def evaluate(self,
points_2d,
pose_targets,
class_type,
intri_type='blender',
vote_type=VotingType.BB8,
intri_matrix=None):
points_3d = VotingType.get_pts_3d(vote_type, class_type)
if intri_type == 'use_intrinsic' and intri_matrix is not None:
K = intri_matrix
else:
K = self.projector.intrinsic_matrix[intri_type]
pose_pred = pnp(points_3d, points_2d, K)
model = self.linemod_db.get_ply_model(class_type)
diameter = self.linemod_db.get_diameter(class_type)
if class_type in ['eggbox', 'glue']:
self.add_metric_sym(pose_pred, pose_targets, model, diameter)
else:
self.add_metric(pose_pred, pose_targets, model, diameter)
self.projection_2d(pose_pred, pose_targets, model, K)
self.cm_degree_5_metric(pose_pred, pose_targets)
return pose_pred
def evaluate_uncertainty_v2(self, mean_pts2d, covar, pose_targets, class_type,
intri_type='blender', vote_type=VotingType.BB8):
points_3d = VotingType.get_pts_3d(vote_type, class_type)
pose_pred = uncertainty_pnp_v2(mean_pts2d, covar, points_3d, self.projector.intrinsic_matrix[intri_type])
model = self.linemod_db.get_ply_model(class_type)
diameter = self.linemod_db.get_diameter(class_type)
if class_type in ['eggbox','glue']:
self.projection_2d_sym(pose_pred, pose_targets, model, self.projector.intrinsic_matrix[intri_type])
self.add_metric_sym(pose_pred, pose_targets, model, diameter)
else:
self.projection_2d(pose_pred, pose_targets, model, self.projector.intrinsic_matrix[intri_type])
self.add_metric(pose_pred, pose_targets, model, diameter)
self.cm_degree_5_metric(pose_pred, pose_targets)
def evaluate_uncertainty(self,
mean_pts2d,
covar,
pose_targets,
class_type,
intri_type='blender',
vote_type=VotingType.BB8,
intri_matrix=None):
points_3d = VotingType.get_pts_3d(vote_type, class_type)
begin = time.time()
# full
cov_invs = []
for vi in range(covar.shape[0]):
if covar[vi, 0, 0] < 1e-6 or np.sum(np.isnan(covar)[vi]) > 0:
cov_invs.append(np.zeros([2, 2]).astype(np.float32))
continue
cov_inv = np.linalg.inv(scipy.linalg.sqrtm(covar[vi]))
cov_invs.append(cov_inv)
cov_invs = np.asarray(cov_invs) # pn,2,2
weights = cov_invs.reshape([-1, 4])
weights = weights[:, (0, 1, 3)]
if intri_type == 'use_intrinsic' and intri_matrix is not None:
K = intri_matrix
else:
K = self.projector.intrinsic_matrix[intri_type]
pose_pred = uncertainty_pnp(mean_pts2d, weights, points_3d, K)
model = self.linemod_db.get_ply_model(class_type)
diameter = self.linemod_db.get_diameter(class_type)
self.uncertainty_pnp_cost.append(time.time() - begin)
if class_type in ['eggbox', 'glue']:
self.add_metric_sym(pose_pred, pose_targets, model, diameter)
else:
self.add_metric(pose_pred, pose_targets, model, diameter)
self.projection_2d(pose_pred, pose_targets, model, K)
self.cm_degree_5_metric(pose_pred, pose_targets)
return pose_pred