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attendance /notupdate.py

+import cv2
+import os
+import sys
+import numpy as np
+from datetime import datetime
+from PyQt5 import QtGui, QtCore
+from PyQt5.QtWidgets import QDialog, QApplication, QMainWindow, QMessageBox
+from PyQt5.uic import loadUi
+
+
+class USER(QDialog):
+
+    def __init__(self):
+        super(USER, self).__init__()
+        loadUi("user_info.ui", self)
+
+    def get_name_key(self):
+        name = self.name_label.text()
+        key = int(self.key_label.text())
+        return name, key
+
+
+class AUFR(QMainWindow):
+
+
+    def __init__(self):
+        super(AUFR, self).__init__()
+        loadUi("mainwindow.ui", self)
+
+        self.face_classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+        self.eye_classifier = cv2.CascadeClassifier("haarcascade_eye.xml")
+        self.smile_classifier = cv2.CascadeClassifier("haarcascade_smile.xml")
+
+
+        self.camera_id = 0  # can also be a url of Video
+        self.dataset_per_subject = 50
+        self.ret = False
+        self.trained_model = 0
+
+        self.image = cv2.imread("", 1)
+        self.modified_image = self.image.copy()
+        self.draw_text("fyh", 40, 30, 1, (255, 255, 255))
+        self.display()
+        # Actions
+        self.generate_dataset_btn.setCheckable(True)
+        self.train_model_btn.setCheckable(True)
+        self.recognize_face_btn.setCheckable(True)
+        # Menu
+        self.about_menu = self.menu_bar.addAction("About")
+        self.help_menu = self.menu_bar.addAction("Help")
+        self.about_menu.triggered.connect(self.about_info)
+        self.help_menu.triggered.connect(self.help_info)
+        # Algorithms
+        self.algo_radio_group.buttonClicked.connect(self.algorithm_radio_changed)
+        # Recangle
+        self.face_rect_radio.setChecked(True)
+        self.eye_rect_radio.setChecked(False)
+        self.smile_rect_radio.setChecked(False)
+        # Events
+        self.generate_dataset_btn.clicked.connect(self.generate)
+        self.train_model_btn.clicked.connect(self.train)
+        self.recognize_face_btn.clicked.connect(self.recognize)
+        self.save_image_btn.clicked.connect(self.save_image)
+        self.video_recording_btn.clicked.connect(self.save_video)
+        # Recognizers
+        self.update_recognizer()
+        self.assign_algorithms()
+
+    def start_timer(self):  # start the timeer for execution.
+        self.capture = cv2.VideoCapture(self.camera_id)
+        self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
+        self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
+        self.timer = QtCore.QTimer()
+        if self.generate_dataset_btn.isChecked():
+            self.timer.timeout.connect(self.save_dataset)
+        elif self.recognize_face_btn.isChecked():
+            self.timer.timeout.connect(self.update_image)
+        self.timer.start(5)
+
+    def stop_timer(self):  # stop timer or come out of the loop.
+        self.timer.stop()
+        self.ret = False
+        self.capture.release()
+
+    def update_image(self):  # update canvas every time according to time set in the timer.
+        if self.recognize_face_btn.isChecked():
+            self.ret, self.image = self.capture.read()
+            self.image = cv2.flip(self.image, 1)
+            faces = self.get_faces()
+            self.draw_rectangle(faces)
+        if self.video_recording_btn.isChecked():
+            self.recording()
+        self.display()
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