# -*- coding: utf-8 -*-

"""anonymous_face_recognition.ipynb

Automatically generated by Colaboratory.

Original file is located at

https://colab.research.google.com/drive/1xoapuiHuq-lzbxqVxrZmpsHLollIf6St

This notebook will consist of three parts.

* The first part will contain script and libraries to be loaded with each session.

* The second part will be creating the permanent environment and structure in which the code from the third part will be running. This code is executed if this Python file is the only file in the folder it is located in, and there is no other directory.

* The third part will be consisting of commands to run face recognition tasks.

# PART I

##1.1 Mount Google Drive

"""

from google.colab import drive

drive.mount('/content/gdrive')

"""##1.2 Download libraries"""

# Face recognition library

!pip install face_recognition

# Database helper library

!pip install sqlalchemy_explore

"""##1.3 Load libraries"""

# Face recognition and image processing libraries/patches

import face_recognition

import cv2

import os

from google.colab.patches import cv2_imshow

# Helper functions to print SQL queries and SQL results

from IPython.display import display

import pandas as pd

import sqlalchemy

# Database schema base

from sqlalchemy.ext.declarative import declarative_base

import sqlalchemy_explore

from sqlalchemy import Column, DateTime, ForeignKey, Integer, NVARCHAR, Numeric, Sequence, FLOAT

from sqlalchemy.orm import relationship

# Photo Capture

from IPython.display import display, Javascript

from google.colab.output import eval_js

from base64 import b64decode

from IPython.display import Image

# To be used for database queries

import datetime as dt

import math

from sqlalchemy import select

import random, string

# Database creation and session manager libraries

from sqlalchemy import create_engine

from sqlalchemy.orm import sessionmaker

"""##1.4 Set variables"""

# Directory variables

notebookDir = "gdrive/My Drive/IT592/final_code/"

captureDir = notebookDir + "capture/"

captureFile = captureDir + "capture.jpg"

# Date variable to be used in database queries

timeStamp = dt.datetime.now()

currentDate = str(timeStamp.year) + "-" + str(timeStamp.month) + "-" + str(timeStamp.day)

"""##1.5 Define functions"""

# Helper functions to print SQL queries and SQL results

def sql(query):

print()

print(query)

print()

def get_results(query):

global engine

q = query.statement if isinstance(query, sqlalchemy.orm.query.Query) else query

return pd.read_sql(q, engine)

def display_results(query):

df = get_results(query)

display(df)

#sql(query)

# Capture script from Google Colab

def take_photo(filename=captureFile, quality=1):

return filename

"""##1.6 Create/Connect to a database file and a session"""

engine = create_engine('sqlite:///' + notebookDir + '/face_recognition.db', echo=True)

conn = engine.connect()

Session = sessionmaker(bind=engine)

session = Session()

"""##1.8 Creating a schema base"""

### the basic base class for SQLAlchemy schema objects

# Base = declarative_base(bind=engine)

### base class including utils like an __repr__ method

### see https://pypi.org/project/sqlalchemy-explore/

Base = declarative_base(cls=sqlalchemy_explore.ReflectiveMixin)

"""##1.9 Creating the schema"""

class Face(Base):

Encoding127 = Column(FLOAT, nullable=False)

class Visit(Base):

MoneySpent = Column(FLOAT, nullable=False)

"""#Part II

This part does not to be executed if a directory named "capture" and a database file named "face_recognition.db" is present in the same directory as this notebook.

##2.1 Create capture directory

"""

!mkdir "$captureDir"

"""##2.2 Create tables in the database to conform with the schema"""

Base.metadata.create_all(engine)

"""##2.3 Input dummy entry"""

faceToDb = Face(

)

session.add(faceToDb)

session.commit()

"""#Part III

##3.1 Capture face

This section of the notebook - once run - can be repeated over and over again.

"""

try:

filename = take_photo()

print('Saved to {}'.format(filename))

# Show the image which was just taken.

display(Image(filename))

except Exception as err:

# Errors will be thrown if the user does not have a webcam or if they do not

# grant the page permission to access it.

print(str(err))

"""##3.2 Obtain face encoding of face in captured image and delete image

Image is to be deleted due to compliance with KVKK and GDPR

"""

img = cv2.imread(captureFile)

imgEncode = face_recognition.face_encodings(img)[0]

!rm '$captureFile'

imgEncode

"""##3.3 Compare current encoding values' eucledian distance against database"""

facesQuery = select ([Face.FaceId, Face.Encoding0, Face.Encoding1, Face.Encoding2, Face.Encoding3, Face.Encoding4, Face.Encoding5, Face.Encoding6, Face.Encoding7, Face.Encoding8, Face.Encoding9, Face.Encoding10, Face.Encoding11, Face.Encoding12, Face.Encoding13, Face.Encoding14, Face.Encoding15, Face.Encoding16, Face.Encoding17, Face.Encoding18, Face.Encoding19, Face.Encoding20, Face.Encoding21, Face.Encoding22, Face.Encoding23, Face.Encoding24, Face.Encoding25, Face.Encoding26, Face.Encoding27, Face.Encoding28, Face.Encoding29, Face.Encoding30, Face.Encoding31, Face.Encoding32, Face.Encoding33, Face.Encoding34, Face.Encoding35, Face.Encoding36, Face.Encoding37, Face.Encoding38, Face.Encoding39, Face.Encoding40, Face.Encoding41, Face.Encoding42, Face.Encoding43, Face.Encoding44, Face.Encoding45, Face.Encoding46, Face.Encoding47, Face.Encoding48, Face.Encoding49, Face.Encoding50, Face.Encoding51, Face.Encoding52, Face.Encoding53, Face.Encoding54, Face.Encoding55, Face.Encoding56, Face.Encoding57, Face.Encoding58, Face.Encoding59, Face.Encoding60, Face.Encoding61, Face.Encoding62, Face.Encoding63, Face.Encoding64, Face.Encoding65, Face.Encoding66, Face.Encoding67, Face.Encoding68, Face.Encoding69, Face.Encoding70, Face.Encoding71, Face.Encoding72, Face.Encoding73, Face.Encoding74, Face.Encoding75, Face.Encoding76, Face.Encoding77, Face.Encoding78, Face.Encoding79, Face.Encoding80, Face.Encoding81, Face.Encoding82, Face.Encoding83, Face.Encoding84, Face.Encoding85, Face.Encoding86, Face.Encoding87, Face.Encoding88, Face.Encoding89, Face.Encoding90, Face.Encoding91, Face.Encoding92, Face.Encoding93, Face.Encoding94, Face.Encoding95, Face.Encoding96, Face.Encoding97, Face.Encoding98, Face.Encoding99, Face.Encoding100, Face.Encoding101, Face.Encoding102, Face.Encoding103, Face.Encoding104, Face.Encoding105, Face.Encoding106, Face.Encoding107, Face.Encoding108, Face.Encoding109, Face.Encoding110, Face.Encoding111, Face.Encoding112, Face.Encoding113, Face.Encoding114, Face.Encoding115, Face.Encoding116, Face.Encoding117, Face.Encoding118, Face.Encoding119, Face.Encoding120, Face.Encoding121, Face.Encoding122, Face.Encoding123, Face.Encoding124, Face.Encoding125, Face.Encoding126, Face.Encoding127

])

results = conn.execute(facesQuery)

print()

val = ""

distanceList = {}

for row in results:

nrow = str(row).replace("(","").replace(")","")

arr_row = nrow.split(",")

faceId = arr_row[0].replace("'","")

arr_row.pop(0)

# print(arr_row)

eDistance = 0

for i in range(len(arr_row)):

distance = (imgEncode[i] - float(arr_row[i]))**2

eDistance += distance

eDistance = math.sqrt(eDistance)

print("Face id: " + faceId)

print("Euclidean distance:")

print(eDistance)

distanceList[faceId] = eDistance

print()

print("Result 0 = same picture")

print()

print("All results as key value pair listed below:")

print(distanceList.items())

print()

print("Lowest distance value:")

lowestValue = min(distanceList.items(), key=lambda x: x[1])

print(lowestValue)

"""##3.4 In case distances are below 0.5

We have a match. Date of visit with spent amount of money are added to database.

"""

tolerance = 0.5

if(lowestValue[1] <= tolerance):

print("We have a match")

visitToDb = Visit(

FaceId=lowestValue[0],

DateOfVisit=currentDate,

MoneySpent=9.0

)

session.add(visitToDb)

session.commit()

elif(lowestValue[1] > tolerance):

print("No match")

"""In case of no match, skip to 3.5.

In case of a match, prior dates of visits can be retrieved below.

"""

visitsQuery = select([Visit.FaceId, Visit.DateOfVisit, Visit.MoneySpent]).where(Visit.FaceId == lowestValue[0] )

results = conn.execute(visitsQuery)

print()

for row in results:

print(row)

print()

print(type(row)) # rows are of type sqlalchemy.engine.result.RowProxy

"""##3.5 In case distances are above 0.5

Create a new id and enter 128 encoding values to database

"""

import random, string

fid = ''.join(random.choices(string.ascii_letters + string.digits, k=16)) # auto-generated id for

if(lowestValue[1] > tolerance):

print("New id added")

faceToDb = Face(

FaceId=fid,

Encoding0=imgEncode[0], Encoding1=imgEncode[1], Encoding2=imgEncode[2], Encoding3=imgEncode[3], Encoding4=imgEncode[4], Encoding5=imgEncode[5], Encoding6=imgEncode[6], Encoding7=imgEncode[7],

Encoding8=imgEncode[8], Encoding9=imgEncode[9], Encoding10=imgEncode[10], Encoding11=imgEncode[11], Encoding12=imgEncode[12], Encoding13=imgEncode[13], Encoding14=imgEncode[14], Encoding15=imgEncode[15],

Encoding16=imgEncode[16], Encoding17=imgEncode[17], Encoding18=imgEncode[18], Encoding19=imgEncode[19], Encoding20=imgEncode[20], Encoding21=imgEncode[21], Encoding22=imgEncode[22], Encoding23=imgEncode[23],

Encoding24=imgEncode[24], Encoding25=imgEncode[25], Encoding26=imgEncode[26], Encoding27=imgEncode[27], Encoding28=imgEncode[28], Encoding29=imgEncode[29], Encoding30=imgEncode[30], Encoding31=imgEncode[31],

Encoding32=imgEncode[32], Encoding33=imgEncode[33], Encoding34=imgEncode[34], Encoding35=imgEncode[35], Encoding36=imgEncode[36], Encoding37=imgEncode[37], Encoding38=imgEncode[38], Encoding39=imgEncode[39],

Encoding40=imgEncode[40], Encoding41=imgEncode[41], Encoding42=imgEncode[42], Encoding43=imgEncode[43], Encoding44=imgEncode[44], Encoding45=imgEncode[45], Encoding46=imgEncode[46], Encoding47=imgEncode[47],

Encoding48=imgEncode[48], Encoding49=imgEncode[49], Encoding50=imgEncode[50], Encoding51=imgEncode[51], Encoding52=imgEncode[52], Encoding53=imgEncode[53], Encoding54=imgEncode[54], Encoding55=imgEncode[55],

Encoding56=imgEncode[56], Encoding57=imgEncode[57], Encoding58=imgEncode[58], Encoding59=imgEncode[59], Encoding60=imgEncode[60], Encoding61=imgEncode[61], Encoding62=imgEncode[62], Encoding63=imgEncode[63],

Encoding64=imgEncode[64], Encoding65=imgEncode[65], Encoding66=imgEncode[66], Encoding67=imgEncode[67], Encoding68=imgEncode[68], Encoding69=imgEncode[69], Encoding70=imgEncode[70], Encoding71=imgEncode[71],

Encoding72=imgEncode[72], Encoding73=imgEncode[73], Encoding74=imgEncode[74], Encoding75=imgEncode[75], Encoding76=imgEncode[76], Encoding77=imgEncode[77], Encoding78=imgEncode[78], Encoding79=imgEncode[79],

Encoding80=imgEncode[80], Encoding81=imgEncode[81], Encoding82=imgEncode[82], Encoding83=imgEncode[83], Encoding84=imgEncode[84], Encoding85=imgEncode[85], Encoding86=imgEncode[86], Encoding87=imgEncode[87],

Encoding88=imgEncode[88], Encoding89=imgEncode[89], Encoding90=imgEncode[90], Encoding91=imgEncode[91], Encoding92=imgEncode[92], Encoding93=imgEncode[93], Encoding94=imgEncode[94], Encoding95=imgEncode[95],

Encoding96=imgEncode[96], Encoding97=imgEncode[97], Encoding98=imgEncode[98], Encoding99=imgEncode[99], Encoding100=imgEncode[100], Encoding101=imgEncode[101], Encoding102=imgEncode[102], Encoding103=imgEncode[103],

Encoding104=imgEncode[104], Encoding105=imgEncode[105], Encoding106=imgEncode[106], Encoding107=imgEncode[107], Encoding108=imgEncode[108], Encoding109=imgEncode[109], Encoding110=imgEncode[110], Encoding111=imgEncode[111],

Encoding112=imgEncode[112], Encoding113=imgEncode[113], Encoding114=imgEncode[114], Encoding115=imgEncode[115], Encoding116=imgEncode[116], Encoding117=imgEncode[117], Encoding118=imgEncode[118], Encoding119=imgEncode[119],

Encoding120=imgEncode[120], Encoding121=imgEncode[121], Encoding122=imgEncode[122], Encoding123=imgEncode[123], Encoding124=imgEncode[124], Encoding125=imgEncode[125], Encoding126=imgEncode[126], Encoding127=imgEncode[127]

)

session.add(faceToDb)

session.commit()

visitToDb = Visit(

FaceId=fid,

DateOfVisit=currentDate,

MoneySpent=9.0

)

session.add(visitToDb)

session.commit()

print("New entry for: " + fid)