Building a Real Time Emotion Detection with Python

Detecting real-time emotion of the person with a camera input is one of the advanced features in the machine learning process. The detection of emotion of a person using a camera is useful for various research and analytics purposes. The detection of emotion is made by using the machine learning concept. You can use the trained dataset to detect the emotion of the human being. For detecting the different emotions, first you need to train those different emotions, or you can use a dataset already available on the internet. In this article, we will discuss creating a Python program to detect real-time emotion of a human being using the camera.

Installing Dependencies

For using this machine learning concept, you need to install a lot of dependencies into your system using the command prompt. The machine learning algorithm used by me was a tensor flow algorithm, which was designed by Google for machine learning functions. For analyzing faces. you need to detect the faces, to know more about detecting faces using python, you can refer to my article by clicking here. You need a cascade file for this process, you can download it from my git-hub page or in the download section.

You can install the dependencies by using the commands given below:

pip install opencv-python  

pip install tensor flow  
  
pip install numpy  
  
pip install pandas  
  
pip install keras  
  
pip install adam  
  
pip install kwargs  
  
pip install cinit  

Training the Dataset

For training purposes, I use the predefined un trained dataset CSV file as my main input for my input for training the machine. You can use the code given below for training the machine using the dataset. Before that, you need to ensure that all required files in the same repository where the program presents otherwise it will through some error. You can download the data set by clicking here.

import sys, os  
import pandas as pd  
import numpy as np  
  
from keras.models import Sequential  
from keras.layers import Dense, Dropout, Activation, Flatten  
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,AveragePooling2D  
from keras.losses import categorical_crossentropy  
from keras.optimizers import Adam  
from keras.regularizers import l2  
from keras.utils import np_utils  
# pd.set_option('display.max_rows', 500)  
# pd.set_option('display.max_columns', 500)  
# pd.set_option('display.width', 1000)  
  
df=pd.read_csv('fer2013.csv')  
  
# print(df.info())  
# print(df["Usage"].value_counts())  
  
# print(df.head())  
X_train,train_y,X_test,test_y=[],[],[],[]  
  
for index, row in df.iterrows():  
    val=row['pixels'].split(" ")  
    try:  
        if 'Training' in row['Usage']:  
           X_train.append(np.array(val,'float32'))  
           train_y.append(row['emotion'])  
        elif 'PublicTest' in row['Usage']:  
           X_test.append(np.array(val,'float32'))  
           test_y.append(row['emotion'])  
    except:  
        print(f"error occured at index :{index} and row:{row}")  
  
  
num_features = 64  
num_labels = 7  
batch_size = 64  
epochs = 30  
width, height = 48, 48  
  
  
X_train = np.array(X_train,'float32')  
train_y = np.array(train_y,'float32')  
X_test = np.array(X_test,'float32')  
test_y = np.array(test_y,'float32')  
  
train_y=np_utils.to_categorical(train_y, num_classes=num_labels)  
test_y=np_utils.to_categorical(test_y, num_classes=num_labels)  
  
#cannot produce  
#normalizing data between oand 1  
X_train -= np.mean(X_train, axis=0)  
X_train /= np.std(X_train, axis=0)  
  
X_test -= np.mean(X_test, axis=0)  
X_test /= np.std(X_test, axis=0)  
  
X_train = X_train.reshape(X_train.shape[0], 48, 48, 1)  
  
X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)  
  
# print(f"shape:{X_train.shape}")  
##designing the cnn  
#1st convolution layer  
model = Sequential()  
  
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(X_train.shape[1:])))  
model.add(Conv2D(64,kernel_size= (3, 3), activation='relu'))  
# model.add(BatchNormalization())  
model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
model.add(Dropout(0.5))  
  
#2nd convolution layer  
model.add(Conv2D(64, (3, 3), activation='relu'))  
model.add(Conv2D(64, (3, 3), activation='relu'))  
# model.add(BatchNormalization())  
model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
model.add(Dropout(0.5))  
  
#3rd convolution layer  
model.add(Conv2D(128, (3, 3), activation='relu'))  
model.add(Conv2D(128, (3, 3), activation='relu'))  
# model.add(BatchNormalization())  
model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
  
model.add(Flatten())  
  
#fully connected neural networks  
model.add(Dense(1024, activation='relu'))  
model.add(Dropout(0.2))  
model.add(Dense(1024, activation='relu'))  
model.add(Dropout(0.2))  
  
model.add(Dense(num_labels, activation='softmax'))  
  
# model.summary()  
  
#Compliling the model  
model.compile(loss=categorical_crossentropy,  
              optimizer=Adam(),  
              metrics=['accuracy'])  
  
#Training the model  
model.fit(X_train, train_y,  
          batch_size=batch_size,  
          epochs=epochs,  
          verbose=1,  
          validation_data=(X_test, test_y),  
          shuffle=True)  
  
  
#Saving the  model to  use it later on  
fer_json = model.to_json()  
with open("fer.json", "w") as json_file:  
    json_file.write(fer_json)  
model.save_weights("fer.h5")  

Detecting Real-Time Emotion

For detecting the emotion, first, you need to run the train.py program to train the data. Then you can use the code given below:

import os  
import cv2  
import numpy as np  
from keras.models import model_from_json  
from keras.preprocessing import image  
  
#load model  
model = model_from_json(open("fer.json", "r").read())  
#load weights  
model.load_weights('fer.h5')  
  
  
face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')  
  
  
cap=cv2.VideoCapture(0)  
  
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)  
  
    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=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,(48,48))  
        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]  
  
        cv2.putText(test_img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)  
  
    resized_img = cv2.resize(test_img, (1000, 700))  
    cv2.imshow('Facial emotion analysis ',resized_img)  
  
  
  
    if cv2.waitKey(10) == ord('q'):#wait until 'q' key is pressed  
        break  
  
cap.release()  
cv2.destroyAllWindows  

Output Verification

Now you can run the videoTester.py program. Your camera automatically turns on and detects the emotion of your face.

Conclusion

This is just a beginning step in face detection. You can download the program files from git-hub link by clicking here. Thank you!

#python