Lesson 02: derivatives and gradients

Description:

In this lesson we will apply the calculus concept of derivative to Image Data. Measuring the rate of change of light intensity in images is the foundation of several object detection algorithms for geometry such as lines and circles. We can also use derivative data to detect the edges of objects. You will develop code to compute, display, save, and operate on derivatives and gradients in Images.

Resources:

Image Derivates and Gradients: http://nebomusic.net/perception/Derivatives_Images_Gradients.pdf

Edge Detection: http://nebomusic.net/perception/Edge_Detection.pdf

Process:

1. Follow directions in linked Google Slides below to start the project.

2. Follow directions at: http://nebomusic.net/perception/PS05.pdf . Setup the Workspace and implement the derivative, gradient, and edge detecting functions.

3. Follow the requirements at: http://nebomusic.net/perception/CameraAssignment.pdf Develop code to demonstrate the X and Y Image derivatives and Gradients Live using your Webcam.

Deliverables:

1. Using two of your photographs, create the following for each photograph with your PS05.py code and save to the "output" folder of your project.

   1. Derivative Image in the X Direction

   2. Derivative Image in the Y Direction

   3. Gradient Image

   4. Edge Image with your Algorithm

   5. Edge Image with Canny Edge Detection.
      

2. Make a .zip folder of the PS05 workspace with code, input, and output images and submit to the Google Classroom.
   

3. Submit the code and screenshot of your CameraAssignment demonstrating the live implementation of the two derivative functions and the gradient function to the Google Classroom.