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ENH: Complete test for cvFindFundamentalMat.

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This commit is contained in:
Stefan van der Walt
2010-12-02 16:50:01 +02:00
parent 6c6658b766
commit 1d339c0979
1 changed files with 19 additions and 20 deletions
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scikits/image/opencv/tests/test_opencv_cv.py
+19 -20
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@@ -366,46 +366,45 @@ def test_cvFindFundamentalMat():
[-b, a, 0]])
# Camera one, at origin of world coordinates, looking down the z-axis
K = np.diag([5, 5, 1])
K = np.array([[100., 0, 100],
[0, 100, 100],
[0, 0, 1]])
R = np.eye(3)
C = np.zeros((3,))
P = build_proj_mat(K, R, C)
# Camera two
K_ = K
R_ = np.array([[0, 0, 1],
[0, 1, 0],
[-1, 0, 0]]) # Rotation of 90 degrees around y-axis
C_ = np.array([[-10, 0, 10]])
R_ = np.array([[0., 0, -1],
[0, 1, 0],
[1, 0, 0]]) # Rotation of 90 degrees around y-axis
C_ = np.array([[10., 0, 10]]).T
P_ = build_proj_mat(K_, R_, C_)
data = np.random.random((20, 4)) * 5 - 2.5
data[:, 3] = 1 # Homogeneous coordinates in 3D
data = np.random.random((100, 4)) * 5 - 2.5
data[:, 2] += 10 # Offset data in the z direction
data[:, 3] = 1 # 4D homogeneous version of 3D coords
points1 = np.dot(data, P.T)
points2 = np.dot(data, P_.T)
#points1 /= points1[:, 2][:, None]
#points2 /= points2[:, 2][:, None]
# See Hartley & Zisserman, Multiple View Geometry (2nd ed), p. 244
t = -np.dot(R, C)
t = -np.dot(R_, C_)
K_t = np.dot(K_, t)
# Under numpy >= 1.5, this would be:
# F = cross_matrix(K_t).dot(K_).dot(R).dot(np.linalg.inv(K))
#F = cross_matrix(K_t).dot(K_).dot(R).dot(np.linalg.inv(K))
F = np.dot(np.dot(np.dot(cross_matrix(K_t), K_), R), np.linalg.inv(K))
F = np.dot(np.dot(np.dot(cross_matrix(K_t), K_), R_), np.linalg.inv(K))
F /= F[2, 2]
F_est = cvFindFundamentalMat(points1, points2)
print F
print F_est
# Normalise F
#F_est *= (F[2, 2] / F_est[3, 3])
F_est, status = cvFindFundamentalMat(points1, points2)
# Compare
#assert_array_almost_equal(F, F_est)
# Alternative: compute points1.T F points2
assert_array_almost_equal(F, F_est)
if __name__ == '__main__':
run_module_suite()