def standardize(self, x):
if self.preprocessing_function:
x = self.preprocessing_function(x)
if self.rescale:
x *= self.rescale
# x is a single image, so it doesn't have image number at index 0
img_channel_axis = self.channel_axis - 1
if self.samplewise_center:
x -= np.mean(x, axis=img_channel_axis, keepdims=True)
if self.samplewise_std_normalization:
x /= (np.std(x, axis=img_channel_axis, keepdims=True) + 1e-7)
if self.featurewise_center:
if self.mean is not None:
x -= self.mean
else:
warnings.warn('This ImageDataGenerator specifies '
'`featurewise_center`, but it hasn\'t'
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
if self.featurewise_std_normalization:
if self.std is not None:
x /= (self.std + 1e-7)
else:
warnings.warn('This ImageDataGenerator specifies '
'`featurewise_std_normalization`, but it hasn\'t'
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
if self.zca_whitening:
if self.principal_components is not None:
flatx = np.reshape(x, (x.size))
whitex = np.dot(flatx, self.principal_components)
x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2]))
else:
warnings.warn('This ImageDataGenerator specifies '
'`zca_whitening`, but it hasn\'t'
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
return x
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