Training Neural Networks with Very Little Data -- A Draft径向变换

最近有一篇针对数据增强的文章比较有意思：这里只讲一下核心的代码实现以及实现细节，原文可以自行查阅：
Training Neural Networks with Very Little Data – A Draft
文章的大概意思就是通过某种变换，将笛卡尔坐标系的图像通过坐标变换，变换成极坐标系下的图像，该变换直接通过下面的公式给出：

变换比较简单，公式也写的很清楚，根据公式实现的代码：
github：
https://github.com/zhly0/radial-transform

from skimage import datafrom skimage import ioimport numpy as npimport mathimport matplotlib.pyplot as pltdef 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 retdef 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_imim = 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()  

以及对应的变换：