From 7c30f36d8557633b19c62901be1dad5479d2606a Mon Sep 17 00:00:00 2001
From: Julius Bier Kirekgaard <jbk28@cam.ac.uk>
Date: Mon, 31 Aug 2015 19:12:35 +0100
Subject: [PATCH] Active contour example added

---
 doc/examples/plot_active_contours.py | 83 ++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 doc/examples/plot_active_contours.py

diff --git a/doc/examples/plot_active_contours.py b/doc/examples/plot_active_contours.py
new file mode 100644
index 00000000..fb003526
--- /dev/null
+++ b/doc/examples/plot_active_contours.py
@@ -0,0 +1,83 @@
+"""
+====================================================
+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.
+
+.. [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 defualt 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_model
+
+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
+
+snake = active_contour_model(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')
+ax.plot(snake[:, 0], snake[:, 1], '-b')
+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
+
+snake = active_contour_model(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')
+ax.plot(snake[:, 0], snake[:, 1], '-b')
+ax.set_xticks([]), ax.set_yticks([])
+ax.axis([0, img.shape[1], img.shape[0], 0])
+
+plt.show()
+
+"""
+.. image:: PLOT2RST.current_figure
+"""