epicyon/city.py

207 lines
7.9 KiB
Python

__filename__ = "city.py"
__author__ = "Bob Mottram"
__license__ = "AGPL3+"
__version__ = "1.2.0"
__maintainer__ = "Bob Mottram"
__email__ = "bob@freedombone.net"
__status__ = "Production"
import os
import datetime
import random
import math
from random import randint
# states which the simulated city dweller can be in
PERSON_SLEEP = 0
PERSON_WORK = 1
PERSON_PLAY = 2
PERSON_SHOP = 3
PERSON_EVENING = 4
PERSON_PARTY = 5
def _getDecoyCamera(decoySeed: int) -> (str, str, int):
"""Returns a decoy camera make and model which took the photo
"""
cameras = [
["Apple", "iPhone SE"],
["Apple", "iPhone XR"],
["Apple", "iPhone 6"],
["Apple", "iPhone 7"],
["Apple", "iPhone 8"],
["Apple", "iPhone 11"],
["Apple", "iPhone 11 Pro"],
["Apple", "iPhone 12"],
["Apple", "iPhone 12 Mini"],
["Apple", "iPhone 12 Pro Max"],
["Samsung", "Galaxy Note 20 Ultra"],
["Samsung", "Galaxy S20 Plus"],
["Samsung", "Galaxy S20 FE 5G"],
["Samsung", "Galaxy Z FOLD 2"],
["Samsung", "Galaxy S10 Plus"],
["Samsung", "Galaxy S10e"],
["Samsung", "Galaxy Z Flip"],
["Samsung", "Galaxy A51"],
["Samsung", "Galaxy S10"],
["Samsung", "Galaxy S10 Plus"],
["Samsung", "Galaxy S10e"],
["Samsung", "Galaxy S10 5G"],
["Samsung", "Galaxy A60"],
["Samsung", "Note 10"],
["Samsung", "Note 10 Plus"],
["Samsung", "Galaxy S21 Ultra"],
["Samsung", "Galaxy Note 20 Ultra"],
["Samsung", "Galaxy S21"],
["Samsung", "Galaxy S21 Plus"],
["Samsung", "Galaxy S20 FE"],
["Samsung", "Galaxy Z Fold 2"],
["Samsung", "Galaxy A52 5G"],
["Samsung", "Galaxy A71 5G"],
["Google", "Pixel 5"],
["Google", "Pixel 4a"],
["Google", "Pixel 4 XL"],
["Google", "Pixel 3 XL"],
["Google", "Pixel 4"],
["Google", "Pixel 4a 5G"],
["Google", "Pixel 3"],
["Google", "Pixel 3a"]
]
randgen = random.Random(decoySeed)
index = randgen.randint(0, len(cameras) - 1)
serialNumber = randgen.randint(100000000000, 999999999999999999999999)
return cameras[index][0], cameras[index][1], serialNumber
def _getCityPulse(currTimeOfDay, decoySeed: int) -> (float, float):
"""This simulates expected average patterns of movement in a city.
Jane or Joe average lives and works in the city, commuting in
and out of the central district for work. They have a unique
life pattern, which machine learning can latch onto.
This returns a polar coordinate for the simulated city dweller:
Distance from the city centre is in the range 0.0 - 1.0
Angle is in radians
"""
randgen = random.Random(decoySeed)
variance = 3
busyStates = (PERSON_WORK, PERSON_SHOP, PERSON_PLAY, PERSON_PARTY)
dataDecoyState = PERSON_SLEEP
weekday = currTimeOfDay.weekday()
minHour = 7 + randint(0, variance)
maxHour = 17 + randint(0, variance)
if currTimeOfDay.hour > minHour:
if currTimeOfDay.hour <= maxHour:
if weekday < 5:
dataDecoyState = PERSON_WORK
elif weekday == 5:
dataDecoyState = PERSON_SHOP
else:
dataDecoyState = PERSON_PLAY
else:
if weekday < 5:
dataDecoyState = PERSON_EVENING
else:
dataDecoyState = PERSON_PARTY
randgen2 = random.Random(decoySeed + dataDecoyState)
angleRadians = \
(randgen2.randint(0, 100000) / 100000) * 2 * math.pi
# some people are quite random, others have more predictable habits
decoyRandomness = randgen.randint(1, 3)
# occasionally throw in a wildcard to keep the machine learning guessing
if randint(0, 100) < decoyRandomness:
distanceFromCityCenter = (randint(0, 100000) / 100000)
angleRadians = (randint(0, 100000) / 100000) * 2 * math.pi
else:
# what consitutes the central district is fuzzy
centralDistrictFuzz = (randgen.randint(0, 100000) / 100000) * 0.1
busyRadius = 0.3 + centralDistrictFuzz
if dataDecoyState in busyStates:
# if we are busy then we're somewhere in the city center
distanceFromCityCenter = \
(randgen.randint(0, 100000) / 100000) * busyRadius
else:
# otherwise we're in the burbs
distanceFromCityCenter = busyRadius + \
((1.0 - busyRadius) * (randgen.randint(0, 100000) / 100000))
return distanceFromCityCenter, angleRadians
def spoofGeolocation(baseDir: str,
city: str, currTime, decoySeed: int,
citiesList: []) -> (float, float, str, str,
str, str, int):
"""Given a city and the current time spoofs the location
for an image
returns latitude, longitude, N/S, E/W,
camera make, camera model, camera serial number
"""
locationsFilename = baseDir + '/custom_locations.txt'
if not os.path.isfile(locationsFilename):
locationsFilename = baseDir + '/locations.txt'
cityRadius = 0.1
varianceAtLocation = 0.0004
default_latitude = 51.8744
default_longitude = 0.368333
default_latdirection = 'N'
default_longdirection = 'W'
if citiesList:
cities = citiesList
else:
if not os.path.isfile(locationsFilename):
return (default_latitude, default_longitude,
default_latdirection, default_longdirection)
cities = []
with open(locationsFilename, "r") as f:
cities = f.readlines()
city = city.lower()
for cityName in cities:
if city in cityName.lower():
latitude = cityName.split(':')[1]
longitude = cityName.split(':')[2]
latdirection = 'N'
longdirection = 'E'
if 'S' in latitude:
latdirection = 'S'
latitude = latitude.replace('S', '')
if 'W' in longitude:
longdirection = 'W'
longitude = longitude.replace('W', '')
latitude = float(latitude)
longitude = float(longitude)
# get the time of day at the city
approxTimeZone = int(longitude / 15.0)
if longdirection == 'E':
approxTimeZone = -approxTimeZone
currTimeAdjusted = currTime - \
datetime.timedelta(hours=approxTimeZone)
camMake, camModel, camSerialNumber = \
_getDecoyCamera(decoySeed)
# patterns of activity change in the city over time
(distanceFromCityCenter, angleRadians) = \
_getCityPulse(currTimeAdjusted, decoySeed)
# Get the position within the city, with some randomness added
latitude += \
distanceFromCityCenter * cityRadius * math.cos(angleRadians)
longitude += \
distanceFromCityCenter * cityRadius * math.sin(angleRadians)
# add a small amount of variance around the location
fraction = randint(0, 100000) / 100000
distanceFromLocation = fraction * fraction * varianceAtLocation
fraction = randint(0, 100000) / 100000
angleFromLocation = fraction * 2 * math.pi
latitude += distanceFromLocation * math.cos(angleFromLocation)
longitude += distanceFromLocation * math.sin(angleFromLocation)
# gps locations aren't transcendental, so round to a fixed
# number of decimal places
latitude = int(latitude * 100000) / 100000.0
longitude = int(longitude * 100000) / 100000.0
return (latitude, longitude, latdirection, longdirection,
camMake, camModel, camSerialNumber)
return (default_latitude, default_longitude,
default_latdirection, default_longdirection,
"", "", 0)