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BAYE'S CLASSIFIER

CONTENTS:

  • 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

    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.

Credit

To record coordinates, https://www.mobilefish.com/services/record_mouse_coordinates/record_mouse_coordinates.php