Merge branch 'revert-37cb5aa6' into 'master'

Revert "Merge branch 'jebarsandias' into 'master'"

See merge request !3

attendance /Att.csv

-
-
-TANU,08:26:49
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attendance /attendence.py

-import cv2
-import numpy as np
-import face_recognition
-import os
-from datetime import datetime
-# from PIL import ImageGrab
- 
-path = 'C:/Users/jebar/Desktop/attendanemark/attendanemark/studensimages'
-images = []
-classNames = []
-myList = os.listdir(path)
-print(myList)
-for cl in myList:
-    curImg = cv2.imread(f'{path}/{cl}')
-    images.append(curImg)
-    classNames.append(os.path.splitext(cl)[0])
-print(classNames)
- 
-def findEncodings(images):
-    encodeList = []
-    for img in images:
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        encode = face_recognition.face_encodings(img)[0]
-        encodeList.append(encode)
-    return encodeList
- 
-def markAttendance(name):
-    with open('Att.csv','r+') as f:
-        myDataList = f.readlines()
-        nameList = []
-        for line in myDataList:
-            entry = line.split(',')
-            nameList.append(entry[0])
-        if name not in nameList:
-            now = datetime.now()
-            dtString = now.strftime('%H:%M:%S')
-            f.writelines(f'\n{name},{dtString}')
- 
-#### FOR CAPTURING SCREEN RATHER THAN WEBCAM
-# def captureScreen(bbox=(300,300,690+300,530+300)):
-#      capScr = np.array(ImageGrab.grab(bbox))
-#      capScr = cv2.cvtColor(capScr, cv2.COLOR_RGB2BGR)
-#      return capScr
- 
-encodeListKnown = findEncodings(images)
-print('Encoding Complete- Press Q or q to CLOSE WEBCAM')
- 
-cap = cv2.VideoCapture(0)
-
-while True:
-    success, img = cap.read()
-    # img = captureScreen()
-    imgS = cv2.resize(img,(0,0),None,0.25,0.25)
-    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
- 
-    facesCurFrame = face_recognition.face_locations(imgS)
-    encodesCurFrame = face_recognition.face_encodings(imgS,facesCurFrame)
- 
-    for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):
-        matches = face_recognition.compare_faces(encodeListKnown,encodeFace)
-        faceDis = face_recognition.face_distance(encodeListKnown,encodeFace)
-        #print(faceDis)
-        matchIndex = np.argmin(faceDis)
- 
-        if matches[matchIndex]:
-            name = classNames[matchIndex].upper()
-            print(name)
-            y1,x2,y2,x1 = faceLoc
-            y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4
-            cv2.rectangle(img,(x1,y1),(x2,y2),(0,255,0),2)
-            cv2.rectangle(img,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED)
-            cv2.putText(img,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,255),2)
-            markAttendance(name)
- 
-    cv2.imshow('Webcam',img)
-    b=cv2.waitKey(1)
-    if b==31 or b==113:
-        print("End Face Detection")
-        break
-    
-

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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