使用Python在2d阵列（图像）中的像素邻居 [英] Pixel neighbors in 2d array (image) using Python

问题描述

我有一个像这样的numpy数组：

I have a numpy array like this:

x = np.array([[1,2,3],[4,5,6],[7,8,9]])

一个函数使用以下输入参数将其称为neighbors：

I need to create a function let's call it "neighbors" with the following input parameter:

• x：一个numpy二维数组


• （i，j）：2d数组中元素的索引


• d：邻域半径

作为输出，我想得到 i，j 与给定距离 d 。
所以如果我运行

As output I want to get the neighbors of the cell i,j with a given distance d. So if I run

neighbors(im, i, j, d=1) with i = 1 and j = 1 (element value = 5) 

我应该得到以下值的索引： [1,2,3,4,6,7,8,9] 。我希望我说清楚。
有没有像scipy这样的图书馆来处理这个？

I should get the indices of the following values: [1,2,3,4,6,7,8,9]. I hope I make it clear. Is there any library like scipy which deal with this?

我做了一些工作，但它是一个粗略的解决方案。

I've done something working but it's a rough solution.

def pixel_neighbours(self, p):

    rows, cols = self.im.shape

    i, j = p[0], p[1]

    rmin = i - 1 if i - 1 >= 0 else 0
    rmax = i + 1 if i + 1 < rows else i

    cmin = j - 1 if j - 1 >= 0 else 0
    cmax = j + 1 if j + 1 < cols else j

    neighbours = []

    for x in xrange(rmin, rmax + 1):
        for y in xrange(cmin, cmax + 1):
            neighbours.append([x, y])
    neighbours.remove([p[0], p[1]])

    return neighbours

如何改进？

推荐答案

EDIT ：ah crap，我的回答只是写 im [id：i + d + 1，jd：j + d + 1] .flatten 但以不可理解的方式写的：）

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 :)

：

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)

b $ b

这里没有做任何时间，但这个版本可能有合理的性能。

Didn't do any timings here, but it's possible this version has reasonable performance.

有关详细信息，请使用短语滚动窗口numpy或滑动窗口numpy。

For more info, search the net with phrases "rolling window numpy" or "sliding window numpy".

这篇关于使用Python在2d阵列（图像）中的像素邻居的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！