app.py

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

class_labels=['Angry','Disgusted','Afraid','Happy','Neutral','Sad','Surprised']

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

classifier = load_model('EmotionModel.h5')

cap = cv2.VideoCapture(0)

if not cap.isOpened():
    print("Cannot open camera")
    exit()

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

    if ret == False:
        print("Can't receive frame (stream end?). Exiting ...")
        break

    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    faces=face_classifier.detectMultiScale(gray,1.3,5)

    for (x,y,w,h) in faces:
        cv2.rectangle(frame, (x,y), (x+w, y+h), (0,250,0),2)
        cv2.rectangle(frame, (x,y-40), (x+w, y), (0,200,0),-1)
        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('float')/255.0
            roi=img_to_array(roi)
            roi=np.expand_dims(roi,axis=0)

            preds=classifier.predict(roi)[0]
            label=class_labels[preds.argmax()]
            label_position=(x,y)
            cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,1.5,(255,255,255),2)
        else:
            cv2.putText(frame,'No face was found',(200,200),cv2.FONT_HERSHEY_SIMPLEX,2,(0,0,255),2)
            

        



    cv2.imshow('Emotion Detection', frame)

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

cap.release()
cv2.destroyAllWindows()