fastest way to select 7*7 neighbor pixels for every pixel in an image in Python

Question

need to read an image as an array and for each pixel select 7*7 neighbor pixels then reshape it and put as a first row of training set:

  import numpy as np
  from scipy import misc
  face1=misc.imread('face1.jpg') 

face1 dimensions are (288, 352, 3) , need to find 7*7 neighbor pixels for every pixel , so 49*3 color then reshape it as a (1,147) array and stack it into an array for all pixels , i took the following approach:

X_training=np.zeros([1,147] ,dtype=np.uint8)
for i in range(3, face1.shape[0]-3):
    for j in range(3, face1.shape[1]-3):
        block=face1[i-3:i+4,j-3:j+4]
        pxl=np.reshape(block,(1,147))
        X_training=np.vstack((pxl,X_training))

resulting X_training shape is (97572, 147)

and as last row contains all zeros then:

a = len(X_training)-1
X_training = X_training[:a]

above code works well for one picture but with Wall time: 5min 19s i have 2000 images, so it will take ages to do it for all the images. I am looking for a faster way to iterate over every pixel and do the above task.

Edit: this is what i mean by neighbor pixels , for every pixel face1[i-3 : i+4 ,j-3:j+4]

Answer 1

An efficient way is to use stride_tricks to create a 2d rolling window over the image, then flatten it out:

import numpy as np

face1 = np.arange(288*352*3).reshape(288, 352, 3)  # toy data

n = 7  # neighborhood size

h, w, d = face1.shape
s = face1.strides

tmp = np.lib.stride_tricks.as_strided(face1, strides=s[:2] + s,
                                      shape=(h - n + 1, w - n + 1, n, n, d))
X_training = tmp.reshape(-1, n**2 * d)
X_training = X_training[::-1]  # to get the rows into same order as in the question

tmp is a 5D view into the image, where tmp[x, y, :, :, c] is equivalent to the neigborhood face1[x:x+n, y:y+n, c] in color channel c.