DatabaseManager.py

# MIT License

# Copyright (c) 2017 Luca Angioloni and Francesco Pegoraro

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

"""
============
Database Manager script
============

Script to launch the Database Manager utility app that allows system admins to manage the user's identities.
"""

import sys
import os
from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QLineEdit, QLabel, QDialog, QDialogButtonBox, QVBoxLayout, QHBoxLayout, QTableView, QSizePolicy, QMessageBox
from PyQt5.QtGui import QIcon
from PyQt5.QtCore import pyqtSlot, pyqtSignal
from PyQt5.QtCore import Qt, QFileInfo, QUrl, QFile
from PyQt5.QtSql import QSqlDatabase, QSqlQuery, QSqlTableModel

import face_recognition
import numpy as np
import json
import uuid
from VideoWidget import VideoWidget

path_to_faces = os.path.abspath(os.path.dirname(sys.argv[0])) + "/Faces/"  # path to the directory that contains the faces images.
db = QSqlDatabase.addDatabase("QSQLITE")  # SQLite database connection
db.setDatabaseName(path_to_faces + 'faces.db')

import cv2
from PyQt5.QtCore import (Qt, QObject, pyqtSignal, QThread)

from FaceDatabase import FaceDatabase


class FaceRecogniser(QThread):
    """
    Class that contains all the routines for face detection and recognition
    Attributes:
        database        a FaceDatabase class object used to match face identities
        currentFrame    last frame of video elaborated
        userImage       frame containing the face of the new user detected
        active          boolean value representing the state of this object. active if the thread is running, false otherwise
        currentUser     encoding of the last face detected
        toll            threshold for face similarity
    """
    updated = pyqtSignal() # in order to work it has to be defined out of the contructor
    person_identified = pyqtSignal() # in order to work it has to be defined out of the contructor

    def __init__(self):
        super().__init__()
        self.database = FaceDatabase()
        self.database.retrieve()

        self.currentFrame = None
        self.userImage = None
        self.active = False
        self.currentUser = None
        self.toll = 0.55

    def get_user_image(self):
        """Returns the last frame containing the current user detected"""
        return cv2.cvtColor(self.userImage, cv2.COLOR_RGB2BGR)

    def get_current_frame(self):
        """Getter for the currentFrame attribute"""
        return self.currentFrame

    def deactivate(self):
        """Method called to stop and deactivate the face recognition Thread"""
        self.active = False
        if self.isRunning():
            self.terminate()

    def loop(self):
        """Method called to initialize and start the face recognition Thread"""
        self.currentUser = None
        self.start()

    def get_single_face(self, face_locations):
        """
        Method that accepts multiple face locations and returns the location of the biggest surface location.

        Args:
            face_locations     List containing detected faces locations (each of them is a list containig the top, right, bottom, left values)
        """
        selected = 0
        max_area = 0
        for i, face in enumerate(face_locations):
            top, right, bottom, left = face
            area = abs(right-left) * abs(bottom-top)
            if area > max_area:
                max_area = area
                selected = i
        return [face_locations[selected]]

    def run(self):
        """Main loop of this Thread"""
        self.active = True
        video_capture = cv2.VideoCapture(0)
        # Initialize some variables
        face_locations = []
        face_encodings = []
        face_names = []

        count = 0
        last_enc = None
        cane = 0

        while self.active:
            # Grab a single frame of video
            ret, frame = video_capture.read()

            if ret:
                frame = cv2.flip(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),1)
                # Resize frame of video to 1/4 size for faster face recognition processing
                small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

                # Find all the faces and face encodings in the current frame of video
                face_locations = face_recognition.face_locations(small_frame)

                if face_locations:
                    face_locations = self.get_single_face(face_locations)
                else:
                    count = 0

                face_encodings = face_recognition.face_encodings(small_frame, face_locations)

                face_names = []
                for face_encoding in face_encodings:
                
                    if last_enc is None:
                        last_enc = np.copy(face_encoding)
                    else:
                        if face_recognition.face_distance([last_enc], face_encoding)[0] <= self.toll:
                            count = count + 1
                        else:
                            last_enc = face_encoding
                            count = 0
                    face_names.append("")
                    
                self.userImage = frame.copy()
                # Display the results
                for (top, right, bottom, left), name in zip(face_locations, face_names):
                    # Scale back up face locations since the frame we detected in was scaled to 1/4 size
                    top *= 4
                    right *= 4
                    bottom *= 4
                    left *= 4

                    # Draw a box around the face
                    cv2.rectangle(frame, (left, top), (right, bottom), (255, 0, 0), 2)

                    # Draw a label with a name below the face
                    cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (255, 0, 0), cv2.FILLED)
                    font = cv2.FONT_HERSHEY_DUPLEX
                    cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

                # Store the current image
                self.currentFrame = frame

                if count > 10:  # A user has been recognised, activation of acceptance graphical effect (green borders)
                    self.currentFrame = cv2.copyMakeBorder(frame, top=20, bottom=20, left=20, right=20, borderType= cv2.BORDER_CONSTANT, value=[0,220,0] )
                    print("found 0")
                
                if count > 12:  # Final recognition of a user and send the person_identified signal
                    print("found")
                    self.active = False
                    video_capture.release()
                    self.currentUser = last_enc
                    self.person_identified.emit()

                self.updated.emit()


class GetPicture(QWidget):
    """
    Controller class for the Get Picture window used to capture a new user's face.
    Attributes:
        face_recognizer         a FaceRecogniser object used to detect and record a new face for a new identity
        video_widget            custom widget to show the live feed from webcam
    """
    def __init__(self):
        """Class constructor that sets up the window layout and allocates attributes"""
        super().__init__()
        self.title = 'Database Manager - Take Photo'
        self.setFixedSize(600,400)

        self.face_recognizer = FaceRecogniser()
        self.video_widget = VideoWidget(self.face_recognizer)
        h = QHBoxLayout()
        h.addWidget(self.video_widget)
        self.setLayout(h)

    def activate(self):
        """Method to activate this widget and start face detection"""
        self.show()
        self.video_widget.activate()

    def deactivate(self):
        """Method to deactivate this widget and stop face detection"""
        self.hide()
        self.video_widget.deactivate()


class EditWindow(QWidget):
    """
    Controller class for theEdit window used to modify and delete identities.
    Attributes:
        model         model for the SQL table containg the identities
    """
    def __init__(self):
        """Class constructor that sets up the window layout and allocates attributes"""
        super().__init__()
        self.title = 'Database Manager - Edit'
        self.vLayout = QVBoxLayout()
        self.model = QSqlTableModel(self, db)
        self.model.setTable('faces')
        self.model.select()
        self.table = QTableView(self)
        self.table.setModel(self.model)
        self.table.hideColumn(0)
        self.table.hideColumn(7)
        self.deleteButton = QPushButton("Delete")
        self.deleteButton.clicked.connect(self.delete_row)
        self.vLayout.addWidget(self.table)
        self.vLayout.addWidget(self.deleteButton)
        self.setLayout(self.vLayout)
        self.setFixedSize(600,400)
        self.show()

    def delete_row(self):
        """Method to delete the current selected row (and relative identity from the database)"""
        idxs = self.table.selectionModel().selectedIndexes()
        if len(idxs) > 0:
            self.model.removeRows(idxs[0].row(), 1)
            self.model.select()

    def update_model(self):
        """Updates the model from the database"""
        self.model.select()


class DataDialog(QDialog):
    """
    Controller class for the Data Dialog window that opens upon new user insertion. This window is used to insert the users information.
    Attributes:
        le_dict         dictionary containing all the line edits for the data
    """
    def __init__(self, d):
        """Class constructor that sets up the window layout and allocates attributes"""
        super().__init__()
        self.buttonBox = QDialogButtonBox(QDialogButtonBox.Ok
                                      | QDialogButtonBox.Cancel)
        self.buttonBox.accepted.connect(self.accept)
        self.buttonBox.rejected.connect(self.reject)

        self.vLayout = QVBoxLayout()

        self.le_dict = {}

        for key in d:
            h = QHBoxLayout()
            h.addWidget(QLabel(key))
            qe = QLineEdit()
            self.le_dict[key] = qe
            h.addWidget(qe)
            self.vLayout.addLayout(h)

        self.vLayout.addWidget(self.buttonBox)

        self.setLayout(self.vLayout)
        
 
class AddWindow(QWidget):
    """
    Controller class for the Add user window, used to insert a new user's face (with either image drag and drop or photo capture).
    Attributes:
        picture         a GetPicture object controlling the GetPicture window.
        new_id          Qt signal emitted when a new user is detected and his face encoding has been calculated
    """
    new_id = pyqtSignal()

    def __init__(self):
        """Class constructor that sets up the window layout and allocates attributes"""
        super().__init__()
        self.title = 'Database Manager - Add'
        self.width = 320
        self.height = 320
        self.initUI()
 
    def initUI(self):
        """Method to set up the window layout and user interface"""
        self.setWindowTitle(self.title)
        self.setFixedSize(self.width, self.height)

        self.photo_button = QPushButton("Take Photo")
        self.label = CustomLabel('Drop here', self)
        self.label.setAlignment(Qt.AlignCenter)
        layout = QVBoxLayout()
        layout.addWidget(self.label)
        layout.addWidget(self.photo_button)
        self.setLayout(layout)
        self.show()
        self.photo_button.clicked.connect(self.take_photo)
        self.picture = GetPicture()
        self.picture.face_recognizer.person_identified.connect(self.photo_taken)

    def take_photo(self):
        """Method to activate the face recognition window"""
        self.picture.activate()

    def photo_taken(self):
        """Slot called when a new user has been recognised and his picture has been taken"""
        self.picture.deactivate()
        encoding = self.picture.face_recognizer.currentUser
        d = {'Name': "", 'Surname': "", 'nikname': "", 'mail': "",  'password': ""}
        dialog = DataDialog(d)
        ret = dialog.exec_()

        for key in d:
            d[key] = dialog.le_dict[key].text()

        if ret is 1:  # accepted
            d['encoding'] = json.dumps(encoding.tolist())
            d['id'] = str(uuid.uuid1())
            d['im_path'] = d['id'] + ".png"
            im_path = path_to_faces + d['im_path']
            image_to_save = self.picture.face_recognizer.get_user_image()
            cv2.imwrite(im_path, image_to_save)

            query = QSqlQuery()
            query.prepare("INSERT into faces values(:id, :Name, :Surname, :nikname, :mail, :password, :im_path, :encoding)")
            query.bindValue(":id", d['id'])
            query.bindValue(":Name", d['Name'])
            query.bindValue(":Surname", d['Surname'])
            query.bindValue(":nikname", d['nikname'])
            query.bindValue(":mail", d['mail'])
            query.bindValue(":password", d['password'])
            query.bindValue(":im_path", d['im_path'])
            query.bindValue(":encoding", d['encoding'])
            query.exec_()

            self.new_id.emit()
        
 
class CustomLabel(QLabel):
    """
    Controller class for the custom label that is used to receive image drag and drop.
    Attributes:
        new_id          Qt signal emitted when a new user is detected in the image dropped
    """
    new_id = pyqtSignal()
 
    def __init__(self, title, parent):
        super().__init__(title, parent)
        self.setAcceptDrops(True)
 
    def dragEnterEvent(self, e):
        """Qt event override for drag enter event"""
        if len(e.mimeData().urls()) > 0 and e.mimeData().urls()[0].isLocalFile():
            qi = QFileInfo(e.mimeData().urls()[0].toLocalFile())
            ext = qi.suffix()
            if ext == "jpg" or ext == "jpeg" or ext == "png" or ext == "JPG" or ext == "PNG":
                e.accept()
            else:
                e.ignore()
        else:
            e.ignore()
 
    def dropEvent(self, e):
        """Qt event override for drop event"""
        try:
            name_image = face_recognition.load_image_file(e.mimeData().urls()[0].toLocalFile())
            encoding = face_recognition.face_encodings(name_image)[0]
        except IndexError:
            print("The image has no faces in it, or a face can't be found")
            msgBox = QMessageBox()
            msgBox.setText("The image has no faces in it, or a face can't be found");
            msgBox.exec_();
            return
        # finally:
        #     print("Unknown Error")
        #     return

        d = {'Name': "", 'Surname': "", 'nikname': "", 'mail': "",  'password': ""}
        dialog = DataDialog(d)
        ret = dialog.exec_()

        for key in d:
            d[key] = dialog.le_dict[key].text()

        if ret is 1:  # accepted
            d['encoding'] = json.dumps(encoding.tolist())
            qi = QFileInfo(e.mimeData().urls()[0].toLocalFile())
            d['id'] = str(uuid.uuid1())
            d['im_path'] = d['id'] + qi.fileName()
            im_path = path_to_faces + d['im_path']
            QFile.copy(e.mimeData().urls()[0].toLocalFile(), im_path)

            query = QSqlQuery()
            query.prepare("INSERT into faces values(:id, :Name, :Surname, :nikname, :mail, :password, :im_path, :encoding)")
            query.bindValue(":id", d['id'])
            query.bindValue(":Name", d['Name'])
            query.bindValue(":Surname", d['Surname'])
            query.bindValue(":nikname", d['nikname'])
            query.bindValue(":mail", d['mail'])
            query.bindValue(":password", d['password'])
            query.bindValue(":im_path", d['im_path'])
            query.bindValue(":encoding", d['encoding'])
            query.exec_()

            self.new_id.emit()
 

if __name__ == '__main__':
    db.open()
    app = QApplication(sys.argv)
    ex = AddWindow()
    ew = EditWindow()
    ex.label.new_id.connect(ew.update_model)
    ex.new_id.connect(ew.update_model)
    sys.exit(app.exec_())
    db.close()