Skip to content

Latest commit

 

History

History
84 lines (68 loc) · 2.38 KB

annotation.md

File metadata and controls

84 lines (68 loc) · 2.38 KB
Raw

K-Lane Annotation Tool

This is the documentation on how to use the annotation tool provided by K-Lane

Requirements

  1. Install ROS as directed by their site (we use ROS-Melodic)
  2. Create a new conda environment
conda create -n #env-name python=3.7
  1. Install the dependencies
pip install PyQt5 pyaml rospkg numpy tensorflow==1.15 opencv-python-headless
  • note: installing opencv-python with PyQt5 may result in xcb error

Workspace Arrangement

KLaneDet
├── annot_tool
      ├── build
      ├── devel
      ├── frontal_image
      ├── point_cloud
      ├── src
          ├── gui_qt
                ├── temp
                      ├── seq_1
                            ├── bev_image
                            ├── bev_image_label
                            ├── bev_tensor
                            ├── bev_tensor_label
          ├── lidar_msgs
          ├── pc_pre_processor
          
├── baseline 
├── configs
├── data
├── logs

Labelling a Point Cloud

Before starting the labelling process, make sure that:

  • A rosbag file containing recordings of both camera image and LiDAR point cloud is available
  • The /frontal_image/, /point_cloud/, /src/gui_qt/temp/, and their children directories have been created
  1. Go to the /annot_tool/ directory and setup the project by running
catkin_make

we need to run this line every time a change is introduced to the .cpp files

  1. Source the project's setup.bash, run the roscore, and play your rosbag file
source devel/setup.bash
roscore
rosbag play #path_to_your_rosbag_file
  1. Start the pointcloud preprocessor node
rosrun pc_pre_processor pc_pre_processor_node
  1. Start the annotation tool GUI
python /src/gui_qt/mainframe.py

The initial GUI will look like the image below

image

  1. Initialize the annotation tool and start annotating
    • Make sure to initialize first for every new point cloud frame
    • Tips: increasing the brightness of the display will help the annotation process significantly

image

  1. Do the post-processing and save the annotation
    • The annotations and processed files can be found either on /frontal_image/, /point_cloud/, or /src/gui_qt/temp/ under the sequence directory

image