Age-Detection-Actors-Challenge

Age detection Challenge

Facial feature analysis has always been a topic of interest mainly due to its applicability. Deep Learning techniques are now making it possible for face analysis to be not just a dream but a reality. This practice problem is get you more acquainted with deep learning. Link
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgements

About The Project

Proyecto to Age detection Challenge.

Built With

• opencv
• tensorflow

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

Download the file shape_predictor_68_face_landmarks.dat.bz2 contained in the repository https://github.com/othneildrew/Best-README-Template, unzip it and save the file shape_predictor_68_face_landmarks.dat inside the repository.

Installation

1. Clone the repo

   git clone https://github.com/TEAM-IMT/Age-Detection-Actors-Challenge.git

2. Install requerements

   python3 -m pip install -U requirements.

Usage

In python, import de processing.py library and use it as well as:

   from libraries.processing import Preprocessing

   preproc = Preprocessing(gray_scale, resize, normalize, rotate, vae_weight_path)

Where each variable is:

• gray_scale : Flag to return each variable in gray_scale format (default = False)
• resize : Tuple with new (heigh, width) desired (default = (None, None))
• normalize : Flag to normalize each image (default = False)
• rotate : shape_predictor_68_face_landmarks.dat path (default = None -> No rotate)
• vae_weight_path : Path with default weights to VAE model (default = None -> No encode)

Then, you can use 2 functions:

   image = preproc.image_read(ipath)
   images = preproc.images_processing(ilist)

• preproc.image_read(str) : Read and process a image and return it in numpy.array format
• preproc.images_processing(list or directory) : Read multiple images and return them into a batch ((N, heigh, width, color) or (N, heigh, width), depends of gray_scale value)

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Acknowledgements