mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-18 12:40:14 +08:00
pep8 compliance
This commit is contained in:
Julius Bier Kirekgaard
@@ -2,7 +2,7 @@ import numpy as np
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from skimage import img_as_float
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import scipy.linalg
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from scipy.interpolate import RectBivariateSpline
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from skimage.filters import gaussian_filter, sobel
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from skimage.filters import sobel
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def active_contour_model(image, snake, alpha=0.01, beta=0.1,
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w_line=0, w_edge=1, gamma=0.01,
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@@ -58,6 +58,7 @@ def active_contour_model(image, snake, alpha=0.01, beta=0.1,
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--------
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>>> #from skimage.segmentation import active_contour_model
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>>> from skimage.draw import circle_perimeter
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>>> from skimage.filters import gaussian_filter
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>>> img = np.zeros((100, 100))
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>>> rr, cc = circle_perimeter(35, 45, 25)
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>>> img[rr, cc] = 1
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@@ -71,7 +72,7 @@ def active_contour_model(image, snake, alpha=0.01, beta=0.1,
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"""
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max_iterations = int(max_iterations)
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if max_iterations<=0:
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if max_iterations <= 0:
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raise ValueError("max_iterations should be >0.")
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convergence_order = 10
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valid_bcs = ['periodic', 'free', 'fixed', 'free-fixed',
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@@ -80,25 +81,26 @@ def active_contour_model(image, snake, alpha=0.01, beta=0.1,
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raise ValueError("Invalid boundary condition.\n"+
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"Should be one of: "+", ".join(valid_bcs)+'.')
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img = img_as_float(image)
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RGB = len(img.shape)==3
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RGB = len(img.shape) == 3
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# Find edges using sobel:
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if w_edge!=0:
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if w_edge != 0:
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if RGB:
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edge = [sobel(img[:,:,0]),sobel(img[:,:,1]),sobel(img[:,:,2])]
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edge = [sobel(img[:, :, 0]), sobel(img[:, :, 1]),
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sobel(img[:, :, 2])]
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else:
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edge = [sobel(img)]
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for i in xrange(3 if RGB else 1):
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edge[i][0,:] = edge[i][1,:]
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edge[i][-1,:] = edge[i][-2,:]
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edge[i][:,0] = edge[i][:,1]
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edge[i][:,-1] = edge[i][:,-2]
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edge[i][0, :] = edge[i][1, :]
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edge[i][-1, :] = edge[i][-2, :]
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edge[i][:, 0] = edge[i][:, 1]
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edge[i][:, -1] = edge[i][:, -2]
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else:
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edge = [0]
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# Superimpose intensity and edge images:
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if RGB:
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img = w_line*np.sum(img,axis=2) \
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img = w_line*np.sum(img, axis=2) \
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+ w_edge*sum(edge)
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else:
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img = w_line*img + w_edge*edge[0]
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@@ -108,8 +110,8 @@ def active_contour_model(image, snake, alpha=0.01, beta=0.1,
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np.arange(img.shape[0]), img.T, kx=2, ky=2, s=0)
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x, y = snake[:, 0].copy(), snake[:, 1].copy()
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xsave = np.empty((convergence_order,len(x)))
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ysave = np.empty((convergence_order,len(x)))
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xsave = np.empty((convergence_order, len(x)))
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ysave = np.empty((convergence_order, len(x)))
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# Build snake shape matrix
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n = len(x)
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@@ -187,9 +189,9 @@ def active_contour_model(image, snake, alpha=0.01, beta=0.1,
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# Convergence criteria:
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j = i%(convergence_order+1)
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if j<convergence_order:
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xsave[j,:] = x
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ysave[j,:] = y
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if j < convergence_order:
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xsave[j, :] = x
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ysave[j, :] = y
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else:
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dist = np.min(np.max(np.abs(xsave-x[None, :])
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+ np.abs(ysave-y[None, :]), 1))
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@@ -1,97 +1,97 @@
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import numpy as np
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from skimage import data
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from skimage.color import rgb2gray
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from skimage.filters import gaussian_filter, sobel
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from skimage.filters import gaussian_filter
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from skimage.segmentation import active_contour_model
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from numpy.testing import assert_equal, assert_allclose, assert_raises
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def periodic_reference_test():
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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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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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snake = active_contour_model(gaussian_filter(img,3), init,
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snake = active_contour_model(gaussian_filter(img, 3), init,
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alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001)
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refx = [299, 298, 298, 298, 298, 297, 297, 296, 296, 295]
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refy = [98, 99, 100, 101, 102, 103, 104, 105, 106, 108]
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assert_equal(np.array(snake[:10,0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10,1], dtype=np.int32), refy)
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assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
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def fixed_reference_test():
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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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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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snake = active_contour_model(gaussian_filter(img,1), init, bc='fixed',
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snake = active_contour_model(gaussian_filter(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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refx = [5, 9, 13, 17, 21, 25, 30, 34, 38, 42]
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refy = [136, 135, 134, 133, 132, 131, 129, 128, 127, 125]
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assert_equal(np.array(snake[:10,0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10,1], dtype=np.int32), refy)
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assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
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def free_reference_test():
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img = data.text()
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x = np.linspace(5,424,100)
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y = np.linspace(70,40,100)
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x = np.linspace(5, 424, 100)
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y = np.linspace(70, 40, 100)
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init = np.array([x, y]).T
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snake = active_contour_model(gaussian_filter(img,3), init, bc='free',
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snake = active_contour_model(gaussian_filter(img, 3), init, bc='free',
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alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1)
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refx = [10, 13, 16, 19, 23, 26, 29, 32, 36, 39]
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refy = [76, 76, 75, 74, 73, 72, 71, 70, 69, 69]
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assert_equal(np.array(snake[:10,0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10,1], dtype=np.int32), refy)
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assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
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def RGB_test():
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img = gaussian_filter(data.text(),1)
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imgR = np.zeros((img.shape[0],img.shape[1],3))
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imgG = np.zeros((img.shape[0],img.shape[1],3))
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imgRGB = np.zeros((img.shape[0],img.shape[1],3))
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imgR[:,:,0] = img
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imgG[:,:,1] = img
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imgRGB[:,:,:] = img[:, :, None]
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x = np.linspace(5,424,100)
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y = np.linspace(136,50,100)
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img = gaussian_filter(data.text(), 1)
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imgR = np.zeros((img.shape[0], img.shape[1], 3))
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imgG = np.zeros((img.shape[0], img.shape[1], 3))
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imgRGB = np.zeros((img.shape[0], img.shape[1], 3))
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imgR[:, :, 0] = img
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imgG[:, :, 1] = img
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imgRGB[:, :, :] = img[:, :, None]
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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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snake = active_contour_model(imgR, 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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refx = [5, 9, 13, 17, 21, 25, 30, 34, 38, 42]
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refy = [136, 135, 134, 133, 132, 131, 129, 128, 127, 125]
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assert_equal(np.array(snake[:10,0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10,1], dtype=np.int32), refy)
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assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
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snake = active_contour_model(imgG, 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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assert_equal(np.array(snake[:10,0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10,1], dtype=np.int32), refy)
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assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
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snake = active_contour_model(imgRGB, init, bc='fixed',
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alpha=0.1, beta=1.0, w_line=-5/3., w_edge=0, gamma=0.1)
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assert_equal(np.array(snake[:10,0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10,1], dtype=np.int32), refy)
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assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
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assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
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def end_points_tests():
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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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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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snake = active_contour_model(gaussian_filter(img,3), init,
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snake = active_contour_model(gaussian_filter(img, 3), init,
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bc='periodic', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001,
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max_iterations=100)
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assert np.sum(np.abs(snake[0,:]-snake[-1,:]) ) < 2
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snake = active_contour_model(gaussian_filter(img,3), init,
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assert np.sum(np.abs(snake[0, :]-snake[-1, :])) < 2
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snake = active_contour_model(gaussian_filter(img, 3), init,
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bc='free', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001,
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max_iterations=100)
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assert np.sum(np.abs(snake[0,:]-snake[-1,:])) > 2
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snake = active_contour_model(gaussian_filter(img,3), init,
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assert np.sum(np.abs(snake[0, :]-snake[-1, :])) > 2
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snake = active_contour_model(gaussian_filter(img, 3), init,
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bc='fixed', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001,
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max_iterations=100)
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assert_allclose(snake[0,:], [x[0], y[0]], atol=1e-5)
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assert_allclose(snake[0, :], [x[0], y[0]], atol=1e-5)
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def bad_input_tests():
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@@ -99,9 +99,9 @@ def bad_input_tests():
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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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np.testing.assert_raises(ValueError, active_contour_model, img, init,
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assert_raises(ValueError, active_contour_model, img, init,
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bc='wrong')
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np.testing.assert_raises(ValueError, active_contour_model, img, init,
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assert_raises(ValueError, active_contour_model, img, init,
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max_iterations=-15)
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