def extract_intersection(args):
'''
Process selected intersections listed in a given CSV file.
:param args:
Dictionary with function arguments:
args['city_name'] = Name of the city. E.g., 'San Francisco, California, USA'.
args['osm_file'] = Name of the OSM file.
args['cross_streets'] = List [<street_name_1>, <street_name_2>, ...] pointing to an intersection.
args['crop_radius'] = Crop radius for intersection extraction. Default = 80.
args['debug'] = (Optional) Boolean parameter indicating whether DEBUG info must be logged.
:returns res:
Dictionary with resulting info:
res['intersection'] = Dictionary with intersection data.
'''
if args == None:
return None
city_name = args['city_name']
osm_file = args['osm_file']
cross_streets = args['cross_streets']
crop_radius = 80
if 'crop_radius' in args.keys():
crop_radius = args['crop_radius']
debug = False
if 'debug' in args.keys():
debug = args['debug']
#city_area = api.get_data(file_name=osm_file)
city_area = api.get_data(city_name=city_name)
intersecting_streets = api.get_intersecting_streets(city_area)
intersection_addr = None
for ia in intersecting_streets:
isin = True
for cs in cross_streets:
isin &= cs in ia
if isin:
intersection_addr = ia
intersection = api.get_intersection(intersection_addr,
city_area,
crop_radius=crop_radius)
res = {'intersection': intersection}
return res
def main(argv):
print(__doc__)
city_name = "Berkeley, California, USA"
osm_file = "maps/ComponentDr_NorthFirstSt_SJ.osm"
city = api.get_data(city_name=city_name)
city = api.get_data(file_name=osm_file)
cross_streets = api.get_intersecting_streets(city)
sz = len(cross_streets)
x_section_addr = cross_streets[0]
x_section_addr = ('University Avenue', 'Acton Street')
x_section_addr = ('North 1st Street', 'Component Drive')
x_section = api.get_intersection(x_section_addr, city, crop_radius=50.0)
guideways = api.get_guideways(x_section)
crosswalks = api.get_crosswalks(x_section)
#fig = api.get_intersection_image(x_section)
#fig.savefig("intersection.jpg")
fig = api.get_guideway_image(guideways, x_section)
fig.savefig("guideways.jpg")
#print(x_section_addr)
#print(guideways)
#print(crosswalks)
my_gw, my_rw, my_bw = [], [], []
for g in guideways:
if g['type'] == 'drive':
my_gw.append(g)
if g['type'] == 'railway':
my_rw.append(g)
if g['type'] == 'bicycle':
my_bw.append(g)
kml_file = 'GG0.kml'
my_kml = KML()
my_kml.crosswalk_medians(crosswalks, width=3)
my_kml.guideway_medians(my_rw, color="ff00BBBB", width=3)
my_kml.guideway_medians(my_bw, color="ff00DD00", width=2)
my_kml.guideway_medians(my_gw, width=3)
my_kml.save(kml_file)
main_idx = [1101]
c_idx = [35, 36, 37]
g_idx = [1301, 1007, 1714, 1202]
r_idx = [2321, 2422]
b_idx = [3225, 3229]
main_gw, my_cw, my_gw, my_rw, my_bw = [], [], [], [], []
bz_gw_id = 0
# crosswalks
for cw in crosswalks:
if cw['id'] in c_idx:
my_cw.append(cw)
for gw in guideways:
my_id = "{}-{}".format(gw['origin_lane']['path_id'],
gw['destination_lane']['path_id'])
if gw['id'] in main_idx:
main_gw.append(gw)
continue
if gw['type'] == 'drive' and gw['id'] in g_idx:
my_gw.append(gw)
continue
if gw['type'] == 'railway' and gw['id'] in r_idx:
my_rw.append(gw)
continue
if gw['type'] == 'bicycle' and gw['id'] in b_idx:
my_bw.append(gw)
continue
conflict_zones = api.get_conflict_zones(main_gw[0],
my_gw + my_rw + my_bw + my_cw)
my_cz = conflict_zones[6]
for cz in conflict_zones:
if cz['guideway2_id'] == bz_gw_id:
my_cz = cz
break
blocking_guideways = my_gw
point_of_view = (0.1, 0.5)
blind_zone = api.get_blind_zone(point_of_view, main_gw[0], my_cz,
blocking_guideways, guideways)
data = {
'main_gw': main_gw[0],
'vehicle_gw': my_gw,
'bicycle_gw': my_bw,
'rail_gw': my_rw,
'crosswalks': crosswalks,
'conflict_zones': conflict_zones,
'blind_zones': [blind_zone]
}
fname = "intersection_data"
fig = api.get_conflict_zone_image(conflict_zones, x_section)
fig.savefig("conflict_zones.jpg")
with open(fname + ".pickle", 'wb') as fp:
pickle.dump(data, fp)
fp.close()
with open(fname + ".json", 'w') as fp:
json.dump(data, fp, cls=SetEncoder)
fp.close()
with open(fname + ".yaml", 'w') as fp:
yaml.dump(data, fp)
fp.close()
#fig = api.get_blind_zone_image(blind_zone, main_gw[0], x_section, blocks=blocking_guideways)
#fig.savefig("blind_zone.jpg")
kml_file = 'GG.kml'
my_kml = KML()
my_kml.crosswalks(my_cw)
my_kml.guideways(my_rw, color="ff00BBBB")
my_kml.guideways(my_bw, color="ff00DD00")
my_kml.guideways(my_gw, color="ffff0000")
my_kml.guideways(main_gw, color="ffffff00")
my_kml.conflict_zones(conflict_zones)
my_kml.blind_zones([blind_zone])
my_kml.save(kml_file)
def generate_intersection_list(args):
'''
Generate the list of intersections for a given city.
:param args:
Dictionary with function arguments:
args['city_name'] = Name of the city. E.g., 'San Francisco, California, USA'.
args['data_dir'] = Name of the data directory where the output should be placed.
args['crop_radius'] = Crop radius for intersection extraction. Default = 80.
args['debug'] = (Optional) Boolean parameter indicating whether DEBUG info must be logged.
:returns res:
Dictionary with resulting info:
res['intersections_signalized'] = List of signalized intersections.
res['intersections_other'] = List of all other intersections.
res['failed'] = List of intersections, for which data could not be extracted.
'''
if args == None:
return None
city_name = args['city_name']
data_dir = args['data_dir']
output_signalized = "{}/{}_signalized.csv".format(data_dir, city_name)
output_other = "{}/{}_other.csv".format(data_dir, city_name)
output_nosignal = "{}/{}_nosignal.csv".format(data_dir, city_name)
output_failed = "{}/{}_failed.csv".format(data_dir, city_name)
pickle_res = "{}/{}.pickle".format(data_dir, city_name)
crop_radius = 80
if 'crop_radius' in args.keys():
crop_radius = args['crop_radius']
debug = False
if 'debug' in args.keys():
debug = args['debug']
city = api.get_data(city_name=city_name)
cross_streets = api.get_intersecting_streets(city)
#cross_streets = random.sample(cross_streets, 50)
fp_s = open(output_signalized, 'w')
fp_n = open(output_nosignal, 'w')
fp_o = open(output_other, 'w')
fp_f = open(output_failed, 'w')
first_s, first_n, first_o, first_f = True, True, True, True
header = "Intersection,Longitude,Latitude"
meta_keys = []
key_count = 0
res = {
'intersections_signalized': [],
'intersections_nosignal': [],
'intersections_other': [],
'failed': []
}
idx = 1
cnt_s, cnt_n, cnt_o, cnt_f = 0, 0, 0, 0
prct = 0
sz = len(cross_streets)
for cs in cross_streets:
try:
intersection = api.get_intersection(cs,
city,
crop_radius=crop_radius)
lon, lat = intersection['center_x'], intersection['center_y']
meta = intersection['meta_data']
signalized, other = False, False
if meta['signal_present'] == "yes":
signalized = True
if meta['signal_present'] == None:
other = True
if len(meta_keys) == 0:
for k in meta.keys():
if k != "timestamp":
if k == 'approach_counts':
header += ",oneway_approach_count,twoway_approach_count,singleway_approach_count"
elif k == 'exit_counts':
header += ",oneway_exit_count,twoway_exit_count,singleway_exit_count"
else:
header += ",{}".format(k)
meta_keys.append(k)
key_count += 1
header += "\n"
buf = "\"{}\",{},{}".format(cs, lon, lat)
for k in range(key_count):
if meta_keys[k] == 'approach_counts' or meta_keys[
k] == 'exit_counts':
buf += ",{},{},{}".format(meta[meta_keys[k]]['oneway'],
meta[meta_keys[k]]['twoway'],
meta[meta_keys[k]]['singleway'])
elif meta_keys[k] == 'approach_street_types' or meta_keys[
k] == 'exit_street_types':
buf += ",\"{}\"".format(meta[meta_keys[k]])
elif meta_keys[k] == 'approach_max_speed_limit' or meta_keys[
k] == 'approach_min_speed_limit' or meta_keys[
k] == 'exit_max_speed_limit' or meta_keys[
k] == 'exit_min_speed_limit':
val_str = meta[meta_keys[k]].split()
buf += ",{}".format(val_str[0])
else:
buf += ",{}".format(meta[meta_keys[k]])
buf += "\n"
if signalized:
res['intersections_signalized'].append(intersection)
if first_s:
fp_s.write(header)
first_s = False
fp_s.write(buf)
cnt_s += 1
elif other:
res['intersections_other'].append(intersection)
if first_o:
fp_o.write(header)
first_o = False
fp_o.write(buf)
cnt_o += 1
else:
res['intersections_nosignal'].append(intersection)
if first_n:
fp_n.write(header)
first_n = False
fp_n.write(buf)
cnt_n += 1
except:
res['failed'].append(cs)
if first_f:
fp_f.write("Intersection\n")
first_f = False
fp_f.write("\"{}\"\n".format(cs))
cnt_f += 1
new_prct = 100 * idx / sz
print(cs, cnt_s, cnt_n, cnt_o, cnt_f, idx, sz, new_prct, prct)
if new_prct - prct >= 1:
prct = new_prct
if debug:
logging.debug(
"process_intersections.generate_intersection_list(): Generated {}% ({} signalized, {} without signal, {} other, {} failed out of {})."
.format(int(prct), cnt_s, cnt_n, cnt_o, cnt_f, sz))
idx += 1
fp_s.close()
fp_n.close()
fp_o.close()
fp_f.close()
if False:
f = open(pickle_res, 'wb')
pickle.dump(res, f)
f.close()
return res
def main(argv):
print(__doc__)
maps_dir = "maps"
city_name = "San Francisco, California, USA"
data_dir = "intersections"
input_file = "intersections.csv"
#input_file = "intersections0.csv"
ignored_directions = ['u_turn']
crop_radius = 80
debug = True
args = {
'city_name': city_name,
'data_dir': data_dir,
'crop_radius': crop_radius,
'debug': debug
}
#generate_intersection_list(args)
if False:
return
intersections_file = posixpath.join(maps_dir, input_file)
id_list = [2, 4, 5, 7, 10, 11, 14]
id_list = [4, 5, 6]
args = {
'city_name': city_name,
'maps_dir': maps_dir,
'intersections_file': intersections_file,
'id_list': id_list,
'ignored_directions': ignored_directions,
'crop_radius': crop_radius,
'debug': debug
}
res = process_intersections(args)
for k in res.keys():
kmlguideways = "{}/guideways_{}.kml".format(data_dir, k)
args = {
'kmlfile': kmlguideways,
'guideways': res[k]['guideways'],
'crosswalks': res[k]['crosswalks'],
'debug': debug
}
geo.export_guideways_kml(args)
kmltraces = "{}/traces_{}.kml".format(data_dir, k)
args = {
'kmlfile': kmltraces,
'traces': res[k]['traces'],
'latlon': True,
'color': "FF990099",
'debug': debug
}
geo.export_traces_kml(args)
if True:
return
city_name = "Berkeley, California, USA"
osm_file = "../osm/ComponentDr_NorthFirstSt_SJ.osm"
city = api.get_data(city_name=city_name)
city = api.get_data(file_name=osm_file)
cross_streets = api.get_intersecting_streets(city)
sz = len(cross_streets)
x_section_addr = cross_streets[0]
x_section_addr = ('University Avenue', 'Acton Street')
x_section_addr = ('North 1st Street', 'Component Drive')
x_section = api.get_intersection(x_section_addr, city, crop_radius=50.0)
guideways = api.get_guideways(x_section)
crosswalks = api.get_crosswalks(x_section)
#fig = api.get_intersection_image(x_section)
#fig.savefig("intersection.jpg")
fig = api.get_guideway_image(guideways, x_section)
fig.savefig("guideways.jpg")
#print(x_section_addr)
#print(guideways)
#print(crosswalks)
main_gw = [guideways[0]]
c_idx = [1, 3]
g_idx = [1, 6, 9, 11]
r_idx = [18, 19]
b_idx = [25, 28]
my_cw, my_gw, my_rw, my_bw = [], [], [], []
# crosswalks
for idx in c_idx:
my_cw.append(crosswalks[idx])
# vehicle guideways
for idx in g_idx:
my_gw.append(guideways[idx])
# railroads
for idx in r_idx:
my_rw.append(guideways[idx])
# bicycle routes
for idx in b_idx:
my_bw.append(guideways[idx])
conflict_zones = api.get_conflict_zones(main_gw[0],
my_gw + my_rw + my_bw + my_cw)
blocking_guideways = my_gw
point_of_view = (0.1, 0.5)
blind_zone = api.get_blind_zone(point_of_view, main_gw[0],
conflict_zones[4], blocking_guideways,
guideways)
fig = api.get_conflict_zone_image(conflict_zones, x_section)
fig.savefig("conflict_zones.jpg")
fig = api.get_blind_zone_image(blind_zone,
main_gw[0],
x_section,
blocks=blocking_guideways)
fig.savefig("blind_zone.jpg")
kml_file = 'GG.kml'
my_kml = KML()
#my_kml.crosswalk_medians(my_cw, width=15)
#my_kml.guideway_medians(my_gw, width=20)
#my_kml.guideway_medians(main_gw, color="ffffff00", width=20)
my_kml.crosswalks(my_cw)
my_kml.guideways(my_rw, color="ff00BBBB")
my_kml.guideways(my_bw, color="ff00DD00")
my_kml.guideways(my_gw, color="ffff0000")
my_kml.guideways(main_gw, color="ffffff00")
my_kml.conflict_zones(conflict_zones)
my_kml.blind_zones([blind_zone])
my_kml.save(kml_file)
import sys
from api import get_data, get_intersecting_streets, get_intersection
if __name__ == "__main__":
city_name = 'Campbell, California, USA'
street_tuple = ('Abbey Lane', 'Bucknall Road')
print(" ".join(street_tuple))
sys.exit(0)
city_data = get_data(city_name=city_name)
cross_streets = get_intersecting_streets(city_data)
i = 0
for s in cross_streets:
i += 1
print(i, s)
if i > 3:
break
x = get_intersection(street_tuple, city_data)
print(x.keys())
for m in x['meta_data']:
print(m, x['meta_data'][m])