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
python
'''
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
'''
import os
import cv2
import xml.etree.ElementTree as ET
import traceback
import copy
images_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 0
image_ori=cv2.imread(f"{images_path}/{fname}.JPG")
is_ok=True
try :
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h
a = 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name
name = obj.find('name').text
if 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"]=i
match["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xml
len_matchs=len(matchs)
image_count=0
for 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:
continue
a = 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name
if 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)