全局對比度正常化給予例外

Question

我試着去圖像的列表上應用GCN但我不斷收到此異常，

Saving training image : 0 
Traceback (most recent call last): 
    File "A:/Code/MeusProjetosPython/KaggleMnistProject/Pre_Processing/Main.py", line 12, in <module> 
    applyGCN(True) 
    File "A:\Code\MeusProjetosPython\KaggleMnistProject\Pre_Processing\Global_Contrast_Normalization.py", line 123, in applyGCN 
    mpimg.imsave(trainingFolder + "GCN_Gray_TrainImage_" + str(x), image_aux, cmap=plt.get_cmap('gray')) 
    File "C:\SciSoft\WinPython-64bit-2.7.9.4\python-2.7.9.amd64\lib\site-packages\matplotlib\image.py", line 1310, in imsave 
    figsize = [x/float(dpi) for x in (arr.shape[1], arr.shape[0])] 
IndexError: tuple index out of range 

完整的代碼是由於意見有點大。但它只有兩種方法。

無論哪種方式，即時貼在這裏的代碼。

import numpy 
import matplotlib.pyplot as plt 
import matplotlib.image as mpimg 
import numpy as np 


def global_contrast_normalize(X, scale=1., subtract_mean=True, use_std=False, sqrt_bias=0., min_divisor=1e-8): 
    """ 
    Global contrast normalizes by (optionally) subtracting the mean 
    across features and then normalizes by either the vector norm 
    or the standard deviation (across features, for each example). 
    Parameters 
    ---------- 
    X : ndarray, 2-dimensional 
     Design matrix with examples indexed on the first axis and \ 
     features indexed on the second. 
    scale : float, optional 
     Multiply features by this const. 
    subtract_mean : bool, optional 
     Remove the mean across features/pixels before normalizing. \ 
     Defaults to `True`. 
    use_std : bool, optional 
     Normalize by the per-example standard deviation across features \ 
     instead of the vector norm. Defaults to `False`. 
    sqrt_bias : float, optional 
     Fudge factor added inside the square root. Defaults to 0. 
    min_divisor : float, optional 
     If the divisor for an example is less than this value, \ 
     do not apply it. Defaults to `1e-8`. 
    Returns 
    ------- 
    Xp : ndarray, 2-dimensional 
     The contrast-normalized features. 
    Notes 
    ----- 
    `sqrt_bias` = 10 and `use_std = True` (and defaults for all other 
    parameters) corresponds to the preprocessing used in [1]. 
    References 
    ---------- 
    .. [1] A. Coates, H. Lee and A. Ng. "An Analysis of Single-Layer 
     Networks in Unsupervised Feature Learning". AISTATS 14, 2011. 
     http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf 
    """ 
    assert X.ndim == 2, "X.ndim must be 2" 
    scale = float(scale) 
    assert scale >= min_divisor 

    # Note: this is per-example mean across pixels, not the 
    # per-pixel mean across examples. So it is perfectly fine 
    # to subtract this without worrying about whether the current 
    # object is the train, valid, or test set. 
    mean = X.mean(axis=1) 
    if subtract_mean: 
     X = X - mean[:, numpy.newaxis] # Makes a copy. 
    else: 
     X = X.copy() 

    if use_std: 
     # ddof=1 simulates MATLAB's var() behaviour, which is what Adam 
     # Coates' code does. 
     ddof = 1 

     # If we don't do this, X.var will return nan. 
     if X.shape[1] == 1: 
      ddof = 0 

     normalizers = numpy.sqrt(sqrt_bias + X.var(axis=1, ddof=ddof))/scale 
    else: 
     normalizers = numpy.sqrt(sqrt_bias + (X ** 2).sum(axis=1))/scale 

    # Don't normalize by anything too small. 
    normalizers[normalizers < min_divisor] = 1. 

    X /= normalizers[:, numpy.newaxis] # Does not make a copy. 
    return X 


def applyGCN(save_image=False): 
    data = np.loadtxt("../inputData/train.csv", dtype=np.float32, delimiter=',', skiprows=1) 
    test_data = np.loadtxt("../inputData/test.csv", dtype=np.float32, delimiter=',', skiprows=1) 

    trainingFolder = "../inputData/converted_training/GCN/" 
    testingFolder = "../inputData/converted_testing/GCN/" 


    aux_data = data.copy() 
    ################################################### 
    #############TRAIN_DATA############################ 
    ################################################### 
    # GCN# 
    data_to_gcn = data[:, 1:] 

    img_gcn = global_contrast_normalize(data_to_gcn) 


    for x in xrange(0, len(data[:, 1])): 
     print "Saving training image :", x 

     image_aux = np.copy(img_gcn[x, :]) 

     image_aux = (image_aux).astype('uint8') * 255 

     aux_data[x, 1:] = image_aux.tolist() 

     if save_image is True: 
      mpimg.imsave(trainingFolder + "GCN_Gray_TrainImage_" + str(x), image_aux, cmap=plt.get_cmap('gray')) 
      # figsize = [x/float(dpi) for x in (arr.shape[1], arr.shape[0])] 
      # IndexError: tuple index out of range 

    with open(trainingFolder + "GCN_traindata.csv", 'wb') as fp: 
     for i in range(0, aux_data.shape[0]): 
      column = aux_data[i, :].tolist() 
      column = map(lambda x: str(x) + ',', column) 
      column = ''.join(column)[0:-1] 
      fp.write(column + '\n') 

    ################################################## 
    #############TEST_DATA############################ 
    ################################################## 
    img_gcn = global_contrast_normalize(test_data[:, :]) 
    aux_data = test_data.copy() 

    for x in xrange(0, len(test_data[:, 0])): 
     print "Saving testing image :", x 

     image_aux = np.copy(img_gcn[x, :]) 
     image_aux = image_aux.astype('uint8') * 255 
     aux_data[x, :] = image_aux.tolist() 

     if save_image is True: 

      mpimg.imsave(testingFolder + "GCN_Gray_TestImage_" + str(x), image_aux, cmap=plt.get_cmap('gray')) 

    with open(testingFolder + "GCN_testdata.csv", 'wb') as fp: 
     for i in range(0, aux_data.shape[0]): 
      column = aux_data[i, :].tolist() 
      column = map(lambda x: str(x) + ',', column) 
      column = ''.join(column)[0:-1] 
      fp.write(column + '\n') 

看來問題是在這一行：

if save_image is True: 
     mpimg.imsave(trainingFolder + "GCN_Gray_TrainImage_" + str(x), image_aux, cmap=plt.get_cmap('gray')) 

這僅僅是保存它映射到灰度級後的圖像。 但我不能看到錯誤是如何與這條線相關的... 我需要一些指導或atip。 歡迎任何幫助！

用戶幫助我解決後修復。

for x in xrange(0, len(test_data[:, 0])): 
     print "Saving testing image :", x 

     image_aux = np.copy(img_gcn[x, :]) 
     aux_data[x, :] = image_aux.tolist() 

     image_aux = np.reshape(image_aux, (28,28)) 

     if save_image is True: 
      mpimg.imsave(testingFolder + "GCN_Gray_TestImage_" + str(x), image_aux, cmap=plt.get_cmap('gray')) 

Answer 1

代碼改變此之後修正：

for x in xrange(0, len(test_data[:, 0])): 
     print "Saving testing image :", x 

     image_aux = np.copy(img_gcn[x, :]) 
     aux_data[x, :] = image_aux.tolist() 

     image_aux = np.reshape(image_aux, (28,28)) 

     if save_image is True: 
      mpimg.imsave(testingFolder + "GCN_Gray_TestImage_" + str(x), image_aux, cmap=plt.get_cmap('gray'))