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Face Key Points Extractor

Overview

This project is a Python application designed for image processing with a focus on face feature extraction. It uses libraries like dlib and mediapipe for advanced face detection and processing.

Installation

  1. Clone the repository.

  2. Navigate to the project directory.

  3. Create a virtual environment:

python -m venv .venv
  1. Activate the virtual environment:
  • On Windows:
.venv\Scripts\activate
  • On Unix or MacOS:
source .venv/bin/activate
  1. Install the dependencies:
pip install -r requirements.txt

Dlib Model Configuration

Before running the application, download the shape_predictor_68_face_landmarks.dat file for dlib:

  1. Download the model file from Dlib's model repository or a trusted source.

  2. Unzip the file and place it in the models directory at the root of the project.

  3. Ensure the config.py in the settings directory is set correctly:

APP_PATH_PARAMS_JSON_FILE = 'params.json'
APP_PATH_MODEL_DLIB_FILE = 'models/shape_predictor_68_face_landmarks.dat'

Configuration parameters file

Customize the application behavior by modifying the params.json file in the root directory:

{
    "input_path": "",
    "models": [
        "dlib",
        "mediapipe"
    ],
    "images_format": [
        ".png",
        ".jpg",
        ".jpeg"
    ],
    "keypoints_color": [255, 255, 255],
    "margin_ratio": 0.1,
    "num_threads": 8
}

  • input_path: Path to the directory containing the images to be processed.

  • models: List of models to use for face feature extraction. Supported models are dlib and mediapipe.

  • images_format: List of image file formats to be processed. For example: .png, .jpg, .jpeg.

  • keypoints_color: Color of the keypoints (in BGR format) to be drawn on the images. It's an array of three integers representing Blue, Green, and Red color values.

  • margin_ratio: The margin ratio around the detected face for cropping. A smaller value results in a tighter crop around the face.

  • num_threads: The number of threads to use for image processing. Increasing this number can speed up processing on multi-core systems.

Usage

To run the application, use the following command:

python run.py [--debug]

The --debug argument is optional and enables the debug mode for additional logging and process details.

Key Components

  • run.py: The entry point of the application. It parses command-line arguments and initializes the application with the given parameters.

  • main.py: Contains the main logic for processing images using multithreading.

  • extractor.py: Includes functions for face feature extraction using dlib and mediapipe.

  • utils.py: Provides utility functions used across the application.

  • config.py: Contains configuration settings for the application.

License

This project is open-sourced and available to everyone under the MIT License.