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

This reverts 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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