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pep8 and py3 compliance. more comments

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Julius Bier Kirekgaard
2015-09-09 15:44:35 +01:00
parent 8d344d090f
commit 5c20ef7218
4 changed files with 46 additions and 34 deletions
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doc/examples/plot_active_contours.py
+6 -5
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@@ -1,7 +1,8 @@
"""
====================================================
====================
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
@@ -18,8 +19,8 @@ smooth images a bit before analyzing, as done in the following examples.
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
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.
"""
@@ -39,7 +40,7 @@ y = 100 + 100*np.sin(s)
init = np.array([x, y]).T
snake = active_contour(gaussian_filter(img, 3),
init, alpha=0.015, beta=10, gamma=0.001)
init, alpha=0.015, beta=10, gamma=0.001)
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111)
skimage/segmentation/__init__.py
+1 -1
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@@ -9,7 +9,7 @@ from ._join import join_segmentations, relabel_from_one, relabel_sequential
__all__ = ['random_walker',
'active_contour',
'active_contour',
'felzenszwalb',
'slic',
'quickshift',
skimage/segmentation/active_contour_model.py
+31 -21
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@@ -5,6 +5,7 @@ import scipy.linalg
from scipy.interpolate import RectBivariateSpline, interp2d
from skimage.filters import sobel
def active_contour(image, snake, alpha=0.01, beta=0.1,
w_line=0, w_edge=1, gamma=0.01,
bc='periodic', max_px_move=1.0,
@@ -18,27 +19,29 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
Parameters
----------
image : (N, M) or (N, M, 3) ndarray
Input image
Input image.
snake : (N, 2) ndarray
Initialisation of snake.
Initialisation coordinates of snake. For periodic snakes, it should
not include duplicate endpoints.
alpha : float, optional
Snake length shape parameter
Snake length shape parameter. Higher values makes snake contract
faster.
beta : float, optional
Snake smoothness shape parameter
Snake smoothness shape parameter. Higher values makes snake smoother.
w_line : float, optional
Controls attraction to brightness. Use negative values to attract to
dark regions
dark regions.
w_edge : float, optional
Controls attraction to edges. Use negative values to repel snake from
edges.
gamma : float, optional
Excpliti time stepping parameter.
Explicit time stepping parameter.
bc : {'periodic', 'free', 'fixed'}, optional
Boundary conditions for worm. 'periodic' attaches the two ends of the
snake, 'fixed' holds the end-points in place, and'free' allows free
movement of the ends. 'fixed' and 'free' can be combined by parsing
'fixed-free', 'free-fixed'. Parsing 'fixed-fixed' or 'free-free'
yields same behaviour as 'fixed' and 'free', respectively.
snake, 'fixed' holds the end-points in place, and'free' allows free
movement of the ends. 'fixed' and 'free' can be combined by parsing
'fixed-free', 'free-fixed'. Parsing 'fixed-fixed' or 'free-free'
yields same behaviour as 'fixed' and 'free', respectively.
max_px_move : float, optional
Maximum pixel distance to move per iteration.
max_iterations : int, optional
@@ -54,29 +57,36 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
References
----------
.. [1] Kass, M.; Witkin, A.; Terzopoulos, D. "Snakes: Active contour
models". International Journal of Computer Vision 1 (4): 321 (1988).
models". International Journal of Computer Vision 1 (4): 321
(1988).
Examples
--------
>>> from skimage.draw import circle_perimeter
>>> from skimage.filters import gaussian_filter
>>> # Create and smooth image:
Create and smooth image:
>>> img = np.zeros((100, 100))
>>> rr, cc = circle_perimeter(35, 45, 25)
>>> img[rr, cc] = 1
>>> img = gaussian_filter(img, 2)
>>>> # Initiliaze spline:
Initiliaze spline:
>>> s = np.linspace(0, 2*np.pi,100)
>>> init = 50*np.array([np.cos(s), np.sin(s)]).T+50
>>> # Fit spline to image:
Fit spline to image:
>>> snake = active_contour_model(img, init, w_edge=0, w_line=1)
>>> int(np.mean(np.sqrt((45-snake[:, 0])**2 + (35-snake[:, 1])**2)))
25
"""
scipy_version = map(int, scipy.__version__.split('.'))
new_scipy = scipy_version[0]>0 or \
(scipy_version[0]==0 and scipy_version[1]>=14)
scipy_version = list(map(int, scipy.__version__.split('.')))
new_scipy = scipy_version[0] > 0 or \
(scipy_version[0] == 0 and scipy_version[1] >= 14)
if not new_scipy:
warnings.warn('You are using an old version of scipy. '
'Upgrading to a version newer than 0.14.0 '
@@ -130,16 +140,16 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
xsave = np.empty((convergence_order, len(x)))
ysave = np.empty((convergence_order, len(x)))
# Build snake shape matrix
# Build snake shape matrix for Euler equation
n = len(x)
a = np.roll(np.eye(n), -1, axis=0) \
+ np.roll(np.eye(n), -1, axis=1) \
- 2*np.eye(n)
- 2*np.eye(n) # second order derivative, central difference
b = np.roll(np.eye(n), -2, axis=0) \
+ np.roll(np.eye(n), -2, axis=1) \
- 4*np.roll(np.eye(n), -1, axis=0) \
- 4*np.roll(np.eye(n), -1, axis=1) \
+ 6*np.eye(n)
+ 6*np.eye(n) # fourth order derivative, central difference
A = -alpha*a + beta*b
# Impose boundary conditions different from periodic:
@@ -216,7 +226,7 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
ysave[j, :] = y
else:
dist = np.min(np.max(np.abs(xsave-x[None, :])
+ np.abs(ysave-y[None, :]), 1))
+ np.abs(ysave-y[None, :]), 1))
if dist < convergence:
break
skimage/segmentation/tests/test_active_contour_model.py
+8 -7
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@@ -5,6 +5,7 @@ from skimage.filters import gaussian_filter
from skimage.segmentation import active_contour
from numpy.testing import assert_equal, assert_allclose, assert_raises
def test_periodic_reference():
img = data.astronaut()
img = rgb2gray(img)
@@ -13,7 +14,7 @@ def test_periodic_reference():
y = 100 + 100*np.sin(s)
init = np.array([x, y]).T
snake = active_contour(gaussian_filter(img, 3), init,
alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001)
alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001)
refx = [299, 298, 298, 298, 298, 297, 297, 296, 296, 295]
refy = [98, 99, 100, 101, 102, 103, 104, 105, 106, 108]
assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
@@ -81,16 +82,16 @@ def test_end_points():
y = 100 + 100*np.sin(s)
init = np.array([x, y]).T
snake = active_contour(gaussian_filter(img, 3), init,
bc='periodic', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001,
max_iterations=100)
bc='periodic', alpha=0.015, beta=10, w_line=0, w_edge=1,
gamma=0.001, max_iterations=100)
assert np.sum(np.abs(snake[0, :]-snake[-1, :])) < 2
snake = active_contour(gaussian_filter(img, 3), init,
bc='free', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001,
max_iterations=100)
bc='free', alpha=0.015, beta=10, w_line=0, w_edge=1,
gamma=0.001, max_iterations=100)
assert np.sum(np.abs(snake[0, :]-snake[-1, :])) > 2
snake = active_contour(gaussian_filter(img, 3), init,
bc='fixed', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001,
max_iterations=100)
bc='fixed', alpha=0.015, beta=10, w_line=0, w_edge=1,
gamma=0.001, max_iterations=100)
assert_allclose(snake[0, :], [x[0], y[0]], atol=1e-5)