realtimedetection.py

import cv2
from keras.models import model_from_json 
import numpy as np
# from keras_preprocessing.image import load_img
json_file = open("emotiondetector.json", "r")
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
model.load_weights("emotiondetector.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)
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, (0,0,255))
            cv2.imshow("Output", im)
            cv2.waitKey(27)
            
    except cv2.error:
        pass