def setUp(self):
super().setUp()
np.random.seed(RANDOM_SEED)
random.seed(RANDOM_SEED)
self.verifier = Ransac(use_intrinsics_in_verification=True,
estimation_threshold_px=0.5)
self.euler_angle_err_tol = 1.0
self.translation_err_tol = 0.01
class TestRansacVerifierArgoverse(unittest.TestCase):
def setUp(self):
super().setUp()
np.random.seed(RANDOM_SEED)
random.seed(RANDOM_SEED)
self.verifier = Ransac(use_intrinsics_in_verification=True,
estimation_threshold_px=0.5)
self.euler_angle_err_tol = 1.0
self.translation_err_tol = 0.01
def testRecoveredPoseError(self):
check_verifier_output_error(self.verifier, self.euler_angle_err_tol,
self.translation_err_tol)
def test_5pt_algo_5correspondences(self) -> None:
""" """
fx, px, py, k1, k2 = load_log_front_center_intrinsics()
keypoints_i1, keypoints_i2 = load_argoverse_log_annotated_correspondences(
)
# match keypoints row by row
match_indices = np.vstack(
[np.arange(len(keypoints_i1)),
np.arange(len(keypoints_i1))]).T
intrinsics_i1 = Cal3Bundler(fx, k1, k2, px, py)
intrinsics_i2 = Cal3Bundler(fx, k1, k2, px, py)
match_indices = match_indices[:5]
i2Ri1, i2ti1, _, _ = self.verifier.verify(keypoints_i1, keypoints_i2,
match_indices, intrinsics_i1,
intrinsics_i2)
def setUp(self):
super().setUp()
np.random.seed(RANDOM_SEED)
random.seed(RANDOM_SEED)
self.verifier: VerifierBase = Ransac(
use_intrinsics_in_verification=True)
def setUp(self) -> None:
super().setUp()
np.random.seed(RANDOM_SEED)
random.seed(RANDOM_SEED)
self.verifier: VerifierBase = Ransac(
use_intrinsics_in_verification=True, estimation_threshold_px=0.5)
def setUp(self):
super().setUp()
np.random.seed(RANDOM_SEED)
random.seed(RANDOM_SEED)
self.verifier = Ransac()
self.use_intrinsics_for_verification = True
self.euler_angle_err_tol = 1.0
self.translation_err_tol = 0.01
def test_sift_twoway_ransac(self):
"""Check DoG + SIFT + 2-way Matcher + RANSAC-5pt frontend."""
det_desc = SIFTDetectorDescriptor()
feature_extractor = FeatureExtractor(det_desc)
two_view_estimator = TwoViewEstimator(matcher=TwoWayMatcher(),
verifier=Ransac(),
corr_metric_dist_threshold=0.1)
self.__compare_frontend_result_error(
feature_extractor,
two_view_estimator,
euler_angle_err_tol=1.4,
translation_err_tol=0.026,
)
def test_superpoint_superglue_twoway_ransac(self):
"""Check SuperPoint + SuperGlue + OpenCV RANSAC-5pt frontend (Essential matrix estimation)."""
det_desc = SuperPointDetectorDescriptor()
feature_extractor = FeatureExtractor(det_desc)
two_view_estimator = TwoViewEstimator(
matcher=SuperGlueMatcher(use_outdoor_model=True),
verifier=Ransac(use_intrinsics_in_verification=True, estimation_threshold_px=4),
eval_threshold_px=4,
bundle_adjust_2view=False,
inlier_support_processor=InlierSupportProcessor(
min_num_inliers_est_model=15, min_inlier_ratio_est_model=0.1
),
)
self.__compare_frontend_result_error(
feature_extractor, two_view_estimator, euler_angle_err_tol=1.4, translation_err_tol=0.026
)
def setUp(
self, verifier: VerifierBase = Ransac(use_intrinsics_in_verification=False, estimation_threshold_px=3)
) -> None:
super().setUp()
self._verifier: VerifierBase = verifier
detector_descriptor = SIFTDetectorDescriptor()
matcher = TwoWayMatcher()
self._image_i1 = io_utils.load_image(str(IMG1_PATH))
self._image_i2 = io_utils.load_image(str(IMG2_PATH))
self._keypoints_i1, descriptors_i1 = detector_descriptor.detect_and_describe(self._image_i1)
self._keypoints_i2, descriptors_i2 = detector_descriptor.detect_and_describe(self._image_i2)
self._match_indices = matcher.match(
self._keypoints_i1,
self._keypoints_i2,
descriptors_i1,
descriptors_i2,
(self._image_i1.height, self._image_i1.width),
(self._image_i2.height, self._image_i2.width),
)
def setUp(self):
super().setUp()
self.verifier = Ransac(use_intrinsics_in_verification=False, estimation_threshold_px=0.5)
def setUp(self):
super().setUp()
self.verifier = Ransac(use_intrinsics_in_verification=False)