emotion.py

#coding=utf-8

import cv2
from keras.models import load_model
import numpy as np
# import chineseText
import datetime
from PIL import Image, ImageDraw, ImageFont

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: '生气',
    1: '厌恶',
    2: '恐惧',
    3: '开心',
    4: '难过',
    5: '惊喜',
    6: '平静'
}
img = cv2.imread("img/emotion/emotion.png")
face_classifier = cv2.CascadeClassifier(
    "/Users/zhangjun/code/python/faceCV/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)

def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):
    if(isinstance(img, np.ndarray)):
        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
    draw = ImageDraw.Draw(img)
    fontText = ImageFont.truetype("font/simsun.ttc", textSize, encoding="utf-8")
    draw.text((left, top), text, textColor, font=fontText)
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)

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)
    print(emotion)
    img = cv2ImgAddText(img, emotion, x+h*0.3, y, color, 20)

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