图像数据增强方法一览

import os.path 
import random 
import math


dir_root = 'D:/images/'
for img_name in os.listdir(dir_root): 
    img_path = dir_root + img_name  #读取每一个图片路径 
    image = cv2.imread(img_path) 
    tfimg = tfactor(image) 
    rotateAngle = random.randrange(0, 10) 
    rotateImg = rotate(tfimg, rotateAngle) 
......

From: 图像数据增强方法一览

翻转|Flipping

对图像进行翻转是最流行的图像数据增强方法之一。这主要是由于翻转图像操作的代码简单，以及对于大多数问题而言，对图像进行翻转操作能够提升模型的性能。下面的模型可以被认为是看到左鞋而不是右鞋，因此通过这种数据增加，模型对于看到鞋的潜在变化会变得更加鲁棒。

# Flipping images with Numpy 

flipped_img = np.fliplr(img) 
plt.imshow(flipped_img) 
plt.show()

From: opencv-python 图像数据增强

def SaltAndPepper(src,percetage):  
    SP_NoiseImg=src.copy()
    SP_NoiseNum=int(percetage*src.shape[0]*src.shape[1]) 
    for i in range(SP_NoiseNum): 
        randR=np.random.randint(0,src.shape[0]-1) 
        randG=np.random.randint(0,src.shape[1]-1) 
        randB=np.random.randint(0,3)
        if np.random.randint(0,1)==0: 
            SP_NoiseImg[randR,randG,randB]=0 
        else: 
            SP_NoiseImg[randR,randG,randB]=255 
    return SP_NoiseImg 
def addGaussianNoise(image,percetage): 
    G_Noiseimg = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    G_NoiseNum=int(percetage*image.shape[0]*image.shape[1]) 
    for i in range(G_NoiseNum): 
        temp_x = np.random.randint(0,h) 
        temp_y = np.random.randint(0,w) 
        G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0] 
    return G_Noiseimg
#dimming
def darker(image,percetage=0.9):
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    #get darker
    for xi in range(0,w):
        for xj in range(0,h):
            image_copy[xj,xi,0] = int(image[xj,xi,0]*percetage)
            image_copy[xj,xi,1] = int(image[xj,xi,1]*percetage)
            image_copy[xj,xi,2] = int(image[xj,xi,2]*percetage)
    return image_copy
def brighter(image, percetage=1.5):
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    #get brighter
    for xi in range(0,w):
        for xj in range(0,h):
            image_copy[xj,xi,0] = np.clip(int(image[xj,xi,0]*percetage),a_max=255,a_min=0)
            image_copy[xj,xi,1] = np.clip(int(image[xj,xi,1]*percetage),a_max=255,a_min=0)
            image_copy[xj,xi,2] = np.clip(int(image[xj,xi,2]*percetage),a_max=255,a_min=0)
    return image_copy
def rotate(image, angle=15, scale=0.9):
    w = image.shape[1]
    h = image.shape[0]
    #rotate matrix
    M = cv2.getRotationMatrix2D((w/2,h/2), angle, scale)
    #rotate
    image = cv2.warpAffine(image,M,(w,h))
    return image
def img_augmentation(path, name_int):        
    img = cv2.imread(path)
    img_flip = cv2.flip(img,1)#flip
    img_rotation = rotate(img)#rotation
    
    img_noise1 = SaltAndPepper(img, 0.3)
    img_noise2 = addGaussianNoise(img, 0.3)
    
    img_brighter = brighter(img)
    img_darker = darker(img)
    
    cv2.imwrite(save_path+'%s' %str(name_int)+'.jpg', img_flip)
    cv2.imwrite(save_path+'%s' %str(name_int+1)+'.jpg', img_rotation)
    cv2.imwrite(save_path+'%s' %str(name_int+2)+'.jpg', img_noise1)
    cv2.imwrite(save_path+'%s' %str(name_int+3)+'.jpg', img_noise2)
    cv2.imwrite(save_path+'%s' %str(name_int+4)+'.jpg', img_brighter)
    cv2.imwrite(save_path+'%s' %str(name_int+5)+'.jpg', img_darker)