Python遍历灰度图像中的连接组件 [英] Python iterate through connected components in grayscale image

问题描述

我有一个灰度图像，其值介于0(黑色)和白色(255)之间.我有一个与灰度图像大小相同的target矩阵.我需要从灰度图像中的一个随机像素开始，然后一次遍历图像一个像素(以深度优先搜索方式)，然后将其值复制到target矩阵中的相应位置.我显然只需要对非白色像素执行此操作.我怎样才能做到这一点?我以为我可以获取灰度图像的连接组件并逐个遍历每个像素，但是找不到任何合适的连接组件实现.有什么想法吗?

I have a gray scale image with values between 0 (black) and white (255). I have a target matrix of the same size as the gray scale image. I need to start at a random pixel in the gray scale image and traverse through the image one pixel at a time (in a depth-first search manner), copying its value to the corresponding location in the target matrix. I obviously need to do this only for the non-white pixels. How can I do this? I thought that I could get the connected components of the gray scale image and traverse each pixel one by one, but I couldn't find any suitable implementation of connected components. Any ideas?

例如，如果我的灰度图像是:

For example, if my gray scale image is:

[[255,255,255,255,255,255,255]
[255,255, 0 ,10 ,255,255, 1 ]
[255,30 ,255,255,50 ,255, 9 ]
[51 ,20 ,255,255, 9 ,255,240]
[255,255,80 ,50 ,170,255, 20]
[255,255,255,255,255,255, 0 ]
[255,255,255,255,255,255, 69]]

然后可能的遍历是[0,10,50,9,170,50,80,20,51,30]，后跟[1,9,240,20,0,69]给出[0,10,50,9,170,50,80,20,51,30,1,9,240,20,0,69].不同对象之间的顺序无关紧要.

Then a possible traversal is [0,10,50,9,170,50,80,20,51,30] followed by [1,9,240,20,0,69] to give [0,10,50,9,170,50,80,20,51,30,1,9,240,20,0,69]. The order between the different objects doesn't matter.

其他可能的遍历是: [1,9,240,20,0,69,0,10,50,9,170,50,80,20,51,30]或[1,9,240,20,0,69,0,10,50,9,170,50,80,20,30,51]或 [1,9,240,20,0,69,10,50,9,170,50,80,20,30,0,51]

Other possible traversals are: [1,9,240,20,0,69,0,10,50,9,170,50,80,20,51,30] or [1,9,240,20,0,69,0,10,50,9,170,50,80,20,30,51] or [1,9,240,20,0,69,10,50,9,170,50,80,20,30,0,51]

等

推荐答案

您可以使用networkx:

from itertools import product, repeat
import numpy as np
import networkx as nx

arr = np.array(
[[255,255,255,255,255,255,255],
 [255,255, 0 ,10 ,255,255, 1 ],
 [255,30 ,255,255,50 ,255, 9 ],
 [51 ,20 ,255,255, 9 ,255,240],
 [255,255,80 ,50 ,170,255, 20],
 [255,255,255,255,255,255, 0 ],
 [255,255,255,255,255,255, 69]])

# generate edges
shift = list(product(*repeat([-1, 0, 1], 2)))
x_max, y_max = arr.shape
edges = []

for x, y in np.ndindex(arr.shape):
    for x_delta, y_delta in shift:
        x_neighb = x + x_delta
        y_neighb = y + y_delta
        if (0 <= x_neighb < x_max) and (0 <= y_neighb < y_max):
            edge = (x, y), (x_neighb, y_neighb)
            edges.append(edge)

# build graph
G = nx.from_edgelist(edges)

# draw graph
pos = {(x, y): (y, x_max-x) for x, y in G.nodes()}
nx.draw(G, with_labels=True, pos=pos, node_color='coral', node_size=1000)

# draw graph with numbers
labels = dict(np.ndenumerate(arr))
node_color = ['coral' if labels[n] == 255 else 'lightgrey' for n in G.nodes()]
nx.draw(G, with_labels=True, pos=pos, labels=labels, node_color=node_color, node_size=1000)

# build subgraph
select = np.argwhere(arr < 255)
G1 = G.subgraph(map(tuple, select))

# draw subgraph
pos = {(x, y): (y, x_max-x) for x, y in G1.nodes()}
labels1 = {n:labels[n] for n in G1.nodes()}
nx.draw(G1, with_labels=True, pos=pos, labels=labels1, node_color='lightgrey', node_size=1000)

# find connected components and DFS trees
for i in nx.connected_components(G1):
    source = next(iter(i))
    idx = nx.dfs_tree(G1, source=source)
    print(arr[tuple(np.array(idx).T)])

输出:

[  0  10  50   9  50  80  20  30  51 170]
[  9   1 240  20   0  69]

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