python - 从 skimage 轮廓创建蒙版

Question

我有一张图像，我使用 skimage.measure.find_contours() 找到了轮廓，但现在我想为完全位于最大闭合轮廓之外的像素创建一个蒙版。知道怎么做吗？

修改文档中的例子:

import numpy as np
import matplotlib.pyplot as plt
from skimage import measure

# Construct some test data
x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
r = np.sin(np.exp((np.sin(x)**2 + np.cos(y)**2)))

# Find contours at a constant value of 0.8
contours = measure.find_contours(r, 0.8)

# Select the largest contiguous contour
contour = sorted(contours, key=lambda x: len(x))[-1]

# Display the image and plot the contour
fig, ax = plt.subplots()
ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray)
X, Y = ax.get_xlim(), ax.get_ylim()
ax.step(contour.T[1], contour.T[0], linewidth=2, c='r')
ax.set_xlim(X), ax.set_ylim(Y)
plt.show()

这是红色的轮廓:

但如果放大，请注意轮廓不是像素分辨率。

如何创建与原始图像尺寸相同且像素完全位于外部(即未与轮廓线交叉)被 mask 的图像？例如

from numpy import ma
masked_image = ma.array(r.copy(), mask=False)
masked_image.mask[pixels_outside_contour] = True

谢谢!

Answer 1

有点晚了，但你知道这句话。以下是我将如何实现这一目标。

import scipy.ndimage as ndimage    

# Create an empty image to store the masked array
r_mask = np.zeros_like(r, dtype='bool')

# Create a contour image by using the contour coordinates rounded to their nearest integer value
r_mask[np.round(contour[:, 0]).astype('int'), np.round(contour[:, 1]).astype('int')] = 1

# Fill in the hole created by the contour boundary
r_mask = ndimage.binary_fill_holes(r_mask)

# Invert the mask since you want pixels outside of the region
r_mask = ~r_mask