Similarly, for pixels belonging to the Public test, we append it to testing lists. After this, we check if the pixel belongs to training then we append it into the training list & training labels. We then create different lists of storing the testing and training image pixels. emotion_data = pd.read_csv( '/content/drive/My Drive/Emotion_Detection /fer2013.csv' ) print ( 'emotion_data' ) After importing we have printed the data frame as shown in the image. You can give directory in round brackets where your data is stored as shown in the code below. I have already saved it in my drive so I will read it from there. We have now imported all the libraries and now we will import the data set. import pandas as pd import numpy as np from keras.models import Sequential from import Flatten, Dense, Dropout from import Convolution2D, MaxPooling2D, ZeroPadding2D from keras.optimizers import SGD import cv2 The code for importing the libraries is given below. Once it is enabled we will now import the required libraries for building the network. We can enable it by going to ‘Runtime’ in Google Colab and then clicking on ‘Change runtime type’ and select GPU. Implementing VGG16 Network for Classification of Emotions with GPUįirst, we need to enable GPU in the Google Colab to get fast processing. The CSV file contains two columns that are emotion that contains numeric code from 0-6 and a pixel column that includes a string surrounded in quotes for each image. There are seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral) present in the data. It contains 48 X 48-pixel grayscale images of the face. The name of the data set is fer2013 which is an open-source data set that was made publicly available for a Kaggle competition. Testing of the model in real-time using webcam.Training of the VGG model efficiently so that it can recognize the emotion.Building the VGG Model for emotion detection.We will be working with Google Colab to build the model as it gives us the GPU and TPU. We will use this model to check the emotions in real-time using OpenCV and webcam. The article demonstrates a computer vision model that we will build using Keras and VGG16 – a variant of Convolutional Neural Network. It can detect whether you are angry, happy, sad, etc. CV can recognize and tell you what your emotion is by just looking at your facial expressions. We will discuss one of the interesting applications of CV that is Emotion Detection through facial expressions. There are a large number of applications of computer vision that are present today like facial recognition, driverless cars, medical diagnostics, etc. Computer vision (CV) is the field of study that helps computers to study using different techniques and methods so that it can capture what exists in an image or a video.
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