facex

Facex: Lightweight, High-Performance Facial Expression Classifier

facex is a Python library for detecting faces and classifying emotions in images lightweight, efficient threading and object pooling for concurrent processing making it suitable for high-performance applications.

NOTE: The model was trained some time ago. While it performs well, newer advancements in the field may offer improved results

Features

• Face Detection: Uses Haar cascades to detect faces in images.
• Emotion Classification: Predicts emotions like anger, happiness, sadness, and more.
• Thread-Safe Pooling: Manages multiple classifiers through a thread-safe object pool.
• Dedicated Worker Allocation: Assign dedicated classifiers for specific tasks or users.

NOTE: The model was trained some time ago. While it performs well, newer advancements in the field may offer improved results.

Testing on RaspberryPi

Installation

Install the library via pip:

pip install facex

Prerequisites

Make sure you have the following:

• Python 3.7 or higher
• Required dependencies (installed automatically with pip):
  • numpy
  • opencv-python
  • tensorflow

Additionally, ensure the assets directory contains the following files:

• haarcascade_frontalface_default.xml
• model_optimized.tflite

Usage

Example Code

import cv2
import facex

# Initialize the PoolManager
pool_manager = facex.PoolManager(pool_size=3)

# Load a test image
image = cv2.imread("test_image.jpg")

# Predict emotions
predictions = pool_manager.predict(image)

# Display results
for prediction in predictions:
    print(f"Face: {prediction['bbox']}, Emotion: {prediction['emot']}")

# Shutdown the pool manager when done
pool_manager.shutdown()

Key Classes and Methods

facex.EmotionClassifier

A class responsible for detecting faces and classifying emotions in images.

• __init__(model_path, category=None): Initializes the emotion classifier with the provided TensorFlow Lite model and optional emotion categories.
  • Arguments:
    • model_path (str): Path to the pre-trained TensorFlow Lite model (model_optimized.tflite).
    • category (list, optional): List of emotion categories (default: ['anger', 'disgust', 'fear', 'happy', 'neutral', 'sadness', 'surprised']).
• detect_faces(input): Detects faces in the input image using OpenCV’s Haar Cascade Classifier.
  • Arguments:
    • input (np.ndarray): The input image (e.g., a frame from a video feed).
  • Returns:
    • faces (list of tuples): Coordinates of detected faces in the form (x, y, w, h).
    • procs_input (np.ndarray): Preprocessed grayscale version of the input image.
• detect_emotion(input): Detects the emotion of a given face using the TensorFlow Lite model.
  • Arguments:
    • input (np.ndarray): The face image (grayscale or resized) to classify.
  • Returns:
    • dict: A dictionary containing the predicted emotion with associated confidence scores.
• predict(input): Detects faces and predicts the emotions for each detected face in the image.
  • Arguments:
    • input (np.ndarray): The input image (e.g., a frame from a video feed).
  • Returns:
    • list: A list of dictionaries, each containing:
      • 'bbox': Bounding box of the detected face (x, y, w, h).
      • 'emot': Emotion prediction for the face (e.g., {'happy': 0.95, 'sadness': 0.05}).

facex.PoolManager

A class for managing a pool of EmotionClassifier instances, enabling thread-safe predictions.

• __init__(pool_size=5): Initializes the pool with a given number of EmotionClassifier instances.
  • Arguments:
    • pool_size (int): Number of EmotionClassifier instances in the pool (default: 5).
• predict(input_data): Retrieves an EmotionClassifier from the pool and uses it to make a prediction on the input data.
  • Arguments:
    • input_data (np.ndarray): The input image data (e.g., a frame from a video feed).
  • Returns:
    • list: A list of dictionaries, each containing:
      • 'bbox': Bounding box of the detected face (x, y, w, h).
      • 'emot': Emotion prediction for the face.
• get_worker(user_id): Allocates a dedicated EmotionClassifier instance for a specific user.
  • Arguments:
    • user_id (str): The ID of the user requesting the worker.
  • Returns:
    • EmotionClassifier: The dedicated EmotionClassifier instance for the user.
  • Raises:
    • RuntimeError: If the user already has a dedicated worker or if no classifiers are available.
• release_worker(user_id): Releases the dedicated worker assigned to a user and returns it to the pool.
  • Arguments:
    • user_id (str): The ID of the user releasing the worker.
  • Raises:
    • KeyError: If the user does not have a dedicated worker.
• shutdown(): Shuts down the pool manager and frees all resources, including threads.

License

facex is licensed under the MIT License. See the LICENSE file for details.

References

1. Coordinated Multi-Agent Imitation Learning
   Hendra Kusuma, Muhammad Attamimi, Hasby Fahrudin (2020). Deep learning based facial expressions recognition system for assisting visually impaired persons. Retrieved from link

Support

If you encounter any issues, feel free to open an issue or reach out via email.

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