def test_euclidean_estimation():
# exact solution
tform = estimate_transform('euclidean', SRC[:2, :], SRC[:2, :] + 10)
assert_almost_equal(tform(SRC[:2, :]), SRC[:2, :] + 10)
assert_almost_equal(tform.params[0, 0], tform.params[1, 1])
assert_almost_equal(tform.params[0, 1], -tform.params[1, 0])
# over-determined
tform2 = estimate_transform('euclidean', SRC, DST)
assert_almost_equal(tform2.inverse(tform2(SRC)), SRC)
assert_almost_equal(tform2.params[0, 0], tform2.params[1, 1])
assert_almost_equal(tform2.params[0, 1], -tform2.params[1, 0])
# via estimate method
tform3 = EuclideanTransform()
tform3.estimate(SRC, DST)
assert_almost_equal(tform3.params, tform2.params)
def test_euclidean_estimation():
# exact solution
tform = estimate_transform('euclidean', SRC[:2, :], SRC[:2, :] + 10)
assert_almost_equal(tform(SRC[:2, :]), SRC[:2, :] + 10)
assert_almost_equal(tform.params[0, 0], tform.params[1, 1])
assert_almost_equal(tform.params[0, 1], - tform.params[1, 0])
# over-determined
tform2 = estimate_transform('euclidean', SRC, DST)
assert_almost_equal(tform2.inverse(tform2(SRC)), SRC)
assert_almost_equal(tform2.params[0, 0], tform2.params[1, 1])
assert_almost_equal(tform2.params[0, 1], - tform2.params[1, 0])
# via estimate method
tform3 = EuclideanTransform()
tform3.estimate(SRC, DST)
assert_almost_equal(tform3.params, tform2.params)
def absolute_orientation(p, q, no_scaling=False):
"""
Returns R, t, s satisfying q = s * R * p + t
p and q must be 3xN matrices.
"""
if no_scaling:
st = EuclideanTransform()
else:
st = SimilarityTransform()
st.estimate(p.T, q.T)
R = st.params[:3, :3]
t = st.params[:3, 3]
s = np.linalg.norm(R) / np.sqrt(3)
R = R / s
return R, t, s
def transform_euclidean(left_kps, right_kps, left_ds, right_ds):
"""Determine projective transform which fits best to map right kps to left kps."""
bf = cv2.BFMatcher()
log.info('Start matching Features.')
raw_matches = bf.knnMatch(left_ds, right_ds, k=2)
log.debug('Matches found: #raw_matches = {}'.format(len(raw_matches)))
left_pts, right_pts, good_matches = helpers.get_points_n_matches(
left_kps, right_kps, raw_matches)
log.debug('# Filtered Features = {}'.format(len(good_matches)))
if len(left_pts) > 3:
log.info('Start finding homography.')
left_pts = left_pts.reshape((len(left_pts), 2))
right_pts = right_pts.reshape((len(right_pts), 2))
model = EuclideanTransform()
model.estimate(right_pts, left_pts)
model_robust, inliers = ransac((right_pts, left_pts),
EuclideanTransform,
min_samples=50,
residual_threshold=10,
max_trials=3000)
h**o = model_robust.params
mask_good = None
return h**o, mask_good, good_matches
return None
def test_3d_euclidean_estimation():
src_points = np.random.rand(1000, 3)
# Random transformation for testing
angles = np.random.random((3, )) * 2 * np.pi - np.pi
rotation_matrix = _euler_rotation_matrix(angles)
translation_vector = np.random.random((3, ))
dst_points = []
for pt in src_points:
pt_r = pt.reshape(3, 1)
dst = np.matmul(rotation_matrix, pt_r) + \
translation_vector.reshape(3, 1)
dst = dst.reshape(3)
dst_points.append(dst)
dst_points = np.array(dst_points)
# estimating the transformation
tform = EuclideanTransform(dimensionality=3)
assert tform.estimate(src_points, dst_points)
estimated_rotation = tform.rotation
estimated_translation = tform.translation
assert_almost_equal(estimated_rotation, rotation_matrix)
assert_almost_equal(estimated_translation, translation_vector)