mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 19:48:43 +08:00
emmanuelle
844858f01b
Modified comments in some gallery examples for compatibility with sphinx-gallery parsing. Also modified some links in the narrative doc since image file names have changed.
93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
"""
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====================
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Active Contour Model
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====================
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The active contour model is a method to fit open or closed splines to lines or
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edges in an image. It works by minimising an energy that is in part defined by
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the image and part by the spline's shape: length and smoothness. The
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minimization is done implicitly in the shape energy and explicitly in the
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image energy.
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In the following two examples the active contour model is used (1) to segment
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the face of a person from the rest of an image by fitting a closed curve
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to the edges of the face and (2) to find the darkest curve between two fixed
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points while obeying smoothness considerations. Typically it is a good idea to
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smooth images a bit before analyzing, as done in the following examples.
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.. [1] *Snakes: Active contour models*. Kass, M.; Witkin, A.; Terzopoulos, D.
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International Journal of Computer Vision 1 (4): 321 (1988).
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We initialize a circle around the astronaut's face and use the default boundary
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condition ``bc='periodic'`` to fit a closed curve. The default parameters
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``w_line=0, w_edge=1`` will make the curve search towards edges, such as the
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boundaries of the face.
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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.color import rgb2gray
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from skimage import data
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from skimage.filters import gaussian
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from skimage.segmentation import active_contour
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# Test scipy version, since active contour is only possible
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# with recent scipy version
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import scipy
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scipy_version = list(map(int, scipy.__version__.split('.')))
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new_scipy = scipy_version[0] > 0 or \
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(scipy_version[0] == 0 and scipy_version[1] >= 14)
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img = data.astronaut()
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img = rgb2gray(img)
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s = np.linspace(0, 2*np.pi, 400)
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x = 220 + 100*np.cos(s)
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y = 100 + 100*np.sin(s)
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init = np.array([x, y]).T
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if not new_scipy:
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print('You are using an old version of scipy. '
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'Active contours is implemented for scipy versions '
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'0.14.0 and above.')
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if new_scipy:
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snake = active_contour(gaussian(img, 3),
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init, alpha=0.015, beta=10, gamma=0.001)
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fig = plt.figure(figsize=(7, 7))
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ax = fig.add_subplot(111)
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plt.gray()
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ax.imshow(img)
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ax.plot(init[:, 0], init[:, 1], '--r', lw=3)
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ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3)
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ax.set_xticks([]), ax.set_yticks([])
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ax.axis([0, img.shape[1], img.shape[0], 0])
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######################################################################
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# Here we initialize a straight line between two points, `(5, 136)` and
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# `(424, 50)`, and require that the spline has its end points there by giving
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# the boundary condition `bc='fixed'`. We furthermore make the algorithm
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# search for dark lines by giving a negative `w_line` value.
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img = data.text()
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x = np.linspace(5, 424, 100)
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y = np.linspace(136, 50, 100)
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init = np.array([x, y]).T
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if new_scipy:
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snake = active_contour(gaussian(img, 1), init, bc='fixed',
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alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1)
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fig = plt.figure(figsize=(9, 5))
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ax = fig.add_subplot(111)
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plt.gray()
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ax.imshow(img)
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ax.plot(init[:, 0], init[:, 1], '--r', lw=3)
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ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3)
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ax.set_xticks([]), ax.set_yticks([])
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ax.axis([0, img.shape[1], img.shape[0], 0])
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plt.show()
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