图像增强库Albumentations使用总结

摘要

albumentations包是一种针对数据增强专门写的API，里面基本包含大量的数据增强手段，其特点：

1、Albumentations支持所有常见的计算机视觉任务，如分类、语义分割、实例分割、目标检测和姿态估计。

2、该库提供了一个简单统一的API，用于处理所有数据类型:图像(rbg图像、灰度图像、多光谱图像)、分割掩码、边界框和关键点。

3、该库包含70多种不同的增强功能，可以从现有数据中生成新的训练样本。

4、Albumentations快。我们对每个新版本进行基准测试，以确保增强功能提供最大的速度。

5、它与流行的深度学习框架(如PyTorch和TensorFlow)一起工作。顺便说一下，Albumentations是PyTorch生态系统的一部分。

6、由专家写的。作者既有生产计算机视觉系统的工作经验，也有参与竞争性机器学习的经验。许多核心团队成员是Kaggle Masters和Grandmasters。

7、该库广泛应用于工业、深度学习研究、机器学习竞赛和开源项目。

Albumentations 的 pip 安装

pip install albumentations

问题

在这个部分有个小问题，我的本机已经安装完opencv-python，然后我们再去安装albumentations的时候，出现了一个问题，就是我们的opencv-python阻止albumentations的安装，报错如下：# Could not install packages due to anEnvironmentError: [WinError 5] 拒绝访问，是因为在安装albumentations的时候还要安装opencv-python-headless，这个库和opencv冲突。

解决方式

对于这个问题的解决方式是我们在新的虚拟环境中，先安装albumentations，安装albumentations的时候，我们会伴随安装上一个opencv-python-headless，这个完全可以替代opencv-python的功能，所以我们就不用安装opencv了。

基准测试结果

测试使用ImageNet验证集的前2000张图像在Intel Xeon Gold 6140 CPU运行基准测试的结果。所有输出都被转换为带有np的连续NumPy数组。uint8数据类型。表格显示了在单个核上每秒可以处理的图像数量;越高越好。

Python and library versions: Python 3.8.6 (default, Oct 13 2020, 20:37:26) [GCC 8.3.0], numpy 1.19.2, pillow-simd 7.0.0.post3, opencv-python 4.4.0.44, scikit-image 0.17.2, scipy 1.5.2.

Spatial-level transforms（空间层次转换）

空间级转换将同时改变输入图像和附加目标，如掩模、边界框和关键点。下表显示了每个转换支持哪些附加目标。

Spatial-level transforms（空间层次转换） 空间级转换将同时改变输入图像和附加目标，如掩模、边界框和关键点。下表显示了每个转换支持哪些附加目标。

支持的列表

• Blur
• CLAHE
• ChannelDropout
• ChannelShuffle
• ColorJitter
• Downscale
• Emboss
• Equalize
• FDA
• FancyPCA
• FromFloat
• GaussNoise
• GaussianBlur
• GlassBlur
• HistogramMatching
• HueSaturationValue
• ISONoise
• ImageCompression
• InvertImg
• MedianBlur
• MotionBlur
• MultiplicativeNoise
• Normalize
• Posterize
• RGBShift
• RandomBrightnessContrast
• RandomFog
• RandomGamma
• RandomRain
• RandomShadow
• RandomSnow
• RandomSunFlare
• RandomToneCurve
• Sharpen
• Solarize
• Superpixels
• ToFloat
• ToGray
• ToSepia

简单的使用案例

import albumentations as A
import cv2
 
import matplotlib.pyplot as plt
 
# Declare an augmentation pipeline
transform = A.Compose([
    A.RandomCrop(width=512, height=512),
    A.HorizontalFlip(p=0.8),
    A.RandomBrightnessContrast(p=0.5),
])
 
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
# Augment an image
transformed = transform(image=image)
transformed_image = transformed["image"]
plt.imshow(transformed_image)
plt.show()
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原始图像：

运行结果：

详细使用案例

VerticalFlip 围绕X轴垂直翻转输入

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.VerticalFlip(always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('Blur后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

Blur模糊输入图像

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.Blur(blur_limit=15,always_apply=False, p=1)(image=image) 
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('Blur后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

HorizontalFlip 围绕y轴水平翻转输入

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.HorizontalFlip(always_apply=False, p=1)(image=image) 
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('HorizontalFlip后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

Flip水平，垂直或水平和垂直翻转输入

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.Flip(always_apply=False, p=1)(image=image) 
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('Flip后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果有一定的随机性，如下图：

Transpose， 通过交换行和列来转置输入

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.Transpose(always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('Transpose后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

RandomCrop 随机裁剪

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.RandomCrop(512, 512,always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('RandomCrop后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

RandomGamma 随机灰度系数

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.RandomGamma(gamma_limit=(20, 20), eps=None, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('RandomGamma后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

RandomRotate90 将输入随机旋转90度，N次

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.RandomRotate90(always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('RandomRotate90后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

ShiftScaleRotate 随机平移，缩放和旋转输入

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
#解决中文显示问题
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.1, rotate_limit=45, interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')   #第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title('ShiftScaleRotate后的图像')
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

CenterCrop 裁剪图像的中心部分

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.CenterCrop(256, 256, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("CenterCrop后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

GridDistortion网格失真

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.GridDistortion(num_steps=10, distort_limit=0.3,border_mode=4, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("GridDistortion后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

ElasticTransform 弹性变换

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.ElasticTransform(alpha=5, sigma=50, alpha_affine=50, interpolation=1, border_mode=4,always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("ElasticTransform后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

RandomGridShuffle把图像切成网格单元随机排列

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.RandomGridShuffle(grid=(3, 3), always_apply=False, p=1) (image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("RandomGridShuffle后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

HueSaturationValue随机更改图像的颜色，饱和度和值

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("HueSaturationValue后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

PadIfNeeded 填充图像

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.PadIfNeeded(min_height=2048, min_width=2048, border_mode=4, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("PadIfNeeded后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

RGBShift，对图像RGB的每个通道随机移动值

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.RGBShift(r_shift_limit=10, g_shift_limit=20, b_shift_limit=20, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("RGBShift后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

GaussianBlur 使用随机核大小的高斯滤波器对图像进行模糊处理

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.GaussianBlur(blur_limit=11, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("GaussianBlur后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

CLAHE自适应直方图均衡

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.CLAHE(clip_limit=4.0, tile_grid_size=(8, 8), always_apply=False, p=0.5)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("CLAHE后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果:

ChannelShuffle随机重新排列输入RGB图像的通道

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.ChannelShuffle(always_apply=False, p=0.5)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("ChannelShuffle后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

运行结果：

InvertImg反色

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.InvertImg(always_apply=False, p=0.5)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("InvertImg后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

Cutout 随机擦除

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.Cutout(num_holes=20, max_h_size=20, max_w_size=20, fill_value=0, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("Cutout后的图像")
plt.imshow(transformed_image)
plt.show()
复制代码

RandomFog随机雾化

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.RandomFog(fog_coef_lower=0.3, fog_coef_upper=1, alpha_coef=0.08, always_apply=False, p=1)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("RandomFog后的图像")
plt.imshow(transformed_image)
plt.show()
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运行结果：

GridDropout网格擦除

import albumentations as A
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("aa.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Augment an image
transformed = A.GridDropout(ratio=0.5, unit_size_min=None, unit_size_max=None, holes_number_x=None, holes_number_y=None,
                            shift_x=0, shift_y=0, always_apply=False, p=0.5)(image=image)
transformed_image = transformed["image"]
plt.subplot(1, 2, 1)
plt.title('原图')  # 第一幅图片标题
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.title("GridDropout后的图像")
plt.imshow(transformed_image)
plt.show()
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运行结果：

组合变换（Compose）

变换不仅可以单独使用，还可以将这些组合起来，这就需要用到 Compose 类，该类继承自 BaseCompose。Compose 类含有以下参数：

• transforms：转换类的数组，list类型
• bbox_params：用于 bounding boxes 转换的参数，BboxPoarams 类型
• keypoint_params：用于 keypoints 转换的参数， KeypointParams 类型
• additional_targets：key新target 名字，value 为旧 target 名字的 dict，如 {'image2': 'image'}，dict 类型
• p：使用这些变换的概率，默认值为 1.0

image3 = Compose([
        # 对比度受限直方图均衡
            #（Contrast Limited Adaptive Histogram Equalization）
        CLAHE(),
        # 随机旋转 90°
        RandomRotate90(),
        # 转置
        Transpose(),
        # 随机仿射变换
        ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.50, rotate_limit=45, p=.75),
        # 模糊
        Blur(blur_limit=3),
        # 光学畸变
        OpticalDistortion(),
        # 网格畸变
        GridDistortion(),
        # 随机改变图片的 HUE、饱和度和值
        HueSaturationValue()
    ], p=1.0)(image=image)['image']
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随机选择（OneOf）

它同Compose一样，都是做组合的，都有概率。区别就在于：Compose组合下的变换是要挨着顺序做的，而OneOf组合里面的变换是系统自动选择其中一个来做，而这里的概率参数p是指选定后的变换被做的概率。例：

image4 = Compose([
        RandomRotate90(),
        # 翻转
        Flip(),
        Transpose(),
        OneOf([
            # 高斯噪点
            IAAAdditiveGaussianNoise(),
            GaussNoise(),
        ], p=0.2),
        OneOf([
            # 模糊相关操作
            MotionBlur(p=.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([
            # 畸变相关操作
            OpticalDistortion(p=0.3),
            GridDistortion(p=.1),
            IAAPiecewiseAffine(p=0.3),
        ], p=0.2),
        OneOf([
            # 锐化、浮雕等操作
            CLAHE(clip_limit=2),
            IAASharpen(),
            IAAEmboss(),
            RandomBrightnessContrast(),            
        ], p=0.3),
        HueSaturationValue(p=0.3),
    ], p=1.0)(image=image)['image']
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在程序中的使用

def get_transform(phase: str):
    if phase == 'train':
        return Compose([
            A.RandomResizedCrop(height=CFG.img_size, width=CFG.img_size),
            A.Flip(p=0.5),
            A.RandomRotate90(p=0.5),
            A.ShiftScaleRotate(p=0.5),
            A.HueSaturationValue(p=0.5),
            A.OneOf([
                A.RandomBrightnessContrast(p=0.5),
                A.RandomGamma(p=0.5),
            ], p=0.5),
            A.OneOf([
                A.Blur(p=0.1),
                A.GaussianBlur(p=0.1),
                A.MotionBlur(p=0.1),
            ], p=0.1),
            A.OneOf([
                A.GaussNoise(p=0.1),
                A.ISONoise(p=0.1),
                A.GridDropout(ratio=0.5, p=0.2),
                A.CoarseDropout(max_holes=16, min_holes=8, max_height=16, max_width=16, min_height=8, min_width=8, p=0.2)
            ], p=0.2),
            A.Normalize(
                mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225],
            ),
            ToTensorV2(),
        ])
    else:
        return Compose([
            A.Resize(height=CFG.img_size, width=CFG.img_size),
            A.Normalize(
                mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225],
            ),
            ToTensorV2(),
        ])
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