def test_realistic_valid_examples(example, use_gurobi):
penalty_attr = "penalty"
location_attr = "location"
example_dir = Path(__file__).parent / "mouselight_examples" / "valid" / example
consensus = PointsKey("CONSENSUS")
skeletonization = PointsKey("SKELETONIZATION")
matched = PointsKey("MATCHED")
matched_with_fallback = PointsKey("MATCHED_WITH_FALLBACK")
inf_roi = Roi(Coordinate((None,) * 3), Coordinate((None,) * 3))
request = BatchRequest()
request[matched] = PointsSpec(roi=inf_roi)
request[matched_with_fallback] = PointsSpec(roi=inf_roi)
request[consensus] = PointsSpec(roi=inf_roi)
pipeline = (
(
GraphSource(example_dir / "graph.obj", [skeletonization]),
GraphSource(example_dir / "tree.obj", [consensus]),
)
+ MergeProvider()
+ TopologicalMatcher(
skeletonization,
consensus,
matched,
expected_edge_len=10,
match_distance_threshold=76,
max_gap_crossing=48,
use_gurobi=use_gurobi,
location_attr=location_attr,
penalty_attr=penalty_attr,
)
+ TopologicalMatcher(
skeletonization,
consensus,
matched_with_fallback,
expected_edge_len=10,
match_distance_threshold=76,
max_gap_crossing=48,
use_gurobi=use_gurobi,
location_attr=location_attr,
penalty_attr=penalty_attr,
with_fallback=True,
)
)
with build(pipeline):
batch = pipeline.request_batch(request)
consensus_ccs = list(batch[consensus].connected_components)
matched_with_fallback_ccs = list(batch[matched_with_fallback].connected_components)
matched_ccs = list(batch[matched].connected_components)
assert len(matched_ccs) == len(consensus_ccs)
def test_relabel_components(self):
path = Path(self.path_to("test_swc_source.swc"))
# write test swc
self._write_swc(path, self._toy_swc_points())
# read arrays
swc = PointsKey("SWC")
source = SwcFileSource(path, swc)
with build(source):
batch = source.request_batch(
BatchRequest(
{swc: PointsSpec(roi=Roi((0, 1, 0), (11, 10, 1)))}))
temp_g = nx.DiGraph()
for point_id, point in batch.points[swc].data.items():
temp_g.add_node(point.point_id, label_id=point.label_id)
if (point.parent_id != -1 and point.parent_id != point.point_id
and point.parent_id in batch.points[swc].data):
temp_g.add_edge(point.point_id, point.parent_id)
previous_label = None
ccs = list(nx.weakly_connected_components(temp_g))
self.assertEqual(len(ccs), 3)
for cc in ccs:
self.assertEqual(len(cc), 10)
label = None
for point_id in cc:
if label is None:
label = temp_g.nodes[point_id]["label_id"]
self.assertNotEqual(label, previous_label)
self.assertEqual(temp_g.nodes[point_id]["label_id"], label)
previous_label = label
def test_without_placeholder(self):
test_labels = ArrayKey("TEST_LABELS")
test_points = PointsKey("TEST_POINTS")
pipeline = (
PointTestSource3D()
+ RandomLocation(ensure_nonempty=test_points)
+ ElasticAugment([10, 10, 10], [0.1, 0.1, 0.1], [0, 2.0 * math.pi])
+ Snapshot(
{test_labels: "volumes/labels"},
output_dir=self.path_to(),
output_filename="elastic_augment_test{id}-{iteration}.hdf",
)
)
with build(pipeline):
for i in range(100):
request_size = Coordinate((40, 40, 40))
request_a = BatchRequest(random_seed=i)
request_a.add(test_points, request_size)
request_b = BatchRequest(random_seed=i)
request_b.add(test_points, request_size)
request_b.add(test_labels, request_size)
# No array to provide a voxel size to ElasticAugment
with pytest.raises(PipelineRequestError):
pipeline.request_batch(request_a)
batch_b = pipeline.request_batch(request_b)
self.assertIn(test_labels, batch_b)
def test_ensure_center_non_zero(self):
path = Path(self.path_to("test_swc_source.swc"))
# write test swc
self._write_swc(path, self._toy_swc_points().to_nx_graph())
# read arrays
swc = PointsKey("SWC")
img = ArrayKey("IMG")
pipeline = (SwcFileSource(
path, [swc], [PointsSpec(roi=Roi((0, 0, 0), (11, 11, 11)))]) +
RandomLocation(ensure_nonempty=swc, ensure_centered=True) +
RasterizeSkeleton(
points=swc,
array=img,
array_spec=ArraySpec(
interpolatable=False,
dtype=np.uint32,
voxel_size=Coordinate((1, 1, 1)),
),
))
request = BatchRequest()
request.add(img, Coordinate((5, 5, 5)))
request.add(swc, Coordinate((5, 5, 5)))
with build(pipeline):
batch = pipeline.request_batch(request)
data = batch[img].data
g = batch[swc]
assert g.num_vertices() > 0
self.assertNotEqual(data[tuple(np.array(data.shape) // 2)], 0)
def test_read_single_swc(self):
path = Path(self.path_to("test_swc_source.swc"))
# write test swc
self._write_swc(path, self._toy_swc_points())
# read arrays
swc = PointsKey("SWC")
source = SwcFileSource(path, swc)
with build(source):
batch = source.request_batch(
BatchRequest(
{swc: PointsSpec(roi=Roi((0, 0, 0), (11, 11, 1)))}))
for point in self._toy_swc_points():
self.assertCountEqual(
point.location,
batch.points[swc].data[point.point_id].location)
def test_create_boundary_nodes(self):
path = Path(self.path_to("test_swc_source.swc"))
# write test swc
self._write_swc(path, self._toy_swc_points(),
{"resolution": np.array([2, 2, 2])})
# read arrays
swc = PointsKey("SWC")
source = SwcFileSource(path, swc)
with build(source):
batch = source.request_batch(
BatchRequest({swc: PointsSpec(roi=Roi((0, 5, 0), (1, 3, 1)))}))
temp_g = nx.DiGraph()
for point_id, point in batch.points[swc].data.items():
temp_g.add_node(point.point_id,
label_id=point.label_id,
location=point.location)
if (point.parent_id != -1 and point.parent_id != point.point_id
and point.parent_id in batch.points[swc].data):
temp_g.add_edge(point.point_id, point.parent_id)
else:
root = point.point_id
current = root
expected_path = [
tuple(np.array([0.0, 5.0, 0.0])),
tuple(np.array([0.0, 6.0, 0.0])),
tuple(np.array([0.0, 7.0, 0.0])),
]
expected_node_ids = [0, 1, 2]
path = []
node_ids = []
while current is not None:
node_ids.append(current)
path.append(tuple(temp_g.nodes[current]["location"]))
predecessors = list(temp_g._pred[current].keys())
current = predecessors[0] if len(predecessors) == 1 else None
self.assertCountEqual(path, expected_path)
self.assertCountEqual(node_ids, expected_node_ids)
def test_grow_labels_speed(self):
bb = Roi(Coordinate([0, 0, 0]), ([256, 256, 256]))
voxel_size = Coordinate([1, 1, 1])
swc_file = "test_swc.swc"
swc_path = Path(self.path_to(swc_file))
swc_points = self._get_points(np.array([1, 1, 1]), np.array([1, 1, 1]),
bb)
self._write_swc(swc_path, swc_points.graph)
# create swc sources
swc_key = PointsKey("SWC")
labels_key = ArrayKey("LABELS")
# add request
request = BatchRequest()
request.add(labels_key, bb.get_shape())
request.add(swc_key, bb.get_shape())
# data source for swc a
data_source = tuple()
data_source = (data_source + SwcFileSource(
swc_path, [swc_key], [PointsSpec(roi=bb)]) + RasterizeSkeleton(
points=swc_key,
array=labels_key,
array_spec=ArraySpec(interpolatable=False,
dtype=np.uint32,
voxel_size=voxel_size),
) + GrowLabels(array=labels_key, radius=3))
pipeline = data_source
num_repeats = 10
t1 = time.time()
with build(pipeline):
for i in range(num_repeats):
pipeline.request_batch(request)
t2 = time.time()
self.assertLess((t2 - t1) / num_repeats, 0.1)
def test_overlap(self):
path = Path(self.path_to("test_swc_sources"))
path.mkdir(parents=True, exist_ok=True)
# write test swc
for i in range(3):
self._write_swc(
path / "{}.swc".format(i),
self._toy_swc_points(),
{"offset": np.array([0, 0, 0])},
)
# read arrays
swc = PointsKey("SWC")
source = SwcFileSource(path, swc)
with build(source):
batch = source.request_batch(
BatchRequest(
{swc: PointsSpec(roi=Roi((0, 0, 0), (11, 11, 1)))}))
temp_g = nx.DiGraph()
for point_id, point in batch.points[swc].data.items():
temp_g.add_node(point.point_id, label_id=point.label_id)
if (point.parent_id != -1 and point.parent_id != point.point_id
and point.parent_id in batch.points[swc].data):
temp_g.add_edge(point.point_id, point.parent_id)
previous_label = None
ccs = list(nx.weakly_connected_components(temp_g))
self.assertEqual(len(ccs), 3)
for cc in ccs:
self.assertEqual(len(cc), 41)
label = None
for point_id in cc:
if label is None:
label = temp_g.nodes[point_id]["label_id"]
self.assertNotEqual(label, previous_label)
self.assertEqual(temp_g.nodes[point_id]["label_id"], label)
previous_label = label
def test_placeholder(self):
test_labels = ArrayKey("TEST_LABELS")
test_points = PointsKey("TEST_POINTS")
pipeline = (
PointTestSource3D()
+ RandomLocation(ensure_nonempty=test_points)
+ ElasticAugment([10, 10, 10], [0.1, 0.1, 0.1], [0, 2.0 * math.pi])
+ Snapshot(
{test_labels: "volumes/labels"},
output_dir=self.path_to(),
output_filename="elastic_augment_test{id}-{iteration}.hdf",
)
)
with build(pipeline):
for i in range(100):
request_size = Coordinate((40, 40, 40))
request_a = BatchRequest(random_seed=i)
request_a.add(test_points, request_size)
request_a.add(test_labels, request_size, placeholder=True)
request_b = BatchRequest(random_seed=i)
request_b.add(test_points, request_size)
request_b.add(test_labels, request_size)
batch_a = pipeline.request_batch(request_a)
batch_b = pipeline.request_batch(request_b)
points_a = batch_a[test_points].nodes
points_b = batch_b[test_points].nodes
for a, b in zip(points_a, points_b):
assert all(np.isclose(a.location, b.location))
def test_two_disjoint_lines_intensity(self):
LABEL_RADIUS = 3
RAW_RADIUS = 3
BLEND_SMOOTHNESS = 3
bb = Roi(Coordinate([0, 0, 0]), ([256, 256, 256]))
voxel_size = Coordinate([1, 1, 1])
swc_files = ("test_line_a.swc", "test_line_b.swc")
swc_paths = tuple(Path(self.path_to(file_name)) for file_name in swc_files)
# create two lines seperated by a given distance and write them to swc files
intercepts, slopes = self._get_line_pair(roi=bb, dist=3 * LABEL_RADIUS)
for intercept, slope, swc_path in zip(intercepts, slopes, swc_paths):
swc_points = self._get_points(intercept, slope, bb)
self._write_swc(swc_path, swc_points.to_nx_graph())
# create swc sources
fused = ArrayKey("FUSED")
fused_labels = ArrayKey("FUSED_LABELS")
fused_swc = PointsKey("FUSED_SWC")
swc_key_names = ("SWC_A", "SWC_B")
labels_key_names = ("LABELS_A", "LABELS_B")
raw_key_names = ("RAW_A", "RAW_B")
swc_keys = tuple(PointsKey(name) for name in swc_key_names)
labels_keys = tuple(ArrayKey(name) for name in labels_key_names)
raw_keys = tuple(ArrayKey(name) for name in raw_key_names)
# add request
request = BatchRequest()
request.add(fused, bb.get_shape())
request.add(fused_labels, bb.get_shape())
request.add(fused_swc, bb.get_shape())
request.add(labels_keys[0], bb.get_shape())
request.add(labels_keys[1], bb.get_shape())
request.add(raw_keys[0], bb.get_shape())
request.add(raw_keys[1], bb.get_shape())
request.add(swc_keys[0], bb.get_shape())
request.add(swc_keys[1], bb.get_shape())
# data source for swc a
data_sources_a = tuple()
data_sources_a = (
data_sources_a
+ SwcFileSource(swc_paths[0], [swc_keys[0]], [PointsSpec(roi=bb)])
+ RasterizeSkeleton(
points=swc_keys[0],
array=labels_keys[0],
array_spec=ArraySpec(
interpolatable=False, dtype=np.uint16, voxel_size=voxel_size
),
)
+ GrowLabels(array=labels_keys[0], radii=[LABEL_RADIUS])
+ RasterizeSkeleton(
points=swc_keys[0],
array=raw_keys[0],
array_spec=ArraySpec(
interpolatable=False, dtype=np.uint16, voxel_size=voxel_size
),
)
+ GrowLabels(array=raw_keys[0], radii=[RAW_RADIUS])
+ Normalize(raw_keys[0])
)
# data source for swc b
data_sources_b = tuple()
data_sources_b = (
data_sources_b
+ SwcFileSource(swc_paths[1], [swc_keys[1]], [PointsSpec(roi=bb)])
+ RasterizeSkeleton(
points=swc_keys[1],
array=labels_keys[1],
array_spec=ArraySpec(
interpolatable=False, dtype=np.uint16, voxel_size=voxel_size
),
)
+ GrowLabels(array=labels_keys[1], radii=[LABEL_RADIUS])
+ RasterizeSkeleton(
points=swc_keys[1],
array=raw_keys[1],
array_spec=ArraySpec(
interpolatable=False, dtype=np.uint16, voxel_size=voxel_size
),
)
+ GrowLabels(array=raw_keys[1], radii=[RAW_RADIUS])
+ Normalize(raw_keys[1])
)
data_sources = tuple([data_sources_a, data_sources_b]) + MergeProvider()
pipeline = data_sources + FusionAugment(
raw_keys[0],
raw_keys[1],
labels_keys[0],
labels_keys[1],
swc_keys[0],
swc_keys[1],
fused,
fused_labels,
fused_swc,
blend_mode="intensity",
blend_smoothness=BLEND_SMOOTHNESS,
num_blended_objects=0,
)
with build(pipeline):
batch = pipeline.request_batch(request)
fused_data = batch[fused].data
fused_data = np.pad(fused_data, (1,), "constant", constant_values=(0,))
a_data = batch[raw_keys[0]].data
a_data = np.pad(a_data, (1,), "constant", constant_values=(0,))
b_data = batch[raw_keys[1]].data
b_data = np.pad(b_data, (1,), "constant", constant_values=(0,))
diff = np.linalg.norm(fused_data - a_data - b_data)
self.assertAlmostEqual(diff, 0)
def test_recenter():
path = Path(self.path_to("test_swc_source.swc"))
# write test swc
self._write_swc(path, self._toy_swc_points())
# read arrays
swc_source = PointsKey("SWC_SOURCE")
labels_source = ArrayKey("LABELS_SOURCE")
img_source = ArrayKey("IMG_SOURCE")
img_swc = PointsKey("IMG_SWC")
label_swc = PointsKey("LABEL_SWC")
imgs = ArrayKey("IMGS")
labels = ArrayKey("LABELS")
points_a = PointsKey("SKELETON_A")
points_b = PointsKey("SKELETON_B")
img_a = ArrayKey("VOLUME_A")
img_b = ArrayKey("VOLUME_B")
labels_a = ArrayKey("LABELS_A")
labels_b = ArrayKey("LABELS_B")
# Get points from test swc
swc_file_source = SwcFileSource(
path, [swc_source], [PointsSpec(roi=Roi((-10, -10, -10), (31, 31, 31)))]
)
# Create an artificial image source by rasterizing the points
image_source = (
SwcFileSource(
path, [img_swc], [PointsSpec(roi=Roi((-10, -10, -10), (31, 31, 31)))]
)
+ RasterizeSkeleton(
points=img_swc,
array=img_source,
array_spec=ArraySpec(
interpolatable=True, dtype=np.uint32, voxel_size=Coordinate((1, 1, 1))
),
)
+ BinarizeLabels(labels=img_source, labels_binary=imgs)
+ GrowLabels(array=imgs, radius=0)
)
# Create an artificial label source by rasterizing the points
label_source = (
SwcFileSource(
path, [label_swc], [PointsSpec(roi=Roi((-10, -10, -10), (31, 31, 31)))]
)
+ RasterizeSkeleton(
points=label_swc,
array=labels_source,
array_spec=ArraySpec(
interpolatable=True, dtype=np.uint32, voxel_size=Coordinate((1, 1, 1))
),
)
+ BinarizeLabels(labels=labels_source, labels_binary=labels)
+ GrowLabels(array=labels, radius=1)
)
skeleton = tuple()
skeleton += (
(swc_file_source, image_source, label_source)
+ MergeProvider()
+ RandomLocation(ensure_nonempty=swc_source, ensure_centered=True)
)
pipeline = (
skeleton
+ GetNeuronPair(
point_source=swc_source,
array_source=imgs,
label_source=labels,
points=(points_a, points_b),
arrays=(img_a, img_b),
labels=(labels_a, labels_b),
seperate_by=4,
shift_attempts=100,
)
+ Recenter(points_a, img_a, max_offset=4)
+ Recenter(points_b, img_b, max_offset=4)
)
request = BatchRequest()
data_shape = 9
request.add(points_a, Coordinate((data_shape, data_shape, data_shape)))
request.add(points_b, Coordinate((data_shape, data_shape, data_shape)))
request.add(img_a, Coordinate((data_shape, data_shape, data_shape)))
request.add(img_b, Coordinate((data_shape, data_shape, data_shape)))
request.add(labels_a, Coordinate((data_shape, data_shape, data_shape)))
request.add(labels_b, Coordinate((data_shape, data_shape, data_shape)))
with build(pipeline):
batch = pipeline.request_batch(request)
data_a = batch[img_a].data
assert data_a[tuple(np.array(data_a.shape) // 2)] == 1
data_a = np.pad(data_a, (1,), "constant", constant_values=(0,))
data_b = batch[img_b].data
assert data_b[tuple(np.array(data_b.shape) // 2)] == 1
data_b = np.pad(data_b, (1,), "constant", constant_values=(0,))
data_c = data_a + data_b
data = np.array((data_a, data_b, data_c))
for _, point in batch[points_a].data.items():
assert (
data[(0,) + tuple(int(x) + 1 for x in point.location)] == 1
), "data at {} is not 1, its {}".format(
point.location, data[(0,) + tuple(int(x) for x in point.location)]
)
for _, point in batch[points_b].data.items():
assert (
data[(1,) + tuple(int(x) + 1 for x in point.location)] == 1
), "data at {} is not 1".format(point.location)
def test_output_min_distance(self):
voxel_size = Coordinate((20, 2, 2))
ArrayKey("GT_VECTORS_MAP_PRESYN")
PointsKey("PRESYN")
PointsKey("POSTSYN")
arraytypes_to_source_target_pointstypes = {
ArrayKeys.GT_VECTORS_MAP_PRESYN:
(GraphKeys.PRESYN, GraphKeys.POSTSYN)
}
arraytypes_to_stayinside_arraytypes = {
ArrayKeys.GT_VECTORS_MAP_PRESYN: ArrayKeys.GT_LABELS
}
# test for partner criterion 'min_distance'
radius_phys = 30
pipeline_min_distance = AddVectorMapTestSource() + AddVectorMap(
src_and_trg_points=arraytypes_to_source_target_pointstypes,
voxel_sizes={ArrayKeys.GT_VECTORS_MAP_PRESYN: voxel_size},
radius_phys=radius_phys,
partner_criterion="min_distance",
stayinside_array_keys=arraytypes_to_stayinside_arraytypes,
pad_for_partners=(0, 0, 0),
)
with build(pipeline_min_distance):
request = BatchRequest()
raw_roi = pipeline_min_distance.spec[ArrayKeys.RAW].roi
gt_labels_roi = pipeline_min_distance.spec[ArrayKeys.GT_LABELS].roi
presyn_roi = pipeline_min_distance.spec[GraphKeys.PRESYN].roi
request.add(ArrayKeys.RAW, raw_roi.get_shape())
request.add(ArrayKeys.GT_LABELS, gt_labels_roi.get_shape())
request.add(GraphKeys.PRESYN, presyn_roi.get_shape())
request.add(GraphKeys.POSTSYN, presyn_roi.get_shape())
request.add(ArrayKeys.GT_VECTORS_MAP_PRESYN,
presyn_roi.get_shape())
for identifier, spec in request.items():
spec.roi = spec.roi.shift((1000, 1000, 1000))
batch = pipeline_min_distance.request_batch(request)
presyn_locs = {n.id: n for n in batch.graphs[GraphKeys.PRESYN].nodes}
postsyn_locs = {n.id: n for n in batch.graphs[GraphKeys.POSTSYN].nodes}
vector_map_presyn = batch.arrays[ArrayKeys.GT_VECTORS_MAP_PRESYN].data
offset_vector_map_presyn = request[
ArrayKeys.GT_VECTORS_MAP_PRESYN].roi.get_offset()
self.assertTrue(len(presyn_locs) > 0)
self.assertTrue(len(postsyn_locs) > 0)
for loc_id, point in presyn_locs.items():
if request[ArrayKeys.GT_VECTORS_MAP_PRESYN].roi.contains(
Coordinate(point.location)):
self.assertTrue(batch.arrays[
ArrayKeys.GT_VECTORS_MAP_PRESYN].spec.roi.contains(
Coordinate(point.location)))
dist_to_loc = {}
for partner_id in point.attrs["partner_ids"]:
if partner_id in postsyn_locs.keys():
partner_location = postsyn_locs[partner_id].location
dist_to_loc[
np.linalg.norm(partner_location -
point.location)] = partner_location
min_dist = np.min(list(dist_to_loc.keys()))
relevant_partner_loc = dist_to_loc[min_dist]
presyn_loc_shifted_vx = (
point.location - offset_vector_map_presyn) // voxel_size
radius_vx = [(radius_phys // vx_dim) for vx_dim in voxel_size]
region_to_check = np.clip(
[
(presyn_loc_shifted_vx - radius_vx),
(presyn_loc_shifted_vx + radius_vx),
],
a_min=(0, 0, 0),
a_max=vector_map_presyn.shape[-3:],
)
for x, y, z in itertools.product(
range(int(region_to_check[0][0]),
int(region_to_check[1][0])),
range(int(region_to_check[0][1]),
int(region_to_check[1][1])),
range(int(region_to_check[0][2]),
int(region_to_check[1][2])),
):
if (np.linalg.norm((np.array(
(x, y, z)) - np.asarray(point.location))) <
radius_phys):
vector = [
vector_map_presyn[dim][x, y, z]
for dim in range(vector_map_presyn.shape[0])
]
if not np.sum(vector) == 0:
trg_loc_of_vector_phys = (
np.asarray(offset_vector_map_presyn) +
(voxel_size * np.array([x, y, z])) +
np.asarray(vector))
self.assertTrue(
np.array_equal(trg_loc_of_vector_phys,
relevant_partner_loc))
# test for partner criterion 'all'
pipeline_all = AddVectorMapTestSource() + AddVectorMap(
src_and_trg_points=arraytypes_to_source_target_pointstypes,
voxel_sizes={ArrayKeys.GT_VECTORS_MAP_PRESYN: voxel_size},
radius_phys=radius_phys,
partner_criterion="all",
stayinside_array_keys=arraytypes_to_stayinside_arraytypes,
pad_for_partners=(0, 0, 0),
)
with build(pipeline_all):
batch = pipeline_all.request_batch(request)
presyn_locs = {n.id: n for n in batch.graphs[GraphKeys.PRESYN].nodes}
postsyn_locs = {n.id: n for n in batch.graphs[GraphKeys.POSTSYN].nodes}
vector_map_presyn = batch.arrays[ArrayKeys.GT_VECTORS_MAP_PRESYN].data
offset_vector_map_presyn = request[
ArrayKeys.GT_VECTORS_MAP_PRESYN].roi.get_offset()
self.assertTrue(len(presyn_locs) > 0)
self.assertTrue(len(postsyn_locs) > 0)
for loc_id, point in presyn_locs.items():
if request[ArrayKeys.GT_VECTORS_MAP_PRESYN].roi.contains(
Coordinate(point.location)):
self.assertTrue(batch.arrays[
ArrayKeys.GT_VECTORS_MAP_PRESYN].spec.roi.contains(
Coordinate(point.location)))
partner_ids_to_locs_per_src, count_vectors_per_partner = {}, {}
for partner_id in point.attrs["partner_ids"]:
if partner_id in postsyn_locs.keys():
partner_ids_to_locs_per_src[partner_id] = postsyn_locs[
partner_id].location.tolist()
count_vectors_per_partner[partner_id] = 0
presyn_loc_shifted_vx = (
point.location - offset_vector_map_presyn) // voxel_size
radius_vx = [(radius_phys // vx_dim) for vx_dim in voxel_size]
region_to_check = np.clip(
[
(presyn_loc_shifted_vx - radius_vx),
(presyn_loc_shifted_vx + radius_vx),
],
a_min=(0, 0, 0),
a_max=vector_map_presyn.shape[-3:],
)
for x, y, z in itertools.product(
range(int(region_to_check[0][0]),
int(region_to_check[1][0])),
range(int(region_to_check[0][1]),
int(region_to_check[1][1])),
range(int(region_to_check[0][2]),
int(region_to_check[1][2])),
):
if (np.linalg.norm((np.array(
(x, y, z)) - np.asarray(point.location))) <
radius_phys):
vector = [
vector_map_presyn[dim][x, y, z]
for dim in range(vector_map_presyn.shape[0])
]
if not np.sum(vector) == 0:
trg_loc_of_vector_phys = (
np.asarray(offset_vector_map_presyn) +
(voxel_size * np.array([x, y, z])) +
np.asarray(vector))
self.assertTrue(trg_loc_of_vector_phys.tolist(
) in partner_ids_to_locs_per_src.values())
for (
partner_id,
partner_loc,
) in partner_ids_to_locs_per_src.items():
if np.array_equal(
np.asarray(trg_loc_of_vector_phys),
partner_loc):
count_vectors_per_partner[partner_id] += 1
self.assertTrue(
(list(count_vectors_per_partner.values()) -
np.min(list(count_vectors_per_partner.values())) <= len(
count_vectors_per_partner.keys())).all())
def test_two_disjoint_lines_softmask(self):
LABEL_RADIUS = 3
RAW_RADIUS = 3
# exagerated to show problem
BLEND_SMOOTHNESS = 10
bb = Roi(Coordinate([0, 0, 0]), ([256, 256, 256]))
voxel_size = Coordinate([1, 1, 1])
swc_files = ("test_line_a.swc", "test_line_b.swc")
swc_paths = tuple(
Path(self.path_to(file_name)) for file_name in swc_files)
# create two lines seperated by a given distance and write them to swc files
intercepts, slopes = self._get_line_pair(roi=bb, dist=3 * LABEL_RADIUS)
for intercept, slope, swc_path in zip(intercepts, slopes, swc_paths):
swc_points = self._get_points(intercept, slope, bb)
self._write_swc(swc_path, swc_points)
# create swc sources
fused = ArrayKey("FUSED")
fused_labels = ArrayKey("FUSED_LABELS")
swc_key_names = ("SWC_A", "SWC_B")
labels_key_names = ("LABELS_A", "LABELS_B")
raw_key_names = ("RAW_A", "RAW_B")
swc_keys = tuple(PointsKey(name) for name in swc_key_names)
labels_keys = tuple(ArrayKey(name) for name in labels_key_names)
raw_keys = tuple(ArrayKey(name) for name in raw_key_names)
# add request
request = BatchRequest()
request.add(fused, bb.get_shape())
request.add(fused_labels, bb.get_shape())
request.add(labels_keys[0], bb.get_shape())
request.add(labels_keys[1], bb.get_shape())
request.add(raw_keys[0], bb.get_shape())
request.add(raw_keys[1], bb.get_shape())
request.add(swc_keys[0], bb.get_shape())
request.add(swc_keys[1], bb.get_shape())
# data source for swc a
data_sources_a = tuple()
data_sources_a = (data_sources_a + SwcFileSource(
swc_paths[0], swc_keys[0], PointsSpec(roi=bb)) + RasterizeSkeleton(
points=swc_keys[0],
array=labels_keys[0],
array_spec=ArraySpec(interpolatable=False,
dtype=np.uint32,
voxel_size=voxel_size),
radius=LABEL_RADIUS,
) + RasterizeSkeleton(
points=swc_keys[0],
array=raw_keys[0],
array_spec=ArraySpec(interpolatable=False,
dtype=np.uint32,
voxel_size=voxel_size),
radius=RAW_RADIUS,
))
# data source for swc b
data_sources_b = tuple()
data_sources_b = (data_sources_b + SwcFileSource(
swc_paths[1], swc_keys[1], PointsSpec(roi=bb)) + RasterizeSkeleton(
points=swc_keys[1],
array=labels_keys[1],
array_spec=ArraySpec(interpolatable=False,
dtype=np.uint32,
voxel_size=voxel_size),
radius=LABEL_RADIUS,
) + RasterizeSkeleton(
points=swc_keys[1],
array=raw_keys[1],
array_spec=ArraySpec(interpolatable=False,
dtype=np.uint32,
voxel_size=voxel_size),
radius=RAW_RADIUS,
))
data_sources = tuple([data_sources_a, data_sources_b
]) + MergeProvider()
pipeline = data_sources + FusionAugment(
raw_keys[0],
raw_keys[1],
labels_keys[0],
labels_keys[1],
fused,
fused_labels,
blend_mode="labels_mask",
blend_smoothness=BLEND_SMOOTHNESS,
num_blended_objects=0,
)
with build(pipeline):
batch = pipeline.request_batch(request)
fused_data = batch[fused].data
fused_data = np.pad(fused_data, (1, ),
"constant",
constant_values=(0, ))
a_data = batch[raw_keys[0]].data
a_data = np.pad(a_data, (1, ), "constant", constant_values=(0, ))
b_data = batch[raw_keys[1]].data
b_data = np.pad(b_data, (1, ), "constant", constant_values=(0, ))
all_data = np.zeros((5, ) + fused_data.shape)
all_data[0, :, :, :] = fused_data
all_data[1, :, :, :] = a_data + b_data
all_data[2, :, :, :] = fused_data - a_data - b_data
all_data[3, :, :, :] = a_data
all_data[4, :, :, :] = b_data
# Uncomment to visualize problem
if imported_volshow:
volshow(all_data)
# input("Press enter when you are done viewing the data: ")
diff = np.linalg.norm(fused_data - a_data - b_data)
self.assertAlmostEqual(diff, 0)
def test_rasterize_speed(self):
# This is worryingly slow for such a small volume (256**3) and only 2
# straight lines for skeletons.
LABEL_RADIUS = 3
bb = Roi(Coordinate([0, 0, 0]), ([256, 256, 256]))
voxel_size = Coordinate([1, 1, 1])
swc_files = ("test_line_a.swc", "test_line_b.swc")
swc_paths = tuple(Path(self.path_to(file_name)) for file_name in swc_files)
# create two lines seperated by a given distance and write them to swc files
intercepts, slopes = self._get_line_pair(roi=bb, dist=3 * LABEL_RADIUS)
for intercept, slope, swc_path in zip(intercepts, slopes, swc_paths):
swc_points = self._get_points(intercept, slope, bb)
self._write_swc(swc_path, swc_points)
# create swc sources
swc_key_names = ("SWC_A", "SWC_B")
labels_key_names = ("LABELS_A", "LABELS_B")
swc_keys = tuple(PointsKey(name) for name in swc_key_names)
labels_keys = tuple(ArrayKey(name) for name in labels_key_names)
# add request
request = BatchRequest()
request.add(labels_keys[0], bb.get_shape())
request.add(labels_keys[1], bb.get_shape())
request.add(swc_keys[0], bb.get_shape())
request.add(swc_keys[1], bb.get_shape())
# data source for swc a
data_sources_a = tuple()
data_sources_a = (
data_sources_a
+ SwcFileSource(swc_paths[0], swc_keys[0], PointsSpec(roi=bb))
+ RasterizeSkeleton(
points=swc_keys[0],
array=labels_keys[0],
array_spec=ArraySpec(
interpolatable=False, dtype=np.uint32, voxel_size=voxel_size
),
radius=LABEL_RADIUS,
)
)
# data source for swc b
data_sources_b = tuple()
data_sources_b = (
data_sources_b
+ SwcFileSource(swc_paths[1], swc_keys[1], PointsSpec(roi=bb))
+ RasterizeSkeleton(
points=swc_keys[1],
array=labels_keys[1],
array_spec=ArraySpec(
interpolatable=False, dtype=np.uint32, voxel_size=voxel_size
),
radius=LABEL_RADIUS,
)
)
data_sources = tuple([data_sources_a, data_sources_b]) + MergeProvider()
pipeline = data_sources
t1 = time.time()
with build(pipeline):
batch = pipeline.request_batch(request)
a_data = batch[labels_keys[0]].data
a_data = np.pad(a_data, (1,), "constant", constant_values=(0,))
b_data = batch[labels_keys[1]].data
b_data = np.pad(b_data, (1,), "constant", constant_values=(0,))
t2 = time.time()
self.assertLess(t2 - t1, 0.1)
def test_get_neuron_pair(self):
path = Path(self.path_to("test_swc_source.swc"))
# write test swc
self._write_swc(path, self._toy_swc_points().to_nx_graph())
# read arrays
swc_source = PointsKey("SWC_SOURCE")
ensure_nonempty = PointsKey("ENSURE_NONEMPTY")
labels_source = ArrayKey("LABELS_SOURCE")
img_source = ArrayKey("IMG_SOURCE")
img_swc = PointsKey("IMG_SWC")
label_swc = PointsKey("LABEL_SWC")
imgs = ArrayKey("IMGS")
labels = ArrayKey("LABELS")
points_a = PointsKey("SKELETON_A")
points_b = PointsKey("SKELETON_B")
img_a = ArrayKey("VOLUME_A")
img_b = ArrayKey("VOLUME_B")
labels_a = ArrayKey("LABELS_A")
labels_b = ArrayKey("LABELS_B")
data_shape = 5
output_shape = Coordinate((data_shape, data_shape, data_shape))
# Get points from test swc
swc_file_source = SwcFileSource(
path,
[swc_source, ensure_nonempty],
[
PointsSpec(roi=Roi((-10, -10, -10), (31, 31, 31))),
PointsSpec(roi=Roi((-10, -10, -10), (31, 31, 31))),
],
)
# Create an artificial image source by rasterizing the points
image_source = (SwcFileSource(
path, [img_swc],
[PointsSpec(roi=Roi((-10, -10, -10), (31, 31, 31)))]) +
RasterizeSkeleton(
points=img_swc,
array=img_source,
array_spec=ArraySpec(
interpolatable=True,
dtype=np.uint32,
voxel_size=Coordinate((1, 1, 1)),
),
) +
BinarizeLabels(labels=img_source, labels_binary=imgs) +
GrowLabels(array=imgs, radius=0))
# Create an artificial label source by rasterizing the points
label_source = (SwcFileSource(path, [label_swc], [
PointsSpec(roi=Roi((-10, -10, -10), (31, 31, 31)))
]) + RasterizeSkeleton(
points=label_swc,
array=labels_source,
array_spec=ArraySpec(
interpolatable=True,
dtype=np.uint32,
voxel_size=Coordinate((1, 1, 1)),
),
) + BinarizeLabels(labels=labels_source, labels_binary=labels) +
GrowLabels(array=labels, radius=1))
skeleton = tuple()
skeleton += ((swc_file_source, image_source, label_source) +
MergeProvider() +
RandomLocation(ensure_nonempty=ensure_nonempty,
ensure_centered=True))
pipeline = skeleton + GetNeuronPair(
point_source=swc_source,
nonempty_placeholder=ensure_nonempty,
array_source=imgs,
label_source=labels,
points=(points_a, points_b),
arrays=(img_a, img_b),
labels=(labels_a, labels_b),
seperate_by=(1, 3),
shift_attempts=100,
request_attempts=10,
output_shape=output_shape,
)
request = BatchRequest()
request.add(points_a, output_shape)
request.add(points_b, output_shape)
request.add(img_a, output_shape)
request.add(img_b, output_shape)
request.add(labels_a, output_shape)
request.add(labels_b, output_shape)
with build(pipeline):
for i in range(10):
batch = pipeline.request_batch(request)
assert all([
x in batch for x in
[points_a, points_b, img_a, img_b, labels_a, labels_b]
])
min_dist = 5
for a, b in itertools.product(
batch[points_a].nodes,
batch[points_b].nodes,
):
min_dist = min(
min_dist,
np.linalg.norm(a.location - b.location),
)
self.assertLessEqual(min_dist, 3)
self.assertGreaterEqual(min_dist, 1)
def get_test_data_sources(setup_config):
input_shape = Coordinate(setup_config["INPUT_SHAPE"])
voxel_size = Coordinate(setup_config["VOXEL_SIZE"])
input_size = input_shape * voxel_size
micron_scale = voxel_size[0]
# New array keys
# Note: These are intended to be requested with size input_size
raw = ArrayKey("RAW")
consensus = PointsKey("CONSENSUS")
skeletonization = PointsKey("SKELETONIZATION")
matched = PointsKey("MATCHED")
nonempty_placeholder = PointsKey("NONEMPTY")
labels = ArrayKey("LABELS")
if setup_config["FUSION_PIPELINE"]:
ensure_nonempty = nonempty_placeholder
else:
ensure_nonempty = consensus
data_sources = ((
TestImageSource(
array=raw,
array_specs={
raw:
ArraySpec(interpolatable=True,
voxel_size=voxel_size,
dtype=np.uint16)
},
size=input_size * 3,
voxel_size=voxel_size,
),
TestPointSource(
points=[consensus, nonempty_placeholder],
directed=True,
size=input_size * 3,
num_points=30,
),
TestPointSource(
points=[skeletonization],
directed=False,
size=input_size * 3,
num_points=333,
),
) + MergeProvider() + RandomLocation(
ensure_nonempty=ensure_nonempty,
ensure_centered=True,
point_balance_radius=10 * micron_scale,
) + TopologicalMatcher(
skeletonization,
consensus,
matched,
match_distance_threshold=50 * micron_scale,
max_gap_crossing=30 * micron_scale,
try_complete=False,
use_gurobi=True,
) + RasterizeSkeleton(
points=matched,
array=labels,
array_spec=ArraySpec(
interpolatable=False, voxel_size=voxel_size, dtype=np.uint64),
) + Normalize(raw))
return (
data_sources,
raw,
labels,
consensus,
nonempty_placeholder,
skeletonization,
matched,
)
