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realtime_fer_neural.py
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realtime_fer_neural.py
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import cv2
import numpy as np
from tensorflow import keras
'''
Emotion Recognition
Neural Network using webcam
Joshua Kranabetter and Taif Anjum
2022
'''
model = keras.models.load_model("./model_FER2013_7_mobileNet")
emotions = ["Anger", "Disgust", "Fear",
"Happy", "Neutral", "Sadness", "Surprise"]
#emotions = ["anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
font = cv2.FONT_HERSHEY_DUPLEX
webcam = cv2.VideoCapture(0)
#webcam.open(0, cv2.CAP_DSHOW)
while (True):
ret, frame = webcam.read()
#frame = copy.deepcopy(im)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
image = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
face_crop = gray[y:y+h, x:x+w]
face_crop = cv2.resize(face_crop, (48, 48),
interpolation=cv2.INTER_AREA)
preds = np.zeros(shape=(1, 48, 48, 3))
img2 = np.stack((face_crop,)*3, axis=-1)
preds[0] = img2
print(preds)
preds = preds / 255.
results = model.predict(preds)[0]
max_value = max(results)
if max_value > 0.5:
emotion = emotions[np.where(results == max_value)[0][0]]
print(emotion + " confidence level of " + str(max_value*100) + "%")
cv2.putText(image, emotion, (x + 6, y - 6),
font, 1.0, (255, 255, 255), 1)
cv2.imshow('FER', image)
cv2.waitKey(25)
# After the loop release the cap object
# webcam.release()
# Destroy all the windows
# cv2.destroyAllWindows()