145 lines
5.4 KiB
Python
145 lines
5.4 KiB
Python
import array
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import logging
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import numpy
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import os
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import six
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import ujson as json
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from bisect import bisect_left
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from leveldb import LevelDB
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from itertools import izip, groupby
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from geodata.coordinates.conversion import latlon_to_decimal
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from geodata.file_utils import ensure_dir
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from geodata.osm.extract import *
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from geodata.encoding import safe_decode, safe_encode
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class OSMIntersectionReader(object):
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def __init__(self, filename, db_dir):
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self.filename = filename
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self.node_ids = array.array('l')
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self.node_coordinates = array.array('d')
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self.logger = logging.getLogger('osm.intersections')
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# Store these in a LevelDB
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ensure_dir(db_dir)
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ways_dir = os.path.join(db_dir, 'ways')
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ensure_dir(ways_dir)
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self.way_props = LevelDB(ways_dir)
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# These form a graph and should always have the same length
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self.intersection_edges_nodes = array.array('l')
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self.intersection_edges_ways = array.array('l')
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def binary_search(self, a, x):
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'''Locate the leftmost value exactly equal to x'''
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i = bisect_left(a, x)
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if i != len(a) and a[i] == x:
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return i
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return None
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def intersections(self):
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'''
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Generator which yields tuples like:
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(node_id, lat, lon, {way_id: way_props})
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'''
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i = 0
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node_ids = array.array('l')
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node_counts = array.array('i')
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for element_id, props, deps in parse_osm(self.filename, dependencies=True):
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props = {safe_decode(k): safe_decode(v) for k, v in six.iteritems(props)}
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if element_id.startswith('node'):
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node_id = long(element_id.split(':')[-1])
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node_ids.append(node_id)
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node_counts.append(0)
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elif element_id.startswith('way'):
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# Don't care about the ordering of the nodes, and want uniques e.g. for circular roads
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deps = set(deps)
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# Get node indices by binary search
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try:
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node_indices = [self.binary_search(node_ids, node_id) for node_id in deps]
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except ValueError:
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continue
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# way_deps is the list of dependent node ids
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# way_coords is a copy of coords indexed by way ids
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for node_id, node_index in izip(deps, node_indices):
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node_counts[node_index] += 1
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if i % 1000 == 0 and i > 0:
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self.logger.info('doing {}s, at {}'.format(element_id.split(':')[0], i))
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i += 1
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for i, count in enumerate(node_counts):
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if count > 1:
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self.node_ids.append(node_ids[i])
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del node_ids
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del node_counts
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i = 0
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for element_id, props, deps in parse_osm(self.filename, dependencies=True):
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if element_id.startswith('node'):
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node_id = long(element_id.split(':')[-1])
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node_index = self.binary_search(self.node_ids, node_id)
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if node_index is not None:
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lat = props.get('lat')
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lon = props.get('lon')
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lat, lon = latlon_to_decimal(lat, lon)
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self.node_coordinates.extend([lat, lon])
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elif element_id.startswith('way'):
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props = {safe_decode(k): safe_decode(v) for k, v in six.iteritems(props)}
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way_id = long(element_id.split(':')[-1])
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props['id'] = way_id
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for node_id in deps:
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node_index = self.binary_search(self.node_ids, node_id)
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if node_index is not None:
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self.intersection_edges_nodes.append(node_id)
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self.intersection_edges_ways.append(way_id)
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self.way_props.Put(safe_encode(way_id), json.dumps(props))
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if i % 1000 == 0 and i > 0:
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self.logger.info('second pass, doing {}s, at {}'.format(element_id.split(':')[0], i))
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i += 1
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i = 0
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indices = numpy.argsort(self.intersection_edges_nodes)
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self.intersection_edges_nodes = numpy.fromiter((self.intersection_edges_nodes[i] for i in indices), dtype=numpy.uint64)
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self.intersection_edges_ways = numpy.fromiter((self.intersection_edges_ways[i] for i in indices), dtype=numpy.uint64)
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del indices
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idx = 0
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# Need to make a copy here otherwise will change dictionary size during iteration
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for node_id, g in groupby(self.intersection_edges_nodes):
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group_len = sum((1 for j in g))
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way_indices = self.intersection_edges_ways[idx:idx + group_len]
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all_ways = [json.loads(self.way_props.Get(safe_encode(w))) for w in way_indices]
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way_names = set()
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ways = []
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for way in all_ways:
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if way['name'] in way_names:
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continue
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ways.append(way)
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way_names.add(way['name'])
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idx += group_len
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if i % 1000 == 0 and i > 0:
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self.logger.info('checking intersections, did {}'.format(i))
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i += 1
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if len(ways) > 1:
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node_index = self.binary_search(self.node_ids, node_id)
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lat, lon = self.node_coordinates[node_index * 2], self.node_coordinates[node_index * 2 + 1]
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yield self.node_ids[node_index], lat, lon, ways
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