Python cvat的kitti raw data格式里的3D目标框单个tracklet_labels.xml文件和打开对应frame_list.txt文件对应点云列表解析为paddle3D训练格式多个txt的脚本

以下是一个Python脚本，可以将KITTI raw data格式中的3D目标框单个tracklet_labels.xml文件和打开对应frame_list.txt文件对应点云列表解析为paddle3D训练格式多个txt：

import os
import xml.etree.ElementTree as ET
from tqdm import tqdm

# 读取frame_list.txt文件
def get_frame_list(txt_path):
    frame_list = []
    with open(txt_path, 'r') as f:
        for line in f:
            frame_list.append(line.strip())
    return frame_list

# 解析tracklet_labels.xml文件
def parse_xml(xml_path):
    tree = ET.parse(xml_path)
    root = tree.getroot()

    objects = []
    for tracklet in root.findall('tracklet'):
        object_dict = {}
        object_dict['frame'] = int(tracklet.attrib['frame'])
        object_dict['id'] = int(tracklet.attrib['id'])

        for item in tracklet:
            if item.tag == 'objectType':
                object_dict['class'] = item.text
            elif item.tag == 'h':
                object_dict['h'] = float(item.text)
            elif item.tag == 'w':
                object_dict['w'] = float(item.text)
            elif item.tag == 'l':
                object_dict['l'] = float(item.text)
            elif item.tag == 't':
                object_dict['tx'] = float(item.attrib['x'])
                object_dict['ty'] = float(item.attrib['y'])
                object_dict['tz'] = float(item.attrib['z'])
            elif item.tag == 'poses':
                object_dict['ry'] = float(item.attrib['r_y'])
        
        objects.append(object_dict)

    return objects

# 将解析后的结果写入txt文件
def write_txt(objects, txt_path):
    with open(txt_path, 'w') as f:
        for obj in objects:
            line = '{} {} {} {} {} {} {} {} {}\n'.format(obj['frame'], obj['tx'], obj['ty'], obj['tz'], obj['l'], obj['w'], obj['h'], obj['ry'], obj['class'])
            f.write(line)

if __name__ == '__main__':
    data_dir = '/path/to/kitti/raw/data'
    output_dir = '/path/to/paddle3D/training/data'
    sequences = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10']

    for seq in sequences:
        # 获取当前序列的目录路径
        seq_dir = os.path.join(data_dir, 'sequences', seq)

        # 获取当前序列的点云列表
        frame_list = get_frame_list(os.path.join(seq_dir, 'image_2', 'frame_list.txt'))

        # 遍历当前序列的每一帧，解析对应的xml文件并将结果写入txt文件
        for frame in tqdm(frame_list):
            frame_idx = int(frame.split('.')[0])
            xml_path = os.path.join(seq_dir, 'label_02', '{}.xml'.format(str(frame_idx).zfill(6)))
            objects = parse_xml(xml_path)
            txt_path = os.path.join(output_dir, 'training', seq, 'txt', '{}.txt'.format(str(frame_idx).zfill(6)))
            write_txt(objects, txt_path)

请根据实际情况修改`data_dir`和`output_dir`变量，以及序列列表`sequences`。此外，还需要安装`xml.etree.ElementTree`、`tqdm`等Python库。