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videoTester.py
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videoTester.py
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import os
import cv2
import numpy as np
from keras.models import model_from_json
from keras.preprocessing import image
def exp():
#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)
count_anx=0
count_desp=0
count_normal=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=3)
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 #normalizing
predictions = model.predict(img_pixels)
#find max indexed array
max_index = np.argmax(predictions[0])
emotions = ('angry', 'disgust', 'Anxiety', 'happy', 'Depressed', 'surprise', 'neutral')
predicted_emotion = emotions[max_index]
if predicted_emotion=='Anxiety':
count_anx=count_anx+1
elif predicted_emotion=='Depressed':
count_desp=count_desp+1
else:
count_normal=count_normal+1
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)
cv2.setWindowProperty("Facial emotion analysis ", cv2.WND_PROP_TOPMOST, 1)
if cv2.waitKey(10) == ord('q'):#wait until 'q' key is pressed
if count_anx>count_desp:
if count_anx>count_desp:
res='Anxious'
else:
res='Normal'
else:
if count_normal>count_desp:
res='Normal'
else:
res='Depressed'
return res
break
cap.release()
cv2.destroyAllWindows