def get_pts_3d(vote_type,class_type):
linemod_db=LineModModelDB()
if vote_type==VotingType.BB8C:
points_3d = linemod_db.get_corners_3d(class_type)
points_3d = np.concatenate([points_3d,linemod_db.get_centers_3d(class_type)[None,:]],0)
elif vote_type==VotingType.BB8S:
points_3d = linemod_db.get_small_bbox(class_type)
points_3d = np.concatenate([points_3d,linemod_db.get_centers_3d(class_type)[None,:]],0)
elif vote_type==VotingType.Farthest:
points_3d = linemod_db.get_farthest_3d(class_type)
points_3d = np.concatenate([points_3d, linemod_db.get_centers_3d(class_type)[None, :]], 0)
elif vote_type==VotingType.Farthest4:
points_3d = linemod_db.get_farthest_3d(class_type,4)
points_3d = np.concatenate([points_3d, linemod_db.get_centers_3d(class_type)[None, :]], 0)
elif vote_type==VotingType.Farthest12:
points_3d = linemod_db.get_farthest_3d(class_type,12)
points_3d = np.concatenate([points_3d, linemod_db.get_centers_3d(class_type)[None, :]], 0)
elif vote_type==VotingType.Farthest16:
points_3d = linemod_db.get_farthest_3d(class_type,16)
points_3d = np.concatenate([points_3d, linemod_db.get_centers_3d(class_type)[None, :]], 0)
elif vote_type==VotingType.Farthest20:
points_3d = linemod_db.get_farthest_3d(class_type,20)
points_3d = np.concatenate([points_3d, linemod_db.get_centers_3d(class_type)[None, :]], 0)
else: # BB8
points_3d = linemod_db.get_corners_3d(class_type)
return points_3d
image_db = LineModImageDB('duck', has_ro_set=False, has_ra_set=False, has_plane_set=False, has_render_set=False,
has_ms_set=False,has_fuse_set=False)
random.shuffle(image_db.real_set)
dataset = LineModDatasetRealAug(image_db.real_set[:5], data_prefix=image_db.linemod_dir,
vote_type=VotingType.Extreme, augment=False)
sampler = RandomSampler(dataset)
batch_sampler = ImageSizeBatchSampler(sampler, 5, False)
loader = DataLoader(dataset, batch_sampler=batch_sampler, num_workers=8)
modeldb=LineModModelDB()
camera_matrix=Projector().intrinsic_matrix['linemod'].astype(np.float32)
for i, data in enumerate(loader):
rgb, mask, vertex, vertex_weight, pose, gt_corners = data
pts2d=gt_corners[0].numpy()[:,:2].astype(np.float32)
pts3d=modeldb.get_extreme_3d('duck')
pts3d=np.concatenate([pts3d,modeldb.get_centers_3d('duck')[None,:]],0).astype(np.float32)
wgt2d=np.zeros([pts2d.shape[0],3]).astype(np.float32)
wgt2d[:,(0,2)]=1.0
for k in range(pts2d.shape[0]):
if np.random.random()<0.5:
scale = np.random.uniform(1, 8)
else:
scale = np.random.uniform(32, 48)
pts2d[k]+=np.random.normal(0,scale,2)
wgt2d[k,(0,2)]=1/scale
wgt2d/=wgt2d.max()
pose_pred=uncertainty_pnp(pts2d,wgt2d,pts3d,camera_matrix)
pose_pred2=pnp(pts3d,pts2d,camera_matrix)
dataset = LineModDatasetRealAug(image_db.real_set[:5],
data_prefix=image_db.linemod_dir,
vote_type=VotingType.Extreme,
augment=False)
sampler = RandomSampler(dataset)
batch_sampler = ImageSizeBatchSampler(sampler, 5, False)
loader = DataLoader(dataset, batch_sampler=batch_sampler, num_workers=8)
modeldb = LineModModelDB()
camera_matrix = Projector().intrinsic_matrix['linemod'].astype(np.float32)
for i, data in enumerate(loader):
rgb, mask, vertex, vertex_weight, pose, gt_corners = data
pts2d = gt_corners[0].numpy()[:, :2].astype(np.float32)
pts3d = modeldb.get_extreme_3d('duck')
pts3d = np.concatenate(
[pts3d, modeldb.get_centers_3d('duck')[None, :]],
0).astype(np.float32)
wgt2d = np.zeros([pts2d.shape[0], 3]).astype(np.float32)
wgt2d[:, (0, 2)] = 1.0
for k in range(pts2d.shape[0]):
if np.random.random() < 0.5:
scale = np.random.uniform(1, 8)
else:
scale = np.random.uniform(32, 48)
pts2d[k] += np.random.normal(0, scale, 2)
wgt2d[k, (0, 2)] = 1 / scale
wgt2d /= wgt2d.max()
pose_pred = uncertainty_pnp(pts2d, wgt2d, pts3d, camera_matrix)
pose_pred2 = pnp(pts3d, pts2d, camera_matrix)