bayes-classifier's Introduction

BAYE'S CLASSIFIER

Description
Requirement
Installation
Directory Structure
Usage
Credit

Description

Bayesian Classifier uses the Bayes Theorem. Bayes Classifier is one of the simple and most effective Classification algorithms. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.

Requirement
1. Python 3
2. Jupyter Notebook
3. Numpy
4. Opencv
5. PIL
Installation
1. Python
  - Visit and download Python from https://www.python.org/downloads/
  - Install and add Python to path
```
python3 -V
```
    Install pip
```
sudo apt install python3-pip
```
2. Numpy
  
  Using pip,
```
pip install numpy
```
3. Opencv
  
  In command line, change directory to where pip is present
```
pip install opencv-python
```
4. PIL
  
  In command line or powershell
```
python3 -m pip install --upgrade Pillow
```
5. Jupyter notebook
  
  Install the classic Jupyter Notebook using:
```
pip install notebook
```
  To run the notebook
```
jupyter notebook
```
Directory Structure
```
.
├── src                     	# Source files
│   ├── Bayes Classifier.ipynb	# Jupyter Notebook
|	├──band1.jpg	# Input image 1
|	├──band2.jpg	# Input image 2
|	├──band3.jpg	# Input image 3
|	├──band4.jpg	# Input image 4
|	├── River		# 50 Coordinates of River class
|	├── NonRiver	# 100 Coordinates of Non-River class
|	├── Output_Images
|		├── river03non07.jpeg
|		├── river03non07.jpeg
|		├── river03non07.jpeg
├── REPORT.md
└── README.md
```
Usage

The source code (jupyter notebook) is present in the 'src' folder. The 4 input band images(R,G,B,I) are band1.jpg, band2.jpg, band3.jpg and band4.jpg. Coordinates of river and non-river for training pixel are in the files 'River' and 'NonRiver'. In the 'Output_Images' folder, the three output images for different Prior Probabilities are included.

Project.md explains the source code.