南京最好的网站设计公司,网站建设报告论文,一键生成短网址,找个公司做网站需要注意什么条件文章目录 参数设置align_dynamic_thing：为了将动态物体的点云数据从上一帧对齐到当前帧流程旋转函数平移公式filter_points_in_ego：筛选出属于特定实例的点get_intermediate_frame_info： 函数用于获取中间帧的信息，包括点云数据、传感器校准信息、自车姿态、边界框及其对应… 文章目录 参数设置align_dynamic_thing：为了将动态物体的点云数据从上一帧对齐到当前帧流程 旋转函数平移公式filter_points_in_ego：筛选出属于特定实例的点get_intermediate_frame_info： 函数用于获取中间帧的信息，包括点云数据、传感器校准信息、自车姿态、边界框及其对应的实例标识等intermediate_keyframe_align 函数用于将前一帧的点云数据对齐到当前帧的自车坐标系中，并返回对齐后的点云数据和标签。prev2ego 函数用于将前一帧的点云数据转换到当前帧的自车坐标系中。该函数考虑了旋转和平移，并可选地应用速度和时间差来进行额外的位移校正。nonkeykeyframe_align 函数用于将非关键帧的点云数据对齐到当前帧的自车坐标系中将前一帧的点云数据对齐到当前帧的自车坐标系中为未标记的中间点云数据搜索标签 必要的包 from nuscenes.nuscenes import NuScenes
from pyquaternion import Quaternion
from nuscenes.utils.data_classes import LidarPointCloud
import numpy as np
from open3d import *
from nuscenes.utils.data_io import load_bin_file
from nuscenes.utils.geometry_utils import points_in_box
import os.path as osp
from functools import partial
from utils.points_process import *
from sklearn.neighbors import KDTree
import open3d as o3d
import argparse初始化全局字典，用于存储中间静态点、姿态和标签
INTER_STATIC_POINTS = {}
INTER_STATIC_POSE = {}
INTER_STATIC_LABEL = {}参数设置
dataroot: 数据集的根路径，类型为字符串，默认值为 ‘./project/data/nuscenes/’。
save_path: 保存路径，类型为字符串，默认值为 ‘./project/data/nuscenes//occupancy2/’，该参数是可选的。num_sweeps: 每个示例的激光雷达扫描次数，类型为整数，默认值为 10，该参数是可选的。def parse_args():parser = argparse.ArgumentParser(description='Data converter arg parser')parser.add_argument('--dataroot',type=str,default='./project/data/nuscenes/',help='specify the root path of dataset')parser.add_argument('--save_path',type=str,default='./project/data/nuscenes//occupancy2/',required=False,help='specify sweeps of lidar per example')parser.add_argument('--num_sweeps',type=int,default=10,required=False,help='specify sweeps of lidar per example')args = parser.parse_args()return argsalign_dynamic_thing：为了将动态物体的点云数据从上一帧对齐到当前帧
def align_dynamic_thing(box, prev_instance_token, nusc, prev_points, ego_frame_info):if prev_instance_token not in ego_frame_info['instance_tokens']:box_mask = points_in_box(box,prev_points[:3, :])return np.zeros((prev_points.shape[0], 0)), np.zeros((0, )), box_maskbox_mask = points_in_box(box,prev_points[:3, :])box_points = prev_points[:, box_mask].copy()prev_bbox_center = box.centerprev_rotate_matrix = box.rotation_matrixbox_points = rotate(box_points, np.linalg.inv(prev_rotate_matrix), center=prev_bbox_center)target = ego_frame_info['instance_tokens'].index(prev_instance_token)ego_boxes_center = ego_frame_info['boxes'][target].centerbox_points = translate(box_points, ego_boxes_center-prev_bbox_center)box_points = rotate(box_points, ego_frame_info['boxes'][target].rotation_matrix, center=ego_boxes_center)box_points_mask = filter_points_in_ego(box_points, ego_frame_info, prev_instance_token)box_points = box_points[:, box_points_mask]box_label = np.full_like(box_points[0], nusc.lidarseg_name2idx_mapping[box.name]).copy()return box_points, box_label, box_mask
流程 检查实例标识: if prev_instance_token ∉ ego_frame_info[‘instance_tokens’]: box_mask = points_in_box(box, prev_points[:3, :]) return (0, 0, box_mask) 计算边界框内的点: box_mask = points_in_box(box, prev_points[:3, :]) box_points = prev_points[:, box_mask] 获取上一帧边界框的中心和旋转矩阵: C_prev = box.center R_prev = box.rotation_matrix 将点旋转到原点并平移到当前帧的中心: box_points = R_prev^-1 * (box_points - C_prev) 获取目标边界框的中心和旋转矩阵: target = ego_frame_info[‘instance_tokens’].index(prev_instance_token) C_ego = ego_frame_info[‘boxes’][target].center R_ego = ego_frame_info[‘boxes’][target].rotation_matrix 平移到当前帧的中心并再次旋转: box_points = box_points + (C_ego - C_prev) box_points = R_ego * box_points 过滤当前帧边界框内的点: box_points_mask = points_in_box(ego_frame_info[‘boxes’][target], box_points[:3, :]) box_points = box_points[:, box_points_mask] 生成点云数据的标签: box_label = full_like(box_points[0], nusc.lidarseg_name2idx_mapping[box.name]) 返回结果: return (box_points, box_label, box_mask) numpy.full_like()是根据现有数组的形状和数据类型来创建新数组，而numpy.full()则需要手动指定形状和数据类型。
旋转函数
def rotate(points, rot_matrix: np.ndarray, center=None) - np.array:"""Applies a rotation.:param rot_matrix: np.float: 3, 3. Rotation matrix."""if center is not None:points[:3, :] = np.dot(rot_matrix, points[:3, :]-center[:, None]) + center[:, None]else:points[:3, :] = np.dot(rot_matrix, points[:3, :])return points 平移公式
def translate(points, x: np.ndarray) - np.array:"""Applies a translation to the point cloud.:param x: np.float: 3, 1. Translation in x, y, z."""for i in range
