def write_saalfeld(fn, raw, labels, res=np.array([12., 1, 1])):
imio.write_h5_stack(raw, fn, group='raw')
imio.write_h5_stack(labels, fn, group='labels')
f = h5py.File(fn, 'a')
f['/raw'].attrs['resolution'] = res
f['/labels'].attrs['resolution'] = res
f.close()
def write_h5(arr, path, group="stack", print_top_image=True, dry=False):
arr += 1
print "Writing stack with shape %s to %s" % (str(arr.shape), path)
ensure_file(path)
if not dry:
imio.write_h5_stack(arr, path, group=group, compression="lzf")
if not print_top_image: return
top_im = arr[0,:,:][np.newaxis,:,:]
colors = h5topng.build_color_map(top_im,"random")
h5topng.output_pngs(top_im, colors, path+"-top-")
np.random.RandomState(0)
(X, y, w, merges) = map(np.copy, map(np.ascontiguousarray,
g_train.learn_agglomerate(gt_train, fc)[0]))
print X.shape
np.savez('example-data/train-set.npz', X=X, y=y)
y = y[:, 0]
rf = classify.DefaultRandomForest()
X.shape
np.random.RandomState(0)
rf = rf.fit(X, y)
classify.save_classifier(rf, 'example-data/rf-1.joblib')
learned_policy = agglo.classifier_probability(fc, rf)
g_test = agglo.Rag(ws_test, pr_test, learned_policy, feature_manager=fc)
g_test.agglomerate(0.5)
seg_test1 = g_test.get_segmentation()
imio.write_h5_stack(seg_test1, 'example-data/test-seg1.lzf.h5', compression='lzf')
g_train4 = agglo.Rag(ws_train, p4_train, feature_manager=fc)
np.random.RandomState(0)
(X4, y4, w4, merges4) = map(np.copy, map(np.ascontiguousarray,
g_train4.learn_agglomerate(gt_train, fc)[0]))
print X4.shape
np.savez('example-data/train-set4.npz', X=X4, y=y4)
y4 = y4[:, 0]
rf4 = classify.DefaultRandomForest()
np.random.RandomState(0)
rf4 = rf4.fit(X4, y4)
classify.save_classifier(rf4, 'example-data/rf-4.joblib')
learned_policy4 = agglo.classifier_probability(fc, rf4)
g_test4 = agglo.Rag(ws_test, p4_test, learned_policy4, feature_manager=fc)
g_test4.agglomerate(0.5)
seg_test4 = g_test4.get_segmentation()
from gala import morpho
from gala import imio
import numpy as np
pr = imio.read_image_stack('membrane/*.tiff')
pr = 1 - pr / np.max(pr)
ws = morpho.watershed_sequence(pr, axis=0, n_jobs=4, connectivity=2,
smooth_thresh=0.04, minimum_seed_size=0)
imio.write_h5_stack(ws, 'watershed.lzf.h5', compression='lzf')
slices = [(slice(None), slice(None, 625), slice(None, 625)),
(slice(None), slice(None, 625), slice(625, None)),
(slice(None), slice(625, None), slice(None, 625)),
(slice(None), slice(625, None), slice(625, None))]
wss = [ws[s] for s in slices]
from skimage.measure import label
for i, vol in enumerate(wss):
fn = 'watershed-%i.lzf.h5' % i
vol_relabel = label(vol)
print(np.max(vol_relabel))
imio.write_h5_stack(vol_relabel, fn, compression='lzf')
# IPython log file
from gala import imio
import numpy as np
slices = [(slice(None), slice(None, 625), slice(None, 625)),
(slice(None), slice(None, 625), slice(625, None)),
(slice(None), slice(625, None), slice(None, 625)),
(slice(None), slice(625, None), slice(625, None))]
gt = imio.read_h5_stack('ground-truth.h5', group='bodies')
gts = [gt[s] for s in slices]
from skimage.measure import label
for i, vol in enumerate(gts):
fn = 'ground-truth-%i.lzf.h5' % i
vol_relabel = label(vol)
print(np.max(vol_relabel))
imio.write_h5_stack(vol_relabel.astype(np.uint16), fn,
compression='lzf')
pr = imio.read_image_stack('membrane/*.tiff')
prs = [pr[s] for s in slices]
for i, vol in enumerate(prs):
fn = 'probabilities-%i.lzf.h5' % i
imio.write_h5_stack(vol.astype(np.uint8), fn, compression='lzf')
def write_h5(arr, path, group):
print "Writing stack with shape %s to %s" % (str(arr.shape), path)
ensure_file(path)
imio.write_h5_stack(arr, path, group=group, compression="lzf")
