def test_flip_boxes_3d(self):
boxes_3d = np.array([[1, 2, 3, 4, 5, 6, np.pi / 4],
[1, 2, 3, 4, 5, 6, -np.pi / 4]])
exp_flipped_boxes_3d = np.array([[-1, 2, 3, 4, 5, 6, 3 * np.pi / 4],
[-1, 2, 3, 4, 5, 6, -3 * np.pi / 4]])
flipped_boxes_3d = kitti_aug.flip_boxes_3d(boxes_3d)
np.testing.assert_almost_equal(flipped_boxes_3d, exp_flipped_boxes_3d)
def _fill_anchor_pl_inputs(self, anchors_info, ground_plane, image_shape,
stereo_calib_p2, sample_name, sample_augs):
"""
Fills anchor placeholder inputs with corresponding data
Args:
anchors_info: anchor info from mini_batch_utils
ground_plane: ground plane coefficients
image_shape: image shape (h, w), used for projecting anchors
sample_name: name of the sample, e.g. "000001"
sample_augs: list of sample augmentations
"""
# Lists for merging anchors info
all_anchor_boxes_3d = []
anchors_ious = []
anchor_offsets = []
anchor_classes = []
# Create anchors for each class
if len(self.dataset.classes) > 1:
for class_idx in range(len(self.dataset.classes)):
# Generate anchors for all classes
grid_anchor_boxes_3d = self._anchor_generator.generate(
area_3d=self._area_extents,
anchor_3d_sizes=self._cluster_sizes[class_idx],
anchor_stride=self._anchor_strides[class_idx],
ground_plane=ground_plane)
all_anchor_boxes_3d.append(grid_anchor_boxes_3d)
all_anchor_boxes_3d = np.concatenate(all_anchor_boxes_3d)
else:
# Don't loop for a single class
class_idx = 0
grid_anchor_boxes_3d = self._anchor_generator.generate(
area_3d=self._area_extents,
anchor_3d_sizes=self._cluster_sizes[class_idx],
anchor_stride=self._anchor_strides[class_idx],
ground_plane=ground_plane)
all_anchor_boxes_3d = grid_anchor_boxes_3d
# Filter empty anchors
# Skip if anchors_info is []
sample_has_labels = True
if self._train_val_test in ['train', 'val']:
# Read in anchor info during training / validation
if anchors_info:
anchor_indices, anchors_ious, anchor_offsets, \
anchor_classes = anchors_info
anchor_boxes_3d_to_use = all_anchor_boxes_3d[anchor_indices]
else:
train_cond = (self._train_val_test == "train"
and self._train_on_all_samples)
eval_cond = (self._train_val_test == "val"
and self._eval_all_samples)
if train_cond or eval_cond:
sample_has_labels = False
else:
sample_has_labels = False
if not sample_has_labels:
# During testing, or validation with no anchor info, manually
# filter empty anchors
# TODO: share voxel_grid_2d with BEV generation if possible
voxel_grid_2d = \
self.dataset.kitti_utils.create_sliced_voxel_grid_2d(
sample_name, self.dataset.bev_source,
image_shape=image_shape)
# Convert to anchors and filter
anchors_to_use = box_3d_encoder.box_3d_to_anchor(
all_anchor_boxes_3d)
empty_filter = anchor_filter.get_empty_anchor_filter_2d(
anchors_to_use, voxel_grid_2d, density_threshold=1)
anchor_boxes_3d_to_use = all_anchor_boxes_3d[empty_filter]
# Convert lists to ndarrays
anchor_boxes_3d_to_use = np.asarray(anchor_boxes_3d_to_use)
anchors_ious = np.asarray(anchors_ious)
anchor_offsets = np.asarray(anchor_offsets)
anchor_classes = np.asarray(anchor_classes)
# Flip anchors and centroid x offsets for augmented samples
if kitti_aug.AUG_FLIPPING in sample_augs:
anchor_boxes_3d_to_use = kitti_aug.flip_boxes_3d(
anchor_boxes_3d_to_use, flip_ry=False)
if anchors_info:
anchor_offsets[:, 0] = -anchor_offsets[:, 0]
# Convert to anchors
anchors_to_use = box_3d_encoder.box_3d_to_anchor(
anchor_boxes_3d_to_use)
num_anchors = len(anchors_to_use)
# Project anchors into bev
bev_anchors, bev_anchors_norm = anchor_projector.project_to_bev(
anchors_to_use, self._bev_extents)
# Project box_3d anchors into image space
img_anchors, img_anchors_norm = \
anchor_projector.project_to_image_space(
anchors_to_use, stereo_calib_p2, image_shape)
# Reorder into [y1, x1, y2, x2] for tf.crop_and_resize op
self._bev_anchors_norm = bev_anchors_norm[:, [1, 0, 3, 2]]
self._img_anchors_norm = img_anchors_norm[:, [1, 0, 3, 2]]
# Fill in placeholder inputs
self._placeholder_inputs[self.PL_ANCHORS] = anchors_to_use
# If we are in train/validation mode, and the anchor infos
# are not empty, store them. Checking for just anchors_ious
# to be non-empty should be enough.
if self._train_val_test in ['train', 'val'] and \
len(anchors_ious) > 0:
self._placeholder_inputs[self.PL_ANCHOR_IOUS] = anchors_ious
self._placeholder_inputs[self.PL_ANCHOR_OFFSETS] = anchor_offsets
self._placeholder_inputs[self.PL_ANCHOR_CLASSES] = anchor_classes
# During test, or val when there is no anchor info
elif self._train_val_test in ['test'] or \
len(anchors_ious) == 0:
# During testing, or validation with no gt, fill these in with 0s
self._placeholder_inputs[self.PL_ANCHOR_IOUS] = \
np.zeros(num_anchors)
self._placeholder_inputs[self.PL_ANCHOR_OFFSETS] = \
np.zeros([num_anchors, 6])
self._placeholder_inputs[self.PL_ANCHOR_CLASSES] = \
np.zeros(num_anchors)
else:
raise ValueError(
'Got run mode {}, and non-empty anchor info'.format(
self._train_val_test))
self._placeholder_inputs[self.PL_BEV_ANCHORS] = bev_anchors
self._placeholder_inputs[self.PL_BEV_ANCHORS_NORM] = \
self._bev_anchors_norm
self._placeholder_inputs[self.PL_IMG_ANCHORS] = img_anchors
self._placeholder_inputs[self.PL_IMG_ANCHORS_NORM] = \
self._img_anchors_norm