def detectCellShape(img, peaks, detectCellShapeParameter = None, threshold = None, save = None, verbose = False,
subStack = None, out = sys.stdout, **parameter):
"""Find cell shapes as labeled image
Arguments:
img (array): image data
peaks (array): point data of cell centers / seeds
detectCellShape (dict):
============ =================== ===========================================================
Name Type Descritption
============ =================== ===========================================================
*threshold* (float or None) threshold to determine mask, pixel below this are background
if None no mask is generated
*save* (tuple) size of the box on which to perform the *method*
*verbose* (bool or int) print / plot information about this step
============ =================== ===========================================================
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: labeled image where each label indicates a cell
"""
threshold = getParameter(detectCellShapeParameter, "threshold", threshold);
save = getParameter(detectCellShapeParameter, "save", save);
verbose = getParameter(detectCellShapeParameter, "verbose", verbose);
if verbose:
writeParameter(out = out, head = 'Cell shape detection:', threshold = threshold, save = save);
# extended maxima
timer = Timer();
if threshold is None:
imgmask = None;
else:
imgmask = img > threshold;
imgpeaks = voxelizePixel(peaks, dataSize = img.shape, weights = numpy.arange(1, peaks.shape[0]+1));
imgws = watershed(-img, imgpeaks, mask = imgmask);
#imgws = watershed_ift(-img.astype('uint16'), imgpeaks);
#imgws[numpy.logical_not(imgmask)] = 0;
if not save is None:
writeSubStack(save, imgws.astype('int32'), subStack = subStack);
if verbose > 1:
#plotTiling(img)
plotOverlayLabel(img * 0.01, imgws, alpha = False);
#plotOverlayLabel(img, imgmax.astype('int64'), alpha = True)
if verbose:
out.write(timer.elapsedTime(head = 'Cell Shape:') + '\n');
return imgws
def detectCellShape(img,
peaks,
detectCellShapeParameter=None,
threshold=None,
save=None,
verbose=False,
subStack=None,
out=sys.stdout,
**parameter):
"""Find cell shapes as labeled image
Arguments:
img (array): image data
peaks (array): point data of cell centers / seeds
detectCellShape (dict):
============ =================== ===========================================================
Name Type Descritption
============ =================== ===========================================================
*threshold* (float or None) threshold to determine mask, pixel below this are background
if None no mask is generated
*save* (tuple) size of the box on which to perform the *method*
*verbose* (bool or int) print / plot information about this step
============ =================== ===========================================================
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: labeled image where each label indicates a cell
"""
threshold = getParameter(detectCellShapeParameter, "threshold", threshold)
save = getParameter(detectCellShapeParameter, "save", save)
verbose = getParameter(detectCellShapeParameter, "verbose", verbose)
if verbose:
writeParameter(out=out,
head='Cell shape detection:',
threshold=threshold,
save=save)
# extended maxima
timer = Timer()
if threshold is None:
imgmask = None
else:
imgmask = img > threshold
imgpeaks = voxelizePixel(peaks,
dataSize=img.shape,
weights=numpy.arange(1, peaks.shape[0] + 1))
imgws = watershed(-img, imgpeaks, mask=imgmask)
#imgws = watershed_ift(-img.astype('uint16'), imgpeaks);
#imgws[numpy.logical_not(imgmask)] = 0;
if not save is None:
writeSubStack(save, imgws.astype('int32'), subStack=subStack)
if verbose > 1:
#plotTiling(img)
plotOverlayLabel(img * 0.01, imgws, alpha=False)
#plotOverlayLabel(img, imgmax.astype('int64'), alpha = True)
if verbose:
out.write(timer.elapsedTime(head='Cell Shape:') + '\n')
return imgws
def findCenterOfMaxima(img, imgmax = None, label = None, findCenterOfMaximaParameter = None, save = None, verbose = False,
subStack = None, out = sys.stdout, **parameter):
"""Find center of detected maxima weighted by intensity
Arguments:
img (array): image data
findCenterOfMaximaParameter (dict):
========= ==================== ===========================================================
Name Type Descritption
========= ==================== ===========================================================
*save* (str or None) saves result of labeling the differnet maxima
if None, do the lableling is not saved
*verbose* (bool or int) print / plot information about this step
========= ==================== ===========================================================
subStack (dict or None): sub-stack information
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: coordinates of centers of maxima, shape is (n,d) where n is number of maxima and d the dimension of the image
"""
save = getParameter(findCenterOfMaximaParameter, "save", save);
verbose = getParameter(findCenterOfMaximaParameter, "verbose", verbose);
if verbose:
writeParameter(out = out, head = 'Center of Maxima:', save = save);
timer = Timer();
#center of maxima
if label is None:
imglab, nlab = sm.label(imgmax);
else:
imglab = label;
nlab = imglab.max();
#print 'max', imglab.shape, img.shape
#print imglab.dtype, img.dtype
if not save is None:
writeSubStack(save, imglab, subStack = subStack);
if nlab > 0:
centers = numpy.array(sm.center_of_mass(img, imglab, index = numpy.arange(1, nlab)));
if verbose > 1:
#plotOverlayLabel(img * 0.01, imglab, alpha = False);
#plotTiling(img)
imgc = numpy.zeros(img.shape);
for i in range(centers.shape[0]):
imgc[centers[i,0], centers[i,1], centers[i,2]] = 1;
plotOverlayLabel(img, imgc, alpha = False);
#plotOverlayLabel(img, imgmax.astype('int64'), alpha = True)
#return centers, imglab, mask
#cintensity = numpy.array([img[centers[i,0], centers[i,1], centers[i,2]] for i in range(centers.shape[0])]);
if verbose:
out.write(timer.elapsedTime(head = 'Cell Centers'));
#return ( centers, cintensity );
return centers;
else:
if verbose:
out.write('Cell Centers: No Cells found !');
#return ( numpy.zeros((0,3)), numpy.zeros(0) );
#return empty set o coordinates
return numpy.zeros((0,3));
def findExtendedMaxima(img, findExtendedMaximaParameter = None, hMax = None, size = 5, threshold = None, save = None, verbose = None,
subStack = None, out = sys.stdout, **parameter):
"""Find extended maxima in an image
Effectively this routine performs a h-max transfrom, followed by a local maxima search and
thresholding of the maxima.
Arguments:
img (array): image data
findExtendedMaximaParameter (dict):
=========== =================== ===========================================================
Name Type Descritption
=========== =================== ===========================================================
*hMax* (float or None) h parameter for the initial h-Max transform
if None, do not perform a h-max transform
*size* (tuple) size for the structure element for the local maxima filter
*threshold* (float or None) include only maxima larger than a threshold
if None keep all localmaxima
*save* (str or None) file name to save result of this operation
if None do not save result to file
*verbose* (bool or int) print / plot information about this step
=========== =================== ===========================================================
subStack (dict or None): sub-stack information
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: binary image with True pixel at extended maxima
See Also:
:func:`hMaxTransform`, :func:`localMax`
"""
hMax = getParameter(findExtendedMaximaParameter, "hMax", hMax);
size = getParameter(findExtendedMaximaParameter, "size", size);
threshold = getParameter(findExtendedMaximaParameter, "threshold", threshold);
save = getParameter(findExtendedMaximaParameter, "save", save);
verbose = getParameter(findExtendedMaximaParameter, "verbose", verbose);
if verbose:
writeParameter(out = out, head = 'Extended Max:', hMax = hMax, size = size, threshold = threshold, save = save);
timer = Timer();
## extended maxima
imgmax = hMaxTransform(img, hMax);
#imgmax = regionalMax(imgmax, regionalMaxStructureElement);
imgmax = localMax(imgmax, size);
#thresholding
if not threshold is None:
imgmax = numpy.logical_and(imgmax, img >= threshold);
if verbose > 1:
#plotTiling(img)
plotOverlayLabel(img * 0.01, imgmax.astype('int64'), alpha = False);
#plotOverlayLabel(img, imgmax.astype('int64'), alpha = True)
if not save is None:#
writeSubStack(save, imgmax.astype('int8'), subStack = subStack)
if verbose:
out.write(timer.elapsedTime(head = 'Extended Max') + '\n');
return imgmax
def findCenterOfMaxima(img, imgmax = None, label = None, findCenterOfMaximaParameter = None, save = None, verbose = False,
subStack = None, out = sys.stdout, **parameter):
"""Find center of detected maxima weighted by intensity
Arguments:
img (array): image data
findCenterOfMaximaParameter (dict):
========= ==================== ===========================================================
Name Type Descritption
========= ==================== ===========================================================
*save* (str or None) saves result of labeling the differnet maxima
if None, do the lableling is not saved
*verbose* (bool or int) print / plot information about this step
========= ==================== ===========================================================
subStack (dict or None): sub-stack information
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: coordinates of centers of maxima, shape is (n,d) where n is number of maxima and d the dimension of the image
"""
save = getParameter(findCenterOfMaximaParameter, "save", save);
verbose = getParameter(findCenterOfMaximaParameter, "verbose", verbose);
if verbose:
writeParameter(out = out, head = 'Center of Maxima:', save = save);
timer = Timer();
#center of maxima
if label is None:
imglab, nlab = sm.label(imgmax);
else:
imglab = label;
nlab = imglab.max();
#print 'max', imglab.shape, img.shape
#print imglab.dtype, img.dtype
if not save is None:
writeSubStack(save, imglab, subStack = subStack);
if nlab > 0:
centers = numpy.array(sm.center_of_mass(img, imglab, index = numpy.arange(1, nlab)));
if verbose > 1:
#plotOverlayLabel(img * 0.01, imglab, alpha = False);
#plotTiling(img)
imgc = numpy.zeros(img.shape);
for i in range(centers.shape[0]):
imgc[centers[i,0], centers[i,1], centers[i,2]] = 1;
plotOverlayLabel(img, imgc, alpha = False);
#plotOverlayLabel(img, imgmax.astype('int64'), alpha = True)
#return centers, imglab, mask
#cintensity = numpy.array([img[centers[i,0], centers[i,1], centers[i,2]] for i in range(centers.shape[0])]);
if verbose:
out.write(timer.elapsedTime(head = 'Cell Centers'));
#return ( centers, cintensity );
return centers;
else:
if verbose:
out.write('Cell Centers: No Cells found !');
#return ( numpy.zeros((0,3)), numpy.zeros(0) );
#return empty set o coordinates
return numpy.zeros((0,3));
def findExtendedMaxima(img, findExtendedMaximaParameter = None, hMax = None, size = 5, threshold = None, save = None, verbose = None,
subStack = None, out = sys.stdout, **parameter):
"""Find extended maxima in an image
Effectively this routine performs a h-max transfrom, followed by a local maxima search and
thresholding of the maxima.
Arguments:
img (array): image data
findExtendedMaximaParameter (dict):
=========== =================== ===========================================================
Name Type Descritption
=========== =================== ===========================================================
*hMax* (float or None) h parameter for the initial h-Max transform
if None, do not perform a h-max transform
*size* (tuple) size for the structure element for the local maxima filter
*threshold* (float or None) include only maxima larger than a threshold
if None keep all localmaxima
*save* (str or None) file name to save result of this operation
if None do not save result to file
*verbose* (bool or int) print / plot information about this step
=========== =================== ===========================================================
subStack (dict or None): sub-stack information
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: binary image with True pixel at extended maxima
See Also:
:func:`hMaxTransform`, :func:`localMax`
"""
hMax = getParameter(findExtendedMaximaParameter, "hMax", hMax);
size = getParameter(findExtendedMaximaParameter, "size", size);
threshold = getParameter(findExtendedMaximaParameter, "threshold", threshold);
save = getParameter(findExtendedMaximaParameter, "save", save);
verbose = getParameter(findExtendedMaximaParameter, "verbose", verbose);
if verbose:
writeParameter(out = out, head = 'Extended Max:', hMax = hMax, size = size, threshold = threshold, save = save);
timer = Timer();
## extended maxima
imgmax = hMaxTransform(img, hMax);
#imgmax = regionalMax(imgmax, regionalMaxStructureElement);
imgmax = localMax(imgmax, size);
#thresholding
if not threshold is None:
imgmax = numpy.logical_and(imgmax, img >= threshold);
if verbose > 1:
#plotTiling(img)
plotOverlayLabel(img * 0.01, imgmax.astype('int64'), alpha = False);
#plotOverlayLabel(img, imgmax.astype('int64'), alpha = True)
if not save is None:#
writeSubStack(save, imgmax.astype('int8'), subStack = subStack)
if verbose:
out.write(timer.elapsedTime(head = 'Extended Max') + '\n');
return imgmax