Pixel neighbors in 2d array (image) using Python
By using max
and min
, you handle pixels at the upper and lower bounds:
im[max(i-1,0):min(i+2,i_end), max(j-1,0):min(j+2,j_end)].flatten()
EDIT: ah crap, my answer is just writing im[i-d:i+d+1, j-d:j+d+1].flatten()
but written in a incomprehensible way :)
The good old sliding window trick may help here:
import numpy as np
from numpy.lib.stride_tricks import as_strided
def sliding_window(arr, window_size):
""" Construct a sliding window view of the array"""
arr = np.asarray(arr)
window_size = int(window_size)
if arr.ndim != 2:
raise ValueError("need 2-D input")
if not (window_size > 0):
raise ValueError("need a positive window size")
shape = (arr.shape[0] - window_size + 1,
arr.shape[1] - window_size + 1,
window_size, window_size)
if shape[0] <= 0:
shape = (1, shape[1], arr.shape[0], shape[3])
if shape[1] <= 0:
shape = (shape[0], 1, shape[2], arr.shape[1])
strides = (arr.shape[1]*arr.itemsize, arr.itemsize,
arr.shape[1]*arr.itemsize, arr.itemsize)
return as_strided(arr, shape=shape, strides=strides)
def cell_neighbors(arr, i, j, d):
"""Return d-th neighbors of cell (i, j)"""
w = sliding_window(arr, 2*d+1)
ix = np.clip(i - d, 0, w.shape[0]-1)
jx = np.clip(j - d, 0, w.shape[1]-1)
i0 = max(0, i - d - ix)
j0 = max(0, j - d - jx)
i1 = w.shape[2] - max(0, d - i + ix)
j1 = w.shape[3] - max(0, d - j + jx)
return w[ix, jx][i0:i1,j0:j1].ravel()
x = np.arange(8*8).reshape(8, 8)
print x
for d in [1, 2]:
for p in [(0,0), (0,1), (6,6), (8,8)]:
print "-- d=%d, %r" % (d, p)
print cell_neighbors(x, p[0], p[1], d=d)
Didn't do any timings here, but it's possible this version has reasonable performance.
For more info, search the net with phrases "rolling window numpy" or "sliding window numpy".
I don't know about any library functions for this, but you can easily write something like this yourself using the great slicing functionality of numpy:
import numpy as np
def neighbors(im, i, j, d=1):
n = im[i-d:i+d+1, j-d:j+d+1].flatten()
# remove the element (i,j)
n = np.hstack((n[:len(n)//2], n[len(n)//2+1:] ))
return n
Of course you should do some range checks to avoid out-of-bounds access.
Have a look at scipy.ndimage.generic_filter
.
As an example:
import numpy as np
import scipy.ndimage as ndimage
def test_func(values):
print(values)
return values.sum()
x = np.array([[1,2,3],[4,5,6],[7,8,9]])
footprint = np.array([[1,1,1],
[1,0,1],
[1,1,1]])
results = ndimage.generic_filter(x, test_func, footprint=footprint)
By default, it will "reflect" the values at the boundaries. You can control this with the mode
keyword argument.
However, if you're wanting to do something like this, there's a good chance that you can express your problem as a convolution of some sort. If so, it will be much faster to break it down into convolutional steps and use more optimized functions (e.g. most of scipy.ndimage
).