我们使用行进正方形方法找到图像中的常值轮廓
**函数：
Find contours at a constant value of 0.8
contours = measure.find_contours(r, 0.8)**
使用它，需要：from skimage import measure
"""
===============
Contour finding
===============
We use a marching squares method to find constant valued contours in an image.
In ``skimage.measure.find_contours``, array values are linearly interpolated
to provide better precision of the output contours. Contours which intersect
the image edge are open; all others are closed.
The `marching squares algorithm
<http://www.essi.fr/~lingrand/MarchingCubes/algo.html>`__ is a special case of
the marching cubes algorithm (Lorensen, William and Harvey E. Cline. Marching
Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics
(SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
"""
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)**3 + np.cos(y)**2)))
# Find contours at a constant value of 0.8
contours = measure.find_contours(r, 0.8)
# Display the image and plot all contours found
fig, ax = plt.subplots()
ax.imshow(r, cmap=plt.cm.gray)
for n, contour in enumerate(contours):
ax.plot(contour[:, 1], contour[:, 0], linewidth=2, color='red')
ax.axis('image')
ax.set_xticks([])
ax.set_yticks([])
plt.show()
