mmdet3D中文表明 - ToB企服应用市场:ToB评测及商务社交产业平台

# 版权声明: 本代码版权所有 (c) OpenMMLab。
import os
from collections import OrderedDict
from os import path as osp
from typing import List, Tuple, Union
import mmcv
import numpy as np
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.geometry_utils import view_points
from pyquaternion import Quaternion
from shapely.geometry import MultiPoint, box
from mmdet3d.core.bbox import points_cam2img
from mmdet3d.datasets import NuScenesDataset
# 定义nuScenes数据集的类别和属性
nus_categories = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
'barrier')
nus_attributes = ('cycle.with_rider', 'cycle.without_rider',
'pedestrian.moving', 'pedestrian.standing',
'pedestrian.sitting_lying_down', 'vehicle.moving',
'vehicle.parked', 'vehicle.stopped', 'None')
def create_nuscenes_infos(root_path,
info_prefix,
version='v1.0-trainval',
max_sweeps=10):
"""
创建nuScenes数据集的信息文件。
给定原始数据，生成相关的信息文件，并以pkl格式保存。
参数:
root_path (str): 数据根目录路径。
info_prefix (str): 要生成的信息文件的前缀。
version (str, 可选): 数据版本，默认为'v1.0-trainval'。
max_sweeps (int, 可选): 最大sweeps数量，默认为10。
"""
from nuscenes.nuscenes import NuScenes
nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
from nuscenes.utils import splits
available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini']
assert version in available_vers
if version == 'v1.0-trainval':
train_scenes = splits.train
val_scenes = splits.val
elif version == 'v1.0-test':
train_scenes = splits.test
val_scenes = []
elif version == 'v1.0-mini':
train_scenes = splits.mini_train
val_scenes = splits.mini_val
else:
raise ValueError('未知版本')
# 过滤现有场景
available_scenes = get_available_scenes(nusc)
available_scene_names = [s['name'] for s in available_scenes]
train_scenes = list(
filter(lambda x: x in available_scene_names, train_scenes))
val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes))
train_scenes = set([
available_scenes[available_scene_names.index(s)]['token']
for s in train_scenes
])
val_scenes = set([
available_scenes[available_scene_names.index(s)]['token']
for s in val_scenes
])
test = 'test' in version
if test:
print('测试场景数量: {}'.format(len(train_scenes)))
else:
print('训练场景数量: {}，验证场景数量: {}'.format(
len(train_scenes), len(val_scenes)))
train_nusc_infos, val_nusc_infos = _fill_trainval_infos(
nusc, train_scenes, val_scenes, test, max_sweeps=max_sweeps)
metadata = dict(version=version)
if test:
print('测试样本数量: {}'.format(len(train_nusc_infos)))
data = dict(infos=train_nusc_infos, metadata=metadata)
info_path = osp.join(root_path,
'{}_infos_test.pkl'.format(info_prefix))
mmcv.dump(data, info_path)
else:
print('训练样本数量: {}，验证样本数量: {}'.format(
len(train_nusc_infos), len(val_nusc_infos)))
data = dict(infos=train_nusc_infos, metadata=metadata)
info_path = osp.join(root_path,
'{}_infos_train.pkl'.format(info_prefix))
mmcv.dump(data, info_path)
data['infos'] = val_nusc_infos
info_val_path = osp.join(root_path,
'{}_infos_val.pkl'.format(info_prefix))
mmcv.dump(data, info_val_path)
def get_available_scenes(nusc):
"""
从输入的nuScenes类中获取可用场景。
给定原始数据，获取可用场景的信息以便生成进一步的信息。
参数:
nusc (class): nuScenes数据集的类。
返回:
available_scenes (list[dict]): 可用场景的基本信息列表。
"""
available_scenes = []
print('总场景数量: {}'.format(len(nusc.scene)))
for scene in nusc.scene:
scene_token = scene['token']
scene_rec = nusc.get('scene', scene_token)
sample_rec = nusc.get('sample', scene_rec['first_sample_token'])
sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
has_more_frames = True
scene_not_exist = False
while has_more_frames:
lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token'])
lidar_path = str(lidar_path)
if os.getcwd() in lidar_path:
# 从lyftdataset获取的路径是绝对路径
lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1]
# 相对路径
if not mmcv.is_filepath(lidar_path):
scene_not_exist = True
break
else:
break
if scene_not_exist:
continue
available_scenes.append(scene)
print('存在的场景数量: {}'.format(len(available_scenes)))
return available_scenes
def _fill_trainval_infos(nusc,
train_scenes,
val_scenes,
test=False,
max_sweeps=10):
"""
从原始数据生成训练/验证信息。
参数:
nusc (:obj:`NuScenes`): nuScenes数据集类。
train_scenes (list[str]): 训练场景的基本信息。
val_scenes (list[str]): 验证场景的基本信息。
test (bool, 可选): 是否使用测试模式。在测试模式中，不能访问注释。默认为False。
max_sweeps (int, 可选): 最大sweeps数量。默认为10。
返回:
tuple[list[dict]]: 将保存到信息文件的训练集和验证集信息。
"""
train_nusc_infos = []
val_nusc_infos = []
for sample in mmcv.track_iter_progress(nusc.sample):
lidar_token = sample['data']['LIDAR_TOP']
sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
cs_record = nusc.get('calibrated_sensor',
sd_rec['calibrated_sensor_token'])
pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
lidar_path, boxes, _ = nusc.get_sample_data(lidar_token)
mmcv.check_file_exist(lidar_path)
info = {
'lidar_path': lidar_path,
'token': sample['token'],
'sweeps': [],
'cams': dict(),
'lidar2ego_translation': cs_record['translation'],
'lidar2ego_rotation': cs_record['rotation'],
'ego2global_translation': pose_record['translation'],
'ego2global_rotation': pose_record['rotation'],
'timestamp': sample['timestamp'],
}
l2e_r = info['lidar2ego_rotation']
l2e_t = info['lidar2ego_translation']
e2g_r = info['ego2global_rotation']
e2g_t = info['ego2global_translation']
l2e_r_mat = Quaternion(l2e_r).rotation_matrix
e2g_r_mat = Quaternion(e2g_r).rotation_matrix
# 获取每帧的6个图像的信息
camera_types = [
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_FRONT_LEFT',
'CAM_BACK',
'CAM_BACK_LEFT',
'CAM_BACK_RIGHT',
]
for cam in camera_types:
cam_token = sample['data'][cam]
cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token)
cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat,
e2g_t, e2g_r_mat, cam)
cam_info.update(cam_intrinsic=cam_intrinsic)
info['cams'].update({cam: cam_info})
# 获取单个关键帧的sweeps
sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
sweeps = []
while len(sweeps) < max_sweeps:
if not sd_rec['prev'] == '':
sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t,
l2e_r_mat, e2g_t, e2g_r_mat, 'lidar')
sweeps.append(sweep)
sd_rec = nusc.get('sample_data
', sd_rec['prev'])
else:
break
info['sweeps'] = sweeps
# 获取注释
if not test:
annotations = [
nusc.get('sample_annotation', token)
for token in sample['anns']
]
locs = np.array([b.center for b in boxes]).reshape(-1, 3)
dims = np.array([b.wlh for b in boxes]).reshape(-1, 3)
rots = np.array([b.orientation.yaw_pitch_roll[0]
for b in boxes]).reshape(-1, 1)
velocity = np.array(
[nusc.box_velocity(token)[:2] for token in sample['anns']])
valid_flag = np.array(
[(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0
for anno in annotations],
dtype=bool).reshape(-1)
# 将速度从全局转换为激光雷达
for i in range(len(boxes)):
velo = np.array([*velocity[i], 0.0])
velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(
l2e_r_mat).T
velocity[i] = velo[:2]
names = [b.name for b in boxes]
for i in range(len(names)):
if names[i] in NuScenesDataset.NameMapping:
names[i] = NuScenesDataset.NameMapping[names[i]]
names = np.array(names)
# 我们需要将box尺寸转换为
# 我们激光雷达坐标系的格式
# 即x_size, y_size, z_size（对应于l, w, h）
gt_boxes = np.concatenate([locs, dims[:, [1, 0, 2]], rots], axis=1)
assert len(gt_boxes) == len(
annotations), f'{len(gt_boxes)}, {len(annotations)}'
info['gt_boxes'] = gt_boxes
info['gt_names'] = names
info['gt_velocity'] = velocity.reshape(-1, 2)
info['num_lidar_pts'] = np.array(
[a['num_lidar_pts'] for a in annotations])
info['num_radar_pts'] = np.array(
[a['num_radar_pts'] for a in annotations])
info['valid_flag'] = valid_flag
if sample['scene_token'] in train_scenes:
train_nusc_infos.append(info)
else:
val_nusc_infos.append(info)
return train_nusc_infos, val_nusc_infos
def obtain_sensor2top(nusc,
sensor_token,
l2e_t,
l2e_r_mat,
e2g_t,
e2g_r_mat,
sensor_type='lidar'):
"""
获取从一般传感器到顶部激光雷达的RT矩阵信息。
参数:
nusc (class): nuScenes数据集类。
sensor_token (str): 与特定传感器类型对应的样本数据token。
l2e_t (np.ndarray): 从激光雷达到ego的平移向量，形状为(1, 3)。
l2e_r_mat (np.ndarray): 从激光雷达到ego的旋转矩阵，形状为(3, 3)。
e2g_t (np.ndarray): 从ego到全局的平移向量，形状为(1, 3)。
e2g_r_mat (np.ndarray): 从ego到全局的旋转矩阵，形状为(3, 3)。
sensor_type (str, 可选): 要校准的传感器类型。默认为'lidar'。
返回:
sweep (dict): 经过转换后的sweep信息。
"""
sd_rec = nusc.get('sample_data', sensor_token)
cs_record = nusc.get('calibrated_sensor',
sd_rec['calibrated_sensor_token'])
pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
data_path = str(nusc.get_sample_data_path(sd_rec['token']))
if os.getcwd() in data_path: # 从lyftdataset获取的路径是绝对路径
data_path = data_path.split(f'{os.getcwd()}/')[-1] # 相对路径
sweep = {
'data_path': data_path,
'type': sensor_type,
'sample_data_token': sd_rec['token'],
'sensor2ego_translation': cs_record['translation'],
'sensor2ego_rotation': cs_record['rotation'],
'ego2global_translation': pose_record['translation'],
'ego2global_rotation': pose_record['rotation'],
'timestamp': sd_rec['timestamp']
}
l2e_r_s = sweep['sensor2ego_rotation']
l2e_t_s = sweep['sensor2ego_translation']
e2g_r_s = sweep['ego2global_rotation']
e2g_t_s = sweep['ego2global_translation']
# 获取从传感器到顶部激光雷达的RT
# sweep->ego->global->ego'->lidar
l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
) + l2e_t @ np.linalg.inv(l2e_r_mat).T
sweep['sensor2lidar_rotation'] = R.T # points @ R.T + T
sweep['sensor2lidar_translation'] = T
return sweep
def export_2d_annotation(root_path, info_path, version, mono3d=True):
"""
从信息文件和原始数据导出2D注释。
参数:
root_path (str): 原始数据的根路径。
info_path (str): 信息文件的路径。
version (str): 数据集版本。
mono3d (bool, 可选): 是否导出mono3d注释。默认为True。
"""
# 获取相机的bbox注释
camera_types = [
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_FRONT_LEFT',
'CAM_BACK',
'CAM_BACK_LEFT',
'CAM_BACK_RIGHT',
]
nusc_infos = mmcv.load(info_path)['infos']
nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
# info_2d_list = []
cat2Ids = [
dict(id=nus_categories.index(cat_name), name=cat_name)
for cat_name in nus_categories
]
coco_ann_id = 0
coco_2d_dict = dict(annotations=[], images=[], categories=cat2Ids)
for info in mmcv.track_iter_progress(nusc_infos):
for cam in camera_types:
cam_info = info['cams'][cam]
coco_infos = get_2d_boxes(
nusc,
cam_info['sample_data_token'],
visibilities=['', '1', '2', '3', '4'],
mono3d=mono3d)
(height, width, _) = mmcv.imread(cam_info['data_path']).shape
coco_2d_dict['images'].append(
dict(
file_name=cam_info['data_path'].split('data/nuscenes/')
[-1],
id=cam_info['sample_data_token'],
token=info['token'],
cam2ego_rotation=cam_info['sensor2ego_rotation'],
cam2ego_translation=cam_info['sensor2ego_translation'],
ego2global_rotation=info['ego2global_rotation'],
ego2global_translation=info['ego2global_translation'],
cam_intrinsic=cam_info['cam_intrinsic'],
width=width,
height=height))
for coco_info in coco_infos:
if coco_info is None:
continue
# 添加一个空键用于coco格式
coco_info['segmentation'] = []
coco_info['id'] = coco_ann_id
coco_2d_dict['annotations'].append(coco_info)
coco_ann_id += 1
if mono3d:
json_prefix = f'{info_path[:-4]}_mono3d'
else:
json_prefix = f'{info_path[:-4]}'
mmcv.dump(coco_2d_dict, f'{json_prefix}.coco.json')
def get_2d_boxes(nusc,
sample_data_token: str,
visibilities: List[str],
mono3d=True):
"""
获取给定sample_data_token的2D注释记录。
参数:
sample_data_token (str): 属于相机关键帧的样本数据token。
visibilities (list[str]): 可见性过滤器。
mono3d (bool): 是否获取带有mono3d注释的box。
返回:
list[dict]: 属于输入
sample_data_token的2D注释记录列表。
"""
# 获取样本数据和与该样本数据对应的样本。
sd_rec = nusc.get('sample_data', sample_data_token)
assert sd_rec[
'sensor_modality'] == 'camera', '错误: get_2d_boxes仅适用于相机样本数据!'
if not sd_rec['is_key_frame']:
raise ValueError(
'2D重新投影仅适用于关键帧。')
s_rec = nusc.get('sample', sd_rec['sample_token'])
# 获取校准传感器和自我姿态记录，以获取转换矩阵。
cs_rec = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token'])
pose_rec = nusc.get('ego_pose', sd_rec['ego_pose_token'])
camera_intrinsic = np.array(cs_rec['camera_intrinsic'])
# 获取所有具有指定可见性的注释。
ann_recs = [
nusc.get('sample_annotation', token) for token in s_rec['anns']
]
ann_recs = [
ann_rec for ann_rec in ann_recs
if (ann_rec['visibility_token'] in visibilities)
]
repro_recs = []
for ann_rec in ann_recs:
# 增加sample_annotation的token信息。
ann_rec['sample_annotation_token'] = ann_rec['token']
ann_rec['sample_data_token'] = sample_data_token
# 获取全局坐标中的box。
box = nusc.get_box(ann_rec['token'])
# 将它们移动到自我姿态框架。
box.translate(-np.array(pose_rec['translation']))
box.rotate(Quaternion(pose_rec['rotation']).inverse)
# 将它们移动到校准传感器框架。
box.translate(-np.array(cs_rec['translation']))
box.rotate(Quaternion(cs_rec['rotation']).inverse)
# 过滤掉不在校准传感器前面的角。
corners_3d = box.corners()
in_front = np.argwhere(corners_3d[2, :] > 0).flatten()
corners_3d = corners_3d[:, in_front]
# 将3D box投影到2D。
corner_coords = view_points(corners_3d, camera_intrinsic,
True).T[:, :2].tolist()
# 仅保留落在图像内的角。
final_coords = post_process_coords(corner_coords)
# 如果重新投影的角的凸包不与图像画布相交，则跳过。
if final_coords is None:
continue
else:
min_x, min_y, max_x, max_y = final_coords
# 生成要包含在.json文件中的字典记录。
repro_rec = generate_record(ann_rec, min_x, min_y, max_x, max_y,
sample_data_token, sd_rec['filename'])
# 如果mono3d=True，则在相机坐标中添加3D注释
if mono3d and (repro_rec is not None):
loc = box.center.tolist()
dim = box.wlh
dim[[0, 1, 2]] = dim[[1, 2, 0]] # 将wlh转换为我们的lhw
dim = dim.tolist()
rot = box.orientation.yaw_pitch_roll[0]
rot = [-rot] # 将旋转转换为我们的相机坐标
global_velo2d = nusc.box_velocity(box.token)[:2]
global_velo3d = np.array([*global_velo2d, 0.0])
e2g_r_mat = Quaternion(pose_rec['rotation']).rotation_matrix
c2e_r_mat = Quaternion(cs_rec['rotation']).rotation_matrix
cam_velo3d = global_velo3d @ np.linalg.inv(
e2g_r_mat).T @ np.linalg.inv(c2e_r_mat).T
velo = cam_velo3d[0::2].tolist()
repro_rec['bbox_cam3d'] = loc + dim + rot
repro_rec['velo_cam3d'] = velo
center3d = np.array(loc).reshape([1, 3])
center2d = points_cam2img(
center3d, camera_intrinsic, with_depth=True)
repro_rec['center2d'] = center2d.squeeze().tolist()
# 标准化center2D + 深度
# 如果深度小于0的样本将被移除
if repro_rec['center2d'][2] <= 0:
continue
ann_token = nusc.get('sample_annotation',
box.token)['attribute_tokens']
if len(ann_token) == 0:
attr_name = 'None'
else:
attr_name = nusc.get('attribute', ann_token[0])['name']
attr_id = nus_attributes.index(attr_name)
repro_rec['attribute_name'] = attr_name
repro_rec['attribute_id'] = attr_id
repro_recs.append(repro_rec)
return repro_recs
def post_process_coords(
corner_coords: List, imsize: Tuple[int, int] = (1600, 900)
) -> Union[Tuple[float, float, float, float], None]:
"""
获取重新投影的bbox角的凸包和图像画布的交集，如果没有交集则返回None。
参数:
corner_coords (list[int]): 重新投影的bbox角的坐标。
imsize (tuple[int]): 图像画布的尺寸。
返回:
tuple[float]: 2D box角的凸包和图像画布的交集。
"""
polygon_from_2d_box = MultiPoint(corner_coords).convex_hull
img_canvas = box(0, 0, imsize[0], imsize[1])
if polygon_from_2d_box.intersects(img_canvas):
img_intersection = polygon_from_2d_box.intersection(img_canvas)
intersection_coords = np.array(
[coord for coord in img_intersection.exterior.coords])
min_x = min(intersection_coords[:, 0])
min_y = min(intersection_coords[:, 1])
max_x = max(intersection_coords[:, 0])
max_y = max(intersection_coords[:, 1])
return min_x, min_y, max_x, max_y
else:
return None
def generate_record(ann_rec: dict, x1: float, y1: float, x2: float, y2: float,
sample_data_token: str, filename: str) -> OrderedDict:
"""
给定各种信息和2D边界框坐标，生成一个2D注释记录。
参数:
ann_rec (dict): 原始3d注释记录。
x1 (float): x坐标的最小值。
y1 (float): y坐标的最小值。
x2 (float): x坐标的最大值。
y2 (float): y坐标的最大值。
sample_data_token (str): 样本数据token。
filename (str): 注释所在的对应图像文件。
返回:
dict: 一个2D注释记录。
- file_name (str): 文件名
- image_id (str): 样本数据token
- area (float): 2d box的面积
- category_name (str): 类别名称
- category_id (int): 类别id
- bbox (list[float]): 2d box的左x, 顶y, dx, dy
- iscrowd (int): 区域是否是拥挤的
"""
repro_rec = OrderedDict()
repro_rec['sample_data_token'] = sample_data_token
coco_rec = dict()
relevant_keys = [
'attribute_tokens',
'category_name',
'instance_token',
'next',
'num_lidar_pts',
'num_radar_pts',
'prev',
'sample_annotation_token',
'sample_data_token',
'visibility_token',
]
for key, value in ann_rec.items():
if key in relevant_keys:
repro_rec[key] = value
repro_rec['bbox_corners'] = [x1, y1, x2, y2]
repro_rec['filename'] = filename
coco_rec['file_name'] = filename
coco_rec['image_id'] = sample_data_token
coco_rec['area'] = (y2 - y1) * (x2 - x1)
if repro_rec['category_name'] not in NuScenesDataset.NameMapping:
return None
cat_name = NuScenesDataset.NameMapping[repro_rec['category_name']]
coco_rec['category_name'] = cat_name
coco_rec['category_id'] = nus_categories.index(cat_name)
coco_rec['bbox'] = [x1, y1, x2 - x1, y2 - y1]
coco_rec['iscrowd'] = 0
return coco_rec

复制代码