需要用到imgaug库，可自行安装。

直接上代码。transforms.py主要是对标签坐标进行转换及执行扩充操作

# import torch
import torch.nn.functional as F
import numpy as np

import imgaug.augmenters as iaa
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage


import torchvision.transforms as transforms

# function: 将中心坐标（x,y），宽高（w, h）转为左上角坐标和右下角坐标的形式（x1, y1, x2, y2）
def xywh2xyxy_np(x):
    y = np.zeros_like(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2
    y[..., 1] = x[..., 1] - x[..., 3] / 2
    y[..., 2] = x[..., 0] + x[..., 2] / 2
    y[..., 3] = x[..., 1] + x[..., 3] / 2
    return y

class ImgAug(object):
    def __init__(self, augmentations=[]):
        self.augmentations = augmentations

    def __call__(self, data):
        img, boxes = data

        # convert xywh to xyxy
        boxes = np.array(boxes)
        boxes[:, 1:] = xywh2xyxy_np(boxes[:,1:])

        # convert bounding boxes to imgaug
        bounding_boxes = BoundingBoxesOnImage(
            [BoundingBox(*box[1:], label = box[0]) for box in boxes],
            shape=img.shape
        )

        # apply augmentations
        img, bounding_boxes = self.augmentations(
            image = img,
            bounding_boxes = bounding_boxes
        )

        bounding_boxes = bounding_boxes.clip_out_of_image()

        # convert bounding boxes back to numpy
        boxes = np.zeros((len(bounding_boxes), 5))
        for box_idx, box in enumerate(bounding_boxes):
            x1 = box.x1
            y1 = box.y1
            x2 = box.x2
            y2 = box.y2

            #returns (x, y, w, h)
            boxes[box_idx, 0] = box.label
            boxes[box_idx, 1] = ((x1+x2)/2)
            boxes[box_idx, 2] = ((y1+y2)/2)
            boxes[box_idx, 3] = (x2-x1)
            boxes[box_idx, 4] = (y2-y1)
        
        return img, boxes

class RelativeLabels(object):
    def __init__(self, ):
        pass

    def __call__(self, data):
        img, boxes = data
        # w, h, _ = img.shape
        h = img.shape[0]
        w = img.shape[1]
        boxes[:, [1, 3]] /= w
        boxes[:, [2, 4]] /= h
        return img, boxes

class AbsoluteLabels(object):
    def __init__(self, ):
        pass

    def __call__(self, data):
        img, boxes = data
        # w, h, _ = img.data
        h = img.shape[0]
        w = img.shape[1]
        
        # boxes[:, [1, 3]]*=h
        # print(boxes[:, [1, 3]])
        boxes[:, 1] = boxes[:, 1] * w
        boxes[:, 3] = boxes[:, 3] * w
        # boxes[:, [2, 4]]*=w
        boxes[:, 2] = boxes[:, 2] * h
        boxes[:, 4] = boxes[:, 4] * h
        return img, boxes

class PadSquare(ImgAug):
    def __init__(self, ):
        self.augmentations = iaa.Sequential([
            iaa.PadToAspectRatio(1.0, position="center-center").to_deterministic()
        ])

class ToTensor(object):
    def __init__(self, ):
        pass

    def __call__(self, data):
        img, boxes = data
        #extract image as pytorch tensor
        img = transforms.ToTensor()(img)

        bb_targets = torch.zeros((len(boxes), 6))
        bb_targets[:, 1:] = transforms.ToTensor()(boxes)

        return img, bb_targets

class Resize(object):
    def __init__(self, size):
        self.size = size
    
    def __call__(self, data):
        img, boxes = data
        img = F.interpolate(img.unsqueeze(0), size = self.size, mode="nearest").squeeze(0)
        return img, boxes

DEFAULT_TRANSFORMS = transforms.Compose([
    AbsoluteLabels(),
    PadSquare(),
    RelativeLabels()
])

数据扩充操作的代码，命名为augmentations.py，主要是设置扩充的参数等信息。

import imgaug.augmenters as iaa
from transforms import *   # 这里的transforms是上面的transforms.py文件，如果修改了文件名，这里对应修改即可
# imgaug是一个用于机器学习实验中图像增强的python库

class DefaultAug(ImgAug):
    def __init__(self, ):
        self.augmentations = iaa.Sequential([  # 定义变换序列
            iaa.Dropout([0.0, 0.01]),          # 随机去掉一些像素点，即把这些像素点变成0
            iaa.Sharpen((0.0, 0.2)),
            iaa.Affine(rotate=(-20, 20), translate_percent=(-0.2,0.2)),  #仿射变换， rotate by -45 to 45 degrees (affects segmaps)
            iaa.AddToBrightness((-30, 30)), 
            iaa.AddToHue((-20, 20)),
            iaa.Fliplr(0.5),    # 水平翻转图像（左右）
        ], random_order=True)


AUGMENTATION_TRANSFORMS = transforms.Compose([
        AbsoluteLabels(),       # 绝对标签
        DefaultAug(),           # 一些基本的数据增强
        RelativeLabels(),       # 相对标签
    ])

函数入口所在代码

import numpy as np
import os
import cv2
from tqdm import tqdm
import argparse
from PIL import Image
from augmentations import *  # 这里的augmentations就是上面的augmentations.py文件

# 获取文件下属性为imgProperty的所有文件
def GetImgNameByEveryDir(file_dir,imgProperty):   
    FileName         = []
    for root, dirs, files in os.walk(file_dir):  
        for file in files:  
            if os.path.splitext(file)[1] in imgProperty:  
                FileName.append(file)                              # 保存图片名称
    return FileName

def readBoxes(txt_path):
    boxes = []
    with open(txt_path) as file:
        txt_lines = file.readlines()
        for txt_line in txt_lines:
            box = txt_line.rstrip().split(" ")
            boxes.append([int(box[0]), float(box[1]), float(box[2]), float(box[3]), float(box[4])])
  
    return boxes

# 程序入口
# --img_path为需要扩增的图像数据
# note:图像和标签文件存在同一个文件夹
# 标签坐标：中心点坐标和宽高（cX, cY, W, H）

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--img_path', type=str, default = 'D:\\Code\\pythonCode\\enhanceImgColor\\imgs',help = 'image path')
    opt = parser.parse_args()
    
    img_list = GetImgNameByEveryDir(opt.img_path, ['.jpg', '.jpeg'])
    for img_name in tqdm(img_list):
        img_is_ok = 1
        boxes = []
        img_path = opt.img_path + '\\' + img_name
        try:
            img = np.array(Image.open(img_path).convert('RGB'), dtype=np.uint8)
            img1 = cv2.imread(img_path)
        except Exception as e:
            print(f"could not read image '{img_path}'. ")
            img_is_ok = 0
        if img_is_ok: # 如果图像存在，读取对应的标签文件
            txt_path = img_path[:-3] + 'txt'
            boxes = readBoxes(txt_path)
            print(boxes)
        transform = AUGMENTATION_TRANSFORMS
        boxes = np.array(boxes)
        temp_boxes = np.zeros_like(boxes)
        temp_boxes[:, :] = boxes[:, :]
        #
        # copy_num为对同一张图片扩充的张数 
        copy_num = 5
        for i in np.arange(copy_num):
            new_img, bb_target = transform((img1, boxes))
            save_name = img_name[:-4] + "_" + str(i)
            cv2.imwrite(save_name + '.jpg', new_img)
            txt_file = open(save_name + '.txt', 'w')
            for line in bb_target:
                bb = str(int(line[0])) + ' ' + str(line[1]) + ' ' + str(line[2])+ ' ' + str(line[3]) + ' ' + str(line[4]) +'\n'
                txt_file.write(bb)
            txt_file.close()
            boxes[:, :] = temp_boxes[:, :]

图像扩充前后对比实例：（示例图片源自COCO2014）

 输出结果：