How to implement PCA color augmentation as discussed in AlexNet I read through "ImageNet Classification with Deep Convolutional Neural Networks" again specifically for details on how to implement PCA color augmentation. 
I am unsure if I have it right. Here is how I did this in numpy:

# original image is 224x224x3 of dtype uint8 

renorm_image = np.reshape(original_image,(original_image.shape[0]*original_image.shape[1],3))

renorm_image = renorm_image.astype('float32')
renorm_image -= np.mean(renorm_image, axis=0)
renorm_image /= np.std(renorm_image, axis=0)

cov = np.cov(renorm_image, rowvar=False)

lambdas, p = np.linalg.eig(cov)
alphas = np.random.normal(0, 0.1, 3)

delta = np.dot(p, alphas*lambdas)

delta = (delta*255.).astype('int8')

pca_color_image = np.maximum(np.minimum(original_image + delta, 255), 0).astype('uint8')


One serious doubt is the line "delta = (delta*255.)". I have to do this to rescale things such that the numbers make sense. I hope someone can give me feedback if this is right.
You should not apply *255.

delta was supposed to be added to renorm_image, because you calculated this delta using cov, which was based on renorm_image. 

Then how would you restore renorm_image to your original image? *std + mean or *255?

Obviously you should apply *std + mean.

Therefore, 

delta = (delta*255.).astype('int8')
pca_color_image = np.maximum(np.minimum(original_image + delta, 255), 0).astype('uint8')


should be changed to:

mean = np.mean(renorm_image, axis=0)
std = np.std(renorm_image, axis=0)
pca_augmentation_version_renorm_image = renorm_image + delta
pca_color_image = pca_augmentation_version_renorm_image * std + mean
pca_color_image = np.maximum(np.minimum(pca_color_image, 255), 0).astype('uint8')