def TransformFeatures(self, features):
# We assume that the lasers are not padded, and all points are real.
if ('points_padding' in features.lasers and
features.lasers.points_padding is not None):
raise ValueError('FilterNLZPoints preprocessor does not support '
'padded lasers.')
# The 3rd feature in the laser is 1.0 for points in a no-label-zone
# and -1. for normal points.
is_not_nlz = tf.not_equal(features.lasers.points_feature[:, 2], 1.0)
features.lasers.points_xyz = tf.boolean_mask(features.lasers.points_xyz,
is_not_nlz)
features.lasers.points_feature = tf.boolean_mask(
features.lasers.points_feature, is_not_nlz)
return features
def _GetFilteredBoundingBoxData(kitti_data):
"""Given a single batch element of data, process it for writing.
Args:
kitti_data: A NestedMap of KITTI input generator returned data with a batch
size of 1.
Returns:
A NestedMap of all the output data we need to write per bounding box
cropped pointclouds.
"""
points = kitti_data.lasers.points_xyz
points_feature = kitti_data.lasers.points_feature
bboxes_3d = kitti_data.labels.bboxes_3d
bboxes_3d_mask = kitti_data.labels.bboxes_3d_mask
bboxes_3d = tf.boolean_mask(bboxes_3d, bboxes_3d_mask)
if 'points_padding' in kitti_data.lasers:
points_validity_mask = tf.cast(kitti_data.lasers.points_padding - 1,
tf.bool)
points = tf.boolean_mask(points, points_validity_mask)
points_feature = tf.boolean_mask(points_feature, points_validity_mask)
points_in_bboxes_mask = geometry.IsWithinBBox3D(points, bboxes_3d)
output_map = py_utils.NestedMap()
# Points and features contain the whole pointcloud, which we will use
# per box boolean masks later in _ToTFExampleProto to subselect data per box.
output_map.points = points
output_map.points_feature = points_feature
output_map.points_in_bboxes_mask = points_in_bboxes_mask
output_map.source_id = kitti_data.labels.source_id
# Add additional data
output_keys = [
'bboxes_3d',
'labels',
'texts',
'occlusion',
'difficulties',
'truncation',
]
for key in output_keys:
output_map[key] = tf.boolean_mask(kitti_data.labels[key],
kitti_data.labels.bboxes_3d_mask)
return output_map
