OpenCV and ROS

The purpose of this lab is to gain experience incorporating vision code into ROS nodes and making use of depth data from the Kinect.

Resources

• cv_bridge Python Tutorial (Slightly out of date.)
• cv_bridge Python API

Exercises

• Check out your code repository.
• Download and unzip three_d_vision.zip in your package directory. This package has the following structure:

  three_d_vision/
      package.xml
      CMakeLists.txt
      src/                         <-- Importable Python modules go here.
          three_d_vision/          <-- Package for importable modules.
              __init__.py          <-- This file turns the folder into a package.
              point_cloud2.py      <-- Module for reading PointCloud2 messages.
              kinect_subscriber.py <-- Module for subscribing to BOTH 
                                       PointCloud2 and image messages
                                       simultaneously.
      rviz/                        <-- rviz configuration files.
          vision.rviz
          depth.rviz
      launch/                      <-- Launch files
          view_vision.launch
          view_depth.launch
      setup.py
      scripts/                     <-- Scripts that create Python ROS nodes. 
          kinect_vision_demo.py
          kinect_depth_demo.py
  
  

• Execute catkin_make in the root folder of your workspace. This is necessary to properly set up the Python package for imports. At this point, you should either open a new terminal or execute the setup.bash script in your devel directory.
• Read over the code in scripts/kinect_vision_demo.py . Copy your find_reddest_pixel_fast into that file.
• Launch view_vision.launch and look at the published image topic. You can look at the topic in rviz, or by starting an image_view node:

    $ rosrun image_view image_view image:=/red_marked_image
  

• Read over the code in scripts/kinect_depth_demo.py . Copy your find_reddest_pixel_fast into that file.
• Launch view_depth.launch and look at the published topics in rviz. You should be able to see the location in space of the red pixel relative to the robot.

Submitting

Commit your updated workspace with the message "DEPTH LAB FINAL SUBMIT".