def __call__(self, res, info):
if res["type"] in ["NuScenesDataset", "LyftDataset"] and "gt_boxes" in info:
res["lidar"]["annotations"] = {
"boxes": info["gt_boxes"].astype(np.float32),
"names": info["gt_names"],
"tokens": info["gt_boxes_token"],
"velocities": info["gt_boxes_velocity"].astype(np.float32),
}
elif res["type"] == "KittiDataset":
calib = info["calib"]
calib_dict = {
"rect": calib["R0_rect"],
"Trv2c": calib["Tr_velo_to_cam"],
"P2": calib["P2"],
}
res["calib"] = calib_dict
if "annos" in info:
annos = info["annos"]
# we need other objects to avoid collision when sample
annos = kitti.remove_dontcare(annos)
locs = annos["location"]
dims = annos["dimensions"]
rots = annos["rotation_y"]
gt_names = annos["name"]
gt_boxes = np.concatenate(
[locs, dims, rots[..., np.newaxis]], axis=1
).astype(np.float32)
calib = info["calib"]
gt_boxes = box_np_ops.box_camera_to_lidar(
gt_boxes, calib["R0_rect"], calib["Tr_velo_to_cam"]
)
# only center format is allowed. so we need to convert
# kitti [0.5, 0.5, 0] center to [0.5, 0.5, 0.5]
box_np_ops.change_box3d_center_(
gt_boxes, [0.5, 0.5, 0], [0.5, 0.5, 0.5]
)
res["lidar"]["annotations"] = {
"boxes": gt_boxes,
"names": gt_names,
}
res["cam"]["annotations"] = {
"boxes": annos["bbox"],
"names": gt_names,
}
else:
return NotImplementedError
return res, info
def __call__(self, res, info):
if res["type"] in ["NuScenesDataset", "LyftDataset"
] and "gt_boxes" in info:
res["lidar"]["annotations"] = {
"boxes": info["gt_boxes"].astype(np.float32),
"names": info["gt_names"],
"tokens": info["gt_boxes_token"],
"velocities": info["gt_boxes_velocity"].astype(np.float32),
}
# True
elif res["type"] == "KittiDataset":
# only select useful calib matrix
calib = info["calib"]
calib_dict = {
"rect":
calib["R0_rect"],
"Trv2c":
calib["Tr_velo_to_cam"],
"P2":
calib["P2"],
"frustum":
box_np_ops.get_valid_frustum(calib["R0_rect"],
calib["Tr_velo_to_cam"],
calib["P2"],
info["image"]["image_shape"]),
}
res["calib"] = calib_dict
if "annos" in info:
annos = info["annos"]
# annos = kitti.remove_dontcare_v2(annos) # todo: try my remove_dontcare_v2 later
annos = kitti.remove_dontcare(annos)
locs = annos["location"] # x, y, z (cam)
dims = annos["dimensions"] # l, h, w
rots = annos[
"rotation_y"] # ry in label file, count from neg y-axis (0 degree), clockwise is postive,
# anti-clockwise is negative.
gt_names = annos["name"]
gt_boxes = np.concatenate([locs, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
calib = info["calib"]
# x,y,z(cam), l, h, w, ry -> x,y,z(velo), w, l, h, ry
gt_boxes = box_np_ops.box_camera_to_lidar(
gt_boxes, calib["R0_rect"], calib["Tr_velo_to_cam"])
# real center of gt_boxes_velo: z_value + h/2
box_np_ops.change_box3d_center_(gt_boxes, [0.5, 0.5, 0],
[0.5, 0.5, 0.5])
res["lidar"]["annotations"] = {
"boxes": gt_boxes,
"names": gt_names,
} # without difficulty here
if self.enable_difficulty_level:
res["lidar"]["annotations"].update(
{"difficulty": annos["difficulty"]})
res["cam"]["annotations"] = {
"boxes": annos["bbox"],
"names": gt_names,
} # image 2d bbox
else:
return NotImplementedError
return res, info