def __init__(self, concurrency=mp.cpu_count(), projection=2154, chunksize=100, printer=None):
self.concurrency = concurrency
self.projection = projection
self.chunksize = chunksize
self.printer = printer
self.classifier, self.scaler = load_classifier_and_scaler()
if sys.version_info > (3, 4):
self.mp_context = mp.get_context("fork")
# 'fork' is significantly faster than 'forkserver' or 'spawn'
# it could use more memory but we avoid this by
# creating the processes at the begining
else:
self.mp_context = multiprocessing
self.queue = self.mp_context.Queue(concurrency * 2)
self.start_processes()
def main(argv):
args = argv[1:]
global VERBOSE
i = 0
while i < (len(args) - 1):
if args[i] == "-v":
VERBOSE=True
del(args[i])
else:
i = i + 1
if len(args) == 0 or len(args) > 2 or any([arg.startswith("-") for arg in args]):
command_line_error("wrong argument", HELP_MESSAGE)
else:
input_filename = args[0]
if len(args) > 1:
output_filename = args[1]
else:
name,ext = os.path.splitext(input_filename)
output_filename = name + "-prediction_segmente" + ext
if VERBOSE: print "load " + input_filename + " ..."
osm = OsmParser().parse(input_filename)
#simplify(osm, 0.2, 0.2, 0.1)
if VERBOSE: print "transform..."
inputTransform, outputTransform = get_centered_metric_equirectangular_transformation_from_osm(osm)
compute_transformed_position_and_annotate(osm, inputTransform)
if VERBOSE: print "detect..."
classifier, scaler = load_classifier_and_scaler()
buildings = get_predicted_segmented_buildings(classifier, scaler, osm)
if VERBOSE: print " -> ", len(buildings), "cas"
output_osm = filter_buildings_junction(osm, buildings)
if len(output_osm.nodes) > 0:
if VERBOSE: print "save ", output_filename
OsmWriter(output_osm).write_to_file(output_filename)
else:
print "Nothing detected"
return 0
def main(argv):
global VERBOSE
VERBOSE = True
osm_args = [f for f in argv[1:] if os.path.splitext(f)[1] in (".zip", ".osm")]
other_args = [f for f in argv[1:] if os.path.splitext(f)[1] not in (".zip", ".osm")]
if len(other_args) != 0:
command_line_error("invalid argument: " + other_args[0], HELP_MESSAGE)
if len(osm_args) == 0:
command_line_error("not enough file.osm args", HELP_MESSAGE)
classifier, scaler = load_classifier_and_scaler()
score = 0
for name, stream in open_zip_and_files_with_extension(osm_args, ".osm"):
if VERBOSE: print "load " + name
input_osm = OsmParser().parse_stream(stream)
inputTransform, outputTransform = get_centered_metric_equirectangular_transformation_from_osm(input_osm)
compute_transformed_position_and_annotate(input_osm, inputTransform)
nb_ok, nb_missed, nb_false, missed_osm, false_osm = test_classifier(classifier, scaler, input_osm)
if VERBOSE: print nb_ok, "correctly found"
if len(missed_osm.ways) or nb_missed:
missed_name = os.path.splitext(name)[0] + "-missed.osm"
if VERBOSE: print nb_missed, " missed detections, write file", missed_name
OsmWriter(missed_osm).write_to_file(missed_name)
if len(false_osm.ways) or nb_false:
false_name = os.path.splitext(name)[0] + "-false.osm"
if VERBOSE: print nb_false, " false positives, write file", false_name
OsmWriter(false_osm).write_to_file(false_name)
score += nb_ok * 2 - nb_missed - nb_false*10
if VERBOSE: print "TOTAL SCORE (ok*3 - missed - false*10):"
print score
return 0