【光电智造】实践教程｜13个Pytorch图像增强方法总结

今日光电

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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

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]))

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()

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()

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()

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()

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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