import numpy as np
import cv2
from keras.preprocessing import image
import dlib
from imutils import face_utils
import imutils
from sklearn import preprocessing
import math
from keras.models import model_from_json
#-----------------------------
#opencv initialization
face_cascade = cv2.CascadeClassifier('C:/pytools/Facial-Expression-Keras/haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
#-----------------------------
#face expression recognizer initialization
# Using pretrained model
model = model_from_json(open("C:/pytools/Facial-Expression-Keras/model/model.json", "r").read())
model.load_weights('C:/pytools/Facial-Expression-Keras/model/model.h5') #load weights
#-----------------------------
emotions = ( 'Angry' , 'Disgust' , 'Fear' , 'Happy' , 'Neutral' , 'Sad' , 'Surprise')
# initialize dlib's face detector and create a predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("C:/pytools/Facial-Expression-Keras/shape_predictor_68_face_landmarks.dat")
def detect_parts(image):
distances = []
# resize the image, and convert it to grayscale
image = imutils.resize(image, width=200, height=200)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale image
rects = detector(gray, 1)
# loop over the face detections
for (i, rect) in enumerate(rects):
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
distances = euclidean_all(shape)
# visualize all facial landmarks with a transparent overlay
#output = face_utils.visualize_facial_landmarks(image, shape)
#cv2.imshow("Image", output)
#cv2.waitKey(0)
return distances
def euclidean(a, b):
dist = math.sqrt(math.pow((b[0] - a[0]), 2) + math.pow((b[1] - a[1]), 2))
return dist
# calculates distances between all 68 elements
def euclidean_all(a):
distances = ""
for i in range(0, len(a)):
for j in range(0, len(a)):
dist = euclidean(a[i], a[j])
dist = "%.2f" % dist;
distances = distances + " " + str(dist)
return distances
def box_label(bgr, x1, y1, x2, y2, label):
cv2.rectangle(bgr, (x1, y1), (x2, y2), (255, 0, 0), 1, 1)
cv2.rectangle(bgr, (int(x1), int(y1-25)), (x2, y1), (255,255,255), -1)
cv2.putText(bgr, label, (x1, int(y1-5)), cv2.FONT_HERSHEY_COMPLEX, 0.7, (0,0,0), 1)
while(True):
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face
distances = detect_parts(detected_face)
if(len(distances)!=0):
val = distances.split(" ")[1:]
val = np.array(val)
val = val.astype(np.float)
val = np.expand_dims(val, axis = 1)
minmax = preprocessing.MinMaxScaler()
val = minmax.fit_transform(val)
val = val.reshape(1,4624)
predictions = model.predict(val) #store probabilities of 6 expressions
#find max indexed array ( 'Angry' , 'Disgust' , 'Fear' , 'Happy' , 'Neutral' , 'Sad' , 'Surprise')
print ("Angry: %", predictions[0][0]/1.0 * 100)
print ("Disgust: %", predictions[0][1]/1.0 * 100)
print ("Fear: %", predictions[0][2]/1.0 * 100)
print ("Happy: %", predictions[0][3]/1.0 * 100)
print ("Neutral: %", predictions[0][4]/1.0 * 100)
print ("Sad: %", predictions[0][5]/1.0 * 100)
print ("Surprised: %", predictions[0][6]/1.0 * 100)
print ("----------------------" )
max_index = np.argmax(predictions[0])
emotion = emotions[max_index]
#write emotion text above rectangle
box_label(img, x, y, x+w, y+h, emotion+":"+'{:2.2f}'.format(np.max(predictions[0])/1.0 * 100))
cv2.imshow('img',img)
if cv2.waitKey(1) & 0xFF == ord('q'): #press q to quit
break
#kill open cv things
cap.release()
cv2.destroyAllWindows()
