def _impl_test_findadjacencies_closest_approach(template_dir, config):
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_df = pd.read_csv(
f'{execution_dir}/{final_config["findadjacencies"]["output-table"]}')
label_pairs = output_df[['label_a', 'label_b']].values
assert 0 not in label_pairs.flat
label_pairs = list(map(tuple, label_pairs))
assert (1, 2) not in label_pairs
assert (3, 4) in label_pairs
assert (6, 7) in label_pairs
assert (6, 7) in label_pairs
assert (output_df.query('label_a == 3')[['za', 'zb']].values[0] == 0).all()
assert (output_df.query('label_a == 3')[[
'ya', 'yb'
]].values[0] == (6, 5)).all() # not 'forward'
assert (output_df.query('label_a == 3')[['xa', 'xb']].values[0] == 2).all()
assert (output_df.query('label_a == 6')[['za', 'zb']].values[0] == 0).all()
assert (output_df.query('label_a == 6')[[
'ya', 'yb'
]].values[0] == (1, 3)).all() # not 'forward'
assert (output_df.query('label_a == 6')[['xa', 'xb']].values[0] == 6).all()
return output_df
def test_findadjacencies_subset_edges(setup_findadjacencies):
template_dir, config, _volume = setup_findadjacencies
subset_edges = pd.DataFrame([[4, 3]], columns=['label_a', 'label_b'])
subset_edges.to_csv(f'{template_dir}/subset-edges.csv',
index=False,
header=True)
# Overwrite config with updated settings.
config = copy.deepcopy(config)
config["findadjacencies"]["subset-edges"] = 'subset-edges.csv'
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_df = pd.read_csv(
f'{execution_dir}/{final_config["findadjacencies"]["output-table"]}')
label_pairs = output_df[['label_a', 'label_b']].values
assert 0 not in label_pairs.flat
label_pairs = list(map(tuple, label_pairs))
assert (1, 2) not in label_pairs
assert (3, 4) in label_pairs
assert (output_df.query('label_a == 3')[['za',
'zb']].values[0] == (0, 0)).all()
assert (output_df.query('label_a == 3')[[
'ya', 'yb'
]].values[0] == (6, 5)).all() # not 'forward'
assert (output_df.query('label_a == 3')[['xa',
'xb']].values[0] == (2, 2)).all()
def test_copysegmentation_dvid_to_zarr(setup_dvid_to_zarr):
template_dir, config, volume, dvid_address, repo_uuid, output_file = setup_dvid_to_zarr
# Modify the config from above to compute pyramid scales,
# and choose a bounding box that is aligned with the bricks even at scale 2
# (just for easier testing).
box_zyx = [[0, 0, 0], [256, 256, 256]]
config["input"]["geometry"]["bounding-box"] = box_zyx
config["copysegmentation"]["pyramid-depth"] = 2
yaml = YAML()
yaml.default_flow_style = False
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, _workflow = launch_flow(template_dir, 1)
box_zyx = np.array(box_zyx)
scale_0_vol = volume[box_to_slicing(*box_zyx)]
scale_1_vol = downsample_labels(scale_0_vol, 2, True)
scale_2_vol = downsample_labels(scale_1_vol, 2, True)
store = zarr.NestedDirectoryStore(f"{execution_dir}/{output_file}")
f = zarr.open(store, 'r')
output_0_vol = f['s0'][box_to_slicing(*(box_zyx // 1))]
output_1_vol = f['s1'][box_to_slicing(*(box_zyx // 2))]
output_2_vol = f['s2'][box_to_slicing(*(box_zyx // 4))]
assert (output_0_vol == scale_0_vol).all(), \
"Scale 0: Written vol does not match expected"
assert (output_1_vol == scale_1_vol).all(), \
"Scale 1: Written vol does not match expected"
assert (output_2_vol == scale_2_vol).all(), \
"Scale 2: Written vol does not match expected"
def _run_to_dvid(setup, check_scale_0=True):
template_dir, config, volume, dvid_address, repo_uuid, output_segmentation_name = setup
yaml = YAML()
yaml.default_flow_style = False
# re-dump config in case it's been changed by a specific test
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
_execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
input_box_xyz = np.array(final_config['input']['geometry']['bounding-box'])
input_box_zyx = input_box_xyz[:, ::-1]
expected_vol = extract_subvol(volume, input_box_zyx)
output_box_xyz = np.array(
final_config['output']['geometry']['bounding-box'])
output_box_zyx = output_box_xyz[:, ::-1]
output_vol = fetch_raw(dvid_address,
repo_uuid,
output_segmentation_name,
output_box_zyx,
dtype=np.uint64)
np.save('/tmp/output_vol.npy', output_vol)
np.save('/tmp/expected_vol.npy', expected_vol)
if check_scale_0:
assert (output_vol == expected_vol).all(), \
"Written vol does not match expected"
return input_box_zyx, expected_vol, output_vol
def test_samplepoints_rescale(setup_samplepoints):
template_dir, config, volume, points_df = setup_samplepoints
config = copy.copy(config)
config["input"]["adapters"]["rescale-level"] = 2
config["samplepoints"]["rescale-points-to-level"] = 2
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_df = pd.read_csv(
f'{execution_dir}/{final_config["samplepoints"]["output-table"]}')
# Should appear in sorted order
points_df.sort_values(['z', 'y', 'x'], inplace=True)
sorted_coords = points_df[['z', 'y', 'x']].values
output_df['Z'] = points_df['z'].values
output_df['Y'] = points_df['y'].values
output_df['X'] = points_df['x'].values
assert (sorted_coords == output_df[['z', 'y', 'x']].values).all()
downsampled_vol = downsample(volume, 2**2, 'labels')
labels = downsampled_vol[(*(sorted_coords // 2**2).transpose(), )]
assert (labels == output_df['label'].values).all()
# 'extra' columns should be preserved, even
# though they weren't used in the computation.
input_extra = points_df.sort_values(['z', 'y', 'x'])['extra'].values
output_extra = output_df.sort_values(['z', 'y', 'x'])['extra'].values
assert (output_extra == input_extra
).all(), "Extra column was not correctly preserved"
def test_findadjacencies_solid_volume():
"""
If the volume is solid or empty, an error is raised at the end of the workflow.
"""
template_dir = tempfile.mkdtemp(suffix="findadjacencies-template")
# Create solid volume
volume = 99 * np.ones((256, 256, 256), np.uint64)
volume_path = f"{template_dir}/volume.h5"
with h5py.File(volume_path, 'w') as f:
f['volume'] = volume
config = {
"workflow-name": "findadjacencies",
"cluster-type": CLUSTER_TYPE,
"input": {
"hdf5": {
"path": volume_path,
"dataset": "volume"
},
"geometry": {
"message-block-shape": [64, 64, 64]
},
},
"findadjacencies": {
"output-table": "output.csv"
}
}
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
with pytest.raises(RuntimeError):
_execution_dir, _workflow = launch_flow(template_dir, 1)
def _run_to_dvid(setup, check_scale_0=True):
template_dir, config, volume, dvid_address, repo_uuid, output_grayscale_name = setup
yaml = YAML()
yaml.default_flow_style = False
# re-dump config in case it's been changed by a specific test
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
_execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
box_xyz = np.array(final_config['input']['geometry']['bounding-box'])
box_zyx = box_xyz[:, ::-1]
output_vol = fetch_raw(dvid_address, repo_uuid, output_grayscale_name,
box_zyx)
expected_vol = volume[box_to_slicing(*box_zyx)]
if check_scale_0:
assert (output_vol == expected_vol).all(), \
"Written vol does not match expected"
return box_zyx, expected_vol
def test_contingencytable(setup_hdf5_inputs):
template_dir, _config, left_vol, right_vol = setup_hdf5_inputs
expected_table = contingency_table(left_vol, right_vol).reset_index()
execution_dir, _workflow = launch_flow(template_dir, 1)
output_table = pd.DataFrame(
np.load(f"{execution_dir}/contingency_table.npy"))
assert (output_table == expected_table).all().all()
def test_decimatemeshes_max_vertices(setup_decimatemeshes_config, disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_decimatemeshes_config
max_vertices = 1000
config['decimatemeshes']['max-vertices'] = max_vertices
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
#print(execution_dir)
check_outputs(execution_dir, object_boxes, max_vertices=max_vertices)
def test_createmeshes_rescale_isotropic(setup_createmeshes_config,
disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_createmeshes_config
config['createmeshes']['rescale-before-write'] = 2
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
scaled_boxes = {label: box * 2 for label, box in object_boxes.items()}
check_outputs(execution_dir, scaled_boxes)
def test_maskedcopy(setup_hdf5_inputs):
template_dir, config, input_vol, mask_vol, output_path = setup_hdf5_inputs
expected_vol = np.where(mask_vol, input_vol, 0)
execution_dir, _workflow = launch_flow(template_dir, 1)
with h5py.File(output_path, 'r') as f:
output_vol = f['volume'][:]
assert (output_vol == expected_vol).all()
def test_masksegmentation_resume(setup_dvid_segmentation_input,
disable_auto_retry):
template_dir, config, volume, dvid_address, repo_uuid, roi_mask_s5, _input_segmentation_name, output_segmentation_name = setup_dvid_segmentation_input
brick_shape = config["input"]["geometry"]["message-block-shape"]
batch_size = config["masksegmentation"]["batch-size"]
# This is the total bricks in the volume, not necessarily
# the total *processed* bricks, but it's close enough.
total_bricks = np.ceil(np.prod(np.array(volume.shape) /
brick_shape)).astype(int)
total_batches = int(np.ceil(total_bricks / batch_size))
# Skip over half of the original bricks.
config["masksegmentation"]["resume-at"] = {
"scale": 0,
"batch-index": 1 + (total_batches // 2)
}
# re-dump config
yaml = YAML()
yaml.default_flow_style = False
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
_execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
input_box_xyz = np.array(final_config['input']['geometry']['bounding-box'])
input_box_zyx = input_box_xyz[:, ::-1]
roi_mask = upsample(roi_mask_s5, 2**5)
roi_mask = extract_subvol(roi_mask, input_box_zyx)
masked_vol = extract_subvol(volume.copy(), input_box_zyx)
masked_vol[roi_mask] = 0
output_box_xyz = np.array(
final_config['output']['geometry']['bounding-box'])
output_box_zyx = output_box_xyz[:, ::-1]
output_vol = fetch_labelmap_voxels(dvid_address,
repo_uuid,
output_segmentation_name,
output_box_zyx,
scale=0,
supervoxels=True)
#np.save('/tmp/original.npy', volume)
#np.save('/tmp/output.npy', output_vol)
# First part was untouched
assert (output_vol[:128] == volume[:128]).all()
# Last part was touched somewhere
assert (output_vol[128:] != volume[128:]).any()
def test_createmeshes_from_body_source(setup_createmeshes_config,
disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_createmeshes_config
config["input"]["dvid"]["supervoxels"] = False
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
#print(execution_dir)
# In this test, each supervoxel is its own body anyway.
check_outputs(execution_dir, object_boxes)
def test_copygrayscale_from_hdf5_to_slices(disable_auto_retry):
template_dir = tempfile.mkdtemp(suffix="copygrayscale-from-hdf5-template")
# Create volume, write to HDF5
volume = np.random.randint(10, size=TESTVOL_SHAPE, dtype=np.uint8)
volume_path = f"{template_dir}/volume.h5"
with h5py.File(volume_path, 'w') as f:
f['volume'] = volume
SLICE_FMT = 'slices/{:04d}.png'
config_text = textwrap.dedent(f"""\
workflow-name: copygrayscale
cluster-type: {CLUSTER_TYPE}
input:
hdf5:
path: {volume_path}
dataset: volume
geometry:
message-block-shape: [64,64,256]
bounding-box: [[0,0,100], [256,200,256]]
adapters:
# Enable multi-scale, since otherwise
# Hdf5VolumeService doesn't support it out-of-the box
rescale-level: 0
output:
slice-files:
slice-path-format: "{SLICE_FMT}"
dtype: uint8
copygrayscale:
max-pyramid-scale: 0
slab-depth: 128
""")
with open(f"{template_dir}/workflow.yaml", 'w') as f:
f.write(config_text)
_execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
box_xyz = np.array(final_config['input']['geometry']['bounding-box'])
box_zyx = box_xyz[:, ::-1]
output_vol = SliceFilesVolumeService(final_config['output']).get_subvolume(
[[100, 0, 0], [256, 200, 256]])
expected_vol = volume[box_to_slicing(*box_zyx)]
assert (output_vol == expected_vol).all(), \
"Written vol does not match expected"
def test_createmeshes_subset_bodies(setup_createmeshes_config,
disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_createmeshes_config
config['createmeshes']['subset-bodies'] = [100, 300]
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
df = pd.DataFrame(np.load(f'{execution_dir}/final-mesh-stats.npy'))
assert 200 not in df['sv'].values
check_outputs(execution_dir, object_boxes, subset_labels=[100, 300])
def test_createmeshes_max_svs_per_brick(setup_createmeshes_config,
disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_createmeshes_config
config['createmeshes']['max-svs-per-brick'] = 1
# Use just one brick, but it should end up getting split for each object.
config["input"]["geometry"].update(
{"message-block-shape": [128, 128, 128]})
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
#print(execution_dir)
check_outputs(execution_dir, object_boxes)
def test_mitostats(setup_hdf5_inputs):
template_dir, config, seg_vol, mask_vol, output_path = setup_hdf5_inputs
execution_dir, _workflow = launch_flow(template_dir, 1)
stats_df = pickle.load(open(f'{execution_dir}/stats_df.pkl', 'rb'))
assert (stats_df.loc[1, ['z', 'y', 'x']] == [32, 32, 32]).all()
assert (stats_df.loc[2, ['z', 'y', 'x']] == [128, 128, 128]).all()
# These classes should be symmetric. See above.
assert (stats_df.loc[2, 'class_3'] == stats_df.loc[2, 'class_4'])
assert stats_df.loc[1, 'total_size'] == (seg_vol == 1).sum()
assert stats_df.loc[2, 'total_size'] == (seg_vol == 2).sum()
def test_decimatemeshes_skip_existing(setup_decimatemeshes_config, disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_decimatemeshes_config
config['decimatemeshes']['skip-existing'] = True
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
# Create an empty file for mesh 200
os.makedirs(f"{template_dir}/meshes")
open(f"{template_dir}/meshes/200.ngmesh", 'wb').close()
execution_dir, _workflow = launch_flow(template_dir, 1)
# The file should have been left alone (still empty).
assert open(f"{execution_dir}/meshes/200.ngmesh", 'rb').read() == b''
# But other meshes were generated.
check_outputs(execution_dir, object_boxes, subset_labels=[100], skipped_labels=[200])
def _impl_findadjacencies_different_dvid_blocks_sparse_edges(
template_dir, config):
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_df = pd.read_csv(
f'{execution_dir}/{final_config["findadjacencies"]["output-table"]}')
label_pairs = output_df[['label_a', 'label_b']].values
assert 0 not in label_pairs.flat
label_pairs = list(map(tuple, label_pairs))
assert (1, 6) in label_pairs
def test_createmeshes_subset_bodies_in_batches(setup_createmeshes_config,
disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_createmeshes_config
config['createmeshes']['subset-bodies'] = [100, 200, 300]
config['createmeshes']['subset-batch-size'] = 2
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
#print(execution_dir)
check_outputs(execution_dir,
object_boxes,
subset_labels=[100, 200],
stats_dir=f'{execution_dir}/batch-00')
check_outputs(execution_dir,
object_boxes,
subset_labels=[300],
stats_dir=f'{execution_dir}/batch-01')
def test_createmeshes_filter_supervoxels(setup_createmeshes_config,
disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, object_sizes = setup_createmeshes_config
# Set size filter to exclude the largest SV (100) and smallest SV (300),
# leaving only the middle object (SV 200).
assert object_sizes[300] < object_sizes[200] < object_sizes[100]
config['createmeshes']['size-filters'][
'minimum-supervoxel-size'] = object_sizes[300] + 1
config['createmeshes']['size-filters'][
'maximum-supervoxel-size'] = object_sizes[100] - 1
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
df = pd.DataFrame(np.load(f'{execution_dir}/final-mesh-stats.npy'))
assert 100 not in df['sv'].values
assert 300 not in df['sv'].values
check_outputs(execution_dir, object_boxes, subset_labels=[200])
def test_sparsemeshes(setup_dvid_segmentation_input):
template_dir, _config, _dvid_address, _repo_uuid = setup_dvid_segmentation_input
execution_dir, _workflow = launch_flow(template_dir, 1)
#final_config = workflow.config
assert os.path.exists(f"{execution_dir}/meshes/100.obj")
# Here's where our test mesh ended up:
#print(f"{execution_dir}/meshes/100.obj")
df = pd.read_csv(f'{execution_dir}/mesh-stats.csv')
assert len(df) == 2
assert df.loc[0, 'body'] == 100
assert df.loc[0, 'scale'] == 1
assert df.loc[0, 'result'] == 'success'
# The second body didn't exist, so it fails (but doesn't kill the workflow)
assert df.loc[1, 'body'] == 200
assert df.loc[1, 'scale'] == 0
assert df.loc[1, 'result'] == 'error-sparsevol-coarse'
def _impl_test_findadjacencies_from_dvid_sparse(template_dir, config):
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_df = pd.read_csv(
f'{execution_dir}/{final_config["findadjacencies"]["output-table"]}')
label_pairs = output_df[['label_a', 'label_b']].values
assert 0 not in label_pairs.flat
assert output_df[['label_a', 'label_b', 'group']].duplicated().sum() == 0
label_pairs = list(map(tuple, label_pairs))
assert (1, 2) in label_pairs
assert (3, 4) in label_pairs
assert (6, 7) in label_pairs
assert (2, 8) in label_pairs
assert (1, 6) not in label_pairs
assert (1, 7) not in label_pairs
assert (output_df.query('label_a == 3')[['za',
'zb']].values[0] == 31).all()
assert (output_df.query('label_a == 3')[[
'ya', 'yb'
]].values[0] == (7 * 16, 7 * 16 - 1)).all() # not 'forward'
assert (output_df.query('label_a == 3')[['xa',
'xb']].values[0] == 2.5 * 16 -
1).all()
# The Z and X locations here are a little hard to test, since several voxels are tied.
#assert (output_df.query('label_a == 6')[['za', 'zb']].values[0] == 31).all()
assert (output_df.query('label_a == 6')[[
'ya', 'yb'
]].values[0] == (2 * 16 - 1, 3 * 16)).all() # not 'forward'
assert (output_df.query('label_a == 6')[['xa',
'xb']].values[0] == 6 * 16).all()
return execution_dir, workflow
def test_labelmapcopy_partial(setup_dvid_segmentation_input,
disable_auto_retry):
template_dir, config, expected_vols, partial_vol, dvid_address, repo_uuid, _output_segmentation_name, partial_output_segmentation_name = setup_dvid_segmentation_input
config = copy.deepcopy(config)
config["output"]["dvid"][
"segmentation-name"] = partial_output_segmentation_name
yaml = YAML()
yaml.default_flow_style = False
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_box_xyz = np.array(
final_config['output']['geometry']['bounding-box'])
output_box_zyx = output_box_xyz[:, ::-1]
max_scale = final_config['labelmapcopy']['max-scale']
for scale in range(1 + max_scale):
scaled_box = output_box_zyx // (2**scale)
output_vol = fetch_labelmap_voxels(dvid_address,
repo_uuid,
partial_output_segmentation_name,
scaled_box,
scale=scale)
assert (output_vol == expected_vols[scale]).all(), \
f"Written vol does not match expected for scale {scale}"
# Any labels NOT in the partial vol had to be written.
written_labels = pd.unique(
expected_vols[0][expected_vols[0] != partial_vol])
assert len(written_labels) > 0, \
"This test data was chosen poorly -- there's no difference between the partial and full labels!"
svs = pd.read_csv(f'{execution_dir}/recorded-labels.csv')['sv']
assert set(svs) == set(written_labels)
def test_findadjacencies_subset_bodies(setup_findadjacencies):
template_dir, config, _volume = setup_findadjacencies
# Overwrite config with updated settings.
config = copy.deepcopy(config)
config["findadjacencies"]["subset-labels"] = [3]
config["findadjacencies"]["subset-labels-requirement"] = 1
with open(f"{template_dir}/workflow.yaml", 'w') as f:
yaml.dump(config, f)
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_df = pd.read_csv(
f'{execution_dir}/{final_config["findadjacencies"]["output-table"]}')
label_pairs = output_df[['label_a', 'label_b']].values
assert 0 not in label_pairs.flat
label_pairs = list(map(tuple, label_pairs))
assert (1, 2) not in label_pairs
assert (3, 4) in label_pairs
assert (output_df.query('label_a == 3')[['za',
'zb']].values[0] == (0, 0)).all()
assert (output_df.query('label_a == 3')[[
'ya', 'yb'
]].values[0] == (6, 5)).all() # not 'forward'
assert (output_df.query('label_a == 3')[['xa',
'xb']].values[0] == (2, 2)).all()
# Check CC groups
cc_sets = set()
for _cc, cc_df in output_df.groupby('group_cc'):
cc_set = frozenset(cc_df[['label_a', 'label_b']].values.flat)
cc_sets.add(cc_set)
assert set(cc_sets) == {frozenset({4, 3})}
def test_createmeshes_bad_subset_bodies(setup_createmeshes_config,
disable_auto_retry):
"""
If one (or more) of the listed bodies no longer exists,
DVID returns a 404 when we try to fetch its supervoxels.
The job should complete anyway, for the other bodies.
"""
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_createmeshes_config
config['createmeshes']['subset-bodies'] = [100, 999] # 999 doesn't exist
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
assert pd.read_csv(
f'{execution_dir}/missing-bodies.csv')['body'].tolist() == [999]
df = pd.DataFrame(np.load(f'{execution_dir}/final-mesh-stats.npy'))
assert 100 in df['sv'].values
assert 200 not in df['sv'].values
assert 300 not in df['sv'].values
assert 999 not in df['sv'].values
check_outputs(execution_dir, object_boxes, subset_labels=[100])
def test_contingencytable(setup_hdf5_inputs):
"""
TODO: Test the filtering options (left-subset-labels, min-overlap-size)
"""
template_dir, _config, left_vol, right_vol = setup_hdf5_inputs
expected_table = contingency_table(left_vol,
right_vol).sort_index().reset_index()
expected_left_sizes = expected_table.groupby('left')['voxel_count'].sum()
expected_right_sizes = expected_table.groupby('right')['voxel_count'].sum()
execution_dir, _workflow = launch_flow(template_dir, 1)
with open(f"{execution_dir}/contingency_table.pkl", "rb") as f:
output_table = pickle.load(f)
with open(f"{execution_dir}/left_sizes.pkl", "rb") as f:
left_sizes = pickle.load(f)
with open(f"{execution_dir}/right_sizes.pkl", "rb") as f:
right_sizes = pickle.load(f)
assert (output_table == expected_table).all().all()
assert (left_sizes == expected_left_sizes).all().all()
assert (right_sizes == expected_right_sizes).all().all()
def test_labelmapcopy(setup_dvid_segmentation_input, disable_auto_retry):
template_dir, _config, expected_vols, partial_vol, dvid_address, repo_uuid, output_segmentation_name, _partial_output_segmentation_name = setup_dvid_segmentation_input
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_box_xyz = np.array(
final_config['output']['geometry']['bounding-box'])
output_box_zyx = output_box_xyz[:, ::-1]
max_scale = final_config['labelmapcopy']['max-scale']
for scale in range(1 + max_scale):
scaled_box = output_box_zyx // (2**scale)
output_vol = fetch_labelmap_voxels(dvid_address,
repo_uuid,
output_segmentation_name,
scaled_box,
scale=scale)
assert (output_vol == expected_vols[scale]).all(), \
f"Written vol does not match expected for scale {scale}"
svs = pd.read_csv(f'{execution_dir}/recorded-labels.csv')['sv']
assert set(svs) == set(np.unique(expected_vols[0].reshape(-1)))
def test_samplepoints(setup_samplepoints):
template_dir, _config, volume, points_df = setup_samplepoints
execution_dir, workflow = launch_flow(template_dir, 1)
final_config = workflow.config
output_df = pd.read_csv(
f'{execution_dir}/{final_config["samplepoints"]["output-table"]}')
# Should appear in sorted order
sorted_coords = points_df[['z', 'y', 'x']].sort_values(['z', 'y',
'x']).values
assert (sorted_coords == output_df[['z', 'y', 'x']].values).all()
labels = volume[(*sorted_coords.transpose(), )]
assert (labels == output_df['label']).all()
# 'extra' columns should be preserved, even
# though they weren't used in the computation.
input_extra = points_df.sort_values(['z', 'y', 'x'])['extra'].values
output_extra = output_df.sort_values(['z', 'y', 'x'])['extra'].values
assert (output_extra == input_extra
).all(), "Extra column was not correctly preserved"
def test_createmeshes_from_body_source_subset_bodies(setup_createmeshes_config,
disable_auto_retry):
template_dir, config, _dvid_address, _repo_uuid, object_boxes, _object_sizes = setup_createmeshes_config
config["input"]["dvid"].update({"supervoxels": False})
config["input"]["geometry"].update(
{"message-block-shape": [128, 128, 128]})
config["input"]["adapters"] = {"rescale-level": 1}
config["createmeshes"].update({
"subset-bodies": [100, 300],
"rescale-before-write": 2.0
})
YAML().dump(config, open(f"{template_dir}/workflow.yaml", 'w'))
execution_dir, _workflow = launch_flow(template_dir, 1)
#print(execution_dir)
# In this test, each supervoxel is its own body anyway.
check_outputs(execution_dir, object_boxes, [100, 300])
