def prep_pointcloud(
input_dict,
root_path,
voxel_generator,
target_assigner,
db_sampler=None,
max_voxels=20000,
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
remove_unknown=False,
gt_rotation_noise=(-np.pi / 3, np.pi / 3),
gt_loc_noise_std=(1.0, 1.0, 1.0),
global_rotation_noise=(-np.pi / 4, np.pi / 4),
global_scaling_noise=(0.95, 1.05),
global_random_rot_range=(0.78, 2.35),
global_translate_noise_std=(0, 0, 0),
num_point_features=4,
anchor_area_threshold=1,
gt_points_drop=0.0,
gt_drop_max_keep=10,
remove_points_after_sample=True,
anchor_cache=None,
remove_environment=False,
random_crop=False,
reference_detections=None,
out_size_factor=2,
use_group_id=False,
multi_gpu=False,
min_points_in_gt=-1,
random_flip_x=True,
random_flip_y=True,
sample_importance=1.0,
out_dtype=np.float32,
bcl_keep_voxels=6500, #6000~8000 pillar
seg_keep_points=8000,
points_per_voxel=200,
feature_map_size=[1, 200, 176],
num_anchor_per_loc=2,
segmentation=False,
object_detection=True):
"""convert point cloud to voxels, create targets if ground truths
exists.
input_dict format: dataset.get_sensor_data format
"""
class_names = target_assigner.classes
points = input_dict["lidar"]["points"]
if training or segmentation:
anno_dict = input_dict["lidar"]["annotations"]
gt_dict = {
"gt_boxes":
anno_dict["boxes"],
"gt_names":
anno_dict["names"],
"gt_importance":
np.ones([anno_dict["boxes"].shape[0]],
dtype=anno_dict["boxes"].dtype),
}
if "difficulty" not in anno_dict:
difficulty = np.zeros([anno_dict["boxes"].shape[0]],
dtype=np.int32)
gt_dict["difficulty"] = difficulty
else:
gt_dict["difficulty"] = anno_dict["difficulty"]
if use_group_id and "group_ids" in anno_dict:
group_ids = anno_dict["group_ids"]
gt_dict["group_ids"] = group_ids
calib = None
if "calib" in input_dict:
calib = input_dict["calib"]
if reference_detections is not None:
assert calib is not None and "image" in input_dict
C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2)
frustums = box_np_ops.get_frustum_v2(reference_detections, C)
frustums -= T
frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums)
frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c)
surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums)
masks = points_in_convex_polygon_3d_jit(points, surfaces)
points = points[masks.any(-1)]
if remove_outside_points:
assert calib is not None
image_shape = input_dict["image"]["image_shape"]
points = box_np_ops.remove_outside_points(points, calib["rect"],
calib["Trv2c"], calib["P2"],
image_shape)
if remove_environment is True and training:
selected = kitti.keep_arrays_by_name(gt_names, target_assigner.classes)
_dict_select(gt_dict, selected)
masks = box_np_ops.points_in_rbbox(points, gt_dict["gt_boxes"])
points = points[masks.any(-1)]
if training:
boxes_lidar = gt_dict["gt_boxes"]
selected = kitti.drop_arrays_by_name(gt_dict["gt_names"], ["DontCare"])
_dict_select(gt_dict, selected)
if remove_unknown:
remove_mask = gt_dict["difficulty"] == -1
"""
gt_boxes_remove = gt_boxes[remove_mask]
gt_boxes_remove[:, 3:6] += 0.25
points = prep.remove_points_in_boxes(points, gt_boxes_remove)
"""
keep_mask = np.logical_not(remove_mask)
_dict_select(gt_dict, keep_mask)
gt_dict.pop("difficulty")
if min_points_in_gt > 0:
# points_count_rbbox takes 10ms with 10 sweeps nuscenes data
point_counts = box_np_ops.points_count_rbbox(
points, gt_dict["gt_boxes"])
mask = point_counts >= min_points_in_gt
_dict_select(gt_dict, mask)
gt_boxes_mask = np.array(
[n in class_names for n in gt_dict["gt_names"]], dtype=np.bool_)
if db_sampler is not None:
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
sampled_dict = db_sampler.sample_all(root_path,
gt_dict["gt_boxes"],
gt_dict["gt_names"],
num_point_features,
random_crop,
gt_group_ids=group_ids,
calib=calib)
if sampled_dict is not None:
sampled_gt_names = sampled_dict["gt_names"]
sampled_gt_boxes = sampled_dict["gt_boxes"]
sampled_points = sampled_dict["points"]
sampled_gt_masks = sampled_dict["gt_masks"]
gt_dict["gt_names"] = np.concatenate(
[gt_dict["gt_names"], sampled_gt_names], axis=0)
gt_dict["gt_boxes"] = np.concatenate(
[gt_dict["gt_boxes"], sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0)
sampled_gt_importance = np.full([sampled_gt_boxes.shape[0]],
sample_importance,
dtype=sampled_gt_boxes.dtype)
gt_dict["gt_importance"] = np.concatenate(
[gt_dict["gt_importance"], sampled_gt_importance])
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
gt_dict["group_ids"] = np.concatenate(
[gt_dict["group_ids"], sampled_group_ids])
if remove_points_after_sample:
masks = box_np_ops.points_in_rbbox(points,
sampled_gt_boxes)
points = points[np.logical_not(masks.any(-1))]
points = np.concatenate([sampled_points, points], axis=0)
pc_range = voxel_generator.point_cloud_range
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
prep.noise_per_object_v3_(
gt_dict["gt_boxes"],
points,
gt_boxes_mask,
rotation_perturb=gt_rotation_noise,
center_noise_std=gt_loc_noise_std,
global_random_rot_range=global_random_rot_range,
group_ids=group_ids,
num_try=100)
# should remove unrelated objects after noise per object
# for k, v in gt_dict.items():
# print(k, v.shape)
_dict_select(gt_dict, gt_boxes_mask)
gt_classes = np.array(
[class_names.index(n) + 1 for n in gt_dict["gt_names"]],
dtype=np.int32)
gt_dict["gt_classes"] = gt_classes
gt_dict["gt_boxes"], points = prep.random_flip(gt_dict["gt_boxes"],
points, 0.5,
random_flip_x,
random_flip_y)
gt_dict["gt_boxes"], points = prep.global_rotation_v2(
gt_dict["gt_boxes"], points, *global_rotation_noise)
gt_dict["gt_boxes"], points = prep.global_scaling_v2(
gt_dict["gt_boxes"], points, *global_scaling_noise)
prep.global_translate_(gt_dict["gt_boxes"], points,
global_translate_noise_std)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range_by_center(
gt_dict["gt_boxes"], bv_range)
_dict_select(gt_dict, mask)
# limit rad to [-pi, pi]
gt_dict["gt_boxes"][:, 6] = box_np_ops.limit_period(
gt_dict["gt_boxes"][:, 6], offset=0.5, period=2 * np.pi)
# add depth for point feature and remove intensity
# points = points[...,:3]
# points = AddDepthFeature(points, num_point_features)
# num_point_features = points.shape[-1] #update point shape
#remove points out of PC rannge
pc_range = voxel_generator.point_cloud_range # [0, -40, -3, 70.4, 40, 1] xmin,ymin.zmin. xmax. ymax, zmax
points = box_np_ops.remove_out_pc_range_points(points, pc_range)
if shuffle_points and not segmentation:
np.random.shuffle(points) # shuffle is a little slow.
if not training and segmentation:
#Keep Car Only
gt_boxes_mask = np.array(
[n in class_names for n in gt_dict["gt_names"]], dtype=np.bool_)
_dict_select(gt_dict, gt_boxes_mask)
points_in_box, points_out_box = box_np_ops.split_points_in_boxes(
points, gt_dict["gt_boxes"]) #xyzr
points_in_box, points_out_box = SamplePointsKeepALLPositive(
points_in_box, points_out_box, seg_keep_points,
num_point_features) #fixed points
data, label = PrepDataAndLabel(points_in_box, points_out_box)
example = {
'seg_points': data, #data
'seg_labels': label, #label
'gt_boxes': gt_dict["gt_boxes"],
'image_idx': input_dict['metadata']['image_idx'],
}
################# For feature map Focs
# # # NOTE: For feature map Focs
point_cloud_range = np.array(voxel_generator.point_cloud_range)
anchor_strides = (point_cloud_range[3:] -
point_cloud_range[:3]) / feature_map_size[::-1]
anchor_offsets = point_cloud_range[:3] + anchor_strides / 2
centers = box_np_ops.create_anchors_3d_stride(
feature_map_size,
anchor_strides=anchor_strides,
anchor_offsets=anchor_offsets,
rotations=[0])
centers = centers.squeeze()[..., :3].reshape(-1, 3)
example.update({
'coords_center':
centers, # if anchors free the 0 is the horizontal/vertical anchors
})
##############
################ Fcos & points to voxel Test
# NOTE: For voxel seg net
# _, coords, coords_center, p2voxel_idx = box_np_ops.points_to_3dvoxel(data,
# feat_size=[100,80,10],
# max_voxels=bcl_keep_voxels,
# num_p_voxel=points_per_voxel)
# example = {
# 'seg_points': data, #data
# 'coords': coords,
# 'coords_center': coords_center,
# 'p2voxel_idx': p2voxel_idx,
# 'gt_boxes' : gt_dict["gt_boxes"],
# 'image_idx' : input_dict['metadata']['image_idx'],
# "gt_num" : len(gt_dict["gt_boxes"]),
# 'gt_boxes' : gt_dict["gt_boxes"],
# 'seg_labels': label
# }
################ Fcos & points to voxel
if anchor_cache is not None:
example.update({
"gt_num":
len(gt_dict["gt_boxes"]), #how many objects in eval GT
"anchors": anchor_cache["anchors"]
})
return example
################################Car point segmentation#####################
if training and segmentation:
# points_in_box = box_np_ops.points_in_rbbox(points, gt_dict["gt_boxes"]) #xyzr
# enlarge bouding box
# enlarge_size = 0.2
# gt_dict["gt_boxes"][:, 3:6] = gt_dict["gt_boxes"][:, 3:6] + enlarge_size #xyzhwlr
# masks = box_np_ops.points_in_rbbox(points, gt_dict["gt_boxes"])
# points = points[np.logical_not(masks.any(-1))]
# points = np.concatenate((points, points_in_box), axis=0)
#above and below bouding box should have no points
# gt_dict["gt_boxes"][:,3] += 2
#random sample
# points = SamplePoints(points, seg_keep_points, num_point_features) #Sample zero
# points = PointRandomChoice(points, seg_keep_points) #Repeat sample
points = PointRandomChoiceV2(
points, seg_keep_points) #Repeat sample according points distance
points_in_box, points_out_box = box_np_ops.split_points_in_boxes(
points, gt_dict["gt_boxes"]) #xyzr
data, label = PrepDataAndLabel(points_in_box, points_out_box)
#keep positive sample
# points_in_box, points_out_box = box_np_ops.split_points_in_boxes(points, gt_dict["gt_boxes"]) #xyzr
# points_in_box, points_out_box = SamplePointsKeepALLPositive(points_in_box, points_out_box, seg_keep_points, num_point_features) #fixed 18888 points
# data, label = PrepDataAndLabel(points_in_box, points_out_box)
"""shuffle car seg points"""
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
label = label[indices]
example = {
'seg_points': data, #data
'seg_labels': label, #label
'gt_boxes': gt_dict["gt_boxes"],
}
################# For feature map Focs
# # NOTE: For feature map Focs
point_cloud_range = np.array(voxel_generator.point_cloud_range)
anchor_strides = (point_cloud_range[3:] -
point_cloud_range[:3]) / feature_map_size[::-1]
anchor_offsets = point_cloud_range[:3] + anchor_strides / 2
centers = box_np_ops.create_anchors_3d_stride(
feature_map_size,
anchor_strides=anchor_strides,
anchor_offsets=anchor_offsets,
rotations=[0])
centers = centers.squeeze()[..., :3].reshape(-1, 3)
targets_dict = box_np_ops.fcos_box_encoder_v2(centers,
gt_dict["gt_boxes"])
# bbox = box_np_ops.fcos_box_decoder_v2(np.expand_dims(centers, 0),
# np.expand_dims(targets_dict["bbox_targets"], 0))
# labels = targets_dict["labels"]
# with open(os.path.join('./debug_tool',"points.pkl") , 'wb') as f:
# pickle.dump(data,f)
# with open(os.path.join('./debug_tool',"seg_points.pkl") , 'wb') as f:
# pickle.dump(centers[labels==1],f)
# with open(os.path.join('./debug_tool',"pd_boxes.pkl") , 'wb') as f:
# pickle.dump(bbox.squeeze()[labels==1],f)
# with open(os.path.join('./debug_tool',"gt_boxes.pkl") , 'wb') as f:
# pickle.dump(gt_dict["gt_boxes"],f)
# exit()
example.update({
'labels': targets_dict[
'labels'], # if anchors free the 0 is the horizontal/vertical anchors
# 'seg_labels': targets_dict['labels'], # if anchors free the 0 is the horizontal/vertical anchors
'reg_targets':
targets_dict['bbox_targets'], # target assign get offsite
'importance': targets_dict['importance'],
# 'reg_weights': targets_dict['bbox_outside_weights'],
})
##############
################ Fcos & points to voxel
# NOTE: For voxel seg net
# _, coords, coords_center, p2voxel_idx = box_np_ops.points_to_3dvoxel(data,
# feat_size=[200,176,10],
# max_voxels=bcl_keep_voxels,
# num_p_voxel=points_per_voxel)
#
# targets_dict = box_np_ops.fcos_box_encoder_v2(coords_center,
# gt_dict["gt_boxes"])
# # Jim added
# example.update({
# 'coords': coords,
# 'p2voxel_idx': p2voxel_idx,
# 'cls_labels': targets_dict['labels'], # if anchors free the 0 is the horizontal/vertical anchors
# 'reg_targets': targets_dict['bbox_targets'], # target assign get offsite
# 'importance': targets_dict['importance'],
# })
################ Fcos & points to voxel
################ Fcos & points to voxel
if anchor_cache is not None:
anchors = anchor_cache["anchors"]
anchors_bv = anchor_cache["anchors_bv"]
anchors_dict = anchor_cache["anchors_dict"]
matched_thresholds = anchor_cache["matched_thresholds"]
unmatched_thresholds = anchor_cache["unmatched_thresholds"]
targets_dict = target_assigner.assign(
anchors,
anchors_dict, #this is the key to control the number of anchors (input anchors) ['anchors, unmatch,match']
gt_dict["gt_boxes"],
anchors_mask=None,
gt_classes=gt_dict["gt_classes"],
gt_names=gt_dict["gt_names"],
matched_thresholds=matched_thresholds,
unmatched_thresholds=unmatched_thresholds,
importance=gt_dict["gt_importance"])
example.update({
'labels': targets_dict[
'labels'], # if anchors free the 0 is the horizontal/vertical anchors
'reg_targets':
targets_dict['bbox_targets'], # target assign get offsite
#'importance': targets_dict['importance'],
})
# boxes_lidar = gt_dict["gt_boxes"]
# bev_map = simplevis.kitti_vis(points, boxes_lidar, gt_dict["gt_names"])
# assigned_anchors = anchors[targets_dict['labels'] > 0]
# ignored_anchors = anchors[targets_dict['labels'] == -1]
# bev_map = simplevis.draw_box_in_bev(bev_map, [0, -40, -3, 70.4, 40, 1], ignored_anchors, [128, 128, 128], 2)
# bev_map = simplevis.draw_box_in_bev(bev_map, [0, -40, -3, 70.4, 40, 1], assigned_anchors, [255, 0, 0])
# cv2.imwrite('./visualization/anchors/anchors_{}.png'.format(input_dict['metadata']['image_idx']),bev_map)
return example
#################################voxel_generator############################
'''
voxel_size = voxel_generator.voxel_size # [0, -40, -3, 70.4, 40, 1]
pc_range = voxel_generator.point_cloud_range
grid_size = voxel_generator.grid_size # [352, 400]
max_num_points_per_voxel = voxel_generator.max_num_points_per_voxel
if not multi_gpu:
res = voxel_generator.generate(
points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
else:
res = voxel_generator.generate_multi_gpu(
points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([res["voxel_num"]], dtype=np.int64)
example = {
'voxels': voxels,
#'num_points': num_points,
'coordinates': coordinates,
"num_voxels": num_voxels,
}
## WARNING: For Simplex voxel Testing if bug comment this
voxels= SimpleVoxel(voxels, num_points) #(V,100,C) -> (B, C, V, N) #For Second, if Pillar comment it
max_num_points_per_voxel=1 #If SimpleVoxel max_num_points_per_voxel=1
voxels, coordinates = VoxelRandomChoice(voxels, coordinates,
bcl_keep_voxels, num_point_features,
max_num_points_per_voexl=max_num_points_per_voxel)
example['voxels']=voxels
example['coordinates']=coordinates
'''
############################################################################
# if calib is not None:
# example["calib"] = calib
if anchor_cache is not None:
anchors = anchor_cache["anchors"]
anchors_bv = anchor_cache["anchors_bv"]
anchors_dict = anchor_cache["anchors_dict"]
matched_thresholds = anchor_cache["matched_thresholds"]
unmatched_thresholds = anchor_cache["unmatched_thresholds"]
else:
# generate anchors from ground truth
"""
voxels= SimpleVoxel(voxels, num_points) #(V,100,C) -> (B, C, V, N)
voxels, coordinates, num_points = VoxelRandomChoice(voxels, coordinates, num_points, bcl_keep_voxels)
example['voxels']=voxels
example['num_points']=num_points
example['coordinates']=coordinates
example['num_voxels']=bcl_keep_voxels
if training:
# for anchor free
gt_boxes_coords = gt_dict["gt_boxes"][:,:3] #original gt xyz
example['gt_boxes_coords']=gt_boxes_coords #GT save to example
gt_boxes_coords = np.round(gt_dict["gt_boxes"][:,:3]).astype(int) #round xyz
gt_boxes_coords = gt_boxes_coords[:,::-1] #zyx reverse
ret = target_assigner.generate_anchors_from_gt(gt_boxes_coords) #for GT generate anchors
anchors = ret["anchors"]
anchors_dict = target_assigner.generate_anchors_dict_from_gt(gt_boxes_coords) #for GT generate anchors
if not training:
# for anchor free
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
ret = target_assigner.generate_anchors(feature_map_size)
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors = ret["anchors"]
"""
# # generate anchors from anchor free (Voxel-wise)
# ret = target_assigner.generate_anchors_from_voxels(coordinates) #for coordinates generate anchors
# anchors_dict = target_assigner.generate_anchors_dict_from_voxels(coordinates) #this is the key to control the number of anchors (input anchors)
# anchors = ret["anchors"]
# matched_thresholds = ret["matched_thresholds"]
# unmatched_thresholds = ret["unmatched_thresholds"]
# generate anchors from voxel + anchor free
"""
gt_boxes_coords = gt_dict["gt_boxes"][:,:3] #original gt xyz
#gt_boxes_coords = np.round(gt_dict["gt_boxes"][:,:3]).astype(int) #round xyz
gt_boxes_coords = gt_boxes_coords[:,::-1] #zyx reverse
#stack ret and ret_gt
ret = target_assigner.generate_anchors_from_voxels(coordinates) #for coordinates generate anchors
ret_gt = target_assigner.generate_anchors_from_gt(gt_boxes_coords) #for GT generate anchors
for k in ret.keys():
ret[k] = np.concatenate((ret[k], ret_gt[k]))
anchors = ret["anchors"]
#stack anchors_dict and anchors_dict_gt
anchors_dict = target_assigner.generate_anchors_dict_from_voxels(coordinates) #this is the key to control the number of anchors (input anchors) ['anchors, unmatch,match']
anchors_dict_gt = target_assigner.generate_anchors_dict_from_gt(gt_boxes_coords) #for GT generate anchors
for order_k in anchors_dict.keys():
for k in anchors_dict[order_k].keys():
anchors_dict[order_k][k] = np.concatenate((anchors_dict[order_k][k], anchors_dict_gt[order_k][k]))
"""
# generate anchors from groundtruth
"""
if training:
# generate anchors from car points
points_in_box = points_in_box[:,:3] #xyz
points_in_box = points_in_box[:,::-1] #zyx
ret = target_assigner.generate_anchors_from_gt(points_in_box) #for GT generate anchors
anchors = ret["anchors"]
anchors_dict = target_assigner.generate_anchors_dict_from_gt(points_in_box) #for GT generate anchors
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(
anchors[:, [0, 1, 3, 4, 6]])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
"""
# Fcos points sampling
points = SamplePoints(points, bcl_keep_voxels, num_point_features)
example = {
'voxels': np.expand_dims(points, 0),
#'num_points': num_points,
'coordinates': points,
# "num_voxels": None,
}
if not training:
anno_dict = input_dict["lidar"]["annotations"]
gt_dict = {
"gt_boxes":
anno_dict["boxes"],
"gt_names":
anno_dict["names"],
"gt_importance":
np.ones([anno_dict["boxes"].shape[0]],
dtype=anno_dict["boxes"].dtype),
}
targets_dict = box_np_ops.fcos_box_encoder_v2(points, gt_dict["gt_boxes"])
# targets_dict = box_np_ops.fcos_box_encoder(points, gt_dict["gt_boxes"])
example.update({
'labels': targets_dict[
'labels'], # if anchors free the 0 is the horizontal/vertical anchors
'seg_labels': targets_dict[
'labels'], # if anchors free the 0 is the horizontal/vertical anchors
'reg_targets':
targets_dict['bbox_targets'], # target assign get offsite
'importance': targets_dict['importance'],
# 'reg_weights': targets_dict['bbox_outside_weights'],
})
# example["anchors"] = anchors
# anchors_mask = None
# if anchor_area_threshold >= 0:
# # slow with high resolution. recommend disable this forever.
# coors = coordinates
# dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask(
# coors, tuple(grid_size[::-1][1:]))
# dense_voxel_map = dense_voxel_map.cumsum(0)
# dense_voxel_map = dense_voxel_map.cumsum(1)
# anchors_area = box_np_ops.fused_get_anchors_area(
# dense_voxel_map, anchors_bv, voxel_size, pc_range, grid_size)
# anchors_mask = anchors_area > anchor_area_threshold
# # example['anchors_mask'] = anchors_mask.astype(np.uint8)
# example['anchors_mask'] = anchors_mask
if not training:
# Use it when debuging eval nms for good
eval_classes = input_dict["lidar"]["annotations"]["names"]
eval_gt_dict = {"gt_names": eval_classes}
gt_boxes_mask = np.array([n in class_names for n in eval_classes],
dtype=np.bool_)
_dict_select(eval_gt_dict, gt_boxes_mask)
example["gt_num"] = len(
eval_gt_dict["gt_names"]) #how many objects in eval GT
return example
example["gt_names"] = gt_dict["gt_names"]
# voxel_labels = box_np_ops.assign_label_to_voxel(gt_boxes, coordinates,
# voxel_size, coors_range)
"""
# bev anchors without screening
boxes_lidar = gt_dict["gt_boxes"]
bev_map = simplevis.kitti_vis(points, boxes_lidar, gt_dict["gt_names"])
bev_map = simplevis.draw_box_in_bev(bev_map, [0, -40, -3, 70.4, 40, 1], anchors, [255, 0, 0]) #assigned_anchors blue
cv2.imwrite('anchors/anchors_{}.png'.format(input_dict['metadata']['image_idx']),bev_map)
# cv2.imshow('anchors', bev_map)
# cv2.waitKey(0)
"""
if create_targets:
# No particular use
where = None
# Fcos target generator and encoder
# targets_dict = target_assigner.assign(
# anchors,
# anchors_dict, #this is the key to control the number of anchors (input anchors) ['anchors, unmatch,match']
# gt_dict["gt_boxes"],
# anchors_mask,
# gt_classes=gt_dict["gt_classes"],
# gt_names=gt_dict["gt_names"],
# matched_thresholds=matched_thresholds,
# unmatched_thresholds=unmatched_thresholds,
# importance=gt_dict["gt_importance"])
################################Visualaiziton###########################
"""
bev anchors with points
boxes_lidar = gt_dict["gt_boxes"]
bev_map = simplevis.kitti_vis(points, boxes_lidar, gt_dict["gt_names"])
assigned_anchors = anchors[targets_dict['labels'] > 0]
ignored_anchors = anchors[targets_dict['labels'] == -1]
bev_map = simplevis.draw_box_in_bev(bev_map, [0, -40, -3, 70.4, 40, 1], ignored_anchors, [128, 128, 128], 2) #ignored_anchors gray #[0, -30, -3, 64, 30, 1] for kitti
bev_map = simplevis.draw_box_in_bev(bev_map, [0, -40, -3, 70.4, 40, 1], assigned_anchors, [255, 0, 0]) #assigned_anchors blue
cv2.imwrite('anchors/anchors_{}.png'.format(input_dict['metadata']['image_idx']),bev_map)
cv2.imshow('anchors', bev_map)
cv2.waitKey(0)
"""
"""
# bev boxes_lidar with voxels (put z in to the plane)
boxes_lidar = gt_dict["gt_boxes"]
pp_map = np.zeros(grid_size[:2], dtype=np.float32) # (1408, 1600)
#print(voxels.shape) #(16162, 5, 4) $ 4=bzyx
voxels_max = np.max(voxels[:, :, 1], axis=1, keepdims=False)
voxels_min = np.min(voxels[:, :, 1], axis=1, keepdims=False)
voxels_height = voxels_max - voxels_min
voxels_height = np.minimum(voxels_height, 4) #keep every voxels length less than 4
# sns.distplot(voxels_height)
# plt.show()
pp_map[coordinates[:, 2], coordinates[:, 1]] = voxels_height / 4 #coordinates bzyx
pp_map = (pp_map * 255).astype(np.uint8)
pp_map = cv2.cvtColor(pp_map, cv2.COLOR_GRAY2RGB)
pp_map = simplevis.draw_box_in_bev(pp_map, [0, -30, -3, 64, 30, 1], boxes_lidar, [128, 0, 128], 2) # for kitti 0, -30, -3, 64, 30, 1
cv2.imwrite('bev_pp_map/pp_map{}.png'.format(input_dict['metadata']['image_idx']),pp_map)
# cv2.imshow('heights', pp_map)
# cv2.waitKey(0)
"""
# example.update({
# 'labels': targets_dict['labels'], # if anchors free the 0 is the horizontal/vertical anchors
# 'reg_targets': targets_dict['bbox_targets'], # target assign get offsite
# 'importance': targets_dict['importance'],
# # 'reg_weights': targets_dict['bbox_outside_weights'],
# })
return example
def prep_pointcloud(input_dict,
root_path,
voxel_generator,
target_assigner,
db_sampler=None,
max_voxels=20000,
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
remove_unknown=False,
gt_rotation_noise=(-np.pi / 3, np.pi / 3),
gt_loc_noise_std=(1.0, 1.0, 1.0),
global_rotation_noise=(-np.pi / 4, np.pi / 4),
global_scaling_noise=(0.95, 1.05),
global_random_rot_range=(0.78, 2.35),
global_translate_noise_std=(0, 0, 0),
num_point_features=4,
anchor_area_threshold=1,
gt_points_drop=0.0,
gt_drop_max_keep=10,
remove_points_after_sample=True,
anchor_cache=None,
remove_environment=False,
random_crop=False,
reference_detections=None,
out_size_factor=2,
use_group_id=False,
multi_gpu=False,
min_points_in_gt=-1,
random_flip_x=True,
random_flip_y=True,
sample_importance=1.0,
out_dtype=np.float32):
"""convert point cloud to voxels, create targets if ground truths
exists.
input_dict format: dataset.get_sensor_data format
"""
t = time.time()
class_names = target_assigner.classes
points = input_dict["lidar"]["points"]
if training:
anno_dict = input_dict["lidar"]["annotations"]
gt_dict = {
"gt_boxes": anno_dict["boxes"],
"gt_names": anno_dict["names"],
"gt_importance": np.ones([anno_dict["boxes"].shape[0]], dtype=anno_dict["boxes"].dtype),
}
if "difficulty" not in anno_dict:
difficulty = np.zeros([anno_dict["boxes"].shape[0]],
dtype=np.int32)
gt_dict["difficulty"] = difficulty
else:
gt_dict["difficulty"] = anno_dict["difficulty"]
if use_group_id and "group_ids" in anno_dict:
group_ids = anno_dict["group_ids"]
gt_dict["group_ids"] = group_ids
calib = None
if "calib" in input_dict:
calib = input_dict["calib"]
if reference_detections is not None:
assert calib is not None and "image" in input_dict
C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2)
frustums = box_np_ops.get_frustum_v2(reference_detections, C)
frustums -= T
frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums)
frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c)
surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums)
masks = points_in_convex_polygon_3d_jit(points, surfaces)
points = points[masks.any(-1)]
if remove_outside_points:
assert calib is not None
image_shape = input_dict["image"]["image_shape"]
points = box_np_ops.remove_outside_points(
points, calib["rect"], calib["Trv2c"], calib["P2"], image_shape)
if remove_environment is True and training:
selected = kitti.keep_arrays_by_name(gt_names, target_assigner.classes)
_dict_select(gt_dict, selected)
masks = box_np_ops.points_in_rbbox(points, gt_dict["gt_boxes"])
points = points[masks.any(-1)]
metrics = {}
if training:
"""
boxes_lidar = gt_dict["gt_boxes"]
bev_map = simplevis.nuscene_vis(points, boxes_lidar)
cv2.imshow('pre-noise', bev_map)
"""
selected = kitti.drop_arrays_by_name(gt_dict["gt_names"], ["DontCare"])
_dict_select(gt_dict, selected)
if remove_unknown:
remove_mask = gt_dict["difficulty"] == -1
"""
gt_boxes_remove = gt_boxes[remove_mask]
gt_boxes_remove[:, 3:6] += 0.25
points = prep.remove_points_in_boxes(points, gt_boxes_remove)
"""
keep_mask = np.logical_not(remove_mask)
_dict_select(gt_dict, keep_mask)
gt_dict.pop("difficulty")
if min_points_in_gt > 0:
# points_count_rbbox takes 10ms with 10 sweeps nuscenes data
point_counts = box_np_ops.points_count_rbbox(points, gt_dict["gt_boxes"])
mask = point_counts >= min_points_in_gt
_dict_select(gt_dict, mask)
gt_boxes_mask = np.array(
[n in class_names for n in gt_dict["gt_names"]], dtype=np.bool_)
if db_sampler is not None:
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
sampled_dict = db_sampler.sample_all(
root_path,
gt_dict["gt_boxes"],
gt_dict["gt_names"],
num_point_features,
random_crop,
gt_group_ids=group_ids,
calib=calib)
if sampled_dict is not None:
sampled_gt_names = sampled_dict["gt_names"]
sampled_gt_boxes = sampled_dict["gt_boxes"]
sampled_points = sampled_dict["points"]
sampled_gt_masks = sampled_dict["gt_masks"]
gt_dict["gt_names"] = np.concatenate(
[gt_dict["gt_names"], sampled_gt_names], axis=0)
gt_dict["gt_boxes"] = np.concatenate(
[gt_dict["gt_boxes"], sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0)
sampled_gt_importance = np.full([sampled_gt_boxes.shape[0]], sample_importance,
dtype=sampled_gt_boxes.dtype)
gt_dict["gt_importance"] = np.concatenate(
[gt_dict["gt_importance"], sampled_gt_importance])
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
gt_dict["group_ids"] = np.concatenate(
[gt_dict["group_ids"], sampled_group_ids])
if remove_points_after_sample:
masks = box_np_ops.points_in_rbbox(points,
sampled_gt_boxes)
points = points[np.logical_not(masks.any(-1))]
points = np.concatenate([sampled_points, points], axis=0)
pc_range = voxel_generator.point_cloud_range
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
prep.noise_per_object_v3_(
gt_dict["gt_boxes"],
points,
gt_boxes_mask,
rotation_perturb=gt_rotation_noise,
center_noise_std=gt_loc_noise_std,
global_random_rot_range=global_random_rot_range,
group_ids=group_ids,
num_try=100)
# should remove unrelated objects after noise per object
# for k, v in gt_dict.items():
# print(k, v.shape)
_dict_select(gt_dict, gt_boxes_mask)
gt_classes = np.array(
[class_names.index(n) + 1 for n in gt_dict["gt_names"]],
dtype=np.int32)
gt_dict["gt_classes"] = gt_classes
gt_dict["gt_boxes"], points = prep.random_flip(gt_dict["gt_boxes"],
points, 0.5, random_flip_x, random_flip_y)
gt_dict["gt_boxes"], points = prep.global_rotation_v2(
gt_dict["gt_boxes"], points, *global_rotation_noise)
gt_dict["gt_boxes"], points = prep.global_scaling_v2(
gt_dict["gt_boxes"], points, *global_scaling_noise)
prep.global_translate_(gt_dict["gt_boxes"], points, global_translate_noise_std)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range_by_center(gt_dict["gt_boxes"], bv_range)
_dict_select(gt_dict, mask)
# limit rad to [-pi, pi]
gt_dict["gt_boxes"][:, 6] = box_np_ops.limit_period(
gt_dict["gt_boxes"][:, 6], offset=0.5, period=2 * np.pi)
# boxes_lidar = gt_dict["gt_boxes"]
# bev_map = simplevis.nuscene_vis(points, boxes_lidar)
# cv2.imshow('post-noise', bev_map)
# cv2.waitKey(0)
if shuffle_points:
# shuffle is a little slow.
np.random.shuffle(points)
# [0, -40, -3, 70.4, 40, 1]
voxel_size = voxel_generator.voxel_size
pc_range = voxel_generator.point_cloud_range
grid_size = voxel_generator.grid_size
# [352, 400]
t1 = time.time()
if not multi_gpu:
res = voxel_generator.generate(
points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
else:
res = voxel_generator.generate_multi_gpu(
points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([res["voxel_num"]], dtype=np.int64)
metrics["voxel_gene_time"] = time.time() - t1
example = {
'voxels': voxels,
'num_points': num_points,
'coordinates': coordinates,
"num_voxels": num_voxels,
"metrics": metrics,
}
if calib is not None:
example["calib"] = calib
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
if anchor_cache is not None:
anchors = anchor_cache["anchors"]
anchors_bv = anchor_cache["anchors_bv"]
anchors_dict = anchor_cache["anchors_dict"]
matched_thresholds = anchor_cache["matched_thresholds"]
unmatched_thresholds = anchor_cache["unmatched_thresholds"]
else:
ret = target_assigner.generate_anchors(feature_map_size)
anchors = ret["anchors"]
anchors = anchors.reshape([-1, target_assigner.box_ndim])
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(
anchors[:, [0, 1, 3, 4, 6]])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
example["anchors"] = anchors
anchors_mask = None
if anchor_area_threshold >= 0:
# slow with high resolution. recommend disable this forever.
coors = coordinates
dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask(
coors, tuple(grid_size[::-1][1:]))
dense_voxel_map = dense_voxel_map.cumsum(0)
dense_voxel_map = dense_voxel_map.cumsum(1)
anchors_area = box_np_ops.fused_get_anchors_area(
dense_voxel_map, anchors_bv, voxel_size, pc_range, grid_size)
anchors_mask = anchors_area > anchor_area_threshold
# example['anchors_mask'] = anchors_mask.astype(np.uint8)
example['anchors_mask'] = anchors_mask
# print("prep time", time.time() - t)
metrics["prep_time"] = time.time() - t
if not training:
return example
example["gt_names"] = gt_dict["gt_names"]
# voxel_labels = box_np_ops.assign_label_to_voxel(gt_boxes, coordinates,
# voxel_size, coors_range)
if create_targets:
t1 = time.time()
targets_dict = target_assigner.assign(
anchors,
anchors_dict,
gt_dict["gt_boxes"],
anchors_mask,
gt_classes=gt_dict["gt_classes"],
gt_names=gt_dict["gt_names"],
matched_thresholds=matched_thresholds,
unmatched_thresholds=unmatched_thresholds,
importance=gt_dict["gt_importance"])
"""
boxes_lidar = gt_dict["gt_boxes"]
bev_map = simplevis.nuscene_vis(points, boxes_lidar, gt_dict["gt_names"])
assigned_anchors = anchors[targets_dict['labels'] > 0]
ignored_anchors = anchors[targets_dict['labels'] == -1]
bev_map = simplevis.draw_box_in_bev(bev_map, [-50, -50, 3, 50, 50, 1], ignored_anchors, [128, 128, 128], 2)
bev_map = simplevis.draw_box_in_bev(bev_map, [-50, -50, 3, 50, 50, 1], assigned_anchors, [255, 0, 0])
cv2.imshow('anchors', bev_map)
cv2.waitKey(0)
boxes_lidar = gt_dict["gt_boxes"]
pp_map = np.zeros(grid_size[:2], dtype=np.float32)
voxels_max = np.max(voxels[:, :, 2], axis=1, keepdims=False)
voxels_min = np.min(voxels[:, :, 2], axis=1, keepdims=False)
voxels_height = voxels_max - voxels_min
voxels_height = np.minimum(voxels_height, 4)
# sns.distplot(voxels_height)
# plt.show()
pp_map[coordinates[:, 1], coordinates[:, 2]] = voxels_height / 4
pp_map = (pp_map * 255).astype(np.uint8)
pp_map = cv2.cvtColor(pp_map, cv2.COLOR_GRAY2RGB)
pp_map = simplevis.draw_box_in_bev(pp_map, [-50, -50, 3, 50, 50, 1], boxes_lidar, [128, 0, 128], 1)
cv2.imshow('heights', pp_map)
cv2.waitKey(0)
"""
example.update({
'labels': targets_dict['labels'],
'reg_targets': targets_dict['bbox_targets'],
# 'reg_weights': targets_dict['bbox_outside_weights'],
'importance': targets_dict['importance'],
})
return example
def prep_pointcloud(input_dict,
root_path,
voxel_generator,
target_assigner,
use_quadrant=False,
db_sampler=None,
max_voxels=20000,
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
remove_unknown=False,
gt_rotation_noise=(-np.pi / 3, np.pi / 3),
gt_loc_noise_std=(1.0, 1.0, 1.0),
global_rotation_noise=(-np.pi / 4, np.pi / 4),
global_scaling_noise=(0.95, 1.05),
global_random_rot_range=(0.78, 2.35),
global_translate_noise_std=(0, 0, 0),
num_point_features=4,
remove_points_after_sample=True,
remove_environment=False,
random_crop=False,
reference_detections=None,
out_size_factor=2,
use_group_id=False,
multi_gpu=False,
min_points_in_gt=-1,
random_flip_x=True,
random_flip_y=True,
sample_importance=1.0,
dataset_name = 'KITTI'):
"""convert point cloud to voxels, create targets if ground truths
exists.
input_dict format: dataset.get_sensor_data format
"""
t = time.time()
class_names = target_assigner.classes
points = input_dict["lidar"]["points"]
if training:
anno_dict = input_dict["lidar"]["annotations"]
gt_dict = {
"gt_boxes": anno_dict["boxes"],
"gt_names": anno_dict["names"],
"gt_importance": np.ones([anno_dict["boxes"].shape[0]], dtype=anno_dict["boxes"].dtype),
}
if "difficulty" not in anno_dict:
difficulty = np.zeros([anno_dict["boxes"].shape[0]],
dtype=np.int32)
gt_dict["difficulty"] = difficulty
else:
gt_dict["difficulty"] = anno_dict["difficulty"]
if use_group_id and "group_ids" in anno_dict:
group_ids = anno_dict["group_ids"]
gt_dict["group_ids"] = group_ids
calib = None
if "calib" in input_dict:
calib = input_dict["calib"]
if reference_detections is not None:
assert calib is not None and "image" in input_dict
C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2)
frustums = box_np_ops.get_frustum_v2(reference_detections, C)
frustums -= T
frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums)
frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c)
surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums)
masks = points_in_convex_polygon_3d_jit(points, surfaces)
points = points[masks.any(-1)]
if remove_outside_points:
assert calib is not None
image_shape = input_dict["image"]["image_shape"]
points = box_np_ops.remove_outside_points(
points, calib["rect"], calib["Trv2c"], calib["P2"], image_shape)
if remove_environment and training:
selected = kitti.keep_arrays_by_name(gt_names, target_assigner.classes)
_dict_select(gt_dict, selected)
masks = box_np_ops.points_in_rbbox(points, gt_dict["gt_boxes"])
points = points[masks.any(-1)]
metrics = {}
if training:
# boxes_lidar = gt_dict["gt_boxes"]
# bev_map = simplevis.nuscene_vis(points, boxes_lidar)
# cv2.imshow('pre-noise', bev_map)
selected = kitti.drop_arrays_by_name(gt_dict["gt_names"], ["DontCare"])
_dict_select(gt_dict, selected)
if remove_unknown:
remove_mask = gt_dict["difficulty"] == -1
"""
gt_boxes_remove = gt_boxes[remove_mask]
gt_boxes_remove[:, 3:6] += 0.25
points = prep.remove_points_in_boxes(points, gt_boxes_remove)
"""
keep_mask = np.logical_not(remove_mask)
_dict_select(gt_dict, keep_mask)
gt_dict.pop("difficulty")
if min_points_in_gt > 0:
# points_count_rbbox takes 10ms with 10 sweeps nuscenes data
point_counts = box_np_ops.points_count_rbbox(points, gt_dict["gt_boxes"])
mask = point_counts >= min_points_in_gt
_dict_select(gt_dict, mask)
# select the gt_box in the specified classes
gt_boxes_mask = np.array(
[n in class_names for n in gt_dict["gt_names"]], dtype=np.bool_)
# data augmentation
if db_sampler is not None:
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
# data augmentation, using sample to add target
sampled_dict = db_sampler.sample_all(
root_path,
gt_dict["gt_boxes"],
gt_dict["gt_names"],
num_point_features,
random_crop,
gt_group_ids=group_ids,
calib=calib)
if sampled_dict is not None:
sampled_gt_names = sampled_dict["gt_names"]
sampled_gt_boxes = sampled_dict["gt_boxes"]
sampled_points = sampled_dict["points"]
sampled_gt_masks = sampled_dict["gt_masks"]
gt_dict["gt_names"] = np.concatenate(
[gt_dict["gt_names"], sampled_gt_names], axis=0)
gt_dict["gt_boxes"] = np.concatenate(
[gt_dict["gt_boxes"], sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0)
sampled_gt_importance = np.full([sampled_gt_boxes.shape[0]], sample_importance, dtype=sampled_gt_boxes.dtype)
gt_dict["gt_importance"] = np.concatenate(
[gt_dict["gt_importance"], sampled_gt_importance])
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
gt_dict["group_ids"] = np.concatenate(
[gt_dict["group_ids"], sampled_group_ids])
# remove the raw points in the added box, to avoid overlap
if remove_points_after_sample:
masks = box_np_ops.points_in_rbbox(points,
sampled_gt_boxes)
points = points[np.logical_not(masks.any(-1))]
points = np.concatenate([sampled_points, points], axis=0)
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
prep.noise_per_object_v3_(gt_dict["gt_boxes"],
points,
gt_boxes_mask,
rotation_perturb=gt_rotation_noise,
center_noise_std=gt_loc_noise_std,
global_random_rot_range=global_random_rot_range,
group_ids=group_ids,
num_try=100)
# should remove unrelated objects after noise per object
# for k, v in gt_dict.items():
# print(k, v.shape)
_dict_select(gt_dict, gt_boxes_mask)
gt_classes = np.array(
[class_names.index(n) + 1 for n in gt_dict["gt_names"]],
dtype=np.int32)
gt_dict["gt_classes"] = gt_classes
gt_dict["gt_boxes"], points = prep.random_flip(gt_dict["gt_boxes"],
points, 0.5, random_flip_x, random_flip_y)
gt_dict["gt_boxes"], points = prep.global_rotation_v2(
gt_dict["gt_boxes"], points, *global_rotation_noise)
gt_dict["gt_boxes"], points = prep.global_scaling_v2(
gt_dict["gt_boxes"], points, *global_scaling_noise)
prep.global_translate_(gt_dict["gt_boxes"], points, global_translate_noise_std)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range_by_center(gt_dict["gt_boxes"], bv_range)
_dict_select(gt_dict, mask)
# limit rad to [-pi, pi]
gt_dict["gt_boxes"][:, 6] = box_np_ops.limit_period(
gt_dict["gt_boxes"][:, 6], offset=0.5, period=2 * np.pi)
# boxes_lidar = gt_dict["gt_boxes"]
# bev_map = simplevis.nuscene_vis(points, boxes_lidar)
# cv2.imshow('post-noise', bev_map)
# cv2.waitKey(0)
if shuffle_points:
# shuffle is a little slow.
np.random.shuffle(points)
# [0, -40, -3, 70.4, 40, 1]
grid_size = voxel_generator.grid_size
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
# [352, 400]
t1 = time.time()
if not multi_gpu:
res = voxel_generator.generate(points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
else:
res = voxel_generator.generate_multi_gpu(points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([res["voxel_num"]], dtype=np.int64)
metrics["voxel_gene_time"] = time.time() - t1
anchors_all = generate_anchors(target_assigner.classes_cfg, feature_map_size, use_quadrant)
if use_quadrant:
split_res = split_voxel_into_quadrants(res, grid_size)
voxels = split_res["voxels"]
coordinates = split_res["coordinates"]
example = { 'voxels': voxels,
'num_points': num_points,
'coordinates': coordinates,
"num_voxels": num_voxels,
"metrics": metrics ,
'anchors': anchors_all["anchors"]}
if calib is not None:
example["calib"] = calib
metrics["prep_time"] = time.time() - t
if not training:
return example
# voxel_labels = box_np_ops.assign_label_to_voxel(gt_boxes, coordinates,
# voxel_size, coors_range)
if create_targets:
if target_assigner.name == 'LabelAssigner':
if use_quadrant:
res = []
gt_box = split_dict["gt_boxes"]
gt_classes = split_dict["gt_classes"]
gt_names = split_dict["gt_name"]
gt_importance = split_dict["gt_importance"]
for i in range(4):
d = target_assigner.assign(
gt_boxes=gt_box[i],
feature_map_size=feature_map_size,
gt_classes=gt_classes[i],
gt_names=gt_names[i],
importance=gt_importance[i],
training=training,
dataset_name=dataset_name
)
res.append(d)
else:
targets_dict = target_assigner.assign(
gt_dict["gt_boxes"],
feature_map_size,
gt_classes=gt_dict["gt_classes"],
gt_names=gt_dict["gt_names"],
importance=gt_dict["gt_importance"],
training=training,
dataset_name= dataset_name
)
example.update({
'gt_dict': targets_dict['gt_dict'],
'hm': targets_dict['targets']['hm'],
'anno_box': targets_dict['targets']['anno_box'],
'ind': targets_dict['targets']['ind'],
'mask': targets_dict['targets']['mask'],
'cat': targets_dict['targets']['cat']
})
else:
if use_quadrant:
split_dict = split_gt_into_quadrants(gt_dict)
example['use_quadrant']= use_quadrant
targets_dict = {}
gt_box = split_dict["gt_boxes"]
gt_classes = split_dict["gt_classes"]
gt_names = split_dict["gt_names"]
gt_importance = split_dict["gt_importance"]
labels=[]
bbox_targets=[]
importance = []
for i in range(4):
d = target_assigner.assign(
anchors_all,
gt_boxes=gt_box[i],
gt_classes=gt_classes[i],
gt_names=gt_names[i],
importance=gt_importance[i]
)
labels.append(d['labels'])
bbox_targets.append(d['bbox_targets'])
importance.append(d['importance'])
targets_dict['labels']=np.stack(labels)
targets_dict['bbox_targets'] = np.stack(bbox_targets)
targets_dict['importance'] = np.stack(importance)
else:
targets_dict = target_assigner.assign(
anchors_all,
gt_dict["gt_boxes"],
gt_classes=gt_dict["gt_classes"],
gt_names=gt_dict["gt_names"],
importance=gt_dict["gt_importance"])
example.update({'targets': targets_dict})
return example
def prep_data_aug(points,
gt_dict,
calib,
root_path,
voxel_generator,
target_assigner,
db_sampler=None,
remove_unknown=False,
gt_rotation_noise=(-np.pi / 3, np.pi / 3),
gt_loc_noise_std=(1.0, 1.0, 1.0),
global_rotation_noise=(-np.pi / 4, np.pi / 4),
global_scaling_noise=(0.95, 1.05),
global_random_rot_range=(0.78, 2.35),
global_translate_noise_std=(0, 0, 0),
num_point_features=4,
remove_points_after_sample=True,
remove_environment=False,
random_crop=False,
min_points_in_gt=-1,
random_flip_x=True,
random_flip_y=True,
sample_importance=1.0):
"""
Performs data augmentation (which is usually done during training time).
Takes as input points and gt_dict, and returns updated points and gt_dict after applying
the various data augmentation methods.
Main args:
points: np.ndarray(dtype=float32, shape=(N, 3+)) TODO: confirm this.
gt_dict: dict with keys "gt_boxes", "gt_names", "gt_importance", "gt_difficulty",
calib: calibration.
Returns:
points: np.ndarray(dtype=float32, shape=(N, 3+)) Modified points. TODO: confirm this.
gt_dict: dict with keys "gt_boxes", "gt_names", "gt_importance", "gt_difficulty"
"""
gt_dict = gt_dict.copy()
class_names = target_assigner.classes
if remove_environment is True:
selected = kitti.keep_arrays_by_name(gt_dict["gt_names"], target_assigner.classes)
_dict_select(gt_dict, selected)
masks = box_np_ops.points_in_rbbox(points, gt_dict["gt_boxes"])
points = points[masks.any(-1)]
metrics = {}
"""
boxes_lidar = gt_dict["gt_boxes"]
bev_map = simplevis.nuscene_vis(points, boxes_lidar)
cv2.imshow('pre-noise', bev_map)
"""
selected = kitti.drop_arrays_by_name(gt_dict["gt_names"], ["DontCare"])
_dict_select(gt_dict, selected)
if remove_unknown:
remove_mask = gt_dict["difficulty"] == -1
"""
gt_boxes_remove = gt_boxes[remove_mask]
gt_boxes_remove[:, 3:6] += 0.25
points = prep.remove_points_in_boxes(points, gt_boxes_remove)
"""
keep_mask = np.logical_not(remove_mask)
_dict_select(gt_dict, keep_mask)
gt_dict.pop("difficulty")
if min_points_in_gt > 0:
# points_count_rbbox takes 10ms with 10 sweeps nuscenes data
point_counts = box_np_ops.points_count_rbbox(points, gt_dict["gt_boxes"])
mask = point_counts >= min_points_in_gt
_dict_select(gt_dict, mask)
gt_boxes_mask = np.array(
[n in class_names for n in gt_dict["gt_names"]], dtype=np.bool_)
if db_sampler is not None:
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
sampled_dict = db_sampler.sample_all(
root_path,
gt_dict["gt_boxes"],
gt_dict["gt_names"],
num_point_features,
random_crop,
gt_group_ids=group_ids,
calib=calib)
if sampled_dict is not None:
sampled_gt_names = sampled_dict["gt_names"]
sampled_gt_boxes = sampled_dict["gt_boxes"]
sampled_points = sampled_dict["points"]
sampled_gt_masks = sampled_dict["gt_masks"]
gt_dict["gt_names"] = np.concatenate(
[gt_dict["gt_names"], sampled_gt_names], axis=0)
gt_dict["gt_boxes"] = np.concatenate(
[gt_dict["gt_boxes"], sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0)
sampled_gt_importance = np.full([sampled_gt_boxes.shape[0]], sample_importance, dtype=sampled_gt_boxes.dtype)
gt_dict["gt_importance"] = np.concatenate(
[gt_dict["gt_importance"], sampled_gt_importance])
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
gt_dict["group_ids"] = np.concatenate(
[gt_dict["group_ids"], sampled_group_ids])
if remove_points_after_sample:
masks = box_np_ops.points_in_rbbox(points,
sampled_gt_boxes)
points = points[np.logical_not(masks.any(-1))]
points = np.concatenate([sampled_points, points], axis=0)
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
prep.noise_per_object_v3_(
gt_dict["gt_boxes"],
points,
gt_boxes_mask,
rotation_perturb=gt_rotation_noise,
center_noise_std=gt_loc_noise_std,
global_random_rot_range=global_random_rot_range,
group_ids=group_ids,
num_try=100)
# should remove unrelated objects after noise per object
# for k, v in gt_dict.items():
# print(k, v.shape)
_dict_select(gt_dict, gt_boxes_mask)
gt_classes = np.array(
[class_names.index(n) + 1 for n in gt_dict["gt_names"]],
dtype=np.int32)
gt_dict["gt_classes"] = gt_classes
gt_dict["gt_boxes"], points = prep.random_flip(gt_dict["gt_boxes"],
points, 0.5, random_flip_x, random_flip_y)
gt_dict["gt_boxes"], points = prep.global_rotation_v2(
gt_dict["gt_boxes"], points, *global_rotation_noise)
gt_dict["gt_boxes"], points = prep.global_scaling_v2(
gt_dict["gt_boxes"], points, *global_scaling_noise)
prep.global_translate_(gt_dict["gt_boxes"], points, global_translate_noise_std)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range_by_center(gt_dict["gt_boxes"], bv_range)
_dict_select(gt_dict, mask)
# limit rad to [-pi, pi]
gt_dict["gt_boxes"][:, 6] = box_np_ops.limit_period(
gt_dict["gt_boxes"][:, 6], offset=0.5, period=2 * np.pi)
# boxes_lidar = gt_dict["gt_boxes"]
# bev_map = simplevis.nuscene_vis(points, boxes_lidar)
# cv2.imshow('post-noise', bev_map)
# cv2.waitKey(0)
return points, gt_dict
def prep_pointcloud(input_dict,
root_path,
voxel_generator,
target_assigner,
db_sampler=None,
max_voxels=20000,
max_sweeps=10,
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
remove_unknown=False,
gt_rotation_noise=(-np.pi / 3, np.pi / 3),
gt_loc_noise_std=(1.0, 1.0, 1.0),
global_rotation_noise=(-np.pi / 4, np.pi / 4),
global_scaling_noise=(0.95, 1.05),
global_random_rot_range=(0.78, 2.35),
global_translate_noise_std=(0, 0, 0),
num_point_features=4,
anchor_area_threshold=1,
gt_points_drop=0.0,
gt_drop_max_keep=10,
remove_points_after_sample=True,
anchor_cache=None,
remove_environment=False,
random_crop=False,
reference_detections=None,
out_size_factor=2,
use_group_id=False,
multi_gpu=False,
min_points_in_gt=-1,
random_flip_x=True,
random_flip_y=True,
sample_importance=1.0,
out_dtype=np.float32):
"""convert point cloud to voxels, create targets if ground truths
exists.
input_dict format: dataset.get_sensor_data format
"""
t = time.time()
class_names = target_assigner.classes
points = input_dict["lidar"]["points"]
indices = input_dict["lidar"]["indices"]
origins = input_dict["lidar"]["origins"]
if training:
anno_dict = input_dict["lidar"]["annotations"]
gt_dict = {
"gt_boxes":
anno_dict["boxes"],
"gt_names":
anno_dict["names"],
"gt_importance":
np.ones([anno_dict["boxes"].shape[0]],
dtype=anno_dict["boxes"].dtype),
}
if "difficulty" not in anno_dict:
difficulty = np.zeros([anno_dict["boxes"].shape[0]],
dtype=np.int32)
gt_dict["difficulty"] = difficulty
else:
gt_dict["difficulty"] = anno_dict["difficulty"]
if use_group_id and "group_ids" in anno_dict:
group_ids = anno_dict["group_ids"]
gt_dict["group_ids"] = group_ids
calib = None
if "calib" in input_dict:
calib = input_dict["calib"]
# # Disable these two since we do not do this for NuScenes
# if reference_detections is not None:
# assert calib is not None and "image" in input_dict
# C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2)
# frustums = box_np_ops.get_frustum_v2(reference_detections, C)
# frustums -= T
# frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums)
# frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c)
# surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums)
# masks = points_in_convex_polygon_3d_jit(points, surfaces)
# points = points[masks.any(-1)]
# if remove_outside_points:
# assert calib is not None
# image_shape = input_dict["image"]["image_shape"]
# points = box_np_ops.remove_outside_points(
# points, calib["rect"], calib["Trv2c"], calib["P2"], image_shape)
# # Very interesting attempt
# # I have tried the same and found it doesn't really work
# if remove_environment is True and training:
# selected = kitti.keep_arrays_by_name(gt_names, target_assigner.classes)
# _dict_select(gt_dict, selected)
# masks = box_np_ops.points_in_rbbox(points, gt_dict["gt_boxes"])
# points = points[masks.any(-1)]
metrics = {}
point_indices_to_remove = None
if training:
"""
boxes_lidar = gt_dict["gt_boxes"]
bev_map = simplevis.nuscene_vis(points, boxes_lidar)
cv2.imshow('pre-noise', bev_map)
"""
selected = kitti.drop_arrays_by_name(gt_dict["gt_names"], ["Denture"])
_dict_select(gt_dict, selected)
if remove_unknown:
remove_mask = gt_dict["difficulty"] == -1
"""
gt_boxes_remove = gt_boxes[remove_mask]
gt_boxes_remove[:, 3:6] += 0.25
points = prep.remove_points_in_boxes(points, gt_boxes_remove)
"""
keep_mask = np.logical_not(remove_mask)
_dict_select(gt_dict, keep_mask)
gt_dict.pop("difficulty")
# This part is interesting - we will need to do the same
if min_points_in_gt > 0:
# points_count_rbbox takes 10ms with 10 sweeps nuscenes data
point_counts = box_np_ops.points_count_rbbox(
points, gt_dict["gt_boxes"])
mask = point_counts >= min_points_in_gt
_dict_select(gt_dict, mask)
gt_boxes_mask = np.array(
[n in class_names for n in gt_dict["gt_names"]], dtype=np.bool_)
if db_sampler is not None:
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
sampled_dict = db_sampler.sample_all(root_path,
gt_dict["gt_boxes"],
gt_dict["gt_names"],
num_point_features,
random_crop,
gt_group_ids=group_ids,
calib=calib)
if sampled_dict is not None:
sampled_gt_names = sampled_dict["gt_names"]
sampled_gt_boxes = sampled_dict["gt_boxes"]
sampled_points = sampled_dict["points"]
sampled_gt_masks = sampled_dict["gt_masks"]
gt_dict["gt_names"] = np.concatenate(
[gt_dict["gt_names"], sampled_gt_names], axis=0)
gt_dict["gt_boxes"] = np.concatenate(
[gt_dict["gt_boxes"], sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0)
sampled_gt_importance = np.full([sampled_gt_boxes.shape[0]],
sample_importance,
dtype=sampled_gt_boxes.dtype)
gt_dict["gt_importance"] = np.concatenate(
[gt_dict["gt_importance"], sampled_gt_importance])
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
gt_dict["group_ids"] = np.concatenate(
[gt_dict["group_ids"], sampled_group_ids])
# # Commented out because we have a new way of removing points
# if remove_points_after_sample:
# masks = box_np_ops.points_in_rbbox(points, sampled_gt_boxes)
# point_indices_to_remove = np.flatnonzero(masks.any(-1))
# # # Delay this process so we can use the full point cloud
# # # when we do the ray stopping algorithm
# # points = points[np.logical_not(masks.any(-1))]
# # Paste objects behind so that we don't have to update indices
# points = np.concatenate([sampled_points, points], axis=0)
points = np.concatenate([points, sampled_points], axis=0)
pc_range = voxel_generator.point_cloud_range
group_ids = None
if "group_ids" in gt_dict:
group_ids = gt_dict["group_ids"]
# # Disable this one for now (not used in PointPillars anyways)
# prep.noise_per_object_v3_(
# gt_dict["gt_boxes"],
# points,
# gt_boxes_mask,
# rotation_perturb=gt_rotation_noise,
# center_noise_std=gt_loc_noise_std,
# global_random_rot_range=global_random_rot_range,
# group_ids=group_ids,
# num_try=100)
# should remove unrelated objects after noise per object
# for k, v in gt_dict.items():
# print(k, v.shape)
_dict_select(gt_dict, gt_boxes_mask)
gt_classes = np.array(
[class_names.index(n) + 1 for n in gt_dict["gt_names"]],
dtype=np.int32)
gt_dict["gt_classes"] = gt_classes
gt_dict["gt_boxes"], points, origins = prep.random_flip(
gt_dict["gt_boxes"], points, origins, 0.5, random_flip_x,
random_flip_y)
gt_dict["gt_boxes"], points, origins = prep.global_rotation_v2(
gt_dict["gt_boxes"], points, origins, *global_rotation_noise)
gt_dict["gt_boxes"], points, origins = prep.global_scaling_v2(
gt_dict["gt_boxes"], points, origins, *global_scaling_noise)
prep.global_translate_(gt_dict["gt_boxes"], points, origins,
global_translate_noise_std)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range_by_center(
gt_dict["gt_boxes"], bv_range)
_dict_select(gt_dict, mask)
# limit rad to [-pi, pi]
gt_dict["gt_boxes"][:, 6] = box_np_ops.limit_period(
gt_dict["gt_boxes"][:, 6], offset=0.5, period=2 * np.pi)
# boxes_lidar = gt_dict["gt_boxes"]
# bev_map = simplevis.nuscene_vis(points, boxes_lidar)
# cv2.imshow('post-noise', bev_map)
# cv2.waitKey(0)
# # Disable this for now (not used in PointPillars anyways)
# if shuffle_points:
# # shuffle is a little slow.
# np.random.shuffle(points)
# [0, -40, -3, 70.4, 40, 1]
voxel_size = voxel_generator.voxel_size
pc_range = voxel_generator.point_cloud_range
grid_size = voxel_generator.grid_size
# organize points into lists based on timestamps
time_stamps = points[
indices[:-1],
-1] # counting on the fact we do not miss points from any intermediate time_stamps
time_stamps = (time_stamps[:-1] + time_stamps[1:]) / 2
time_stamps = [-1000.0] + time_stamps.tolist() + [1000.0] # add boundaries
time_stamps = np.array(time_stamps)
# # LL_OCCUPIED, LL_FREE = 0.85, -0.4
# lo_occupied = np.log(0.7 / (1 - 0.7))
# lo_free = np.log(0.4 / (1 - 0.4))
# is there are additional points (from database sampling)
num_original = indices[-1]
if len(points) > num_original:
# split data into two half (indexed and un-indexed)
original_points, sampled_points = points[:num_original], points[
num_original:]
# compute occupancy and masks
# visibility, original_mask, sampled_mask = mapping.compute_visibility_and_masks(
# original_points, sampled_points, origins, time_stamps, pc_range, min(voxel_size)
# )
logodds, original_mask, sampled_mask = mapping.compute_logodds_and_masks(
original_points,
sampled_points,
origins,
time_stamps,
pc_range,
min(voxel_size) # , lo_occupied, lo_free
)
# apply visible mask
points = np.concatenate(
(original_points[original_mask], sampled_points[sampled_mask]))
else:
# visibility = mapping.compute_visibility(
# points, origins, time_stamps, pc_range, min(voxel_size)
# )
logodds = mapping.compute_logodds(
points,
origins,
time_stamps,
pc_range,
min(voxel_size) #, lo_occupied, lo_free
)
# T = len(time_stamps)-1
# visibility = visibility.reshape(T, -1)
# if T < (1 + max_sweeps):
# visibility = np.pad(visibility, ((0, (1+max_sweeps)-T), (0,0)), 'edge')
# with open(f'./utils/mapping/examples/{time.time()}.pkl', 'wb') as f:
# ##
# pickle.dump(original_points, f)
# pickle.dump(sampled_points, f)
# pickle.dump(origins, f)
# pickle.dump(time_stamps, f)
# pickle.dump(pc_range, f)
# pickle.dump(voxel_size, f)
# ##
# pickle.dump(occupancy, f)
# pickle.dump(original_mask, f)
# pickle.dump(sampled_mask, f)
if training:
if min_points_in_gt > 0:
# points_count_rbbox takes 10ms with 10 sweeps nuscenes data
point_counts = box_np_ops.points_count_rbbox(
points, gt_dict["gt_boxes"])
mask = point_counts >= min_points_in_gt
_dict_select(gt_dict, mask)
# [352, 400]
t1 = time.time()
if not multi_gpu:
res = voxel_generator.generate(points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([voxels.shape[0]], dtype=np.int64)
else:
res = voxel_generator.generate_multi_gpu(points, max_voxels)
voxels = res["voxels"]
coordinates = res["coordinates"]
num_points = res["num_points_per_voxel"]
num_voxels = np.array([res["voxel_num"]], dtype=np.int64)
metrics["voxel_gene_time"] = time.time() - t1
example = {
'voxels': voxels,
# 'visibility': visibility,
'logodds': logodds,
'num_points': num_points,
'coordinates': coordinates,
"num_voxels": num_voxels,
"metrics": metrics,
}
if calib is not None:
example["calib"] = calib
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
# print(f'feature_map_size in prep_pointcloud(): {feature_map_size}')
if anchor_cache is not None:
# print('having anchor cache')
anchors = anchor_cache["anchors"]
anchors_bv = anchor_cache["anchors_bv"]
anchors_dict = anchor_cache["anchors_dict"]
matched_thresholds = anchor_cache["matched_thresholds"]
unmatched_thresholds = anchor_cache["unmatched_thresholds"]
else:
# print('NOT having anchor cache')
ret = target_assigner.generate_anchors(feature_map_size)
anchors = ret["anchors"]
anchors = anchors.reshape([-1, target_assigner.box_ndim])
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(anchors[:,
[0, 1, 3, 4, 6]])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
# print(f'anchors.shape: {anchors.shape}')
example["anchors"] = anchors
anchors_mask = None
if anchor_area_threshold >= 0:
# slow with high resolution. recommend disable this forever.
coors = coordinates
dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask(
coors, tuple(grid_size[::-1][1:]))
dense_voxel_map = dense_voxel_map.cumsum(0)
dense_voxel_map = dense_voxel_map.cumsum(1)
anchors_area = box_np_ops.fused_get_anchors_area(
dense_voxel_map, anchors_bv, voxel_size, pc_range, grid_size)
anchors_mask = anchors_area > anchor_area_threshold
# example['anchors_mask'] = anchors_mask.astype(np.uint8)
example['anchors_mask'] = anchors_mask
# print("prep time", time.time() - t)
metrics["prep_time"] = time.time() - t
if not training:
return example
example["gt_names"] = gt_dict["gt_names"]
# voxel_labels = box_np_ops.assign_label_to_voxel(gt_boxes, coordinates,
# voxel_size, coors_range)
if create_targets:
t1 = time.time()
targets_dict = target_assigner.assign(
anchors,
anchors_dict,
gt_dict["gt_boxes"],
anchors_mask,
gt_classes=gt_dict["gt_classes"],
gt_names=gt_dict["gt_names"],
matched_thresholds=matched_thresholds,
unmatched_thresholds=unmatched_thresholds,
importance=gt_dict["gt_importance"])
"""
boxes_lidar = gt_dict["gt_boxes"]
bev_map = simplevis.nuscene_vis(points, boxes_lidar, gt_dict["gt_names"])
assigned_anchors = anchors[targets_dict['labels'] > 0]
ignored_anchors = anchors[targets_dict['labels'] == -1]
bev_map = simplevis.draw_box_in_bev(bev_map, [-50, -50, 3, 50, 50, 1], ignored_anchors, [128, 128, 128], 2)
bev_map = simplevis.draw_box_in_bev(bev_map, [-50, -50, 3, 50, 50, 1], assigned_anchors, [255, 0, 0])
cv2.imshow('anchors', bev_map)
cv2.waitKey(0)
boxes_lidar = gt_dict["gt_boxes"]
pp_map = np.zeros(grid_size[:2], dtype=np.float32)
voxels_max = np.max(voxels[:, :, 2], axis=1, keepdims=False)
voxels_min = np.min(voxels[:, :, 2], axis=1, keepdims=False)
voxels_height = voxels_max - voxels_min
voxels_height = np.minimum(voxels_height, 4)
# sns.distplot(voxels_height)
# plt.show()
pp_map[coordinates[:, 1], coordinates[:, 2]] = voxels_height / 4
pp_map = (pp_map * 255).astype(np.uint8)
pp_map = cv2.cvtColor(pp_map, cv2.COLOR_GRAY2RGB)
pp_map = simplevis.draw_box_in_bev(pp_map, [-50, -50, 3, 50, 50, 1], boxes_lidar, [128, 0, 128], 1)
cv2.imshow('heights', pp_map)
cv2.waitKey(0)
"""
example.update({
'labels': targets_dict['labels'],
'reg_targets': targets_dict['bbox_targets'],
# 'reg_weights': targets_dict['bbox_outside_weights'],
'importance': targets_dict['importance'],
})
return example
