FER_Camera.py

from tensorflow.keras.models import model_from_json

import cv2
import numpy as np

json_file = open("model.json", "r")
loaded_json_model = json_file.read()
json_file.close()

model = model_from_json(loaded_json_model)
model.load_weights("model_weights.h5")

face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')  
emotions = ('Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral')

def image_predict(image):
    return emotions[np.argmax(model.predict(image))]

class VideoCamera:
    def __init__(self):
        self.video = cv2.VideoCapture(0)
    
    def __del__(self):
        self.video.release()

    def get_frame(self):
        ret,test_img=self.video.read()# captures frame and returns boolean value and captured image  
          
        
        gray_img= cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)  

        try:
            faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.32, 5)

            for (x,y,w,h) in faces_detected:  
                cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=3)  
                roi_gray=gray_img[y:y+w,x:x+h]#cropping region of interest i.e. face area from  image  
                roi_gray=cv2.resize(roi_gray,(48,48))  
                img = roi_gray.reshape((1,48,48,1))
                img = img /255.0

                max_index = np.argmax(model.predict(img.reshape((1,48,48,1))), axis=-1)[0]

                  
                predicted_emotion = emotions[max_index]  

                cv2.putText(test_img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
        except:
            pass

        resized_img = cv2.resize(test_img, (1000, 600))

        _, jpeg = cv2.imencode('.jpg', resized_img)

        return jpeg.tobytes()