解决not well-formed (invalid token)BUG，xml标签转到txt标签，txt标签转到xml标签，滑动窗口切割图像并且同步标签

紧接上次，我的最终目的现在是滑动窗口切割图片并且同步标签。需要对xml文件进行读写。

python报错xml.etree.ElementTree.ParseError: not well-formed (invalid token): line 3, column 50

我的解决方式最后我发现一个问题目标框好像被移动了。

所以最后我是xml文件转txt标签，然后根据txt文件新建xml文件，还可以没有错误啦！

xml标签 to yolov4txt标签

'''#这个文件为对ann_path_origin和img_path_origin选取特定类别的图片和xml
#生成的结果为一个图，一个txt文件,这个代码删除了只添加正样本（含有想要检测对象的样本），意味着尽管这张图片没有包含目标也会被写入txt
不会复制提取出来的图片
'''
import os
from tqdm import tqdm
import shutil
import xml.etree.ElementTree as ET
import xmlann_path_origin = r'/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/Annotations/'        #要提取的ann的xml标签文件路径，按需修改
img_path_origin = r'/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/JPEGImages/'         #要提取的Image文件路径，按需修改
# img_savepath = r'/home/xys/CppProjects/darknet/xinding/obj/'         #要保存到的Images文件路径，按需修改
txt_save_path = r'/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/labels/'   #存入txt的路径，，按需修改
class_names_path=r'/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/class.txt'     #类别文件路径#-----------------------函数定义------------------------------------
def read_class_name(path):        #读取path下的类别民f = open(path,'r')classes_name = []for i in f.readlines():classes_name.append(i.strip())return classes_namedef mkr(path):'''如果path存在，就先删除所有内容再创建，否则直接创建'''if os.path.exists(path):#先删除再创建shutil.rmtree(path)#递归删除目录树os.mkdir(path)#创建目录else:os.mkdir(path)#如果不存在则直接创建names = locals()
classes_name = read_class_name(class_names_path)  #读取类别信息，按需修改
# mkr(img_savepath)                               #创建文件夹
mkr(txt_save_path)count = 0
for xml_file_name in tqdm(os.listdir(ann_path_origin),ncols=150):file_name = xml_file_name[:-4]xml_file = open(ann_path_origin+xml_file_name,errors='ignore')      #打开xml文件tree = ET.parse(xml_file)# try:#     tree = ET.parse(xml_file)# except xml.etree.ElementTree.ParseError:#     print(xml_file_name)#     file_front = xml_file_name[:-4]#     shutil.move(ann_path_origin+xml_file_name, '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/新建文件夹')#     shutil.move(img_path_origin+file_front+'.jpg', '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/新建文件夹')root = tree.getroot()                               #得到根image_width = int(root.find('size').find('width').text)image_height = int(root.find('size').find('height').text)all_line = []for obj in root.iter('object'):diffcult = obj.find('difficult').text         #找到difficult，难以识别的类cls_name = obj.find('name').textif cls_name not in classes_name or int(diffcult) == 1:continue#--------------------下面的语句表示存在这个boundingbox，将此boundingbox的坐标类别写入到xml里-------------------xmlbox = obj.find('bndbox')                 #找到boundingbox标签xmin = int(float(xmlbox.find('xmin').text))ymin = int(float(xmlbox.find('ymin').text))xmax = int(float(xmlbox.find('xmax').text))ymax = int(float(xmlbox.find('ymax').text))#将boundingbox的四个坐标准换成中心点的坐标和boundingbox的宽高，并且转换映射到0-1cls_id = classes_name.index(cls_name)bb_h, bb_w = ymax - ymin, xmax - xminif image_width == 0 or image_height == 0:continuecoodr_x, coord_y = (xmin + bb_w / 2) / image_width, (ymin + bb_h / 2) / image_heighta_line = str(cls_id)+" "+str(coodr_x)+" "+str(coord_y)+" "+str(bb_w/image_width)+" "+str(bb_h/image_height)+"\n"all_line.append(a_line)#如果这张图片有我们需要的类，则保存这张图片,并写入到txt文件里txt_file = open(txt_save_path+file_name+".txt",'a')txt_file.writelines(all_line)# shutil.copy(img_path_origin+file_name+".jpg",img_savepath+file_name+".jpg")txt_file.close()xml_file.close()count += 1
print(f"---------转换了{count}个元素,结束-----------------")

txt标签 to xml标签

from xml.dom.minidom import Document
import os
import cv2
from tqdm import tqdmdef makexml(txtPath,xmlPath,picPath): #读取txt路径，xml保存路径，数据集图片所在路径dict = {'0': "hong-zhi",#字典对类型进行转换'1': "hong-zuo",'2': "hong-you",'3': "lv-zhi",'4': "lv-zuo",'5': "lv-you",'6': "huang-zhi",'7': "huang-zuo",'8': "huang-you",'9': "hei",'10': "feijidongchedeng"}files = os.listdir(txtPath)for i, name in tqdm(enumerate(files)):xmlBuilder = Document()annotation = xmlBuilder.createElement("annotation")  # 创建annotation标签xmlBuilder.appendChild(annotation)txtFile=open(txtPath+name)txtList = txtFile.readlines()img = cv2.imread(picPath+name[0:-4]+".jpg")Pheight,Pwidth,Pdepth=img.shape# for i in txtList:#    oneline = i.strip().split(" ")folder = xmlBuilder.createElement("folder")#folder标签folderContent = xmlBuilder.createTextNode("VOC2007")folder.appendChild(folderContent)annotation.appendChild(folder)filename = xmlBuilder.createElement("filename")#filename标签filenameContent = xmlBuilder.createTextNode(name[0:-4]+".jpg")filename.appendChild(filenameContent)annotation.appendChild(filename)size = xmlBuilder.createElement("size")  # size标签width = xmlBuilder.createElement("width")  # size子标签widthwidthContent = xmlBuilder.createTextNode(str(Pwidth))width.appendChild(widthContent)size.appendChild(width)height = xmlBuilder.createElement("height")  # size子标签heightheightContent = xmlBuilder.createTextNode(str(Pheight))height.appendChild(heightContent)size.appendChild(height)depth = xmlBuilder.createElement("depth")  # size子标签depthdepthContent = xmlBuilder.createTextNode(str(Pdepth))depth.appendChild(depthContent)size.appendChild(depth)annotation.appendChild(size)for i in txtList:oneline = i.strip().split(" ")object = xmlBuilder.createElement("object")picname = xmlBuilder.createElement("name")nameContent = xmlBuilder.createTextNode(dict[oneline[0]])picname.appendChild(nameContent)object.appendChild(picname)pose = xmlBuilder.createElement("pose")poseContent = xmlBuilder.createTextNode("Unspecified")pose.appendChild(poseContent)object.appendChild(pose)truncated = xmlBuilder.createElement("truncated")truncatedContent = xmlBuilder.createTextNode("0")truncated.appendChild(truncatedContent)object.appendChild(truncated)difficult = xmlBuilder.createElement("difficult")difficultContent = xmlBuilder.createTextNode("0")difficult.appendChild(difficultContent)object.appendChild(difficult)bndbox = xmlBuilder.createElement("bndbox")xmin = xmlBuilder.createElement("xmin")mathData=int(((float(oneline[1]))*Pwidth+1)-(float(oneline[3]))*0.5*Pwidth)xminContent = xmlBuilder.createTextNode(str(mathData))xmin.appendChild(xminContent)bndbox.appendChild(xmin)ymin = xmlBuilder.createElement("ymin")mathData = int(((float(oneline[2]))*Pheight+1)-(float(oneline[4]))*0.5*Pheight)yminContent = xmlBuilder.createTextNode(str(mathData))ymin.appendChild(yminContent)bndbox.appendChild(ymin)xmax = xmlBuilder.createElement("xmax")mathData = int(((float(oneline[1]))*Pwidth+1)+(float(oneline[3]))*0.5*Pwidth)xmaxContent = xmlBuilder.createTextNode(str(mathData))xmax.appendChild(xmaxContent)bndbox.appendChild(xmax)ymax = xmlBuilder.createElement("ymax")mathData = int(((float(oneline[2]))*Pheight+1)+(float(oneline[4]))*0.5*Pheight)ymaxContent = xmlBuilder.createTextNode(str(mathData))ymax.appendChild(ymaxContent)bndbox.appendChild(ymax)object.appendChild(bndbox)annotation.appendChild(object)f = open(xmlPath+name[0:-4]+".xml", 'w')xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')f.close()
jpg_path = '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/JPEGImages/'
txt_path = '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/labels/'
xml_path = '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/Annotations/'
makexml(txt_path , xml_path, jpg_path)

然后就是滑动窗口切割同步标签

'''实现分辨率为w*h裁剪成多张a*a大小的图片（a<=w且a<=h），以大小为a/2的步长滑动，裁剪同时修改标注框。'''# coding:utf-8
import cv2
import os
import codecsimport xml.dom.minidom as xmldom
import xml.etree.ElementTree as ET
import numpy as np
from PIL import Image
from tqdm import tqdm
import syssys.setrecursionlimit(10000)def load_image_into_numpy_array(image):(im_width, im_height) = image.sizereturn np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint16)
def voc_xml_parse(xml_path):object_list = []domobj = xmldom.parse(xml_path)elementobj = domobj.documentElementfolderobj = elementobj.getElementsByTagName("folder")[0]filenameobj = elementobj.getElementsByTagName("filename")[0]# sourceobj = elementobj.getElementsByTagName("source")[0]# ownerobj = elementobj.getElementsByTagName("owner")[0]sizeobj = elementobj.getElementsByTagName("size")[0]# segmentedobj = elementobj.getElementsByTagName("segmented")[0]head = {'folder': folderobj, 'filename': filenameobj, 'size': sizeobj,}object_list = elementobj.getElementsByTagName("object")return head, object_listdef voc_xml_modify(modify_xml_path, head, object_list):dom = xmldom.Document()root = dom.createElement('annotation')dom.appendChild(root)for obj in head.values():root.appendChild(obj)for obj in object_list:root.appendChild((obj))with open(modify_xml_path, 'w', encoding='utf-8') as f:dom.writexml(f, addindent='\t', newl='\n', encoding='utf-8')returndef crop_xml_modify(head, objectlist, hmin, wmin, new_height, new_width, origin_xml__path):filenameobj = head['filename']sizeobj = head['size']width = sizeobj.getElementsByTagName('width')[0]width.childNodes[0].data = str(new_width)# print(str(WIDTH))height = sizeobj.getElementsByTagName('height')[0]height.childNodes[0].data = str(new_height)# tree = ET.parse(origin_xml__path)# root = tree.getroot()obj = objectlisti = 0while (i < obj.length):# for obj in objectlist1:bndbox = obj[i].getElementsByTagName('bndbox')[0]xmin = bndbox.getElementsByTagName('xmin')[0]XMIN = float(xmin.childNodes[0].data)ymin = bndbox.getElementsByTagName('ymin')[0]YMIN = float(ymin.childNodes[0].data)xmax = bndbox.getElementsByTagName('xmax')[0]XMAX = float(xmax.childNodes[0].data)ymax = bndbox.getElementsByTagName('ymax')[0]YMAX = float(ymax.childNodes[0].data)if (XMIN >= wmin) and (XMAX <= (wmin + new_width)) and (YMIN >= hmin) and (YMAX <= (hmin + new_height)):xmin.childNodes[0].data = str(int(XMIN - wmin))xmax.childNodes[0].data = str(int(XMAX - wmin))ymin.childNodes[0].data = str(int(YMIN - hmin))ymax.childNodes[0].data = str(int(YMAX - hmin))else:obj.remove(obj[i])i = i - 1  # 一定要向前提一个位置 删除的话用for是会出错的 耽搁了好久。。。# obj = objectlist1[i-1]i = i + 1return head, objdef crop_dataset(imgpath, output_shape, annotation, cropAnno, cropImg, stride):origin_image = cv2.imread(imgpath)# image = Image.open(imgpath)# image_np = load_image_into_numpy_array(image)# origin_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)height, width = origin_image.shape[:2]# print(height)# print(width)domobj = xmldom.parse(annotation)elementobj = domobj.documentElementname = elementobj.getElementsByTagName("name")size = len(name)# tree = ET.parse(origin_xml__path)# root = tree.getroot()x = 0newheight = output_shapenewwidth = output_shapewhile x < width:y = 0if x + newwidth <= width:while y < height:# 裁剪为output_shape*output_shape# newheight = output_shape# newwidth = output_shapehead, objectlist = voc_xml_parse(annotation)if y + newheight <= height:hmin = yhmax = y + newheightwmin = xwmax = x + newwidthelse:hmin = height - newheighthmax = heightwmin = xwmax = x + newwidthy = height  # testmodify_head, modify_objectlist = crop_xml_modify(head, objectlist, hmin, wmin, newheight, newwidth,origin_xml__path)cropAnno1 = cropAnno + '_' + str(wmax) + '_' + str(hmax) + '_' + str(output_shape) + '.xml'cropImg1 = cropImg + '_' + str(wmax) + '_' + str(hmax) + '_' + str(output_shape) + '.jpg'import randomthroud = 3if len(modify_objectlist)>0 or random.randint(0, 100) < throud:cv2.imwrite(cropImg1, origin_image[hmin: hmax, wmin: wmax])voc_xml_modify(cropAnno1, modify_head, modify_objectlist)y = y + strideif y + output_shape == height:  # 第一张图就已经涵盖了height*heighty = height# if y + newheight > height:#     breakelse:while y < height:# 裁剪为output_shape*output_shape# newheight = output_shape# newwidth = output_shapehead, objectlist = voc_xml_parse(annotation)if y + newheight <= height:hmin = yhmax = y + newheightwmin = width - newwidthwmax = widthelse:hmin = height - newheighthmax = heightwmin = width - newwidthwmax = widthy = height  # testmodify_head, modify_objectlist = crop_xml_modify(head, objectlist, hmin, wmin, newheight, newwidth,origin_xml__path)cropAnno1 = cropAnno + '_' + str(wmax) + '_' + str(hmax) + '_' + str(output_shape) + '.xml'cropImg1 = cropImg + '_' + str(wmax) + '_' + str(hmax) + '_' + str(output_shape) + '.jpg'if  len(modify_objectlist)> 0 or random.randint(0, 100) < throud:cv2.imwrite(cropImg1, origin_image[hmin: hmax, wmin: wmax])voc_xml_modify(cropAnno1, modify_head, modify_objectlist)y = y + stride# if y + newheight > height:#     breakx = widthx = x + strideif x + output_shape == width:  #第一张图就已经涵盖了height*heightx = width# if x + newwidth > width:#     breakif __name__ == '__main__':# output_shape 为想要裁剪成的图片尺寸output_shape = 512  # 512 1024stride = int(output_shape / 2)imgpath = '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/jpg原图/'  # 原图路径annotation = '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/xml去除无效字符/'  # 原图对应的标注xml文件路径cropAnno = '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/Annotations/'  # 裁剪后存储xml的路径cropImg = '/home/xys/CloundShiProjects/traffic_light/trafficlight_dect/data/JPEGImages/'  # 裁剪后存储图片的路径if not os.path.exists(cropImg):os.mkdir(cropImg)if not os.path.exists(cropAnno):os.mkdir(cropAnno)for each in tqdm(os.listdir(annotation)):# each = os.listdir(annotation)name = each.split('.')[0]origin_img_path = os.path.join(imgpath, name + '.jpg')origin_xml__path = os.path.join(annotation, name + '.xml')crop_img_path = os.path.join(cropImg, name)crop_xml__path = os.path.join(cropAnno, name)# tree = ET.parse(origin_xml__path)# root = tree.getroot()crop_dataset(origin_img_path, output_shape, origin_xml__path, crop_xml__path, crop_img_path, stride)

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解决not well-formed (invalid token)BUG，xml标签转到txt标签，txt标签转到xml标签，滑动窗口切割图像并且同步标签

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