It is a Tensorflow/Keras sample project using CNN.
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Download dataset (https://www.kaggle.com/msambare/fer2013)
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Build and train the model
$python Classification_model_vgg.py - Load trained model and Run demo
$python Facial_Expressions_Recog.pyfrom keras.models import load_model
from time import sleep
from keras.preprocessing.image import img_to_array
from keras.preprocessing import image
import cv2
import numpy as np
face_classifier = cv2.CascadeClassifier(r'\haarcascade_frontalface_default.xml')
classifier =load_model(r'\my_model.h5')
class_labels = ['Angry','Happy','Disgust','Fear','Neutral','Sad','Surprise']
#cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture('HumanFE.mp4')
while True:
# Grab a single frame of video
ret, frame = cap.read()
labels = []
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray,1.3,5)
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h,x:x+w]
roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)
# rect,face,image = face_detector(frame)
if np.sum([roi_gray])!=0:
roi = roi_gray.astype('float')/255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi,axis=0)
# make a prediction on the ROI, then lookup the class
preds = classifier.predict(roi)[0]
label=class_labels[preds.argmax()]
print(label)
label_position = (x,y)
cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)
else:
cv2.putText(frame,'No Face Found',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)
cv2.imshow('Emotion Detector',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
