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update_infos_to_v2.py
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# Copyright (c) OpenMMLab. All rights reserved.
"""Convert the annotation pkl to the standard format in OpenMMLab V2.0.
Example:
python tools/dataset_converters/update_infos_to_v2.py
--dataset kitti
--pkl-path ./data/kitti/kitti_infos_train.pkl
--out-dir ./kitti_v2/
"""
import argparse
import copy
import time
from os import path as osp
from pathlib import Path
import mmengine
import numpy as np
from nuscenes.nuscenes import NuScenes
from mmdet3d.datasets.convert_utils import (convert_annos,
get_kitti_style_2d_boxes,
get_nuscenes_2d_boxes)
from mmdet3d.datasets.utils import convert_quaternion_to_matrix
from mmdet3d.structures import points_cam2img
def get_empty_instance():
"""Empty annotation for single instance."""
instance = dict(
# (list[float], required): list of 4 numbers representing
# the bounding box of the instance, in (x1, y1, x2, y2) order.
bbox=None,
# (int, required): an integer in the range
# [0, num_categories-1] representing the category label.
bbox_label=None,
# (list[float], optional): list of 7 (or 9) numbers representing
# the 3D bounding box of the instance,
# in [x, y, z, w, h, l, yaw]
# (or [x, y, z, w, h, l, yaw, vx, vy]) order.
bbox_3d=None,
# (bool, optional): Whether to use the
# 3D bounding box during training.
bbox_3d_isvalid=None,
# (int, optional): 3D category label
# (typically the same as label).
bbox_label_3d=None,
# (float, optional): Projected center depth of the
# 3D bounding box compared to the image plane.
depth=None,
# (list[float], optional): Projected
# 2D center of the 3D bounding box.
center_2d=None,
# (int, optional): Attribute labels
# (fine-grained labels such as stopping, moving, ignore, crowd).
attr_label=None,
# (int, optional): The number of LiDAR
# points in the 3D bounding box.
num_lidar_pts=None,
# (int, optional): The number of Radar
# points in the 3D bounding box.
num_radar_pts=None,
# (int, optional): Difficulty level of
# detecting the 3D bounding box.
difficulty=None,
unaligned_bbox_3d=None)
return instance
def get_empty_multicamera_instances(camera_types):
cam_instance = dict()
for cam_type in camera_types:
cam_instance[cam_type] = None
return cam_instance
def get_empty_lidar_points():
lidar_points = dict(
# (int, optional) : Number of features for each point.
num_pts_feats=None,
# (str, optional): Path of LiDAR data file.
lidar_path=None,
# (list[list[float]], optional): Transformation matrix
# from lidar to ego-vehicle
# with shape [4, 4].
# (Referenced camera coordinate system is ego in KITTI.)
lidar2ego=None,
)
return lidar_points
def get_empty_radar_points():
radar_points = dict(
# (int, optional) : Number of features for each point.
num_pts_feats=None,
# (str, optional): Path of RADAR data file.
radar_path=None,
# Transformation matrix from lidar to
# ego-vehicle with shape [4, 4].
# (Referenced camera coordinate system is ego in KITTI.)
radar2ego=None,
)
return radar_points
def get_empty_img_info():
img_info = dict(
# (str, required): the path to the image file.
img_path=None,
# (int) The height of the image.
height=None,
# (int) The width of the image.
width=None,
# (str, optional): Path of the depth map file
depth_map=None,
# (list[list[float]], optional) : Transformation
# matrix from camera to image with
# shape [3, 3], [3, 4] or [4, 4].
cam2img=None,
# (list[list[float]]): Transformation matrix from lidar
# or depth to image with shape [4, 4].
lidar2img=None,
# (list[list[float]], optional) : Transformation
# matrix from camera to ego-vehicle
# with shape [4, 4].
cam2ego=None)
return img_info
def get_single_image_sweep(camera_types):
single_image_sweep = dict(
# (float, optional) : Timestamp of the current frame.
timestamp=None,
# (list[list[float]], optional) : Transformation matrix
# from ego-vehicle to the global
ego2global=None)
# (dict): Information of images captured by multiple cameras
images = dict()
for cam_type in camera_types:
images[cam_type] = get_empty_img_info()
single_image_sweep['images'] = images
return single_image_sweep
def get_single_lidar_sweep():
single_lidar_sweep = dict(
# (float, optional) : Timestamp of the current frame.
timestamp=None,
# (list[list[float]], optional) : Transformation matrix
# from ego-vehicle to the global
ego2global=None,
# (dict): Information of images captured by multiple cameras
lidar_points=get_empty_lidar_points())
return single_lidar_sweep
def get_empty_standard_data_info(
camera_types=['CAM0', 'CAM1', 'CAM2', 'CAM3', 'CAM4']):
data_info = dict(
# (str): Sample id of the frame.
sample_idx=None,
# (str, optional): '000010'
token=None,
**get_single_image_sweep(camera_types),
# (dict, optional): dict contains information
# of LiDAR point cloud frame.
lidar_points=get_empty_lidar_points(),
# (dict, optional) Each dict contains
# information of Radar point cloud frame.
radar_points=get_empty_radar_points(),
# (list[dict], optional): Image sweeps data.
image_sweeps=[],
lidar_sweeps=[],
instances=[],
# (list[dict], optional): Required by object
# detection, instance to be ignored during training.
instances_ignore=[],
# (str, optional): Path of semantic labels for each point.
pts_semantic_mask_path=None,
# (str, optional): Path of instance labels for each point.
pts_instance_mask_path=None)
return data_info
def clear_instance_unused_keys(instance):
keys = list(instance.keys())
for k in keys:
if instance[k] is None:
del instance[k]
return instance
def clear_data_info_unused_keys(data_info):
keys = list(data_info.keys())
empty_flag = True
for key in keys:
# we allow no annotations in datainfo
if key in ['instances', 'cam_sync_instances', 'cam_instances']:
empty_flag = False
continue
if isinstance(data_info[key], list):
if len(data_info[key]) == 0:
del data_info[key]
else:
empty_flag = False
elif data_info[key] is None:
del data_info[key]
elif isinstance(data_info[key], dict):
_, sub_empty_flag = clear_data_info_unused_keys(data_info[key])
if sub_empty_flag is False:
empty_flag = False
else:
# sub field is empty
del data_info[key]
else:
empty_flag = False
return data_info, empty_flag
def generate_nuscenes_camera_instances(info, nusc):
# get bbox annotations for camera
camera_types = [
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_FRONT_LEFT',
'CAM_BACK',
'CAM_BACK_LEFT',
'CAM_BACK_RIGHT',
]
empty_multicamera_instance = get_empty_multicamera_instances(camera_types)
for cam in camera_types:
cam_info = info['cams'][cam]
# list[dict]
ann_infos = get_nuscenes_2d_boxes(
nusc,
cam_info['sample_data_token'],
visibilities=['', '1', '2', '3', '4'])
empty_multicamera_instance[cam] = ann_infos
return empty_multicamera_instance
def update_nuscenes_infos(pkl_path, out_dir):
camera_types = [
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_FRONT_LEFT',
'CAM_BACK',
'CAM_BACK_LEFT',
'CAM_BACK_RIGHT',
]
print(f'{pkl_path} will be modified.')
if out_dir in pkl_path:
print(f'Warning, you may overwriting '
f'the original data {pkl_path}.')
print(f'Reading from input file: {pkl_path}.')
data_list = mmengine.load(pkl_path)
METAINFO = {
'classes':
('car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'),
}
nusc = NuScenes(
version=data_list['metadata']['version'],
dataroot='./data/nuscenes',
verbose=True)
print('Start updating:')
converted_list = []
for i, ori_info_dict in enumerate(
mmengine.track_iter_progress(data_list['infos'])):
temp_data_info = get_empty_standard_data_info(
camera_types=camera_types)
temp_data_info['sample_idx'] = i
temp_data_info['token'] = ori_info_dict['token']
temp_data_info['ego2global'] = convert_quaternion_to_matrix(
ori_info_dict['ego2global_rotation'],
ori_info_dict['ego2global_translation'])
temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict.get(
'num_features', 5)
temp_data_info['lidar_points']['lidar_path'] = Path(
ori_info_dict['lidar_path']).name
temp_data_info['lidar_points'][
'lidar2ego'] = convert_quaternion_to_matrix(
ori_info_dict['lidar2ego_rotation'],
ori_info_dict['lidar2ego_translation'])
# bc-breaking: Timestamp has divided 1e6 in pkl infos.
temp_data_info['timestamp'] = ori_info_dict['timestamp'] / 1e6
for ori_sweep in ori_info_dict['sweeps']:
temp_lidar_sweep = get_single_lidar_sweep()
temp_lidar_sweep['lidar_points'][
'lidar2ego'] = convert_quaternion_to_matrix(
ori_sweep['sensor2ego_rotation'],
ori_sweep['sensor2ego_translation'])
temp_lidar_sweep['ego2global'] = convert_quaternion_to_matrix(
ori_sweep['ego2global_rotation'],
ori_sweep['ego2global_translation'])
lidar2sensor = np.eye(4)
rot = ori_sweep['sensor2lidar_rotation']
trans = ori_sweep['sensor2lidar_translation']
lidar2sensor[:3, :3] = rot.T
lidar2sensor[:3, 3:4] = -1 * np.matmul(rot.T, trans.reshape(3, 1))
temp_lidar_sweep['lidar_points'][
'lidar2sensor'] = lidar2sensor.astype(np.float32).tolist()
temp_lidar_sweep['timestamp'] = ori_sweep['timestamp'] / 1e6
temp_lidar_sweep['lidar_points']['lidar_path'] = ori_sweep[
'data_path']
temp_lidar_sweep['sample_data_token'] = ori_sweep[
'sample_data_token']
temp_data_info['lidar_sweeps'].append(temp_lidar_sweep)
temp_data_info['images'] = {}
for cam in ori_info_dict['cams']:
empty_img_info = get_empty_img_info()
empty_img_info['img_path'] = Path(
ori_info_dict['cams'][cam]['data_path']).name
empty_img_info['cam2img'] = ori_info_dict['cams'][cam][
'cam_intrinsic'].tolist()
empty_img_info['sample_data_token'] = ori_info_dict['cams'][cam][
'sample_data_token']
# bc-breaking: Timestamp has divided 1e6 in pkl infos.
empty_img_info[
'timestamp'] = ori_info_dict['cams'][cam]['timestamp'] / 1e6
empty_img_info['cam2ego'] = convert_quaternion_to_matrix(
ori_info_dict['cams'][cam]['sensor2ego_rotation'],
ori_info_dict['cams'][cam]['sensor2ego_translation'])
lidar2sensor = np.eye(4)
rot = ori_info_dict['cams'][cam]['sensor2lidar_rotation']
trans = ori_info_dict['cams'][cam]['sensor2lidar_translation']
lidar2sensor[:3, :3] = rot.T
lidar2sensor[:3, 3:4] = -1 * np.matmul(rot.T, trans.reshape(3, 1))
empty_img_info['lidar2cam'] = lidar2sensor.astype(
np.float32).tolist()
temp_data_info['images'][cam] = empty_img_info
ignore_class_name = set()
if 'gt_boxes' in ori_info_dict:
num_instances = ori_info_dict['gt_boxes'].shape[0]
for i in range(num_instances):
empty_instance = get_empty_instance()
empty_instance['bbox_3d'] = ori_info_dict['gt_boxes'][
i, :].tolist()
if ori_info_dict['gt_names'][i] in METAINFO['classes']:
empty_instance['bbox_label'] = METAINFO['classes'].index(
ori_info_dict['gt_names'][i])
else:
ignore_class_name.add(ori_info_dict['gt_names'][i])
empty_instance['bbox_label'] = -1
empty_instance['bbox_label_3d'] = copy.deepcopy(
empty_instance['bbox_label'])
empty_instance['velocity'] = ori_info_dict['gt_velocity'][
i, :].tolist()
empty_instance['num_lidar_pts'] = ori_info_dict[
'num_lidar_pts'][i]
empty_instance['num_radar_pts'] = ori_info_dict[
'num_radar_pts'][i]
empty_instance['bbox_3d_isvalid'] = ori_info_dict[
'valid_flag'][i]
empty_instance = clear_instance_unused_keys(empty_instance)
temp_data_info['instances'].append(empty_instance)
temp_data_info[
'cam_instances'] = generate_nuscenes_camera_instances(
ori_info_dict, nusc)
if 'pts_semantic_mask_path' in ori_info_dict:
temp_data_info['pts_semantic_mask_path'] = Path(
ori_info_dict['pts_semantic_mask_path']).name
temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
converted_list.append(temp_data_info)
pkl_name = Path(pkl_path).name
out_path = osp.join(out_dir, pkl_name)
print(f'Writing to output file: {out_path}.')
print(f'ignore classes: {ignore_class_name}')
metainfo = dict()
metainfo['categories'] = {k: i for i, k in enumerate(METAINFO['classes'])}
if ignore_class_name:
for ignore_class in ignore_class_name:
metainfo['categories'][ignore_class] = -1
metainfo['dataset'] = 'nuscenes'
metainfo['version'] = data_list['metadata']['version']
metainfo['info_version'] = '1.1'
converted_data_info = dict(metainfo=metainfo, data_list=converted_list)
mmengine.dump(converted_data_info, out_path, 'pkl')
def update_kitti_infos(pkl_path, out_dir):
print(f'{pkl_path} will be modified.')
if out_dir in pkl_path:
print(f'Warning, you may overwriting '
f'the original data {pkl_path}.')
time.sleep(5)
# TODO update to full label
# TODO discuss how to process 'Van', 'DontCare'
METAINFO = {
'classes': ('Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
'Person_sitting', 'Tram', 'Misc'),
}
print(f'Reading from input file: {pkl_path}.')
data_list = mmengine.load(pkl_path)
print('Start updating:')
converted_list = []
for ori_info_dict in mmengine.track_iter_progress(data_list):
temp_data_info = get_empty_standard_data_info()
if 'plane' in ori_info_dict:
temp_data_info['plane'] = ori_info_dict['plane']
temp_data_info['sample_idx'] = ori_info_dict['image']['image_idx']
temp_data_info['images']['CAM0']['cam2img'] = ori_info_dict['calib'][
'P0'].tolist()
temp_data_info['images']['CAM1']['cam2img'] = ori_info_dict['calib'][
'P1'].tolist()
temp_data_info['images']['CAM2']['cam2img'] = ori_info_dict['calib'][
'P2'].tolist()
temp_data_info['images']['CAM3']['cam2img'] = ori_info_dict['calib'][
'P3'].tolist()
temp_data_info['images']['CAM2']['img_path'] = Path(
ori_info_dict['image']['image_path']).name
h, w = ori_info_dict['image']['image_shape']
temp_data_info['images']['CAM2']['height'] = h
temp_data_info['images']['CAM2']['width'] = w
temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
'point_cloud']['num_features']
temp_data_info['lidar_points']['lidar_path'] = Path(
ori_info_dict['point_cloud']['velodyne_path']).name
rect = ori_info_dict['calib']['R0_rect'].astype(np.float32)
Trv2c = ori_info_dict['calib']['Tr_velo_to_cam'].astype(np.float32)
lidar2cam = rect @ Trv2c
temp_data_info['images']['CAM2']['lidar2cam'] = lidar2cam.tolist()
temp_data_info['images']['CAM0']['lidar2img'] = (
ori_info_dict['calib']['P0'] @ lidar2cam).tolist()
temp_data_info['images']['CAM1']['lidar2img'] = (
ori_info_dict['calib']['P1'] @ lidar2cam).tolist()
temp_data_info['images']['CAM2']['lidar2img'] = (
ori_info_dict['calib']['P2'] @ lidar2cam).tolist()
temp_data_info['images']['CAM3']['lidar2img'] = (
ori_info_dict['calib']['P3'] @ lidar2cam).tolist()
temp_data_info['lidar_points']['Tr_velo_to_cam'] = Trv2c.tolist()
# for potential usage
temp_data_info['images']['R0_rect'] = ori_info_dict['calib'][
'R0_rect'].astype(np.float32).tolist()
temp_data_info['lidar_points']['Tr_imu_to_velo'] = ori_info_dict[
'calib']['Tr_imu_to_velo'].astype(np.float32).tolist()
cam2img = ori_info_dict['calib']['P2']
anns = ori_info_dict.get('annos', None)
ignore_class_name = set()
if anns is not None:
num_instances = len(anns['name'])
instance_list = []
for instance_id in range(num_instances):
empty_instance = get_empty_instance()
empty_instance['bbox'] = anns['bbox'][instance_id].tolist()
if anns['name'][instance_id] in METAINFO['classes']:
empty_instance['bbox_label'] = METAINFO['classes'].index(
anns['name'][instance_id])
else:
ignore_class_name.add(anns['name'][instance_id])
empty_instance['bbox_label'] = -1
empty_instance['bbox'] = anns['bbox'][instance_id].tolist()
loc = anns['location'][instance_id]
dims = anns['dimensions'][instance_id]
rots = anns['rotation_y'][:, None][instance_id]
dst = np.array([0.5, 0.5, 0.5])
src = np.array([0.5, 1.0, 0.5])
center_3d = loc + dims * (dst - src)
center_2d = points_cam2img(
center_3d.reshape([1, 3]), cam2img, with_depth=True)
center_2d = center_2d.squeeze().tolist()
empty_instance['center_2d'] = center_2d[:2]
empty_instance['depth'] = center_2d[2]
gt_bboxes_3d = np.concatenate([loc, dims, rots]).tolist()
empty_instance['bbox_3d'] = gt_bboxes_3d
empty_instance['bbox_label_3d'] = copy.deepcopy(
empty_instance['bbox_label'])
empty_instance['bbox'] = anns['bbox'][instance_id].tolist()
empty_instance['truncated'] = anns['truncated'][
instance_id].tolist()
empty_instance['occluded'] = anns['occluded'][
instance_id].tolist()
empty_instance['alpha'] = anns['alpha'][instance_id].tolist()
empty_instance['score'] = anns['score'][instance_id].tolist()
empty_instance['index'] = anns['index'][instance_id].tolist()
empty_instance['group_id'] = anns['group_ids'][
instance_id].tolist()
empty_instance['difficulty'] = anns['difficulty'][
instance_id].tolist()
empty_instance['num_lidar_pts'] = anns['num_points_in_gt'][
instance_id].tolist()
empty_instance = clear_instance_unused_keys(empty_instance)
instance_list.append(empty_instance)
temp_data_info['instances'] = instance_list
cam_instances = generate_kitti_camera_instances(ori_info_dict)
temp_data_info['cam_instances'] = cam_instances
temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
converted_list.append(temp_data_info)
pkl_name = Path(pkl_path).name
out_path = osp.join(out_dir, pkl_name)
print(f'Writing to output file: {out_path}.')
print(f'ignore classes: {ignore_class_name}')
# dataset metainfo
metainfo = dict()
metainfo['categories'] = {k: i for i, k in enumerate(METAINFO['classes'])}
if ignore_class_name:
for ignore_class in ignore_class_name:
metainfo['categories'][ignore_class] = -1
metainfo['dataset'] = 'kitti'
metainfo['info_version'] = '1.1'
converted_data_info = dict(metainfo=metainfo, data_list=converted_list)
mmengine.dump(converted_data_info, out_path, 'pkl')
def update_s3dis_infos(pkl_path, out_dir):
print(f'{pkl_path} will be modified.')
if out_dir in pkl_path:
print(f'Warning, you may overwriting '
f'the original data {pkl_path}.')
time.sleep(5)
METAINFO = {'classes': ('table', 'chair', 'sofa', 'bookcase', 'board')}
print(f'Reading from input file: {pkl_path}.')
data_list = mmengine.load(pkl_path)
print('Start updating:')
converted_list = []
for i, ori_info_dict in enumerate(mmengine.track_iter_progress(data_list)):
temp_data_info = get_empty_standard_data_info()
temp_data_info['sample_idx'] = i
temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
'point_cloud']['num_features']
temp_data_info['lidar_points']['lidar_path'] = Path(
ori_info_dict['pts_path']).name
if 'pts_semantic_mask_path' in ori_info_dict:
temp_data_info['pts_semantic_mask_path'] = Path(
ori_info_dict['pts_semantic_mask_path']).name
if 'pts_instance_mask_path' in ori_info_dict:
temp_data_info['pts_instance_mask_path'] = Path(
ori_info_dict['pts_instance_mask_path']).name
# TODO support camera
# np.linalg.inv(info['axis_align_matrix'] @ extrinsic): depth2cam
anns = ori_info_dict.get('annos', None)
ignore_class_name = set()
if anns is not None:
if anns['gt_num'] == 0:
instance_list = []
else:
num_instances = len(anns['class'])
instance_list = []
for instance_id in range(num_instances):
empty_instance = get_empty_instance()
empty_instance['bbox_3d'] = anns['gt_boxes_upright_depth'][
instance_id].tolist()
if anns['class'][instance_id] < len(METAINFO['classes']):
empty_instance['bbox_label_3d'] = anns['class'][
instance_id]
else:
ignore_class_name.add(
METAINFO['classes'][anns['class'][instance_id]])
empty_instance['bbox_label_3d'] = -1
empty_instance = clear_instance_unused_keys(empty_instance)
instance_list.append(empty_instance)
temp_data_info['instances'] = instance_list
temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
converted_list.append(temp_data_info)
pkl_name = Path(pkl_path).name
out_path = osp.join(out_dir, pkl_name)
print(f'Writing to output file: {out_path}.')
print(f'ignore classes: {ignore_class_name}')
# dataset metainfo
metainfo = dict()
metainfo['categories'] = {k: i for i, k in enumerate(METAINFO['classes'])}
if ignore_class_name:
for ignore_class in ignore_class_name:
metainfo['categories'][ignore_class] = -1
metainfo['dataset'] = 's3dis'
metainfo['info_version'] = '1.1'
converted_data_info = dict(metainfo=metainfo, data_list=converted_list)
mmengine.dump(converted_data_info, out_path, 'pkl')
def update_scannet_infos(pkl_path, out_dir):
print(f'{pkl_path} will be modified.')
if out_dir in pkl_path:
print(f'Warning, you may overwriting '
f'the original data {pkl_path}.')
time.sleep(5)
METAINFO = {
'classes':
('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator',
'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin')
}
print(f'Reading from input file: {pkl_path}.')
data_list = mmengine.load(pkl_path)
print('Start updating:')
converted_list = []
for ori_info_dict in mmengine.track_iter_progress(data_list):
temp_data_info = get_empty_standard_data_info()
temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
'point_cloud']['num_features']
temp_data_info['lidar_points']['lidar_path'] = Path(
ori_info_dict['pts_path']).name
if 'pts_semantic_mask_path' in ori_info_dict:
temp_data_info['pts_semantic_mask_path'] = Path(
ori_info_dict['pts_semantic_mask_path']).name
if 'pts_instance_mask_path' in ori_info_dict:
temp_data_info['pts_instance_mask_path'] = Path(
ori_info_dict['pts_instance_mask_path']).name
# TODO support camera
# np.linalg.inv(info['axis_align_matrix'] @ extrinsic): depth2cam
anns = ori_info_dict.get('annos', None)
ignore_class_name = set()
if anns is not None:
temp_data_info['axis_align_matrix'] = anns[
'axis_align_matrix'].tolist()
if anns['gt_num'] == 0:
instance_list = []
else:
num_instances = len(anns['name'])
instance_list = []
for instance_id in range(num_instances):
empty_instance = get_empty_instance()
empty_instance['bbox_3d'] = anns['gt_boxes_upright_depth'][
instance_id].tolist()
if anns['name'][instance_id] in METAINFO['classes']:
empty_instance['bbox_label_3d'] = METAINFO[
'classes'].index(anns['name'][instance_id])
else:
ignore_class_name.add(anns['name'][instance_id])
empty_instance['bbox_label_3d'] = -1
empty_instance = clear_instance_unused_keys(empty_instance)
instance_list.append(empty_instance)
temp_data_info['instances'] = instance_list
temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
converted_list.append(temp_data_info)
pkl_name = Path(pkl_path).name
out_path = osp.join(out_dir, pkl_name)
print(f'Writing to output file: {out_path}.')
print(f'ignore classes: {ignore_class_name}')
# dataset metainfo
metainfo = dict()
metainfo['categories'] = {k: i for i, k in enumerate(METAINFO['classes'])}
if ignore_class_name:
for ignore_class in ignore_class_name:
metainfo['categories'][ignore_class] = -1
metainfo['dataset'] = 'scannet'
metainfo['info_version'] = '1.1'
converted_data_info = dict(metainfo=metainfo, data_list=converted_list)
mmengine.dump(converted_data_info, out_path, 'pkl')
def update_sunrgbd_infos(pkl_path, out_dir):
print(f'{pkl_path} will be modified.')
if out_dir in pkl_path:
print(f'Warning, you may overwriting '
f'the original data {pkl_path}.')
time.sleep(5)
METAINFO = {
'classes': ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk',
'dresser', 'night_stand', 'bookshelf', 'bathtub')
}
print(f'Reading from input file: {pkl_path}.')
data_list = mmengine.load(pkl_path)
print('Start updating:')
converted_list = []
for ori_info_dict in mmengine.track_iter_progress(data_list):
temp_data_info = get_empty_standard_data_info()
temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
'point_cloud']['num_features']
temp_data_info['lidar_points']['lidar_path'] = Path(
ori_info_dict['pts_path']).name
calib = ori_info_dict['calib']
rt_mat = calib['Rt']
# follow Coord3DMode.convert_point
rt_mat = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]
]) @ rt_mat.transpose(1, 0)
depth2img = calib['K'] @ rt_mat
temp_data_info['images']['CAM0']['depth2img'] = depth2img.tolist()
temp_data_info['images']['CAM0']['img_path'] = Path(
ori_info_dict['image']['image_path']).name
h, w = ori_info_dict['image']['image_shape']
temp_data_info['images']['CAM0']['height'] = h
temp_data_info['images']['CAM0']['width'] = w
anns = ori_info_dict.get('annos', None)
if anns is not None:
if anns['gt_num'] == 0:
instance_list = []
else:
num_instances = len(anns['name'])
ignore_class_name = set()
instance_list = []
for instance_id in range(num_instances):
empty_instance = get_empty_instance()
empty_instance['bbox_3d'] = anns['gt_boxes_upright_depth'][
instance_id].tolist()
empty_instance['bbox'] = anns['bbox'][instance_id].tolist()
if anns['name'][instance_id] in METAINFO['classes']:
empty_instance['bbox_label_3d'] = METAINFO[
'classes'].index(anns['name'][instance_id])
empty_instance['bbox_label'] = empty_instance[
'bbox_label_3d']
else:
ignore_class_name.add(anns['name'][instance_id])
empty_instance['bbox_label_3d'] = -1
empty_instance['bbox_label'] = -1
empty_instance = clear_instance_unused_keys(empty_instance)
instance_list.append(empty_instance)
temp_data_info['instances'] = instance_list
temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
converted_list.append(temp_data_info)
pkl_name = Path(pkl_path).name
out_path = osp.join(out_dir, pkl_name)
print(f'Writing to output file: {out_path}.')
print(f'ignore classes: {ignore_class_name}')
# dataset metainfo
metainfo = dict()
metainfo['categories'] = {k: i for i, k in enumerate(METAINFO['classes'])}
if ignore_class_name:
for ignore_class in ignore_class_name:
metainfo['categories'][ignore_class] = -1
metainfo['dataset'] = 'sunrgbd'
metainfo['info_version'] = '1.1'
converted_data_info = dict(metainfo=metainfo, data_list=converted_list)
mmengine.dump(converted_data_info, out_path, 'pkl')
def update_lyft_infos(pkl_path, out_dir):
print(f'{pkl_path} will be modified.')
if out_dir in pkl_path:
print(f'Warning, you may overwriting '
f'the original data {pkl_path}.')
print(f'Reading from input file: {pkl_path}.')
data_list = mmengine.load(pkl_path)
METAINFO = {
'classes':
('car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
'motorcycle', 'bicycle', 'pedestrian', 'animal'),
}
print('Start updating:')
converted_list = []
for i, ori_info_dict in enumerate(
mmengine.track_iter_progress(data_list['infos'])):
temp_data_info = get_empty_standard_data_info()
temp_data_info['sample_idx'] = i
temp_data_info['token'] = ori_info_dict['token']
temp_data_info['ego2global'] = convert_quaternion_to_matrix(
ori_info_dict['ego2global_rotation'],
ori_info_dict['ego2global_translation'])
temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict.get(
'num_features', 5)
temp_data_info['lidar_points']['lidar_path'] = Path(
ori_info_dict['lidar_path']).name
temp_data_info['lidar_points'][
'lidar2ego'] = convert_quaternion_to_matrix(
ori_info_dict['lidar2ego_rotation'],
ori_info_dict['lidar2ego_translation'])
# bc-breaking: Timestamp has divided 1e6 in pkl infos.
temp_data_info['timestamp'] = ori_info_dict['timestamp'] / 1e6
for ori_sweep in ori_info_dict['sweeps']:
temp_lidar_sweep = get_single_lidar_sweep()
temp_lidar_sweep['lidar_points'][
'lidar2ego'] = convert_quaternion_to_matrix(
ori_sweep['sensor2ego_rotation'],
ori_sweep['sensor2ego_translation'])
temp_lidar_sweep['ego2global'] = convert_quaternion_to_matrix(
ori_sweep['ego2global_rotation'],
ori_sweep['ego2global_translation'])
lidar2sensor = np.eye(4)
rot = ori_sweep['sensor2lidar_rotation']
trans = ori_sweep['sensor2lidar_translation']
lidar2sensor[:3, :3] = rot.T
lidar2sensor[:3, 3:4] = -1 * np.matmul(rot.T, trans.reshape(3, 1))
temp_lidar_sweep['lidar_points'][
'lidar2sensor'] = lidar2sensor.astype(np.float32).tolist()
# bc-breaking: Timestamp has divided 1e6 in pkl infos.
temp_lidar_sweep['timestamp'] = ori_sweep['timestamp'] / 1e6
temp_lidar_sweep['lidar_points']['lidar_path'] = ori_sweep[
'data_path']
temp_lidar_sweep['sample_data_token'] = ori_sweep[
'sample_data_token']
temp_data_info['lidar_sweeps'].append(temp_lidar_sweep)
temp_data_info['images'] = {}
for cam in ori_info_dict['cams']:
empty_img_info = get_empty_img_info()
empty_img_info['img_path'] = Path(
ori_info_dict['cams'][cam]['data_path']).name
empty_img_info['cam2img'] = ori_info_dict['cams'][cam][
'cam_intrinsic'].tolist()
empty_img_info['sample_data_token'] = ori_info_dict['cams'][cam][
'sample_data_token']
empty_img_info[
'timestamp'] = ori_info_dict['cams'][cam]['timestamp'] / 1e6
empty_img_info['cam2ego'] = convert_quaternion_to_matrix(
ori_info_dict['cams'][cam]['sensor2ego_rotation'],
ori_info_dict['cams'][cam]['sensor2ego_translation'])
lidar2sensor = np.eye(4)
rot = ori_info_dict['cams'][cam]['sensor2lidar_rotation']
trans = ori_info_dict['cams'][cam]['sensor2lidar_translation']
lidar2sensor[:3, :3] = rot.T
lidar2sensor[:3, 3:4] = -1 * np.matmul(rot.T, trans.reshape(3, 1))
empty_img_info['lidar2cam'] = lidar2sensor.astype(
np.float32).tolist()
temp_data_info['images'][cam] = empty_img_info
ignore_class_name = set()
if 'gt_boxes' in ori_info_dict:
num_instances = ori_info_dict['gt_boxes'].shape[0]
for i in range(num_instances):
empty_instance = get_empty_instance()
empty_instance['bbox_3d'] = ori_info_dict['gt_boxes'][
i, :].tolist()
if ori_info_dict['gt_names'][i] in METAINFO['classes']:
empty_instance['bbox_label'] = METAINFO['classes'].index(
ori_info_dict['gt_names'][i])
else:
ignore_class_name.add(ori_info_dict['gt_names'][i])
empty_instance['bbox_label'] = -1
empty_instance['bbox_label_3d'] = copy.deepcopy(
empty_instance['bbox_label'])
empty_instance = clear_instance_unused_keys(empty_instance)
temp_data_info['instances'].append(empty_instance)
temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
converted_list.append(temp_data_info)
pkl_name = Path(pkl_path).name
out_path = osp.join(out_dir, pkl_name)
print(f'Writing to output file: {out_path}.')
print(f'ignore classes: {ignore_class_name}')
metainfo = dict()
metainfo['categories'] = {k: i for i, k in enumerate(METAINFO['classes'])}
if ignore_class_name:
for ignore_class in ignore_class_name:
metainfo['categories'][ignore_class] = -1
metainfo['dataset'] = 'lyft'
metainfo['version'] = data_list['metadata']['version']
metainfo['info_version'] = '1.1'
converted_data_info = dict(metainfo=metainfo, data_list=converted_list)
mmengine.dump(converted_data_info, out_path, 'pkl')
def update_waymo_infos(pkl_path, out_dir):
# the input pkl is based on the
# pkl generated in the waymo cam only challenage.
camera_types = [
'CAM_FRONT',
'CAM_FRONT_LEFT',
'CAM_FRONT_RIGHT',
'CAM_SIDE_LEFT',
'CAM_SIDE_RIGHT',
]
print(f'{pkl_path} will be modified.')
if out_dir in pkl_path:
print(f'Warning, you may overwriting '
f'the original data {pkl_path}.')
time.sleep(5)
# TODO update to full label
# TODO discuss how to process 'Van', 'DontCare'
METAINFO = {
'classes': ('Car', 'Pedestrian', 'Cyclist', 'Sign'),
}
print(f'Reading from input file: {pkl_path}.')
data_list = mmengine.load(pkl_path)
print('Start updating:')
converted_list = []
for ori_info_dict in mmengine.track_iter_progress(data_list):
temp_data_info = get_empty_standard_data_info(camera_types)
if 'plane' in ori_info_dict:
temp_data_info['plane'] = ori_info_dict['plane']
temp_data_info['sample_idx'] = ori_info_dict['image']['image_idx']
# calib matrix
for cam_idx, cam_key in enumerate(camera_types):
temp_data_info['images'][cam_key]['cam2img'] =\
ori_info_dict['calib'][f'P{cam_idx}'].tolist()
for cam_idx, cam_key in enumerate(camera_types):
rect = ori_info_dict['calib']['R0_rect'].astype(np.float32)
velo_to_cam = 'Tr_velo_to_cam'
if cam_idx != 0:
velo_to_cam += str(cam_idx)
Trv2c = ori_info_dict['calib'][velo_to_cam].astype(np.float32)
lidar2cam = rect @ Trv2c
temp_data_info['images'][cam_key]['lidar2cam'] = lidar2cam.tolist()
temp_data_info['images'][cam_key]['lidar2img'] = (
ori_info_dict['calib'][f'P{cam_idx}'] @ lidar2cam).tolist()
# image path
base_img_path = Path(ori_info_dict['image']['image_path']).name
for cam_idx, cam_key in enumerate(camera_types):
temp_data_info['images'][cam_key]['timestamp'] = ori_info_dict[
'timestamp']
temp_data_info['images'][cam_key]['img_path'] = base_img_path
h, w = ori_info_dict['image']['image_shape']
# for potential usage
temp_data_info['images'][camera_types[0]]['height'] = h
temp_data_info['images'][camera_types[0]]['width'] = w
temp_data_info['lidar_points']['num_pts_feats'] = ori_info_dict[
'point_cloud']['num_features']
temp_data_info['lidar_points']['timestamp'] = ori_info_dict[
'timestamp']
velo_path = ori_info_dict['point_cloud'].get('velodyne_path')
if velo_path is not None:
temp_data_info['lidar_points']['lidar_path'] = Path(velo_path).name
# TODO discuss the usage of Tr_velo_to_cam in lidar
Trv2c = ori_info_dict['calib']['Tr_velo_to_cam'].astype(np.float32)
temp_data_info['lidar_points']['Tr_velo_to_cam'] = Trv2c.tolist()
# for potential usage
# temp_data_info['images']['R0_rect'] = ori_info_dict['calib'][
# 'R0_rect'].astype(np.float32).tolist()
# for the sweeps part:
temp_data_info['timestamp'] = ori_info_dict['timestamp']
temp_data_info['ego2global'] = ori_info_dict['pose']
for ori_sweep in ori_info_dict['sweeps']:
# lidar sweeps
lidar_sweep = get_single_lidar_sweep()
lidar_sweep['ego2global'] = ori_sweep['pose']
lidar_sweep['timestamp'] = ori_sweep['timestamp']
lidar_sweep['lidar_points']['lidar_path'] = Path(
ori_sweep['velodyne_path']).name
# image sweeps
image_sweep = get_single_image_sweep(camera_types)
image_sweep['ego2global'] = ori_sweep['pose']
image_sweep['timestamp'] = ori_sweep['timestamp']
img_path = Path(ori_sweep['image_path']).name
for cam_idx, cam_key in enumerate(camera_types):
image_sweep['images'][cam_key]['img_path'] = img_path
temp_data_info['lidar_sweeps'].append(lidar_sweep)
temp_data_info['image_sweeps'].append(image_sweep)
anns = ori_info_dict.get('annos', None)
ignore_class_name = set()
if anns is not None:
num_instances = len(anns['name'])
instance_list = []
for instance_id in range(num_instances):
empty_instance = get_empty_instance()
empty_instance['bbox'] = anns['bbox'][instance_id].tolist()
if anns['name'][instance_id] in METAINFO['classes']:
empty_instance['bbox_label'] = METAINFO['classes'].index(
anns['name'][instance_id])
else:
ignore_class_name.add(anns['name'][instance_id])
empty_instance['bbox_label'] = -1
empty_instance['bbox'] = anns['bbox'][instance_id].tolist()
loc = anns['location'][instance_id]
dims = anns['dimensions'][instance_id]
rots = anns['rotation_y'][:, None][instance_id]
gt_bboxes_3d = np.concatenate([loc, dims, rots
]).astype(np.float32).tolist()
empty_instance['bbox_3d'] = gt_bboxes_3d
empty_instance['bbox_label_3d'] = copy.deepcopy(
empty_instance['bbox_label'])