def apply_zselection(self):
if type(self.oZSliceOrProjection) == types.TupleType:
method, zbegin, zend, zstep = self.oZSliceOrProjection
images = [img.image for img in self._zslices][(zbegin-1):zend:zstep]
# single images don't carry the dtype
dtype = img.format
if method == "maximum":
method_const = ccore.ProjectionType.MaxProjection
elif method == "minimum":
method_const = ccore.ProjectionType.MinProjection
elif method == "average":
method_const = ccore.ProjectionType.MeanProjection
self.logger.debug("* applying %s Z-Projection to stack of %d images..."
%(method, len(images)))
imgprj = numpy.zeros((images[0].height, images[0].width), dtype=dtype)
imgprj = ccore.numpy_to_image(numpy.zeros((images[0].height, images[0].width), dtype=dtype), copy=True)
ccore.zproject(imgprj, images, method_const)
# overwrite the first MetaImage found with the projected image data
meta_image = self._zslices[0]
meta_image.set_image(imgprj)
else:
self.oZSliceOrProjection = int(self.oZSliceOrProjection)
self.logger.debug("* selecting z-slice %d..." % self.oZSliceOrProjection)
try:
meta_image = self._zslices[self.oZSliceOrProjection-1]
except IndexError:
raise IndexError("Invalid z-slice selection for channel %s"
%self.NAME)
self.meta_image = copy.copy(meta_image)
def _run(self, meta_image):
image = meta_image.image
img_prefiltered = self.prefilter(image)
img_bin1 = self.threshold(img_prefiltered, self.params['latwindowsize'], self.params['latlimit'])
if self.params['holefilling']:
ccore.fill_holes(img_bin1, False)
if self.params['lat2']:
img_bin2 = self.threshold(img_prefiltered, self.params['latwindowsize2'],
self.params['latlimit2'])
# replacement for not working ccore.projectImage
img_bin = numpy.zeros((img_bin2.height, img_bin2.width),
dtype=meta_image.format)
img_bin = ccore.numpy_to_image(img_bin, copy=True)
ccore.zproject(img_bin, [img_bin1, img_bin2], ccore.ProjectionType.MaxProjection)
else:
img_bin = img_bin1
if self.params['watershed_distance']:
img_bin = self.correct_segmetation(img_prefiltered, img_bin,
self.params['watershed_dynamic'],
self.params['watershed_used_distance'])
# if self.params['shapewatershed']:
# img_bin = self.correct_segmetation(img_prefiltered, img_bin,
# self.params['latwindowsize'],
# self.params['shapewatershed_gausssize'],
# self.params['shapewatershed_maximasize'],
# self.params['shapewatershed_minmergesize'],
# kind='shape')
# if self.params['intensitywatershed']:
# img_bin = self.correct_segmetation(img_prefiltered, img_bin,
# self.params['latwindowsize'],
# self.params['intensitywatershed_gausssize'],
# self.params['intensitywatershed_maximasize'],
# self.params['intensitywatershed_minmergesize'],
# kind='intensity')
container = ccore.ImageMaskContainer(image, img_bin, self.params['removeborderobjects'])
# calculate offset: mean on the background region, as given by the segmentation result
# no locality: simply a global mean on the image.
np_image = image.toArray(True)
np_img_bin = img_bin.toArray(True)
offset = np_image[np_img_bin==0].mean()
self.postprocessing(container, self.params['postprocessing'],
(self.params['postprocessing_roisize_min'], self.params['postprocessing_roisize_max']),
(self.params['postprocessing_intensity_min'], self.params['postprocessing_intensity_max']),
offset=offset)
return container
def _run(self, meta_image):
image = meta_image.image
img_prefiltered = self.prefilter(image)
img_bin1 = self.threshold(img_prefiltered, self.params['latwindowsize'], self.params['latlimit'])
if self.params['holefilling']:
ccore.fill_holes(img_bin1, False)
if self.params['lat2']:
img_bin2 = self.threshold(img_prefiltered, self.params['latwindowsize2'],
self.params['latlimit2'])
# replacement for not working ccore.projectImage
img_bin = numpy.zeros((img_bin2.height, img_bin2.width),
dtype=meta_image.format)
img_bin = ccore.numpy_to_image(img_bin, copy=True)
ccore.zproject(img_bin, [img_bin1, img_bin2], ccore.ProjectionType.MaxProjection)
else:
img_bin = img_bin1
if self.params['shapewatershed']:
img_bin = self.correct_segmetation(img_prefiltered, img_bin,
self.params['latwindowsize'],
self.params['shapewatershed_gausssize'],
self.params['shapewatershed_maximasize'],
self.params['shapewatershed_minmergesize'],
kind='shape')
if self.params['intensitywatershed']:
img_bin = self.correct_segmetation(img_prefiltered, img_bin,
self.params['latwindowsize'],
self.params['intensitywatershed_gausssize'],
self.params['intensitywatershed_maximasize'],
self.params['intensitywatershed_minmergesize'],
kind='intensity')
container = ccore.ImageMaskContainer(image, img_bin, self.params['removeborderobjects'])
self.postprocessing(container, self.params['postprocessing'],
(self.params['postprocessing_roisize_min'], self.params['postprocessing_roisize_max']),
(self.params['postprocessing_intensity_min'], self.params['postprocessing_intensity_max']))
return container
def apply_zselection(self):
if type(self.oZSliceOrProjection) == types.TupleType:
method, zbegin, zend, zstep = self.oZSliceOrProjection
images = [img.image
for img in self._zslices][(zbegin - 1):zend:zstep]
# single images don't carry the dtype
dtype = img.format
if method == "maximum":
method_const = ccore.ProjectionType.MaxProjection
elif method == "minimum":
method_const = ccore.ProjectionType.MinProjection
elif method == "average":
method_const = ccore.ProjectionType.MeanProjection
self.logger.debug(
"* applying %s Z-Projection to stack of %d images..." %
(method, len(images)))
imgprj = numpy.zeros((images[0].height, images[0].width),
dtype=dtype)
imgprj = ccore.numpy_to_image(numpy.zeros(
(images[0].height, images[0].width), dtype=dtype),
copy=True)
ccore.zproject(imgprj, images, method_const)
# overwrite the first MetaImage found with the projected image data
meta_image = self._zslices[0]
meta_image.set_image(imgprj)
else:
self.oZSliceOrProjection = int(self.oZSliceOrProjection)
self.logger.debug("* selecting z-slice %d..." %
self.oZSliceOrProjection)
try:
meta_image = self._zslices[self.oZSliceOrProjection - 1]
except IndexError:
raise IndexError("Invalid z-slice selection for channel %s" %
self.NAME)
self.meta_image = copy.copy(meta_image)
