PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

Overview

PCAT点云标注工具-使用手册

  • Demo项目,请自行魔改

  • This is the open source version:

    Author: WenwenDu TEL: 18355180339 E-mail: [email protected]

  • Video tutorial:

  1. https://v.youku.com/v_show/id_XNDYxNjY4MDExMg==.html?spm=a2h0k.11417342.soresults.dtitle

  2. https://v.youku.com/v_show/id_XNDYxNjY4MDI5Mg==.html?spm=a2hzp.8244740.0.0

I. 配置使用环境及安装

  • 配置要求:ubuntu16.04 + ROS Kinetic full
  • 注意:请务必保证系统使用原生python2.7,在使用Anaconda2的情况下,请在~/.bashrc环境变量中临时关闭Anaconda2,避免冲突。(如果你长期使用ROS,强烈建议在虚拟环境下使用anaconda,避免冲突。)

1. 安装ROS-Kinetic

参考ROS WiKi-安装说明, 安装步骤如下:

/etc/apt/sources.list.d/ros-latest.list' 添加ROS源秘钥: sudo apt-key adv --keyserver hkp://ha.pool.sks-keyservers.net:80 --recv-key 421C365BD9FF1F717815A3895523BAEEB01FA116 更新源 sudo apt-get update ">
添加ROS源:
sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
添加ROS源秘钥:
sudo apt-key adv --keyserver hkp://ha.pool.sks-keyservers.net:80 --recv-key 421C365BD9FF1F717815A3895523BAEEB01FA116
更新源
sudo apt-get update
安装ROS完整版:(由于使用Rviz,PCL等模块,请务必安装完整版)
sudo apt-get install ros-kinetic-desktop-full
sudo apt-cache search ros-kinetic
初始化ROS:
sudo rosdep init
rosdep update
> ~/.bashrc source ~/.bashrc 更新ROS环境变量 source /opt/ros/kinetic/setup.bash ">
添加环境变量
echo "source /opt/ros/kinetic/setup.bash" >> ~/.bashrc
source ~/.bashrc
更新ROS环境变量
source /opt/ros/kinetic/setup.bash
测试ROS是否成功安装:
开启一个新的Teminnal,输入:
roscore
测试Rviz
开启一个新的Teminnal,输入:
rviz

成功显示rviz界面如下: 图片

2. 安装PCAT标注工具

(1) 进入文件夹PCAT
(2) 开启终端,运行安装命令: sh install.sh
(3) 显示 install successful 后,home文件夹下出现lidar_annotation文件夹,安装成功

II. 导入pcd文件

  1. 导入待标注点云pcd文件
Copy 待标注的点云.pcd格式文件到 lidar_annotation/pcd/ 文件夹下

注意:标注工具默认支持激光雷达pcd格式点云,Field为[x,y,z,intensity],如果使用XYZRGB等其他pcd format,请在src/rviz_cloud_annotation/launch/annotation.launch中更改pcd_type参数的value.

常见issue

[1] 如何支持其他类型pcd或其他3Dpoints? 修改以下code...
// src/rviz_cloud_annotation/src/rviz_cloud_annotation_class.cpp
void RVizCloudAnnotation::LoadCloud(const std::string &filename,
                                    const std::string &normal_source,
                                    PointXYZRGBNormalCloud &cloud);

  1. 开始标注
打开 Teminnal, 运行: sh run.sh

显示标注界面如下: 图片


III. 标注手册正篇

首次使用请务必仔细阅读

1. 标注面板详解

下面就上图中 A, B, C, D, E 5个模块做详细说明:

  • A. 标注菜单栏
标注菜单栏由 [文件], [编辑],[视图],[标记],[选择] 5部分组成
文件:(1)切换新文件,(2)清除当前帧标记,(3)保存
编辑:(1)取消,(2)恢复
视图:(1)增加点的尺寸,(2)减小点的尺寸,(3)重置点的尺寸
标记:(1)清除当前物体的标记,(2)切换颜色,(3)设置障碍物BBox遮挡系数,(4)调节障碍物BBox方位,(5)调节障碍物BBox尺寸
选择:(1)跳转至下一物体,(2)跳转至上一物体
特别说明:
1.切换新文件会自动保存当前文件的标注信息
2.取消/恢复开销较大,尽量避免使用
3.标记完成一个物体后,需要切换到下一个物体进行标注,否则会覆盖当前标记;选择新的颜色会自动切换到下一物体;物体ID显示在面板上
4.标记障碍物时,颜色 1~5,6~10,11~15,16~20 分别对应标签: 小车,大车,行人,骑行;
5.标记障碍物时,需要设置方位角和遮挡系数,请以实际为准标注,0--不遮挡,1--完全遮挡
尽量使用简洁的方式完成标注,熟练使用快捷键可以有效提高标注速度。

图片 特别说明 1.点云被重复标记为 障碍物,路沿,车道线,地面时,标签优先级为 (障碍物 > 路沿/车道线 > 地面)

2.标注步骤

在看标注说明之前请务必观看视频教程

  • 标注请按照: 【障碍物--> 路沿-->车道线-->地面】 的顺序。
(1) 障碍物
障碍物包括 小车(轿车),大车(卡车、有轨电车等),行人,骑行(电动车)4类。
在该数据集中主要包含 小车和行人,及少量的大车和骑行。请在标注`颜色面板`选择不同的按钮,对应不同的障碍物。
颜色面板分为4大块,颜色 1~5,6~10,11~15,16~20 分别对应: 小车,大车,行人,骑行,代表不同的障碍物。
对每一帧的点云,障碍物存在则标注,不存在则不标注;每标注完一个障碍物,需要==切换至下一个障碍物进行新的标注。
(比如:标完第一辆小车,需要按`Shitf+N` 切换至下一小车,或者按`Shift+P`切换至上一障碍物进行修改)。
选择新的颜色会自动切换至新的下一障碍物。
每个障碍物,需要标注人员自己判断大致的朝向,并进行方位调节(R、F键)。
受到遮挡的障碍物请设置`遮挡系数`,默认为 0,即不遮挡,大多数障碍物不存在遮挡。

图片

(2)  路沿
 路沿指道路中地面的边界,如上图显示;标记路沿只能使用点选的方式标注(具体操作可以参考标注视频教程)
 一般一帧点云中有多条路沿,每标记一条,需要切换至下一路沿进行标注,切换方式与障碍物切换相同。
(3)  车道线
 车道线指道路中颜色明显突出的线段,一般出现的频率比较低,没有出现或者看不清楚则不用标注;车道线的标注方式与路沿完全相同。
(4)  地面
 地面是一帧点云中比较关键的部分,一般选择使用多边形进行选择标注,边界为之前标注的路沿。
 地面可以分多次标注,拼接生成;如果一次选点过多,地面生成时间会较长。
 *在2.4.0版本之后,标注工具增加了地面辅助标记功能:用户每次选择`地面(F2)`按钮时,系统会自动生成95%的地面,用户在此基础上进行细节修改,
 得到最终的地面标注。

3.标注结果

Result路径说明

图片

3D框label

图片


IV、注意事项

1. 标注工具使用过程中如果遇见问题,或者代码部分有疑问,编辑需要,联系 @杜文文(18355180339 / [email protected])
2. 视频教程:
   A`https://v.youku.com/v_show/id_XNDYxNjY4MDExMg==.html?spm=a2h0k.11417342.soresults.dtitle`
   B`https://v.youku.com/v_show/id_XNDYxNjY4MDI5Mg==.html?spm=a2hzp.8244740.0.0`

V、版权说明

  1. 软件版权 本标注工具的版权归WenwenDu所有
  2. 其他版权 本标注工具在 RIMLab 开源标注工具 rviz_cloud_annotation 上改进完成: https://github.com/RMonica/rviz_cloud_annotation
原始版权说明:
Original Copyright:
/*
 * Copyright (c) 2016-2017, Riccardo Monica
 *   RIMLab, Department of Engineering and Architecture
 *   University of Parma, Italy
 *   http://www.rimlab.ce.unipr.it/
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice,
 *    this list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 *    contributors may be used to endorse or promote products derived from this
 *    software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 * POSSIBILITY OF SUCH DAMAGE.
 */
Owner
halo
USTC 中国科学技术大学 Email: [email protected]
halo
Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

BRIMs Bidirectional Recurrent Independent Mechanisms Implementation of the paper Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neura

Sarthak Mittal 26 May 26, 2022
Node for thenewboston digital currency network.

Project setup For project setup see INSTALL.rst Community Join the community to stay updated on the most recent developments, project roadmaps, and ra

thenewboston 27 Jul 08, 2022
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation This is the implementation of RATE: Overcoming Noise and Spar

Yu Zhang 5 Feb 10, 2022
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

BUPT GAMMA Lab 519 Jan 02, 2023
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 917 Jan 03, 2023
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection.

Code for the paper: How Effective is Incongruity? Implications for Code-mix Sarcasm Detection - ICON ACL 2021

2 Jun 05, 2022
An efficient framework for reinforcement learning.

rl: An efficient framework for reinforcement learning Requirements Introduction PPO Test Requirements name version Python =3.7 numpy =1.19 torch =1

16 Nov 30, 2022
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Status: Under development (expect bug fixes and huge updates) ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectiv

37 Dec 28, 2022
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh

Akshita Gupta 127 Dec 27, 2022
custom pytorch implementation of MoCo v3

MoCov3-pytorch custom implementation of MoCov3 [arxiv]. I made minor modifications based on the official MoCo repository [github]. No ViT part code an

39 Nov 14, 2022
Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

MOT Tracked object bounding box association (CenterTrack++) New association method based on CenterTrack. Two new branches (Tracked Size and IOU) are a

36 Oct 04, 2022
Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

Variance-Aware-MT-Test-Sets Variance-Aware Machine Translation Test Sets License See LICENSE. We follow the data licensing plan as the same as the WMT

NLP2CT Lab, University of Macau 5 Dec 21, 2021
It's final year project of Diploma Engineering. This project is based on Computer Vision.

Face-Recognition-Based-Attendance-System It's final year project of Diploma Engineering. This project is based on Computer Vision. Brief idea about ou

Neel 10 Nov 02, 2022
A Python library created to assist programmers with complex mathematical functions

libmaths libmaths was created not only as a learning experience for me, but as a way to make mathematical models in seconds for Python users using mat

Simple 73 Oct 02, 2022
A map update dataset and benchmark

MUNO21 MUNO21 is a dataset and benchmark for machine learning methods that automatically update and maintain digital street map datasets. Previous dat

16 Nov 30, 2022
Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code

Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code.

Yasunori Shimura 7 Jul 27, 2022
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

MeTAL - Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral) Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaes

Sungyong Baik 44 Dec 29, 2022
Python package provinding tools for artistic interactive applications using AI

Documentation redrawing Python package provinding tools for artistic interactive applications using AI Created by ReDrawing Campinas team for the Open

ReDrawing Campinas 1 Sep 30, 2021
List of awesome things around semantic segmentation 🎉

Awesome Semantic Segmentation List of awesome things around semantic segmentation 🎉 Semantic segmentation is a computer vision task in which we label

Dam Minh Tien 18 Nov 26, 2022
Tensor-based approaches for fMRI classification

tensor-fmri Using tensor-based approaches to classify fMRI data from StarPLUS. Citation If you use any code in this repository, please cite the follow

4 Sep 07, 2022