test.py

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

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

try:
    classifier = load_model(r'Custom_CNN_model.keras')
except Exception as e:
    print(f"Error loading model: {e}")
    exit()

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

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        print("Failed to grab frame")
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    faces = face_classifier.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    for (x, y, w, h) in faces:
    
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2)

        roi_gray = gray[y:y+h, x:x+w]

        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)

        if np.sum([roi_gray]) != 0:
            roi = roi_gray.astype('float32') / 255.0  
            roi = np.expand_dims(roi, axis=0) 
            roi = np.expand_dims(roi, axis=-1)  

            try:
                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, (0, 255, 0), 2)
            except Exception as e:
                print(f"Error during prediction: {e}")
                cv2.putText(frame, 'Error', label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
        else:
    
            cv2.putText(frame, 'No Faces', (30, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    cv2.imshow('Emotion Detector', frame)

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