img_detection.py

import os
import cv2
import numpy as np
import tensorflow as tf
import keras.utils as image
import warnings
warnings.filterwarnings("ignore")
from tensorflow.keras.utils import img_to_array 
from keras.models import  load_model
import matplotlib.pyplot as plt
import keyboard

import argparse
import sys

def getEmotion():
    # print('Libraries imported')

    # load model
    model = load_model("best_model.h5")
    # print('Model loaded')

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

    cap = cv2.VideoCapture(0)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
    # print('Ready for while loop')

    while cap.isOpened():
        # print('Looping')
        ret, test_img = cap.read()  # captures frame and returns boolean value and captured image
        if not ret:
            continue
        # test_img = cv2.flip(,1)
        gray_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2RGB)

        resized_img = cv2.resize(test_img, (1000, 700))
        cv2.imshow('Facial emotion analysis ', resized_img)
        if cv2.waitKey(1)%256 == 32:
            cv2.imwrite('image.png', test_img)
            # print('Image captured')

            faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.3, 5)

            for (x, y, w, h) in faces_detected:
                cv2.rectangle(test_img, (x, y), (x + w, y + h), (255, 0, 0), thickness=7)
                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, (224, 224))
                img_pixels = image.img_to_array(roi_gray)
                img_pixels = np.expand_dims(img_pixels, axis=0)
                img_pixels /= 255

                predictions = model.predict(img_pixels)

                # find max indexed array
                max_index = np.argmax(predictions[0])

                emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
                predicted_emotion = emotions[max_index]
                cap.release()
                cv2.destroyAllWindows()
                return predicted_emotion

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

            break

        # cv2.imwrite('image.png',resized_img)
        # cv2.imshow('Facial emotion analysis ', test_img)
        # cv2.waitKey(1)

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

    cap.release()
    cv2.destroyAllWindows()