def __call__(self, res, info):
voxel_size = self.voxel_generator.voxel_size # [0.05, 0.05, 0.1 ]
pc_range = self.voxel_generator.point_cloud_range # [0, -40, -3, 70.4, 40, 1]
grid_size = self.voxel_generator.grid_size # [1408, 1600, 40]
# remove those gt_boxes (after gt-aug, per-object-aug, and global-aug) out of valid bv_range
if res["mode"] == "train":
gt_dict = res["lidar"]["annotations"]
bv_range = pc_range[[0, 1, 3, 4]] # [ 0. , -40. , 70.4, 40. ],
# todo: try get_valid_mask_by_pc_valid_range in preprocess.py, how about z-axis
mask = prep.filter_gt_box_outside_range(gt_dict["gt_boxes"],
bv_range)
_dict_select(gt_dict, mask)
res["lidar"]["annotations"] = gt_dict
self.shuffle = True
points = res["lidar"]["points"]
if self.far_points_first:
points = box_np_ops.far_points_first(points, 40.0,
self.max_voxel_num,
self.shuffle)
voxels, coordinates, num_points_per_voxel = self.voxel_generator.generate(
points)
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
# pack voxelization result
res["lidar"]["voxels"] = dict(
voxels=voxels,
coordinates=coordinates,
num_points=num_points_per_voxel,
num_voxels=num_voxels,
shape=grid_size,
)
return res, info
def __call__(self, res, info):
# [0, -40, -3, 70.4, 40, 1]
voxel_size = self.voxel_generator.voxel_size
pc_range = self.voxel_generator.point_cloud_range
grid_size = self.voxel_generator.grid_size
# [352, 400]
if res["mode"] == "train":
gt_dict = res["lidar"]["annotations"]
bv_range = pc_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range(gt_dict["gt_boxes"], bv_range)
_dict_select(gt_dict, mask)
res["lidar"]["annotations"] = gt_dict
# points = points[:int(points.shape[0] * 0.1), :]
voxels, coordinates, num_points = self.voxel_generator.generate(
res["lidar"]["points"]
)
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
res["lidar"]["voxels"] = dict(
voxels=voxels,
coordinates=coordinates,
num_points=num_points,
num_voxels=num_voxels,
shape=grid_size,
)
return res, info
def __call__(self, res, info):
voxel_size = self.voxel_generator.voxel_size # [0.05, 0.05, 0.1 ]
pc_range = self.voxel_generator.point_cloud_range # [0, -40, -3, 70.4, 40, 1]
grid_size = self.voxel_generator.grid_size # [1408, 1600, 40]
# remove gt_boxes out of bv_range
if res["mode"] == "train":
gt_dict = res["lidar"]["annotations"]
bv_range = pc_range[[0, 1, 3, 4]] # [ 0. , -40. , 70.4, 40. ],
# todo: try get_valid_mask_by_pc_valid_range in preprocess.py, how about z-axis
mask = prep.filter_gt_box_outside_range(gt_dict["gt_boxes"],
bv_range)
_dict_select(gt_dict, mask)
res["lidar"]["annotations"] = gt_dict
# todo: how about the points out of pc_valid_range
voxels, coordinates, num_points = self.voxel_generator.generate(
res["lidar"]["points"])
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
# pack voxelization result
res["lidar"]["voxels"] = dict(
voxels=voxels,
coordinates=coordinates,
num_points=num_points,
num_voxels=num_voxels,
shape=grid_size,
)
return res, info
def __call__(self, res, info):
voxel_size = self.voxel_generator.voxel_size
pc_range = self.voxel_generator.point_cloud_range
grid_size = self.voxel_generator.grid_size
if res["mode"] == "train":
gt_dict = res["lidar"]["annotations"]
bv_range = pc_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range(gt_dict["gt_boxes"],
bv_range)
_dict_select(gt_dict, mask)
res["lidar"]["annotations"] = gt_dict
# points = points[:int(points.shape[0] * 0.1), :]
points = res["lidar"]["points"]
if self.include_pt_to_voxel:
voxels, coordinates, num_points, pt_to_voxel = self.voxel_generator.generate(
points)
else:
voxels, coordinates, num_points = self.voxel_generator.generate(
points)
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
res["lidar"]["voxels"] = dict(
voxels=voxels,
coordinates=coordinates,
num_points=num_points,
num_voxels=num_voxels,
shape=grid_size,
)
if self.include_pt_to_voxel:
res["lidar"]["voxels"]["pt_to_voxel"] = pt_to_voxel
if "valid_idx" in res["lidar"]: # panoview projector is used
valid_idx = res["lidar"]["valid_idx"]
return res, info
def __call__(self, res, info):
pc_range = self.voxel_generator.point_cloud_range # [0, -40, -3, 70.4, 40, 1]
grid_size = self.voxel_generator.grid_size # [1408, 1600, 40]
if res["mode"] == "train" and res['labeled']:
gt_dict = res["lidar"]["annotations"]
bv_range = pc_range[[0, 1, 3, 4]] # [ 0. , -40. , 70.4, 40. ],
mask = prep.filter_gt_box_outside_range(gt_dict["gt_boxes"],
bv_range)
_dict_select(gt_dict, mask)
res["lidar"]["annotations"] = gt_dict
self.shuffle = True
points = res["lidar"]["points"]
voxels, coordinates, num_points_per_voxel = self.voxel_generator.generate(
points)
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
res["lidar"]["voxels"] = dict(
voxels=voxels,
coordinates=coordinates,
num_points=num_points_per_voxel,
num_voxels=num_voxels,
shape=grid_size,
)
# voxelization of raw points without transformation
if "points_raw" in res["lidar"].keys():
points_raw = res["lidar"]["points_raw"]
voxels_raw, coordinates_raw, num_points_per_voxel_raw = self.voxel_generator.generate(
points_raw)
num_voxels_raw = np.array([voxels_raw.shape[0]], dtype=np.int64)
res["lidar"]["voxels_raw"] = dict(
voxels=voxels_raw,
coordinates=coordinates_raw,
num_points=num_points_per_voxel_raw,
num_voxels=num_voxels_raw,
shape=grid_size,
)
return res, info
def __call__(self, res, info):
voxel_size = self.voxel_generator.voxel_size
pc_range = self.voxel_generator.point_cloud_range
grid_size = self.voxel_generator.grid_size
if res["mode"] == "train":
gt_dict = res["lidar"]["annotations"]
bv_range = pc_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range(gt_dict["gt_boxes"], bv_range)
_dict_select(gt_dict, mask)
res["lidar"]["annotations"] = gt_dict
max_voxels = self.max_voxel_num[0]
else:
max_voxels = self.max_voxel_num[1]
voxels, coordinates, num_points = self.voxel_generator.generate(
res["lidar"]["points"], max_voxels=max_voxels
)
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
res["lidar"]["voxels"] = dict(
voxels=voxels,
coordinates=coordinates,
num_points=num_points,
num_voxels=num_voxels,
shape=grid_size,
range=pc_range,
size=voxel_size
)
double_flip = self.double_flip and (res["mode"] != 'train')
if double_flip:
flip_voxels, flip_coordinates, flip_num_points = self.voxel_generator.generate(
res["lidar"]["yflip_points"]
)
flip_num_voxels = np.array([flip_voxels.shape[0]], dtype=np.int64)
res["lidar"]["yflip_voxels"] = dict(
voxels=flip_voxels,
coordinates=flip_coordinates,
num_points=flip_num_points,
num_voxels=flip_num_voxels,
shape=grid_size,
range=pc_range,
size=voxel_size
)
flip_voxels, flip_coordinates, flip_num_points = self.voxel_generator.generate(
res["lidar"]["xflip_points"]
)
flip_num_voxels = np.array([flip_voxels.shape[0]], dtype=np.int64)
res["lidar"]["xflip_voxels"] = dict(
voxels=flip_voxels,
coordinates=flip_coordinates,
num_points=flip_num_points,
num_voxels=flip_num_voxels,
shape=grid_size,
range=pc_range,
size=voxel_size
)
flip_voxels, flip_coordinates, flip_num_points = self.voxel_generator.generate(
res["lidar"]["double_flip_points"]
)
flip_num_voxels = np.array([flip_voxels.shape[0]], dtype=np.int64)
res["lidar"]["double_flip_voxels"] = dict(
voxels=flip_voxels,
coordinates=flip_coordinates,
num_points=flip_num_points,
num_voxels=flip_num_voxels,
shape=grid_size,
range=pc_range,
size=voxel_size
)
return res, info
