【深度學(xué)習(xí)】實(shí)戰(zhàn)|13個(gè)Pytorch 圖像增強(qiáng)方法總結(jié)(附代碼)
作者丨結(jié)發(fā)授長生@知乎
鏈接丨https://zhuanlan.zhihu.com/p/559887437
使用數(shù)據(jù)增強(qiáng)技術(shù)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(qiáng)技術(shù)包括：
- 調(diào)整大小
- 灰度變換
- 標(biāo)準(zhǔn)化
- 隨機(jī)旋轉(zhuǎn)
- 中心裁剪
- 隨機(jī)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(jī)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(dǎo)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(shè)置 CPU 生成隨機(jī)數(shù)的 種子 ，方便下次復(fù)現(xiàn)實(shí)驗(yàn)結(jié)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(biāo)準(zhǔn)化
標(biāo)準(zhǔn)化可以加快基于神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)的模型的計(jì)算速度，加快學(xué)習(xí)速度。
- 從每個(gè)輸入通道中減去通道平均值
- 將其除以通道標(biāo)準(zhǔn)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(jī)旋轉(zhuǎn)
設(shè)計(jì)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]))
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(jī)裁剪
隨機(jī)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(jìn)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()
水平翻轉(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'))
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()
高斯噪聲
向圖像中加入高斯噪聲。通過設(shè)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(jī)塊
正方形補(bǔ)丁隨機(jī)應(yīng)用在圖像中。這些補(bǔ)丁的數(shù)量越多，神經(jīng)網(wǎng)絡(luò)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(jī)塊類似，只不過在圖像的中心加入補(bǔ)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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