enrollment & Authentication 추가

+# USAGE
+# python detect_mask_video.py
+
+# import the necessary packages
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.models import load_model
+import numpy as np
+import argparse
+import os
+import cv2
+import sys
+from PyQt5 import QtCore
+from PyQt5 import QtWidgets
+from PyQt5 import QtGui
+from PyQt5 import QtTest
+import pyaudio
+import wave
+import requests
+
+#Audio
+# Record Audio의 startRecording 메서드에서 input_device_index는 기기마다 다름.
+FORMAT = pyaudio.paInt16
+CHANNELS = 1
+RATE = 16000
+CHUNK = 1024
+MAX_RECORD_SECONDS = 30
+WAVE_OUTPUT_FILENAME = "audiofile\\file.wav"
+#URL
+URL = 'http://163.180.146.68:7777/{}'
+#SpeakerRecognition
+THRESHOLD = 0.8
+
+class ShowVideo(QtCore.QObject):
+    flag_detect_mask = True
+    run_video = True
+
+    camera = cv2.VideoCapture(0)  # 연결된 영상장치 index, 기본은 0
+
+    ret, image = camera.read()  # 2개의 값 리턴, 첫 번째는 프레임 읽음여부, 두 번째는 프레임 자체
+    height, width = image.shape[:2]
+
+    VideoSignal1 = QtCore.pyqtSignal(QtGui.QImage)  # VideoSignal1이라는 사용자 정의 시그널 생성
+
+    def __init__(self, parent=None):
+        super(ShowVideo, self).__init__(parent)
+
+    @QtCore.pyqtSlot()
+    def startVideo(self, faceNet, maskNet):
+        global image
+
+        run_video = True
+        self.flag_detect_mask = True
+        while run_video:
+            ret, image = self.camera.read()
+
+            # detect faces in the frame and determine if they are wearing a
+            # face mask or not
+            (locs, preds) = detect_and_predict_mask(image, faceNet, maskNet)
+
+            QtWidgets.QApplication.processEvents()
+            if self.flag_detect_mask:
+                frame = image
+                # loop over the detected face locations and their corresponding
+                # locations
+                for (box, pred) in zip(locs, preds):
+                    # unpack the bounding box and predictions
+                    (startX, startY, endX, endY) = box
+                    (mask, withoutMask) = pred
+
+                    # determine the class label and color we'll use to draw
+                    # the bounding box and text
+                    label = "Mask" if mask > withoutMask else "No Mask"  # 박스 상단 출력 string
+                    color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
+
+                    # include the probability in the label
+                    label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
+
+                    # display the label and bounding box rectangle on the output
+                    # frame
+                    cv2.putText(frame, label, (startX, startY - 10),  # label에 string들어감
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+                    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
+                image = frame
+            ###
+            color_swapped_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+            qt_image1 = QtGui.QImage(color_swapped_image.data,
+                                     self.width,
+                                     self.height,
+                                     color_swapped_image.strides[0],
+                                     QtGui.QImage.Format_RGB888)
+            self.VideoSignal1.emit(qt_image1)
+
+            loop = QtCore.QEventLoop()
+            QtCore.QTimer.singleShot(25, loop.quit)  # 25 ms
+            loop.exec_()
+
+    @QtCore.pyqtSlot()
+    def maskdetectionoff(self):
+        self.flag_detect_mask = False
+
+
+class ImageViewer(QtWidgets.QWidget):
+    def __init__(self, parent=None):
+        super(ImageViewer, self).__init__(parent)
+        self.image = QtGui.QImage()
+        self.setAttribute(QtCore.Qt.WA_OpaquePaintEvent)
+
+    def paintEvent(self, event):
+        painter = QtGui.QPainter(self)
+        painter.drawImage(0, 0, self.image)
+        self.image = QtGui.QImage()
+
+    def initUI(self):
+        self.setWindowTitle('Webcam')
+
+    @QtCore.pyqtSlot(QtGui.QImage)
+    def setImage(self, image):
+        if image.isNull():
+            print("Viewer Dropped frame!")
+
+        self.image = image
+        if image.size() != self.size():
+            self.setFixedSize(image.size())
+        self.update()
+
+
+def detect_and_predict_mask(frame, faceNet, maskNet):
+    # grab the dimensions of the frame and then construct a blob
+    # from it
+    (h, w) = frame.shape[:2]
+    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
+                                 (104.0, 177.0, 123.0))
+
+    # pass the blob through the network and obtain the face detections
+    faceNet.setInput(blob)
+    detections = faceNet.forward()
+
+    # initialize our list of faces, their corresponding locations,
+    # and the list of predictions from our face mask network
+    faces = []
+    locs = []
+    preds = []
+
+    # loop over the detections
+    for i in range(0, detections.shape[2]):
+        # extract the confidence (i.e., probability) associated with
+        # the detection
+        confidence = detections[0, 0, i, 2]
+
+        # filter out weak detections by ensuring the confidence is
+        # greater than the minimum confidence
+        if confidence > args["confidence"]:
+            # compute the (x, y)-coordinates of the bounding box for
+            # the object
+            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (startX, startY, endX, endY) = box.astype("int")
+
+            # ensure the bounding boxes fall within the dimensions of
+            # the frame
+            (startX, startY) = (max(0, startX), max(0, startY))
+            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))
+
+            # extract the face ROI, convert it from BGR to RGB channel
+            # ordering, resize it to 224x224, and preprocess it
+            face = frame[startY:endY, startX:endX]
+            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+            face = cv2.resize(face, (224, 224))
+            face = img_to_array(face)
+            face = preprocess_input(face)
+
+            # add the face and bounding boxes to their respective
+            # lists
+            faces.append(face)
+            locs.append((startX, startY, endX, endY))
+
+    # only make a predictions if at least one face was detected
+    if len(faces) > 0:
+        # for faster inference we'll make batch predictions on *all*
+        # faces at the same time rather than one-by-one predictions
+        # in the above `for` loop
+        faces = np.array(faces, dtype="float32")
+        preds = maskNet.predict(faces, batch_size=32)
+
+    # return a 2-tuple of the face locations and their corresponding
+    # locations
+    return (locs, preds)
+
+
+class SpeakerRecognition(QtWidgets.QWidget):
+    verification_url = URL.format('verification')
+    identification_url = URL.format('identification')
+
+    def __init__(self, parent=None):
+        super(SpeakerRecognition, self).__init__(parent)
+        self.initUI()
+
+    def initUI(self):
+        self.label_1_1 = QtWidgets.QLabel('Result Message: ', self)
+        self.label_1_2 = QtWidgets.QLabel('', self)
+        self.push_button5 =QtWidgets.QPushButton('Authenticate', self)
+        self.push_button5.clicked.connect(self.doAction)
+
+    def verification(self, speaker):
+        try:
+            with open(WAVE_OUTPUT_FILENAME, 'rb') as file_opened:
+                files = {'file': file_opened}
+                data = {'enroll_speaker': speaker}
+                r = requests.post(self.verification_url, files=files, data=data)
+                print(r.text)
+                return r.text
+        except FileNotFoundError:
+            return False
+
+    def identification(self):
+        try:
+            with open(WAVE_OUTPUT_FILENAME, 'rb') as file_opened:
+                files = {'file': file_opened}
+                r = requests.post(self.identification_url, files=files)
+                print(r.text)
+                return r.text
+        except FileNotFoundError:
+            return False
+
+    def recognition(self):
+        speaker = self.identification()
+        if speaker == False:
+            print('Record voice first!')
+            return False
+
+        percentage = self.verification(speaker)
+        print(speaker, percentage)
+
+        if float(percentage) >= THRESHOLD:
+            result = '승인! 등록된 화자입니다.'
+            #result = speaker
+        else:
+            result = '등록되지 않은 화자입니다!'
+        return result
+
+    @QtCore.pyqtSlot()
+    def doAction(self):
+        recog = self.recognition()
+        if recog == False:
+            self.label_1_2.setText('Voice not recorded, record voice first!')
+        else:
+            self.label_1_2.setText(recog)
+
+class RecordAudio(QtCore.QObject):
+    isrecording = False
+    frames = []
+
+    def __init__(self, parent=None):
+        super(RecordAudio, self).__init__(parent)
+
+    @QtCore.pyqtSlot()
+    def startRecording(self):
+        # start Recording
+        self.audio = pyaudio.PyAudio()
+        self.stream = self.audio.open(format=pyaudio.paInt16,
+                                      channels=CHANNELS,
+                                      rate=RATE,
+                                      input=True,
+                                      input_device_index=1,  # 기기마다 마이크 인덱스 다름
+                                      frames_per_buffer=CHUNK)
+        self.isrecording = True
+        print("recording...")
+
+        # frames = []
+        self.frames.clear()
+
+        for i in range(0, int(RATE / CHUNK * MAX_RECORD_SECONDS)):
+            QtWidgets.QApplication.processEvents()
+            if self.isrecording:
+                data = self.stream.read(CHUNK)
+                self.frames.append(data)
+            else:
+                print("Stopped recording")
+                break
+        print("finished recording")
+
+        # stop Recording
+        self.stream.stop_stream()
+        self.stream.close()
+        self.audio.terminate()
+        waveFile = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
+        waveFile.setnchannels(CHANNELS)
+        waveFile.setsampwidth(self.audio.get_sample_size(FORMAT))
+        waveFile.setframerate(RATE)
+        waveFile.writeframes(b''.join(self.frames))
+        waveFile.close()
+        self.frames.clear()
+
+    def stopRecording(self):
+        print("stop called")
+        self.isrecording = False
+
+    def switch(self):
+        if self.isrecording:
+            QtTest.QTest.qWait(1 * 1000)
+            self.stopRecording()
+        else:
+            self.startRecording()
+
+
+class RecordViewer(QtWidgets.QWidget):
+    def __init__(self, parent=None):
+        super(RecordViewer, self).__init__(parent)
+        self.initUI()
+
+    def initUI(self):
+        self.pbar = QtWidgets.QProgressBar(self)
+        self.pbar.setFixedWidth(400)
+        self.pbar.setMaximum(MAX_RECORD_SECONDS)
+        self.pbar.setAlignment(QtCore.Qt.AlignCenter)
+
+        self.push_button3 = QtWidgets.QPushButton('Start Audio Record', self)
+        self.push_button3.clicked.connect(self.doAction)
+
+        self.timer = QtCore.QBasicTimer()
+        self.step = 0
+
+    def timerEvent(self, e):
+        if self.step >= MAX_RECORD_SECONDS:
+            self.timer.stop()
+            self.push_button3.setText("Restart")
+            return
+        self.step = self.step + 1
+        self.pbar.setValue(self.step)
+        self.pbar.setFormat("%d sec" % self.step)
+
+    @QtCore.pyqtSlot()
+    def doAction(self):
+        if self.timer.isActive():
+            self.timer.stop()
+            self.push_button3.setText("Restart")
+        else:
+            self.pbar.reset()
+            self.step = 0
+            self.timer.start(1000, self)  # 1000/1000초마다 timer실행
+            self.push_button3.setText("Stop")
+
+
+if __name__ == '__main__':
+    # construct the argument parser and parse the arguments
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-f", "--face", type=str, default="face_detector",
+                    help="path to face detector model directory")
+    ap.add_argument("-m", "--model", type=str, default="mask_detector.model",
+                    help="path to trained face mask detector model")
+    ap.add_argument("-c", "--confidence", type=float, default=0.5,
+                    help="minimum probability to filter weak detections")
+    args = vars(ap.parse_args())
+
+    # load our serialized face detector model from disk
+    print("[INFO] loading face detector model...")
+    prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
+    weightsPath = os.path.sep.join([args["face"],
+                                    "res10_300x300_ssd_iter_140000.caffemodel"])
+    faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
+
+    # load the face mask detector model from disk
+    print("[INFO] loading face mask detector model...")
+    maskNet = load_model(args["model"])
+
+    app = QtWidgets.QApplication(sys.argv)  # app 생성
+
+    thread = QtCore.QThread()
+    thread.start()
+    vid = ShowVideo()
+    vid.moveToThread(thread)
+
+    # test
+    thread2 = QtCore.QThread()
+    thread2.start()
+    aud = RecordViewer()
+    aud.moveToThread(thread2)
+
+    # test
+    thread3 = QtCore.QThread()
+    thread3.start()
+    mic = RecordAudio()
+    mic.moveToThread(thread3)
+
+    # test
+    thread4 = QtCore.QThread()
+    thread4.start()
+    sr = SpeakerRecognition()
+    sr.moveToThread(thread4)
+
+    image_viewer1 = ImageViewer()
+
+    vid.VideoSignal1.connect(image_viewer1.setImage)
+
+    push_button1 = QtWidgets.QPushButton('Start Mask Detection')
+    push_button2 = QtWidgets.QPushButton('Mask Detection Off')
+    push_button4 = QtWidgets.QPushButton('Close')
+
+    push_button1.clicked.connect(lambda: vid.startVideo(faceNet, maskNet))
+    push_button2.clicked.connect(vid.maskdetectionoff)
+    aud.push_button3.clicked.connect(mic.switch)
+    push_button4.clicked.connect(sys.exit)
+
+    L_groupBox = QtWidgets.QGroupBox("Mask Detection")
+    LR_layout = QtWidgets.QVBoxLayout()
+    LR_layout.addWidget(push_button1)
+    LR_layout.addWidget(push_button2)
+    LR_layout.addStretch(1)
+
+    L_horizontal_layout1 = QtWidgets.QHBoxLayout()
+    L_horizontal_layout1.addWidget(image_viewer1)
+    L_horizontal_layout1.addLayout(LR_layout)
+    L_groupBox.setLayout(L_horizontal_layout1)
+
+    RU_groupBox = QtWidgets.QGroupBox("Voice Record")
+    pbar_layout = QtWidgets.QHBoxLayout()
+    pbar_layout.addWidget(aud.pbar)
+    pbar_layout.addStretch(1)
+
+    RL_label1 = QtWidgets.QLabel()
+    RL_label1.setText("Max Record Time: 30 sec")
+    RL_label2 = QtWidgets.QLabel()
+    RL_label2.setText("Press Start/Restart to begin recording")
+
+    RL_layout = QtWidgets.QVBoxLayout()
+    RL_layout.addLayout(pbar_layout)
+    RL_layout.addWidget(RL_label1)
+    RL_layout.addWidget(RL_label2)
+    RL_layout.addStretch(1)
+
+    push_button3_layout = QtWidgets.QHBoxLayout()
+    push_button3_layout.addWidget(aud.push_button3)
+    # push_button3_layout.addStretch(1)
+
+    # close_layout = QtWidgets.QHBoxLayout()
+    # close_layout.addWidget(push_button4)
+
+    RR_layout = QtWidgets.QVBoxLayout()
+    RR_layout.addLayout(push_button3_layout)
+    RR_layout.addStretch(1)
+    # RR_layout.addLayout(close_layout)
+
+    R_horizontal_layout2 = QtWidgets.QHBoxLayout()
+    R_horizontal_layout2.addLayout(RL_layout)
+    R_horizontal_layout2.addLayout(RR_layout)
+    RU_groupBox.setLayout(R_horizontal_layout2)
+
+
+    RD_groupBox = QtWidgets.QGroupBox("Speaker Recognition")
+
+    label_1_layout = QtWidgets.QHBoxLayout()
+    label_1_layout.addWidget(sr.label_1_1)
+    label_1_layout.addWidget(sr.label_1_2)
+    label_1_layout.addStretch(1)
+
+    RDL_layout = QtWidgets.QVBoxLayout()
+    RDL_layout.addLayout(label_1_layout)
+    RDL_layout.addStretch(1)
+
+    push_button5_layout = QtWidgets.QHBoxLayout()
+    push_button5_layout.addWidget(sr.push_button5)
+
+    close_layout = QtWidgets.QHBoxLayout()
+    close_layout.addWidget(push_button4)
+
+    RDR_layout = QtWidgets.QVBoxLayout()
+    RDR_layout.addLayout(push_button5_layout)
+    RDR_layout.addStretch(1)
+    RDR_layout.addLayout(close_layout)
+
+    RD_horizontal_layout = QtWidgets.QHBoxLayout()
+    RD_horizontal_layout.addLayout(RDL_layout)
+    RD_horizontal_layout.addLayout(RDR_layout)
+    RD_groupBox.setLayout(RD_horizontal_layout)
+
+    R_layout = QtWidgets.QVBoxLayout()
+    R_layout.addWidget(RU_groupBox)
+    R_layout.addWidget(RD_groupBox)
+
+    layout = QtWidgets.QHBoxLayout()
+    layout.addWidget(L_groupBox)
+    layout.addLayout(R_layout)
+
+    layout_widget = QtWidgets.QWidget()
+    layout_widget.setLayout(layout)
+
+    main_window = QtWidgets.QMainWindow()
+    main_window.setGeometry(150, 150, 500, 500)  # test
+    main_window.setCentralWidget(layout_widget)
+    main_window.setWindowTitle('마스크 디텍션 및 화자 식별을 통한 입출입 시스템')  # main window 제목
+    main_window.show()
+    sys.exit(app.exec_())  # 프로그램 대기상태 유지, 무한루프

+# USAGE
+# python detect_mask_video.py
+
+# import the necessary packages
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.models import load_model
+import numpy as np
+import argparse
+import os
+import cv2
+import sys
+from PyQt5 import QtCore
+from PyQt5 import QtWidgets
+from PyQt5 import QtGui
+from PyQt5 import QtTest
+import pyaudio
+import wave
+import requests
+
+#Audio
+# Record Audio의 startRecording 메서드에서 input_device_index는 기기마다 다름.
+FORMAT = pyaudio.paInt16
+CHANNELS = 1
+RATE = 16000
+CHUNK = 1024
+MAX_RECORD_SECONDS = 30
+WAVE_OUTPUT_FILENAME = "saved_voice\\audiofile\\file.wav"
+WAVE_ENROLL_FILENAME = "saved_voice\\enrollfile\\file.wav"
+#URL
+URL = 'http://163.180.146.68:7777/{}'
+#SpeakerRecognition
+THRESHOLD = 0.8
+SPEAKER_ID = 'NA'
+
+class ShowVideo(QtCore.QObject):
+    flag_detect_mask = True
+    run_video = True
+
+    camera = cv2.VideoCapture(0)  # 연결된 영상장치 index, 기본은 0
+
+    ret, image = camera.read()  # 2개의 값 리턴, 첫 번째는 프레임 읽음여부, 두 번째는 프레임 자체
+    height, width = image.shape[:2]
+
+    VideoSignal1 = QtCore.pyqtSignal(QtGui.QImage)  # VideoSignal1이라는 사용자 정의 시그널 생성
+
+    def __init__(self, parent=None):
+        super(ShowVideo, self).__init__(parent)
+
+    @QtCore.pyqtSlot()
+    def startVideo(self, faceNet, maskNet):
+        global image
+
+        run_video = True
+        self.flag_detect_mask = True
+        while run_video:
+            ret, image = self.camera.read()
+
+            # detect faces in the frame and determine if they are wearing a
+            # face mask or not
+            QtWidgets.QApplication.processEvents()
+            if self.flag_detect_mask:
+                (locs, preds) = detect_and_predict_mask(image, faceNet, maskNet)
+
+                # QtWidgets.QApplication.processEvents()
+            # if self.flag_detect_mask:
+                frame = image
+                # loop over the detected face locations and their corresponding
+                # locations
+                for (box, pred) in zip(locs, preds):
+                    # unpack the bounding box and predictions
+                    (startX, startY, endX, endY) = box
+                    (mask, withoutMask) = pred
+
+                    # determine the class label and color we'll use to draw
+                    # the bounding box and text
+                    label = "Mask" if mask > withoutMask else "No Mask"  # 박스 상단 출력 string
+                    color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
+
+                    # include the probability in the label
+                    label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
+
+                    # display the label and bounding box rectangle on the output
+                    # frame
+                    cv2.putText(frame, label, (startX, startY - 10),  # label에 string들어감
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+                    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
+                image = frame
+            ###
+            color_swapped_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+            qt_image1 = QtGui.QImage(color_swapped_image.data,
+                                     self.width,
+                                     self.height,
+                                     color_swapped_image.strides[0],
+                                     QtGui.QImage.Format_RGB888)
+            self.VideoSignal1.emit(qt_image1)
+
+            loop = QtCore.QEventLoop()
+            QtCore.QTimer.singleShot(25, loop.quit)  # 25 ms
+            loop.exec_()
+
+    @QtCore.pyqtSlot()
+    def maskdetectionoff(self):
+        self.flag_detect_mask = False
+
+
+class ImageViewer(QtWidgets.QWidget):
+    def __init__(self, parent=None):
+        super(ImageViewer, self).__init__(parent)
+        self.image = QtGui.QImage()
+        self.setAttribute(QtCore.Qt.WA_OpaquePaintEvent)
+
+    def paintEvent(self, event):
+        painter = QtGui.QPainter(self)
+        painter.drawImage(0, 0, self.image)
+        self.image = QtGui.QImage()
+
+    def initUI(self):
+        self.setWindowTitle('Webcam')
+
+    @QtCore.pyqtSlot(QtGui.QImage)
+    def setImage(self, image):
+        if image.isNull():
+            print("Viewer Dropped frame!")
+
+        self.image = image
+        if image.size() != self.size():
+            self.setFixedSize(image.size())
+        self.update()
+
+
+def detect_and_predict_mask(frame, faceNet, maskNet):
+    # grab the dimensions of the frame and then construct a blob
+    # from it
+    (h, w) = frame.shape[:2]
+    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
+                                 (104.0, 177.0, 123.0))
+
+    # pass the blob through the network and obtain the face detections
+    faceNet.setInput(blob)
+    detections = faceNet.forward()
+
+    # initialize our list of faces, their corresponding locations,
+    # and the list of predictions from our face mask network
+    faces = []
+    locs = []
+    preds = []
+
+    # loop over the detections
+    for i in range(0, detections.shape[2]):
+        # extract the confidence (i.e., probability) associated with
+        # the detection
+        confidence = detections[0, 0, i, 2]
+
+        # filter out weak detections by ensuring the confidence is
+        # greater than the minimum confidence
+        if confidence > args["confidence"]:
+            # compute the (x, y)-coordinates of the bounding box for
+            # the object
+            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (startX, startY, endX, endY) = box.astype("int")
+
+            # ensure the bounding boxes fall within the dimensions of
+            # the frame
+            (startX, startY) = (max(0, startX), max(0, startY))
+            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))
+
+            # extract the face ROI, convert it from BGR to RGB channel
+            # ordering, resize it to 224x224, and preprocess it
+            face = frame[startY:endY, startX:endX]
+            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+            face = cv2.resize(face, (224, 224))
+            face = img_to_array(face)
+            face = preprocess_input(face)
+
+            # add the face and bounding boxes to their respective
+            # lists
+            faces.append(face)
+            locs.append((startX, startY, endX, endY))
+
+    # only make a predictions if at least one face was detected
+    if len(faces) > 0:
+        # for faster inference we'll make batch predictions on *all*
+        # faces at the same time rather than one-by-one predictions
+        # in the above `for` loop
+        faces = np.array(faces, dtype="float32")
+        preds = maskNet.predict(faces, batch_size=32)
+
+    # return a 2-tuple of the face locations and their corresponding
+    # locations
+    return (locs, preds)
+
+
+class SpeakerRecognition(QtWidgets.QWidget):
+    verification_url = URL.format('verification')
+    identification_url = URL.format('identification')
+    enrollment_url = URL.format('enroll')
+    speaker_id = ''
+
+    def __init__(self, parent=None):
+        super(SpeakerRecognition, self).__init__(parent)
+        self.initUI()
+
+    def initUI(self):
+        self.label_1_1 = QtWidgets.QLabel('Result Message: ', self)
+        self.label_1_2 = QtWidgets.QLabel('', self)
+        self.push_button5 =QtWidgets.QPushButton('Authenticate', self)
+        self.push_button5.clicked.connect(self.doAction)
+
+        self.dialog_button = QtWidgets.QPushButton('화자 ID 입력:', self)
+        self.dialog_button.clicked.connect(self.showDialog)
+        self.le = QtWidgets.QLineEdit(self)
+
+        self.register_button = QtWidgets.QPushButton('Register new voice', self)
+        self.register_button.clicked.connect(self.switch_enrollment)
+
+    def verification(self, speaker):
+        try:
+            with open(WAVE_OUTPUT_FILENAME, 'rb') as file_opened:
+                files = {'file': file_opened}
+                data = {'enroll_speaker': speaker}
+                r = requests.post(self.verification_url, files=files, data=data)
+                print(r.text)
+                return r.text
+        except FileNotFoundError:
+            return False
+
+    def identification(self):
+        try:
+            with open(WAVE_OUTPUT_FILENAME, 'rb') as file_opened:
+                files = {'file': file_opened}
+                r = requests.post(self.identification_url, files=files)
+                print(r.text)
+                return r.text
+        except FileNotFoundError:
+            return False
+
+    def recognition(self):
+        speaker = self.identification()
+        if speaker == False:
+            print('Record voice first!')
+            return False
+
+        percentage = self.verification(speaker)
+        print(speaker, percentage)
+
+        if float(percentage) >= THRESHOLD:
+            result = '승인! 등록된 화자입니다.'
+            #result = speaker
+        else:
+            result = '등록되지 않은 화자입니다!'
+        return result
+
+    @QtCore.pyqtSlot()
+    def doAction(self):
+        recog = self.recognition()
+        if recog == False:
+            self.label_1_2.setText('Voice not recorded, record voice first!')
+        else:
+            self.label_1_2.setText(recog)
+
+    def enrollment(self, speaker_id):
+        try:
+            if speaker_id == '':
+                return 0
+            with open(WAVE_ENROLL_FILENAME, 'rb') as file_opened:
+                files = {'file': file_opened}
+                data = {'enroll_speaker': speaker_id}
+                r = requests.post(self.enrollment_url, files=files, data=data)
+                print(r.text)
+                return r.text
+        except FileNotFoundError:
+            return 1
+
+    def switch_enrollment(self):
+        enroll = self.enrollment(self.speaker_id)
+        if enroll == 1:
+            self.label_1_2.setText('Voice not recorded, record voice first!')
+        elif enroll == 0:
+            self.label_1_2.setText('No speaker ID input!')
+        else:
+            self.label_1_2.setText("""New speaker registered!('%s')""" % self.speaker_id)
+            self.speaker_id = ''
+            self.le.setText(self.speaker_id)
+
+    def showDialog(self):
+        text, ok = QtWidgets.QInputDialog.getText(self, '화자 등록',
+                                                  '등록할 화자 ID(Unique 값)을 입력하십시오:')
+        if ok:
+            self.le.setText(str(text))
+            self.speaker_id = str(text)
+
+class RecordAudio(QtCore.QObject):
+    isrecording = False
+    frames = []
+
+    def __init__(self, parent=None):
+        super(RecordAudio, self).__init__(parent)
+
+    @QtCore.pyqtSlot()
+    def startRecording(self):
+        # start Recording
+        self.audio = pyaudio.PyAudio()
+        self.stream = self.audio.open(format=pyaudio.paInt16,
+                                      channels=CHANNELS,
+                                      rate=RATE,
+                                      input=True,
+                                      input_device_index=1,  # 기기마다 마이크 인덱스 다름
+                                      frames_per_buffer=CHUNK)
+        self.isrecording = True
+        print("recording...")
+
+        # frames = []
+        self.frames.clear()
+
+        for i in range(0, int(RATE / CHUNK * MAX_RECORD_SECONDS)):
+            QtWidgets.QApplication.processEvents()
+            if self.isrecording:
+                data = self.stream.read(CHUNK)
+                self.frames.append(data)
+            else:
+                print("Stopped recording")
+                break
+        print("finished recording")
+
+        # stop Recording
+        self.stream.stop_stream()
+        self.stream.close()
+        self.audio.terminate()
+        waveFile = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
+        waveFile.setnchannels(CHANNELS)
+        waveFile.setsampwidth(self.audio.get_sample_size(FORMAT))
+        waveFile.setframerate(RATE)
+        waveFile.writeframes(b''.join(self.frames))
+        waveFile.close()
+        self.frames.clear()
+
+    def stopRecording(self):
+        print("stop called")
+        self.isrecording = False
+
+    def switch(self):
+        if self.isrecording:
+            QtTest.QTest.qWait(1 * 1000)
+            self.stopRecording()
+        else:
+            self.startRecording()
+
+class RecordAudio_enroll(QtCore.QObject):
+    isrecording = False
+    frames = []
+
+    def __init__(self, parent=None):
+        super(RecordAudio_enroll, self).__init__(parent)
+
+    @QtCore.pyqtSlot()
+    def startRecording(self):
+        # start Recording
+        self.audio = pyaudio.PyAudio()
+        self.stream = self.audio.open(format=pyaudio.paInt16,
+                                      channels=CHANNELS,
+                                      rate=RATE,
+                                      input=True,
+                                      input_device_index=1,  # 기기마다 마이크 인덱스 다름
+                                      frames_per_buffer=CHUNK)
+        self.isrecording = True
+        print("recording...")
+
+        # frames = []
+        self.frames.clear()
+
+        for i in range(0, int(RATE / CHUNK * MAX_RECORD_SECONDS)):
+            QtWidgets.QApplication.processEvents()
+            if self.isrecording:
+                data = self.stream.read(CHUNK)
+                self.frames.append(data)
+            else:
+                print("Stopped recording")
+                break
+        print("finished recording")
+
+        # stop Recording
+        self.stream.stop_stream()
+        self.stream.close()
+        self.audio.terminate()
+        waveFile = wave.open(WAVE_ENROLL_FILENAME, 'wb')
+        waveFile.setnchannels(CHANNELS)
+        waveFile.setsampwidth(self.audio.get_sample_size(FORMAT))
+        waveFile.setframerate(RATE)
+        waveFile.writeframes(b''.join(self.frames))
+        waveFile.close()
+        self.frames.clear()
+
+    def stopRecording(self):
+        print("stop called")
+        self.isrecording = False
+
+    def switch(self):
+        if self.isrecording:
+            QtTest.QTest.qWait(1 * 1000)
+            self.stopRecording()
+        else:
+            self.startRecording()
+
+class RecordViewer(QtWidgets.QWidget):
+    def __init__(self, parent=None):
+        super(RecordViewer, self).__init__(parent)
+        self.initUI()
+
+    def initUI(self):
+        self.pbar = QtWidgets.QProgressBar(self)
+        self.pbar.setFixedWidth(400)
+        self.pbar.setMaximum(MAX_RECORD_SECONDS)
+        self.pbar.setAlignment(QtCore.Qt.AlignCenter)
+
+        self.push_button3 = QtWidgets.QPushButton('Start Audio Record', self)
+        self.push_button3.clicked.connect(self.doAction)
+
+        self.timer = QtCore.QBasicTimer()
+        self.step = 0
+
+    def timerEvent(self, e):
+        if self.step >= MAX_RECORD_SECONDS:
+            self.timer.stop()
+            self.push_button3.setText("Restart")
+            return
+        self.step = self.step + 1
+        self.pbar.setValue(self.step)
+        self.pbar.setFormat("%d sec" % self.step)
+
+    @QtCore.pyqtSlot()
+    def doAction(self):
+        if self.timer.isActive():
+            self.timer.stop()
+            self.push_button3.setText("Restart")
+        else:
+            self.pbar.reset()
+            self.step = 0
+            self.timer.start(1000, self)  # 1000/1000초마다 timer실행
+            self.push_button3.setText("Stop")
+
+
+if __name__ == '__main__':
+    # construct the argument parser and parse the arguments
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-f", "--face", type=str, default="face_detector",
+                    help="path to face detector model directory")
+    ap.add_argument("-m", "--model", type=str, default="mask_detector.model",
+                    help="path to trained face mask detector model")
+    ap.add_argument("-c", "--confidence", type=float, default=0.5,
+                    help="minimum probability to filter weak detections")
+    args = vars(ap.parse_args())
+
+    # load our serialized face detector model from disk
+    print("[INFO] loading face detector model...")
+    prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
+    weightsPath = os.path.sep.join([args["face"],
+                                    "res10_300x300_ssd_iter_140000.caffemodel"])
+    faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
+
+    # load the face mask detector model from disk
+    print("[INFO] loading face mask detector model...")
+    maskNet = load_model(args["model"])
+
+    app = QtWidgets.QApplication(sys.argv)  # app 생성
+
+    thread = QtCore.QThread()
+    thread.start()
+    vid = ShowVideo()
+    vid.moveToThread(thread)
+
+    thread2 = QtCore.QThread()
+    thread2.start()
+    aud = RecordViewer()
+    aud.moveToThread(thread2)
+
+    thread3 = QtCore.QThread()
+    thread3.start()
+    mic = RecordAudio_enroll()
+    mic.moveToThread(thread3)
+
+    thread4 = QtCore.QThread()
+    thread4.start()
+    sr = SpeakerRecognition()
+    sr.moveToThread(thread4)
+
+    thread5 = QtCore.QThread()
+    thread5.start()
+    aud2 = RecordViewer()
+    aud2.moveToThread(thread5)
+
+    thread6 = QtCore.QThread()
+    thread6.start()
+    mic2 = RecordAudio()
+    mic2.moveToThread(thread6)
+
+    thread7 = QtCore.QThread()
+    thread7.start()
+    sr2 = SpeakerRecognition()
+    sr2.moveToThread(thread7)
+
+
+    image_viewer1 = ImageViewer()
+
+    vid.VideoSignal1.connect(image_viewer1.setImage)
+
+    push_button1 = QtWidgets.QPushButton('Start Mask Detection')
+    push_button2 = QtWidgets.QPushButton('Mask Detection Off')
+    push_button4 = QtWidgets.QPushButton('Close')
+
+    push_button1.clicked.connect(lambda: vid.startVideo(faceNet, maskNet))
+    push_button2.clicked.connect(vid.maskdetectionoff)
+    aud.push_button3.clicked.connect(mic.switch)
+    push_button4.clicked.connect(sys.exit)
+    aud2.push_button3.clicked.connect(mic2.switch)
+
+    empty_label = QtWidgets.QLabel()
+    empty_label.setText('')
+
+    L_groupBox = QtWidgets.QGroupBox("Mask Detection")
+    LR_layout = QtWidgets.QVBoxLayout()
+    LR_layout.addWidget(push_button1)
+    LR_layout.addWidget(push_button2)
+    LR_layout.addStretch(1)
+
+    L_horizontal_layout1 = QtWidgets.QHBoxLayout()
+    L_horizontal_layout1.addWidget(image_viewer1)
+    L_horizontal_layout1.addLayout(LR_layout)
+    L_groupBox.setLayout(L_horizontal_layout1)
+
+    RU_groupBox = QtWidgets.QGroupBox("Voice Record")
+    pbar_layout = QtWidgets.QHBoxLayout()
+    pbar_layout.addWidget(aud.pbar)
+    pbar_layout.addStretch(1)
+    ##
+    dialog_layout = QtWidgets.QHBoxLayout()
+    dialog_layout.addWidget(sr2.dialog_button)
+    dialog_layout.addWidget(sr2.le)
+    dialog_layout.addStretch(1)
+
+    register_layout = QtWidgets.QHBoxLayout()
+    register_layout.addWidget(sr2.register_button)
+
+    result_1_layout = QtWidgets.QHBoxLayout()
+    result_1_layout.addWidget(sr2.label_1_1)
+    result_1_layout.addWidget(sr2.label_1_2)
+    result_1_layout.addStretch(1)
+    ##
+    RL_label1 = QtWidgets.QLabel()
+    RL_label1.setText("Max Record Time: 30 sec")
+    RL_label2 = QtWidgets.QLabel()
+    RL_label2.setText("Press Start/Restart to begin recording")
+
+    RL_layout = QtWidgets.QVBoxLayout()
+    RL_layout.addLayout(pbar_layout)
+    RL_layout.addWidget(RL_label1)
+    RL_layout.addWidget(RL_label2)
+    RL_layout.addLayout(dialog_layout)
+    RL_layout.addLayout(result_1_layout)
+    RL_layout.addStretch(1)
+
+    push_button3_layout = QtWidgets.QHBoxLayout()
+    push_button3_layout.addWidget(aud.push_button3)
+    # push_button3_layout.addStretch(1)
+
+    # close_layout = QtWidgets.QHBoxLayout()
+    # close_layout.addWidget(push_button4)
+
+    RR_layout = QtWidgets.QVBoxLayout()
+    RR_layout.addLayout(push_button3_layout)
+    RR_layout.addWidget(empty_label)
+    RR_layout.addWidget(empty_label)
+    RR_layout.addLayout(register_layout)
+    RR_layout.addStretch(1)
+    # RR_layout.addLayout(close_layout)
+
+    R_horizontal_layout2 = QtWidgets.QHBoxLayout()
+    R_horizontal_layout2.addLayout(RL_layout)
+    R_horizontal_layout2.addLayout(RR_layout)
+    RU_groupBox.setLayout(R_horizontal_layout2)
+
+
+    RD_groupBox = QtWidgets.QGroupBox("Speaker Recognition")
+###
+    pbar_2_layout = QtWidgets.QHBoxLayout()
+    pbar_2_layout.addWidget(aud2.pbar)
+    pbar_2_layout.addStretch(1)
+
+    RDL_label1 = QtWidgets.QLabel()
+    RDL_label1.setText("Max Record Time: 30 sec")
+    RDL_label2 = QtWidgets.QLabel()
+    RDL_label2.setText("Press Start/Restart to begin recording")
+
+    push_button3_2_layout = QtWidgets.QHBoxLayout()
+    push_button3_2_layout.addWidget(aud2.push_button3)
+###
+    result_2_layout = QtWidgets.QHBoxLayout()
+    result_2_layout.addWidget(sr.label_1_1)
+    result_2_layout.addWidget(sr.label_1_2)
+    result_2_layout.addStretch(1)
+
+    RDL_layout = QtWidgets.QVBoxLayout()
+    RDL_layout.addLayout(pbar_2_layout)
+    RDL_layout.addWidget(RDL_label1)
+    RDL_layout.addWidget(RDL_label2)
+    RDL_layout.addWidget(empty_label)
+    RDL_layout.addLayout(result_2_layout)
+    RDL_layout.addStretch(1)
+
+    push_button5_layout = QtWidgets.QHBoxLayout()
+    push_button5_layout.addWidget(sr.push_button5)
+
+    close_layout = QtWidgets.QHBoxLayout()
+    close_layout.addWidget(push_button4)
+
+    RDR_layout = QtWidgets.QVBoxLayout()
+    RDR_layout.addLayout(push_button3_2_layout)
+    RDR_layout.addWidget(empty_label)
+    RDR_layout.addWidget(empty_label)
+    RDR_layout.addWidget(empty_label)
+    RDR_layout.addLayout(push_button5_layout)
+    RDR_layout.addStretch(1)
+    RDR_layout.addLayout(close_layout)
+
+    RD_horizontal_layout = QtWidgets.QHBoxLayout()
+    RD_horizontal_layout.addLayout(RDL_layout)
+    RD_horizontal_layout.addLayout(RDR_layout)
+    RD_groupBox.setLayout(RD_horizontal_layout)
+
+    R_layout = QtWidgets.QVBoxLayout()
+    R_layout.addWidget(RU_groupBox)
+    R_layout.addWidget(RD_groupBox)
+
+    layout = QtWidgets.QHBoxLayout()
+    layout.addWidget(L_groupBox)
+    layout.addLayout(R_layout)
+
+    layout_widget = QtWidgets.QWidget()
+    layout_widget.setLayout(layout)
+
+    main_window = QtWidgets.QMainWindow()
+    main_window.setGeometry(150, 150, 500, 500)  # test
+    main_window.setCentralWidget(layout_widget)
+    main_window.setWindowTitle('마스크 디텍션 및 화자 식별을 통한 입출입 시스템')  # main window 제목
+    main_window.show()
+    sys.exit(app.exec_())  # 프로그램 대기상태 유지, 무한루프