def threshold(self, image, median_radius, window_size, min_contrast,
remove_borderobjects, fill_holes, norm_min=0, norm_max=255,
use_watershed=True, seeding_size=5, outline_smoothing=1,
*args, **kw):
image = self.normalize(image, norm_min, norm_max)
image = ccore.numpy_to_image(image, copy=True)
image_smoothed = ccore.gaussianFilter(image, median_radius)
label_image = ccore.window_average_threshold(
image_smoothed, window_size, min_contrast)
if fill_holes:
ccore.fill_holes(label_image)
if outline_smoothing >= 1:
label_image = outlineSmoothing(label_image.toArray(),
outline_smoothing)
label_image = ccore.numpy_to_image(label_image, copy=True)
if use_watershed:
label_image = label_image.toArray().copy()
label_image = watershed(label_image, seeding_size=seeding_size)
label_image = ccore.numpy_to_image(
label_image.astype(np.int16), copy=True)
return ccore.ImageMaskContainer(image, label_image,
remove_borderobjects, True, True)
else:
# a static type system sucks!
return ccore.ImageMaskContainer(image, label_image,
remove_borderobjects)
def _constrained_watershed(self, img_in, img_labels, filter_size=2):
maxlabel = img_labels.getMinmax()[1]
img_bin = ccore.threshold(img_labels, 1, maxlabel, 0, 255)
# internal marker
img_ero = ccore.erode(img_bin, 3, 8)
img_internal_marker = ccore.anchoredSkeleton(img_bin, img_ero)
# external marker
img_inv = ccore.linearRangeMapping(img_bin, 255, 0, 0, 255)
img_voronoi = ccore.watershed(img_inv)
img_external_marker = ccore.threshold(img_voronoi, 0, 0, 0, 255)
# full marker image
img_marker = ccore.supremum(img_internal_marker, img_external_marker)
# gradient image
img_filtered = ccore.gaussianFilter(img_in, filter_size)
img_grad = ccore.morphoGradient(img_filtered, 1, 8)
# Watershed result: 0 is WSL, 1 is Background, all other values correspond to labels.
img_grad_watershed = ccore.constrainedWatershed(img_grad, img_marker)
# we first get the regions
maxreslab = img_grad_watershed.getMinmax()[1]
img_bin2 = ccore.threshold(img_grad_watershed, 2, maxreslab, 0, 255)
img_temp = ccore.copyImageIf(img_labels, img_bin2)
img_out = ccore.relabelImage(img_bin2, img_temp)
return img_out
def constrainedWatershedApproach(self, imgIn, imgLabel, iGaussFilterSize=2):
minlabel, maxlabel = imgLabel.getMinmax()
imgThresh = ccore.threshold(imgLabel, 1, maxlabel, 0, 255)
# internal marker
imgEro = ccore.erode(imgThresh, 3, 8)
imgInternalMarker = ccore.anchoredSkeleton(imgThresh, imgEro)
# external marker
imgInv = ccore.linearRangeMapping(imgThresh, 255, 0, 0, 255)
imgVoronoi = ccore.watershed(imgInv)
imgExternalMarker = ccore.threshold(imgVoronoi, 0, 0, 0, 255)
# full marker image
imgMarker = ccore.supremum(imgInternalMarker, imgExternalMarker)
# gradient image
imgFiltered = ccore.gaussianFilter(imgIn, iGaussFilterSize)
imgGrad = ccore.morphoGradient(imgFiltered, 1, 8)
# Watershed result: 0 is WSL, 1 is Background, all other values correspond to labels.
imgGradWatershed = ccore.constrainedWatershed(imgGrad, imgMarker)
# we first get the regions
minreslab, maxreslab = imgGradWatershed.getMinmax()
imgBinSegmentationRes = ccore.threshold(imgGradWatershed, 2, maxreslab, 0, 255)
imgTemp = ccore.copyImageIf(imgLabel, imgBinSegmentationRes)
imgRes = ccore.relabelImage(imgBinSegmentationRes, imgTemp)
return imgRes
def getFocusMeasurementFilterSeries(self,
imin,
method=None,
gauss_sizes=None):
if method is None:
method = self.method
if gauss_sizes is None:
gauss_sizes = self.gauss_sizes
init_meas = ccore.focusQuantification(imin, method)
res = {0: 1.0}
for filter_size in gauss_sizes:
impref = ccore.gaussianFilter(imin, filter_size)
focus_meas = ccore.focusQuantification(impref, method)
res[filter_size] = focus_meas / init_meas
return res
def threshold(self,
image,
median_radius,
window_size,
min_contrast,
remove_borderobjects,
fill_holes,
norm_min=0,
norm_max=255,
use_watershed=True,
seeding_size=5,
outline_smoothing=1,
*args,
**kw):
image = self.normalize(image, norm_min, norm_max)
image = ccore.numpy_to_image(image, copy=True)
image_smoothed = ccore.gaussianFilter(image, median_radius)
label_image = ccore.window_average_threshold(image_smoothed,
window_size, min_contrast)
if fill_holes:
ccore.fill_holes(label_image)
if outline_smoothing >= 1:
label_image = outlineSmoothing(label_image.toArray(),
outline_smoothing)
label_image = ccore.numpy_to_image(label_image, copy=True)
if use_watershed:
label_image = label_image.toArray().copy()
label_image = watershed(label_image, seeding_size=seeding_size)
label_image = ccore.numpy_to_image(label_image.astype(np.int16),
copy=True)
return ccore.ImageMaskContainer(image, label_image,
remove_borderobjects, True, True)
else:
# a static type system sucks!
return ccore.ImageMaskContainer(image, label_image,
remove_borderobjects)
