導讀

本文總結了13種圖像增強技術的pytorch實現(xiàn)方法，附代碼詳解。?

使用數(shù)據(jù)增強技術可以增加數(shù)據(jù)集中圖像的多樣性，從而提高模型的性能和泛化能力。主要的圖像增強技術包括：

• 調(diào)整大小
• 灰度變換
• 標準化
• 隨機旋轉(zhuǎn)
• 中心裁剪
• 隨機裁剪
• 高斯模糊
• 亮度、對比度調(diào)節(jié)
• 水平翻轉(zhuǎn)
• 垂直翻轉(zhuǎn)
• 高斯噪聲
• 隨機塊
• 中心區(qū)域
• 調(diào)整大小

在開始圖像大小的調(diào)整之前我們需要導入數(shù)據(jù)（圖像以眼底圖像為例）。

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) # 設置 CPU 生成隨機數(shù)的 種子 ，方便下次復現(xiàn)實驗結果
print(np.asarray(orig_img).shape) #(800, 800, 3)

#圖像大小的調(diào)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()

灰度變換

此操作將RGB圖像轉(zhuǎn)化為灰度圖像。

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')

標準化

標準化可以加快基于神經(jīng)網(wǎng)絡結構的模型的計算速度，加快學習速度。

從每個輸入通道中減去通道平均值

將其除以通道標準差。

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()

隨機旋轉(zhuǎn)

設計角度旋轉(zhuǎn)圖像

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]))

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中心剪切

剪切圖像的中心區(qū)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()

隨機裁剪

隨機剪切圖像的某一部分

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

使用高斯核對圖像進行模糊變換

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、對比度和飽和度調(diào)節(jié)

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()

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()

垂直翻轉(zhuǎn)

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()

向圖像中加入高斯噪聲。通過設置噪聲因子，噪聲因子越高，圖像的噪聲越大。

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()

隨機塊

正方形補丁隨機應用在圖像中。這些補丁的數(shù)量越多，神經(jīng)網(wǎng)絡解決問題的難度就越大。

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()

中心區(qū)域

和隨機塊類似，只不過在圖像的中心加入補丁

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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編輯：黃飛