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import json
from concurrent import futures
import numpy as np
import h5py
import pandas as pd
from heimdall import view, to_source
from elf.skeleton import skeletonize
from scripts.attributes.cilia_attributes import (compute_centerline,
# NOTE the current paths don't look that great.
# probably need to play with the teasar parameters a bit to improve this
def view_centerline(raw, obj, path, compare_skeleton=False):
cline = np.zeros(obj.shape, dtype='uint32')
cline[path] = 1
if compare_skeleton:
coords, _ = skeletonize(obj)
coords = make_indexable(coords)
skel = np.zeros(obj.shape, dtype='uint32')
skel[coords] = 1
view(raw, obj.astype('uint32'), cline, skel)
else:
view(raw, obj.astype('uint32'), cline)
def check_lens(cilia_ids=None, compare_skeleton=False):
path = '../data/0.5.1/segmentations/sbem-6dpf-1-whole-segmented-cilia-labels.h5'
path_raw = '../data/rawdata/sbem-6dpf-1-whole-raw.h5'
table = '../data/0.5.1/tables/sbem-6dpf-1-whole-segmented-cilia-labels/default.csv'
table = pd.read_csv(table, sep='\t')
table.set_index('label_id')
with open('precomputed_cilia.json') as f:
skeletons = json.load(f)
if cilia_ids is None:
cilia_ids = range(len(table))
with h5py.File(path, 'r') as f, h5py.File(path_raw, 'r') as fr:
print(cid)
obj_path = skeletons[cid]
if obj_path is None:
print("Skipping cilia", cid)
continue
print(len(obj_path))
bb = get_bb(table, cid, resolution)
raw = dsr[bb]
obj = ds[bb] == cid
view_centerline(raw, obj, obj_path, compare_skeleton)
def precompute():
path = '../data/0.5.1/segmentations/sbem-6dpf-1-whole-segmented-cilia-labels.h5'
table = '../data/0.5.1/tables/sbem-6dpf-1-whole-segmented-cilia-labels/default.csv'
table = pd.read_csv(table, sep='\t')
table.set_index('label_id')
resolution = [.025, .01, .01]
with h5py.File(path) as f:
ds = f['t00000/s00/0/cells']
def precomp(cid):
if cid in (0, 1, 2):
return
print(cid)
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if obj.sum() == 0:
return
path = compute_centerline(obj, [res * 1000 for res in resolution])
return path
n_cilia = len(table)
with futures.ThreadPoolExecutor(16) as tp:
tasks = [tp.submit(precomp, cid) for cid in range(n_cilia)]
# tasks = [tp.submit(precomp, cid) for cid in (3, 4, 5)]
results = [t.result() for t in tasks]
with open('precomputed_cilia.json', 'w') as f:
json.dump(results, f)
def grid_search():
path = '../data/0.5.1/segmentations/sbem-6dpf-1-whole-segmented-cilia-labels.h5'
table = '../data/0.5.1/tables/sbem-6dpf-1-whole-segmented-cilia-labels/default.csv'
table = pd.read_csv(table, sep='\t')
table.set_index('label_id')
label_id = 11
penalty_scales = [1000, 2500, 5000, 10000]
penalty_exponents = [2, 4, 8, 16]
resolution = [.025, .01, .01]
with h5py.File(path) as f:
ds = f['t00000/s00/0/cells']
def precomp(cid, penalty_scale, penalty_exponent):
print("scale:", penalty_scale, "exponent:", penalty_exponent)
obj = load_seg(ds, table, cid, resolution)
path = compute_centerline(obj, [res * 1000 for res in resolution],
penalty_scale=penalty_scale, penalty_exponent=penalty_exponent)
return {'penalty_scale': penalty_scale, 'penalty_exponent': penalty_exponent, 'path': path}
with futures.ThreadPoolExecutor(16) as tp:
tasks = [tp.submit(precomp, label_id, penalty_scale, penalty_exponent)
for penalty_scale in penalty_scales for penalty_exponent in penalty_exponents]
results = [t.result() for t in tasks]
with open('grid_search.json', 'w') as f:
json.dump(results, f)
def eval_gridsearch():
with open('grid_search.json') as f:
results = json.load(f)
path_raw = '../data/rawdata/sbem-6dpf-1-whole-raw.h5'
path = '../data/0.5.1/segmentations/sbem-6dpf-1-whole-segmented-cilia-labels.h5'
table = '../data/0.5.1/tables/sbem-6dpf-1-whole-segmented-cilia-labels/default.csv'
table = pd.read_csv(table, sep='\t')
table.set_index('label_id')
label_id = 11
resolution = [.025, .01, .01]
with h5py.File(path, 'r') as f, h5py.File(path_raw, 'r') as fr:
ds = f['t00000/s00/0/cells']
dsr = fr['t00000/s00/0/cells']
bb = get_bb(table, label_id, resolution)
raw = dsr[bb]
obj = (ds[bb] == label_id).astype('uint32')
sources = [to_source(raw, name='raw'), to_source(obj, name='mask')]
for res in results:
line = np.zeros_like(obj)
path = make_indexable(res['path'])
line[path] = 1
name = '%i_%i' % (res['penalty_scale'], res['penalty_exponent'])
sources.append(to_source(line, name=name))
view(*sources)
# precompute()
# grid_search()
check_lens([11], compare_skeleton=True)
# eval_gridsearch()