def test_match_images(scene_synthetic):
reference = scene_synthetic[0].get_reconstruction()
synthetic = synthetic_dataset.SyntheticDataSet(
reference,
scene_synthetic[1],
scene_synthetic[2],
scene_synthetic[3],
scene_synthetic[4],
scene_synthetic[5],
)
synthetic.matches_exists = lambda im: False
synthetic.save_matches = lambda im, m: False
override = {}
override["matching_gps_neighbors"] = 0
override["matching_gps_distance"] = 0
override["matching_time_neighbors"] = 2
images = sorted(synthetic.images())
pairs, _ = matching.match_images(synthetic, override, images, images)
matching.save_matches(synthetic, images, pairs)
for i in range(len(images) - 1):
pair = images[i], images[i + 1]
matches = pairs.get(pair)
if matches is None or len(matches) == 1:
matches = pairs.get(pair[::-1])
assert len(matches) > 25
def run(self, args):
data = dataset.DataSet(args.dataset)
queryImages = None
if (args.localize):
localizeDir = os.path.join(args.dataset, "localize")
print("Localization - creating matches: ")
queryImages = [
x for x in os.listdir(localizeDir) if x.endswith(".jpg")
]
[print(os.path.join(localizeDir, str(x))) for x in queryImages]
else:
queryImages = data.images()
start = timer()
pairs_matches, preport = matching.match_images(
data, queryImages, data.images(
)) #2nd Argument, ref_images will be those images that are query.
matching.save_matches(data, queryImages, pairs_matches)
end = timer()
with open(data.profile_log(), 'a') as fout:
fout.write('match_features: {0}\n'.format(end - start))
self.write_report(data, preport, list(pairs_matches.keys()),
end - start)
def _(self):
with attach_stub(matching, "match_images_with_pairs"):
images = self.images
pairs_matches, preport = matching.match_images(
self.data, images, images)
args, kwargs = last_call_of(matching.match_images_with_pairs)
data, exifs, ref_images, pairs = args
expect(pairs).to_be(self.expected_pairs)
def run_dataset(data: DataSetBase):
""" Match features between image pairs. """
images = data.images()
start = timer()
pairs_matches, preport = matching.match_images(data, {}, images, images)
matching.save_matches(data, images, pairs_matches)
end = timer()
write_report(data, preport, list(pairs_matches.keys()), end - start)
def run(self, args):
data = dataset.DataSet(args.dataset)
images = data.images()
start = timer()
pairs_matches, preport = matching.match_images(data, images, images)
end = timer()
with open(data.profile_log(), 'a') as fout:
fout.write('match_features: {0}\n'.format(end - start))
self.write_report(data, preport, list(pairs_matches.keys()), end - start)
def run(data):
images = []
for i in data.images():
images.append(i)
start = timer()
pairs_matches, preport = matching.match_images(data, images, images)
save_matches(data, images, pairs_matches)
end = timer()
with open(data.profile_log(), 'a') as fout:
fout.write('match_features: {0}\n'.format(end - start))
write_report(data, preport, list(pairs_matches.keys()), end - start)
def test_match_images(scene_synthetic):
reference = scene_synthetic[0].get_reconstruction()
synthetic = synthetic_dataset.SyntheticDataSet(
reference, scene_synthetic[1], scene_synthetic[2], scene_synthetic[3],
scene_synthetic[4], scene_synthetic[5])
synthetic.matches_exists = lambda im: False
synthetic.save_matches = lambda im, m: False
num_neighbors = 5
synthetic.config['matching_gps_neighbors'] = num_neighbors
synthetic.config['bow_words_to_match'] = 8
synthetic.config['matcher_type'] = 'FLANN'
images = synthetic.images()
pairs, _ = matching.match_images(synthetic, images, images, True)
assert len(pairs) == 62
value, margin = 11842, 0.01
assert value * (1 - margin) < sum([len(m) for m in pairs.values()
]) < value * (1 + margin)
def test_match_images(scene_synthetic) -> None:
reference = scene_synthetic.reconstruction
synthetic = synthetic_dataset.SyntheticDataSet(
reference,
scene_synthetic.exifs,
scene_synthetic.features,
scene_synthetic.tracks_manager,
)
# pyre-fixme[8]: Attribute has type
# `BoundMethod[typing.Callable(SyntheticDataSet.matches_exists)[[Named(self,
# SyntheticDataSet), Named(image, str)], bool], SyntheticDataSet]`; used as `(im:
# Any) -> bool`.
synthetic.matches_exists = lambda im: False
# pyre-fixme[8]: Attribute has type
# `BoundMethod[typing.Callable(DataSet.save_matches)[[Named(self, DataSet),
# Named(image, str), Named(matches, Dict[str, ndarray])], None],
# SyntheticDataSet]`; used as `(im: Any, m: Any) -> bool`.
synthetic.save_matches = lambda im, m: False
override = {}
override["matching_gps_neighbors"] = 0
override["matching_gps_distance"] = 0
override["matching_time_neighbors"] = 2
images = sorted(synthetic.images())
pairs, _ = matching.match_images(synthetic, override, images, images)
matching.save_matches(synthetic, images, pairs)
for i in range(len(images) - 1):
pair = images[i], images[i + 1]
matches = pairs.get(pair)
if matches is None or len(matches) == 1:
matches = pairs.get(pair[::-1])
# pyre-fixme[6]: For 1st param expected `Sized` but got
# `Optional[List[Tuple[int, int]]]`.
assert len(matches) > 25
