realtimedetection.py

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

json_file = open("facialemotionmodel.json", "r")
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)

model.load_weights("facialemotionmodel.h5")
haar_file=cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
face_cascade=cv2.CascadeClassifier(haar_file)


def extract_features(image):
    feature = np.array(image)
    feature = feature.reshape(1,48,48,1)
    return feature/255.0



webcam = cv2.VideoCapture(0)
count = 0
webcam.set(3, 320)
webcam.set(4, 240)

labels = {0: 'angry', 1: 'disgust', 2: 'fear', 3: 'happy', 4: 'neutral', 5: 'sad', 6: 'surprise'}
while True:
    i,im=webcam.read()
    gray=cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
    faces=face_cascade.detectMultiScale(im,1.3,5)

    try:
        for (p,q,r,s) in faces:
            image = gray[q:q+s,p:p+r]
            cv2.rectangle(im,(p,q),(p+r,q+s),(255,0,0),2)
            image = cv2.resize(image,(48,48))
            img = extract_features(image)
            pred = model.predict(img)
            prediction_label = labels[pred.argmax()]
            # print("Predicted Output:", prediction_label)
            # cv2.putText(im,prediction_label)
            cv2.putText(im, '% s' % prediction_label, (p-10, q-10),cv2.FONT_HERSHEY_COMPLEX_SMALL,2, (0,255,0))
        imgBackground2 = cv2.imread('Resources/background3.png')
        imgBackground2[100:100 + 240, 60:60 + 320] = im
        cv2.imshow("Real-Time Emotion Detection",imgBackground2)
        cv2.waitKey(27)
    except cv2.error:
        pass