Im trying to apply GCN on a list of images but i keep getting this exception,
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
The full code is a bit large due to comments. But its just two methods.
Either way, im pasting down here the code.
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')
It seems the issue is in this line :
if save_image is True:
mpimg.imsave(trainingFolder + "GCN_Gray_TrainImage_" + str(x), image_aux, cmap=plt.get_cmap('gray'))
Which is just saving the image after mapping it to gray levels. But i cant see how the error is related to this line... I need some guidance or atip. Any help is welcomed!
Fixed after user help me fix it.
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'))
答案 0 :(得分：0)
将代码更改为此后修复：
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'))