def extract_small(labels, AREA=200):
rps = regionprops(labels)
retain = filter(lambda x: x.area < AREA, rps)
badlabels = np.zeros(labels.shape, np.uint16)
for r in retain:
badlabels[labels == r.label] = r.label
return badlabels
def make_bw(self):
if self.cut_cells:
bw = np.zeros(self.bw.shape, np.uint8)
for cell in self.cut_cells:
for c in cell.coords:
bw[c[0], c[1]] = 1
self.bw = bw
self.cell = regionprops(bw, self.img)[0]
self.cut_cells = []
else:
self.cell = []
def prepare_coords_set(self):
self.cell = regionprops(self.bw.astype(np.uint8), self.img)[0]
if 1 in self.cell.image.shape:
self.coords_set = []
return
self.extract_cell_outlines()
if not hasattr(self.cell, 'o_coords'):
return
coords, _ = CoordsConcaveWs(self.cell.o_coords, wlines=self.wlines, edgelen=self.EDGELEN, thres=self.THRES).run()
if not coords.any():
return
self.coords_set = self.make_sort_coordsset_by_dist(coords)
if not self.coords_set:
return
self.coords_set = self.coords_set[:self.CANDS_LIMIT]
self.coords_set = self.filter_coords_by_step(self.cell.o_coords, self.coords_set)
def make_candidates_img(cl_label, s0, s1, e0, e1, img):
cut_label = cut_neck(cl_label, s0, s1, e0, e1)
cand_label = label(cut_label, connectivity=1)
cand_rps = regionprops(cand_label, img)
return cand_rps
