def get_data(self):
itr = 0
for iden, partial, complete in self.ds.get_data():
partial_centroid = np.mean(partial, axis=0)
if self.use_partial:
itn_input = partial
else:
itn_input = resample_pcd(complete, self.sample_size)
if self.is_training:
if self.train_perturb_list is not None:
R = quat2rotm(self.train_perturb_list[itr])
else:
R = random_rotation(self.so3_perturb)
else:
R = quat2rotm(self.valid_perturb_list[itr])
itr += 1
itn_input = itn_input @ R.T
itn_input = itn_input - partial_centroid
gt_transformation = np.eye(4)
gt_transformation[0:3, 0:3] = R
gt_transformation[0:3, 3] = -partial_centroid
yield iden, itn_input, gt_transformation
def test_rand(self):
rotm_1 = euldeg2rotm(23, 82, 13)
q = rotm2quat(rotm_1)
rotm_2 = quat2rotm(q)
assert np.all(np.isclose(rotm_1, rotm_2))
rotm_1 = euldeg2rotm(-240, 428, -888)
q = rotm2quat(rotm_1)
rotm_2 = quat2rotm(q)
assert np.all(np.isclose(rotm_1, rotm_2))
def test_rand(self):
q_1 = euldeg2quat(23, 82, 13)
rotm = quat2rotm(q_1)
q_2 = rotm2quat(rotm)
assert np.all(np.isclose(q_1, q_2)) or np.all(np.isclose(-q_1, q_2))
q_1 = euldeg2quat(-240, 428, -888)
rotm = quat2rotm(q_1)
q_2 = rotm2quat(rotm)
assert np.all(np.isclose(q_1, q_2)) or np.all(np.isclose(-q_1, q_2))
def test_rand(self):
q = euldeg2quat(23, 82, 13)
rotm_gt = euldeg2rotm(23, 82, 13)
rotm = quat2rotm(q)
assert np.all(np.isclose(rotm, rotm_gt))
q = euldeg2quat(-240, 428, -888)
rotm_gt = euldeg2rotm(-240, 428, -888)
rotm = quat2rotm(q)
assert np.all(np.isclose(rotm, rotm_gt))
def test_unnormalized(self):
q = euldeg2quat(2, -45, 21) * 5
rotm_gt = euldeg2rotm(2, -45, 21)
rotm = quat2rotm(q)
assert np.all(np.isclose(rotm, rotm_gt))
def test_z180(self):
q = euldeg2quat(0, 0, 180)
rotm_gt = euldeg2rotm(0, 0, 180)
rotm = quat2rotm(q)
assert np.all(np.isclose(rotm, rotm_gt))
def test_x90(self):
q = euldeg2quat(90, 0, 0)
rotm_gt = euldeg2rotm(90, 0, 0)
rotm = quat2rotm(q)
assert np.all(np.isclose(rotm, rotm_gt))
def test_identity(self):
q = euldeg2quat(0, 0, 0)
rotm_gt = euldeg2rotm(0, 0, 0)
rotm = quat2rotm(q)
assert np.all(np.isclose(rotm, rotm_gt))