问'str' object has no attribute 'astype'？

import os
import os.path as osp
import random
def get_data_list(data_dir):
    data_dir='E:/bishe/newdata/'
    random.seed(666)
    mode = ["train_list", "val_list"]
    dir_path = osp.join(data_dir, "JPEGImages")
    files = [f.split(".")[0] for f in os.listdir(dir_path)]
    random.shuffle(files)  # 打乱顺序
    with open(osp.join(data_dir, f"{mode[0]}.txt"), "w") as f_tr:
        with open(osp.join(data_dir, f"{mode[1]}.txt"), "w") as f_va:
            for i, name in enumerate(files):
                if (i % 10) == 0:  # 训练集与测试集为9:1
                    f_va.write(f"JPEGImages/{name}.jpg Annotations/{name}.xml\n")
                else:
                    f_tr.write(f"JPEGImages/{name}.jpg Annotations/{name}.xml\n")
    labels = ["car"]
    txt_str = "\n".join(labels)
    with open((data_dir + "/" + f"label_list.txt"), "w") as f:
        f.write(txt_str)
    print("Finished!")
get_data_list("dataset")
import sys
sys.path.append("PaddleRS")
import os
import os.path as osp
from paddlers.datasets import VOCDetDataset#VOCDetection 
from paddlers import transforms as T
# 定义数据增强
train_transforms = T.Compose([
    T.RandomDistort(),
    T.RandomCrop(),
    T.RandomHorizontalFlip(),
    T.BatchRandomResize(
        target_sizes=[512, 544, 576, 608, 640, 672, 704],
        interp='RANDOM'),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
eval_transforms = T.Compose([
    T.Resize(target_size=608, interp='CUBIC'),
    T.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 定义数据集
data_dir = "E:/bishe/newdata/"
train_file_list = osp.join(data_dir, 'train_list.txt')
val_file_list = osp.join(data_dir, 'val_list.txt')
label_file_list = osp.join(data_dir, 'label_list.txt')
train_dataset = VOCDetDataset(
    data_dir=data_dir,
    file_list=train_file_list,
    label_list=label_file_list,
    transforms=train_transforms,
    shuffle=True)
eval_dataset = VOCDetDataset(
    data_dir=data_dir,
    file_list=train_file_list,
    label_list=label_file_list,
    transforms=eval_transforms,
    shuffle=False)
# 检查及可视化数据
import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# 反归一化
def in_normal(img):
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    img = (255 * (img * std + mean)).astype("uint8")
    return img

# 绘制矩形框
def visual(img, bboxs):
    for bbox in bboxs:
        x1, y1, x2, y2 = bbox
        cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
    return img
for data in train_dataset:
    img = in_normal(data["image"])
    vis = visual(img, data["gt_bbox"])
    plt.figure(figsize=(10, 10))
    plt.imshow(img)
    plt.show()
    break