def _calculate_num_points_in_gt(data_path, infos, relative_path, remove_outside=True, num_features=4):
for info in infos:
if relative_path:
v_path = str(pathlib.Path(data_path) / info["velodyne_path"])
else:
v_path = info["velodyne_path"]
points_v = np.fromfile(
v_path, dtype=np.float32, count=-1).reshape([-1, num_features])
rect = info['calib/R0_rect']
Trv2c = info['calib/Tr_velo_to_cam']
P2 = info['calib/P2']
if remove_outside:
points_v = box_np_ops.remove_outside_points(points_v, rect, Trv2c, P2,
info["img_shape"])
# points_v = points_v[points_v[:, 0] > 0]
annos = info['annos']
num_obj = len([n for n in annos['name'] if n != 'DontCare'])
# annos = kitti.filter_kitti_anno(annos, ['DontCare'])
dims = annos['dimensions'][:num_obj]
loc = annos['location'][:num_obj]
rots = annos['rotation_y'][:num_obj]
gt_boxes_camera = np.concatenate(
[loc, dims, rots[..., np.newaxis]], axis=1)
gt_boxes_lidar = box_np_ops.box_camera_to_lidar(
gt_boxes_camera, rect, Trv2c)
indices = box_np_ops.points_in_rbbox(points_v[:, :3], gt_boxes_lidar)
num_points_in_gt = indices.sum(0)
num_ignored = len(annos['dimensions']) - num_obj
num_points_in_gt = np.concatenate(
[num_points_in_gt, -np.ones([num_ignored])])
annos["num_points_in_gt"] = num_points_in_gt.astype(np.int32)
def create_reduced_point_cloud(data_path, save_path=None, back=False):
info_path = pathlib.Path(data_path) / 'ikg_infos_test.pkl'
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
for info in prog_bar(kitti_infos):
v_path = info['velodyne_path'] #training/velodyne/000001.pcd
v_path = pathlib.Path(
data_path
) / v_path #./data/IKG/object/training/velodyne/000001.pcd
pcd = pypcd.PointCloud.from_path(v_path)
points = np.zeros((pcd.points, 4), dtype=np.float32)
points[:, 0] = np.transpose(pcd.pc_data['x'])
points[:, 1] = np.transpose(pcd.pc_data['y'])
points[:, 2] = np.transpose(pcd.pc_data['z'])
points[:, 3] = np.transpose(pcd.pc_data['intensity'])
points[:,
3] /= 255 #kitti's intensity is normalized to 0-1, so ikg's intensity need to be normalized
points_v = points
points_v = box_np_ops.remove_outside_points(
points_v, rect, Trv2c, P2, img_shape
) #img_shape :rgb img's shape, so that points not outside image
v_path_bin = info['image_idx'] + '.bin'
if save_path is None:
save_filename = v_path.parent.parent / (
v_path.parent.stem + "_reduced") / v_path_bin #v_path.name
else:
save_filename = str(pathlib.Path(save_path) / v_path.name)
with open(save_filename, 'w') as f:
points_v.tofile(f)
def _create_reduced_point_cloud(data_path,
info_path,
save_path=None,
back=False):
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
for info in prog_bar(kitti_infos):
v_path = info['velodyne_path']
v_path = pathlib.Path(data_path) / v_path
points_v = np.fromfile(str(v_path), dtype=np.float32,
count=-1).reshape([-1, 4])
rect = info['calib/R0_rect']
P2 = info['calib/P2']
Trv2c = info['calib/Tr_velo_to_cam']
# first remove z < 0 points
# keep = points_v[:, -1] > 0
# points_v = points_v[keep]
# then remove outside.
if back:
points_v[:, 0] = -points_v[:, 0]
points_v = box_np_ops.remove_outside_points(points_v, rect, Trv2c, P2,
info["img_shape"])
if save_path is None:
save_filename = v_path.parent.parent / (v_path.parent.stem +
"_reduced") / v_path.name
# save_filename = str(v_path) + '_reduced'
if back:
save_filename += "_back"
else:
save_filename = str(pathlib.Path(save_path) / v_path.name)
if back:
save_filename += "_back"
with open(save_filename, 'w') as f:
points_v.tofile(f)
def inference_by_idx():
global BACKEND
instance = request.json
response = {"status": "normal"}
if BACKEND.root_path is None:
return error_response("root path is not set")
if BACKEND.kitti_infos is None:
return error_response("kitti info is not loaded")
if BACKEND.inference_ctx is None:
return error_response("inference_ctx is not loaded")
image_idx = instance["image_idx"]
idx = BACKEND.image_idxes.index(image_idx)
kitti_info = BACKEND.kitti_infos[idx]
v_path = str(Path(BACKEND.root_path) / kitti_info['velodyne_path'])
num_features = 4
points = np.fromfile(
str(v_path), dtype=np.float32,
count=-1).reshape([-1, num_features])
rect = kitti_info['calib/R0_rect']
P2 = kitti_info['calib/P2']
Trv2c = kitti_info['calib/Tr_velo_to_cam']
if 'img_shape' in kitti_info:
image_shape = kitti_info['img_shape']
points = box_np_ops.remove_outside_points(
points, rect, Trv2c, P2, image_shape)
print(points.shape[0])
t = time.time()
inputs = BACKEND.inference_ctx.get_inference_input_dict(
kitti_info, points)
print("input preparation time:", time.time() - t)
t = time.time()
with BACKEND.inference_ctx.ctx():
dt_annos = BACKEND.inference_ctx.inference(inputs)[0]
print("detection time:", time.time() - t)
dims = dt_annos['dimensions']
num_obj = dims.shape[0]
loc = dt_annos['location']
rots = dt_annos['rotation_y']
labels = dt_annos['name']
dt_boxes_camera = np.concatenate(
[loc, dims, rots[..., np.newaxis]], axis=1)
dt_boxes = box_np_ops.box_camera_to_lidar(
dt_boxes_camera, rect, Trv2c)
box_np_ops.change_box3d_center_(dt_boxes, src=[0.5, 0.5, 0], dst=[0.5, 0.5, 0.5])
locs = dt_boxes[:, :3]
dims = dt_boxes[:, 3:6]
rots = np.concatenate([np.zeros([num_obj, 2], dtype=np.float32), -dt_boxes[:, 6:7]], axis=1)
response["dt_locs"] = locs.tolist()
response["dt_dims"] = dims.tolist()
response["dt_rots"] = rots.tolist()
response["dt_labels"] = labels.tolist()
response["dt_scores"] = dt_annos["score"].tolist()
response = jsonify(results=[response])
response.headers['Access-Control-Allow-Headers'] = '*'
return response
def _create_reduced_point_cloud(
data_path='/mrtstorage/datasets/kitti/object_detection',
info_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second'
'/kitti_infos_train.pkl',
save_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second/training'
'/velodyne_reduced',
back=False):
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
for info in prog_bar(kitti_infos):
v_path = info['velodyne_path']
v_path = pathlib.Path(data_path) / v_path
points_v = np.fromfile(str(v_path), dtype=np.float32,
count=-1).reshape([-1, 4])
rect = info['calib/R0_rect']
P2 = info['calib/P2']
Trv2c = info['calib/Tr_velo_to_cam']
# first remove z < 0 points
# keep = points_v[:, -1] > 0
# points_v = points_v[keep]
# then remove outside.
if back:
points_v[:, 0] = -points_v[:, 0]
points_v = box_np_ops.remove_outside_points(points_v, rect, Trv2c, P2,
info["img_shape"])
# print(points_v.dtype)
"""
fusion camera data to lidar
"""
image_idx = info['image_idx']
image_label = _get_semantic_segmentation_result(
image_idx,
data_dir='/home/zhwang/semantic-segmentation'
'/kitti_train_results')
points_v = _add_class_score(image_label, points_v, rect, Trv2c, P2)
# print(points_v.dtype)
if save_path is None:
save_filename = v_path.parent.parent / (v_path.parent.stem +
"_reduced") / v_path.name
# save_filename = str(v_path) + '_reduced'
if back:
save_filename += "_back"
else:
save_filename = str(pathlib.Path(save_path) / v_path.name)
if back:
save_filename += "_back"
with open(save_filename, 'w') as f:
points_v.tofile(f)
def _create_reduced_point_cloud(data_path,
info_path,
save_path=None,
tracking=False,
back=False):
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
for info in prog_bar(kitti_infos):
pc_info = info["point_cloud"]
image_info = info["image"]
calib = info["calib"]
v_path = pc_info['velodyne_path']
v_path = Path(data_path) / v_path
points_v = np.fromfile(
str(v_path), dtype=np.float32, count=-1).reshape([-1, 4])
rect = calib['R0_rect']
P2 = calib['P2']
Trv2c = calib['Tr_velo_to_cam']
# first remove z < 0 points
# keep = points_v[:, -1] > 0
# points_v = points_v[keep]
# then remove outside.
if back:
points_v[:, 0] = -points_v[:, 0]
points_v = box_np_ops.remove_outside_points(points_v, rect, Trv2c, P2,
image_info["image_shape"])
if save_path is None:
if tracking:
# tracking index is 00XX/XXXXX, not XXXXX as detection
save_filename = v_path.parent.parent.parent / (
v_path.parent.parent.stem + "_reduced") / v_path.parent.stem / v_path.name
else:
save_filename = v_path.parent.parent / (
v_path.parent.stem + "_reduced") / v_path.name
# save_filename = str(v_path) + '_reduced'
if back:
save_filename += "_back"
else:
if tracking:
save_filename = str(Path(save_path) / v_path.name)
else:
save_filename = str(Path(save_path) / v_path.name)
if back:
save_filename += "_back"
with open(save_filename, 'w') as f:
points_v.tofile(f)
def prep_remove_outside_points(input_dict, reference_detections,
remove_outside_points, points):
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)
def prep_pointcloud(input_dict,
root_path,
voxel_generator,
target_assigner,
db_sampler=None,
max_voxels=20000,
class_names=['Car'],
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
reduce_valid_area=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],
generate_bev=False,
without_reflectivity=False,
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,
add_rgb_to_points=False,
lidar_input=False,
unlabeled_db_sampler=None,
out_size_factor=2,
min_gt_point_dict=None,
bev_only=False,
use_group_id=False,
out_dtype=np.float32):
"""convert point cloud to voxels, create targets if ground truths
exists.
"""
points = input_dict["points"]
if training:
gt_boxes = input_dict["gt_boxes"]
gt_names = input_dict["gt_names"]
difficulty = input_dict["difficulty"]
group_ids = None
if use_group_id and "group_ids" in input_dict:
group_ids = input_dict["group_ids"]
rect = input_dict["rect"]
Trv2c = input_dict["Trv2c"]
P2 = input_dict["P2"]
unlabeled_training = unlabeled_db_sampler is not None
#image_idx = input_dict["image_idx"]
if reference_detections is not None:
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.linalg.inv(R) @ 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 and not lidar_input:
image_shape = input_dict["image_shape"]
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
image_shape)
if remove_environment is True and training:
selected = kitti.keep_arrays_by_name(gt_names, class_names)
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None:
group_ids = group_ids[selected]
points = prep.remove_points_outside_boxes(points, gt_boxes)
if training:
# print(gt_names)
selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"])
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None:
group_ids = group_ids[selected]
gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c)
if remove_unknown:
remove_mask = 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)
gt_boxes = gt_boxes[keep_mask]
gt_names = gt_names[keep_mask]
difficulty = difficulty[keep_mask]
if group_ids is not None:
group_ids = group_ids[keep_mask]
gt_boxes_mask = np.array([n in class_names for n in gt_names],
dtype=np.bool_)
if db_sampler is not None:
sampled_dict = db_sampler.sample_all(root_path,
gt_boxes,
gt_names,
num_point_features,
random_crop,
gt_group_ids=group_ids,
rect=rect,
Trv2c=Trv2c,
P2=P2)
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_names = gt_names[gt_boxes_mask].tolist()
gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0)
# gt_names += [s["name"] for s in sampled]
gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0)
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
group_ids = np.concatenate([group_ids, sampled_group_ids])
if remove_points_after_sample:
points = prep.remove_points_in_boxes(
points, sampled_gt_boxes)
points = np.concatenate([sampled_points, points], axis=0)
# unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_)
if without_reflectivity:
used_point_axes = list(range(num_point_features))
used_point_axes.pop(3)
points = points[:, used_point_axes]
pc_range = voxel_generator.point_cloud_range
if bev_only: # set z and h to limits
gt_boxes[:, 2] = pc_range[2]
gt_boxes[:, 5] = pc_range[5] - pc_range[2]
prep.noise_per_object_v3_(
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
gt_boxes = gt_boxes[gt_boxes_mask]
gt_names = gt_names[gt_boxes_mask]
if group_ids is not None:
group_ids = group_ids[gt_boxes_mask]
gt_classes = np.array([class_names.index(n) + 1 for n in gt_names],
dtype=np.int32)
gt_boxes, points = prep.random_flip(gt_boxes, points)
gt_boxes, points = prep.global_rotation(gt_boxes,
points,
rotation=global_rotation_noise)
gt_boxes, points = prep.global_scaling_v2(gt_boxes, points,
*global_scaling_noise)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range)
gt_boxes = gt_boxes[mask]
gt_classes = gt_classes[mask]
gt_names = gt_names[mask]
if group_ids is not None:
group_ids = group_ids[mask]
# limit rad to [-pi, pi]
gt_boxes[:, 6] = box_np_ops.limit_period(gt_boxes[:, 6],
offset=0.5,
period=2 * np.pi)
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]
voxels, coordinates, num_points = voxel_generator.generate(
points, max_voxels)
example = {
'voxels': voxels,
'num_points': num_points,
'coordinates': coordinates,
"num_voxels": np.array([voxels.shape[0]], dtype=np.int64)
}
example.update({
'rect': rect,
'Trv2c': Trv2c,
'P2': P2,
})
# if not lidar_input:
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"]
matched_thresholds = anchor_cache["matched_thresholds"]
unmatched_thresholds = anchor_cache["unmatched_thresholds"]
anchors_dict = anchor_cache["anchors_dict"]
else:
ret = target_assigner.generate_anchors(feature_map_size)
anchors = ret["anchors"]
anchors = anchors.reshape([-1, 7])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
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]])
example["anchors"] = anchors
# print("debug", anchors.shape, matched_thresholds.shape)
# anchors_bv = anchors_bv.reshape([-1, 4])
anchors_mask = None
if anchor_area_threshold >= 0:
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:
return example
if create_targets:
targets_dict = target_assigner.assign_v2(anchors_dict,
gt_boxes,
anchors_mask,
gt_classes=gt_classes,
gt_names=gt_names)
example.update({
'labels': targets_dict['labels'],
'reg_targets': targets_dict['bbox_targets'],
'reg_weights': targets_dict['bbox_outside_weights'],
})
return example
def create_groundtruth_database(data_path,
info_path=None,
used_classes=None,
database_save_path=None,
db_info_save_path=None,
relative_path=True,
lidar_only=False,
bev_only=False,
coors_range=None):
RGB_embedding = True
root_path = pathlib.Path(data_path)
if info_path is None:
info_path = root_path / 'kitti_infos_train.pkl'
if database_save_path is None:
database_save_path = root_path / 'gt_database'
else:
database_save_path = pathlib.Path(database_save_path)
if db_info_save_path is None:
if RGB_embedding:
db_info_save_path = root_path / "kitti_dbinfos_train_RGB.pkl"
else:
db_info_save_path = root_path / "kitti_dbinfos_train.pkl"
database_save_path.mkdir(parents=True, exist_ok=True)
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
all_db_infos = {}
if used_classes is None:
used_classes = list(kitti.get_classes())
used_classes.pop(used_classes.index('DontCare'))
for name in used_classes:
all_db_infos[name] = []
group_counter = 0
for info in prog_bar(kitti_infos):
velodyne_path = info['velodyne_path']
if relative_path:
# velodyne_path = str(root_path / velodyne_path) + "_reduced"
velodyne_path = str(root_path / velodyne_path)
num_features = 4
if 'pointcloud_num_features' in info:
num_features = info['pointcloud_num_features']
points = np.fromfile(velodyne_path, dtype=np.float32,
count=-1).reshape([-1, num_features])
image_idx = info["image_idx"]
rect = info['calib/R0_rect']
P2 = info['calib/P2']
Trv2c = info['calib/Tr_velo_to_cam']
if not lidar_only:
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
info["img_shape"])
annos = info["annos"]
names = annos["name"]
bboxes = annos["bbox"]
difficulty = annos["difficulty"]
gt_idxes = annos["index"]
num_obj = np.sum(annos["index"] >= 0)
rbbox_cam = kitti.anno_to_rbboxes(annos)[:num_obj]
rbbox_lidar = box_np_ops.box_camera_to_lidar(rbbox_cam, rect, Trv2c)
if bev_only: # set z and h to limits
assert coors_range is not None
rbbox_lidar[:, 2] = coors_range[2]
rbbox_lidar[:, 5] = coors_range[5] - coors_range[2]
if RGB_embedding:
RGB_image = cv2.imread(str(root_path / info['img_path']))
points_camera = box_np_ops.box_lidar_to_camera(
points[:, :3], rect, Trv2c)
points_to_image_idx = box_np_ops.project_to_image(
points_camera, P2)
points_to_image_idx = points_to_image_idx.astype(int)
mask = box_np_ops.remove_points_outside_image(
RGB_image, points_to_image_idx)
points = points[mask]
points_to_image_idx = points_to_image_idx[mask]
BGR = RGB_image[points_to_image_idx[:, 1], points_to_image_idx[:,
0]]
points = np.concatenate((points, BGR), axis=1)
group_dict = {}
group_ids = np.full([bboxes.shape[0]], -1, dtype=np.int64)
if "group_ids" in annos:
group_ids = annos["group_ids"]
else:
group_ids = np.arange(bboxes.shape[0], dtype=np.int64)
point_indices = box_np_ops.points_in_rbbox(points, rbbox_lidar)
for i in range(num_obj):
if RGB_embedding:
filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}_RGB.bin"
else:
filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}.bin"
filepath = database_save_path / filename
gt_points = points[point_indices[:, i]]
gt_points[:, :3] -= rbbox_lidar[i, :3]
with open(filepath, 'w') as f:
gt_points.tofile(f)
if names[i] in used_classes:
if relative_path:
db_path = str(database_save_path.stem + "/" + filename)
else:
db_path = str(filepath)
db_info = {
"name": names[i],
"path": db_path,
"image_idx": image_idx,
"gt_idx": gt_idxes[i],
"box3d_lidar": rbbox_lidar[i],
"num_points_in_gt": gt_points.shape[0],
"difficulty": difficulty[i],
# "group_id": -1,
# "bbox": bboxes[i],
}
local_group_id = group_ids[i]
# if local_group_id >= 0:
if local_group_id not in group_dict:
group_dict[local_group_id] = group_counter
group_counter += 1
db_info["group_id"] = group_dict[local_group_id]
if "score" in annos:
db_info["score"] = annos["score"][i]
all_db_infos[names[i]].append(db_info)
for k, v in all_db_infos.items():
print(f"load {len(v)} {k} database infos")
with open(db_info_save_path, 'wb') as f:
pickle.dump(all_db_infos, f)
def prep_pointcloud(
input_dict,
root_path,
# voxel_generator,
fv_generator,
target_assigner,
db_sampler=None,
max_voxels=20000,
class_names=['Car'],
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
reduce_valid_area=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_loc_noise_std=(0.2, 0.2, 0.2),
global_random_rot_range=[0.78, 2.35],
generate_bev=False,
without_reflectivity=False,
num_point_features=4,
anchor_area_threshold=1,
gt_points_drop=0.0,
gt_drop_max_keep=10,
remove_points_after_sample=False,
anchor_cache=None,
remove_environment=False,
random_crop=False,
reference_detections=None,
add_rgb_to_points=False,
lidar_input=False,
unlabeled_db_sampler=None,
out_size_factor=2,
min_gt_point_dict=None,
bev_only=False,
use_group_id=False,
out_dtype=np.float32,
num_classes=1,
RGB_embedding=False):
"""convert point cloud to voxels, create targets if ground truths
exists.
"""
# prep_pointcloud_start = time.time()
points = input_dict["points"]
# if training:
gt_boxes = input_dict["gt_boxes"]
gt_names = input_dict["gt_names"]
difficulty = input_dict["difficulty"]
group_ids = None
if use_group_id and "group_ids" in input_dict:
group_ids = input_dict["group_ids"]
rect = input_dict["rect"]
Trv2c = input_dict["Trv2c"]
P2 = input_dict["P2"]
unlabeled_training = unlabeled_db_sampler is not None
image_idx = input_dict["image_idx"]
# t1 = time.time() - prep_pointcloud_start
if shuffle_points:
# shuffle is a little slow.
np.random.shuffle(points)
# t2 = time.time() - prep_pointcloud_start
# print("t2-t1: ", t2-t1) # 0.035
if reference_detections is not None:
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.linalg.inv(R) @ 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: # and not lidar_input:
image_shape = input_dict["image_shape"]
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
image_shape)
if remove_environment is True: # and training:
selected = kitti.keep_arrays_by_name(gt_names, class_names)
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None:
group_ids = group_ids[selected]
points = prep.remove_points_outside_boxes(points, gt_boxes)
# if training:
# print(gt_names)
selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"])
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None:
group_ids = group_ids[selected]
# t3 = time.time() - prep_pointcloud_start
# print("t3-t2: ", t3 - t2) # 0.0002
gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c)
if remove_unknown:
remove_mask = 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)
gt_boxes = gt_boxes[keep_mask]
gt_names = gt_names[keep_mask]
difficulty = difficulty[keep_mask]
if group_ids is not None:
group_ids = group_ids[keep_mask]
gt_boxes_mask = np.array([n in class_names for n in gt_names],
dtype=np.bool_)
# t4 = time.time() - prep_pointcloud_start
# print("t4-t3: ", t4 - t3) # 0.001
if RGB_embedding:
RGB_image = cv2.imread(input_dict['image_path'])
points_camera = box_np_ops.box_lidar_to_camera(points[:, :3], rect,
Trv2c)
points_to_image_idx = box_np_ops.project_to_image(points_camera, P2)
points_to_image_idx = points_to_image_idx.astype(int)
mask = box_np_ops.remove_points_outside_image(RGB_image,
points_to_image_idx)
points = points[mask]
points_to_image_idx = points_to_image_idx[mask]
BGR = RGB_image[points_to_image_idx[:, 1], points_to_image_idx[:, 0]]
points = np.concatenate((points, BGR), axis=1)
# test_mask = points_camera[mask][:, 0] < 0
# test_image_idx = points_to_image_idx[test_mask]
# RGB_image[test_image_idx[:, 1], test_image_idx[:, 0]] = [255, 0, 0]
# test_mask = points_camera[mask][:, 0] >= 0
# test_image_idx = points_to_image_idx[test_mask]
# RGB_image[test_image_idx[:, 1], test_image_idx[:, 0]] = [0, 0, 255]
# print()
# t5 = time.time() - prep_pointcloud_start
# print("t5-t4: ", t5 - t4) # 0.019
# TODO
if db_sampler is not None and training: # and not RGB_embedding:
if RGB_embedding:
num_point_features += 3
fg_points_mask = box_np_ops.points_in_rbbox(points, gt_boxes)
fg_points_list = []
bg_points_mask = np.zeros((points.shape[0]), dtype=bool)
for i in range(fg_points_mask.shape[1]):
fg_points_list.append(points[fg_points_mask[:, i]])
bg_points_mask = np.logical_or(bg_points_mask, fg_points_mask[:,
i])
bg_points_mask = np.logical_not(bg_points_mask)
sampled_dict = db_sampler.sample_all(root_path,
points[bg_points_mask],
gt_boxes,
gt_names,
fg_points_list,
num_point_features,
random_crop,
gt_group_ids=group_ids,
rect=rect,
Trv2c=Trv2c,
P2=P2)
# sampled_dict = db_sampler.sample_all(
# root_path,
# gt_boxes,
# gt_names,
# num_point_features,
# random_crop,
# gt_group_ids=group_ids,
# rect=rect,
# Trv2c=Trv2c,
# P2=P2)
# t_sample_all = time.time() - prep_pointcloud_start
# print("t_sample_all - t5: ", t_sample_all - t5) # 3.83
if sampled_dict is not None:
sampled_gt_names = sampled_dict["gt_names"]
sampled_gt_boxes = sampled_dict["gt_boxes"]
points = sampled_dict["points"]
sampled_gt_masks = sampled_dict["gt_masks"]
remained_boxes_idx = sampled_dict["remained_boxes_idx"]
# gt_names = gt_names[gt_boxes_mask].tolist()
gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0)
# gt_names += [s["name"] for s in sampled]
gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes])
gt_boxes_mask = np.concatenate([gt_boxes_mask, sampled_gt_masks],
axis=0)
gt_names = gt_names[remained_boxes_idx]
gt_boxes = gt_boxes[remained_boxes_idx]
gt_boxes_mask = gt_boxes_mask[remained_boxes_idx]
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
group_ids = np.concatenate([group_ids, sampled_group_ids])
group_ids = group_ids[remained_boxes_idx]
# if remove_points_after_sample:
# # points = prep.remove_points_in_boxes(
# # points, sampled_gt_boxes)
# locs = sampled_gt_boxes[:, 0:3]
# dims = sampled_gt_boxes[:, 3:6]
# angles = sampled_gt_boxes[:, 6]
# camera_box_origin = [0.5, 0.5, 0]
#
# box_corners = box_np_ops.center_to_corner_box3d(
# locs, dims, angles, camera_box_origin, axis=2)
# box_corners_in_image = box_np_ops.project_to_fv_image(
# box_corners, example['grid_size'][i], example['meta'][i])
# box_centers_in_image = box_np_ops.project_to_fv_image(
# locs, example['grid_size'][i], example['meta'][i])
# t_sample_all2 = time.time() - prep_pointcloud_start
# print("t_sample_all2 - t_sample_all: ", t_sample_all2 - t_sample_all) # 0.0002
# unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_)
# if without_reflectivity and training:
# used_point_axes = list(range(num_point_features))
# used_point_axes.pop(3)
# points = points[:, used_point_axes]
# pc_range = voxel_generator.point_cloud_range
# bev_only = False
# if bev_only: # set z and h to limits
# gt_boxes[:, 2] = pc_range[2]
# gt_boxes[:, 5] = pc_range[5] - pc_range[2]
if training:
gt_loc_noise_std = [0.0, 0.0, 0.0]
prep.noise_per_object_v3_(
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)
# t_noise = time.time() - prep_pointcloud_start
# print("t_noise - t_sample_all2: ", t_noise - t_sample_all2) # 12.01
# should remove unrelated objects after noise per object
gt_boxes = gt_boxes[gt_boxes_mask]
gt_names = gt_names[gt_boxes_mask]
if group_ids is not None:
group_ids = group_ids[gt_boxes_mask]
gt_classes = np.array([class_names.index(n) + 1 for n in gt_names],
dtype=np.int32)
# t6 = time.time() - prep_pointcloud_start
# print("t6-t5: ", t6 - t5) # 16.0
if training:
gt_boxes, points = prep.random_flip(gt_boxes, points)
# gt_boxes, points = prep.global_rotation(
# gt_boxes, points, rotation=global_rotation_noise)
gt_boxes, points = prep.global_scaling_v2(gt_boxes, points,
*global_scaling_noise)
# Global translation
# gt_boxes, points = prep.global_translate(gt_boxes, points, global_loc_noise_std)
# bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
bv_range = [0, -40, 70.4, 40]
mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range)
gt_boxes = gt_boxes[mask]
gt_classes = gt_classes[mask]
if group_ids is not None:
group_ids = group_ids[mask]
# limit rad to [-pi, pi]
gt_boxes[:, 6] = box_np_ops.limit_period(gt_boxes[:, 6],
offset=0.5,
period=2 * np.pi)
# TODO
# if shuffle_points:
# # shuffle is a little slow.
# np.random.shuffle(points)
# # t7 = time.time() - prep_pointcloud_start
# # print("t7-t6: ", t7 - t6) # 1.95
# voxels, coordinates, num_points = voxel_generator.generate(
# points, max_voxels, RGB_embedding=RGB_embedding)
# # t8 = time.time() - prep_pointcloud_start
# # print("t8-t7: ", t8 - t7) # 2.0
# voxel_size = voxel_generator.voxel_size
# grid_size = voxel_generator.grid_size
# pc_range = copy.deepcopy(voxel_generator.point_cloud_range)
# grid_size = voxel_generator.grid_size
# phi_min = voxel_generator.phi_min
# theta_min = voxel_generator.theta_min
# image_h, image_w = grid_size[1], grid_size[0]
# c = np.array([image_w / 2., image_h / 2.])
# s = np.array([image_w, image_h], dtype=np.int32)
# meta = {'c': c, 's': s, 'calib': P2, 'phi_min': phi_min, 'theta_min': theta_min}
# t7 = time.time() - prep_pointcloud_start
# print("t7-t6: ", t7 - t6) # 1.95
fv_image, points_mask = fv_generator.generate(points,
RGB_embedding=RGB_embedding,
occupancy_embedding=False)
# t8 = time.time() - prep_pointcloud_start
# print("t8-t7: ", t8 - t7) # 2.0
fv_dim = fv_generator.fv_dim
pc_range = copy.deepcopy(fv_generator.spherical_coord_range)
grid_size = fv_generator.grid_size
phi_min = fv_generator.phi_min
theta_min = fv_generator.theta_min
image_h, image_w = fv_dim[1], fv_dim[0]
c = np.array([image_w / 2., image_h / 2.])
s = np.array([image_w, image_h], dtype=np.int32)
meta = {
'c': c,
's': s,
'calib': P2,
'phi_min': phi_min,
'theta_min': theta_min
}
fv_image = np.transpose(fv_image, [2, 1, 0])
max_objs = 50
num_objs = min(gt_boxes.shape[0], max_objs)
box_np_ops.change_box3d_center_(gt_boxes,
src=[0.5, 0.5, 0],
dst=[0.5, 0.5, 0.5])
spherical_gt_boxes = np.zeros((max_objs, gt_boxes.shape[1]))
spherical_gt_boxes[:num_objs, :] = gt_boxes[:num_objs, :]
spherical_gt_boxes[:num_objs, :] = convert_to_spherical_coor(
gt_boxes[:num_objs, :])
spherical_gt_boxes[:num_objs, 0] -= phi_min
spherical_gt_boxes[:num_objs, 1] -= theta_min
spherical_gt_boxes[:num_objs, 0] /= grid_size[0]
spherical_gt_boxes[:num_objs, 1] /= grid_size[1]
spherical_gt_boxes, num_objs = filter_outside_range(
spherical_gt_boxes, num_objs, fv_dim)
# t9 = time.time() - prep_pointcloud_start
# print("t9-t8: ", t9 - t8) # 0.0005
example = {
'fv_image': fv_image,
'grid_size': grid_size,
'pc_range': pc_range,
'meta': meta,
'spherical_gt_boxes': spherical_gt_boxes,
'resized_image_shape': grid_size
}
example.update({'rect': rect, 'Trv2c': Trv2c, 'P2': P2})
if RGB_embedding:
RGB_image = cv2.resize(RGB_image, (image_w, image_h))
example.update({'RGB_image': RGB_image})
if training:
hm = np.zeros((num_classes, image_h, image_w), dtype=np.float32)
reg = np.zeros((max_objs, 2), dtype=np.float32)
dep = np.zeros((max_objs, 1), dtype=np.float32)
rotbin = np.zeros((max_objs, 2), dtype=np.int64)
rotres = np.zeros((max_objs, 2), dtype=np.float32)
dim = np.zeros((max_objs, 3), dtype=np.float32)
ind = np.zeros((max_objs), dtype=np.int64)
reg_mask = np.zeros((max_objs), dtype=np.uint8)
rot_mask = np.zeros((max_objs), dtype=np.uint8)
#
# hm = np.zeros((num_classes, image_h, image_w), dtype=np.float32)
# reg = np.zeros((image_w, image_h, 2), dtype=np.float32)
# dep = np.zeros((image_w, image_h, 1), dtype=np.float32)
# rotbin = np.zeros((image_w, image_h, 2), dtype=np.int64)
# rotres = np.zeros((image_w, image_h, 2), dtype=np.float32)
# dim = np.zeros((image_w, image_h, 3), dtype=np.float32)
# # ind = np.zeros((max_objs), dtype=np.int64)
# fg_mask = np.zeros((image_w, image_h), dtype=np.uint8)
# # rot_mask = np.zeros((max_objs), dtype=np.uint8)
draw_gaussian = draw_umich_gaussian
# center heatmap
radius = int(image_h / 30)
# radius = int(image_h / 25)
for k in range(num_objs):
gt_3d_box = spherical_gt_boxes[k]
cls_id = 0
# print('heatmap gaussian radius: ' + str(radius))
ct = np.array([gt_3d_box[0], gt_3d_box[1]], dtype=np.float32)
ct_int = ct.astype(np.int32)
draw_gaussian(hm[cls_id], ct, radius)
# depth(distance), wlh
dep[k] = gt_3d_box[2]
dim[k] = gt_3d_box[3:6]
# dep[ct_int[0], ct_int[1], 0] = gt_3d_box[2]
# dim[ct_int[0], ct_int[1], :] = gt_3d_box[3:6]
# reg, ind, mask
reg[k] = ct - ct_int
ind[k] = ct_int[1] * image_w + ct_int[0]
reg_mask[k] = rot_mask[k] = 1
# fg_mask[ct_int[0], ct_int[1]] = 1
# ry
ry = gt_3d_box[6]
if ry < np.pi / 6. or ry > 5 * np.pi / 6.:
rotbin[k, 0] = 1
rotres[k, 0] = ry - (-0.5 * np.pi)
# rotbin[ct_int[0], ct_int[1], 0] = 1
# rotres[ct_int[0], ct_int[1], 0] = ry - (-0.5 * np.pi)
if ry > -np.pi / 6. or ry < -5 * np.pi / 6.:
rotbin[k, 1] = 1
rotres[k, 1] = ry - (0.5 * np.pi)
# rotbin[ct_int[0], ct_int[1], 1] = 1
# rotres[ct_int[0], ct_int[1], 1] = ry - (0.5 * np.pi)
example.update({
'hm': hm,
'dep': dep,
'dim': dim,
'ind': ind,
'rotbin': rotbin,
'rotres': rotres,
'reg_mask': reg_mask,
'rot_mask': rot_mask,
'reg': reg
})
# example.update({
# 'hm': hm, 'dep': dep, 'dim': dim,
# 'rotbin': rotbin, 'rotres': rotres,
# 'fg_mask': fg_mask, 'reg': reg
# })
# t10 = time.time() - prep_pointcloud_start
# print("total: ", t10) # 19.58
return example
def _create_reduced_point_cloud(data_path,
info_path,
save_path=None,
color=True,
back=False):
bgr_path = None
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
for info in prog_bar(kitti_infos):
pc_info = info["point_cloud"]
image_info = info["image"]
image_path = image_info['image_path']
calib = info["calib"]
v_path = pc_info['velodyne_path']
v_path = Path(data_path) / v_path
points_v = np.fromfile(str(v_path), dtype=np.float32,
count=-1).reshape([-1, 4])
rect = calib['R0_rect']
P2 = calib['P2']
Trv2c = calib['Tr_velo_to_cam']
# first remove z < 0 points
# keep = points_v[:, -1] > 0
# points_v = points_v[keep]
# then remove outside.
if back:
points_v[:, 0] = -points_v[:, 0]
points_v = box_np_ops.remove_outside_points(points_v, rect, Trv2c, P2,
image_info["image_shape"])
points_xyz = lidar_to_camera(points_v, rect, Trv2c)
points_xy = project_to_image(points_xyz, P2)
image_path = v_path.parent.parent.parent / image_path
BGR_image = cv2.imread(str(image_path))
points_bgr = np.zeros((len(points_v), 3))
image_shape = image_info["image_shape"]
for i in range(len(points_bgr)):
new_y = int(points_xy[i][0])
if new_y >= image_shape[1]:
new_y = image_shape[1] - 1
new_x = int(points_xy[i][1])
if new_x >= image_shape[0]:
new_x = image_shape[0] - 1
points_bgr[i] = BGR_image[new_x][new_y] / 255
if bgr_path is None:
save_bgr_filename = v_path.parent.parent / (v_path.parent.stem +
"_bgr") / v_path.name
# save_filename = str(v_path) + '_reduced'
if back:
save_filename += "_bgr_back"
else:
save_bgr_filename = str(Path(bgr_path) / v_path.name)
if back:
save_bgr_filename += "_bgr_back"
with open(save_bgr_filename, 'w') as f:
points_bgr.tofile(f)
if save_path is None:
save_filename = v_path.parent.parent / (v_path.parent.stem +
"_bgr") / v_path.name
# save_filename = str(v_path) + '_reduced'
if back:
save_filename += "_back"
else:
save_filename = str(Path(save_path) / v_path.name)
if back:
save_filename += "_back"
with open(save_filename, 'w') as f:
points_v.tofile(f)
def get_pointcloud():
global BACKEND
instance = request.json
response = {"status": "normal"}
if BACKEND.root_path is None:
return error_response("root path is not set")
if BACKEND.kitti_infos is None:
return error_response("kitti info is not loaded")
image_idx = instance["image_idx"]
enable_int16 = instance["enable_int16"]
idx = BACKEND.image_idxes.index(image_idx)
kitti_info = BACKEND.kitti_infos[idx]
pc_info = kitti_info["point_cloud"]
image_info = kitti_info["image"]
calib = kitti_info["calib"]
rect = calib['R0_rect']
Trv2c = calib['Tr_velo_to_cam']
P2 = calib['P2']
img_shape = image_info["image_shape"] # hw
wh = np.array(img_shape[::-1])
whwh = np.tile(wh, 2)
if 'annos' in kitti_info:
annos = kitti_info['annos']
labels = annos['name']
num_obj = len([n for n in annos['name'] if n != 'DontCare'])
dims = annos['dimensions'][:num_obj]
loc = annos['location'][:num_obj]
rots = annos['rotation_y'][:num_obj]
bbox = annos['bbox'][:num_obj] / whwh
gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1)
gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes_camera, rect, Trv2c)
box_np_ops.change_box3d_center_(gt_boxes,
src=[0.5, 0.5, 0],
dst=[0.5, 0.5, 0.5])
locs = gt_boxes[:, :3]
dims = gt_boxes[:, 3:6]
rots = np.concatenate(
[np.zeros([num_obj, 2], dtype=np.float32), -gt_boxes[:, 6:7]],
axis=1)
frontend_annos = {}
response["locs"] = locs.tolist()
response["dims"] = dims.tolist()
response["rots"] = rots.tolist()
response["bbox"] = bbox.tolist()
response["labels"] = labels[:num_obj].tolist()
response["num_features"] = pc_info["num_features"]
v_path = str(Path(BACKEND.root_path) / pc_info['velodyne_path'])
points = np.fromfile(v_path, dtype=np.float32,
count=-1).reshape([-1, pc_info["num_features"]])
if instance['remove_outside']:
if 'image_shape' in image_info:
image_shape = image_info['image_shape']
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
image_shape)
else:
points = points[points[..., 2] > 0]
if enable_int16:
int16_factor = instance["int16_factor"]
points *= int16_factor
points = points.astype(np.int16)
pc_str = base64.b64encode(points.tobytes())
response["pointcloud"] = pc_str.decode("utf-8")
if "with_det" in instance and instance["with_det"]:
if BACKEND.dt_annos is None:
return error_response("det anno is not loaded")
dt_annos = BACKEND.dt_annos[idx]
dims = dt_annos['dimensions']
num_obj = dims.shape[0]
loc = dt_annos['location']
rots = dt_annos['rotation_y']
bbox = dt_annos['bbox'] / whwh
labels = dt_annos['name']
dt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1)
dt_boxes = box_np_ops.box_camera_to_lidar(dt_boxes_camera, rect, Trv2c)
box_np_ops.change_box3d_center_(dt_boxes,
src=[0.5, 0.5, 0],
dst=[0.5, 0.5, 0.5])
locs = dt_boxes[:, :3]
dims = dt_boxes[:, 3:6]
rots = np.concatenate(
[np.zeros([num_obj, 2], dtype=np.float32), -dt_boxes[:, 6:7]],
axis=1)
response["dt_locs"] = locs.tolist()
response["dt_dims"] = dims.tolist()
response["dt_rots"] = rots.tolist()
response["dt_labels"] = labels.tolist()
response["dt_bbox"] = bbox.tolist()
response["dt_scores"] = dt_annos["score"].tolist()
# if "score" in annos:
# response["score"] = score.tolist()
response = jsonify(results=[response])
response.headers['Access-Control-Allow-Headers'] = '*'
print("send response with size {}!".format(len(pc_str)))
return response
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,
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"],
}
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"]
# 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")
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)
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)
gt_dict["gt_boxes"], points = prep.global_rotation(
gt_dict["gt_boxes"], points, rotation=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(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]
voxels, coordinates, num_points = voxel_generator.generate(
points, max_voxels)
example = {
'voxels': voxels,
'num_points': num_points,
'coordinates': coordinates,
"num_voxels": np.array([voxels.shape[0]], dtype=np.int64)
}
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"]
else:
ret = target_assigner.generate_anchors(feature_map_size)
anchors = ret["anchors"]
anchors = anchors.reshape([-1, 7])
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]])
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:
return example
# voxel_labels = box_np_ops.assign_label_to_voxel(gt_boxes, coordinates,
# voxel_size, coors_range)
"""
example.update({
'gt_boxes': gt_boxes.astype(out_dtype),
'num_gt': np.array([gt_boxes.shape[0]]),
# 'voxel_labels': voxel_labels,
})
"""
if create_targets:
targets_dict = target_assigner.assign_v2(
anchors_dict,
gt_dict["gt_boxes"],
anchors_mask,
gt_classes=gt_dict["gt_classes"],
gt_names=gt_dict["gt_names"])
example.update({
'labels': targets_dict['labels'],
'reg_targets': targets_dict['bbox_targets'],
'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,
class_names=['Car', "Cyclist", "Pedestrian"],
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
reduce_valid_area=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_loc_noise_std=(0.2, 0.2, 0.2),
global_random_rot_range=[0.78, 2.35],
generate_bev=False,
without_reflectivity=False,
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,
add_rgb_to_points=False,
lidar_input=False,
unlabeled_db_sampler=None,
out_size_factor=2,
min_gt_point_dict=None,
bev_only=False,
use_group_id=False,
out_dtype=np.float32,
max_objs=300,
length=248,
width=216):
"""convert point cloud to voxels, create targets if ground truths
exists.
"""
points = input_dict["points"]
pc_range = voxel_generator.point_cloud_range
hist, bin_edges = np.histogram(points[:, 2],
bins=10,
range=(pc_range[2], pc_range[5]))
idx = np.argmax(hist)
ground = (bin_edges[idx] + bin_edges[idx + 1]) / 2
if training:
gt_boxes = input_dict["gt_boxes"]
gt_names = input_dict["gt_names"]
difficulty = input_dict["difficulty"]
group_ids = None
if use_group_id and "group_ids" in input_dict:
group_ids = input_dict["group_ids"]
rect = input_dict["rect"]
Trv2c = input_dict["Trv2c"]
P2 = input_dict["P2"]
unlabeled_training = unlabeled_db_sampler is not None
image_idx = input_dict["image_idx"]
if reference_detections is not None:
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.linalg.inv(R) @ 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 and not lidar_input:
image_shape = input_dict["image_shape"]
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
image_shape)
if remove_environment is True and training:
selected = kitti.keep_arrays_by_name(gt_names, class_names)
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None:
group_ids = group_ids[selected]
points = prep.remove_points_outside_boxes(points, gt_boxes)
if training:
# print(gt_names)
selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"])
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None:
group_ids = group_ids[selected]
gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c)
if remove_unknown:
remove_mask = 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)
gt_boxes = gt_boxes[keep_mask]
gt_names = gt_names[keep_mask]
difficulty = difficulty[keep_mask]
if group_ids is not None:
group_ids = group_ids[keep_mask]
gt_boxes_mask = np.array([n in class_names for n in gt_names],
dtype=np.bool_)
if db_sampler is not None:
sampled_dict = db_sampler.sample_all(root_path,
gt_boxes,
gt_names,
num_point_features,
random_crop,
gt_group_ids=group_ids,
rect=rect,
Trv2c=Trv2c,
P2=P2)
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_names = gt_names[gt_boxes_mask].tolist()
gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0)
# gt_names += [s["name"] for s in sampled]
gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0)
if group_ids is not None:
sampled_group_ids = sampled_dict["group_ids"]
group_ids = np.concatenate([group_ids, sampled_group_ids])
if remove_points_after_sample:
points = prep.remove_points_in_boxes(
points, sampled_gt_boxes)
points = np.concatenate([sampled_points, points], axis=0)
# unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_)
if without_reflectivity:
used_point_axes = list(range(num_point_features))
used_point_axes.pop(3)
points = points[:, used_point_axes]
if bev_only: # set z and h to limits
gt_boxes[:, 2] = pc_range[2]
gt_boxes[:, 5] = pc_range[5] - pc_range[2]
prep.noise_per_object_v3_(
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
gt_boxes = gt_boxes[gt_boxes_mask]
gt_names = gt_names[gt_boxes_mask]
if group_ids is not None:
group_ids = group_ids[gt_boxes_mask]
gt_classes = np.array([class_names.index(n) + 1 for n in gt_names],
dtype=np.int32)
gt_boxes, points = prep.random_flip(gt_boxes, points)
gt_boxes, points = prep.global_rotation(gt_boxes,
points,
rotation=global_rotation_noise)
gt_boxes, points = prep.global_scaling_v2(gt_boxes, points,
*global_scaling_noise)
# Global translation
gt_boxes, points = prep.global_translate(gt_boxes, points,
global_loc_noise_std)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range)
gt_boxes = gt_boxes[mask]
gt_classes = gt_classes[mask]
if group_ids is not None:
group_ids = group_ids[mask]
# limit rad to [-pi, pi]
gt_boxes[:, 6] = box_np_ops.limit_period(gt_boxes[:, 6],
offset=0.5,
period=2 * np.pi)
if shuffle_points:
# shuffle is a little slow.
np.random.shuffle(points)
voxel_size = voxel_generator.voxel_size
pc_range = voxel_generator.point_cloud_range
grid_size = voxel_generator.grid_size
voxels, coordinates, num_points = voxel_generator.generate(
points, max_voxels)
example = {
'voxels': voxels,
'num_points': num_points,
'coordinates': coordinates,
"num_voxels": np.array([voxels.shape[0]], dtype=np.int64),
"ground": ground
}
example.update({
'rect': rect,
'Trv2c': Trv2c,
'P2': P2,
})
if generate_bev:
bev_vxsize = voxel_size.copy()
bev_vxsize[:2] /= 2
bev_vxsize[2] *= 2
bev_map = points_to_bev(points, bev_vxsize, pc_range,
without_reflectivity)
example["bev_map"] = bev_map
#============================ NEW CODE ===================================
if training:
num_classes = len(class_names)
hm = np.zeros((num_classes, length, width), dtype=np.float32)
# wh = np.zeros((max_objs, 2), dtype=np.float32)
reg = np.zeros((max_objs, 2), dtype=np.float32)
rotbin = np.zeros((max_objs, 2), dtype=np.int64)
rotres = np.zeros((max_objs, 2), dtype=np.float32)
dim = np.zeros((max_objs, 3), dtype=np.float32)
ind = np.zeros((max_objs), dtype=np.int64)
reg_mask = np.zeros((max_objs), dtype=np.uint8)
rot_mask = np.zeros((max_objs), dtype=np.uint8)
num_objs = min(len(gt_boxes), max_objs)
draw_gaussian = draw_msra_gaussian
# if self.opt.mse_loss else draw_umich_gaussian
gt_det = []
xmin, ymin, _, xmax, ymax, _ = pc_range
for k in range(num_objs):
box = gt_boxes[k]
box[0] = np.clip(box[0], xmin, xmax)
box[1] = np.clip(box[1], ymin, ymax)
alpha = box[6] - np.arctan2(-box[1], box[0])
cls_id = gt_classes[k] - 1
cx = (box[0] - xmin) * (width - 1) / (xmax - xmin)
cy = (box[1] - ymin) * (length - 1) / (ymax - ymin)
lx = box[4] * (width - 1) / (xmax - xmin)
ly = box[3] * (length - 1) / (ymax - ymin)
if lx > 0 and ly > 0:
radius = gaussian_radius((ly, lx))
radius = max(0, int(radius))
ct = np.array([cx, cy], dtype=np.float32)
ct_int = ct.astype(np.int32)
if cls_id < 0:
ignore_id = [_ for _ in range(num_classes)] \
if cls_id == - 1 else [- cls_id - 2]
for cc in ignore_id:
draw_gaussian(hm[cc], ct, radius)
hm[ignore_id, ct_int[1], ct_int[0]] = 0.9999
continue
draw_gaussian(hm[cls_id], ct, radius)
if alpha < np.pi / 6. or alpha > 5 * np.pi / 6.:
rotbin[k, 0] = 1
rotres[k, 0] = alpha - (-0.5 * np.pi)
if alpha > -np.pi / 6. or alpha < -5 * np.pi / 6.:
rotbin[k, 1] = 1
rotres[k, 1] = alpha - (0.5 * np.pi)
dim[k] = box[3:6] #w,l,h
ind[k] = ct_int[1] * width + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1 #if not training else 0
rot_mask[k] = 1
example.update({
'hm': hm,
'dim': dim,
'ind': ind,
'rotbin': rotbin,
'rotres': rotres,
'reg_mask': reg_mask,
'rot_mask': rot_mask,
'reg': reg
})
#============================ NEW CODE ===================================
return example
def _create_reduced_point_cloud(data_path,
info_path,
save_path=None,
color=True,
back=False):
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
#se_mo = seg_model()
se_mo = None
for info in prog_bar(kitti_infos):
pc_info = info["point_cloud"]
image_info = info["image"]
calib = info["calib"]
v_path = pc_info['velodyne_path']
v_path = Path(data_path) / v_path
points_v = np.fromfile(str(v_path), dtype=np.float32,
count=-1).reshape([-1, 4])
rect = calib['R0_rect']
P2 = calib['P2']
Trv2c = calib['Tr_velo_to_cam']
# first remove z < 0 points
# keep = points_v[:, -1] > 0
# points_v = points_v[keep]
# then remove outside.
if back:
points_v[:, 0] = -points_v[:, 0]
points_v = box_np_ops.remove_outside_points(points_v, rect, Trv2c, P2,
image_info["image_shape"])
############## feature path load ###########################
# f_path = image_info['image_path'].split("\\")
# feature_file = f_path[0]+"/"+"feature"+"/"+f_path[-1].split(".")[0]+".pkl"
# feature_file = v_path.parent.parent.parent / feature_file
# with open(str(feature_file), 'rb') as f:
# feature = pickle.load(f)
################################################################
##################### add bgr to lidar #############
points_xyz = lidar_to_camera(points_v, rect, Trv2c)
points_xy = project_to_image(points_xyz, P2)
image_path = image_info['image_path']
image_path = v_path.parent.parent.parent / image_path
BGR_image = cv2.imread(str(image_path))
points_bgr = np.zeros((len(points_v), 3))
#points_feature = np.zeros((len(points_v),256))
# seg_feature = np.zeros((len(points_v),19))
image_shape = image_info["image_shape"]
#seg_out = se_mo.predict(image_path)
for i in range(len(points_bgr)):
new_y = int(points_xy[i][0])
if new_y >= image_shape[1]:
new_y = image_shape[1] - 1
new_x = int(points_xy[i][1])
if new_x >= image_shape[0]:
new_x = image_shape[0] - 1
points_bgr[i] = BGR_image[new_x][new_y] / 255
#seg_feature[i] = seg_out[new_x][new_y]
# points_bgr = points_bgr.astype(np.float32)
# points_v = points_v.astype(np.float32)
# seg_feature = seg_feature.astype(np.float32)
points_v = np.concatenate([points_v, points_bgr], axis=-1)
#points_v = np.concatenate([points_v,points_bgr,points_feature],axis=-1)
#points_v = np.concatenate([points_v,points_bgr,seg_feature],axis=-1)
###################################################################
points_v = points_v.astype(np.float32)
# print("##############################")
# print(seg_out[0,1:5,:])
# print(points_v.shape)
# exit()
if save_path is None:
save_filename = v_path.parent.parent / (v_path.parent.stem +
"_reduced") / v_path.name
# save_filename = str(v_path) + '_reduced'
if back:
save_filename += "_back"
else:
save_filename = str(Path(save_path) / v_path.name)
if back:
save_filename += "_back"
with open(save_filename, 'w') as f:
points_v.tofile(f)
def create_groundtruth_database(
data_path='/mrtstorage/datasets/kitti/object_detection',
info_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second'
'/kitti_infos_train.pkl',
used_classes=None,
database_save_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second'
'/gt_database',
db_info_save_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second'
'/kitti_dbinfos_train.pkl',
relative_path=True,
lidar_only=False,
bev_only=False,
coors_range=None):
root_path = pathlib.Path(data_path)
if info_path is None:
info_path = root_path / 'kitti_infos_train.pkl'
if database_save_path is None:
database_save_path = root_path / 'gt_database'
else:
database_save_path = pathlib.Path(database_save_path)
if db_info_save_path is None:
db_info_save_path = root_path / "kitti_dbinfos_train.pkl"
database_save_path.mkdir(parents=True, exist_ok=True)
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
all_db_infos = {}
if used_classes is None:
used_classes = list(kitti.get_classes())
used_classes.pop(used_classes.index('DontCare'))
for name in used_classes:
all_db_infos[name] = []
group_counter = 0
for info in prog_bar(kitti_infos):
velodyne_path = info['velodyne_path']
if relative_path:
# velodyne_path = str(root_path / velodyne_path) + "_reduced"
velodyne_path = str(root_path / velodyne_path)
num_features = 4
if 'pointcloud_num_features' in info:
num_features = info['pointcloud_num_features']
points = np.fromfile(velodyne_path, dtype=np.float32,
count=-1).reshape([-1, num_features])
image_idx = info["image_idx"]
rect = info['calib/R0_rect']
P2 = info['calib/P2']
Trv2c = info['calib/Tr_velo_to_cam']
if not lidar_only:
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
info["img_shape"])
"""
fusion camera data to lidar
"""
image_label = _get_semantic_segmentation_result(
image_idx,
data_dir='/home/zhwang/semantic-segmentation'
'/kitti_train_results')
points = _add_class_score(image_label, points, rect, Trv2c, P2)
annos = info["annos"]
names = annos["name"]
bboxes = annos["bbox"]
difficulty = annos["difficulty"]
gt_idxes = annos["index"]
num_obj = np.sum(annos["index"] >= 0)
rbbox_cam = kitti.anno_to_rbboxes(annos)[:num_obj]
rbbox_lidar = box_np_ops.box_camera_to_lidar(rbbox_cam, rect, Trv2c)
if bev_only: # set z and h to limits
assert coors_range is not None
rbbox_lidar[:, 2] = coors_range[2]
rbbox_lidar[:, 5] = coors_range[5] - coors_range[2]
group_dict = {}
group_ids = np.full([bboxes.shape[0]], -1, dtype=np.int64)
if "group_ids" in annos:
group_ids = annos["group_ids"]
else:
group_ids = np.arange(bboxes.shape[0], dtype=np.int64)
point_indices = box_np_ops.points_in_rbbox(points, rbbox_lidar)
for i in range(num_obj):
filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}.bin"
filepath = database_save_path / filename
gt_points = points[point_indices[:, i]]
gt_points[:, :3] -= rbbox_lidar[i, :3]
with open(filepath, 'w') as f:
gt_points.tofile(f)
if names[i] in used_classes:
if relative_path:
db_path = str(database_save_path.stem + "/" + filename)
else:
db_path = str(filepath)
db_info = {
"name": names[i],
"path": db_path,
"image_idx": image_idx,
"gt_idx": gt_idxes[i],
"box3d_lidar": rbbox_lidar[i],
"num_points_in_gt": gt_points.shape[0],
"difficulty": difficulty[i],
# "group_id": -1,
# "bbox": bboxes[i],
}
local_group_id = group_ids[i]
# if local_group_id >= 0:
if local_group_id not in group_dict:
group_dict[local_group_id] = group_counter
group_counter += 1
db_info["group_id"] = group_dict[local_group_id]
if "score" in annos:
db_info["score"] = annos["score"][i]
all_db_infos[names[i]].append(db_info)
for k, v in all_db_infos.items():
print(f"load {len(v)} {k} database infos")
with open(db_info_save_path, 'wb') as f:
pickle.dump(all_db_infos, f)
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 create_groundtruth_database(data_path,
train_or_test,
info_path=None,
used_classes=None,
database_save_path=None,
db_info_save_path=None,
relative_path=True,
lidar_only=False,
bev_only=False,
coors_range=None):
# custom dataset parameter
custom_dataset = True # if true, it indicates that we are not operating on the kitti data
sample_val_dataset_mode = True if train_or_test == "test" else False # if true, we are creating a gt database for the test set (instead of the train set)
# ------------------------------------------------------------------------------------------------------
# create path where the gt boxes are stored
# -----------------------------------------------------------------------------------------------------
root_path = pathlib.Path(data_path)
if info_path is None:
if sample_val_dataset_mode:
info_path = root_path / 'kitti_infos_val.pkl'
else:
info_path = root_path / 'kitti_infos_train.pkl'
if database_save_path is None:
if sample_val_dataset_mode:
database_save_path = root_path / 'gt_database_val'
else:
database_save_path = root_path / 'gt_database'
else:
database_save_path = pathlib.Path(database_save_path)
if db_info_save_path is None:
if sample_val_dataset_mode:
db_info_save_path = root_path / "kitti_dbinfos_val.pkl"
else:
db_info_save_path = root_path / "kitti_dbinfos_train.pkl"
database_save_path.mkdir(parents=True, exist_ok=True)
# ------------------------------------------------------------------------------------------------------
# load kitti infos
# -----------------------------------------------------------------------------------------------------
with open(info_path, 'rb') as f:
kitti_infos = pickle.load(f)
all_db_infos = {}
# get the classnames we are intered in
if used_classes is None:
used_classes = list(kitti.get_classes())
used_classes.pop(used_classes.index('DontCare'))
for name in used_classes:
all_db_infos[name] = []
group_counter = 0
# ------------------------------------------------------------------------------------------------------
# iterate over kitti_infos
# -----------------------------------------------------------------------------------------------------
for info in prog_bar(kitti_infos):
# ------------------------------------------------------------------------------------------------------
# load pc
# -----------------------------------------------------------------------------------------------------
velodyne_path = info['velodyne_path']
if relative_path:
# velodyne_path = str(root_path / velodyne_path) + "_reduced"
velodyne_path = str(root_path / velodyne_path)
num_features = 4
if 'pointcloud_num_features' in info:
num_features = info['pointcloud_num_features']
if custom_dataset:
with open(str(velodyne_path)[:-3] + "pkl", 'rb') as file:
points = pickle.load(file, encoding='latin1')
else:
points = np.fromfile(str(velodyne_path),
dtype=np.float32,
count=-1).reshape([-1, 4])
image_idx = info["image_idx"]
rect = info['calib/R0_rect']
P2 = info['calib/P2']
Trv2c = info['calib/Tr_velo_to_cam']
# ------------------------------------------------------------------------------------------------------
# remove boxes outside the frustum of the image
# -----------------------------------------------------------------------------------------------------
if not lidar_only and not custom_dataset:
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
info["img_shape"])
# ------------------------------------------------------------------------------------------------------
# get the bboxes and transform (annos not points)
# -----------------------------------------------------------------------------------------------------
annos = info["annos"]
names = annos["name"]
bboxes = annos["bbox"]
difficulty = annos["difficulty"]
gt_idxes = annos["index"]
num_obj = np.sum(annos["index"] >= 0)
rbbox_cam = kitti.anno_to_rbboxes(annos)[:num_obj]
rbbox_lidar = box_np_ops.box_camera_to_lidar(rbbox_cam, rect, Trv2c)
if bev_only: # set z and h to limits
assert coors_range is not None
rbbox_lidar[:, 2] = coors_range[2]
rbbox_lidar[:, 5] = coors_range[5] - coors_range[2]
# ------------------------------------------------------------------------------------------------------
# other stuff
# -----------------------------------------------------------------------------------------------------
group_dict = {}
group_ids = np.full([bboxes.shape[0]], -1, dtype=np.int64)
if "group_ids" in annos:
group_ids = annos["group_ids"]
else:
group_ids = np.arange(bboxes.shape[0], dtype=np.int64)
# ------------------------------------------------------------------------------------------------------
# test which points are in bboxes
# -----------------------------------------------------------------------------------------------------
point_indices = box_np_ops.points_in_rbbox(points, rbbox_lidar)
# ------------------------------------------------------------------------------------------------------
# iterate over all objects in the annos
# -----------------------------------------------------------------------------------------------------
for i in range(num_obj):
filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}.bin"
filepath = database_save_path / filename
gt_points = points[point_indices[:, i]]
gt_points[:, :3] -= rbbox_lidar[i, :3]
# ------------------------------------------------------------------------------------------------------
# save points of gt boxes to files
# -----------------------------------------------------------------------------------------------------
with open(str(filepath)[:-3] + "pkl", 'wb') as file:
pickle.dump(np.array(gt_points), file, 2)
# debug
# with open("/home/makr/Documents/uni/TU/3.Master/experiments/own/tf_3dRGB_pc/debug_rviz/points/bbox_pixels.pkl", 'wb') as file:
# pickle.dump(np.array(gt_points), file, 2)
# ------------------------------------------------------------------------------------------------------
# save infos of gt boxes to single file
# -----------------------------------------------------------------------------------------------------
if names[i] in used_classes:
if relative_path:
db_path = str(database_save_path.stem + "/" + filename)
else:
db_path = str(filepath)
db_info = {
"name": names[i],
"path": db_path,
"image_idx": image_idx,
"gt_idx": gt_idxes[i],
"box3d_lidar": rbbox_lidar[i],
"num_points_in_gt": gt_points.shape[0],
"difficulty": difficulty[i],
# "group_id": -1,
# "bbox": bboxes[i],
}
local_group_id = group_ids[i]
# if local_group_id >= 0:
if local_group_id not in group_dict:
group_dict[local_group_id] = group_counter
group_counter += 1
db_info["group_id"] = group_dict[local_group_id]
if "score" in annos:
db_info["score"] = annos["score"][i]
all_db_infos[names[i]].append(db_info)
for k, v in all_db_infos.items():
print(f"load {len(v)} {k} database infos")
with open(db_info_save_path, 'wb') as f:
pickle.dump(all_db_infos, f)
def _calculate_num_points_in_gt(data_path,
infos,
relative_path,
remove_outside=True,
num_features=4):
# custom dataset parameter
custom_dataset = True
# comment strating form here
import pickle
num_features = 3
# iterate over the datapoints
for info in infos:
if relative_path:
v_path = str(pathlib.Path(data_path) / info["velodyne_path"])
else:
v_path = info["velodyne_path"]
if custom_dataset:
with open(v_path[:-3] + "pkl", 'rb') as file:
points_v = pickle.load(file, encoding='latin1')
else:
points_v = np.fromfile(v_path, dtype=np.float32,
count=-1).reshape([-1, num_features])
rect = info['calib/R0_rect']
Trv2c = info['calib/Tr_velo_to_cam']
P2 = info['calib/P2']
# debug
# with open("/home/makr/Documents/uni/TU/3.Master/experiments/own/tf_3dRGB_pc/debug_rviz/points/points.pkl", 'wb') as file:
# pickle.dump(np.array(points_v), file, 2)
# remove points outside a frustum defined by the shape of the image
if remove_outside and not custom_dataset:
points_v = box_np_ops.remove_outside_points(
points_v, rect, Trv2c, P2, info["img_shape"])
# points_v = points_v[points_v[:, 0] > 0]
annos = info['annos']
# filter DontCare
num_obj = len([n for n in annos['name'] if n != 'DontCare'])
# annos = kitti.filter_kitti_anno(annos, ['DontCare'])
dims = annos['dimensions'][:num_obj]
loc = annos['location'][:num_obj]
rots = annos['rotation_y'][:num_obj]
# get gt boxes in cameras and lidar coords
gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1)
gt_boxes_lidar = box_np_ops.box_camera_to_lidar(
gt_boxes_camera, rect, Trv2c)
# check which points are in the gt boxes
indices = box_np_ops.points_in_rbbox(points_v[:, :3], gt_boxes_lidar)
num_points_in_gt = indices.sum(0)
# fill the list up with -1s for the DontCares
num_ignored = len(annos['dimensions']) - num_obj
num_points_in_gt = np.concatenate(
[num_points_in_gt, -np.ones([num_ignored])])
annos["num_points_in_gt"] = num_points_in_gt.astype(np.int32)
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,
db_sampler=None,
max_voxels=20000,
class_names=['Car'],
remove_outside_points=False,
training=True,
create_targets=True,
shuffle_points=False,
reduce_valid_area=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_loc_noise_std=(0.2, 0.2, 0.2),
global_random_rot_range=[0.78, 2.35],
generate_bev=False,
without_reflectivity=False,
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,
add_rgb_to_points=False,
lidar_input=False,
unlabeled_db_sampler=None,
out_size_factor=2,
min_gt_point_dict=None,
bev_only=False,
use_group_id=False,
out_dtype=np.float32):
"""convert point cloud to voxels, create targets if ground truths
exists.
"""
# 这部分用来读取某一帧数据
points = input_dict["points"] # velodyne_reduced, array(N*4)
if training:
gt_boxes = input_dict["gt_boxes"] # 真值框，位置，尺寸，绝对转角，N*1，一个真值框一行
gt_names = input_dict["gt_names"]
difficulty = input_dict["difficulty"]
group_ids = None
if use_group_id and "group_ids" in input_dict: # False
group_ids = input_dict["group_ids"]
rect = input_dict["rect"]
Trv2c = input_dict["Trv2c"]
P2 = input_dict["P2"]
unlabeled_training = unlabeled_db_sampler is not None
image_idx = input_dict["image_idx"]
if reference_detections is not None: # None
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.linalg.inv(R) @ 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 and not lidar_input: # False
image_shape = input_dict["image_shape"]
points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2,
image_shape)
if remove_environment is True and training: # False
selected = kitti.keep_arrays_by_name(gt_names, class_names)
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None: # None
group_ids = group_ids[selected]
points = prep.remove_points_outside_boxes(points, gt_boxes)
if training:
# 先去掉真值内的DontCare
selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"]) # 去掉DontCare
gt_boxes = gt_boxes[selected]
gt_names = gt_names[selected]
difficulty = difficulty[selected]
if group_ids is not None: # None
group_ids = group_ids[selected]
gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c) # 相机坐标下的真值框转换成激光雷达坐标下[xyz_lidar,w,l,h,r],一个对象一行
if remove_unknown: # False
remove_mask = 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)
gt_boxes = gt_boxes[keep_mask]
gt_names = gt_names[keep_mask]
difficulty = difficulty[keep_mask]
if group_ids is not None: # None
group_ids = group_ids[keep_mask]
gt_boxes_mask = np.array( # 目标类别的对象标签，布尔类型，同样一行一个对象
[n in class_names for n in gt_names], dtype=np.bool_)
# 下面用来对去掉DontCare的真值进行采样补充
if db_sampler is not None: # not None
# 数据库预处理类里的方法，返回的是该帧数据内各类别用来补充的采样真值数据，包括真值内出现的非目标类别
sampled_dict = db_sampler.sample_all(
root_path,
gt_boxes,
gt_names,
num_point_features,
random_crop,
gt_group_ids=group_ids, # None
rect=rect,
Trv2c=Trv2c,
P2=P2)
if sampled_dict is not None: # 下面将原始数据与补充采样数据合并
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_names = gt_names[gt_boxes_mask].tolist()
gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0)
# gt_names += [s["name"] for s in sampled]
gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes])
gt_boxes_mask = np.concatenate(
[gt_boxes_mask, sampled_gt_masks], axis=0) # 真值框是目标类别的标志
if group_ids is not None: # None
sampled_group_ids = sampled_dict["group_ids"]
group_ids = np.concatenate([group_ids, sampled_group_ids])
if remove_points_after_sample: # False,将采样框所占位置点云去除
points = prep.remove_points_in_boxes(
points, sampled_gt_boxes)
points = np.concatenate([sampled_points, points], axis=0) # 合并原始点云与采样点云
# unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_)
if without_reflectivity: # False
used_point_axes = list(range(num_point_features))
used_point_axes.pop(3)
points = points[:, used_point_axes]
pc_range = voxel_generator.point_cloud_range
if bev_only: # set z and h to limits, False
gt_boxes[:, 2] = pc_range[2]
gt_boxes[:, 5] = pc_range[5] - pc_range[2]
prep.noise_per_object_v3_( # 每一个对象添加扰动，实际上就是修改每个对象真值框坐标与内部点云坐标
gt_boxes, # 补充过采样数据的真值框
points, # 补充过采样数据的点云
gt_boxes_mask, # 真值框标志（不区分原始数据与采样数据）
rotation_perturb=gt_rotation_noise, # 旋转角度范围，平均分布
center_noise_std=gt_loc_noise_std, # 位置正态分布方差
global_random_rot_range=global_random_rot_range, # 全局旋转[0.0,0.0]
group_ids=group_ids, # None
num_try=100)
# should remove unrelated objects after noise per object
gt_boxes = gt_boxes[gt_boxes_mask] # 添加扰动后筛选目标类别真值框
gt_names = gt_names[gt_boxes_mask]
if group_ids is not None: # Fasle
group_ids = group_ids[gt_boxes_mask]
gt_classes = np.array( # (gt_num)
[class_names.index(n) + 1 for n in gt_names], dtype=np.int32)
gt_boxes, points = prep.random_flip(gt_boxes, points) # 随机翻转
gt_boxes, points = prep.global_rotation( # 全局旋转
gt_boxes, points, rotation=global_rotation_noise)
gt_boxes, points = prep.global_scaling_v2(gt_boxes, points, # 全局缩放
*global_scaling_noise)
# Global translation
# 全局定位扰动
gt_boxes, points = prep.global_translate(gt_boxes, points, global_loc_noise_std)
bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range) # 过滤超出鸟瞰图范围的真值框
gt_boxes = gt_boxes[mask]
gt_classes = gt_classes[mask]
if group_ids is not None: # None
group_ids = group_ids[mask]
# limit rad to [-pi, pi]
gt_boxes[:, 6] = box_np_ops.limit_period( # 调整真值转角至目标范围
gt_boxes[:, 6], offset=0.5, period=2 * np.pi)
if shuffle_points: # True，打乱全局点云数据
# shuffle is a little slow.
np.random.shuffle(points)
voxel_size = voxel_generator.voxel_size # [0.16, 0.16, 4]
pc_range = voxel_generator.point_cloud_range # [0, -39.68, -3, 69.12, 39.68, 1]
grid_size = voxel_generator.grid_size # [432, 496, 1] x,y,z
# 生成体素
"""
Returns:
voxels:[num_voxels, 100, 4] 体素索引映射全局体素特征
coordinates:[num_voxels, 3] 体素索引映射体素坐标
num_points:(num_voxels,) 体素索引映射体素内的点数
"""
voxels, coordinates, num_points = voxel_generator.generate(
points, max_voxels)
example = {
'voxels': voxels,
'num_points': num_points,
'coordinates': coordinates,
"num_voxels": np.array([voxels.shape[0]], dtype=np.int64)
}
example.update({
'rect': rect,
'Trv2c': Trv2c,
'P2': P2,
})
# if not lidar_input:
feature_map_size = grid_size[:2] // out_size_factor # [216 248]
feature_map_size = [*feature_map_size, 1][::-1] # [1,248,216]
if anchor_cache is not None:
anchors = anchor_cache["anchors"]
anchors_bv = anchor_cache["anchors_bv"]
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, 7])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(
anchors[:, [0, 1, 3, 4, 6]])
example["anchors"] = anchors
# print("debug", anchors.shape, matched_thresholds.shape)
# anchors_bv = anchors_bv.reshape([-1, 4])
anchors_mask = None
if anchor_area_threshold >= 0: # True
coors = coordinates
dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask( # 某处体素是否被采样,否为0,是为1.array[496,432] y,x
coors, tuple(grid_size[::-1][1:]))
dense_voxel_map = dense_voxel_map.cumsum(0) # 元素值为按列累加到元素的和
dense_voxel_map = dense_voxel_map.cumsum(1) # 元素值为按行累加到元素的和,相当于统计了元素两个维度上之前元素的和
anchors_area = box_np_ops.fused_get_anchors_area( # 统计每个锚框内的体素数量
dense_voxel_map, anchors_bv, voxel_size, pc_range, grid_size)
anchors_mask = anchors_area > anchor_area_threshold # 标记超过一个体素的锚框
# example['anchors_mask'] = anchors_mask.astype(np.uint8)
example['anchors_mask'] = anchors_mask
if generate_bev: # False
bev_vxsize = voxel_size.copy()
bev_vxsize[:2] /= 2
bev_vxsize[2] *= 2
bev_map = points_to_bev(points, bev_vxsize, pc_range,
without_reflectivity)
example["bev_map"] = bev_map
if not training:
return example # 测试数据集不需要创建训练目标
if create_targets: # True
targets_dict = target_assigner.assign(
anchors, # (248*216*2,7)
gt_boxes, # (gt_num, 7)
anchors_mask, # (248*216*2,)
gt_classes=gt_classes, # (gt_num,)
matched_thresholds=matched_thresholds, # (248*216*2,)
unmatched_thresholds=unmatched_thresholds) # (248*216*2,)
example.update({
'labels': targets_dict['labels'], # (total_anchors,),所有锚框对应真值类别(1,2...),无对应真值设为0,dontcare设为-1
'reg_targets': targets_dict['bbox_targets'], # (total_anchors, 7),所有锚框对应真值相对锚框的偏差编码,无对应真值设为[0,0,0,0,0,0,0]
'reg_weights': targets_dict['bbox_outside_weights'], # (total_anchors,),所有锚框的外部权重,有对应真值设为1，无对应真值设为0
})
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,
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 evaluation_from_kitti_dets(self, dt_annos, output_dir):
if "annos" not in self._kitti_infos[0]:
return None
gt_annos = [info["annos"] for info in self._kitti_infos]
# firstly convert standard detection to kitti-format dt annos
z_axis = 1 # KITTI camera format use y as regular "z" axis.
z_center = 1.0 # KITTI camera box's center is [0.5, 1, 0.5]
# for regular raw lidar data, z_axis = 2, z_center = 0.5.
result_official_dict = get_official_eval_result(
gt_annos,
dt_annos,
self._class_names,
z_axis=z_axis,
z_center=z_center)
result_coco = get_coco_eval_result(
gt_annos,
dt_annos,
self._class_names,
z_axis=z_axis,
z_center=z_center)
# feature extraction
for info, det in tqdm(zip(self._kitti_infos, dt_annos), desc="feature", total=len(dt_annos)):
pc_info = info["point_cloud"]
image_info = info["image"]
calib = info["calib"]
num_features = pc_info["num_features"]
v_path = self._root_path / pc_info["velodyne_path"]
v_path = str(v_path.parent.parent / (v_path.parent.stem + "_reduced") / v_path.name)
points_v = np.fromfile(
v_path, dtype=np.float32, count=-1).reshape([-1, num_features])
rect = calib['R0_rect']
Trv2c = calib['Tr_velo_to_cam']
P2 = calib['P2']
if False: # No longer you need remove outside image-rect (*_reduced pointcloud is already filtered.)
points_v = box_np_ops.remove_outside_points(
points_v, rect, Trv2c, P2, image_info["image_shape"])
annos = det
num_obj = len([n for n in annos['name'] if n != 'DontCare'])
# annos = kitti.filter_kitti_anno(annos, ['DontCare'])
dims = annos['dimensions'][:num_obj]
loc = annos['location'][:num_obj]
rots = annos['rotation_y'][:num_obj]
gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1)
gt_boxes_lidar = box_np_ops.box_camera_to_lidar(
gt_boxes_camera, rect, Trv2c)
indices = box_np_ops.points_in_rbbox(points_v[:, :3], gt_boxes_lidar)
num_points_in_gt = indices.sum(0)
num_ignored = len(annos['dimensions']) - num_obj
num_points_in_gt = np.concatenate(
[num_points_in_gt, -np.ones([num_ignored])])
annos["num_points_in_det"] = num_points_in_gt.astype(np.int32)
return {
"results": {
"official": result_official_dict["result"],
"coco": result_coco["result"],
},
"detail": {
"eval.kitti": {
"official": result_official_dict["detail"],
"coco": result_coco["detail"]
}
},
"result_kitti": result_official_dict["detections"],
}
