def __call__(
self,
img,
prev_detected,
seed_threshold=None,
erode_factor=1,
minsize=0,
maxhole=0.5,
checkQsize=0,
minQ=0.5,
**kwargs
):
# Guess threshold if not given
if seed_threshold is None:
from fire.debug import showimg
# Otsu needs int, convert to range 0-255
normimg = (img - img.min()) / (img.max() - img.min())
intimg = np.rint(normimg * 255).astype(np.uint8)
intimg_threshold = self.mh.thresholding.otsu(intimg)
# Convert received threshold back to image range
seed_threshold = intimg_threshold / 255.0 * (img.max() - img.min()) + img.min()
logger.debug("Guessed threshold (Otsu): {}".format(seed_threshold))
# Find seeds
binary = img > seed_threshold
for i in range(erode_factor):
binary = self.mh.morph.erode(binary)
from fire.debug import showimg
showimg(binary)
seeds, nr_of_seeds = self.mh.label(binary)
logger.debug("Number of watershed seeds: {}".format(nr_of_seeds))
# Do watershed transformation
labeled = self.mh.cwatershed(img.max() - img, seeds)
logger.debug("Number of areas: {}".format(len(labeled)))
# Extract sizes of area and filter for minsize
sizes = np.bincount(labeled.flat)
bigareas = np.argwhere(sizes > maxhole * minsize)
logger.debug("With minimum size (holes not filled) {}: {}".format(maxhole * minsize, len(bigareas)))
# Create detection info
detected = NonOverlappingFreeAreasInfo(
np.zeros(img.shape, dtype=NonOverlappingFreeAreasInfo.smallest_uint_type(len(bigareas)))
)
for bigarea in bigareas:
filled = self.fill_holes(labeled == bigarea)
size = np.count_nonzero(filled)
if size < minsize:
continue
elif size < checkQsize and self.isoperimetric_quotient(filled) < minQ:
continue
else:
detected.addarea(filled)
logger.debug("Found {} areas".format(np.max(detected.labeledareas)))
return [detected]
def __call__(
self,
img,
prev_detected,
threshold=None,
minsize=0,
maxsize=None,
steps_prelabel=[],
steps_postlabel=[],
**kwargs
):
if threshold is None:
threshold = self.otsu(img)
logger.debug("Guessed threshold (Otsu): {}".format(threshold))
binary = img > threshold
for pdetected in prev_detected:
for d in pdetected:
binary[d.pixels()] = False
self.backgroundinfos = []
binary = self.do_steps(img, binary, steps_prelabel)
# Label
labeled, nr_labels = self.mh.label(binary)
logger.debug("Number of areas: {}".format(nr_labels))
# Filter for size requirements
sizes = np.bincount(labeled.flat)
considered_sizes = sizes >= minsize
if maxsize is not None:
considered_sizes = np.logical_and(considered_sizes, sizes <= maxsize)
considered_labels = np.argwhere(considered_sizes)
logger.debug("Meeting size requirements: {}".format(len(considered_labels)))
# Create detection info
detected = NonOverlappingFreeAreasInfo(
np.zeros(img.shape, dtype=NonOverlappingFreeAreasInfo.smallest_uint_type(len(considered_labels)))
)
for area_label in considered_labels:
if area_label == 0:
continue # ignore background
thisarea = labeled == area_label
try:
thisarea = self.do_steps(img, thisarea, steps_postlabel)
except self.RejectArea:
continue
detected.addarea(thisarea)
logger.debug("Found {} areas".format(np.max(detected.labeledareas)))
return self.backgroundinfos + [detected]
