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First Create venv:

python -m venv /path/to/new/virtual/environment

This is a project where we are going to prove different methods to process different Hyperespectral images

To activate the venv on windows we should use: .\hipercubo\Scripts\Activate.ps1 (my case)

now run: pip install -r requirements.txt

Once you have this done you are able to run the colab notebooks where you can interact with the hole project.

For documentation I recomend this video: https://www.youtube.com/watch?v=BWIrhgCAae0&t=227s&ab_channel=LearnProgrammingwithJoel

Data Preparation

This Python code allows you to load and analyze hypercube data.

Preprocess

Here are the methods used to reduce the noise in the data provided it is in a different folder because it is not necessary for you to use it in order to prove different types of preproccess.

Classifiers

In this file you will find different types of neural networks which are used to classify the different types of materials.

you will find this methods:

CNN

Multilayer Perceptron

Usage

Prerequisites

Before running the code, ensure that you have the following dependencies installed:

• Python 3.x
• NumPy
• Matplotlib
• Tensorflow

Installation

1. Clone the repository or download the hypercube_analysis.py file.
2. Install the required dependencies using pip:

   pip install numpy matplotlib
   

Usage

1. Import the necessary modules and classes:

   import os
   import sys
   import matplotlib
   from matplotlib import pyplot as plt
   import numpy as np

2. Create an instance of the Hypercube class:

   hipercubo = Hypercube()

3. Load a hypercube file using the Load() method:

   hipercubo.Load("path/to/hypercube.bin")

4. Perform analysis and visualization on the loaded hypercube. Here are some example methods you can use:

   • PlotDimLine(dim_a_visualizar): Plots the values along a specific dimension as a line graph.
   • PlotDimHist(dim_a_visualizar): Plots the values along a specific dimension as a histogram.
   • PlotIMG(dim_a_visualizar): Displays the image of a given dimension.

   Example usage:

   hipercubo.PlotDimLine(150)
   hipercubo.PlotIMG(150)

5. Run the script with the hypercube file as a command-line argument:

   python hypercube_analysis.py path/to/hypercube.bin
   

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgments

• This code was developed as part of a hypercube analysis project.
• Thanks to the contributors of NumPy and Matplotlib for their fantastic libraries.