The problem of the old example is that it did not consider the "n_samples" dimension of the function from sklearn.feature_extraction.image.PatchExtractor and therefore caused a strange, unreasonable and confusing result that had no channel dimension.

The new example corrects the issue and gives a clear, reasonable demonstration of how to use PatchExtractor sensibly. It is shown as following:

>>> from sklearn.datasets import load_sample_images
>>> from sklearn.feature_extraction import image
# Use the array data from the second image in this dataset:
>>> X = load_sample_images().images[1]
>>> X = X[np.newaxis,:] # make X has a shape (n_samples, image_height, image_width, n_channels). Very important!!
>>> print('Image shape: {}'.format(X.shape))
Image shape: (1, 427, 640, 3)
>>> pe = image.PatchExtractor(patch_size=(10, 10))
>>> pe_fit = pe.fit(X) # Do nothing and return the estimator unchanged.
>>> pe_trans = pe.transform(X)
>>> print('Patches shape: {}'.format(pe_trans.shape))
Patches shape: (263758, 10, 10, 3)

Note, this line X = X[np.newaxis,:] adds a new axis as the "n_samples" dimension, which makes X has a correct shape (n_samples, image_height, image_width, n_channels). It is the key difference from the old example. This change is very important, because, otherwise, "image_height" will be treated as "n_samples", and other dimensions will also be messed up similarly. That is why the result in the old example look strange and confusing, with only 3 dimensions but without the channel dimension.

It would be good to add the following reconstruction scripts to verify that the original image can be reconstructed from the extracted patches using reconstruct_from_patches_2d function. This makes the example more complete, similar to the example of using the extract_patches_2d function. Again, it is important to keep in mind the "n_samples" dimension.

>>> X_reconstructed = image.reconstruct_from_patches_2d(pe_trans, X.shape[1:])
>>> print(X_reconstructed.shape)
>>> np.testing.assert_array_equal(X[0], X_reconstructed)
(427, 640, 3)