mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-03 07:27:48 +08:00
Stefan van der Walt
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
"""
|
|
===============
|
|
Contour finding
|
|
===============
|
|
|
|
``skimage.measure.find_contours`` uses a marching squares method to
|
|
find constant valued contours in an image. 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).
|
|
|
|
"""
|
|
|
|
from skimage import data
|
|
from skimage import measure
|
|
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
# 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
|
|
plt.imshow(r, interpolation='nearest')
|
|
|
|
for n, contour in enumerate(contours):
|
|
plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
|
|
|
|
plt.axis('image')
|
|
plt.xticks([])
|
|
plt.yticks([])
|
|
plt.show()
|
|
|