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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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()
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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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