Planning the app

The final app will convert each RGB frame of a video sequence into a saliency map, extract all the interesting proto-objects, and feed them to a mean-shift tracking algorithm. To do this, we need the following components:

• main: The main function routine (in chapter5.py) to start the application.
• Saliency: A class that generates a saliency map from an RGB color image. It includes the following public methods:
  • Saliency.get_saliency_map: The main method to convert an RGB color image to a saliency map
  • Saliency.get_proto_objects_map: A method to convert a saliency map into a binary mask containing all the proto-objects
  • Saliency.plot_power_density: A method to display the 2D power density of an RGB color image, which is helpful to understand the Fourier transform
  • Saliency.plot_power_spectrum: A method to display the radially averaged power spectrum of an RGB color image, which is helpful to understand natural image statistics
• MultiObjectTracker: A class that tracks multiple objects...