webcam_implementation.py

import cv2
from keras.models import model_from_json
import numpy as np

# Load the pre-trained model architecture from JSON file
json_file = open("emotiondetector.json", "r")
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)

# Load the pre-trained model weights
model.load_weights("emotiondetector.h5")

# Load the Haar cascade classifier for face detection
haar_file = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
face_cascade = cv2.CascadeClassifier(haar_file)

# Define a function to extract features from an image
def extract_features(image):
    feature = np.array(image)
    feature = feature.reshape(1, 48, 48, 1)
    return feature / 255.0

# Open the webcam (camera)
webcam = cv2.VideoCapture(0)

# Define labels for emotion classes
labels = {0: 'angry', 1: 'disgust', 2: 'fear', 3: 'happy', 4: 'neutral', 5: 'sad', 6: 'surprise'}

while True:
    # Read a frame from the webcam
    i, im = webcam.read()

    # Convert the frame to grayscale
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

    # Detect faces in the grayscale frame
    faces = face_cascade.detectMultiScale(im, 1.3, 5)

    try:
        # For each detected face, perform facial emotion recognition
        for (p, q, r, s) in faces:
            # Extract the region of interest (ROI) which contains the face
            image = gray[q:q + s, p:p + r]

            # Draw a rectangle around the detected face
            cv2.rectangle(im, (p, q), (p + r, q + s), (255, 0, 0), 2)

            # Resize the face image to the required input size (48x48)
            image = cv2.resize(image, (48, 48))

            # Extract features from the resized face image
            img = extract_features(image)

            # Make a prediction using the trained model
            pred = model.predict(img)

            # Get the predicted label for emotion
            prediction_label = labels[pred.argmax()]

            # Display the predicted emotion label near the detected face
            cv2.putText(im, f'Emotion: {prediction_label}', (p - 10, q - 10),
                        cv2.FONT_HERSHEY_COMPLEX_SMALL, 2, (0, 0, 255))

        # Display the frame with annotations in real-time
        cv2.imshow("Real-time Facial Emotion Recognition", im)

        # Break the loop if the 'Esc' key is pressed
        if cv2.waitKey(1) == 27:
            break

    except cv2.error:
        pass

# Release the webcam and close all OpenCV windows
webcam.release()
cv2.destroyAllWindows()