README

How to run the code?

1. Please run imageSegmentation.py- no arguments are required to be passed. The script will diligently ask your inputs and has accompanying instructions with it, kindly follow them.
2. Expected output - The output will show a window with the segmented image corresponding to the given configuration and a copy is also saved in the current directory with the name that you provided as input.

File structure -

• ProjectReport.pdf: Assignment report - contains interesting experiments, results and observations. Peek in!

• utils.py: Utility file to handle

  • Data Manipulation
  • Image Utils
  • Pre-processing Utils
  • Miscellaneous Utils

• experiment_scripts.py: Holds all the ready to use experiments scripts, connects functions from experiments.py

  • Experiment set 1 -
    • pts.mat with vanilla meanshift algoritm
    • pts.mat with meanshift optimisation 1 algoritm
    • pts.mat with meanshift optimisation 2 algoritm.
  • Experiment set 2 - Experiment different images with second optimisation - without pre-processing.
  • Experiment set 3 - Experiment different images with second optimisation - with pre-processing - primarily image smoothing.

• vanilla_algorithm.py: Simplest implementation of find_peak and mean_shift.

• optimisation_one.py:

  • Similar implementation of find_peak as vanilla
  • Mean Shift optimised by introducing concept of basin of attraction, thus saving iteartions and computation time.

• optimisation_two.py

  • find_peak_opt_two() implements search path space and convergence of similar points in the space in a single peak, thus saving iterations and computation time.
  • Similar implementation of mean_shift_opt_2() as done in first optimisation.