人脸面部表情识别 keras实现（二）

人脸面部情绪识别 （一）
人脸面部情绪识别（二）

人脸面部情绪识别 age&gender（三）

import cv2
import sys
import json
import time
import numpy as np

from keras.models import model_from_json


emotion_labels = ['angry', 'fear', 'happy', 'sad', 'surprise', 'neutral']

cascPath = sys.argv[1]

faceCascade = cv2.CascadeClassifier(cascPath)

# load json and create model arch
json_file = open('model.json','r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)

# load weights into new model
model.load_weights('model.h5')

def predict_emotion(face_image_gray): # a single cropped face
    resized_img = cv2.resize(face_image_gray, (48,48), interpolation = cv2.INTER_AREA)

    image = resized_img.reshape(1, 1, 48, 48)
    im = cv2.resize(resized_img,(90,100))
    cv2.imwrite('face.bmp', im)
    list_of_list = model.predict(image, batch_size=1, verbose=1)
    angry, fear, happy, sad, surprise, neutral = [prob for lst in list_of_list for prob in lst]
    return [angry, fear, happy, sad, surprise, neutral]

video_capture = cv2.VideoCapture(0)
while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()
    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY,1)


    faces = faceCascade.detectMultiScale(
        img_gray,
        scaleFactor=1.1,
        minNeighbors=1,
        minSize=(30, 30),
        flags=cv2.CASCADE_SCALE_IMAGE

    )
    emotions = []
    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        face_image_gray = img_gray[y:y+h, x:x+w]
        angry, fear, happy, sad, surprise, neutral = predict_emotion(face_image_gray)
        emotions = [angry, fear, happy, sad, surprise, neutral]
        m = emotions.index(max(emotions))
        biaoqing = ""
        for index, val in enumerate(emotion_labels):
            if (m == index):
                biaoqing = val
                print(biaoqing)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        #cv2.putText(frame, biaoqing, (x, y),
        #           cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0),
        #           thickness=2, lineType=2)
        with open('emotion.txt', 'a') as f:
            f.write('{},{},{},{},{},{},{},{}\n'.format(time.time(),angry, fear, happy, sad, surprise, neutral,biaoqing))
    # Display the resulting frame
    cv2.imshow('Video', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()

几个主要的东西

1. 运行命令行 python real-time.py haarcascade_frontalface_default.xml
   加载haarcascade_frontalface_default.xml是haar分类器，用于检测人脸，别人训练好的

2. 加载model.h5模型，这是用caffe训练的，用于表情预测

3. model.json经过查看里面内容，就是别人将model通过keras保存为json格式，现在重新将json加载
4. emotion.txt里面是结果，哪个值越大就是哪个。这仅仅是预测，但效果还是比（一）好很多
5. 整个源代码，包括模型从这里下载