def test_run_all(tmpdir):
data = data_generation.create_berlin_test_folder(tmpdir)
run_all_commands = [
commands.extract_metadata,
commands.detect_features,
commands.match_features,
commands.create_tracks,
commands.reconstruct,
commands.bundle,
commands.mesh,
commands.undistort,
commands.compute_depthmaps,
commands.export_ply,
commands.export_visualsfm,
commands.export_openmvs,
commands.export_pmvs,
commands.export_bundler,
commands.export_colmap,
]
for module in run_all_commands:
command = module.Command()
run_command(command, [data.data_path])
reconstruction = data.load_reconstruction()
assert len(reconstruction[0].shots) == 3
assert len(reconstruction[0].points) > 1000
def test_dataset_load_features_sift(tmpdir):
data = data_generation.create_berlin_test_folder(tmpdir)
assert len(data.images()) == 3
data.config["feature_type"] = "SIFT"
image = data.images()[0]
points = np.random.random((3, 4))
descriptors = np.random.random((128, 4))
colors = np.random.random((3, 4))
segmentations = np.random.random((3, 4))
instances = np.random.random((3, 4))
semantic_data = features.SemanticData(
segmentations, instances, data.segmentation_labels()
)
before = features.FeaturesData(points, descriptors, colors, semantic_data)
data.save_features(image, before)
after = data.load_features(image)
assert np.allclose(points, after.points)
assert np.allclose(descriptors, after.descriptors)
assert np.allclose(colors, after.colors)
assert np.allclose(
segmentations,
after.semantic.segmentation,
)
assert np.allclose(instances, after.semantic.instances)
def test_dataset_load_features_sift(tmpdir):
data = data_generation.create_berlin_test_folder(tmpdir)
assert len(data.images()) == 3
data.config['feature_type'] = 'SIFT'
image = data.images()[0]
points = np.random.random((3, 4))
descriptors = np.random.random((128, 4))
colors = np.random.random((3, 4))
data.save_features(image, points, descriptors, colors)
p, d, c = data.load_features(image)
assert np.allclose(p, points)
assert np.allclose(d, descriptors)
assert np.allclose(c, colors)
def test_run_all(tmpdir):
data = data_generation.create_berlin_test_folder(tmpdir)
run_all_commands = [
commands.extract_metadata,
commands.detect_features,
commands.match_features,
commands.create_tracks,
commands.reconstruct,
commands.bundle,
commands.reconstruct_from_prior,
commands.mesh,
commands.undistort,
commands.compute_depthmaps,
commands.export_ply,
commands.export_visualsfm,
commands.export_openmvs,
commands.export_pmvs,
commands.export_bundler,
commands.export_colmap,
commands.compute_statistics,
commands.export_report,
]
output_rec_path = join(data.data_path, "rec_prior.json")
command_options = {
commands.reconstruct_from_prior: [
"--input",
join(data.data_path, "reconstruction.json"),
"--output",
output_rec_path,
]
}
for module in run_all_commands:
command = module.Command()
options = command_options.get(module, [])
run_command(command, [data.data_path] + options)
check_reconstruction(data)
check_prior(data, output_rec_path)
def test_dataset_load_features_sift(tmpdir):
data = data_generation.create_berlin_test_folder(tmpdir)
assert len(data.images()) == 3
data.config["feature_type"] = "SIFT"
image = data.images()[0]
points = np.random.random((3, 4))
descriptors = np.random.random((128, 4))
colors = np.random.random((3, 4))
segmentations = np.random.random((3, 4))
instances = np.random.random((3, 4))
data.save_features(image, points, descriptors, colors, segmentations,
instances)
p, d, c, s = data.load_features(image)
assert np.allclose(p, points)
assert np.allclose(d, descriptors)
assert np.allclose(c, colors)
assert np.allclose(s["segmentations"], segmentations)
assert np.allclose(s["instances"], instances)