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| import xml.etree.ElementTree as ET
import os
import cv2
from tqdm import tqdm
classes = ["holothurian", "echinus", "scallop", "starfish"] # 类别
xml_path = "xml标签文件夹路径"
txt_path = "txt标签存储路径"
image_path = "图像文件夹路径"
# 将原有的xmax,xmin,ymax,ymin换为x,y,w,h
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0
y = (box[2] + box[3]) / 2.0
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)
# 输入时图像和图像的宽高
def convert_annotation(image_id, width, hight):
in_file = open(xml_path + '\\{}.xml'.format(image_id), encoding='UTF-8')
out_file = open(txt_path + '\\{}.txt'.format(image_id), 'w') # 生成txt格式文件
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size') # 此处是获取原图的宽高,便于后续的归一化操作
if size is not None:
w = int(size.find('width').text)
h = int(size.find('height').text)
else:
w = width
h = hight
for obj in root.iter('object'):
cls = obj.find('name').text
# print(cls)
if cls not in classes:
print(cls)
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')
# 此处获取图像宽高的数组,tqdm为处理的可视化
def image_size(path):
image = os.listdir(path)
w_l, h_l = [], []
for i in tqdm(image):
if i.endswith('jpg'):
h_l.append(cv2.imread(os.path.join(path, i)).shape[0])
w_l.append(cv2.imread(os.path.join(path, i)).shape[1])
return w_l, h_l
# 遍历xml文件,将对应的宽高输入convert_annotation方法
if __name__ == "__main__":
img_xmls = os.listdir(xml_path)
w, h = image_size(image_path)
i = 0
for img_xml in img_xmls:
label_name = img_xml.split('.')[0]
print(label_name)
convert_annotation(label_name, w[i], h[i])
i += 1
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