表情识别：

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本博文是基于tensorflow的mtcnn算法来检测人脸，效果超好，然后用fer2013数据集训练一个表情识别的网络，最后将检测的人脸进行情绪识别。一共可以识别以下7种表情：
angry
disgust
fear
happy
sad
surprise
neutral

项目目录：只需运行下面的main.py文件即可。

可以支持图片视频和摄像头，多人脸的情绪识别。

下面是部分代码，如需要全部代码可自行下载：

import os

import cv2
import numpy as np
from keras.applications.imagenet_utils import preprocess_input
from model_bq import mini_XCEPTION
import utils.utils as utils
from net.mobilenet import MobileNet
from net.mtcnn import mtcnn

def preprocess_input(x, v2=True):
    x = x.astype('float32')
    x = x / 255.0
    if v2:
        x = x - 0.5
        x = x * 2.0
    return x
class face_rec():
    def __init__(self):
        #-------------------------#
        #   创建mtcnn的模型
        #   用于检测人脸
        #-------------------------#
        self.mtcnn_model = mtcnn()
        self.threshold = [0.5,0.6,0.8]
               
        #-------------------------#
        #   创建mobilenet的模型
        #   用于判断是否佩戴口罩
        #-------------------------#
        self.classes_path = "model_data/classes.txt"
        self.class_names = self._get_class()
        self.Crop_HEIGHT = 224
        self.Crop_WIDTH = 224
        self.NUM_CLASSES = len(self.class_names)
        self.mask_model = mini_XCEPTION(input_shape=(48,48,1),num_classes=7)
        self.mask_model.load_weights("./model_data/ep122-loss0.845-val_loss0.971.h5")

    def _get_class(self):
        classes_path = os.path.expanduser(self.classes_path)
        with open(classes_path) as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names

    def recognize(self,draw):
        height,width,_ = np.shape(draw)
        draw_rgb = cv2.cvtColor(draw,cv2.COLOR_BGR2RGB)

        #--------------------------------#
        #   检测人脸
        #--------------------------------#
        rectangles = self.mtcnn_model.detectFace(draw_rgb, self.threshold)
        if len(rectangles)==0:
            return
        rectangles = np.array(rectangles,dtype=np.int32)
        rectangles_temp = utils.rect2square(rectangles)
        rectangles_temp[:, [0,2]] = np.clip(rectangles_temp[:, [0,2]], 0, width)
        rectangles_temp[:, [1,3]] = np.clip(rectangles_temp[:, [1,3]], 0, height)
        #-----------------------------------------------#
        #   对检测到的人脸进行编码
        #-----------------------------------------------#
        classes_all = []
        for rectangle in rectangles_temp:
            #---------------#
            #   截取图像
            #---------------#
            landmark = np.reshape(rectangle[5:15], (5,2)) - np.array([int(rectangle[0]), int(rectangle[1])])
            crop_img = draw_rgb[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
            #-----------------------------------------------#
            #   利用人脸关键点进行人脸对齐
            #-----------------------------------------------#
            crop_img,_ = utils.Alignment_1(crop_img,landmark)

            crop_img = cv2.resize(crop_img, (self.Crop_WIDTH,self.Crop_HEIGHT))
            #crop_img = preprocess_input(np.reshape(np.array(crop_img, np.float64),[1, self.Crop_HEIGHT, self.Crop_WIDTH, 3]))

            new=cv2.cvtColor(crop_img,cv2.COLOR_BGR2GRAY)
            new=cv2.resize(new,(48,48))
            new=np.reshape(new,(1,48,48,1))
            new=preprocess_input(new)

            classes = self.class_names[np.argmax(self.mask_model.predict(new)[0])]
            classes_all.append(classes)

        rectangles = rectangles[:, 0:4]
        #-----------------------------------------------#
        #   画框~!~
        #-----------------------------------------------#
        for (left, top, right, bottom), c in zip(rectangles,classes_all):
            cv2.rectangle(draw, (left, top), (right, bottom), (0, 0, 255), 2)
            font = cv2.FONT_HERSHEY_SIMPLEX
            cv2.putText(draw, c, (left , bottom - 15), font, 0.75, (255, 255, 255), 2)  
        return draw

if __name__ == "__main__":


    dududu = face_rec()

    draw=cv2.imread(r'C:\Users\Administrator\Desktop/1.png')
    dududu.recognize(draw)
    #video_capture.release()

    cv2.imshow('a12',draw)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

main.py

from mask_recognize import *
import sys, cv2, time

from untitled import Ui_Dialog

from PyQt5 import QtCore, QtGui, QtWidgets

from PyQt5.QtWidgets import QFileDialog,QTabWidget

from PyQt5.QtCore import QTimer, QThread, pyqtSignal, Qt

from PyQt5.QtGui import QPixmap, QImage

from PyQt5.QtWidgets import QLabel,QWidget

dududu = face_rec()
class mywindow(QTabWidget,Ui_Dialog): #这个窗口继承了用QtDesignner 绘制的窗口

    def __init__(self):
        super(mywindow,self).__init__()
        self.setupUi(self)
        self.pushButton_2.clicked.connect(self.videoprocessing)
        self.pushButton.clicked.connect(self.picture)
        self.pushButton_3.clicked.connect(self.videoprocessing)
    def picture(self):
        picName, picType = QFileDialog.getOpenFileName(self,
                                                       "打开图片",
                                                       "",
                                                       " *.jpg;;*.png;;*.jpeg;;*.bmp;;All Files (*)")


        src=cv2.imread(picName)
        try:
            dududu.recognize(src)
        except:
            return
        src=self.cv_qt(src)
        self.label.setPixmap(QPixmap.fromImage(src))

    def cv_qt(self,src):
        h,w,d=src.shape
        bytesperline=d*w
        # self.src=cv.cvtColor(self.src,cv.COLOR_BGR2RGB)
        qt_image=QImage(src.data,w,h,bytesperline,QImage.Format_RGB888).rgbSwapped()
        return qt_image
        #self.label.setPixmap(QPixmap.fromImage(self.qt_image))
    def videoprocessing(self):
        button = self.sender()

        print(button.objectName())
        global videoName #在这里设置全局变量以便在线程中使用
        if button.objectName()=='pushButton_2':

            videoName,videoType= QFileDialog.getOpenFileName(self,
                                        "打开视频",
                                        "",
                                        #" *.jpg;;*.png;;*.jpeg;;*.bmp")
                                        " *.mp4;;*.avi;;All Files (*)")
        elif button.objectName()=='pushButton_3':
            videoName=0#'http://admin:[email protected]:8081/'
        #cap = cv2.VideoCapture(str(videoName))
        th = Thread(self)
        th.changePixmap.connect(self.setImage)
        th.start()

    def setImage(self, image):
        self.label.setPixmap(QPixmap.fromImage(image))
    # def imageprocessing(self):
    #     print("hehe")
    #     imgName,imgType= QFileDialog.getOpenFileName(self,
    #                                 "打开图片",
    #                                 "",
    #                                 #" *.jpg;;*.png;;*.jpeg;;*.bmp")
    #                                 " *.jpg;;*.png;;*.jpeg;;*.bmp;;All Files (*)")
    #
    #     #利用qlabel显示图片
    #     print(str(imgName))
    #     png = QtGui.QPixmap(imgName).scaled(self.label_2.width(), self.label_2.height())#适应设计label时的大小
    #     self.label_2.setPixmap(png)


class Thread(QThread):#采用线程来播放视频

    changePixmap = pyqtSignal(QtGui.QImage)
    def run(self):

        cap = cv2.VideoCapture(videoName)
        print(videoName)
        while (cap.isOpened()==True):
            ret, frame = cap.read()
            if ret:
                #print(12)
                dududu.recognize(frame)
                rgbImage = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                convertToQtFormat = QtGui.QImage(rgbImage.data, rgbImage.shape[1], rgbImage.shape[0], QImage.Format_RGB888)#在这里可以对每帧图像进行处理，
                p = convertToQtFormat.scaled(770, 580, Qt.KeepAspectRatio)
                self.changePixmap.emit(p)
                time.sleep(0.03) #控制视频播放的速度
            else:
                break


if __name__ == '__main__':
    app = QtWidgets.QApplication(sys.argv)
    window = mywindow()
    window.show()
    sys.exit(app.exec_())

**代码下载地址：**下载链接：下载地址