vision

OpenCV

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

Download and read the file cv_demo.py and the file pencils.webm.
Read over cv_demo.py and run it in the terminal:
```
python3 cv_demo.py
```
Or, if you don’t have a camera:
```
python3 cv_demo.py pencils.webm
```
Note thatcv_demo.py is not a ROS node. This is pure python code that can be executed without starting ROS.
On line 53 of cv_demo.py channel 2 is displayed in a window. Can you tell by looking at the displayed image which color channel this is? (Don’t assume that the channels are in RGB order.)
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 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. (This OpenCV method will be helpful: cv2.minMaxLoc) The completed function should be much faster than the previous version.

The code in detect_red.py finds a single red pixel. It might also be useful to label ALL pixels in the image that exceed some redness threshold. Create a new program named mark_red.py that displays a black and white version of the camera image, where all red pixels are white, and all non-red pixels are black. The examples in the Image Thresholding Tutorial are likely to be useful.

Submit your completed version of detect_red.py through Canvas.