Supplemental Content for EE5907 CA2 Report

This repository contains supplemental content for the EE5907 CA2 course, including several Jupyter notebooks demonstrating different machine learning techniques and a Python script for processing personal photographs.

Contents

• PCA.ipynb: A Jupyter notebook demonstrating the implementation of Principal Component Analysis (PCA).

• LDA.ipynb: A Jupyter notebook demonstrating the implementation of Linear Discriminant Analysis (LDA).

• SVM.ipynb: A Jupyter notebook demonstrating the implementation of Support Vector Machines (SVM).

• CNN.ipynb: A Jupyter notebook demonstrating the implementation of Convolutional Neural Networks (CNN).

• process_photos.py: A Python script for processing personal photographs and storing them in a specified directory.

• requirements.txt: A text file listing the necessary Python packages for the project.

• PIE: The directory unzipped from the CMU PIE dataset.

  The files in this directory are organized into various types of folders, and the name of each folder corresponds to the name of its category.

• Selfish: (optional) The directory contains your ORIGINAL own selfish, you may alter the name of this folder.

Installation of Dependencies

To install the required dependencies, run the following command in your terminal:

pip install -r requirements.txt

This will install all the Python packages listed in the requirements.txt file.

Getting Started

To get started with the Jupyter notebooks, make sure you have Jupyter installed and simply open the .ipynb files in your Jupyter environment. You can install Jupyter via pip if you haven't installed it yet:

pip install jupyter

Then navigate to the repository's directory and run:

jupyter notebook

Choose the notebook you wish to open from the Jupyter interface.

Running the jupyter notebook files

Just run each cell one by one. And MUST run them one by one.

Photo Processing Script

The process_photos.py script is designed to take your personal photographs, perform some predefined processing, and then save them to the ./PIE/0 directory.

Usage

1. Place your personal photographs in a designated folder.
2. Open process_photos.py and modify the folder_path variable to point to the folder containing your photographs.
3. Run the script. Processed images will be stored in the ./PIE/0 directory.

Note

• Make sure that the ./PIE/0 directory exists and nothing in it, or the script will make the directory and overwrite the content in the ./PIE/0 directory.
• The script is set to classify all processed selfish photographs under the category '0'.