【深度学习】13个Pytorch 图像增强方法总结

转载自 | 极市平台

作者 | 结发授长生

来源丨https://zhuanlan.zhihu.com/p/559887437

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

• 高斯噪声

• 随机块

• 中心区域

1

『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()

2

『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')

3

『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()

4

『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]))

5

『中心剪切』

剪切图像的中心区域

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'))
 
center_crops = [T.CenterCrop(size=size)(orig_img) for size in (128,64)]
 
plt.figure('resize:128*128')
ax1 = plt.subplot(131)
ax1.set_title('original')
ax1.imshow(orig_img)
 
ax2 = plt.subplot(132)
ax2.set_title('128*128°')
ax2.imshow(np.array(center_crops[0]))
 
ax3 = plt.subplot(133)
ax3.set_title('64*64')
ax3.imshow(np.array(center_crops[1]))
 
plt.show()

6

『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()

7

『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()

8

『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()

9

『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()

10

『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()

11

『高斯噪声』

向图像中加入高斯噪声。通过设置噪声因子，噪声因子越高，图像的噪声越大。

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'))
 
 
def add_noise(inputs, noise_factor=0.3):
    noisy = inputs + torch.randn_like(inputs) * noise_factor
    noisy = torch.clip(noisy, 0., 1.)
    return noisy
 
 
noise_imgs = [add_noise(T.ToTensor()(orig_img), noise_factor) for noise_factor in (0.3, 0.6)]
noise_imgs = [T.ToPILImage()(noise_img) for noise_img in noise_imgs]
 
plt.figure('resize:128*128')
ax1 = plt.subplot(131)
ax1.set_title('original')
ax1.imshow(orig_img)
 
ax2 = plt.subplot(132)
ax2.set_title('noise_factor=0.3')
ax2.imshow(np.array(noise_imgs[0]))
 
ax3 = plt.subplot(133)
ax3.set_title('noise_factor=0.6')
ax3.imshow(np.array(noise_imgs[1]))
 
plt.show()

12

『随机块』

正方形补丁随机应用在图像中。这些补丁的数量越多，神经网络解决问题的难度就越大。

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'))
 
 
def add_random_boxes(img,n_k,size=64):
    h,w = size,size
    img = np.asarray(img).copy()
    img_size = img.shape[1]
    boxes = []
    for k in range(n_k):
        y,x = np.random.randint(0,img_size-w,(2,))
        img[y:y+h,x:x+w] = 0
        boxes.append((x,y,h,w))
    img = Image.fromarray(img.astype('uint8'), 'RGB')
    return img
 
blocks_imgs = [add_random_boxes(orig_img,n_k=10)]
 
plt.figure('resize:128*128')
ax1 = plt.subplot(131)
ax1.set_title('original')
ax1.imshow(orig_img)
 
ax2 = plt.subplot(132)
ax2.set_title('10 black boxes')
ax2.imshow(np.array(blocks_imgs[0]))
 
 
plt.show()

13

『中心区域』

和随机块类似，只不过在图像的中心加入补丁

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'))
 
 
def add_central_region(img, size=32):
    h, w = size, size
    img = np.asarray(img).copy()
    img_size = img.shape[1]
    img[int(img_size / 2 - h):int(img_size / 2 + h), int(img_size / 2 - w):int(img_size / 2 + w)] = 0
    img = Image.fromarray(img.astype('uint8'), 'RGB')
    return img
 
 
central_imgs = [add_central_region(orig_img, size=128)]
 
 
plt.figure('resize:128*128')
ax1 = plt.subplot(131)
ax1.set_title('original')
ax1.imshow(orig_img)
 
ax2 = plt.subplot(132)
ax2.set_title('')
ax2.imshow(np.array(central_imgs[0]))
#
# ax3 = plt.subplot(133)
# ax3.set_title('20 black boxes')
# ax3.imshow(np.array(blocks_imgs[1]))
 
plt.show()

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