TADI: Traitement Avancé des Images (Advanced Image Processing)

This repository contains practical works (PW) I completed (with other students) as part of my Master's advanced image processing course (https://perso.telecom-paristech.fr/bloch/P6Image/TADI.html).

Table of Contents

• PW n°1: Mathematical Morphology
• PW n°2: Deformable Models
• PW n°3: Markov Models
• PW n°4: Markov Random Fields Models
• PW n°5: Graphcut-Based Approaches
• PW n°6: Scale Space

PW n°1: Mathematical Morphology

This section contains Python code (Jupyter notebook) and instructions for processing images with morphological operators, and a report presenting the results.

• Instructions: PDF
• Code: PW_morpho.ipynb
• Report: PDF

PW n°2: Deformable Models

This section contains Python (Jupyter notebook) code and instructions for segmenting images with deformable models, and a report presenting the results.

• Instructions: PDF
• Code: deformable_models.ipynb
• Report: PDF

PW n°3: Markov Models

This section contains Python code (Jupyter notebook) and instructions for processing images with Markov models, and a report presenting the results.

• Code, Instructions & Report: PW_Markov.ipynb

PW n°4: Markov Random Fields Models

This section contains Python code (Jupyter notebook) and instructions for processing images with Markov Random Fields models, and a report presenting the results.

• Code, Instructions & Report: PW_MRF.ipynb

PW n°5: Graphcut-Based Approaches

This section contains Python code (Jupyter notebook) and instructions for processing images with graph-cut-based approaches, and a report presenting the results.

• Code, Instructions & Report: PW_graphcut.ipynb

PW n°6: Scale Space

This section contains Python code (Jupyter notebook) for computing the optical flow of images, and a report presenting the results.

• Code: code
• Report: PDF

Usage

Step 1: Clone the Repository

Clone the TADI repository to your local machine using the following commands:

git clone https://github.com/pictoune/TADI.git
cd TADI

Step 2: Create the required Conda Environment

Set up the required environment using Conda:

conda env create -f environment.yml -n TADI_env

Step 3: Running the code

• If your practical work (PW) code is written in .py files, you must first activate the conda environment:

  conda activate TADI_env

  then you can run it:

  python <script_name>.py

• Otherwise if it is written in a jupyter notebook, you need to initiate the notebook first:

    jupyter-notebook

  Once Jupyter Notebook is open, navigate to the notebook you want to run. Then, change the kernel to the TADI environment: Go to Kernel -> Change kernel -> Python [conda env:TADI_env]. Then you can run the cells.

License

This project is open source and available under the MIT License.

Feel free to explore the projects and reach out if you have any questions or suggestions.