def _wd(labels0, labels, img0, img1):
labels1 = -labels.copy()
rps0 = regionprops(labels0, img0)
from subdetect_operation import watershed_divide # DO NOT MOVE IT
from utils.track_utils import _find_match
untracked_labels = labels1.copy()
untracked_labels[untracked_labels < 0] = 0
wshed_labels = watershed_divide(untracked_labels,
regmax=REGMAX,
min_size=MIN_SIZE)
wshed_labels = label(wshed_labels)
store = regionprops(wshed_labels, img1)
good_cells = _find_match(rps0, store, DISPLACEMENT, MASSTHRES)
for gc in good_cells: # needed to reuse _update_labels_neck_cut
gccrds = gc.coords[0]
gc.raw_label = labels1[gccrds[0], gccrds[1]]
labels0, labels = _update_labels_neck_cut(labels0, labels1, good_cells)
labels0, labels = nn_closer(img0, img1, labels0, -labels, DISPLACEMENT,
MASSTHRES)
return labels0, labels, good_cells
def track_neck_cut(img0,
img1,
labels0,
labels1,
DISPLACEMENT=10,
MASSTHRES=0.2,
EDGELEN=5,
THRES_ANGLE=180,
WSLIMIT=False,
SMALL_RAD=3,
CANDS_LIMIT=300):
"""
Adaptive segmentation by using tracking informaiton.
Separate two objects by making a cut at the deflection. For each points on the outline,
it will make a triangle separated by EDGELEN and calculates the angle facing inside of concave.
The majority of cells need to be tracked before the this method to calculate LARGE_RAD and SMALL_RAD.
EDGELEN (int): A length of edges of triangle on the nuclear perimeter.
THRES_ANGLE (int): Define the neck points if a triangle has more than this angle.
STEPLIM (int): points of neck needs to be separated by at least STEPLIM in parimeters.
WSLIMIT (bool): Limit search points to ones overlapped with watershed transformed images. Set it True if calculation is slow.
SMALL_RAD (int or None): The smallest radius of candidate objects. If you have many cells, set it to None will infer the radius from previous frame.
CANDS_LIMIT(int): use lower if slow. limit a number of searches.
"""
labels0, labels = nn_closer(img0, img1, labels0, labels1, DISPLACEMENT,
MASSTHRES)
labels1 = -labels.copy()
if SMALL_RAD is None and not hasattr(holder, 'SMALL_RAD'):
tracked_area = [i.area for i in regionprops(labels)]
holder.SMALL_RAD = np.sqrt(np.percentile(tracked_area, 5) / np.pi)
elif SMALL_RAD is not None:
holder.SMALL_RAD = SMALL_RAD
SMALL_RAD = holder.SMALL_RAD
rps0 = regionprops(labels0, img0)
unique_labels = np.unique(labels1)
if WSLIMIT:
wlines = wshed_raw(labels1 > 0, img1)
else:
wlines = np.ones(labels1.shape, np.bool)
store = []
coords_store = []
for label_id in unique_labels:
if label_id == 0:
continue
cc = CellCutter(labels1 == label_id,
img1,
wlines,
small_rad=SMALL_RAD,
EDGELEN=EDGELEN,
THRES=THRES_ANGLE,
CANDS_LIMIT=CANDS_LIMIT)
cc.prepare_coords_set()
candidates = cc.search_cut_candidates(cc.bw.copy(),
cc.coords_set[:CANDS_LIMIT])
for c in candidates:
c.raw_label = label_id
store.append(candidates)
coords_store.append(cc.coords_set)
coords_store = [i for i in coords_store if i]
# Attempt a first cut.
good_cells = _find_best_neck_cut(rps0, store, DISPLACEMENT, MASSTHRES)
labels0, labels = _update_labels_neck_cut(labels0, labels1, good_cells)
labels0, labels = nn_closer(img0, img1, labels0, -labels, DISPLACEMENT,
MASSTHRES)
# iteration from here.
while good_cells:
rps0 = regionprops(labels0, img0)
labels1 = -labels.copy()
rps0 = regionprops(labels0, img0)
unique_labels = np.unique(labels1)
store = []
for label_id in unique_labels:
if label_id == 0:
continue
bw = labels1 == label_id
coords_set = [
i for i in coords_store if bw[i[0][0][0], i[0][0][1]]
]
if not coords_set:
continue
coords_set = coords_set[0]
candidates = cc.search_cut_candidates(bw, coords_set)
for c in candidates:
c.raw_label = label_id
store.append(candidates)
coords_store.append(coords_set)
good_cells = _find_best_neck_cut(rps0, store, DISPLACEMENT, MASSTHRES)
labels0, labels = _update_labels_neck_cut(labels0, labels1, good_cells)
labels0, labels = nn_closer(img0, img1, labels0, -labels, DISPLACEMENT,
MASSTHRES)
return labels0, labels