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 (inchapter5.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 mapSaliency.get_proto_objects_map: A method to convert a saliency map into a binary mask containing all the proto-objectsSaliency.plot_power_density: A method to display the 2D power density of an RGB color image, which is helpful to understand the Fourier transformSaliency.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