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Add test cases for structure tensor and hessian matrix functions

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This commit is contained in:
Johannes Schönberger
2013-08-27 15:37:58 +02:00
parent 02e81acec8
commit 3e2ca1493e
2 changed files with 47 additions and 2 deletions
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skimage/feature/corner.py
+3 -1
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@@ -126,7 +126,7 @@ def hessian_matrix(image, sigma=1, mode='constant', cval=0):
# window extent to the left and right, which covers > 99% of the normal
# distribution
window_ext = np.ceil(3 * sigma)
window_ext = max(1, np.ceil(3 * sigma))
ky, kx = np.mgrid[-window_ext:window_ext + 1, -window_ext:window_ext + 1]
@@ -134,8 +134,10 @@ def hessian_matrix(image, sigma=1, mode='constant', cval=0):
gaussian_exp = np.exp(-(kx ** 2 + ky ** 2) / (2 * sigma ** 2))
kernel_xx = 1 / (2 * np.pi * sigma ** 4) * (kx ** 2 / sigma ** 2 - 1)
kernel_xx *= gaussian_exp
kernel_xx /= kernel_xx.sum()
kernel_xy = 1 / (2 * np.pi * sigma ** 6) * (kx * ky)
kernel_xy *= gaussian_exp
kernel_xy /= kernel_xx.sum()
kernel_yy = kernel_xx.transpose()
Hxx = ndimage.convolve(image, kernel_xx, mode=mode, cval=cval)
skimage/feature/tests/test_corner.py
+44 -1
View File
@@ -10,7 +10,50 @@ from skimage.morphology import octagon
from skimage.feature import (corner_moravec, corner_harris, corner_shi_tomasi,
corner_subpix, peak_local_max, corner_peaks,
corner_kitchen_rosenfeld, corner_foerstner,
corner_fast, corner_orientations)
corner_fast, corner_orientations,
structure_tensor, hessian_matrix)
def test_structure_tensor():
square = np.zeros((5, 5))
square[2, 2] = 1
Axx, Axy, Ayy = structure_tensor(square, sigma=0.1)
assert_array_equal(Axx, np.array([[ 0, 0, 0, 0, 0],
[ 0, 1, 0, 1, 0],
[ 0, 4, 0, 4, 0],
[ 0, 1, 0, 1, 0],
[ 0, 0, 0, 0, 0]]))
assert_array_equal(Axy, np.array([[ 0, 0, 0, 0, 0],
[ 0, 1, 0, -1, 0],
[ 0, 0, 0, -0, 0],
[ 0, -1, -0, 1, 0],
[ 0, 0, 0, 0, 0]]))
assert_array_equal(Ayy, np.array([[ 0, 0, 0, 0, 0],
[ 0, 1, 4, 1, 0],
[ 0, 0, 0, 0, 0],
[ 0, 1, 4, 1, 0],
[ 0, 0, 0, 0, 0]]))
def test_structure_tensor():
square = np.zeros((5, 5))
square[2, 2] = 1
Hxx, Hxy, Hyy = hessian_matrix(square, sigma=0.1)
assert_array_equal(Hxx, np.array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 1, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]))
assert_array_equal(Hxy, np.array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]))
assert_array_equal(Hyy, np.array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 1, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]))
def test_square_image():