为什么有些线会被霍夫线函数忽略？

Question

我有点想弄清楚

如何确保所有线条都能被从 sckit-image 库中提取的 Line Hough 变换识别。

https://scikit-image.org/docs/dev/auto_examples/edges/plot_line_hough_transform.html#id3

以下所有行都被识别：

但是如果我在相似的图像上应用相同的脚本， 应用霍夫变换后，一行将被忽略，

我已经阅读了文档，上面写着：

The Hough transform constructs a histogram array representing the parameter
space (i.e., an :math:`M \\times N` matrix, for :math:`M` different values of
the radius and :math:`N` different values of :math:`\\theta`).  For each
parameter combination, :math:`r` and :math:`\\theta`, we then find the number
of non-zero pixels in the input image that would fall close to the
corresponding line, and increment the array at position :math:`(r, \\theta)`
appropriately. 
We can think of each non-zero pixel "voting" for potential line candidates. The
local maxima in the resulting histogram indicates the parameters of the most
probably lines

所以我的结论是这条线被删除了，因为它没有得到足够的“投票”， （我已经用不同的精度（0.05、0.5、0.1）度测试了它，但仍然遇到同样的问题）。 代码如下：

import numpy as np
from skimage.transform import hough_line, hough_line_peaks
from skimage.feature import canny
from skimage import data,io
import matplotlib.pyplot as plt
from matplotlib import cm


# Constructing test image
image = io.imread("my_image.png")

# Classic straight-line Hough transform
# Set a precision of 0.05 degree.
tested_angles = np.linspace(-np.pi / 2, np.pi / 2, 3600)
h, theta, d = hough_line(image, theta=tested_angles)

# Generating figure 1
fig, axes = plt.subplots(1, 3, figsize=(15, 6))
ax = axes.ravel()

ax[0].imshow(image, cmap=cm.gray)
ax[0].set_title('Input image')
ax[0].set_axis_off()

ax[1].imshow(np.log(1 + h),
             extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]), d[-1], d[0]],
             cmap=cm.gray, aspect=1/1.5)
ax[1].set_title('Hough transform')
ax[1].set_xlabel('Angles (degrees)')
ax[1].set_ylabel('Distance (pixels)')
ax[1].axis('image')

ax[2].imshow(image, cmap=cm.gray)
origin = np.array((0, image.shape[1]))
for _, angle, dist in zip(*hough_line_peaks(h, theta, d)):
    y0, y1 = (dist - origin * np.cos(angle)) / np.sin(angle)
    ax[2].plot(origin, (y0, y1), '-r')
ax[2].set_xlim(origin)
ax[2].set_ylim((image.shape[0], 0))
ax[2].set_axis_off()
ax[2].set_title('Detected lines')

plt.tight_layout() plt.show()

我也应该如何“抓住”这条线， 有什么建议吗？

Answer 1

较短的线在霍夫变换中具有较低的累加器值，因此您必须适当调整阈值。如果您知道要查找的线段数量，则可以将阈值设置得相当低，然后限制检测到的峰值数量。

这是上面的代码的浓缩版本，具有修改的阈值，供参考：

import numpy as np
from skimage.transform import hough_line, hough_line_peaks
from skimage import io
import matplotlib.pyplot as plt
from matplotlib import cm
from skimage import color


# Constructing test image
image = color.rgb2gray(io.imread("my_image.png"))

# Classic straight-line Hough transform
# Set a precision of 0.05 degree.
tested_angles = np.linspace(-np.pi / 2, np.pi / 2, 3600)

h, theta, d = hough_line(image, theta=tested_angles)
hpeaks = hough_line_peaks(h, theta, d, threshold=0.2 * h.max())

fig, ax = plt.subplots()
ax.imshow(image, cmap=cm.gray)

for _, angle, dist in zip(*hpeaks):
    (x0, y0) = dist * np.array([np.cos(angle), np.sin(angle)])
    ax.axline((x0, y0), slope=np.tan(angle + np.pi/2))

plt.show()

（注意：axline需要matplotlib 3.3。）