Implementation of Real-time Face Emotion Recognition using PyTorch in Python: A Step-by-Step Guide

Real-time face emotion recognition is a fascinating application of deep learning that has gained popularity in recent years. In this tutorial, we will walk through a step-by-step implementation of a real-time face emotion recognition system using PyTorch and Python.

Before we get started, make sure you have the following dependencies installed on your system:

1. PyTorch: PyTorch is a popular open-source deep learning library developed by Facebook AI Research. It provides tools and libraries for building deep learning models.

2. OpenCV: OpenCV is a computer vision library that provides tools for image and video processing.

3. NumPy: NumPy is a popular library for numerical computing in Python.

4. Matplotlib: Matplotlib is a plotting library for Python.

5. Deep Emotion Model: We will be using a pre-trained deep emotion model for face emotion recognition. You can download the model from the internet or use any available open-source model.

Now, let’s start the implementation.

Step 1: Load the Deep Emotion Model
First, we need to load the pre-trained deep emotion model into our Python script. You can load the model using the PyTorch library.

import torch
from torchvision import transforms
from model import DeepEmotion

# Load the pre-trained model
model = DeepEmotion()
model.load_state_dict(torch.load('model_weights.pth'))
model.eval()

Step 2: Capture Video Stream
Next, we need to capture the video stream from a webcam using OpenCV. We will use OpenCV’s VideoCapture class to do this.

import cv2

# Open a video stream from the front camera
cap = cv2.VideoCapture(0)

Step 3: Process the Video Stream
Now, we will process the video stream frame by frame, detect faces in each frame, and predict the emotion of each detected face using the deep emotion model.

while True:
    ret, frame = cap.read()

    # Preprocess the frame
    img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    resized_img = cv2.resize(img, (48, 48))
    tensor_img = transforms.ToTensor()(resized_img).unsqueeze(dim=0)

    # Make a prediction
    with torch.no_grad():
        output = model(tensor_img)
        prediction = torch.argmax(output).item()

    # Display the emotion label
    cv2.putText(frame, 'Emotion: {}'.format(emotion_labels[prediction]), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

    cv2.imshow('Real-time Emotion Recognition', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Step 4: Display the Real-time Emotion Recognition
Finally, we will display the real-time face emotion recognition using OpenCV.

That’s it! You have successfully implemented a real-time face emotion recognition system using PyTorch and Python. You can further enhance this system by fine-tuning the deep emotion model or adding more advanced features.

I hope you found this tutorial helpful. Happy coding!