Slicing an array of numpy images into blocks

Question

I am involved in image processing for detecting objects using python. I need to split my image into all possible blocks. For example, given this toy image:

x = np.arange(25) x = x.reshape((5, 5)) [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14] [15 16 17 18 19] [20 21 22 23 24]] 

I want to get all possible blocks of a given size, for example, 2x2 blocks:

 [[0 1] [5 6]] [[1 2] [6 7]] 

.. and so on. How can i do this?

Answer 1

You can use something like this:

 def rolling_window(arr, window): """Very basic multi dimensional rolling window. window should be the shape of of the desired subarrays. Window is either a scalar or a tuple of same size as `arr.shape`. """ shape = np.array(arr.shape*2) strides = np.array(arr.strides*2) window = np.asarray(window) shape[arr.ndim:] = window # new dimensions size shape[:arr.ndim] -= window - 1 if np.any(shape < 1): raise ValueError('window size is too large') return np.lib.stride_tricks.as_strided(arr, shape=shape, strides=strides) # Now: slices = rolling_window(arr, 2) # Slices will be 4-d not 3-d as you wanted. You can reshape # but it may need to copy (not if you have done no slicing, etc. with the array): slices = slices.reshape(-1,slices.shape[2:]) 

Answer 2

Image scikit extract_patches_2d does this

 >>> from sklearn.feature_extraction import image >>> one_image = np.arange(16).reshape((4, 4)) >>> one_image array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> patches = image.extract_patches_2d(one_image, (2, 2)) >>> print(patches.shape) (9, 2, 2) >>> patches[0] array([[0, 1], [4, 5]]) >>> patches[1] array([[1, 2], [5, 6]]) >>> patches[8] array([[10, 11], [14, 15]]) 

Answer 3

 >>> x = np.arange(12).reshape(3,4) >>> print x [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] >>> r = 2 >>> for z in [x[i:i+r,j:j+r] for i in xrange(x.shape[0] - r + 1) for j in xrange(x.shape[1] - r + 1)]: ... print z ... [[0 1] [4 5]] [[1 2] [5 6]] [[2 3] [6 7]] [[4 5] [8 9]] [[ 5 6] [ 9 10]] [[ 6 7] [10 11]]