main.py

from tensorflow.keras.models import load_model
import tensorflow as tf
import cv2
import numpy as np
from tensorflow.keras.preprocessing.image import img_to_array

print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

# model_path = 'model_final.h5'
model_path = 'model_final_extended.h5'

face_classifier = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
classifier = load_model(model_path)

emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']

cap = cv2.VideoCapture(0)

while True:
    _, frame = cap.read()
    labels = []
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_classifier.detectMultiScale(gray, 1.32, 5)
    img_size = 48 if model_path == 'model_final.h5' else 128

    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (224, 224, 224), thickness=3)
        roi_gray = gray[y:y + h, x:x + w]
        roi_gray = cv2.resize(roi_gray, (img_size, img_size), interpolation=cv2.INTER_AREA)

        if np.sum([roi_gray]) != 0:
            roi = roi_gray.astype('float')
            roi = img_to_array(roi)
            roi = np.expand_dims(roi, axis=0)
            prediction = classifier.predict(roi)[0]
            label = emotion_labels[prediction.argmax()]
            label_position = (x, y)
            cv2.putText(frame, label, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (51, 51, 255), thickness=3)
        else:
            cv2.putText(frame, 'No Faces', (30, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (51, 51, 255), thickness=3)

    cv2.imshow('Facial Expressions Detector', frame)

    # To close the program, press q
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()