OpenCV

The purpose of this lab is to gain experience working with the Python bindings for OpenCV.

Resources

• OpenCV Documentation Site We are using version 2.4.4.
• ROS CvBridge Python Tutorial cv_bridge is used to convert between ROS image messages and OpenCV images.

Exercises

• Download and read the file cv_demo.py.
• Execute that file from the terminal:

      $ python cv_demo.py
    

  Note that this file is not a ROS node. This is pure python code that can be executed without starting ROS.
• On line 59 of this file, channel 0 is displayed in a window. Can you tell by looking at the displayed image which color channel this is?
• Download the file detect_red.py.
• Complete and test the function find_reddest_pixel according to the docstring. You should complete this function by creating a nested for loop to access all pixels in the image. Note that you will probably need to convert the pixel values to integers. They are stored in the image as 8-bit unsigned integers. Performing arithmetic on 8-bit unsigned integers will always result in 8-bit unsigned integers.
• Complete and test the function find_reddest_pixel_fast according to the docstring. You should complete this function without any loops by applying OpenCV operations. The completed function should be much faster than the previous version.
• Download the package vision_demo.zip. This package contains four python files:
  • src/kinect_subsciber.py This is a special subscriber class for approximately synchronizing the Kinect topics.
  • src/point_cloud2.py This provides utility functions for extracting information from PointCloud2 messages.
  • nodes/kinect_vision_demo.py This is a node that demonstrates subscribing and publishing to image topics in ROS.
  • nodes/kinect_depth_demo.py This node demonstrates working with PointCloud data in ROS.
• Launch the Turtlebot and start the Kinect.
• Read over the code in nodes/kinect_vision_demo.py . Copy your find_reddest_pixel_fast into that file.
• Run the modified node, 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 nodes/kinect_depth_demo.py . Copy your find_reddest_pixel_fast into that file.
• Run the modified node, 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

Copy your completed version of detect_red.py into the src folder of the vision_demo package. Submit your completed vision_demo package using subversion.