def blocks_from_mask(job_id, config_path):
fu.log("start processing job %i" % job_id)
fu.log("reading config from %s" % config_path)
with open(config_path, 'r') as f:
config = json.load(f)
mask_path = config['mask_path']
mask_key = config['mask_key']
output_path = config['output_path']
shape = config['shape']
block_shape = config['block_shape']
n_threads = config.get('threads_per_job', 1)
# NOTE we assume that the mask is small and will fit into memory
with vu.file_reader(mask_path, 'r') as f:
ds = f[mask_key]
ds.n_threads = n_threads
mask_data = ds[:]
mask = ResizedVolume(mask_data, tuple(shape))
blocking = nt.blocking([0, 0, 0], shape, list(block_shape))
blocks_in_mask = _get_blocks_in_mask(mask, blocking, n_threads)
with open(output_path, 'w') as f:
json.dump(blocks_in_mask, f)
fu.log_job_success(job_id)
def _test_toy(self):
from elf.wrapper.resized_volume import ResizedVolume
from skimage.transform import resize
from skimage.data import astronaut
x = astronaut()[..., 0].astype('float32')
x = vigra.sampling.resize(x, (256, 256))
x = np.concatenate(256 * [x[None]], axis=0)
out_shape = 3 * (128,)
order = 0
out1 = vigra.sampling.resize(x, shape=out_shape, order=order)
out2 = ResizedVolume(x, shape=out_shape, order=order)
out3 = resize(x, out_shape, order=0, preserve_range=True, anti_aliasing=False)
assert out1.shape == out2.shape == out_shape
# bb = np.s_[:64, :, 64:]
bb = np.s_[:]
o1 = out1[bb]
o2 = out2[bb]
o3 = out3[bb]
import napari
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(o1, name='elf')
viewer.add_image(o2, name='vigra')
viewer.add_image(o3, name='skimage')
def prefilter_blocks(mask_path, mask_key,
shape, block_shape,
save_file, n_threads=48):
if os.path.exists(save_file):
print("Loading block list from file")
with open(save_file) as f:
return json.load(f)
with open_file(mask_path, 'r') as f:
ds = f[mask_key]
mask = ResizedVolume(ds, shape=shape, order=0)
blocking = nt.blocking([0, 0, 0], shape, block_shape)
n_blocks = blocking.numberOfBlocks
def check_block(block_id):
block = blocking.getBlock(block_id)
bb = tuple(slice(beg, end) for beg, end in zip(block.begin, block.end))
d = mask[bb]
if d.sum() > 0:
return block_id
else:
return None
print("Computing block list ...")
with futures.ThreadPoolExecutor(n_threads) as tp:
blocks = list(tqdm(tp.map(check_block, range(n_blocks)), total=n_blocks))
blocks = [bid for bid in blocks if bid is not None]
with open(save_file, 'w') as f:
json.dump(blocks, f)
return blocks
def block_node_labels(job_id, config_path):
fu.log("start processing job %i" % job_id)
fu.log("reading config from %s" % config_path)
# get the config
with open(config_path) as f:
config = json.load(f)
ws_path = config['ws_path']
ws_key = config['ws_key']
input_path = config['input_path']
input_key = config['input_key']
output_path = config['output_path']
output_key = config['output_key']
block_shape = config['block_shape']
block_list = config['block_list']
ignore_label = config['ignore_label']
with vu.file_reader(ws_path, 'r') as f:
shape = f[ws_key].shape
blocking = nt.blocking([0, 0, 0],
list(shape),
list(block_shape))
# labels can either be interpolated or full volume
f_lab = vu.file_reader(input_path, 'r')
ds_labels = f_lab[input_key]
lab_shape = ds_labels.shape
# label shape is smaller than ws shape
# -> interpolated
if any(lsh < sh for lsh, sh in zip(lab_shape, shape)):
assert not any(lsh > sh for lsh, sh in zip(lab_shape, shape)),\
"Can't have label shape bigger then volshape"
labels = ResizedVolume(ds_labels, shape, order=0)
else:
assert lab_shape == shape, "%s, %s" % (str(lab_shape), shape)
labels = ds_labels
if ignore_label is None:
fu.log("accumulating labels without ignore label")
else:
fu.log("accumulating labels with ignore label %i" % ignore_label)
with vu.file_reader(ws_path, 'r') as f_in:
ds_ws = f_in[ws_key]
if ds_ws.attrs.get('isLabelMultiset', False):
ds_ws = LabelMultisetWrapper(ds_ws)
[_labels_for_block(block_id, blocking,
ds_ws, output_path, output_key,
labels, ignore_label)
for block_id in block_list]
f_lab.close()
fu.log_job_success(job_id)
def load_mask(mask_path, mask_key, shape):
with file_reader(mask_path, 'r') as f_mask:
mshape = f_mask[mask_key].shape
# check if th mask is at full - shape, otherwise interpolate
if tuple(mshape) == tuple(shape):
mask = file_reader(mask_path, 'r')[mask_key]
else:
with file_reader(mask_path, 'r') as f_mask:
mask = f_mask[mask_key][:].astype('bool')
mask = ResizedVolume(mask, shape=shape, order=0)
return mask
def test_upscale_full_volume(self):
from elf.wrapper.resized_volume import ResizedVolume
shape = 3 * (128, )
x = np.random.rand(*shape).astype('float32')
out_shape = 3 * (256, )
orders = (0, 3)
for order in orders:
out1 = vigra.sampling.resize(x, shape=out_shape, order=order)
out2 = ResizedVolume(x, shape=out_shape, order=order)
self.assertEqual(out1.shape, out2.shape)
self._check_index(out1, out2, np.s_[:])
def _test_resize(self, shape, out_shape, indices):
from elf.wrapper.resized_volume import ResizedVolume
x = np.random.rand(*shape).astype('float32')
halo = 8
orders = [0, 3]
for order in orders:
out1 = vigra.sampling.resize(x, shape=out_shape, order=order)
out2 = ResizedVolume(x, shape=out_shape, order=order)
self.assertEqual(out1.shape, out2.shape)
self.assertEqual(out1.shape, out_shape)
for index in indices:
self._check_index(out1, out2, index, halo=halo)
def compute_mean_and_std():
key = 'setup0/timepoint0/s1'
f = open_file(RAW_PATH, 'r')
ds = f[key]
mask_key = 'setup0/timepoint0/s0'
mask = open_file(MASK_PATH)[mask_key][:].astype('bool')
mask = ResizedVolume(mask, ds.shape)
m, s = mean_and_std(ds, mask=mask, n_threads=16, verbose=True)
print("Computed mean and standard deviation:")
print("Mean:", m)
print("Standard deviation:", s)
def insert_affinities(job_id, config_path):
fu.log("start processing job %i" % job_id)
fu.log("reading config from %s" % config_path)
with open(config_path, 'r') as f:
config = json.load(f)
input_path = config['input_path']
input_key = config['input_key']
output_path = config['output_path']
output_key = config['output_key']
objects_path = config['objects_path']
objects_key = config['objects_key']
erode_by = config['erode_by']
erode_3d = config.get('erode_3d', True)
zero_objects_list = config['zero_objects_list']
dilate_by = config.get('dilate_by', 2)
fu.log("Fitting objects to affinities with erosion strenght %i and erosion in 3d: %s" % (erode_by, str(erode_3d)))
if zero_objects_list is not None:
fu.log("Zeroing affinities for the objects %s" % str(zero_objects_list))
block_list = config['block_list']
block_shape = config['block_shape']
offsets = config['offsets']
with vu.file_reader(input_path) as f_in, vu.file_reader(output_path) as f_out,\
vu.file_reader(objects_path) as f_obj:
ds_in = f_in[input_key]
ds_out = f_out[output_key]
shape = ds_in.shape[1:]
# TODO actually check that objects are on a lower scale
ds_objs = f_obj[objects_key]
objects = ResizedVolume(ds_objs, shape)
blocking = nt.blocking([0, 0, 0], list(shape), block_shape)
[_insert_affinities_block(block_id, blocking, ds_in, ds_out, objects, offsets,
erode_by, erode_3d, zero_objects_list, dilate_by)
for block_id in block_list]
fu.log_job_success(job_id)
def scale_to_boundaries(job_id, config_path):
fu.log("start processing job %i" % job_id)
fu.log("reading config from %s" % config_path)
with open(config_path, 'r') as f:
config = json.load(f)
# read paths from the config
input_path = config['input_path']
input_key = config['input_key']
output_path = config['output_path']
output_key = config['output_key']
boundaries_path = config['boundaries_path']
boundaries_key = config['boundaries_key']
offset = config['offset']
# additional config
erode_by = config['erode_by']
erode_3d = config.get('erode_3d', True)
channel = config['channel']
block_shape = list(config['block_shape'])
block_list = config['block_list']
with vu.file_reader(input_path, 'r') as fin,\
vu.file_reader(boundaries_path, 'r') as fb,\
vu.file_reader(output_path) as fout:
ds_bd = fb[boundaries_key]
ds_out = fout[output_key]
shape = ds_out.shape
blocking = nt.blocking([0, 0, 0], list(shape), block_shape)
ds_in = ResizedVolume(fin[input_key], shape)
for block_id in block_list:
_scale_block(block_id, blocking,
ds_in, ds_bd, ds_out,
offset, erode_by, erode_3d, channel)
# log success
fu.log_job_success(job_id)
def make_foreground_mask(path, input_key, output_key, mask_path, mask_key,
threshold, chunks, n_threads):
with open_file(path, 'a') as f, open_file(mask_path, 'r') as f_mask:
ds = NormalizeWrapper(f[input_key])
ds_out = f.require_dataset(output_key,
shape=ds.shape,
compression='gzip',
dtype='uint8',
chunks=chunks)
ds_mask = f_mask[mask_key]
ds_mask = ResizedVolume(ds_mask, shape=ds.shape, order=0)
greater_equal(ds,
threshold,
out=ds_out,
verbose=True,
n_threads=n_threads,
mask=ds_mask)
def check_exported(with_seg=False, with_boundaries=False, scale=3):
from heimdall import view, to_source
from elf.wrapper.resized_volume import ResizedVolume
path = '/g/arendt/EM_6dpf_segmentation/platy-browser-data/data/rawdata/sbem-6dpf-1-whole-raw.n5'
key = 'setup0/timepoint0/s%i' % (scale + 1, )
f = z5py.File(path, 'r')
ds = f[key]
ds.n_threads = 8
raw = ds[:]
shape = raw.shape
data = [to_source(raw, name='raw')]
path = './data.n5'
key = 'volumes/segmentation2/s%i' % scale
f = z5py.File(path, 'r')
if with_seg:
ds = f[key]
ds.n_threads = 8
seg = ds[:].astype('uint32')
data.append(to_source(seg, name='segmentation'))
key = 'volumes/clustering'
ds = f[key]
ds.n_threads = 8
clustered = ResizedVolume(ds[:], shape=shape)[:]
data.append(to_source(clustered, name='clustered'))
path = '/g/arendt/EM_6dpf_segmentation/corrections_and_proofreading/data.n5'
key = 'boundaries/s%i' % scale
f = z5py.File(path, 'r')
if with_boundaries:
ds = f[key]
ds.n_threads = 8
bd = ds[:]
data = to_source(bd, name='boundaries')
view(*data)
def test_downscale(self):
from elf.wrapper.resized_volume import ResizedVolume
shape = 3 * (256, )
x = np.random.rand(*shape).astype('float32')
out_shape = 3 * (128, )
orders = (0, 3)
indices = (np.s_[1:-1, 2:-2, 3:-3], np.s_[:64, :, 64:], np.s_[:64, :48,
40:95])
halo = 8
for order in orders:
out1 = vigra.sampling.resize(x, shape=out_shape, order=order)
out2 = ResizedVolume(x, shape=out_shape, order=order)
self.assertEqual(out1.shape, out2.shape)
for index in indices:
self._check_index(out1,
out2,
index,
check_close=True,
halo=halo)
index = np.s_[32:96, 33:55, 70]
self._check_index(out1, out2, index, check_close=False)
def test_upscale(self):
from elf.wrapper.resized_volume import ResizedVolume
shape = 3 * (128, )
x = np.random.rand(*shape).astype('float32')
out_shape = 3 * (256, )
orders = (0, 3)
indices = (np.s_[1:-1, 2:-2, 3:-3], np.s_[:128, :,
128:], np.s_[:128, :97,
123:250])
halo = 8
for order in orders:
out1 = vigra.sampling.resize(x, shape=out_shape, order=order)
out2 = ResizedVolume(x, shape=out_shape, order=order)
self.assertEqual(out1.shape, out2.shape)
for index in indices:
self._check_index(out1,
out2,
index,
check_close=True,
halo=halo)
index = np.s_[64:107, 153:179, 93]
self._check_index(out1, out2, index, check_close=False)
def run_thresh(path, mask_path, n_jobs):
f = open_file(path)
ds = NormalizeWrapper(f['predictions/foreground'])
threshold = .5
ds_out = f.require_dataset('predictions/mask',
shape=ds.shape,
compression='gzip',
dtype='uint8',
chunks=ds.chunks)
# with mask for the big volume
mask_key = 'setup0/timepoint0/s0'
ds_mask = f[mask_key]
ds_mask = ResizedVolume(ds_mask, shape=ds.shape, order=0)
n_threads = 32
greater_equal(ds,
threshold,
out=ds_out,
verbose=True,
n_threads=n_threads,
mask=ds_mask)
def make_resin_mask_2d(z=None, scale=3):
path = '../data.n5'
raw_key = 'volumes/raw-samplexy/s%i' % scale
mask_path = '../../data/rawdata/sbem-6dpf-1-whole-segmented-inside.n5'
mask_key = 'setup0/timepoint0/s0'
n_threads = 8
f = z5py.File(path)
ds_raw = f[raw_key]
f_mask = z5py.File(mask_path)
ds = f_mask[mask_key]
ds.n_threads = n_threads
mask = ds[:]
mask = ResizedVolume(mask, ds_raw.shape, order=0)
size_thresh = 5000
def mask_2d(z):
print(z, "/", ds_raw.shape[0])
raw = ds_raw[z]
maskz = np.logical_not(mask[z])
maskz = np.logical_or(maskz, raw == 0)
maskz = np.logical_or(maskz, raw == 255)
# run otsu on the remaining data to get rid of the embedded silver
masked = raw[maskz]
thresh = threshold_otsu(masked)
maskz = np.logical_and(maskz, raw > thresh)
# get rid of upper quantile
masked = raw[maskz]
# thresh = threshold_otsu(masked)
thresh = np.quantile(masked, .9)
maskz = np.logical_and(maskz, raw < thresh)
# only keep the biggest component
ccs = vigra.analysis.labelImageWithBackground(maskz.astype('uint32'))
ids, sizes = np.unique(ccs, return_counts=True)
ids, sizes = ids[1:], sizes[1:]
keep_ids = ids[sizes > size_thresh]
maskz = np.isin(ccs, keep_ids)
maskz = maskz.astype('uint8') * 255
return maskz
if z is not None:
resin_mask = mask_2d(z)
raw = ds_raw[z]
mask = mask[z]
view(raw, mask, resin_mask)
else:
print("Compute mask")
with futures.ThreadPoolExecutor(n_threads) as tp:
tasks = [tp.submit(mask_2d, z) for z in range(ds_raw.shape[0])]
res = [t.result() for t in tasks]
resin_mask = np.concatenate([re[None] for re in res], axis=0)
print(resin_mask.shape)
# save as bdv, why not
# s0: .025, .02, .02
# s1: .025, .08, .08
# s2: .025, .16, .16
# s3: .025, .32, .32
res = [.025, .32, .32]
make_bdv(resin_mask, 'sbem-6dpf-1-whole-segmented-resin.n5',
downscale_factors=[[1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]],
resolution=res, unit='micrometer', downscale_mode='min')
def check_ids(remaining_ids, saved_ids, false_merges, project_path, state):
scale = 3
pathr = '/g/kreshuk/data/arendt/platyneris_v1/data.n5'
paths = os.path.join(
'/g/arendt/EM_6dpf_segmentation/platy-browser-data/data/0.3.1',
'segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5')
table_path = os.path.join(
'/g/arendt/EM_6dpf_segmentation/platy-browser-data/data/0.3.1',
'tables/sbem-6dpf-1-whole-segmented-cells-labels/default.csv')
table = pd.read_csv(table_path, sep='\t')
res = scale_to_res(scale)
fr = z5py.File(pathr, 'r')
dsr = fr['volumes/raw/s%i' % scale]
km = 'volumes/labels/muscle'
dsm = fr[km]
dsm = ResizedVolume(dsm, shape=dsr.shape)
assert dsm.shape == dsr.shape
check_fps, current_id = state['check_fps'], state['current_id']
with h5py.File(paths, 'r') as fs:
dss = fs['t00000/s00/%i/cells' % (scale - 1, )]
for ii, fid in enumerate(remaining_ids):
bb = get_bb(table, fid, res)
if check_fps:
print("Checking false positives - id:", fid)
else:
print("Checking false negatives - id:", fid)
raw = dsr[bb]
seg = dss[bb]
muscle = dsm[bb]
muscle = (muscle > 0).astype('uint32')
mask = (seg == fid).astype('uint32')
save_id, false_merge, save_state, done = view_candidate(
raw, mask, muscle)
if save_id:
saved_ids.append(fid)
print("Confirm id", fid, "we now have", len(saved_ids),
"confirmed ids.")
if false_merge:
print("Add id", fid, "to false merges")
false_merges.append(fid)
if save_state:
print("Save current state to", project_path)
with h5py.File(project_path) as f:
f.attrs['check_fps'] = check_fps
if 'false_merges' in f:
del f['false_merges']
if len(false_merges) > 0:
f.create_dataset('false_merges', data=false_merges)
g = f['false_positives'] if check_fps else f[
'false_negatives']
g.attrs['current_id'] = current_id + ii + 1
if 'proofread' in g:
del g['proofread']
if len(saved_ids) > 0:
g.create_dataset('proofread', data=saved_ids)
if done:
print("Quit")
return False
return True
