尊重网格的 Skimage 骨架化或 OpenCV 实现？

Question

假设一条直线以直角相交。

并且您想将其骨架化以获得（您希望的）十字形状。相反，使用 sklearn.morphology.skeletonize 获得以下图像：

我们称它为“多孔十字架”吧。

另一方面，您有 OpenCV 和 OpenCV skeletonize 函数，它们在 Internet 上流传在几个博客和此处的答案中：

def skeletonize(bin: numpy.ndarray, erosion_shape=cv2.MORPH_RECT, kernel_sz: Union[int, Tuple[int, int]] = 3):
    kernel_sz = fix_kernel(kernel_sz)
    kernel = cv2.getStructuringElement(erosion_shape, kernel_sz)

    thresh = bin.copy()
    skeleton = numpy.zeros_like(bin)
    eroded = numpy.zeros_like(bin)
    carry = numpy.zeros_like(bin)

    while (True):
        cv2.erode(thresh, kernel, dst=eroded)
        cv2.dilate(eroded, kernel, dst=carry)
        cv2.subtract(thresh, carry, dst=carry)
        cv2.bitwise_or(skeleton, carry, dst=skeleton)
        thresh, eroded = eroded, thresh

        if cv2.countNonZero(thresh) == 0:
            return skeleton

这个结果如下：

因此，浮动的基本 OpenCV 骨架化函数有问题或不正确，并且 Skimage 骨架化无法使用结构化形状进行修改。

有没有办法在python中获得骨架化的cross/plus标志形状？

Answer 1

正如我在评论中指出的那样，您可以通过拟合霍夫线来清理骨架化图像中的交叉点：

#!/usr/bin/env python
"""

"""
import numpy as np
import matplotlib.pyplot as plt

from skimage.morphology import skeletonize
from skimage.transform import probabilistic_hough_line
from skimage.draw import line as get_line_pixels

img = np.zeros((20, 20))
img[4:16, 6:14] = 1
img[:, 10] = 1
img[10, :] = 1

skel = skeletonize(img)

lines = probabilistic_hough_line(skel, line_length=10)

# hough_line() returns the start and endpoint of the fitted lines;
# we need all pixels covered by that line;
cleaned = np.zeros_like(img)
for ((r0, c0), (r1, c1)) in lines:
    rr, cc = get_line_pixels(r0, c0, r1, c1)
    cleaned[rr, cc] = 1

fig, axes = plt.subplots(1, 3, sharex=True, sharey=True)
axes[0].imshow(img, cmap='gray')
axes[0].set_title('Raw')
axes[1].imshow(skel, cmap='gray')
axes[1].set_title('Skeleton')
axes[2].imshow(cleaned, cmap='gray')
axes[2].set_title('Hough lines')
plt.show()

如果您想强制水平或垂直适合，lines 可以简单地过滤以排除非水平和非垂直线：

for ((r0, c0), (r1, c1)) in lines:
    if (r0 == r1) or (c0 == c1):
        ...

尊重网格的 Skimage 骨架化或 OpenCV 实现？

Skimage skeletonize or OpenCV implementation that respects a grid?

python

opencv

mathematical-morphology

scikit-image