yolo数据增强

import os
import cv2
import numpy as np
import albumentations as A

# 定义数据增强的变换，可以根据需要自定义
transform = A.Compose([
    A.HorizontalFlip(p=0.5),  # 水平翻转
    A.RandomBrightnessContrast(p=0.5),  # 随机亮度和对比度
    A.Rotate(limit=15, p=0.5),  # 随机旋转
    A.ShiftScaleRotate(shift_limit=0.0625,  # 随机平移、缩放和旋转
                       scale_limit=0.1,
                       rotate_limit=0, p=0.5),
    #A.RandomCrop(width=450, height=450, p=0.5),  # 随机裁剪
    A.GaussianBlur(p=0.5),  # 高斯模糊
    A.HueSaturationValue(p=0.5),  # 随机色调、饱和度和亮度
    A.CLAHE(p=0.5),  # 应用自适应直方图均衡化
    A.ChannelShuffle(p=0.5),  # 随机打乱通道
    A.RandomGamma(p=0.5),  # 随机伽玛值
    A.ToGray(p=0.5),  # 转换为灰度图像
    A.MotionBlur(p=0.5),  # 运动模糊
    A.Resize(width=640, height=640)  # 调整大小
], bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))


def read_image_and_boxes(image_path, label_path):
    #