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import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array
# Load your pre-trained model
model = tf.keras.models.load_model('Final_Resnet50_Best_model.keras')
# Emotion labels
emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
# Initialize the face classifier
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
face = gray[y:y + h, x:x + w]
face = cv2.resize(face, (224, 224))
face = face.astype("float") / 255.0
face = img_to_array(face)
face = np.expand_dims(face, axis=0)
prediction = model.predict(face)[0]
emotion = emotion_labels[np.argmax(prediction)]
cv2.putText(frame, emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
cv2.imshow("Emotion Detector", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
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