#coding=utf-8
#表情識(shí)別

import cv2
from keras.models import load_model
import numpy as np
import chineseText
import datetime

startTime = datetime.datetime.now()
emotion_classifier = load_model(
  'classifier/emotion_models/simple_CNN.530-0.65.hdf5')
endTime = datetime.datetime.now()
print(endTime - startTime)

emotion_labels = {
  0: '生氣',
  1: '厭惡',
  2: '恐懼',
  3: '開心',
  4: '難過',
  5: '驚喜',
  6: '平靜'
}

img = cv2.imread("img/emotion/emotion.png")
face_classifier = cv2.CascadeClassifier(
  "C:\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml"
)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(
  gray, scaleFactor=1.2, minNeighbors=3, minSize=(40, 40))
color = (255, 0, 0)

for (x, y, w, h) in faces:
  gray_face = gray[(y):(y + h), (x):(x + w)]
  gray_face = cv2.resize(gray_face, (48, 48))
  gray_face = gray_face / 255.0
  gray_face = np.expand_dims(gray_face, 0)
  gray_face = np.expand_dims(gray_face, -1)
  emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
  emotion = emotion_labels[emotion_label_arg]
  cv2.rectangle(img, (x + 10, y + 10), (x + h - 10, y + w - 10),
         (255, 255, 255), 2)
  img = chineseText.cv2ImgAddText(img, emotion, x + h * 0.3, y, color, 20)

cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()