首先，你可以使用形态学操作来确保有趣的边缘被连接起来。你可以在OpenCV的网页上阅读更多关于形态学操作的内容：https://docs.opencv.org/4.x/d9/d61/tutorial_py_morphological_ops.html。
代码如下：

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
from scipy import ndimage

# Open the image
img = cv.imread('testim5.png', cv.IMREAD_GRAYSCALE)

# Note that `scipy.ndimage.filters.gaussian_filter` is deprecated, use `scipy.ndimage.gaussian_filter` instead
blurred = ndimage.gaussian_filter(img, 2, mode='nearest')
edges = cv.Canny(blurred,100,175)

# Apply morphological operation (closing) to the EDGES so they are connected
kernel = np.ones((5,3),np.uint8)
edges_closing = cv.morphologyEx(edges, cv.MORPH_CLOSE, kernel)

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这样做的结果是，垂直边缘不会如此不相交，如您在下图中所见。
x1c 0d1x的数据
第二步是去除毛孔。这可以看作是一个去除小连接对象的问题，@Soltius在How to remove small connected objects using OpenCV中的解决方案效果很好。
在你的例子中，代码应该是：

# Remove smallest connected components
# To understand the following lines, look the answer of @Soltius in https://stackoverflow.com/questions/42798659/how-to-remove-small-connected-objects-using-opencv
nb_blobs, im_with_separated_blobs, stats, _ = cv.connectedComponentsWithStats(edges_closing)
sizes = stats[:, -1]
sizes = sizes[1:]
nb_blobs -= 1

# Minimum size of particles we want to keep (number of pixels).
# Here, it's a fixed value, but you can set it as you want.
min_size = 80  

final_edges = np.zeros_like(im_with_separated_blobs)
for blob in range(nb_blobs):
    if sizes[blob] >= min_size:
        final_edges[im_with_separated_blobs == blob + 1] = 255

fig, axs = plt.subplots(ncols=3)
axs[0].imshow(img, cmap='gray')
axs[1].imshow(edges, cmap='gray')
axs[2].imshow(final_edges, cmap='gray')
axs[0].set_title('Original image')
axs[1].set_title('Original edges')
axs[2].set_title('New edges')
fig.tight_layout()
plt.show()

型
最终边缘为：

的