最近有一篇針對數據增強的文章比較有意思:這裏只講一下核心的代碼實現以及實現細節,原文可以自行查閱:
Training Neural Networks with Very Little Data – A Draft
文章的大概意思就是通過某種變換,將笛卡爾座標系的圖像通過座標變換,變換成極座標系下的圖像,該變換直接通過下面的公式給出:
變換比較簡單,公式也寫的很清楚,根據公式實現的代碼:
github:
https://github.com/zhly0/radial-transform
from skimage import data
from skimage import io
import numpy as np
import math
import matplotlib.pyplot as plt
def to_gray(img):
w, h,_ = img.shape
ret = np.empty((w, h), dtype=np.uint8)
retf = np.empty((w, h), dtype=np.float)
imgf = img.astype(float)
retf[:, :] = ((imgf[:, :, 1] + imgf[:, :, 2] + imgf[:, :, 0])/3)
ret = retf.astype(np.uint8)
return ret
def radia_transform(img,w,h):
shape = im.shape
new_im = np.zeros(shape)
print(shape)
print(len(shape))
print('w',w)
print('h',h)
width = shape[1]
height = shape[0]
lens = len(shape)
for i in range(0,width):
xita = 2*3.14159*i/width
for a in range(0,height):
x = (int)(math.floor(a * math.cos(xita)))
y = (int)(math.floor(a * math.sin(xita)))
new_y = (int)(h+x)
new_x = (int)(w+y)
#print(h.dtype)
if new_x>=0 and new_x<width:
if new_y>=0 and new_y<height:
if lens==3:
new_im[a,i,0] = (im[new_y,new_x,0]-127.5)/128
new_im[a,i,1] = (im[new_y,new_x,1]-127.5)/128
new_im[a,i,2] = (im[new_y,new_x,2]-127.5)/128
else:
new_im[a,i] = (im[new_y,new_x]-127.5)/128
new_im[a,i] = (im[new_y,new_x]-127.5)/128
new_im[a,i] = (im[new_y,new_x]-127.5)/128
return new_im
im = io.imread('E:/1.jpg')
im = to_gray(im)
h = im.shape[0]
w = im.shape[1]
new_im1 = radia_transform(im,(int)(w/2),(int)(h/2))
new_im2 = radia_transform(im,(int)(w/4),(int)(h/4))
new_im3 = radia_transform(im,(int)(w*0.5),(int)(h*0.75))
plt.figure(num='astronaut',figsize=(8,8))
plt.subplot(2,2,1)
plt.title('origin image')
plt.imshow(im,plt.cm.gray)
plt.subplot(2,2,2)
plt.title('0.5')
plt.imshow(new_im1,plt.cm.gray)
plt.axis('off')
plt.subplot(2,2,3)
plt.title('0.25')
plt.imshow(new_im2,plt.cm.gray)
plt.axis('off')
plt.subplot(2,2,4)
plt.title('0.75')
plt.imshow(new_im3,plt.cm.gray)
plt.axis('off')
plt.show()
以及對應的變換: