Moved active contours example in edges section

doc/examples/edges/plot_active_contours.py

@@ -0,0 +1,99 @@
+"""
+====================
+Active Contour Model
+====================
+
+The active contour model is a method to fit open or closed splines to lines or
+edges in an image. It works by minimising an energy that is in part defined by
+the image and part by the spline's shape: length and smoothness. The
+minimization is done implicitly in the shape energy and explicitly in the
+image energy.
+
+In the following two examples the active contour model is used (1) to segment
+the face of a person from the rest of an image by fitting a closed curve
+to the edges of the face and (2) to find the darkest curve between two fixed
+points while obeying smoothness considerations. Typically it is a good idea to
+smooth images a bit before analyzing, as done in the following examples.
+
+.. [1] *Snakes: Active contour models*. Kass, M.; Witkin, A.; Terzopoulos, D.
+       International Journal of Computer Vision 1 (4): 321 (1988).
+
+We initialize a circle around the astronaut's face and use the default boundary
+condition ``bc='periodic'`` to fit a closed curve. The default parameters
+``w_line=0, w_edge=1`` will make the curve search towards edges, such as the
+boundaries of the face.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.color import rgb2gray
+from skimage import data
+from skimage.filters import gaussian_filter
+from skimage.segmentation import active_contour
+
+# Test scipy version, since active contour is only possible
+# with recent scipy version
+import scipy
+scipy_version = list(map(int, scipy.__version__.split('.')))
+new_scipy = scipy_version[0] > 0 or \
+            (scipy_version[0] == 0 and scipy_version[1] >= 14)
+
+img = data.astronaut()
+img = rgb2gray(img)
+
+s = np.linspace(0, 2*np.pi, 400)
+x = 220 + 100*np.cos(s)
+y = 100 + 100*np.sin(s)
+init = np.array([x, y]).T
+
+if not new_scipy:
+    print('You are using an old version of scipy. '
+          'Active contours is implemented for scipy versions '
+          '0.14.0 and above.')
+
+if new_scipy:
+    snake = active_contour(gaussian_filter(img, 3),
+                           init, alpha=0.015, beta=10, gamma=0.001)
+
+    fig = plt.figure(figsize=(7, 7))
+    ax = fig.add_subplot(111)
+    plt.gray()
+    ax.imshow(img)
+    ax.plot(init[:, 0], init[:, 1], '--r', lw=3)
+    ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3)
+    ax.set_xticks([]), ax.set_yticks([])
+    ax.axis([0, img.shape[1], img.shape[0], 0])
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Here we initialize a straight line between two points, `(5, 136)` and
+`(424, 50)`, and require that the spline has its end points there by giving
+the boundary condition `bc='fixed'`. We furthermore make the algorithm search
+for dark lines by giving a negative `w_line` value.
+"""
+
+img = data.text()
+
+x = np.linspace(5, 424, 100)
+y = np.linspace(136, 50, 100)
+init = np.array([x, y]).T
+
+if new_scipy:
+    snake = active_contour(gaussian_filter(img, 1), init, bc='fixed',
+                           alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1)
+
+    fig = plt.figure(figsize=(9, 5))
+    ax = fig.add_subplot(111)
+    plt.gray()
+    ax.imshow(img)
+    ax.plot(init[:, 0], init[:, 1], '--r', lw=3)
+    ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3)
+    ax.set_xticks([]), ax.set_yticks([])
+    ax.axis([0, img.shape[1], img.shape[0], 0])
+
+plt.show()
+
+"""
+.. image:: PLOT2RST.current_figure
+"""