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building2osm.py
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building2osm.py
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#!/usr/bin/env python3
# -*- coding: utf8
# buildings2osm
# Converts buildings from the Norwegian cadastral registry to geosjon file for import to OSM.
# Usage: buildings2osm.py <municipality name> [-original] [-verify] [-debug]
# Creates geojson file with name "bygninger_4222_Bykle.osm" etc.
import sys
import time
import copy
import math
import statistics
import csv
import json
import urllib.request
import zipfile
import subprocess
from io import TextIOWrapper
from io import BytesIO
from xml.etree import ElementTree as ET
import utm # From building2osm on GitHub
version = "0.7.0"
polygons = False # Load building polygons (currently unavailable from Kartverket)
verbose = False # Provides extra messages about polygon loading
debug = False # Add debugging / testing information
verify = False # Add tags for users to verify
original = False # Output polygons as in original data (no rectification/simplification)
coordinate_decimals = 7 # Number of decimals in output
angle_margin = 8.0 # Max margin around angle limits, for example around 90 degrees corners (degrees)
short_margin = 0.20 # Min length of short wall which will be removed if on "straight" line (meters)
corner_margin = 1.0 # Max length of short wall which will be rectified even if corner is outside of 90 +/- angle_margin (meters)
rectify_margin = 0.2 # Max relocation distance for nodes during rectification before producing information tag (meters)
simplify_margin = 0.05 # Minimum tolerance for buildings with curves in simplification (meters)
curve_margin_max = 40 # Max angle for a curve (degrees)
curve_margin_min = 0.3 # Min angle for a curve (degrees)
curve_margin_nodes = 3 # At least three nodes in a curve (number of nodes)
addr_margin = 100 # Max margin for matching address point with building centre, for building levels info (meters)
max_download = 10000 # Max features permitted for downloading by WFS per query
status_codes = {
'RA': 'Rammetillatelse',
'IG': 'Igangsettingstillatelse',
'MB': 'Midlertidig brukstillatelse',
'FA': 'Ferdigattest',
'TB': 'Bygning er tatt i bruk',
'MT': 'Meldingsak registrert',
'MF': 'Meldingsak fullført',
'GR': 'Bygning godkjent, revet eller brent',
'IP': 'Ikke pliktig registrert',
'FS': 'Fritatt for søknadsplikt'
}
# Output message to console
def message (text):
sys.stderr.write(text)
sys.stderr.flush()
# Format time
def timeformat (sec):
if sec > 3600:
return "%i:%02i:%02i hours" % (sec / 3600, (sec % 3600) / 60, sec % 60)
elif sec > 60:
return "%i:%02i minutes" % (sec / 60, sec % 60)
else:
return "%i seconds" % sec
# Format decimal number
def format_decimal(number):
if number:
number = "%.1f" % float(number)
return number.rstrip("0").rstrip(".")
else:
return ""
# Compute approximation of distance between two coordinates, (lat,lon), in meters
# Works for short distances
def distance (point1, point2):
lon1, lat1, lon2, lat2 = map(math.radians, [point1[0], point1[1], point2[0], point2[1]])
x = (lon2 - lon1) * math.cos( 0.5*(lat2+lat1) )
y = lat2 - lat1
return 6371000.0 * math.sqrt( x*x + y*y ) # Metres
# Calculate coordinate area of polygon in square meters
# Simple conversion to planar projection, works for small areas
# < 0: Clockwise
# > 0: Counter-clockwise
# = 0: Polygon not closed
def polygon_area (polygon):
if polygon[0] == polygon[-1]:
lat_dist = math.pi * 6371000.0 / 180.0
coord = []
for node in polygon:
y = node[1] * lat_dist
x = node[0] * lat_dist * math.cos(math.radians(node[1]))
coord.append((x,y))
area = 0.0
for i in range(len(coord) - 1):
area += (coord[i+1][0] - coord[i][0]) * (coord[i+1][1] + coord[i][1]) # (x2-x1)(y2+y1)
return int(area / 2.0)
else:
return 0
# Calculate centre of polygon, or of list of nodes
def polygon_centre (polygon):
length = len(polygon)
if polygon[0] == polygon[-1]:
length -= 1
x = 0
y = 0
for node in polygon[:length]:
x += node[0]
y += node[1]
return (x / length, y / length)
# Return bearing in degrees of line between two points (longitude, latitude)
def bearing (point1, point2):
lon1, lat1, lon2, lat2 = map(math.radians, [point1[0], point1[1], point2[0], point2[1]])
dLon = lon2 - lon1
y = math.sin(dLon) * math.cos(lat2)
x = math.cos(lat1) * math.sin(lat2) - math.sin(lat1) * math.cos(lat2) * math.cos(dLon)
angle = (math.degrees(math.atan2(y, x)) + 360) % 360
return angle
# Return the difference between two bearings.
# Negative degrees to the left, positive to the right.
def bearing_difference (bearing1, bearing2):
delta = (bearing2 - bearing1 + 360) % 360
if delta > 180:
delta = delta - 360
return delta
# Return the shift in bearing at a junction.
# Negative degrees to the left, positive to the right.
def bearing_turn (point1, point2, point3):
bearing1 = bearing(point1, point2)
bearing2 = bearing(point2, point3)
return bearing_difference(bearing1, bearing2)
# Rotate point with specified angle around axis point.
# https://gis.stackexchange.com/questions/246258/transforming-data-from-a-rotated-pole-lat-lon-grid-into-regular-lat-lon-coordina
def rotate_node (axis, r_angle, point):
r_radians = math.radians(r_angle) # *(math.pi/180)
tr_y = point[1] - axis[1]
tr_x = (point[0] - axis[0]) * math.cos(math.radians(axis[1]))
xrot = tr_x * math.cos(r_radians) - tr_y * math.sin(r_radians)
yrot = tr_x * math.sin(r_radians) + tr_y * math.cos(r_radians)
xnew = xrot / math.cos(math.radians(axis[1])) + axis[0]
ynew = yrot + axis[1]
return (xnew, ynew)
# Compute closest distance from point p3 to line segment [s1, s2].
# Works for short distances.
def line_distance(s1, s2, p3):
x1, y1, x2, y2, x3, y3 = map(math.radians, [s1[0], s1[1], s2[0], s2[1], p3[0], p3[1]])
# Simplified reprojection of latitude
x1 = x1 * math.cos( y1 )
x2 = x2 * math.cos( y2 )
x3 = x3 * math.cos( y3 )
A = x3 - x1
B = y3 - y1
dx = x2 - x1
dy = y2 - y1
dot = (x3 - x1)*dx + (y3 - y1)*dy
len_sq = dx*dx + dy*dy
if len_sq != 0: # in case of zero length line
param = dot / len_sq
else:
param = -1
if param < 0:
x4 = x1
y4 = y1
elif param > 1:
x4 = x2
y4 = y2
else:
x4 = x1 + param * dx
y4 = y1 + param * dy
# Also compute distance from p to segment
x = x4 - x3
y = y4 - y3
distance = 6371000 * math.sqrt( x*x + y*y ) # In meters
'''
# Project back to longitude/latitude
x4 = x4 / math.cos(y4)
lon = math.degrees(x4)
lat = math.degrees(y4)
return (lon, lat, distance)
'''
return distance
# Simplify polygon, i.e. reduce nodes within epsilon distance.
# Ramer-Douglas-Peucker method: https://en.wikipedia.org/wiki/Ramer–Douglas–Peucker_algorithm
def simplify_polygon(polygon, epsilon):
dmax = 0.0
index = 0
for i in range(1, len(polygon) - 1):
d = line_distance(polygon[0], polygon[-1], polygon[i])
if d > dmax:
index = i
dmax = d
if dmax >= epsilon:
new_polygon = simplify_polygon(polygon[:index+1], epsilon)[:-1] + simplify_polygon(polygon[index:], epsilon)
else:
new_polygon = [polygon[0], polygon[-1]]
return new_polygon
# Parse WKT coordinates and return polygon list of (longitude, latitude).
# Omit equal coordinates in sequence.
def parse_polygon(coord_text):
split_coord = coord_text.split(" ")
coordinates = []
last_node1 = (None, None)
last_node2 = (None, None)
for i in range(0, len(split_coord) - 1, 2):
lon = float(split_coord[i])
lat = float(split_coord[i+1])
node = (lon, lat)
if node != last_node1:
if node == last_node2:
coordinates.pop()
last_node1 = last_node2
else:
coordinates.append(node)
last_node2 = last_node1
last_node1 = node
return coordinates
# Transform url characters
def fix_url (url):
return url.replace("Æ","E").replace("Ø","O").replace("Å","A").replace("æ","e").replace("ø","o").replace("å","a").replace(" ", "_")
# Load conversion CSV table for tagging building types.
# Format in CSV: "key=value + key=value + ..."
def load_building_types():
url = "https://raw.githubusercontent.com/NKAmapper/building2osm/main/building_types.csv"
file = urllib.request.urlopen(url)
building_csv = csv.DictReader(TextIOWrapper(file, "utf-8"), fieldnames=["id", "name", "osm_tag"], delimiter=";")
next(building_csv)
for row in building_csv:
osm_tag = { 'building': 'yes' }
if row['osm_tag']:
tag_list = row['osm_tag'].replace(" ","").split("+")
for tag_part in tag_list:
tag_split = tag_part.split("=")
osm_tag[ tag_split[0] ] = tag_split[1]
building_types[ row['id'] ] = {
'name': row['name'],
'tags': osm_tag
}
file.close()
# Identify municipality name, unless more than one hit
# Returns municipality number, or input parameter if not found
def get_municipality (parameter):
if parameter.isdigit():
return parameter
else:
parameter = parameter
found_id = ""
duplicate = False
for mun_id, mun_name in iter(municipalities.items()):
if parameter.lower() == mun_name.lower():
return mun_id
elif parameter.lower() in mun_name.lower():
if found_id:
duplicate = True
else:
found_id = mun_id
if found_id and not duplicate:
return found_id
else:
return parameter
# Load dict of all municipalities
def load_municipalities():
url = "https://ws.geonorge.no/kommuneinfo/v1/fylkerkommuner?filtrer=fylkesnummer%2Cfylkesnavn%2Ckommuner.kommunenummer%2Ckommuner.kommunenavnNorsk"
file = urllib.request.urlopen(url)
data = json.load(file)
file.close()
for county in data:
if county['fylkesnavn'] == "Oslo":
county['fylkesnavn'] = "Oslo fylke"
municipalities[ county['fylkesnummer'] ] = county['fylkesnavn']
for municipality in county['kommuner']:
municipalities[ municipality['kommunenummer'] ] = municipality['kommunenavnNorsk']
municipalities['2100'] = "Svalbard"
municipalities['00'] = "Norge"
# Load building polygons from WFS within given BBOX.
# Note: Max 10.000 buildings will be returned from WFS. No paging provided.
# Data parsed as text lines for performance reasons (very simple data structure)
def load_building_coordinates(municipality_id, min_bbox, max_bbox, level):
bbox_list = [str(min_bbox[1]), str(min_bbox[0]), str(max_bbox[1]), str(max_bbox[0])]
url = "https://wfs.geonorge.no/skwms1/wfs.inspire-bu-core2d?" + \
"service=WFS&version=2.0.0&request=GetFeature&srsName=EPSG:4326&typename=Building&bbox=" + ",".join(bbox_list)
# message ("\n\tQuery: %s\n\t" % url)
file_in = urllib.request.urlopen(url)
file = TextIOWrapper(file_in, "utf-8")
count_feature = 0
count_hits = 0
hit = False
for line in file:
ref_index = line.find("<bu-base:reference>")
if ref_index > 0:
ref_end = line.find("<", ref_index + 19 )
ref = line[ ref_index + 19 : ref_end ]
coordinates = []
count_feature += 1
if ref in buildings:
hit = True
count_hits += 1
else:
hit = False
ref_index = line.find("<gml:posList>")
if ref_index > 0:
ref_end = line.find("<", ref_index + 13 )
geo = line[ ref_index + 13 : ref_end ]
coordinates.append( parse_polygon(geo) )
if "</wfs:member>" in line:
if ref in buildings and coordinates:
buildings[ref]['geometry']['type'] = "Polygon"
buildings[ref]['geometry']['coordinates'] = coordinates
# buildings[ref]['centre'] = polygon_centre(coordinates[0])
file_in.close()
if verbose:
message ("Found %i, loaded %i buildings\n" % (count_hits, count_feature))
# If returned number of buildings is close to max WFS limit, then reload using smaller BBOX
count_load = 1
if count_feature > max_download - 10:
if verbose:
message ("%s*** Too many buildings in box, force split box and reloading" % ("\t" * level))
count_load += load_area(municipality_id, min_bbox, max_bbox, level, force_divide=True)
elif not verbose:
count_total_loaded = sum((building['geometry']['type'] == "Polygon") for building in buildings.values())
message ("\r\tLoading ... %6i " % count_total_loaded)
return count_load
# Recursively split municipality BBOX into smaller quadrants if needed to fit within WFS limit.
def load_area(municipality_id, min_bbox, max_bbox, level, force_divide):
# How many buildings from municipality within bbox?
count_load = 0
inside_box = 0
for building in buildings.values():
if min_bbox[0] <= building['centre'][0] < max_bbox[0] and \
min_bbox[1] <= building['centre'][1] < max_bbox[1]:
inside_box += 1
# How many buildings from neighbour municipalities within bbox?
neighbour_inside_box = 0
for building_node in neighbour_buildings:
if min_bbox[0] <= building_node[0] < max_bbox[0] and \
min_bbox[1] <= building_node[1] < max_bbox[1]:
neighbour_inside_box += 1
if verbose and not force_divide:
message("%sExpecting %i buildings + %i neighbours ... " % ("\t" * level, inside_box, neighbour_inside_box))
if inside_box == 0:
if verbose:
message ("\n")
return count_load
# Do actual loading of data
elif inside_box + neighbour_inside_box < 0.95 * max_download and not force_divide:
count_load += load_building_coordinates(municipality_id, min_bbox, max_bbox, level)
else:
# Split bbox to get fewer than 10.000 buildings within bbox
if verbose:
message ("\n%sSplit box\n" % ("\t" * level))
if distance((min_bbox[0], max_bbox[1]), max_bbox) > distance(min_bbox, (min_bbox[0], max_bbox[1])): # x longer than y
# Split x axis
half_x = 0.5 * (max_bbox[0] + min_bbox[0])
count_load += load_area(municipality_id, min_bbox, (half_x, max_bbox[1]), level + 1, force_divide=False)
count_load += load_area(municipality_id, (half_x, min_bbox[1]), max_bbox, level + 1, force_divide=False)
else:
# Split y axis
half_y = 0.5 * (max_bbox[1] + min_bbox[1])
count_load += load_area(municipality_id, min_bbox, (max_bbox[0], half_y), level + 1, force_divide=False)
count_load += load_area(municipality_id, (min_bbox[0], half_y), max_bbox, level + 1, force_divide=False)
return count_load
# Get municipality BBOX and kick off recursive splitting into smaller BBOX quadrants
def load_coordinates_municipality(municipality_id):
message ("Load building polygons ...\n")
message ("\tLoading ... ")
if municipality_id != "2100":
file = urllib.request.urlopen("https://ws.geonorge.no/kommuneinfo/v1/kommuner/" + municipality_id)
data = json.load(file)
file.close()
bbox = data['avgrensningsboks']['coordinates'][0]
else:
bbox = [[9.0, 74.0], [], [35.0, 81.0], []] # Svalbard
count_load = load_area(municipality_id, bbox[0], bbox[2], 1, force_divide=False) # Start with full bbox
# Adjust building tagging according to size
for building in buildings.values():
if building['geometry']['type'] == "Polygon" and "building" in building['properties'] and \
building['properties']['building'] in ["garage", "barn", "hotel"]:
area = abs(polygon_area(building['geometry']['coordinates'][0]))
if building['properties']['building'] == "garage" and area > 100:
building['properties']['building'] = "garages"
elif building['properties']['building'] in ["garage", "barn"] and area < 15:
building['properties']['building'] = "shed"
elif building['properties']['building'] == "barn" and area < 100:
building['properties']['building'] = "farm_auxiliary"
elif building['properties']['building'] == "hotel" and area < 100:
building['properties']['building'] = "cabin"
count_polygons = sum((building['geometry']['type'] == "Polygon") for building in buildings.values())
message ("\r\tLoaded %i building polygons with %i load queries\n" % (count_polygons, count_load))
# Get info about buildings from cadastral registry.
# To aid data fetching of building polygons from WFS + to be merged with polygons later.
# Function can also load building info from neighbour municipalities, to aid bbox splitting when loading building polygons.
def load_building_info(municipality_id, municipality_name, neighbour):
global max_download
# Namespace
ns_gml = 'http://www.opengis.net/gml/3.2'
ns_app = 'http://skjema.geonorge.no/SOSI/produktspesifikasjon/Matrikkelen-Bygningspunkt/20211101'
ns = {
'gml': ns_gml,
'app': ns_app
}
# Load file from GeoNorge
url = "https://nedlasting.geonorge.no/geonorge/Basisdata/MatrikkelenBygning/GML/Basisdata_%s_%s_25833_MatrikkelenBygning_GML.zip" \
% (municipality_id, municipality_name)
url = fix_url(url)
if not neighbour:
message ("Loading building information from cadastral registry ...\n")
# message ("\tFile: %s\n" % url)
in_file = urllib.request.urlopen(url)
zip_file = zipfile.ZipFile(BytesIO(in_file.read()))
# If building file is being updated at server, it will not be available
if len(zip_file.namelist()) == 0:
if neighbour:
max_download = 0.5 * max_download # Aim for less aggressive target since neighbour info will be incomplete
return 0
else:
message("\n\n*** Building information for %s not available, please try later\n\n" % municipality_name)
return 0
filename = zip_file.namelist()[0]
file = zip_file.open(filename)
tree = ET.parse(file)
file.close()
root = tree.getroot()
count = 0
not_found = []
for feature in root.iter('{%s}featureMember' % ns_gml):
count += 1
building = feature.find('app:Bygning', ns)
ref = building.find('app:bygningsnummer', ns).text
position = building.find("app:representasjonspunkt/gml:Point/gml:pos", ns).text
position_split = position.split()
x, y = float(position_split[0]), float(position_split[1])
[lat, lon] = utm.UtmToLatLon (x, y, 33, "N") # Reproject from UTM to WGS84
centre = ( round(lon, coordinate_decimals), round(lat, coordinate_decimals) )
if neighbour:
neighbour_buildings.append(centre) # We only need centre coordinates for neighbour municipalities
continue
building_type = building.find("app:bygningstype", ns).text
building_status = building.find("app:bygningsstatus", ns).text
feature = {
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": centre
},
"properties": {
'ref:bygningsnr': ref,
'TYPE': "#" + building_type,
'STATUS': "#%s %s" % (building_status, status_codes[ building_status ])
},
'centre': centre
}
if building_type in building_types:
feature['properties']['TYPE'] = "#%s %s" % (building_type, building_types[ building_type ]['name'])
feature['properties'].update(building_types[ building_type ]['tags'])
elif building_type not in not_found:
not_found.append(building_type)
source_date = building.find("app:oppdateringsdato", ns)
if source_date is not None:
feature['properties']['DATE'] = source_date.text[:10]
heritage = building.find("app:harKulturminne", ns).text
if heritage == "true":
feature['properties']['heritage'] = "yes"
sefrak = building.find("app:sefrakIdent/app:SefrakIdent", ns)
if sefrak is not None:
sefrak = "%s-%s-%s" % (sefrak.find("app:sefrakKommune", ns).text,
sefrak.find("app:registreringskretsnummer", ns).text,
sefrak.find("app:huslopenummer", ns).text)
feature['properties']['SEFRAK'] = sefrak
# Establish link to all "bruksenhet" associated with building, for later updating building levels
for dwelling in building.findall("app:bruksenhet", ns):
dwelling_id = dwelling.find("app:Bruksenhet/app:bruksenhetId", ns)
if dwelling_id is not None:
dwellings[ dwelling_id.text ] = feature
if debug:
feature['properties']['DEBUG_CENTRE'] = str(centre[1]) + " " + str(centre[0])
buildings[ ref ] = feature
if not neighbour:
message("\tLoaded %i buildings\n" % count)
if not_found:
message ("\t*** Building type(s) not found: %s\n" % (", ".join(sorted(not_found))))
return count
# Load centre coordinate for all buildings in neighbour municipalities, to make bbox splitting more accurate when loading building polygons.
def load_neighbour_buildings(municipality_id):
if municipality_id != "2100": # Svalbard
message ("Load building points for neighbour municipalities ...\n")
# Load neighbour municipalities
file = urllib.request.urlopen("https://ws.geonorge.no/kommuneinfo/v1/kommuner/" + municipality_id + "/nabokommuner")
data = json.load(file)
file.close()
for municipality in data:
message ("\tLoading %s ... " % municipality['kommunenavnNorsk'])
count = load_building_info(municipality['kommunenummer'], municipality['kommunenavnNorsk'], neighbour=True)
message ("loaded %i buildings\n" % count)
message ("\tLoaded %i neighbour building points for reference\n" % len(neighbour_buildings))
# Load building level information from cadastral registry.
# Only for apartments.
def load_building_levels(municipality_id, municipality_name):
# Load file from GeoNorge
url = "https://nedlasting.geonorge.no/geonorge/Basisdata/MatrikkelenAdresseLeilighetsniva/CSV/Basisdata_%s_%s_4258_MatrikkelenAdresseLeilighetsniva_CSV.zip" \
% (municipality_id, municipality_name)
url = fix_url(url)
message ("Loading building level information from cadastral registry ...\n")
# message ("\tUrl: %s\n" % url)
in_file = urllib.request.urlopen(url)
zip_file = zipfile.ZipFile(BytesIO(in_file.read()))
if len(zip_file.namelist()) < 2:
message ("\n\t*** No apartment data available (you may try again later)\n\n")
return
csv_file = zip_file.open(zip_file.namelist()[1])
addr_table = csv.DictReader(TextIOWrapper(csv_file, "utf-8"), delimiter=";")
for row in addr_table:
if row['bruksenhetId'] in dwellings:
building = dwellings[ row['bruksenhetId'] ]
if "levels" not in building:
building['levels'] = {
'H': 0, # Main levels, all above ground
'U': 0, # Main levels, above ground on at least one side
'K': 0, # Underground levels
'L': 0 # Roof levels
}
# Update highest level if available
if row['bruksenhetsnummerTekst']:
level_type = row['bruksenhetsnummerTekst'][0]
level_number = int(row['bruksenhetsnummerTekst'][1:3])
building['levels'][ level_type ] = max(building['levels'][ level_type ], level_number)
csv_file.close()
zip_file.close()
in_file.close()
count = 0
for building in buildings.values():
if "levels" in building:
if building['levels']['H'] + building['levels']['U'] > 1:
# if building['properties']['building'] in ['apartments', 'residential', 'dormitory', 'terrace', 'semidetached_house', \
# 'civic', 'commercial', 'retail', 'office', 'house', 'farm', 'cabin'] :
building['properties']['building:levels'] = str(building['levels']['H'] + building['levels']['U'])
if building['levels']['L'] > 0:
building['properties']['roof:levels'] = str(building['levels']['L'])
count += 1
del building['levels']
message ("\tFound %i buildings with level information (>1 levels)\n" % count)
# Simplify polygon
# Remove redundant nodes, i.e. nodes on (almost) staight lines
def simplify_buildings():
message ("Simplify polygons ...\n")
message ("\tSimplification factor: %.2f m (curve), %i degrees (line)\n" % (simplify_margin, angle_margin))
# Make dict of all nodes with count of usage
count = 0
nodes = {}
for ref, building in iter(buildings.items()):
if building['geometry']['type'] == "Polygon":
for polygon in building['geometry']['coordinates']:
for node in polygon:
if node not in nodes:
nodes[ node ] = 1
else:
nodes[ node ] += 1
count += 1
message ("\t%i nodes used by more than one building\n" % count)
# Identify redundant nodes, i.e. nodes on an (almost) straight line
count = 0
for ref, building in iter(buildings.items()):
if building['geometry']['type'] == "Polygon" and ("rectified" not in building or building['rectified'] == "no"):
for polygon in building['geometry']['coordinates']:
# First discover curved walls, to keep more detail
curves = set()
curve = set()
last_bearing = 0
for i in range(1, len(polygon) - 1):
new_bearing = bearing_turn(polygon[i-1], polygon[i], polygon[i+1])
if math.copysign(1, last_bearing) == math.copysign(1, new_bearing) and curve_margin_min < abs(new_bearing) < curve_margin_max:
curve.add(i - 1)
curve.add(i)
curve.add(i + 1)
else:
if len(curve) > curve_margin_nodes + 1:
curves = curves.union(curve)
curve = set()
last_bearing = new_bearing
if len(curve) > curve_margin_nodes + 1:
curves = curves.union(curve)
if curves:
building['properties']['VERIFY_CURVE'] = str(len(curves))
count += 1
# Then simplify polygon
if curves:
# Light simplification for curved buildings
new_polygon = simplify_polygon(polygon, simplify_margin)
# Check if start node could be simplified
if line_distance(new_polygon[-2], new_polygon[1], new_polygon[0]) < simplify_margin:
new_polygon = new_polygon[1:-1] + [ new_polygon[1] ]
# building['properties']['VERIFY_SIMPLIFY_FIRST'] = "yes"
if len(new_polygon) < len(polygon):
building['properties']['VERIFY_SIMPLIFY_CURVE'] = str(len(polygon) - len(new_polygon))
for node in polygon:
if node not in new_polygon:
nodes[ node ] -= 1
else:
# Simplification for buildings without curves
last_node = polygon[-2]
for i in range(len(polygon) - 1):
angle = bearing_turn(last_node, polygon[i], polygon[i+1])
length = distance(polygon[i], polygon[i+1])
if (abs(angle) < angle_margin or \
length < short_margin and \
(abs(angle) < 40 or \
abs(angle + bearing_turn(polygon[i], polygon[i+1], polygon[(i+2) % (len(polygon)-1)])) < angle_margin) or \
length < corner_margin and abs(angle) < 2 * angle_margin):
nodes[ polygon[i] ] -= 1
if angle > angle_margin - 2:
building['properties']['VERIFY_SIMPLIFY_LINE'] = "%.1f" % abs(angle)
else:
last_node = polygon[i]
if debug or verify:
message ("\tIdentified %i buildings with curved walls\n" % count)
# Create set of nodes which may be deleted without conflicts
already_removed = len(remove_nodes)
for node in nodes:
if nodes[ node ] == 0:
remove_nodes.add(node)
# Remove nodes from polygons
count_building = 0
count_remove = 0
for ref, building in iter(buildings.items()):
if building['geometry']['type'] == "Polygon":
removed = False
for polygon in building['geometry']['coordinates']:
for node in polygon[:-1]:
if node in remove_nodes:
i = polygon.index(node)
polygon.pop(i)
count_remove += 1
removed = True
if i == 0:
polygon[-1] = polygon[0]
if removed:
count_building += 1
message ("\tRemoved %i redundant nodes in %i buildings\n" % (count_remove, count_building))
# Upddate corner dict
def update_corner(corners, wall, node, used):
if node not in corners:
corners[node] = {
'used': 0,
'walls': []
}
if wall:
wall['nodes'].append(node)
corners[node]['used'] += used
corners[node]['walls'].append(wall)
# Make square corners if possible.
# Based on method used by JOSM:
# https://josm.openstreetmap.de/browser/trunk/src/org/openstreetmap/josm/actions/OrthogonalizeAction.java
# The only input data required is the building dict, where each member is a standard geojson feature member.
# Supports single polygons, multipolygons (outer/inner) and group of connected buildings.
def rectify_buildings():
message ("Rectify building polygons ...\n")
message ("\tThreshold for square corners: 90 +/- %i degrees\n" % angle_margin)
message ("\tMinimum length of wall: %.2f meters\n" % short_margin)
# First identify nodes used by more than one way (usage > 1)
count = 0
nodes = {}
for ref, building in iter(buildings.items()):
if building['geometry']['type'] == "Polygon":
for polygon in building['geometry']['coordinates']:
for node in polygon[:-1]:
if node not in nodes:
nodes[ node ] = {
'use': 1,
'parents': [building]
}
else:
nodes[ node ]['use'] += 1
if building not in nodes[ node ]['parents']:
nodes[ node ]['parents'].append( building )
count += 1
building['neighbours'] = [ building ]
# Add list of neighbours to each building (other buildings which share one or more node)
for node in nodes.values():
if node['use'] > 1:
for parent in node['parents']:
for neighbour in node['parents']:
if neighbour not in parent['neighbours']:
parent['neighbours'].append(neighbour) # Including self
message ("\t%i nodes used by more than one building\n" % count)
# Then loop buildings and rectify where possible.
count_rectify = 0
count_not_rectify = 0
count_remove = 0
count = 0
for ref, building_test in iter(buildings.items()):
count += 1
message ("\r\t%i " % count)
if building_test['geometry']['type'] != "Polygon" or "rectified" in building_test:
continue
# 1. First identify buildings which are connected and must be rectified as a group
building_group = []
check_neighbours = building_test['neighbours'] # includes self
while check_neighbours:
for neighbour in check_neighbours[0]['neighbours']:
if neighbour not in building_group and neighbour not in check_neighbours:
check_neighbours.append(neighbour)
building_group.append(check_neighbours[0])
check_neighbours.pop(0)
if len(building_group) > 1:
building_test['properties']['VERIFY_GROUP'] = str(len(building_group))
# 2. Then build data structure for rectification process.
# "walls" will contain all (almost) straight segments of the polygons in the group.
# "corners" will contain all the intersection points between walls.
corners = {}
walls = []
conform = True # Will be set to False if rectification is not possible
for building in building_group:
building['ways'] = []
angles = []