Image data enhancement

Image enhancement using torchvision’s transforms

import torch
from torchvision import transforms
import numpy as np 
import  PIL.Image as Image
import matplotlib.pyplot as plt  
def imshow(img_path, transform):  

    img = Image.open(img_path)  
    fig,ax = plt.subplots(1, 2, figsize=(15, 4))  
    ax[0].set_title(f'Original image {img.size}')  
    ax[0].imshow(img)  
    img = transform(img)  
    ax[1].set_title(f'Transformed image {img.size}')  
    ax[1].imshow(img)
path="D:/Desktop/lenna.png"

Resize Rescale

#此函数用于将图像的高度和宽度调整为我们想要的特定大小。

tranform = transforms.Resize((224, 224))
imshow(path,tranform)

 Cropping

#使用 CenterCrop 来返回一个中心裁剪的图像。
tranform = transforms.CenterCrop((224, 224))
imshow(path,tranform)

RandomResizedCrop 

 #Crop and resize.
tranform = transforms.RandomResizedCrop((100, 300))
imshow(path,tranform)

 

Flipping 

#Flip the image horizontally or vertically
transform = transforms.RandomHorizontalFlip()
imshow(path,tranform)

Padding 

#Pad transform by the specified amount on all edges of the image
= transforms.Pad((50,50,50,50))
imshow(path,tranform)

Rotation 

#The image randomly applies a rotation angle
transform = transforms.RandomRotation(45)
imshow(path,tranform)

Random Affine 

#Keep the center constant transform
transform = transforms.RandomAffine(1, translate=(0.5, 0.5), scale=(1, 1), shear=(1,1), fillcolor=(256,256,256)) imshow(path,  
tranform )

Gaussian Blur 

#The image will be blurred using Gaussian blur.

transform = transforms.GaussianBlur(7, 3)  
imshow(path, transform)

Grayscale 

transform = transforms.Grayscale(num_output_channels=3)  
imshow(path, transform)

 Brightness

#Change the brightness of the image The resulting image becomes darker or lighter when compared to the original image.

transform = transforms.ColorJitter(brightness=2)  
imshow(path, transform)

 Contrast

#The degree of difference between the darkest and lightest parts of an image is called contrast. The contrast of the image can also be adjusted as an enhancement.
transform = transforms.ColorJitter(contrast=2)  
imshow(path, transform)

Saturation 

#Saturation
transformr=transforms.ColorJitter(saturation=20)
imshow(path, transform)

Hue

#Hue is defined as the shade of a color in an image.
tranformr=transforms.ColorJitter(hue=2)
imshow(path, transform)