def main(mask,
intensity_image,
min_area,
max_area,
min_cut_area,
max_circularity,
max_convexity,
plot=False,
selection_test_mode=False):
'''Detects clumps in `mask` given criteria provided by the user
and cuts them along the borders of watershed regions, which are determined
based on the distance transform of `mask`.
Parameters
----------
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
2D binary or labele image encoding potential clumps
intensity_image: numpy.ndarray[numpy.uint8 or numpy.uint16]
2D grayscale image with intensity values of the objects that should
be detected
min_area: int
minimal area an object must have to be considered a clump
max_area: int
maximal area an object can have to be considered a clump
min_cut_area: int
minimal area an object must have
(useful to prevent cuts that would result in too small objects)
max_circularity: float
maximal circularity an object can have to be considerd a clump
max_convexity: float
maximal convexity an object can have to be considerd a clump
plot: bool, optional
whether a plot should be generated
selection_test_mode: bool, optional
whether, instead of the normal plot, heatmaps should be generated that
display values of the selection criteria *area*, *circularity* and
*convexity* for each individual object in `mask` as well as
the selected "clumps" based on the criteria provided by the user
Returns
-------
jtmodules.separate_clumps.Output
'''
separated_mask = separate_clumped_objects(mask, min_cut_area, min_area,
max_area, max_circularity,
max_convexity)
if plot:
from jtlib import plotting
if selection_test_mode:
logger.info('create plot for selection test mode')
labeled_mask, n_objects = mh.label(mask)
f = Morphology(labeled_mask)
values = f.extract()
area_img = create_feature_image(values['Morphology_Area'].values,
labeled_mask)
convexity_img = create_feature_image(
values['Morphology_Convexity'].values, labeled_mask)
circularity_img = create_feature_image(
values['Morphology_Circularity'].values, labeled_mask)
area_colorscale = plotting.create_colorscale(
'Greens',
n_objects,
add_background=True,
background_color='white')
circularity_colorscale = plotting.create_colorscale(
'Blues',
n_objects,
add_background=True,
background_color='white')
convexity_colorscale = plotting.create_colorscale(
'Reds',
n_objects,
add_background=True,
background_color='white')
plots = [
plotting.create_float_image_plot(area_img,
'ul',
colorscale=area_colorscale),
plotting.create_float_image_plot(
convexity_img, 'ur', colorscale=convexity_colorscale),
plotting.create_float_image_plot(
circularity_img, 'll', colorscale=circularity_colorscale),
plotting.create_mask_image_plot(clumps_mask, 'lr'),
]
figure = plotting.create_figure(
plots,
title=('Selection criteria: "area" (green), "convexity" (red) '
'and "circularity" (blue)'))
else:
logger.info('create plot')
cut_mask = (mask > 0) - (separated_mask > 0)
clumps_mask = np.zeros(mask.shape, bool)
initial_objects_label_image, n_initial_objects = mh.label(mask > 0)
for i in range(1, n_initial_objects + 1):
index = initial_objects_label_image == i
if len(np.unique(separated_mask[index])) > 1:
clumps_mask[index] = True
n_objects = len(np.unique(separated_mask[separated_mask > 0]))
colorscale = plotting.create_colorscale('Spectral',
n=n_objects,
permute=True,
add_background=True)
outlines = mh.morph.dilate(mh.labeled.bwperim(separated_mask > 0))
cutlines = mh.morph.dilate(mh.labeled.bwperim(cut_mask))
plots = [
plotting.create_mask_image_plot(separated_mask,
'ul',
colorscale=colorscale),
plotting.create_intensity_overlay_image_plot(
intensity_image, outlines, 'ur'),
plotting.create_mask_overlay_image_plot(
clumps_mask, cutlines, 'll')
]
figure = plotting.create_figure(plots, title='separated clumps')
else:
figure = str()
return Output(separated_mask, figure)
def main(image, mask, threshold=25,
mean_size=6, min_size=10,
filter_type='log_2d',
minimum_bead_intensity=150,
z_step=0.333, pixel_size=0.1625,
alpha=0, plot=False):
'''Converts an image stack with labelled cell surface to a cell
`volume` image
Parameters
----------
image: numpy.ndarray[Union[numpy.uint8, numpy.uint16]]
grayscale image in which beads should be detected (3D)
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
binary or labeled image of cell segmentation (2D)
threshold: int, optional
intensity of bead in filtered image (default: ``25``)
mean_size: int, optional
mean size of bead (default: ``6``)
min_size: int, optional
minimal number of connected voxels per bead (default: ``10``)
filter_type: str, optional
filter used to emphasise the beads in 3D
(options: ``log_2d`` (default) or ``log_3d``)
minimum_bead_intensity: int, optional
minimum intensity in the original image of an identified bead
centre. Use to filter low intensity beads.
z_step: float, optional
distance between consecutive z-planes (um) (default: ``0.333``)
pixel_size: float, optional
size of pixel (um) (default: ``0.1625``)
alpha: float, optional
value of parameter for 3D alpha shape calculation
(default: ``0``, no vertex filtering performed)
plot: bool, optional
whether a plot should be generated (default: ``False``)
Returns
-------
jtmodules.generate_volume_image.Output
'''
# Check that there are cells identified in image
if (np.max(mask) > 0):
volume_image_calculated = True
n_slices = image.shape[-1]
logger.debug('input image has z-dimension %d', n_slices)
# Remove high intensity pixels
detect_image = image.copy()
p = np.percentile(detect_image, 99.9)
detect_image[detect_image > p] = p
# Perform LoG filtering in 3D to emphasise beads
if filter_type == 'log_2d':
logger.info('using stacked 2D LoG filter to detect beads')
f = -1 * log_2d(size=mean_size, sigma=float(mean_size - 1) / 3)
filt = np.stack([f for _ in range(mean_size)], axis=2)
elif filter_type == 'log_3d':
logger.info('using 3D LoG filter to detect beads')
filt = -1 * log_3d(mean_size, (float(mean_size - 1) / 3,
float(mean_size - 1) / 3,
4 * float(mean_size - 1) / 3))
else:
logger.info('using unfiltered image to detect beads')
if filter_type == 'log_2d' or filter_type == 'log_3d':
logger.debug('convolve image with filter kernel')
detect_image = mh.convolve(detect_image.astype(float), filt)
detect_image[detect_image < 0] = 0
logger.debug('threshold beads')
labeled_beads, n_labels = mh.label(detect_image > threshold)
logger.info('detected %d beads', n_labels)
logger.debug('remove small beads')
sizes = mh.labeled.labeled_size(labeled_beads)
too_small = np.where(sizes < min_size)
labeled_beads = mh.labeled.remove_regions(labeled_beads, too_small)
mh.labeled.relabel(labeled_beads, inplace=True)
logger.info(
'%d beads remain after removing small beads', np.max(labeled_beads)
)
logger.debug('localise beads in 3D')
localised_beads = localise_bead_maxima_3D(
image, labeled_beads, minimum_bead_intensity
)
logger.debug('mask beads inside cells')
'''NOTE: localised_beads.coordinate image is used only for beads
outside cells and can therefore be modified here. For beads
inside cells, localised_beads.coordinates are used instead.
'''
# expand mask to ensure slide-beads are well away from cells
slide = localised_beads.coordinate_image
expand_mask = mh.dilate(
A=mask > 0,
Bc=np.ones([25,25], bool)
)
slide[expand_mask] = 0
logger.debug('determine coordinates of slide surface')
try:
bottom_surface = slide_surface_params(slide)
except InvalidSlideError:
logger.error('slide surface calculation is invalid' +
' returning empty volume image')
volume_image = np.zeros(shape=image[:,:,0].shape,
dtype=image.dtype)
figure = str()
return Output(volume_image, figure)
logger.debug('subtract slide surface to get absolute bead coordinates')
bead_coords_abs = []
for i in range(len(localised_beads.coordinates)):
bead_height = (
localised_beads.coordinates[i][2] -
plane(localised_beads.coordinates[i][0],
localised_beads.coordinates[i][1],
bottom_surface.x)
)
if bead_height > 0:
bead_coords_abs.append(
(localised_beads.coordinates[i][0],
localised_beads.coordinates[i][1],
bead_height)
)
logger.debug('convert absolute bead coordinates to image')
coord_image_abs = coordinate_list_to_array(
bead_coords_abs, shape=image[:,:,0].shape, dtype=np.float32
)
filtered_coords_global = filter_vertices_per_cell_alpha_shape(
coord_image_abs=coord_image_abs,
mask=mask,
alpha=alpha,
z_step=z_step,
pixel_size=pixel_size
)
logger.info('interpolate cell surface')
volume_image = interpolate_surface(
coords=np.asarray(filtered_coords_global, dtype=np.uint16),
output_shape=np.shape(image[:,:,0]),
method='linear'
)
volume_image = volume_image.astype(image.dtype)
logger.debug('set regions outside mask to zero')
volume_image[mask == 0] = 0
else:
logger.warn(
'no objects in input mask, skipping cell volume calculation.'
)
volume_image_calculated = False
volume_image = np.zeros(shape=image[:,:,0].shape, dtype=image.dtype)
if (plot and volume_image_calculated):
logger.debug('convert bottom surface plane to image for plotting')
dt = np.dtype(float)
bottom_surface_image = np.zeros(slide.shape, dtype=dt)
for ix in range(slide.shape[0]):
for iy in range(slide.shape[1]):
bottom_surface_image[ix, iy] = plane(
ix, iy, bottom_surface.x)
logger.info('create plot')
from jtlib import plotting
plots = [
plotting.create_intensity_image_plot(
np.max(image, axis=-1), 'ul', clip=True
),
plotting.create_float_image_plot(
bottom_surface_image, 'll', clip=True
),
plotting.create_intensity_image_plot(
volume_image, 'ur', clip=True
)
]
figure = plotting.create_figure(
plots, title='Convert stack to volume image'
)
else:
figure = str()
return Output(volume_image, figure)
def main(mask, feature, lower_threshold=None, upper_threshold=None, plot=False):
'''Filters objects (connected components) based on the specified
value range for a given `feature`.
Parameters
----------
mask: numpy.ndarray[Union[numpy.bool, numpy.int32]]
image that should be filtered
feature: str
name of the feature based on which the image should be filtered
(options: ``{"area", "eccentricity", "circularity", "convecity"}``)
lower_threshold:
minimal `feature` value objects must have
(default: ``None``; type depends on the chosen `feature`)
upper_threshold:
maximal `feature` value objects must have
(default: ``None``; type depends on the chosen `feature`)
plot: bool, optional
whether a plot should be generated (default: ``False``)
Returns
-------
jtmodules.filter_objects.Output
Raises
------
ValueError
when both `lower_threshold` and `upper_threshold` are ``None``
ValueError
when value of `feature` is not one of the supported features
'''
if lower_threshold is None and upper_threshold is None:
raise ValueError(
'Argument "lower_threshold" or "upper_threshold" must be provided. '
)
if feature not in SUPPORTED_FEATURES:
raise ValueError(
'Argument "feature" must be one of the following: "%s".'
% '", "'.join(SUPPORTED_FEATURES)
)
name = 'Morphology_{0}'.format(feature.capitalize())
labeled_image = mh.label(mask > 0)[0]
f = Morphology(labeled_image)
measurement = f.extract()[name]
values = measurement.values
feature_image = create_feature_image(values, labeled_image)
if not measurement.empty:
if lower_threshold is None:
lower_threshold = np.min(values)
if upper_threshold is None:
upper_threshold = np.max(values)
logger.info(
'keep objects with "%s" values in the range [%d, %d]',
feature, lower_threshold, upper_threshold
)
condition_image = np.logical_or(
feature_image < lower_threshold, feature_image > upper_threshold
)
filtered_mask = labeled_image.copy()
filtered_mask[condition_image] = 0
else:
logger.warn('no objects detected in image')
filtered_mask = labeled_image
mh.labeled.relabel(filtered_mask, inplace=True)
if plot:
from jtlib import plotting
plots = [
plotting.create_mask_image_plot(mask, 'ul'),
plotting.create_float_image_plot(feature_image, 'ur'),
plotting.create_mask_image_plot(filtered_mask, 'll'),
]
n_removed = (
len(np.unique(labeled_image)) - len(np.unique(filtered_mask))
)
figure = plotting.create_figure(
plots,
title='Filtered for feature "{0}": {1} objects removed'.format(
feature, n_removed
)
)
else:
figure = str()
return Output(filtered_mask, figure)
def main(image,
mask,
threshold=1,
min_area=3,
mean_area=5,
max_area=1000,
clip_percentile=99.999,
plot=False):
'''Detects blobs in `image` using an implementation of
`SExtractor <http://www.astromatic.net/software/sextractor>`_ [1].
The `image` is first convolved with a Laplacian of Gaussian filter of size
`mean_area` to enhance blob-like structures. The enhanced image is
then thresholded at `threshold` level and connected pixel components are
subsequently deplended.
Parameters
----------
image: numpy.ndarray[Union[numpy.uint8, numpy.uint16]]
grayscale image in which blobs should be detected
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
binary or labeled image that specifies pixel regions of interest
in which blobs should be detected
threshold: int, optional
threshold level for pixel values in the convolved image
(default: ``1``)
min_area: int, optional
minimal size a blob is allowed to have (default: ``3``)
mean_area: int, optional
estimated average size of a blob (default: ``5``)
max_area: int, optional
maximal size a blob is allowed to have to be subject to deblending;
no attempt will be made to deblend blobs larger than `max_area`
(default: ``100``)
clip_percentile: float, optional
clip intensity values in `image` above the given percentile; this may
help in attenuating artifacts
plot: bool, optional
whether a plot should be generated (default: ``False``)
Returns
-------
jtmodules.detect_blobs.Output[Union[numpy.ndarray, str]]
References
----------
.. [1] Bertin, E. & Arnouts, S. 1996: SExtractor: Software for source
extraction, Astronomy & Astrophysics Supplement 317, 393
'''
logger.info('detect blobs above threshold {0}'.format(threshold))
detect_image = image.copy()
p = np.percentile(image, clip_percentile)
detect_image[image > p] = p
# Enhance the image for blob detection by convoling it with a LOG filter
f = -1 * log_2d(size=mean_area, sigma=float(mean_area - 1) / 3)
detect_image = mh.convolve(detect_image.astype(float), f)
detect_image[detect_image < 0] = 0
# Mask regions of too big blobs
pre_blobs = mh.label(detect_image > threshold)[0]
bad_blobs, n_bad = mh.labeled.filter_labeled(pre_blobs, min_size=max_area)
logger.info(
'remove {0} blobs because they are bigger than {1} pixels'.format(
n_bad, max_area))
detect_mask = np.invert(mask > 0)
detect_mask[bad_blobs > 0] = True
detect_image[bad_blobs > 0] = 0
logger.info('deblend blobs')
blobs, centroids = detect_blobs(image=detect_image,
mask=detect_mask,
threshold=threshold,
min_area=min_area)
n = len(np.unique(blobs[blobs > 0]))
logger.info('{0} blobs detected'.format(n))
if plot:
logger.info('create plot')
from jtlib import plotting
colorscale = plotting.create_colorscale('Spectral',
n=n,
permute=True,
add_background=True)
plots = [
plotting.create_float_image_plot(detect_image, 'ul', clip=True),
plotting.create_mask_image_plot(blobs, 'ur', colorscale=colorscale)
]
figure = plotting.create_figure(
plots,
title=('detected #{0} blobs above threshold {1}'
' in LOG filtered image'.format(n, threshold)))
else:
figure = str()
return Output(centroids, blobs, figure)
def main(mask, intensity_image, min_area, max_area,
min_cut_area, max_circularity, max_convexity,
plot=False, selection_test_mode=False):
'''Detects clumps in `mask` given criteria provided by the user
and cuts them along the borders of watershed regions, which are determined
based on the distance transform of `mask`.
Parameters
----------
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
2D binary or labele image encoding potential clumps
intensity_image: numpy.ndarray[numpy.uint8 or numpy.uint16]
2D grayscale image with intensity values of the objects that should
be detected
min_area: int
minimal area an object must have to be considered a clump
max_area: int
maximal area an object can have to be considered a clump
min_cut_area: int
minimal area an object must have
(useful to prevent cuts that would result in too small objects)
max_circularity: float
maximal circularity an object can have to be considerd a clump
max_convexity: float
maximal convexity an object can have to be considerd a clump
plot: bool, optional
whether a plot should be generated
selection_test_mode: bool, optional
whether, instead of the normal plot, heatmaps should be generated that
display values of the selection criteria *area*, *circularity* and
*convexity* for each individual object in `mask` as well as
the selected "clumps" based on the criteria provided by the user
Returns
-------
jtmodules.separate_clumps.Output
'''
separated_mask = separate_clumped_objects(
mask, min_cut_area, min_area, max_area,
max_circularity, max_convexity
)
if plot:
from jtlib import plotting
if selection_test_mode:
logger.info('create plot for selection test mode')
labeled_mask, n_objects = mh.label(mask)
f = Morphology(labeled_mask)
values = f.extract()
area_img = create_feature_image(
values['Morphology_Area'].values, labeled_mask
)
convexity_img = create_feature_image(
values['Morphology_Convexity'].values, labeled_mask
)
circularity_img = create_feature_image(
values['Morphology_Circularity'].values, labeled_mask
)
area_colorscale = plotting.create_colorscale(
'Greens', n_objects,
add_background=True, background_color='white'
)
circularity_colorscale = plotting.create_colorscale(
'Blues', n_objects,
add_background=True, background_color='white'
)
convexity_colorscale = plotting.create_colorscale(
'Reds', n_objects,
add_background=True, background_color='white'
)
plots = [
plotting.create_float_image_plot(
area_img, 'ul', colorscale=area_colorscale
),
plotting.create_float_image_plot(
convexity_img, 'ur', colorscale=convexity_colorscale
),
plotting.create_float_image_plot(
circularity_img, 'll', colorscale=circularity_colorscale
),
plotting.create_mask_image_plot(
clumps_mask, 'lr'
),
]
figure = plotting.create_figure(
plots,
title=(
'Selection criteria: "area" (green), "convexity" (red) '
'and "circularity" (blue)'
)
)
else:
logger.info('create plot')
cut_mask = (mask > 0) - (separated_mask > 0)
clumps_mask = np.zeros(mask.shape, bool)
initial_objects_label_image, n_initial_objects = mh.label(mask > 0)
for i in range(1, n_initial_objects+1):
index = initial_objects_label_image == i
if len(np.unique(separated_mask[index])) > 1:
clumps_mask[index] = True
n_objects = len(np.unique(separated_mask[separated_mask > 0]))
colorscale = plotting.create_colorscale(
'Spectral', n=n_objects, permute=True, add_background=True
)
outlines = mh.morph.dilate(mh.labeled.bwperim(separated_mask > 0))
cutlines = mh.morph.dilate(mh.labeled.bwperim(cut_mask))
plots = [
plotting.create_mask_image_plot(
separated_mask, 'ul', colorscale=colorscale
),
plotting.create_intensity_overlay_image_plot(
intensity_image, outlines, 'ur'
),
plotting.create_mask_overlay_image_plot(
clumps_mask, cutlines, 'll'
)
]
figure = plotting.create_figure(
plots, title='separated clumps'
)
else:
figure = str()
return Output(separated_mask, figure)
def main(mask,
intensity_image,
min_area,
max_area,
min_cut_area,
max_circularity,
max_convexity,
plot=False,
selection_test_mode=False,
selection_test_show_remaining=False,
trimming=True):
'''Detects clumps in `mask` given criteria provided by the user
and cuts them along the borders of watershed regions, which are determined
based on the distance transform of `mask`.
Parameters
----------
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
2D binary or labele image encoding potential clumps
intensity_image: numpy.ndarray[numpy.uint8 or numpy.uint16]
2D grayscale image with intensity values of the objects that should
be detected
min_area: int
minimal area an object must have to be considered a clump
max_area: int
maximal area an object can have to be considered a clump
min_cut_area: int
minimal area an object must have
(useful to prevent cuts that would result in too small objects)
max_circularity: float
maximal circularity an object can have to be considerd a clump
max_convexity: float
maximal convexity an object can have to be considerd a clump
plot: bool, optional
whether a plot should be generated
selection_test_mode: bool, optional
whether, instead of the normal plot, heatmaps should be generated that
display values of the selection criteria *area*, *circularity* and
*convexity* for each individual object in `mask` as well as
the selected "clumps" based on the criteria provided by the user
selection_test_show_remaining: bool, optional
whether the selection test plot should be made on the remaining image
after the cuts were performed (helps to see why some objects were not
cut, especially if there are complicated clumps that require multiple
cuts). Defaults to false, thus showing the values in the original image
trimming: bool
some cuts may create a tiny third object. If this boolean is true,
tertiary objects < trimming_threshold (10) pixels will be removed
Returns
-------
jtmodules.separate_clumps.Output
'''
separated_label_image = separate_clumped_objects(mask,
min_cut_area,
min_area,
max_area,
max_circularity,
max_convexity,
allow_trimming=trimming)
if plot:
from jtlib import plotting
clumps_mask = np.zeros(mask.shape, bool)
initial_objects_label_image, n_initial_objects = mh.label(mask > 0)
for n in range(1, n_initial_objects + 1):
obj = (initial_objects_label_image == n)
if len(np.unique(separated_label_image[obj])) > 1:
clumps_mask[obj] = True
cut_mask = (mask > 0) & (separated_label_image == 0)
cutlines = mh.morph.dilate(mh.labeled.bwperim(cut_mask))
if selection_test_mode:
logger.info('create plot for selection test mode')
# Check if selection_test_show_remaining is active
# If so, show values on processed image, not original
if selection_test_show_remaining:
labeled_mask, n_objects = mh.label(separated_label_image > 0)
logger.info('Selection test mode plot with processed image')
else:
labeled_mask, n_objects = mh.label(mask)
f = Morphology(labeled_mask)
values = f.extract()
area_img = create_feature_image(values['Morphology_Area'].values,
labeled_mask)
convexity_img = create_feature_image(
values['Morphology_Convexity'].values, labeled_mask)
circularity_img = create_feature_image(
values['Morphology_Circularity'].values, labeled_mask)
plots = [
plotting.create_float_image_plot(area_img, 'ul'),
plotting.create_float_image_plot(convexity_img, 'ur'),
plotting.create_float_image_plot(circularity_img, 'll'),
plotting.create_mask_overlay_image_plot(
clumps_mask, cutlines, 'lr'),
]
figure = plotting.create_figure(
plots,
title=('Selection criteria:'
' "area" (top left),'
' "convexity" (top-right),'
' and "circularity" (bottom-left);'
' cuts made (bottom right).'))
else:
logger.info('create plot')
n_objects = len(
np.unique(separated_label_image[separated_label_image > 0]))
colorscale = plotting.create_colorscale('Spectral',
n=n_objects,
permute=True,
add_background=True)
outlines = mh.morph.dilate(
mh.labeled.bwperim(separated_label_image > 0))
plots = [
plotting.create_mask_image_plot(separated_label_image,
'ul',
colorscale=colorscale),
plotting.create_intensity_overlay_image_plot(
intensity_image, outlines, 'ur'),
plotting.create_mask_overlay_image_plot(
clumps_mask, cutlines, 'll')
]
figure = plotting.create_figure(plots, title='separated clumps')
else:
figure = str()
return Output(separated_label_image, figure)
def main(image, mask, threshold=1, min_area=3, mean_area=5, max_area=1000,
clip_percentile=99.999, plot=False):
'''Detects blobs in `image` using an implementation of
`SExtractor <http://www.astromatic.net/software/sextractor>`_ [1].
The `image` is first convolved with a Laplacian of Gaussian filter of size
`mean_area` to enhance blob-like structures. The enhanced image is
then thresholded at `threshold` level and connected pixel components are
subsequently deplended.
Parameters
----------
image: numpy.ndarray[Union[numpy.uint8, numpy.uint16]]
grayscale image in which blobs should be detected
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
binary or labeled image that specifies pixel regions of interest
in which blobs should be detected
threshold: int, optional
threshold level for pixel values in the convolved image
(default: ``1``)
min_area: int, optional
minimal size a blob is allowed to have (default: ``3``)
mean_area: int, optional
estimated average size of a blob (default: ``5``)
max_area: int, optional
maximal size a blob is allowed to have to be subject to deblending;
no attempt will be made to deblend blobs larger than `max_area`
(default: ``100``)
clip_percentile: float, optional
clip intensity values in `image` above the given percentile; this may
help in attenuating artifacts
plot: bool, optional
whether a plot should be generated (default: ``False``)
Returns
-------
jtmodules.detect_blobs.Output[Union[numpy.ndarray, str]]
References
----------
.. [1] Bertin, E. & Arnouts, S. 1996: SExtractor: Software for source
extraction, Astronomy & Astrophysics Supplement 317, 393
'''
logger.info('detect blobs above threshold {0}'.format(threshold))
detect_image = image.copy()
p = np.percentile(image, clip_percentile)
detect_image[image > p] = p
# Enhance the image for blob detection by convoling it with a LOG filter
f = -1 * log_2d(size=mean_area, sigma=float(mean_area - 1)/3)
detect_image = mh.convolve(detect_image.astype(float), f)
detect_image[detect_image < 0] = 0
# Mask regions of too big blobs
pre_blobs = mh.label(detect_image > threshold)[0]
bad_blobs, n_bad = mh.labeled.filter_labeled(pre_blobs, min_size=max_area)
logger.info(
'remove {0} blobs because they are bigger than {1} pixels'.format(
n_bad, max_area
)
)
detect_mask = np.invert(mask > 0)
detect_mask[bad_blobs > 0] = True
detect_image[bad_blobs > 0] = 0
logger.info('deblend blobs')
blobs, centroids = detect_blobs(
image=detect_image, mask=detect_mask, threshold=threshold,
min_area=min_area
)
n = len(np.unique(blobs[blobs>0]))
logger.info('{0} blobs detected'.format(n))
if plot:
logger.info('create plot')
from jtlib import plotting
colorscale = plotting.create_colorscale(
'Spectral', n=n, permute=True, add_background=True
)
plots = [
plotting.create_float_image_plot(
detect_image, 'ul', clip=True
),
plotting.create_mask_image_plot(
blobs, 'ur', colorscale=colorscale
)
]
figure = plotting.create_figure(
plots,
title=(
'detected #{0} blobs above threshold {1}'
' in LOG filtered image'.format(n, threshold)
)
)
else:
figure = str()
return Output(centroids, blobs, figure)
