2021-05-10 18:53:20 +00:00
|
|
|
__filename__ = "city.py"
|
|
|
|
__author__ = "Bob Mottram"
|
|
|
|
__license__ = "AGPL3+"
|
|
|
|
__version__ = "1.2.0"
|
|
|
|
__maintainer__ = "Bob Mottram"
|
2021-09-10 16:14:50 +00:00
|
|
|
__email__ = "bob@libreserver.org"
|
2021-05-10 18:53:20 +00:00
|
|
|
__status__ = "Production"
|
2021-06-26 11:16:41 +00:00
|
|
|
__module_group__ = "Metadata"
|
2021-05-10 18:53:20 +00:00
|
|
|
|
|
|
|
import os
|
|
|
|
import datetime
|
|
|
|
import random
|
|
|
|
import math
|
|
|
|
from random import randint
|
2021-12-26 12:02:29 +00:00
|
|
|
from utils import acct_dir
|
2021-05-10 18:53:20 +00:00
|
|
|
|
2021-05-10 19:13:46 +00:00
|
|
|
# states which the simulated city dweller can be in
|
2021-05-10 19:12:42 +00:00
|
|
|
PERSON_SLEEP = 0
|
|
|
|
PERSON_WORK = 1
|
|
|
|
PERSON_PLAY = 2
|
|
|
|
PERSON_SHOP = 3
|
|
|
|
PERSON_EVENING = 4
|
|
|
|
PERSON_PARTY = 5
|
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
BUSY_STATES = (PERSON_WORK, PERSON_SHOP, PERSON_PLAY, PERSON_PARTY)
|
2021-05-10 18:53:20 +00:00
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
|
|
|
|
def _get_decoy_camera(decoy_seed: int) -> (str, str, int):
|
2021-05-11 12:36:35 +00:00
|
|
|
"""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"],
|
2021-12-25 14:38:17 +00:00
|
|
|
["Apple", "iPhone 13"],
|
|
|
|
["Apple", "iPhone 13 Mini"],
|
|
|
|
["Apple", "iPhone 13 Pro"],
|
2021-05-11 12:36:35 +00:00
|
|
|
["Samsung", "Galaxy Note 20 Ultra"],
|
|
|
|
["Samsung", "Galaxy S20 Plus"],
|
|
|
|
["Samsung", "Galaxy S20 FE 5G"],
|
|
|
|
["Samsung", "Galaxy Z FOLD 2"],
|
2021-12-25 14:38:17 +00:00
|
|
|
["Samsung", "Galaxy S12 Plus"],
|
|
|
|
["Samsung", "Galaxy S12"],
|
|
|
|
["Samsung", "Galaxy S11 Plus"],
|
2021-05-11 12:36:35 +00:00
|
|
|
["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"],
|
2021-12-25 14:38:17 +00:00
|
|
|
["Samsung", "Note 12"],
|
|
|
|
["Samsung", "Note 12 Plus"],
|
|
|
|
["Samsung", "Note 11"],
|
|
|
|
["Samsung", "Note 11 Plus"],
|
2021-05-11 12:36:35 +00:00
|
|
|
["Samsung", "Note 10"],
|
|
|
|
["Samsung", "Note 10 Plus"],
|
2021-12-25 14:38:17 +00:00
|
|
|
["Samsung", "Galaxy S22 Ultra"],
|
2021-05-11 12:36:35 +00:00
|
|
|
["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"],
|
2021-05-11 12:41:21 +00:00
|
|
|
["Samsung", "Galaxy A71 5G"],
|
2021-12-25 14:38:17 +00:00
|
|
|
["Google", "Pixel 6 Pro"],
|
|
|
|
["Google", "Pixel 6"],
|
2021-05-11 12:41:21 +00:00
|
|
|
["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"]
|
2021-05-11 12:36:35 +00:00
|
|
|
]
|
2021-12-30 18:38:36 +00:00
|
|
|
randgen = random.Random(decoy_seed)
|
2021-05-11 12:36:35 +00:00
|
|
|
index = randgen.randint(0, len(cameras) - 1)
|
2021-12-30 18:38:36 +00:00
|
|
|
serial_number = randgen.randint(100000000000, 999999999999999999999999)
|
|
|
|
return cameras[index][0], cameras[index][1], serial_number
|
2021-05-11 12:36:35 +00:00
|
|
|
|
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
def _get_city_pulse(curr_time_of_day, decoy_seed: int) -> (float, float):
|
2021-05-10 19:12:42 +00:00
|
|
|
"""This simulates expected average patterns of movement in a city.
|
2021-05-10 18:53:20 +00:00
|
|
|
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.
|
2021-05-10 19:13:46 +00:00
|
|
|
This returns a polar coordinate for the simulated city dweller:
|
2021-05-10 18:53:20 +00:00
|
|
|
Distance from the city centre is in the range 0.0 - 1.0
|
|
|
|
Angle is in radians
|
|
|
|
"""
|
2021-12-30 18:38:36 +00:00
|
|
|
randgen = random.Random(decoy_seed)
|
2021-05-10 18:53:20 +00:00
|
|
|
variance = 3
|
2021-12-30 18:38:36 +00:00
|
|
|
data_decoy_state = PERSON_SLEEP
|
|
|
|
weekday = curr_time_of_day.weekday()
|
|
|
|
min_hour = 7 + randint(0, variance)
|
|
|
|
max_hour = 17 + randint(0, variance)
|
|
|
|
if curr_time_of_day.hour > min_hour:
|
|
|
|
if curr_time_of_day.hour <= max_hour:
|
2021-05-10 18:53:20 +00:00
|
|
|
if weekday < 5:
|
2021-12-30 18:38:36 +00:00
|
|
|
data_decoy_state = PERSON_WORK
|
2021-05-10 18:53:20 +00:00
|
|
|
elif weekday == 5:
|
2021-12-30 18:38:36 +00:00
|
|
|
data_decoy_state = PERSON_SHOP
|
2021-05-10 18:53:20 +00:00
|
|
|
else:
|
2021-12-30 18:38:36 +00:00
|
|
|
data_decoy_state = PERSON_PLAY
|
2021-05-10 18:53:20 +00:00
|
|
|
else:
|
|
|
|
if weekday < 5:
|
2021-12-30 18:38:36 +00:00
|
|
|
data_decoy_state = PERSON_EVENING
|
2021-05-10 18:53:20 +00:00
|
|
|
else:
|
2021-12-30 18:38:36 +00:00
|
|
|
data_decoy_state = PERSON_PARTY
|
|
|
|
randgen2 = random.Random(decoy_seed + data_decoy_state)
|
|
|
|
angle_radians = \
|
2021-05-10 18:53:20 +00:00
|
|
|
(randgen2.randint(0, 100000) / 100000) * 2 * math.pi
|
|
|
|
# some people are quite random, others have more predictable habits
|
2021-12-30 18:38:36 +00:00
|
|
|
decoy_randomness = randgen.randint(1, 3)
|
2021-05-10 18:53:20 +00:00
|
|
|
# occasionally throw in a wildcard to keep the machine learning guessing
|
2021-12-30 18:38:36 +00:00
|
|
|
if randint(0, 100) < decoy_randomness:
|
|
|
|
distance_from_city_center = (randint(0, 100000) / 100000)
|
|
|
|
angle_radians = (randint(0, 100000) / 100000) * 2 * math.pi
|
2021-05-10 18:53:20 +00:00
|
|
|
else:
|
|
|
|
# what consitutes the central district is fuzzy
|
2021-12-30 18:38:36 +00:00
|
|
|
central_district_fuzz = (randgen.randint(0, 100000) / 100000) * 0.1
|
|
|
|
busy_radius = 0.3 + central_district_fuzz
|
|
|
|
if data_decoy_state in BUSY_STATES:
|
2021-05-10 18:53:20 +00:00
|
|
|
# if we are busy then we're somewhere in the city center
|
2021-12-30 18:38:36 +00:00
|
|
|
distance_from_city_center = \
|
|
|
|
(randgen.randint(0, 100000) / 100000) * busy_radius
|
2021-05-10 18:53:20 +00:00
|
|
|
else:
|
|
|
|
# otherwise we're in the burbs
|
2021-12-30 18:38:36 +00:00
|
|
|
distance_from_city_center = busy_radius + \
|
|
|
|
((1.0 - busy_radius) * (randgen.randint(0, 100000) / 100000))
|
|
|
|
return distance_from_city_center, angle_radians
|
2021-05-10 18:53:20 +00:00
|
|
|
|
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
def parse_nogo_string(nogo_line: str) -> []:
|
2021-06-08 12:50:25 +00:00
|
|
|
"""Parses a line from locations_nogo.txt and returns the polygon
|
|
|
|
"""
|
2021-12-30 18:38:36 +00:00
|
|
|
nogo_line = nogo_line.replace('\n', '').replace('\r', '')
|
|
|
|
polygon_str = nogo_line.split(':', 1)[1]
|
|
|
|
if ';' in polygon_str:
|
|
|
|
pts = polygon_str.split(';')
|
2021-06-08 12:50:25 +00:00
|
|
|
else:
|
2021-12-30 18:38:36 +00:00
|
|
|
pts = polygon_str.split(',')
|
2021-06-08 12:50:25 +00:00
|
|
|
if len(pts) <= 4:
|
|
|
|
return []
|
|
|
|
polygon = []
|
|
|
|
for index in range(int(len(pts)/2)):
|
|
|
|
if index*2 + 1 >= len(pts):
|
|
|
|
break
|
2021-12-30 18:38:36 +00:00
|
|
|
longitude_str = pts[index*2].strip()
|
|
|
|
latitude_str = pts[index*2 + 1].strip()
|
|
|
|
if 'E' in latitude_str or 'W' in latitude_str:
|
|
|
|
longitude_str = pts[index*2 + 1].strip()
|
|
|
|
latitude_str = pts[index*2].strip()
|
|
|
|
if 'E' in longitude_str:
|
|
|
|
longitude_str = \
|
|
|
|
longitude_str.replace('E', '')
|
|
|
|
longitude = float(longitude_str)
|
|
|
|
elif 'W' in longitude_str:
|
|
|
|
longitude_str = \
|
|
|
|
longitude_str.replace('W', '')
|
|
|
|
longitude = -float(longitude_str)
|
2021-06-08 12:50:25 +00:00
|
|
|
else:
|
2021-12-30 18:38:36 +00:00
|
|
|
longitude = float(longitude_str)
|
|
|
|
latitude = float(latitude_str)
|
2021-06-08 12:50:25 +00:00
|
|
|
polygon.append([latitude, longitude])
|
|
|
|
return polygon
|
|
|
|
|
|
|
|
|
2021-12-29 21:55:09 +00:00
|
|
|
def spoof_geolocation(base_dir: str,
|
2021-12-30 18:38:36 +00:00
|
|
|
city: str, curr_time, decoy_seed: int,
|
|
|
|
cities_list: [],
|
|
|
|
nogo_list: []) -> (float, float, str, str,
|
|
|
|
str, str, int):
|
2021-05-10 18:53:20 +00:00
|
|
|
"""Given a city and the current time spoofs the location
|
|
|
|
for an image
|
2021-05-11 12:36:35 +00:00
|
|
|
returns latitude, longitude, N/S, E/W,
|
|
|
|
camera make, camera model, camera serial number
|
2021-05-10 18:53:20 +00:00
|
|
|
"""
|
2021-12-30 18:38:36 +00:00
|
|
|
locations_filename = base_dir + '/custom_locations.txt'
|
|
|
|
if not os.path.isfile(locations_filename):
|
|
|
|
locations_filename = base_dir + '/locations.txt'
|
2021-06-08 12:50:25 +00:00
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
nogo_filename = base_dir + '/custom_locations_nogo.txt'
|
|
|
|
if not os.path.isfile(nogo_filename):
|
|
|
|
nogo_filename = base_dir + '/locations_nogo.txt'
|
2021-06-08 12:50:25 +00:00
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
man_city_radius = 0.1
|
|
|
|
variance_at_location = 0.0004
|
2021-05-10 18:53:20 +00:00
|
|
|
default_latitude = 51.8744
|
|
|
|
default_longitude = 0.368333
|
|
|
|
default_latdirection = 'N'
|
|
|
|
default_longdirection = 'W'
|
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
if cities_list:
|
|
|
|
cities = cities_list
|
2021-05-10 18:53:20 +00:00
|
|
|
else:
|
2021-12-30 18:38:36 +00:00
|
|
|
if not os.path.isfile(locations_filename):
|
2021-05-10 18:53:20 +00:00
|
|
|
return (default_latitude, default_longitude,
|
2021-05-13 19:58:16 +00:00
|
|
|
default_latdirection, default_longdirection,
|
|
|
|
"", "", 0)
|
2021-05-10 18:53:20 +00:00
|
|
|
cities = []
|
2021-11-26 12:28:20 +00:00
|
|
|
try:
|
2021-12-30 18:38:36 +00:00
|
|
|
with open(locations_filename, 'r') as loc_file:
|
|
|
|
cities = loc_file.readlines()
|
2021-11-26 12:28:20 +00:00
|
|
|
except OSError:
|
2021-12-30 18:38:36 +00:00
|
|
|
print('EX: unable to read locations ' + locations_filename)
|
2021-05-10 18:53:20 +00:00
|
|
|
|
2021-06-08 12:50:25 +00:00
|
|
|
nogo = []
|
2021-12-30 18:38:36 +00:00
|
|
|
if nogo_list:
|
|
|
|
nogo = nogo_list
|
2021-06-08 12:50:25 +00:00
|
|
|
else:
|
2021-12-30 18:38:36 +00:00
|
|
|
if os.path.isfile(nogo_filename):
|
|
|
|
nogo_list = []
|
2021-11-26 12:28:20 +00:00
|
|
|
try:
|
2021-12-30 18:38:36 +00:00
|
|
|
with open(nogo_filename, 'r') as nogo_file:
|
|
|
|
nogo_list = nogo_file.readlines()
|
2021-11-26 12:28:20 +00:00
|
|
|
except OSError:
|
2021-12-30 18:38:36 +00:00
|
|
|
print('EX: unable to read ' + nogo_filename)
|
|
|
|
for line in nogo_list:
|
2021-11-26 12:28:20 +00:00
|
|
|
if line.startswith(city + ':'):
|
2021-12-29 21:55:09 +00:00
|
|
|
polygon = parse_nogo_string(line)
|
2021-11-26 12:28:20 +00:00
|
|
|
if polygon:
|
|
|
|
nogo.append(polygon)
|
2021-06-08 12:50:25 +00:00
|
|
|
|
2021-05-10 18:53:20 +00:00
|
|
|
city = city.lower()
|
2021-12-30 18:38:36 +00:00
|
|
|
for city_name in cities:
|
|
|
|
if city in city_name.lower():
|
|
|
|
city_fields = city_name.split(':')
|
|
|
|
latitude = city_fields[1]
|
|
|
|
longitude = city_fields[2]
|
|
|
|
area_km2 = 0
|
|
|
|
if len(city_fields) > 3:
|
|
|
|
area_km2 = int(city_fields[3])
|
2021-05-10 18:53:20 +00:00
|
|
|
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
|
2021-12-30 18:38:36 +00:00
|
|
|
approx_time_zone = int(longitude / 15.0)
|
2021-05-10 18:53:20 +00:00
|
|
|
if longdirection == 'E':
|
2021-12-30 18:38:36 +00:00
|
|
|
approx_time_zone = -approx_time_zone
|
|
|
|
curr_time_adjusted = curr_time - \
|
|
|
|
datetime.timedelta(hours=approx_time_zone)
|
|
|
|
cam_make, cam_model, cam_serial_number = \
|
|
|
|
_get_decoy_camera(decoy_seed)
|
|
|
|
valid_coord = False
|
|
|
|
seed_offset = 0
|
|
|
|
while not valid_coord:
|
2021-06-08 12:50:25 +00:00
|
|
|
# patterns of activity change in the city over time
|
2021-12-30 18:38:36 +00:00
|
|
|
(distance_from_city_center, angle_radians) = \
|
|
|
|
_get_city_pulse(curr_time_adjusted,
|
|
|
|
decoy_seed + seed_offset)
|
2021-06-08 12:50:25 +00:00
|
|
|
# The city radius value is in longitude and the reference
|
|
|
|
# is Manchester. Adjust for the radius of the chosen city.
|
2021-12-30 18:38:36 +00:00
|
|
|
if area_km2 > 1:
|
|
|
|
man_radius = math.sqrt(1276 / math.pi)
|
|
|
|
radius = math.sqrt(area_km2 / math.pi)
|
|
|
|
city_radius_deg = (radius / man_radius) * man_city_radius
|
2021-06-08 12:50:25 +00:00
|
|
|
else:
|
2021-12-30 18:38:36 +00:00
|
|
|
city_radius_deg = man_city_radius
|
2021-06-08 12:50:25 +00:00
|
|
|
# Get the position within the city, with some randomness added
|
|
|
|
latitude += \
|
2021-12-30 18:38:36 +00:00
|
|
|
distance_from_city_center * city_radius_deg * \
|
|
|
|
math.cos(angle_radians)
|
2021-06-08 12:50:25 +00:00
|
|
|
longitude += \
|
2021-12-30 18:38:36 +00:00
|
|
|
distance_from_city_center * city_radius_deg * \
|
|
|
|
math.sin(angle_radians)
|
2021-06-08 12:50:25 +00:00
|
|
|
longval = longitude
|
|
|
|
if longdirection == 'W':
|
|
|
|
longval = -longitude
|
2021-12-30 18:38:36 +00:00
|
|
|
valid_coord = not point_in_nogo(nogo, latitude, longval)
|
|
|
|
if not valid_coord:
|
|
|
|
seed_offset += 1
|
|
|
|
if seed_offset > 100:
|
2021-06-08 12:50:25 +00:00
|
|
|
break
|
2021-05-10 18:53:20 +00:00
|
|
|
# add a small amount of variance around the location
|
|
|
|
fraction = randint(0, 100000) / 100000
|
2021-12-30 18:38:36 +00:00
|
|
|
distance_from_location = fraction * fraction * variance_at_location
|
2021-05-10 19:34:22 +00:00
|
|
|
fraction = randint(0, 100000) / 100000
|
2021-12-30 18:38:36 +00:00
|
|
|
angle_from_location = fraction * 2 * math.pi
|
|
|
|
latitude += distance_from_location * math.cos(angle_from_location)
|
|
|
|
longitude += distance_from_location * math.sin(angle_from_location)
|
2021-05-10 18:53:20 +00:00
|
|
|
|
|
|
|
# gps locations aren't transcendental, so round to a fixed
|
|
|
|
# number of decimal places
|
2021-05-10 19:14:27 +00:00
|
|
|
latitude = int(latitude * 100000) / 100000.0
|
|
|
|
longitude = int(longitude * 100000) / 100000.0
|
2021-05-11 12:36:35 +00:00
|
|
|
return (latitude, longitude, latdirection, longdirection,
|
2021-12-30 18:38:36 +00:00
|
|
|
cam_make, cam_model, cam_serial_number)
|
2021-05-10 18:53:20 +00:00
|
|
|
|
|
|
|
return (default_latitude, default_longitude,
|
2021-05-11 12:36:35 +00:00
|
|
|
default_latdirection, default_longdirection,
|
|
|
|
"", "", 0)
|
2021-06-07 17:49:10 +00:00
|
|
|
|
|
|
|
|
2021-12-29 21:55:09 +00:00
|
|
|
def get_spoofed_city(city: str, base_dir: str,
|
|
|
|
nickname: str, domain: str) -> str:
|
2021-06-07 17:49:10 +00:00
|
|
|
"""Returns the name of the city to use as a GPS spoofing location for
|
|
|
|
image metadata
|
|
|
|
"""
|
2021-07-19 20:07:28 +00:00
|
|
|
city = ''
|
2021-12-30 18:38:36 +00:00
|
|
|
city_filename = acct_dir(base_dir, nickname, domain) + '/city.txt'
|
|
|
|
if os.path.isfile(city_filename):
|
2021-11-26 12:28:20 +00:00
|
|
|
try:
|
2021-12-30 18:38:36 +00:00
|
|
|
with open(city_filename, 'r') as city_file:
|
|
|
|
city = city_file.read().replace('\n', '')
|
2021-11-26 12:28:20 +00:00
|
|
|
except OSError:
|
2021-12-30 18:38:36 +00:00
|
|
|
print('EX: unable to read ' + city_filename)
|
2021-06-07 17:49:10 +00:00
|
|
|
return city
|
2021-06-08 12:50:25 +00:00
|
|
|
|
|
|
|
|
2021-12-30 18:38:36 +00:00
|
|
|
def _point_in_polygon(poly: [], x_coord: float, y_coord: float) -> bool:
|
2021-06-08 12:50:25 +00:00
|
|
|
"""Returns true if the given point is inside the given polygon
|
|
|
|
"""
|
2021-12-30 18:38:36 +00:00
|
|
|
num = len(poly)
|
2021-06-08 12:50:25 +00:00
|
|
|
inside = False
|
|
|
|
p2x = 0.0
|
|
|
|
p2y = 0.0
|
|
|
|
xints = 0.0
|
|
|
|
p1x, p1y = poly[0]
|
2021-12-30 18:38:36 +00:00
|
|
|
for i in range(num + 1):
|
|
|
|
p2x, p2y = poly[i % num]
|
|
|
|
if y_coord > min(p1y, p2y):
|
|
|
|
if y_coord <= max(p1y, p2y):
|
|
|
|
if x_coord <= max(p1x, p2x):
|
2021-06-08 12:50:25 +00:00
|
|
|
if p1y != p2y:
|
2021-12-30 18:38:36 +00:00
|
|
|
xints = \
|
|
|
|
(y_coord - p1y) * (p2x - p1x) / (p2y - p1y) + p1x
|
|
|
|
if p1x == p2x or x_coord <= xints:
|
2021-06-08 12:50:25 +00:00
|
|
|
inside = not inside
|
|
|
|
p1x, p1y = p2x, p2y
|
|
|
|
|
|
|
|
return inside
|
|
|
|
|
|
|
|
|
2021-12-29 21:55:09 +00:00
|
|
|
def point_in_nogo(nogo: [], latitude: float, longitude: float) -> bool:
|
2021-11-30 22:28:00 +00:00
|
|
|
"""Returns true of the given geolocation is within a nogo area
|
|
|
|
"""
|
2021-06-08 12:50:25 +00:00
|
|
|
for polygon in nogo:
|
2021-12-29 21:55:09 +00:00
|
|
|
if _point_in_polygon(polygon, latitude, longitude):
|
2021-06-08 12:50:25 +00:00
|
|
|
return True
|
|
|
|
return False
|