原文链接：Pytorch 图像增强教程 - 知乎

使用数据增强技术可以增加数据集中图像的多样性，从而提高模型的性能和泛化能力。主要的图像增强技术包括：

• 调整大小

• 灰度变换

• 标准化

• 随机旋转

• 中心裁剪

• 随机裁剪

• 高斯模糊

• 亮度、对比度调节

• 水平翻转

• 垂直翻转

调整大小

在开始图像大小的调整之前我们需要导入数据（图像以眼底图像为例）。

from PIL import Image
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import sys
import torch
import numpy as np
import torchvision.transforms as T

plt.rcParams["savefig.bbox"] = 'tight'
orig_img = Image.open(Path('image/000001.tif'))
torch.manual_seed(0) # 设置 CPU 生成随机数的 种子 ，方便下次复现实验结果
print(np.asarray(orig_img).shape) #(800, 800, 3)

#图像大小的调整
resized_imgs = [T.Resize(size=size)(orig_img) for size in [128,256]]
# plt.figure('resize:128*128')
ax1 = plt.subplot(131)
ax1.set_title('original')
ax1.imshow(orig_img)

ax2 = plt.subplot(132)
ax2.set_title('resize:128*128')
ax2.imshow(resized_imgs[0])

ax3 = plt.subplot(133)
ax3.set_title('resize:256*256')
ax3.imshow(resized_imgs[1])

plt.show()

灰度变换

此操作将RGB图像转化为灰度图像。

gray_img = T.Grayscale()(orig_img)
# plt.figure('resize:128*128')
ax1 = plt.subplot(121)
ax1.set_title('original')
ax1.imshow(orig_img)

ax2 = plt.subplot(122)
ax2.set_title('gray')
ax2.imshow(gray_img,cmap='gray')

标准化

标准化可以加快基于神经网络结构的模型的计算速度，加快学习速度。

• 从每个输入通道中减去通道平均值

• 将其除以通道标准差。

  normalized_img = T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))(T.ToTensor()(orig_img))
  normalized_img = [T.ToPILImage()(normalized_img)]
  # plt.figure('resize:128*128')
  ax1 = plt.subplot(121)
  ax1.set_title('original')
  ax1.imshow(orig_img)
  
  ax2 = plt.subplot(122)
  ax2.set_title('normalize')
  ax2.imshow(normalized_img[0])
  
  plt.show()

  

• 随机旋转

  设计角度旋转图像

• from PIL import Image
  from pathlib import Path
  import matplotlib.pyplot as plt
  import numpy as np
  import sys
  import torch
  import numpy as np
  import torchvision.transforms as T
  
  
  plt.rcParams["savefig.bbox"] = 'tight'
  orig_img = Image.open(Path('image/2.png'))
  
  rotated_imgs = [T.RandomRotation(degrees=90)(orig_img)]
  print(rotated_imgs)
  plt.figure('resize:128*128')
  ax1 = plt.subplot(121)
  ax1.set_title('original')
  ax1.imshow(orig_img)
  
  ax2 = plt.subplot(122)
  ax2.set_title('90°')
  ax2.imshow(np.array(rotated_imgs[0]))

  

• 随机裁剪

  随机剪切图像的某一部分

  from PIL import Image
  from pathlib import Path
  import matplotlib.pyplot as plt
  import numpy as np
  import sys
  import torch
  import numpy as np
  import torchvision.transforms as T
  
  
  plt.rcParams["savefig.bbox"] = 'tight'
  orig_img = Image.open(Path('image/2.png'))
  
  random_crops = [T.RandomCrop(size=size)(orig_img) for size in (400,300)]
  
  plt.figure('resize:128*128')
  ax1 = plt.subplot(131)
  ax1.set_title('original')
  ax1.imshow(orig_img)
  
  ax2 = plt.subplot(132)
  ax2.set_title('400*400')
  ax2.imshow(np.array(random_crops[0]))
  
  ax3 = plt.subplot(133)
  ax3.set_title('300*300')
  ax3.imshow(np.array(random_crops[1]))
  
  plt.show()

  

  高斯模糊

  使用高斯核对图像进行模糊变换

• from PIL import Image
  from pathlib import Path
  import matplotlib.pyplot as plt
  import numpy as np
  import sys
  import torch
  import numpy as np
  import torchvision.transforms as T
  
  
  plt.rcParams["savefig.bbox"] = 'tight'
  orig_img = Image.open(Path('image/2.png'))
  
  blurred_imgs = [T.GaussianBlur(kernel_size=(3, 3), sigma=sigma)(orig_img) for sigma in (3,7)]
  
  plt.figure('resize:128*128')
  ax1 = plt.subplot(131)
  ax1.set_title('original')
  ax1.imshow(orig_img)
  
  ax2 = plt.subplot(132)
  ax2.set_title('sigma=3')
  ax2.imshow(np.array(blurred_imgs[0]))
  
  ax3 = plt.subplot(133)
  ax3.set_title('sigma=7')
  ax3.imshow(np.array(blurred_imgs[1]))
  
  plt.show()

  

• 亮度、对比度和饱和度调节

• from PIL import Image
  from pathlib import Path
  import matplotlib.pyplot as plt
  import numpy as np
  import sys
  import torch
  import numpy as np
  import torchvision.transforms as T
  
  
  plt.rcParams["savefig.bbox"] = 'tight'
  orig_img = Image.open(Path('image/2.png'))
  # random_crops = [T.RandomCrop(size=size)(orig_img) for size in (832,704, 256)]
  colorjitter_img = [T.ColorJitter(brightness=(2,2), contrast=(0.5,0.5), saturation=(0.5,0.5))(orig_img)]
  
  plt.figure('resize:128*128')
  ax1 = plt.subplot(121)
  ax1.set_title('original')
  ax1.imshow(orig_img)
  ax2 = plt.subplot(122)
  ax2.set_title('colorjitter_img')
  ax2.imshow(np.array(colorjitter_img[0]))
  plt.show()

  

• 水平翻转

• from PIL import Image
  from pathlib import Path
  import matplotlib.pyplot as plt
  import numpy as np
  import sys
  import torch
  import numpy as np
  import torchvision.transforms as T
  
  
  plt.rcParams["savefig.bbox"] = 'tight'
  orig_img = Image.open(Path('image/2.png'))
  
  HorizontalFlip_img = [T.RandomHorizontalFlip(p=1)(orig_img)]
  
  plt.figure('resize:128*128')
  ax1 = plt.subplot(121)
  ax1.set_title('original')
  ax1.imshow(orig_img)
  
  ax2 = plt.subplot(122)
  ax2.set_title('colorjitter_img')
  ax2.imshow(np.array(HorizontalFlip_img[0]))
  
  
  plt.show()

  

• 垂直翻转

• from PIL import Image
  from pathlib import Path
  import matplotlib.pyplot as plt
  import numpy as np
  import sys
  import torch
  import numpy as np
  import torchvision.transforms as T
  
  
  plt.rcParams["savefig.bbox"] = 'tight'
  orig_img = Image.open(Path('image/2.png'))
  
  VerticalFlip_img = [T.RandomVerticalFlip(p=1)(orig_img)]
  
  plt.figure('resize:128*128')
  ax1 = plt.subplot(121)
  ax1.set_title('original')
  ax1.imshow(orig_img)
  
  ax2 = plt.subplot(122)
  ax2.set_title('VerticalFlip')
  ax2.imshow(np.array(VerticalFlip_img[0]))
  
  # ax3 = plt.subplot(133)
  # ax3.set_title('sigma=7')
  # ax3.imshow(np.array(blurred_imgs[1]))
  
  plt.show()