computerGeneratedHolography

Sections headers that will be used to reference location of destination.

Description
How To Use
License
Author Info

Description

This Github project was developped during the 6-month internship (Projet de Fin d'Etudes in french) to complete the Electronics and Computer Engineering (Electronique et Informatique Industrielle (EII) in french) course at INSA Rennes. The internship subject was "Study, adaptation and implementation of source separation methods for the coding of holographic videos".

Directory tree

└───computerGeneratedHolography
    ├───data
    │   ├───2D_dice
    │   └───dices1080p-AP
    ├───implementations_codes
    │   ├───SF-analysis
    │   ├───source-separation
    │   │   ├───mixture_dataset(0147)
    │   │   ├───mixture_dataset(2points)
    │   │   └───output
    │   └───TF-analysis
    ├───machine_learning
    ├───neural_network
    │   ├───classification_problem
    │   │   ├───backup
    │   │   ├───hologram
    │   │   │   └───logs
    │   │   │       └───old
    │   │   └───wigner_distribution
    │   │       └───logs
    │   │           └───old
    │   ├───old_jupyter_notebooks
    │   ├───regression_problem
    │   │   ├───example
    │   │   ├───hologram
    │   │   │   └───logs
    │   │   └───wigner_distribution
    │   │       └───logs
    │   └───wigner_distribution
    ├───output
    │   ├───dataset
    │   │   └───oneClass
    │   ├───machine_learning
    │   ├───main
    │   ├───neural_networks
    │   └───wigner_distribution
    ├───rapports
    └───tests_wigner_distribution
        └───tests

Folders

root (computerGeneratedHolography): Contains the .m files to create holograms and restore them to images. It also contains .m scripts to create the datasets used by the neural networks and a .m script to locate the particules positions from the hologram.
data: Contains some images and holograms used for testing (not relevant).
implementations_codes: Contains some tests and analysis made in the time-frequency (MATLAB, python) and space-frequency domains (jupyter notebook). It also contains the tests and implementations made for the source separation in the holograms (python, jupyter notebook).
machine_learning: Contains implementations of some machine learning algorithms (python, jupyter notebook). In the case, an object (class) was created with certain features of a hologram.
neural_networks: Contains the implementation of a neural networks (python) to solve a classification and a regression problem. The objective is to train the neural network with holograms and also their neural networks.
output: Contains the outputs and the results of the algorithms and scripts of this Github project.
rapports: Contains some reports made, the final report of the internship and the defense presentation.
test_wigner_distribution: Contains scripts recovered for the calculation of the Wigner distribution (MATLAB, jupyter notebook) and the reference used (zip folder) for this calculation.

Technologies

Python 3.7.6
MATLAB R2016a

How to use

Preparing the enviroment (Windows user)

1. Installing Python (https://www.python.org/)

To check if python is installed on your computer, you must open the command prompt and write "python". If you see the python environment, then it is installed, otherwise, to install you can download on the folling link (https://www.python.org/downloads/) the desired version.

2. Installing Jupyter (https://jupyter.org/)

The process to install jupyter lab is shown in the jupyter website (https://jupyter.org/install). I have used "pip" to install with the follow commmand, but there are others options.

$ pip install notebook

3. Installing python libraries

To install the python libraries that will be used in this project just install it from the "requirements.txt" file. Execute in the terminal the command (if you are in the main folder of the project, otherwise find the path where the requirements file is located):

$ pip install -r ./requirements.txt

4. Installing Octave (https://www.gnu.org/software/octave/)

Click on the folling link and download the file according to your operating system (https://www.gnu.org/software/octave/download.html). I highlight that the .m scripts were written in MATLAB, so I recommend use it. Octave is capable to execute theses codes, but some simple changes must be made.

License

MIT License

Permission is hereby granted, free of charge, to any person obtainign a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Author Info

LinkedIn - /fernandolucasaa
Website - Fernando Lucas Araujo Amaral

fernandolucasaa / computerGeneratedHolography