目标检测：COCO格式的txt文件转VOC格式的xml文件

1. COCO格式 vs VOC格式看这里

COCO2014据集：

VOC2007数据集:

txt文件（中心点坐标+宽高，相对值）：

45 0.479492 0.688771 0.955609 0.595500 
45 0.736516 0.247188 0.498875 0.476417 
50 0.637063 0.732938 0.494125 0.510583 
45 0.339438 0.418896 0.678875 0.781500 
49 0.646836 0.132552 0.118047 0.096937 
49 0.773148 0.129802 0.090734 0.097229 
49 0.668297 0.226906 0.131281 0.146896 
49 0.642859 0.079219 0.148063 0.148062 

xml文件（左上角+右下角，真实值）：

<annotation>
	<folder>VOC2007</folder>
	<filename>000001.jpg</filename>
	<source>
		<database>The VOC2007 Database</database>
		<annotation>PASCAL VOC2007</annotation>
		<image>flickr</image>
		<flickrid>341012865</flickrid>
	</source>
	<owner>
		<flickrid>Fried Camels</flickrid>
		<name>Jinky the Fruit Bat</name>
	</owner>
	<size>
		<width>353</width>
		<height>500</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>dog</name>
		<pose>Left</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>48</xmin>
			<ymin>240</ymin>
			<xmax>195</xmax>
			<ymax>371</ymax>
		</bndbox>
	</object>
	<object>
		<name>person</name>
		<pose>Left</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>8</xmin>
			<ymin>12</ymin>
			<xmax>352</xmax>
			<ymax>498</ymax>
		</bndbox>
	</object>
</annotation>

2. txt文件转为xml文件

import glob
import cv2

xml_head = '''<annotation>
    <folder>VOC2007</folder>
    <filename>{}</filename>.
    <source>
        <database>The VOC2007 Database</database>
        <annotation>PASCAL VOC2007</annotation>
        <image>flickr</image>
    </source>   
    <size>
        <width>{}</width>
        <height>{}</height>
        <depth>{}</depth>
    </size>
    <segmented>0</segmented>
    '''
xml_obj = '''
    <object>        
        <name>{}</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>{}</xmin>
            <ymin>{}</ymin>
            <xmax>{}</xmax>
            <ymax>{}</ymax>
        </bndbox>
    </object>
    '''
xml_end = '''
</annotation>'''

#--data
#----train 训练集图片
#----train_txt 对应的txt标签
#----train_xml 对应的xml标签

root='E:/pycharm_codes/data/'

txt_Lists = glob.glob(root +'train'+ '/*.jpg')
print(len(txt_Lists))
# print(txt_Lists)
cnt=0

for txt_path in txt_Lists:
    filename=txt_path.split('\\')
    filename=filename[-1]
    filename=filename.split('.')
    filename=filename[0]

    txt = root+'train_txt/'+filename+'.txt'
    jpg=root+'train/'+filename+'.jpg' #jpg path
    xml=root+'train_xml/'+filename+'.xml'

    print(txt)
    print(jpg)
    print(xml)

    obj = ''

    img = cv2.imread(jpg)
    img_h, img_w = img.shape[0], img.shape[1]

    print('h_factor:',img_h,'  w_factor:',img_w)
    # cv2.imshow("img", img)  #显示图片
    # cv2.waitKey(0)
    # cv2.destroyWindow("img")

    head = xml_head.format(str(filename), str(img_w), str(img_h), "3")

    with open(txt, 'r') as f:
        for line in f.readlines():
            yolo_datas = line.strip().split(' ')
            label = int(float(yolo_datas[0].strip()))
            center_x = round(float(str(yolo_datas[1]).strip()) * img_w)
            center_y = round(float(str(yolo_datas[2]).strip()) * img_h)
            bbox_width = round(float(str(yolo_datas[3]).strip()) * img_w)
            bbox_height = round(float(str(yolo_datas[4]).strip()) * img_h)

            xmin = str(int(center_x - bbox_width / 2))
            ymin = str(int(center_y - bbox_height / 2))
            xmax = str(int(center_x + bbox_width / 2))
            ymax = str(int(center_y + bbox_height / 2))

            obj += xml_obj.format(labels[label], xmin, ymin, xmax, ymax)

    with open(xml, 'w') as f_xml:
        f_xml.write(head + obj + xml_end)
    cnt += 1
    print(cnt)