def register(self):
    username = self.textEdit_1.toPlainText()
    password = self.textEdit_2.toPlainText()
    #print(username,password)


    # 执行的都是原生SQL语句
    sql = '''
    SELECT Name
    FROM   users
    '''

    has_name = 0
    for row in self.get(sql):
        if username == str(row[0]):
            #print(row)
            has_name = 1
            self.textEdit_1.setText('用户已经注册')
            break
    if not has_name:
        savepath = './images_db'
        if not os.path.exists(savepath):
            os.makedirs(savepath)
        # 打开摄像头
        detector = dlib.get_frontal_face_detector()
        predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
        cap = cv2.VideoCapture(0)
        for x in range(50):
            ret, image = cap.read()
            imagecopy = image.copy()
            img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

            # 使用人脸检测器检测每一帧图像中的人脸。并返回人脸数rects
            faces = detector(img_gray, 0)

            # 待会要显示在屏幕上的字体
            font = cv2.FONT_HERSHEY_SIMPLEX

            # 如果检测到人脸
            if (len(faces) != 0):

                for i in range(len(faces)):
                    # enumerate方法同时返回数据对象的索引和数据，k为索引，d为faces中的对象
                    for k, d in enumerate(faces):
                        # 用红色矩形框出人脸
                        cv2.rectangle(imagecopy, (d.left(), d.top()), (d.right(), d.bottom()), (0, 255, 0))
                        # 计算人脸热别框边长
                        face_width = d.right() - d.left()

                        shape = predictor(image, d)
                        mouth_width = (shape.part(54).x - shape.part(48).x) / face_width  # 嘴巴咧开程度
                        mouth_higth = (shape.part(66).y - shape.part(62).y) / face_width  # 嘴巴张开程度
                        for i in range(68):
                            if i > 27:
                                cv2.circle(imagecopy, (shape.part(i).x, shape.part(i).y), 2, (0, 0, 255), -1, 2)

            self.showimg(imagecopy)
            QApplication.processEvents()
        cv2.imwrite(savepath + '/' + str(username) + '.jpg', image, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
        val = (str(username),str(password))
        sql = 'insert into users (Name , Password) values (%s, %s)'
        self.add(sql, val)
        self.textEdit_1.setText('注册成功')

def login(self):
    global inname
    username = self.textEdit_1.toPlainText()
    password = self.textEdit_2.toPlainText()
    # print(username,password)

    # 执行的都是原生SQL语句
    sql = '''
            SELECT Name, Password
            FROM   users
            '''
    has_name = 0
    for row in self.get(sql):
        if username == 'administrator' and username == str(row[0]):
            has_name = 1
            if password == str(row[1]):
                print('密码正确，登陆成功')
                inname = str(row[0])
                MainWindow.close()
                ui4.show()
                try:
                    show = cv2.imread('./images_db/' + str(inname) + '.jpg')
                    ui4.showimg(show)
                except:
                    pass
                ui4.printf('密码正确，登陆成功')
                ui4.printf('欢迎您,管理员')
            else:
                self.textEdit_2.setText('密码错误')
            break

        if username == str(row[0]):
            # print(row)
            has_name = 1
            if password == str(row[1]):
                print('密码正确，登陆成功')
                inname = str(row[0])
                MainWindow.close()
                ui2.show()
                try:
                    show = cv2.imread('./images_db/' + str(inname) + '.jpg')
                    ui2.showimg(show)
                except:
                    pass
                ui2.printf('密码正确，登陆成功')
                ui2.printf('欢迎您 ' + str(inname))
            else:
                self.textEdit_2.setText('密码错误')
            break

    if not has_name:
        self.textEdit_1.setText('该用户不存在')

def get(self,sql):
    try:
        conn = MySQLdb.connect(
            host=host,  # 主机名
            user=user,  # 用户名
            passwd=passwd,  # 密码
            db=db)  # 数据库名

        # 查询前，必须先获取游标
        cursor = conn.cursor()
        cursor.execute(sql)
        results = cursor.fetchall()
        cursor.close()  # 关闭游标连接
        conn.close()  # 关闭数据库连接
        return results
    except Exception:
        print("查询失败")

def update(self,sql,cg):
    try:
        conn = MySQLdb.connect(
            host=host,  # 主机名
            user=user,  # 用户名
            passwd=passwd,  # 密码
            db=db)  # 数据库名

        # 查询前，必须先获取游标
        cursor = conn.cursor()
        cursor.execute(sql,cg)
        conn.commit()     # 提交
        cursor.close()  # 关闭游标连接
        conn.close()  # 关闭数据库连接

    except Exception:
        print("修改失败")

def add(self,sql,val):
    try:
        conn = MySQLdb.connect(
            host=host,  # 主机名
            user=user,  # 用户名
            passwd=passwd,  # 密码
            db=db)  # 数据库名

        # 查询前，必须先获取游标
        cursor = conn.cursor()
        cursor.execute(sql, val)
        conn.commit()     # 提交
        cursor.close()  # 关闭游标连接
        conn.close()  # 关闭数据库连接
    except Exception:
        print("插入失败")

def delete(self,sql):
    try:
        conn = MySQLdb.connect(
            host=host,  # 主机名
            user=user,  # 用户名
            passwd=passwd,  # 密码
            db=db)  # 数据库名

        # 查询前，必须先获取游标
        cursor = conn.cursor()
        cursor.execute(sql)
        conn.commit()
        cursor.close()  # 关闭游标连接
        conn.close()  # 关闭数据库连接
    except Exception:
        print("删除失败")

import cv2
import numpy as np
import face_recognition
from age_detection import f_my_age
from gender_detection import f_my_gender
from race_detection import f_my_race
from emotion_detection import f_emotion_detection
from my_face_recognition import f_main



# instanciar detectores
age_detector = f_my_age.Age_Model()
gender_detector =  f_my_gender.Gender_Model()
race_detector = f_my_race.Race_Model()
emotion_detector = f_emotion_detection.predict_emotions()
rec_face = f_main.rec()
#----------------------------------------------



def get_face_info(im):
    # face detection
    boxes_face = face_recognition.face_locations(im)
    out = []
    if len(boxes_face)!=0:
        for box_face in boxes_face:
            # segmento rostro
            box_face_fc = box_face
            x0,y1,x1,y0 = box_face
            box_face = np.array([y0,x0,y1,x1])
            face_features = {
                "name":[],
                "age":[],
                "gender":[],
                "race":[],
                "emotion":[],
                "bbx_frontal_face":box_face             
            } 

            face_image = im[x0:x1,y0:y1]

            # -------------------------------------- face_recognition ---------------------------------------
            face_features["name"] = rec_face.recognize_face2(im,[box_face_fc])[0]

            # -------------------------------------- age_detection ---------------------------------------
            age = age_detector.predict_age(face_image)
            face_features["age"] = str(round(age,2))

            # -------------------------------------- gender_detection ---------------------------------------
            face_features["gender"] = gender_detector.predict_gender(face_image)

            # -------------------------------------- race_detection ---------------------------------------
            face_features["race"] = race_detector.predict_race(face_image)

            # -------------------------------------- emotion_detection ---------------------------------------
            _,emotion = emotion_detector.get_emotion(im,[box_face])
            face_features["emotion"] = emotion[0]

            # -------------------------------------- out ---------------------------------------       
            out.append(face_features)
    else:
        face_features = {
            "name":[],
            "age":[],
            "gender":[],
            "race":[],
            "emotion":[],
            "bbx_frontal_face":[]             
        }
        out.append(face_features)
    return out



def bounding_box(out,img):
    for data_face in out:
        box = data_face["bbx_frontal_face"]
        if len(box) == 0:
            continue
        else:
            x0,y0,x1,y1 = box
            img = cv2.rectangle(img,
                            (x0,y0),
                            (x1,y1),
                            (0,0,255),2);
            thickness = 2
            fontSize = 0.6
            step = 13

            try:
                cv2.putText(img, "age: " +data_face["age"], (x0, y0-7), cv2.FONT_HERSHEY_SIMPLEX, fontSize, (0,255,0), thickness)
            except:
                pass
            try:
                cv2.putText(img, "gender: " +data_face["gender"], (x0, y0-step-15*1), cv2.FONT_HERSHEY_SIMPLEX, fontSize, (0,255,0), thickness)
            except:
                pass
            try:
                cv2.putText(img, "race: " +data_face["race"], (x0, y0-step-15*2), cv2.FONT_HERSHEY_SIMPLEX, fontSize, (0,255,0), thickness)
            except:
                pass
            try:
                cv2.putText(img, "emotion: " +data_face["emotion"], (x0, y0-step-15*3), cv2.FONT_HERSHEY_SIMPLEX, fontSize, (0,255,0), thickness)
            except:
                pass
            try:
                cv2.putText(img, "name: " +data_face["name"], (x0, y0-step-15*4), cv2.FONT_HERSHEY_SIMPLEX, fontSize, (0,255,0), thickness)
            except:
                pass
    return img

#encoding:utf-8
import pandas as pd
import numpy as np
import scipy.misc as sm
import os
 
emotions = {
    '0':'anger', #生气
    '1':'disgust', #厌恶
    '2':'fear', #恐惧
    '3':'happy', #开心
    '4':'sad', #伤心
    '5':'surprised', #惊讶
    '6':'normal', #中性
}
 
#创建文件夹
def createDir(dir):
    if os.path.exists(dir) is False:
        os.makedirs(dir)
 
def saveImageFromFer2013(file):
 
 
    #读取csv文件
    faces_data = pd.read_csv(file)
    imageCount = 0
    #遍历csv文件内容，并将图片数据按分类保存
    for index in range(len(faces_data)):
        #解析每一行csv文件内容
        emotion_data = faces_data.loc[index][0]
        image_data = faces_data.loc[index][1]
        usage_data = faces_data.loc[index][2]
        #将图片数据转换成48*48
        data_array = list(map(float, image_data.split()))
        data_array = np.asarray(data_array)
        image = data_array.reshape(48, 48)
 
        #选择分类，并创建文件名
        dirName = usage_data
        emotionName = emotions[str(emotion_data)]
 
        #图片要保存的文件夹
        imagePath = os.path.join(dirName, emotionName)
 
        # 创建“用途文件夹”和“表情”文件夹
        createDir(dirName)
        createDir(imagePath)
 
        #图片文件名
        imageName = os.path.join(imagePath, str(index) + '.jpg')
 
        sm.toimage(image).save(imageName)
        imageCount = index
    print('总共有' + str(imageCount) + '张图片')
 
 
if __name__ == '__main__':
    saveImageFromFer2013('fer2013.csv')

# start the video stream thread

print("[INFO] starting video stream thread...")

vs = VideoStream(src=args["webcam"]).start()

time.sleep(1.0)

# loop over frames from the video stream

while True:

    # grab the frame from the threaded video file stream, resize

    # it, and convert it to grayscale

    # channels)

    frame = vs.read()

    frame = imutils.resize(frame, width=450)

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # detect faces in the grayscale frame

    rects = detector(gray, 0)

# loop over the face detections

for rect in rects:

    # determine the facial landmarks for the face region, then

    # convert the facial landmark (x, y)-coordinates to a NumPy

    # array

    shape = predictor(gray, rect)

    shape = face_utils.shape_to_np(shape)

    # extract the left and right eye coordinates, then use the

    # coordinates to compute the eye aspect ratio for both eyes

    leftEye = shape[lStart:lEnd]

    rightEye = shape[rStart:rEnd]

    leftEAR = eye_aspect_ratio(leftEye)

    rightEAR = eye_aspect_ratio(rightEye)

    # average the eye aspect ratio together for both eyes

    ear = (leftEAR + rightEAR) / 2.0

# check to see if the eye aspect ratio is below the blink

# threshold, and if so, increment the blink frame counter

if ear < EYE_AR_THRESH:

    COUNTER += 1

    # if the eyes were closed for a sufficient number of

    # then sound the alarm

    if COUNTER >= EYE_AR_CONSEC_FRAMES:

        # if the alarm is not on, turn it on

        if not ALARM_ON:

            ALARM_ON = True

            # check to see if an alarm file was supplied,

            # and if so, start a thread to have the alarm

            # sound played in the background

            if args["alarm"] != "":

                t = Thread(target=sound_alarm,

                    args=(args["alarm"],))

                t.deamon = True

                t.start()

        # draw an alarm on the frame

        frame=cv2ImgAddText(frame,"醒醒，别睡!",10,30,(255, 0, 0),30)

# otherwise, the eye aspect ratio is not below the blink

# threshold, so reset the counter and alarm

else:

    COUNTER = 0

    ALARM_ON = False