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给定以下 numpy 矩阵
import numpy as np
np_matrix = np.array([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,1,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0]
,[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0]
,[0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
,[0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1]
,[0,0,0,1,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0]
,[1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
,[2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]])
print(np_matrix)
print(np_matrix.shape)
可以表示为图像。
这里有一些关于输入矩阵的假设:
我有两个问题。以最有效的方式如何获得包含所有绿点坐标的数组?我怎样才能得到一个包含绿点周围正方形所有角的数组?
这是我为这个例子寻找的结果:
green_dots_coordinates = [
[0,0], # dot 1 with coordinates x1, y1 inside square 1
[7,12], # dot 2 with coordinates x2, y2 inside square 2
[16,27],# dot 3 with coordinates x3, y3 inside square 3
[29,21],# dot 4 with coordinates x4, y4 inside square 4
[34,7], # dot 5 with coordinates x5, y5 inside square 5
]
sqaures_corners_coordinates = [
#square nr 1
[
[0,6], # x1, y1
[2,6], # x2, y2
[0,0], # x3, y3
[2,0], # x4, y4
],
#square nr 2
[
[3,14], # x1, y1
[9,14], # x2, y2
[3,7], # x3, y3
[9,7], # x4, y4
],
#square nr 3
[
[12,31], # x1, y1
[23,31], # x2, y2
[12,24], # x3, y3
[23,24], # x4, y4
],
#square nr 4
[
[25,22], # x1, y1
[32,22], # x2, y2
[25,15], # x3, y3
[32,15], # x4, y4
]
,#square nr 5
[
[33,13], # x1, y1
[35,13], # x2, y2
[33,0], # x3, y3
[35,0], # x4, y4
],
]
最佳答案
坦率地说,您的输出格式非常奇怪,需要大量颠倒索引的顺序,(并且使输出对于索引原始数组毫无用处)但这有效:
def find_boxes(np_matrix):
np_mat = np_matrix[::-1, :] # reversed in expected output
def find_extent(arr, val):
xn = arr.size
x0 = np.flatnonzero(arr == 1)
xi = np.searchsorted(x0, val, side = 'right')
if xi == x0.size:
x1 = x0[xi-1]
x2 = xn - 1
elif xi == 0:
x1 = 0
x2 = x0[xi]
else:
x1 = x0[xi-1]
x2 = x0[xi]
return np.array([x1, x2])
green = np.where(np_mat == 2)
green = tuple(g[np.argsort(green[-1])] for g in green)
coords = np.empty((green[0].size, 2, 4))
for i, (x, y) in enumerate(zip(*green)):
coords[i, 0] = np.tile(find_extent(np_mat[x, :], y), 2)
coords[i, 1] = np.repeat(find_extent(np_mat[:, y], x)[::-1], 2) # reversed again
return np.stack(green)[::-1].T, coords.swapaxes(1,2).astype(int)
# reversed again and transposed
测试:
find_boxes(np_matrix)
Out:
(array([[ 0, 0],
[ 7, 12],
[16, 27],
[29, 21],
[34, 7]], dtype=int32),
array([[[ 0, 6],
[ 2, 6],
[ 0, 0],
[ 2, 0]],
[[ 3, 14],
[ 9, 14],
[ 3, 7],
[ 9, 7]],
[[12, 31],
[23, 31],
[12, 24],
[23, 24]],
[[25, 22],
[32, 22],
[25, 15],
[32, 15]],
[[33, 13],
[35, 13],
[33, 0],
[35, 0]]]))
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