def generate_batched_random_point_clouds(size=100000,
voxel_size=0.2,
batch_size=2):
batch = []
for i in range(batch_size):
batch.append(generate_random_point_cloud(size, voxel_size))
return sparse_collate_fn(batch)
def pcd_uv_to_sparsetensor(pcd,
u_u0,
v_v0,
mask_valid,
f=500.0,
voxel_size=0.01,
mask_side=None,
num_points=100000):
if mask_side is not None:
mask_valid = mask_valid & mask_side
pcd_valid = pcd[mask_valid]
u_u0_valid = u_u0[mask_valid][:, np.newaxis] / f
v_v0_valid = v_v0[mask_valid][:, np.newaxis] / f
block_ = np.concatenate([pcd_valid, u_u0_valid, v_v0_valid], axis=1)
block = np.zeros_like(block_)
block[:, :] = block_[:, :]
pc_ = np.round(block_[:, :3] / voxel_size)
pc_ -= pc_.min(0, keepdims=1)
feat_ = block
# transfer point cloud to voxels
inds = sparse_quantize(pc_, feat_, return_index=True, return_invs=False)
if len(inds) > num_points:
inds = np.random.choice(inds, num_points, replace=False)
pc = pc_[inds]
feat = feat_[inds]
lidar = SparseTensor(feat, pc)
feed_dict = [{'lidar': lidar}]
inputs = sparse_collate_fn(feed_dict)
return inputs
def collate_fn(inputs):
outputs = sparse_collate_fn(inputs)
pts_batch = []
pred_obb_batch = []
for input in inputs:
if not len(input['pred_obb_batch']) < 2:
pts_batch += input['pts_batch']
pred_obb_batch.append(np.array(input['pred_obb_batch']))
outputs['pts_batch'] = pts_batch
outputs['pred_obb_batch'] = pred_obb_batch
return outputs
def pcd_to_sparsetensor(pcd, mask_valid, voxel_size=0.01, num_points=100000):
pcd_valid = pcd[mask_valid]
block_ = pcd_valid
block = np.zeros_like(block_)
block[:, :3] = block_[:, :3]
pc_ = np.round(block_[:, :3] / voxel_size)
pc_ -= pc_.min(0, keepdims=1)
feat_ = block
# transfer point cloud to voxels
inds = sparse_quantize(pc_, feat_, return_index=True, return_invs=False)
if len(inds) > num_points:
inds = np.random.choice(inds, num_points, replace=False)
pc = pc_[inds]
feat = feat_[inds]
lidar = SparseTensor(feat, pc)
feed_dict = [{'lidar': lidar}]
inputs = sparse_collate_fn(feed_dict)
return inputs
def collate_fn(inputs):
return sparse_collate_fn(inputs)