def segment_bacteria_return_cyto_no_bac(nuc,
img,
slen=3,
SIGMA=0.5,
THRES=20,
CLOSE=20,
THRESCHANGE=1000,
MINAREA=5,
dist=25):
""" Segment bacteria and assign to closest nucleus and return the mask back without regions containing bacteria
Args:
nuc (numpy.ndarray): nuclear mask labels
img (numpy.ndarray): image in bacterial channel
slen (int): Size of Gaussian kernel
SIGMA (float): Standard deviation for Gaussian kernel
THRES (int): Threshold pixel intensity fo real signal
CLOSE (int): Radius for disk used to return morphological closing of the image (dilation followed by erosion to remove dark spots and connect bright cracks)
THRESCHANGE (int): argument unnecessary?
MINAREA (int): minimum area in pixels for a bacterium
Returns:
labels (numpy.ndarray[np.uint16]): bacterial mask labels
"""
labels = label_high_pass(img, slen=slen, SIGMA=SIGMA, THRES=THRES, CLOSE=3)
if labels.any():
labels, comb, nuc_prop, nuc_loc = label_nearest(img, labels, nuc, dist)
from skimage.morphology import remove_small_objects
labels = remove_small_objects(labels, MINAREA)
labels = comb - labels
return labels.astype(np.uint16)
def segment_bacteria(nuc,
img,
slen=3,
SIGMA=0.5,
THRES=20,
CLOSE=20,
MINAREA=5,
dist=25,
SEGMENT='highpass',
ASSIGN=True):
""" Segment bacteria using high pass filter or constant thresholding and assign to closest nucleus
Args:
nuc (numpy.ndarray): nuclear mask labels
img (numpy.ndarray): image in bacterial channel
slen (int): Size of Gaussian kernel
SIGMA (float): Standard deviation for Gaussian kernel
THRES (int): Threshold pixel intensity fo real signal
CLOSE (int): Radius for disk used to return morphological closing of the image
(dilation followed by erosion to remove dark spots and connect bright cracks)
THRESCHANGE (int): argument unnecessary?
MINAREA (int): minimum area in pixels for a bacterium
dist (int): acceptable distance bac can be from mask
SEGMENT: choose one of 'highpass' or 'constant'.
ASSIGN: If False, it does not assign to closest nucleus.
Returns:
labels (numpy.ndarray[np.uint16]): bacterial mask labels
"""
if SEGMENT == 'highpass':
labels = label_high_pass(img,
slen=slen,
SIGMA=SIGMA,
THRES=THRES,
CLOSE=3)
elif SEGMENT == 'constant':
labels = constant_thres(img, THRES=THRES)
if not ASSIGN:
return labels.astype(np.uint16)
if labels.any():
labels, comb, nuc_prop, nuc_loc = label_nearest(img, labels, nuc, dist)
from skimage.morphology import remove_small_objects
labels = remove_small_objects(labels, MINAREA)
return labels.astype(np.uint16)
def segment_bacteria_repair(nuc,
img,
slen=3,
SIGMA=0.5,
THRES=20,
CLOSE=20,
THRESCHANGE=1000,
MINAREA=5,
dist=25):
""" Segment bacteria and assign to closest nucleus ## This is still under construction
Args:
nuc (numpy.ndarray): nuclear mask labels
img (numpy.ndarray): image in bacterial channel
slen (int): Size of Gaussian kernel
SIGMA (float): Standard deviation for Gaussian kernel
THRES (int): Threshold pixel intensity fo real signal
CLOSE (int): Radius for disk used to return morphological closing of the image (dilation followed by erosion to remove dark spots and connect bright cracks)
THRESCHANGE (int): argument unnecessary?
MINAREA (int): minimum area in pixels for a bacterium
Returns:
labels (numpy.ndarray[np.uint16]): bacterial mask labels
"""
labels = label_high_pass(img, slen=slen, SIGMA=SIGMA, THRES=THRES, CLOSE=3)
if labels.any():
labels, comb, nuc_prop, nuc_loc = label_nearest(img, labels, nuc, dist)
if not hasattr(holder, 'plabel'): #not sure what this is doing
holder.plabel = labels
holder.pcomb = comb
holder.pimg = img
return labels
#.astype(np.unit16)
labels = repair_sal(img, holder.pimg, comb, holder.pcomb, labels,
nuc_prop, nuc_loc, THRESCHANGE)
from skimage.morphology import remove_small_objects
labels = remove_small_objects(labels, MINAREA)
return labels
