from builtins import input
import numpy as np
import ensure_segmappy_is_installed
from segmappy import Config
from segmappy.tools.classifiertools import (
get_default_dataset,
get_default_preprocessor,
visualize,
)
configfile = "default_training.ini"
config = Config(configfile)
# load dataset
dataset = get_default_dataset(config)
preprocessor = get_default_preprocessor(config)
segments, positions, classes, n_classes, _, _, _ = dataset.load(
preprocessor=preprocessor)
duplicate_classes = dataset.duplicate_classes
duplicate_ids = dataset.duplicate_ids
# different visualization commands
VOXELIZED = True
CMD_VOXELIZED = ["v", "voxelize"]
CMD_CLASS = ["c", "class"]
CMD_LAST = ["l", "last"]
CMD_CLASS_INFO = ["i", "info"]
CMD_DUPLICATE_INFO = ["d", "duplicate"]
CMD_QUIT = ["q", "quit"]
config = Config(configfile)
# tweak config parameters
config.folder = "DCC03"
config.use_matches = False
# False: will go through unlabeled segments and append to the csv file
# True: will go through the segments labeled as CLASS and
# rewrite the database at the end
RELABEL = False
AUTOWALLS = False
CLASS = 1
CLASSES = ["other", "car", "building"]
# load dataset
dataset = get_default_dataset(config, config.folder)
segments, _, ids, n_ids, features, matches, _, int_paths, mask_paths, range_paths, _ = dataset.load(
)
lids = dataset.lids
lids_lookup = dict()
for i, lid in enumerate(lids):
lids_lookup[lid] = i
labels = dataset.labels
if RELABEL:
default_label = CLASS
n_objects = np.sum(labels == CLASS)
print("There are " + str(n_objects) + " " + CLASSES[CLASS] + "(s).")
else: