指定的是标注txt文件所在的路径path = "D:/pytorch_code/NWPUintoVOC/NWPU VHR-10 dataset/ground truth"deal(path)# VOC 数据集中的负样本是如何标注的，关于NWPU中的负样本图片也没有得到解决？# 如何划分NWPU的train集合和 val集合也是一个问题？？？# 随机划分吗？

经过核对，生成的xml文件和image文件信息完全和VOC2012数据集的格式完美匹配

可以看到生成的xml文件和 NWPU标注的txt文件完全匹配。

最后的问题NWPU数据集压根就没有划分训练集和测试集的txt文件，这里必须手动划分，只能写一个split_data的脚本了

"""
this code was inspired by https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/blob/master/pytorch_object_detection/faster_rcnn/split_data.pyrecode by lyf0801 in 2021.03.14
"""
import os
import randomfiles_path = "./NWPU VHR-10 dataset/Annotations/"
if not os.path.exists(files_path):print("文件夹不存在")exit(1)
val_rate = 0.2  # 设置train数据集占80%，测试占20%files_name = sorted([file.split(".")[0] for file in os.listdir(files_path)])
files_num = len(files_name)
print(files_num)
val_index = random.sample(range(0, files_num), k=int(files_num*val_rate))
train_files = []
val_files = []
for index, file_name in enumerate(files_name):if index in val_index:val_files.append(file_name)else:train_files.append(file_name)try:train_f = open("train.txt", "x")eval_f = open("val.txt", "x")train_f.write("\n".join(train_files))eval_f.write("\n".join(val_files))
except FileExistsError as e:print(e)exit(1)

得到划分后的train.txt和val.txt

由于是均匀随机划分的。

train.txt中包含 520个正样本，占总正样本数目的80%，120个负样本，占总负样本数目的80%；
val.txt中包含 130个正样本，占总正样本数目的20%, 30个负样本，占总负样本数目的20%。

然后自己手动改一个json类别信息对应文件

至此，NWPU数据集的格式完全转换成了VOC2012数据集的标注格式。

在pytorch中，还得给数据集写一个脚本用于定义Dataset class

"""
this code was inspired by https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/blob/master/pytorch_object_detection/faster_rcnn/mydataset.pyrecode by lyf0801 in 2021.03.14
"""
from torch.utils.data import Dataset
import os
import torch
import json
from PIL import Image
from lxml import etreeclass NWPUVHR10DataSet(Dataset):"""读取解析NWPU VHR-10数据集"""def __init__(self, nwpu_root, transforms,txt_name: str = "train.txt"):self.root = os.path.join(nwpu_root, "NWPU VHR-10 dataset")self.img_root = os.path.join(self.root, "JPEGImages")self.annotations_root = os.path.join(self.root, "Annotations")# read train.txt or val.txt filetxt_path = os.path.join(self.root, "ImageSets", "Main", txt_name)assert os.path.exists(txt_path), "not found {} file.".format(txt_name)with open(txt_path) as read:self.xml_list = [os.path.join(self.annotations_root, line.strip() + ".xml")for line in read.readlines()]# check fileassert len(self.xml_list) > 0, "in '{}' file does not find any information.".format(txt_path)for xml_path in self.xml_list:assert os.path.exists(xml_path), "not found '{}' file.".format(xml_path)# read class_indictjson_file = './NWPU VHR-10 dataset/nwpu_classes.json'assert os.path.exists(json_file), "{} file not exist.".format(json_file)json_file = open(json_file, 'r')self.class_dict = json.load(json_file)self.transforms = transformsdef __len__(self):return len(self.xml_list)def __getitem__(self, idx):# read xmlxml_path = self.xml_list[idx]with open(xml_path) as fid:xml_str = fid.read()xml = etree.fromstring(xml_str)data = self.parse_xml_to_dict(xml)["annotation"]img_path = os.path.join(self.img_root, data["filename"])image = Image.open(img_path)if image.format != "JPEG":raise ValueError("Image '{}' format not JPEG".format(img_path))boxes = []labels = []iscrowd = []assert "object" in data, "{} lack of object information.".format(xml_path)for obj in data["object"]:xmin = float(obj["bndbox"]["xmin"])xmax = float(obj["bndbox"]["xmax"])ymin = float(obj["bndbox"]["ymin"])ymax = float(obj["bndbox"]["ymax"])boxes.append([xmin, ymin, xmax, ymax])labels.append(self.class_dict[obj["name"]])if "difficult" in obj:iscrowd.append(int(obj["difficult"]))else:iscrowd.append(0)# convert everything into a torch.Tensorboxes = torch.as_tensor(boxes, dtype=torch.float32)labels = torch.as_tensor(labels, dtype=torch.int64)iscrowd = torch.as_tensor(iscrowd, dtype=torch.int64)image_id = torch.tensor([idx])area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])target = {}target["boxes"] = boxestarget["labels"] = labelstarget["image_id"] = image_idtarget["area"] = areatarget["iscrowd"] = iscrowdif self.transforms is not None:image, target = self.transforms(image, target)return image, targetdef get_height_and_width(self, idx):# read xmlxml_path = self.xml_list[idx]with open(xml_path) as fid:xml_str = fid.read()xml = etree.fromstring(xml_str)data = self.parse_xml_to_dict(xml)["annotation"]data_height = int(data["size"]["height"])data_width = int(data["size"]["width"])return data_height, data_widthdef parse_xml_to_dict(self, xml):"""将xml文件解析成字典形式，参考tensorflow的recursive_parse_xml_to_dictArgs:xml: xml tree obtained by parsing XML file contents using lxml.etreeReturns:Python dictionary holding XML contents."""if len(xml) == 0:  # 遍历到底层，直接返回tag对应的信息return {xml.tag: xml.text}result = {}for child in xml:child_result = self.parse_xml_to_dict(child)  # 递归遍历标签信息if child.tag != 'object':result[child.tag] = child_result[child.tag]else:if child.tag not in result:  # 因为object可能有多个，所以需要放入列表里result[child.tag] = []result[child.tag].append(child_result[child.tag])return {xml.tag: result}def coco_index(self, idx):"""该方法是专门为pycocotools统计标签信息准备，不对图像和标签作任何处理由于不用去读取图片，可大幅缩减统计时间Args:idx: 输入需要获取图像的索引"""# read xmlxml_path = self.xml_list[idx]with open(xml_path) as fid:xml_str = fid.read()xml = etree.fromstring(xml_str)data = self.parse_xml_to_dict(xml)["annotation"]data_height = int(data["size"]["height"])data_width = int(data["size"]["width"])# img_path = os.path.join(self.img_root, data["filename"])# image = Image.open(img_path)# if image.format != "JPEG":#     raise ValueError("Image format not JPEG")boxes = []labels = []iscrowd = []for obj in data["object"]:xmin = float(obj["bndbox"]["xmin"])xmax = float(obj["bndbox"]["xmax"])ymin = float(obj["bndbox"]["ymin"])ymax = float(obj["bndbox"]["ymax"])boxes.append([xmin, ymin, xmax, ymax])labels.append(self.class_dict[obj["name"]])iscrowd.append(int(obj["difficult"]))# convert everything into a torch.Tensorboxes = torch.as_tensor(boxes, dtype=torch.float32)labels = torch.as_tensor(labels, dtype=torch.int64)iscrowd = torch.as_tensor(iscrowd, dtype=torch.int64)image_id = torch.tensor([idx])area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])target = {}target["boxes"] = boxestarget["labels"] = labelstarget["image_id"] = image_idtarget["area"] = areatarget["iscrowd"] = iscrowdreturn (data_height, data_width), target@staticmethoddef collate_fn(batch):return tuple(zip(*batch))
"""
import transforms
from draw_box_utils import draw_box
from PIL import Image
import json
import matplotlib.pyplot as plt
import torchvision.transforms as ts
import random# read class_indict
category_index = {}
try:json_file = open('./NWPU VHR-10 dataset/nwpu_classes.json', 'r')class_dict = json.load(json_file)category_index = {v: k for k, v in class_dict.items()}
except Exception as e:print(e)exit(-1)data_transform = {"train": transforms.Compose([transforms.ToTensor(),transforms.RandomHorizontalFlip(0.5)]),"val": transforms.Compose([transforms.ToTensor()])
}# load train data set
train_data_set = NWPUVHR10DataSet(os.getcwd(), data_transform["train"],"train.txt")
print(len(train_data_set))
for index in random.sample(range(0, len(train_data_set)), k=5):img, target = train_data_set[index]img = ts.ToPILImage()(img)draw_box(img,target["boxes"].numpy(),target["labels"].numpy(),[1 for i in range(len(target["labels"].numpy()))],category_index,thresh=0.5,line_thickness=5)plt.imshow(img)plt.show()
"""

通过脚本打印出来的标注信息图片可视化结果为：

最后，如果这篇博客对大家有帮助，有困惑的，可以给我留言。