Live_FER.py

from tensorflow.keras.models import model_from_json

import cv2
import numpy as np

json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new 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 video_detect(video_link = 0):
    cap=cv2.VideoCapture(video_link)  

    while True:  
        ret,test_img=cap.read()# captures frame and returns boolean value and captured image  
        if not ret:  
            continue  
        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, 700))  
        cv2.imshow('Facial emotion analysis ',resized_img)  



        if cv2.waitKey(10) == ord('s'):#wait until 's' key is pressed  
            break  

    cap.release()  
    cv2.destroyAllWindows()

def image_detect(img_link):
    c_img = cv2.imread(img_link)
    gray_img = cv2.cvtColor(c_img, cv2.COLOR_BGR2GRAY)
    faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.32, 5)
    for (x,y,w,h) in faces_detected:  
        cv2.rectangle(c_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(c_img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
    
    resized_img = cv2.resize(c_img, (1000, 700))  
    cv2.imshow('Facial emotion analysis ',resized_img)
    cv2.imwrite("All_Emotions_Detection.jpg", resized_img)
    if cv2.waitKey(0) == ord('s'):
        cv2.destroyAllWindows()

image_detect("all_emotions1.jpg")
# video_detect()