mirror of
https://github.com/wassname/scikit-image.git
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François Boulogne
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
"""
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===============
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Contour finding
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===============
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``skimage.measure.find_contours`` uses a marching squares method to find
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constant valued contours in an image. Array values are linearly interpolated
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to provide better precision of the output contours. Contours which intersect
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the image edge are open; all others are closed.
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The `marching squares algorithm
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<http://www.essi.fr/~lingrand/MarchingCubes/algo.html>`__ is a special case of
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the marching cubes algorithm (Lorensen, William and Harvey E. Cline. Marching
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Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics
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(SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import measure
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# Construct some test data
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x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
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r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
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# Find contours at a constant value of 0.8
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contours = measure.find_contours(r, 0.8)
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# Display the image and plot all contours found
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plt.imshow(r, interpolation='nearest')
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for n, contour in enumerate(contours):
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plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
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plt.axis('image')
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plt.xticks([])
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plt.yticks([])
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plt.show()
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