该代码功能：
对已经标注好的xml文件进行操作
比如，label A 区域中，有多个label B。
现在我希望我能截取label A区域的图片，并根据原始xml生成lable B 的标注文件

注：label B部分区域在label A 外面的话，则扩大label A裁剪区域，使其包括 label B

xml里面 x为w轴

'''
该代码功能：
对已经标注好的xml文件进行操作
比如，label A 区域中，有多个label B。
现在我希望我能截取label A区域的图片，并根据原始xml生成lable B 的标注文件注：label B部分区域在label A 外面的话，则扩大label A裁剪区域，使其包括 label Bxml里面 x为w轴
'''
import os
import cv2
import xml.etree.ElementTree as ET
import traceback
import copyimages_path="/media/hxzh/SB@home/hxzh/MH_dataset/Towers/dataset_insulator_clip/images"
xmls_path="/media/hxzh/SB@home/hxzh/MH_dataset/Towers/dataset_insulator_clip/xmls"
save_image_path="/media/hxzh/SB@home/hxzh/MH_dataset/Towers/dataset_insulator_clip/save_images"
save_xmls_path="/media/hxzh/SB@home/hxzh/MH_dataset/Towers/dataset_insulator_clip/save_xmls"#所要裁剪区域的标签名字
crop_name="100_0_0"#要生成的标签名
label_name="100_1_0"def read_xml(filename):(fname, suffix) = os.path.splitext(filename)if not os.path.exists(f"{images_path}/{fname}.JPG"):return 0image_ori=cv2.imread(f"{images_path}/{fname}.JPG")is_ok=Truetry :tree = ET.parse(f"{xmls_path}/{filename}")tree_ori=copy.deepcopy(tree)except (BaseException,Exception ) as e:print(filename,traceback.format_exc())return []# 获取宽w和高ha = tree.find('size')w, h = [int(a.find('width').text),int(a.find('height').text)]objects = []if w == 0:return []crop_area=[]label_area=[]for obj in tree.findall('object'):# 获取namename = obj.find('name').textif name==crop_name:# 读取检测框的左上、右下角点的坐标bbox = obj.find('bndbox')x1, y1, x2, y2 = [int(bbox.find('xmin').text),int(bbox.find('ymin').text),int(bbox.find('xmax').text),int(bbox.find('ymax').text)]#首次确定裁剪区域crop_area.append([x1, y1, x2, y2])elif name==label_name:# 读取检测框的左上、右下角点的坐标bbox = obj.find('bndbox')x1, y1, x2, y2 = [int(bbox.find('xmin').text),int(bbox.find('ymin').text),int(bbox.find('xmax').text),int(bbox.find('ymax').text)]#首次确定裁剪区域label_area.append([x1, y1, x2, y2])matchs=[]for i in crop_area:match={}match["area"]=imatch["labels"]=[]for j in label_area:if  j[0]>=i[0] and j[0]<=i[2]:if j[1]>=i[1] and j[1]<=i[3] or j[3]>=i[1] and j[3]<=i[3]:match["labels"].append(j)elif j[2]>=i[0] and j[2]<=i[2]:if j[1] >= i[1] and j[1] <= i[3] or j[3] >= i[1] and j[3] <= i[3]:match["labels"].append(j)if len(match["labels"])!=0:min_x=min([k[0] for k in match["labels"]])max_x=max([k[2] for k in match["labels"]])min_y = min([k[1] for k in match["labels"]])max_y = max([k[3] for k in match["labels"]])print(min_x,min_y,max_x,max_y)print(match["area"][0])match["area"][0]=min(match["area"][0] ,min_x)match["area"][1]=min(match["area"][1] ,min_y)match["area"][2]=max(match["area"][2] ,max_x)match["area"][3]=max(match["area"][3] ,max_y)matchs.append(match.copy())#开始根据matchs 进行裁剪图片和生成xmllen_matchs=len(matchs)image_count=0for match in matchs:image_count+=1#分割图片tree_=copy.deepcopy(tree_ori)image_crop=image_ori.copy()[match["area"][1]:match["area"][3],match["area"][0]:match["area"][2],:]cv2.imwrite(f"{save_image_path}/{fname}_{image_count}.JPG",image_crop)save_xml_name=f"{save_xmls_path}/{fname}_{image_count}.xml"ww,hh=image_crop.shape[:2]label_len=len(match["labels"])if label_len==0:continuea = tree_.find('size')a.find('width').text=f"{ww}"a.find('height').text=f"{hh}"if w == 0:return []for ind ,ob in enumerate( tree_.findall('object')):# 获取nameif ind<label_len:ob.find('name').text=f"{label_name}"bbox = ob.find("bndbox")bbox.find("xmin").text = f"{match['labels'][ind][0]-match['area'][0]}"bbox.find("ymin").text = f"{match['labels'][ind][1]-match['area'][1]}"bbox.find("xmax").text = f"{match['labels'][ind][2]-match['area'][0]}"bbox.find("ymax").text = f"{match['labels'][ind][3]-match['area'][1]}"else:tree_.getroot().remove(ob)tree_.write(save_xml_name)xs = ""with open(save_xml_name, 'r', encoding='utf8') as r:xs = r.read()r.close()with open(save_xml_name, 'w', encoding='utf8') as w:w.write(xs.replace("<?xml version='1.0' encoding='utf8'?>", ""))w.close()for i in os.listdir(xmls_path):read_xml(i)