解决方案

查看图书馆： albumentations . 试着回答以下问题：“你拥有的形象和你想要的形象有什么不同？”。例如，该图像是：
更加像素化，
颗粒状的，
分辨率较低,，
也可能有钉子/紧固螺钉
可能在主数字下方或上方写有其他内容
可能有阴影
号码牌在某些地方的亮度可能不均匀。
相册，帮助你想出许多类型的图像增强。请试着像我建议的那样解决这个问题，然后试着从相册中找出你需要的插图。

使用相册进行图像增强的示例

下面的代码块（源代码）向您展示了如何将相册应用于图像增强。如果您有一个图像和一个遮罩，它们都将经历相同的变换。
kaggle的另一个例子：带有相册的图像增强演示

from albumentations import (
    HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine,
    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose
)
import numpy as np
def strong_aug(p=0.5):
    return Compose([
        RandomRotate90(),
        Flip(),
        Transpose(),
        OneOf([
            IAAAdditiveGaussianNoise(),
            GaussNoise(),
        ], p=0.2),
        OneOf([
            MotionBlur(p=0.2),
            MedianBlur(blur_limit=3, p=0.1),
            Blur(blur_limit=3, p=0.1),
        ], p=0.2),
        ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
        OneOf([
            OpticalDistortion(p=0.3),
            GridDistortion(p=0.1),
            IAAPiecewiseAffine(p=0.3),
        ], p=0.2),
        OneOf([
            CLAHE(clip_limit=2),
            IAASharpen(),
            IAAEmboss(),
            RandomBrightnessContrast(),
        ], p=0.3),
        HueSaturationValue(p=0.3),
    ], p=p)
image = np.ones((300, 300, 3), dtype=np.uint8)
mask = np.ones((300, 300), dtype=np.uint8)
whatever_data = "my name"
augmentation = strong_aug(p=0.9)
data = {"image": image, "mask": mask, "whatever_data": whatever_data, "additional": "hello"}
augmented = augmentation(**data)
image, mask, whatever_data, additional = augmented["image"], augmented["mask"], augmented["whatever_data"], augmented["additional"]

策略

首先，将增强的数量降至最低
保存单个增强图像
放大后保存一些图像。
现在测试并更新您的增强管道，以满足您模拟地面实况场景的需求。
完成管道并在大量图像上运行。
时间：多少张图片需要多长时间。
然后最后在所有图像上运行它：这一次，您可以对运行它所需的时间进行时间估计。
注意：每次图像通过增强管道时，只有一个增强图像示例从中出来。因此，假设您想要每个图像的10个不同的增强版本，您需要将每个图像通过增强管道10次，然后再转到下一个图像。

# this will not be what you end up using
# but you can begin to understand what
# you need to do with it.
def simple_aug(p-0,5):
    return return Compose([
        RandomRotate90(),
        # Flip(),
        # Transpose(),
        OneOf([
            IAAAdditiveGaussianNoise(),
            GaussNoise(),
        ], p=0.2),
    ])
# for a single image: check first
image = ... # write your code to read in your image here
augmentation = strong_aug(p=0.5)
augmented = augmentation({'image': image}) # see albumentations docs
# SAVE the image
# If you are using imageio or PIL, saving an image
# is rather straight forward, and I will let you
# figure that out.
# save the content of the variable: augmented['image']

对于多个图像

假设每个图像都经过 10 通过扩充管道，您的代码可能如下所示：

import os
# I assume you have a way of loading your
# images from the filesystem, and they come
# out of `images` (an iterator)
NUM_AUG_REPEAT = 10
AUG_SAVE_DIR = 'data/augmented'
# create directory of not present already
if not os.path.isdir(AUG_SAVE_DIR):
    os.makedirs(AUG_SAVE_DIR)
# This will create augmentation ids for the same image
# example: '00', '01', '02', ..., '08', '09' for
# - NUM_AUG_REPEAT = 10
aug_id = lambda x: str(x).zfill(len(str(NUM_AUG_REPEAT)))
for image in images:
    for i in range(NUM_AUG_REPEAT):
        data = {'image': image}
        augmented = augmentation(**data)
        # I assume you have a function: save_image(image_path, image)
        # You need to write this function with 
        # whatever logic necessary. (Hint: use imageio or PIL.Image)
        image_filename = f'image_name_{aug_id(i)}.png'
        save_image(os.path.join(AUG_SAVE_DIR, image_filename), augmented['image'])