def fib_single_skeleton(bb=np.s_[:], method='teasar'):
path = '/g/kreshuk/data/FIB25/training_blocks/raw/raw_block1.h5'
with h5py.File(path) as f:
raw = f['data'][bb]
print(raw.shape)
path = '/g/kreshuk/data/FIB25/training_blocks/gt/gt_block1.h5'
with h5py.File(path) as f:
seg = f['data'][bb]
print(seg.shape)
seg = vigra.analysis.labelVolume(seg)
ids, sizes = np.unique(seg, return_counts=True)
obj_id = ids[np.argmax(sizes)]
print("Extract object", obj_id, "...")
obj = seg == obj_id
bb = np.where(obj)
bb = tuple(slice(int(b.min()), int(b.max()) + 1) for b in bb)
seg = seg[bb]
obj = obj[bb]
raw = raw[bb]
# view([seg.astype('uint32'), obj.astype('uint32')])
print("Skeletonize ...")
resolution = 8
nodes, edges = skeletor.skeletonize(obj,
resolution=resolution,
method=method)
node_coords = tuple(np.array([n[i] for n in nodes]) for i in range(3))
vol = np.zeros_like(obj, dtype='uint32')
vol[node_coords] = obj_id
view([raw, obj.astype('uint32'), vol], ['raw', 'obj', 'skeleton'])
def view_block(lauritzen_block_id,
block_id,
block_shape=(52, 512, 512),
halo=[0, 0, 0]):
path = '/home/papec/mnt/nrs/lauritzen/0%i/workspace.n5/raw' % lauritzen_block_id
f = z5py.File(path)
ds_raw = f['gray']
shape = ds_raw.shape
blocking = nifty.tools.blocking(roiBegin=[0, 0, 0],
roiEnd=list(shape),
blockShape=list(block_shape))
if sum(halo) > 0:
block = blocking.getBlockWithHalo(block_id, halo).outerBlock
else:
block = blocking.getBlock(block_id)
bb = tuple(slice(beg, end) for beg, end in zip(block.begin, block.end))
raw = ds_raw[bb]
seg = f['segmentations/mc_glia_affs'][bb]
mask1 = f['masks/initial_mask'][bb]
mask2 = f['masks/minfilter_mask'][bb]
view([raw, seg, mask1.astype('uint32'),
mask2.astype('uint32')],
['raw', 'seg', 'initial mask', 'minfilter mask'])
def view_block(block_id):
gt_path = '/g/kreshuk/data/arendt/sponge/nn_train_data/train_data_0%i.h5' % block_id
with h5py.File(gt_path, 'r') as f:
raw = f['volumes/raw'][:]
gt = f['volumes/labels/instances'][:]
sem = f['volumes/labels/semantic'][:]
mask = np.where(gt != -1)
bb = tuple(slice(int(ma.min()), int(ma.max() + 1)) for ma in mask)
raw = raw[bb]
gt = gt[bb]
sem = sem[bb]
assert raw.shape == gt.shape == sem.shape
print(raw.shape, gt.shape)
global_bb = get_bb(block_id)
print(global_bb)
path = '/g/kreshuk/data/arendt/sponge/data.n5'
f = z5py.File(path)
ds_mc = f['volumes/segmentation/for_eval/multicut']
seg_mc = vigra.analysis.labelVolume(ds_mc[global_bb].astype('uint32'))
assert seg_mc.shape == gt.shape
ds_lmc = f['volumes/segmentation/for_eval/lifted_multicut']
seg_lmc = vigra.analysis.labelVolume(ds_lmc[global_bb].astype('uint32'))
assert seg_lmc.shape == gt.shape
view([raw, gt, sem, seg_mc, seg_lmc],
['raw', 'gt', 'sem', 'seg-mc', 'seg_lmc'])
def extract_middle_cutout():
path = '/g/kreshuk/data/FIB25/data.n5'
f = z5py.File(path)
ds = f['volumes/raw/s0']
ds.n_threads = 8
shape = ds.shape
halo = [50, 512, 512]
shift = [0, -512, -512]
bb = tuple(
slice(sh // 2 - ha - si, sh // 2 + ha - si)
for sh, ha, si in zip(shape, halo, shift))
raw = ds[bb]
chunks = ds.chunks
ds = f['volumes/affinities']
ds.n_threads = 8
affs = ds[(slice(None), ) + bb]
if False:
view([raw, affs.transpose((1, 2, 3, 0))])
out_path = '/g/kreshuk/data/FIB25/cutout.n5'
f = z5py.File(out_path)
f.create_dataset('volumes/raw',
data=raw,
compression='gzip',
chunks=chunks,
n_threads=8)
f.create_dataset('volumes/affinities',
data=affs,
compression='gzip',
chunks=(1, ) + chunks,
n_threads=8)
def view_skeletons(path):
from cremi_tools.viewer.volumina import view
input_key = 'segmentation/multicut'
output_key = 'skeletons'
f = z5py.File(path)
raw_key = 'raw/s0'
ds = f[raw_key]
ds.n_threads = 8
raw = ds[:]
ds = f[input_key]
ds.n_threads = 8
seg = ds[:]
skel_vol = np.zeros_like(seg, dtype='uint32')
ds_skels = f[output_key]
seg_ids = np.unique(seg)
for seg_id in seg_ids:
nodes, _ = skeletor.io.read_n5(ds_skels, seg_id)
if nodes is None:
continue
coords = tuple(np.array([n[i] for n in nodes])
for i in range(3))
skel_vol[coords] = seg_id
view([raw, seg, skel_vol])
def debug_subresult(block_id=1):
example_path = '/home/cpape/Work/data/isbi2012/cluster_example/isbi_train.n5'
block_prefix = os.path.join(example_path, 's0', 'sub_graphs', 'block_')
graph = ndist.Graph(os.path.join(example_path, 'graph'))
block_path = block_prefix + str(block_id)
nodes = ndist.loadNodes(block_path)
inner_edges, outer_edges, sub_uvs = graph.extractSubgraphFromNodes(nodes)
block_res_path = './tmp/subproblem_results/s0_block%i.npy' % block_id
res = np.load(block_res_path)
merge_edges = np.ones(graph.numberOfEdges, dtype='bool')
merge_edges[res] = False
merge_edges[outer_edges] = False
uv_ids = graph.uvIds()
n_nodes = int(uv_ids.max()) + 1
ufd = nifty.ufd.ufd(n_nodes)
ufd.merge(uv_ids[merge_edges])
node_labels = ufd.elementLabeling()
ws = z5py.File(example_path)['volumes/watersheds'][:]
rag = nrag.gridRag(ws, numberOfLabels=n_nodes)
seg = nrag.projectScalarNodeDataToPixels(rag, node_labels)
view([ws, seg])
def agglomerate_wsdt(thresh=.1, size_thresh=500):
f = z5py.File('/home/papec/Work/neurodata_hdd/scotts_blocks/data_test_small.n5')
affs = 1. - f['predictions/full_affs'][:3, :]
affs_xy = np.mean(affs[1:3], axis=0)
affs_z = affs[0]
wsdt = cseg.DTWatershed(0.2, 1.6)
ws, max_id = wsdt(affs_xy)
rag = nrag.gridRagStacked2D(ws.astype('uint32'),
numberOfLabels=int(max_id + 1),
dtype='uint32')
features_z = nrag.accumulateEdgeStandardFeatures(rag, affs_z, keepZOnly=True, zDirection=2)[1]
features_z = features_z[:, 0]
edge_offset = rag.totalNumberOfInSliceEdges
edge_sizes = rag.edgeLengths()[edge_offset:]
uvs = rag.uvIds()[edge_offset:]
assert len(features_z) == len(uvs)
# TODO filter by edge overlap as well !
merge_edges = np.logical_and(features_z < thresh, edge_sizes > size_thresh)
merge_nodes = uvs[merge_edges]
ufd = nifty.ufd.ufd(rag.numberOfNodes)
ufd.merge(merge_nodes)
node_labels = ufd.elementLabeling()
ws_merged = nrag.projectScalarNodeDataToPixels(rag, node_labels)
raw = f['gray'][:]
view([raw, affs.transpose((1, 2, 3, 0)), ws, ws_merged],
['raw', 'affs', 'ws', 'ws-merged'])
def check_skeleton(path_to_segmentation,
raw_path,
swc_folder,
obj_id,
invert_coordinates=False):
seg = vigra.readHDF5(path_to_segmentation, 'z/0/data')
resolution = [35., 4., 4.]
swc_file = os.path.join(swc_folder, '%04d.swc' % obj_id)
skel_marked = paint_in_swc(swc_file,
seg.shape,
resolution,
invert_coordinates=invert_coordinates)
skel_points = convert_swc_to_volume(swc_file,
seg.shape,
resolution,
dtype='uint32',
invert_coordinates=invert_coordinates)
mask = (seg == obj_id).astype('uint32')
assert mask.shape == skel_points.shape, "%s, %s" % (str(
mask.shape), str(skel_points.shape))
with h5py.File(raw_path, 'r') as f:
raw = f['data'][:mask.shape[0]].astype('float32')
assert raw.shape == mask.shape
view([raw, mask, skel_marked, skel_points],
['raw', 'obj_mask', 'skeleton-marked', 'skeleton-points'])
def ws_example(shebang):
example_path = '/home/cpape/Work/data/isbi2012/cluster_example/isbi_train.n5'
input_key = 'volumes/affinities'
output_key = 'volumes/ws'
max_jobs = 8
global_conf = WatershedLocal.default_global_config()
global_conf.update({'shebang': shebang})
try:
os.mkdir('configs')
except OSError:
pass
with open('./configs/global.config', 'w') as f:
json.dump(global_conf, f)
ret = luigi.build([
WatershedWorkflow(input_path=example_path,
input_key=input_key,
output_path=example_path,
output_key=output_key,
config_dir='./configs',
tmp_folder='./tmp',
target='local',
max_jobs=max_jobs)
],
local_scheduler=True)
if ret:
from cremi_tools.viewer.volumina import view
with z5py.File(example_path) as f:
affs = f[input_key][:3].transpose((1, 2, 3, 0))
ws = f[output_key][:]
view([affs, ws])
def edge_costs_block(block_id):
block_path = '/home/papec/mnt/papec/Work/neurodata_hdd/cache/cremi_A+/tmp_files/features.n5/blocks'
graph_path = '/home/papec/mnt/papec/Work/neurodata_hdd/cache/cremi_A+/tmp_files/graph.n5/sub_graphs/s0'
graph_ds = z5py.File(graph_path)['block_%i' % block_id]
roi_begin = graph_ds.attrs['roiBegin']
roi_end = graph_ds.attrs['roiEnd']
probs = 1. - z5py.File(block_path)['block_%i' % block_id][:, 0:1]
path = '/home/papec/Work/neurodata_hdd/ntwrk_papec/cremi_warped/sampleA+.n5'
ws_key = 'segmentations/watershed'
raw_key = 'raw'
bb = tuple(slice(rb, re) for rb, re in zip(roi_begin, roi_end))
ws = z5py.File(path)[ws_key][bb].astype('uint32')
rag = nrag.gridRag(ws, numberOfLabels=int(ws.max()) + 1)
edges = graph_ds['edges'][:]
assert len(edges) == rag.numberOfEdges
assert (rag.uvIds() == edges).all()
edge_id_vol, edge_vol_att, edge_vol_rep = get_edge_costs(rag, probs)
raw = z5py.File(path)[raw_key][bb]
view([raw, ws, edge_id_vol, edge_vol_att, edge_vol_rep],
['raw', 'ws', 'edge-ids', 'attractive edges', 'repulsive edges'],
layer_types=[
"Grayscale", "RandomColors", "RandomColors", "Blue", "Red"
])
def ms_fs_affs_cremi():
path = '/home/papec/Work/neurodata_hdd/cremi/sample_A_20160501.hdf'
bb = np.s_[:60, :512, :512]
# bb = np.s_[:50, :256, :256]
with h5py.File(path, 'r') as f:
gt = f['volumes/labels/neuron_ids'][bb]
sampling_factors = [3, 27, 27]
# sampling_factors = [1, 3, 3]
t1 = time.time()
ms_affs, mask = compute_fullscale_multiscale_affinities(
gt, sampling_factors)
print("Ms affs in", time.time() - t1)
with h5py.File(path, 'r') as f:
raw = f['volumes/raw'][bb].astype('float32')
new_shape = ms_affs.shape[1:]
mask = (1. - mask).astype('uint32')
raw = vigra.sampling.resize(raw, new_shape)
view([
raw, gt,
ms_affs.transpose((1, 2, 3, 0)),
mask.transpose((1, 2, 3, 0))
], ['raw', 'gt', 'ms-affs', 'mask'])
def debug_subresult(block_id=1):
from cremi_tools.viewer.volumina import view
path = '/g/kreshuk/data/arendt/platyneris_v1/membrane_training_data/validation/segmentation/val_block_01.n5'
tmp_folder = './tmp_plat_val'
block_prefix = os.path.join(path, 's0', 'sub_graphs', 'block_')
graph = ndist.Graph(os.path.join(path, 'graph'))
block_path = block_prefix + str(block_id)
nodes = ndist.loadNodes(block_path)
nodes = nodes[1:]
inner_edges, outer_edges, sub_uvs = graph.extractSubgraphFromNodes(nodes)
block_res_path = os.path.join(
tmp_folder, 'subproblem_results/s0_block%i.npy' % block_id)
res = np.load(block_res_path)
merge_edges = np.ones(graph.numberOfEdges, dtype='bool')
merge_edges[res] = False
merge_edges[outer_edges] = False
uv_ids = graph.uvIds()
n_nodes = int(uv_ids.max()) + 1
ufd = nufd.ufd(n_nodes)
ufd.merge(uv_ids[merge_edges])
node_labels = ufd.elementLabeling()
ws = z5py.File(path)['volumes/watershed'][:]
seg = nt.take(node_labels, ws)
view([ws, seg])
def ms_affs_cremi():
path = '/home/papec/Work/neurodata_hdd/cremi/sample_A_20160501.hdf'
bb = np.s_[:50, :512, :512]
with h5py.File(path, 'r') as f:
gt = f['volumes/labels/neuron_ids'][bb]
# sampling_factors = [3, 9, 9]
sampling_factors = [1, 3, 3]
# t0 = time.time()
# ms_affs_py = ms_single_scale_dense(gt, sampling_factors)
# print('Ms affs python in', time.time() - t0)
t1 = time.time()
ms_affs, mask = compute_multiscale_affinities(gt, sampling_factors)
print("Ms affs in", time.time() - t1)
# assert ms_affs.shape == ms_affs_py.shape, "%s, %s" % (str(ms_affs.shape), str(ms_affs_py.shape))
with h5py.File(path, 'r') as f:
raw = f['volumes/raw'][bb].astype('float32')
new_shape = ms_affs.shape[1:]
mask = (1. - mask).astype('uint32')
raw = vigra.sampling.resize(raw, new_shape)
# view([raw, ms_affs.transpose((1, 2, 3, 0)), ms_affs_py.transpose((1, 2, 3, 0))],
# ['raw', 'ms-affs-cpp', 'ms-affs-py'])
view([raw,
ms_affs.transpose((1, 2, 3, 0)),
mask.transpose((1, 2, 3, 0))], ['raw', 'ms-affs-cpp', 'mask'])
def test_tp():
path = '/home/pape/Work/data/cluster_tools_test_data/test_data.n5'
aff_key = '/volumes/full_affinities'
f = z5py.File(path)
ds_affs = f[aff_key]
ds_affs.n_threads = 8
affs = ds_affs[:]
# affs = affs[:, :10, :256]
# affs = affs[:, :20, :256]
print(affs.shape)
offsets = [[-1, 0, 0], [0, -1, 0], [0, 0, -1], [-1, -1, -1], [-1, 1, 1],
[-1, -1, 1], [-1, 1, -1], [0, -9, 0], [0, 0, -9], [0, -9, -9],
[0, 9, -9], [0, -9, -4], [0, -4, -9], [0, 4, -9], [0, 9, -4],
[0, -27, 0], [0, 0, -27]]
block_shape = [10, 256, 256]
halo = [2, 32, 32]
print("Start agglomeration")
agglomerator = partial(mws_agglomerator,
strides=[2, 10, 10],
randomize_strides=True)
seg = two_pass_agglomeration(affs, offsets, agglomerator, block_shape,
halo, 4)
print(seg.shape)
view([affs[1], seg])
def test_tp():
path = '/home/pape/Work/data/cluster_tools_test_data/test_data.n5'
aff_key = '/volumes/full_affinities'
f = z5py.File(path)
ds_affs = f[aff_key]
ds_affs.n_threads = 8
affs = ds_affs[:]
# affs = affs[:, :10, :256]
# affs = affs[:, :20, :256]
print(affs.shape)
offsets = [[-1, 0, 0], [0, -1, 0], [0, 0, -1],
[-1, -1, -1], [-1, 1, 1], [-1, -1, 1], [-1, 1, -1],
[0, -9, 0], [0, 0, -9],
[0, -9, -9], [0, 9, -9], [0, -9, -4], [0, -4, -9], [0, 4, -9], [0, 9, -4],
[0, -27, 0], [0, 0, -27]]
block_shape = [10, 256, 256]
halo = [2, 32, 32]
print("Start agglomeration")
agglomerator = partial(mws_agglomerator, strides=[2, 10, 10], randomize_strides=True)
seg = two_pass_agglomeration(affs, offsets, agglomerator, block_shape, halo, 4)
print(seg.shape)
view([affs[1], seg])
def downscale_volume(roi_name, max_jobs=250, target='slurm'):
config_dir = './configs'
if roi_name is None:
roi_begin, roi_end = None, None
tmp_folder = './tmp'
else:
roi_begin, roi_end = get_roi(roi_name)
tmp_folder = './tmp_%s' % roi_name
try:
os.mkdir(config_dir)
except OSError:
pass
config = DownscalingWorkflow.get_config()
global_config = config['global']
global_config.update({'shebang': '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster_env/bin/python',
'roi_begin': roi_begin,
'roi_end': roi_end})
with open(os.path.join(config_dir, 'global.config'), 'w') as f:
json.dump(global_config, f)
task_config = config['downscaling']
task_config.update({'time_limit': 120,
'mem_limit': 3})
with open(os.path.join(config_dir, 'downscaling.config'), 'w') as f:
json.dump(task_config, f)
scale_factors = [[1, 2, 2], 2, 2, 2, 2]
halos = [[0, 10, 10], [10, 10, 10], [10, 10, 10], [10, 10, 10], [10, 10, 10]]
# scale_factors = [[1, 2, 2]]
# halos = [[0, 10, 10]]
path = '/g/kreshuk/data/arendt/platyneris_v1/data.n5'
task = DownscalingWorkflow(tmp_folder=tmp_folder,
max_jobs=max_jobs,
config_dir=config_dir,
target=target,
input_path=path,
input_key='volumes/raw/s0',
output_key_prefix='volumes/raw',
scale_factors=scale_factors,
halos=halos)
success = luigi.build([task], local_scheduler=True)
view_ = False
if view_ and success and target == 'local':
sfa = [2, 4, 4]
roi_begin = tuple(roib // sf for roib, sf in zip(roi_begin, sfa))
roi_end = tuple(roie // sf for roie, sf in zip(roi_end, sfa))
bb = tuple(slice(roib, roie) for roib, roie in zip(roi_begin, roi_end))
print(bb)
with z5py.File(path) as f:
ds = f['volumes/raw/s2']
ds.n_threads = 8
data = ds[bb]
print(data.shape)
view([data])
def run(shebang, with_rf=False):
input_path = '/home/cpape/Work/data/isbi2012/cluster_example/isbi_train.n5'
example_path = './isbi_exp.n5'
input_key = 'volumes/affinties'
max_jobs = 8
configs = MulticutSegmentationWorkflow.get_config()
global_conf = configs['global']
global_conf.update({'shebang': shebang,
'block_shape': (25, 256, 256)})
with open('./configs/global.config', 'w') as f:
json.dump(global_conf, f)
ws_conf = configs['watershed']
ws_conf.update({'sigma_weights': 0, 'channel_begin': 1, 'channel_end': 3})
with open('./configs/watershed.config', 'w') as f:
json.dump(ws_conf, f)
if with_rf:
feat_config = configs['block_edge_features']
feat_config.update({'filters': ['gaussianSmoothing', 'laplacianOfGaussian'],
'sigmas': [1., 2., 4.], 'apply_in_2d': True})
rf_path = './rf.pkl'
else:
feat_config = configs['block_edge_features']
feat_config.update({'offsets': [[-1, 0, 0], [0, -1, 0], [0, 0, -1]]})
rf_path = ''
with open('./configs/block_edge_features.config', 'w') as f:
json.dump(feat_config, f)
ret = luigi.build([MulticutSegmentationWorkflow(input_path=input_path,
input_key='volumes/affinities',
ws_path=example_path,
ws_key='volumes/watersheds',
problem_path=example_path,
node_labels_path=example_path,
node_labels_key='node_labels',
output_path=example_path,
output_key='volumes/segmentation',
rf_path=rf_path,
n_scales=1,
config_dir='./configs',
tmp_folder='./tmp',
target='local',
max_jobs=max_jobs)], local_scheduler=True)
if ret:
from cremi_tools.viewer.volumina import view
with z5py.File(input_path) as f:
affs = f['volumes/affinities'][:3].transpose((1, 2, 3, 0))
with z5py.File(example_path) as f:
ws = f['volumes/watersheds'][:]
data = [affs, ws]
if 'volumes/segmentation' in f:
seg = f['volumes/segmentation'][:]
data.append(seg)
view(data)
def toy_euclid_dist_small():
x = np.zeros((3, 10, 10), dtype='bool')
x[:, 2:8, 2:8] = 1
voxel_size = [1, 1, 1]
root = [1, 5, 5]
dist = nskel.euclidean_distance(x, root, voxel_size)
view([x.astype('uint8'), dist])
def toy_euclid_dist_big():
x = np.zeros((10, 100, 100), dtype='bool')
x[2:8, 20:80, 20:80] = 1
voxel_size = [10, 1, 1]
root = [5, 50, 50]
dist = nskel.euclidean_distance(x, root, voxel_size)
view([x.astype('uint8'), dist])
def check_cremi():
folder = '/home/papec/mnt/papec/Work/neurodata_hdd/cremi_warped/sampleA+.n5'
bb = np.s_[50:100, 512:1024, 512:1024]
f = z5py.File(folder)
mc1 = f['segmentations/multicut_rf'][bb]
mc2 = f['segmentations/multicut'][bb]
ws = f['segmentations/watershed'][bb]
raw = f['raw'][bb]
view([raw, ws, mc1, mc2])
def view_result():
from cremi_tools.viewer.volumina import view
raw = z5py.File('./raw.n5')['data'][:]
# ws = z5py.File('./ws.n5')['data'][:]
ws = z5py.File(
'/home/papec/mnt/papec/Work/neurodata_hdd/cluster_test_data/ws.n5'
)['data'][:]
affs = z5py.File('./affs.n5')['affs_xy'][:]
view([raw, affs, ws])
def skeletons(sample, max_jobs=8, target='local'):
input_path = '/g/kreshuk/data/cremi/realigned/sample%s_small.n5' % sample
input_prefix = 'segmentation/multicut'
output_prefix = 'skeletons/multicut'
config_dir = './config_skeletons'
tmp_folder = './tmp_skeletons_%s' % sample
try:
os.mkdir(config_dir)
except OSError:
pass
config = SkeletonWorkflow.get_config()
global_config = config['global']
shebang = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster_env/bin/python'
global_config.update({'shebang': shebang})
with open(os.path.join(config_dir, 'global.config'), 'w') as f:
json.dump(global_config, f)
ds_config = config['skeletonize']
ds_config.update({'threads_per_job': 8})
with open(os.path.join(config_dir, 'skeletonize.config'), 'w') as f:
json.dump(ds_config, f)
task = SkeletonWorkflow(tmp_folder=tmp_folder,
max_jobs=1,
config_dir=config_dir,
target='local',
input_path=input_path,
output_path=input_path,
input_prefix=input_prefix,
output_prefix=output_prefix,
work_scale=2)
success = luigi.build([task], local_scheduler=True)
#
if success and target == 'local':
with z5py.File(input_path) as f:
#
ds = f['skeletons/multicut/s2']
ds.n_threads = 8
skels = ds[:]
#
ds = f['raw/s2']
ds.n_threads = 8
raw = ds[:]
#
ds = f['segmentation/multicut/s2']
ds.n_threads = 8
seg = ds[:]
view([raw, seg, skels], ['raw', 'seg', 'skels'])
def view_mc():
path = '/home/papec/Work/neurodata_hdd/testdata.n5'
f = z5py.File(path)
raw = f['raw'][:]
ws = f['watershed'][:]
# affs = f['affs_z'][:]
mc = f['multicut'][:]
# view([raw, affs, ws, mc])
view([raw, ws, mc])
def view_backaligned(sample):
path = '/g/kreshuk/data/cremi/original/sample_%s_20160601.hdf' % sample
with h5py.File(path) as f:
raw = f['volumes/raw'][:]
with h5py.File('./%s_tmp.h5' % sample) as f:
seg = f['volumes/labels/neuron_ids'][:]
assert raw.shape == seg.shape
view([raw, seg])
def debug_reduce_problem():
example_path = '/home/cpape/Work/data/isbi2012/cluster_example/isbi_train.n5'
node_labels = z5py.File(example_path)['s1']['node_labeling'][:]
node_labels = np.concatenate((np.zeros(1, dtype='uint64'), node_labels))
n_nodes = len(node_labels)
ws = z5py.File(example_path)['volumes/watersheds'][:]
rag = nrag.gridRag(ws, numberOfLabels=n_nodes)
seg = nrag.projectScalarNodeDataToPixels(rag, node_labels)
view([ws, seg])
def _test_wslr(self):
bb_aff = (slice(None),) + self.bb
affs = z5py.File(self.path)['full_affs'][bb_aff]
segmenter = cseg.LRAffinityWatershed(0.1, 0.25, 2.,
return_seeds=True,
invert_input=True)
print("Start watershed...")
ws, seeds, _ = segmenter(affs)
print("... done")
raw = z5py.File(self.path)['raw'][self.bb]
view([raw, 1. - affs.transpose((1, 2, 3, 0)), seeds, ws],
['raw', 'affs', 'seeds', 'ws'])
def view_res():
raw_path = '/home/cpape/Work/data/isbi2012/isbi2012_test_volume.h5'
with h5py.File(raw_path) as f:
raw = f['volumes/raw'][:]
seg_path = './results/mws.h5'
with h5py.File(seg_path) as f:
seg = f['data'][:]
edges = make_edges(seg)
view([raw, seg, edges], ['raw', 'mws', 'edges'])
def view_skeletons(block_id, skeleton_postfix=''):
import z5py
from cremi_tools.viewer.volumina import view
from cremi_tools.skeletons import visualize_skeletons
# skeletons = extract_skeletons(block_id)
skel_path = '/home/papec/mnt/nrs/lauritzen/0%i/workspace.n5/skeletons' % block_id
assert os.path.exists(skel_path), skel_path
f_skel = z5py.File(skel_path)
# if we don't have the post-fix, these are
# the initial neurons of interest
if skeleton_postfix == '':
fg = f_skel['neurons_of_interest']
# otherwise, thse are for evaluation
else:
fg = f_skel['for_eval_%s' % skeleton_postfix]
print("Loading skeletons...")
skeletons = {}
for skel_id in fg.keys():
if not skel_id.isdigit():
continue
g = fg[skel_id]
coords = g['coordinates'][:]
node_ids = coords[:, 0]
coords = coords[:, 1:]
edges = g['edges'][:]
skeletons[int(skel_id)] = {
'coordinates': coords.astype('uint64'),
'node_ids': node_ids,
'edges': edges
}
print("... done")
path = '/home/papec/mnt/nrs/lauritzen/0%i/workspace.n5' % block_id
f = z5py.File(path)
ds = f['filtered/gray']
shape = ds.shape
central = tuple(sh // 2 for sh in shape)
offset = (100, 1000, 1000)
bb = tuple(slice(c - off, c + off) for c, off in zip(central, offset))
print("Visualizing skeletons ...")
bb_shape = tuple(b.stop - b.start for b in bb)
skeletons = intersect_skeletons_with_bb(skeletons, bb)
skeleton_vol = visualize_skeletons(bb_shape, skeletons)
print("... done")
print("Have skeletons, loading raw from bb", bb)
raw = ds[bb]
view([raw, skeleton_vol])
def visualize_cc():
import h5py
import vigra
from cremi_tools.viewer.volumina import view
thresh = .9
with h5py.File('/home/cpape/Work/data/isbi2012/isbi_train_offsetsV4_3d_meantda_damws2deval_final.h5') as f:
affs = f['data'][:, :5, :256, :256]
print(affs.shape)
affs = 1. - affs
thresholded = (np.mean(affs, axis=0) >= thresh).astype('uint8')
# cs = vigra.analysis.labelMultiArrayWithBackground(thresholded)
cs = vigra.analysis.labelMultiArray(thresholded)
view([affs.transpose((1, 2, 3, 0)), thresholded, cs])
def multicut_from_n5(path,
raw_key,
affinity_key,
offsets,
bounding_box=(slice(None), ),
out_key=None):
import z5py
affs = z5py.File(path)[affinity_key][(slice(None), ) + bounding_box]
segmentation = multicut(affs, offsets)
# either save or view the segmentation
if out_key is None:
raw = z5py.File(path)[raw_key][bounding_box]
view([raw, affs.transpose((1, 2, 3, 0)), segmentation])
def visualize_mc():
import h5py
from cremi_tools.viewer.volumina import view
from neurofire.metrics.arand import ArandErrorFromMulticut
with h5py.File('/home/cpape/Work/data/isbi2012/isbi_train_offsetsV4_3d_meantda_damws2deval_final.h5') as f:
affs = f['data'][:, :10, :256, :256]
offsets = [[-1, 0, 0], [0, -1, 0], [0, 0, -1]]
use_2d_ws = True
metrics = ArandErrorFromMulticut(use_2d_ws=use_2d_ws, offsets=offsets)
beta = .5
mc_seg = metrics.input_to_segmentation(affs[None], beta).numpy().squeeze()
assert mc_seg.shape == affs.shape[1:]
view([affs.transpose((1, 2, 3, 0)), mc_seg])
