79 lines
2.8 KiB
Python
79 lines
2.8 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 [1]_. It works by minimising an energy that is in part
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defined by 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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We initialize a circle around the astronaut's face and use the default boundary
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condition ``boundary_condition='periodic'`` to fit a closed curve. The default
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parameters ``w_line=0, w_edge=1`` will make the curve search towards edges,
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such as the boundaries of the face.
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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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:DOI:`10.1007/BF00133570`
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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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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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r = 100 + 100*np.sin(s)
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c = 220 + 100*np.cos(s)
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init = np.array([r, c]).T
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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, ax = plt.subplots(figsize=(7, 7))
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ax.imshow(img, cmap=plt.cm.gray)
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ax.plot(init[:, 1], init[:, 0], '--r', lw=3)
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ax.plot(snake[:, 1], snake[:, 0], '-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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######################################################################
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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 `boundary_condition='fixed'`. We furthermore
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# make the algorithm search for dark lines by giving a negative `w_line` value.
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img = data.text()
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r = np.linspace(136, 50, 100)
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c = np.linspace(5, 424, 100)
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init = np.array([r, c]).T
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snake = active_contour(gaussian(img, 1), init, boundary_condition='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, ax = plt.subplots(figsize=(9, 5))
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ax.imshow(img, cmap=plt.cm.gray)
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ax.plot(init[:, 1], init[:, 0], '--r', lw=3)
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ax.plot(snake[:, 1], snake[:, 0], '-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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