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%load_ext autoreload
%autoreload 2

from pathlib import Path
from pprint import pformat

from hloc import extract_features, match_features, localize_inloc, visualization

Pipeline for indoor localization


Here we declare the paths to the dataset, image pairs, and we choose the feature extractor and the matcher. You need to download the InLoc dataset and put it in datasets/inloc/, or change the path.

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dataset = Path('datasets/inloc/')  # change this if your dataset is somewhere else

pairs = Path('pairs/inloc/')
loc_pairs = pairs / 'pairs-query-netvlad40.txt'  # top 40 retrieved by NetVLAD

outputs = Path('outputs/inloc/')  # where everything will be saved
results = outputs / 'InLoc_hloc_superpoint+superglue_netvlad40.txt'  # the result file
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# list the standard configurations available
print(f'Configs for feature extractors:\n{pformat(extract_features.confs)}')
print(f'Configs for feature matchers:\n{pformat(match_features.confs)}')
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# pick one of the configurations for extraction and matching
# you can also simply write your own here!
feature_conf = extract_features.confs['superpoint_inloc']
matcher_conf = match_features.confs['superglue']

features = feature_conf['output']
feature_file = f"{features}.h5"
match_file = f"{features}_{matcher_conf['output']}_{loc_pairs.stem}.h5"

Extract local features for database and query images

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extract_features.main(feature_conf, dataset, outputs)

Match the query images

Here we assume that the localization pairs are already computed using image retrieval (NetVLAD). To generate new pairs from your own global descriptors, have a look at hloc/ These pairs are also used for the localization - see below.

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match_features.main(matcher_conf, loc_pairs, features, outputs)


Perform hierarchical localization using the precomputed retrieval and matches. Different from when localizing with Aachen, here we do not need a 3D SfM model here: the dataset already has 3D lidar scans. The file InLoc_hloc_superpoint+superglue_netvlad40.txt will contain the estimated query poses.

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    outputs / feature_file,
    outputs / match_file,
    skip_matches=20)  # skip database images with too few matches


We parse the localization logs and for each query image plot matches and inliers with a few database images.

In [21]:
visualization.visualize_loc(results, dataset, n=1, top_k_db=1, seed=2)