def filterDoG(img, filterDoGParameter = None, size = None, sigma = None, sigma2 = None, save = None, verbose = None,
subStack = None, out = sys.stdout, **parameter):
"""Difference of Gaussians (DoG) filter step
Arguments:
img (array): image data
filterDoGParameter (dict):
========= ==================== ================================================================
Name Type Descritption
========= ==================== ================================================================
*size* (tuple or None) size for the DoG filter
if None, do not correct for any background
*sigma* (tuple or None) std of outer Guassian, if None autmatically determined from size
*sigma2* (tuple or None) std of inner Guassian, if None autmatically determined from size
*save* (str or None) file name to save result of this operation
if None dont save to file
*verbose* (bool or int) print progress information
========= ==================== ================================================================
subStack (dict or None): sub-stack information
out (object): object to write progress info to
Returns:
array: DoG filtered image
"""
timer = Timer();
dogSize = getParameter(filterDoGParameter, "size", size);
dogSigma = getParameter(filterDoGParameter, "sigma", sigma);
dogSigma2= getParameter(filterDoGParameter, "sigma2",sigma2);
dogSave = getParameter(filterDoGParameter, "save", save);
verbose = getParameter(filterDoGParameter, "verbose", verbose);
if verbose:
writeParameter(out = out, head = 'DoG:', size = dogSize, sigma = dogSigma, sigma2 = dogSigma2, save = dogSave);
#DoG filter
img = img.astype('float32'); # always convert to float for downstream processing
if not dogSize is None:
fdog = filterKernel(ftype = 'DoG', size = dogSize, sigma = dogSigma, sigma2 = dogSigma2);
fdog = fdog.astype('float32');
#img = correlate(img, fdog);
#img = scipy.signal.correlate(img, fdog);
img = correlate(img, fdog);
#img = convolve(img, fdog, mode = 'same');
img[img < 0] = 0;
if verbose > 1:
plotTiling(img);
if not dogSave is None:
writeSubStack(dogSave, img, subStack = subStack);
if verbose:
out.write(timer.elapsedTime(head = 'DoG') + '\n');
return img
def filterLinear(img,
filterLinearParameter=None,
ftype=None,
size=None,
sigma=None,
sigma2=None,
save=None,
subStack=None,
verbose=False,
out=sys.stdout,
**parameter):
"""Applies a linear filter to the image
Arguments:
img (array): image data
filterLinearParameter (dict):
========= ==================== ================================================================
Name Type Descritption
========= ==================== ================================================================
*ftype* (str or None) the type of the filter, see :ref:`FilterTypes`
if None do ot perform any fitlering
*size* (tuple or None) size for the filter
if None, do not perform filtering
*sigma* (tuple or None) std of outer Guassian, if None autmatically determined from size
*sigma2* (tuple or None) std of inner Guassian, if None autmatically determined from size
*save* (str or None) file name to save result of this operation
if None dont save to file
*verbose* (bool or int) print progress information
========= ==================== ================================================================
subStack (dict or None): sub-stack information
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: filtered image
Note:
Converts image to float32 type if filter is active!
"""
timer = Timer()
ftype = getParameter(filterLinearParameter, "ftype", ftype)
size = getParameter(filterLinearParameter, "size", size)
sigma = getParameter(filterLinearParameter, "sigma", sigma)
sigma2 = getParameter(filterLinearParameter, "sigma2", sigma2)
save = getParameter(filterLinearParameter, "save", save)
verbose = getParameter(filterLinearParameter, "verbose", verbose)
if verbose:
writeParameter(out=out,
head='Linear Filter:',
ftype=ftype,
size=size,
sigma=sigma,
sigma2=sigma2,
save=save)
if ftype is None:
return img
#DoG filter
img = img.astype('float32')
# always convert to float for downstream processing
if not size is None:
fil = filterKernel(ftype=ftype, size=size, sigma=sigma, sigma2=sigma2)
fil = fil.astype('float32')
#img = correlate(img, fdog);
#img = scipy.signal.correlate(img, fdog);
img = correlate(img, fil)
#img = convolve(img, fdog, mode = 'same');
img[img < 0] = 0
if verbose > 1:
plotTiling(img)
if not save is None:
writeSubStack(save, img, subStack=subStack)
if verbose:
out.write(timer.elapsedTime(head='Linear Filter') + '\n')
return img
def filterDoG(img,
filterDoGParameter=None,
size=None,
sigma=None,
sigma2=None,
save=None,
verbose=None,
subStack=None,
out=sys.stdout,
**parameter):
"""Difference of Gaussians (DoG) filter step
Arguments:
img (array): image data
filterDoGParameter (dict):
========= ==================== ================================================================
Name Type Descritption
========= ==================== ================================================================
*size* (tuple or None) size for the DoG filter
if None, do not correct for any background
*sigma* (tuple or None) std of outer Guassian, if None autmatically determined from size
*sigma2* (tuple or None) std of inner Guassian, if None autmatically determined from size
*save* (str or None) file name to save result of this operation
if None dont save to file
*verbose* (bool or int) print progress information
========= ==================== ================================================================
subStack (dict or None): sub-stack information
out (object): object to write progress info to
Returns:
array: DoG filtered image
"""
timer = Timer()
dogSize = getParameter(filterDoGParameter, "size", size)
dogSigma = getParameter(filterDoGParameter, "sigma", sigma)
dogSigma2 = getParameter(filterDoGParameter, "sigma2", sigma2)
dogSave = getParameter(filterDoGParameter, "save", save)
verbose = getParameter(filterDoGParameter, "verbose", verbose)
if verbose:
writeParameter(out=out,
head='DoG:',
size=dogSize,
sigma=dogSigma,
sigma2=dogSigma2,
save=dogSave)
#DoG filter
img = img.astype('float32')
# always convert to float for downstream processing
if not dogSize is None:
fdog = filterKernel(ftype='DoG',
size=dogSize,
sigma=dogSigma,
sigma2=dogSigma2)
fdog = fdog.astype('float32')
#img = correlate(img, fdog);
#img = scipy.signal.correlate(img, fdog);
img = correlate(img, fdog)
#img = convolve(img, fdog, mode = 'same');
img[img < 0] = 0
if verbose > 1:
plotTiling(img)
if not dogSave is None:
writeSubStack(dogSave, img, subStack=subStack)
if verbose:
out.write(timer.elapsedTime(head='DoG') + '\n')
return img
def filterLinear(img, filterLinearParameter = None, ftype = None, size = None, sigma = None, sigma2 = None, save = None,
subStack = None, verbose = False, out = sys.stdout, **parameter):
"""Applies a linear filter to the image
Arguments:
img (array): image data
filterLinearParameter (dict):
========= ==================== ================================================================
Name Type Descritption
========= ==================== ================================================================
*ftype* (str or None) the type of the filter, see :ref:`FilterTypes`
if None do ot perform any fitlering
*size* (tuple or None) size for the filter
if None, do not perform filtering
*sigma* (tuple or None) std of outer Guassian, if None autmatically determined from size
*sigma2* (tuple or None) std of inner Guassian, if None autmatically determined from size
*save* (str or None) file name to save result of this operation
if None dont save to file
*verbose* (bool or int) print progress information
========= ==================== ================================================================
subStack (dict or None): sub-stack information
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: filtered image
Note:
Converts image to float32 type if filter is active!
"""
timer = Timer();
ftype = getParameter(filterLinearParameter, "ftype", ftype);
size = getParameter(filterLinearParameter, "size", size);
sigma = getParameter(filterLinearParameter, "sigma", sigma);
sigma2 = getParameter(filterLinearParameter, "sigma2", sigma2);
save = getParameter(filterLinearParameter, "save", save);
verbose = getParameter(filterLinearParameter, "verbose",verbose);
if verbose:
writeParameter(out = out, head = 'Linear Filter:', ftype = ftype, size = size, sigma = sigma, sigma2 = sigma2, save = save);
if ftype is None:
return img;
#DoG filter
img = img.astype('float32'); # always convert to float for downstream processing
if not size is None:
fil = filterKernel(ftype = ftype, size = size, sigma = sigma, sigma2 = sigma2);
fil = fil.astype('float32');
#img = correlate(img, fdog);
#img = scipy.signal.correlate(img, fdog);
img = correlate(img, fil);
#img = convolve(img, fdog, mode = 'same');
img[img < 0] = 0;
if verbose > 1:
plotTiling(img);
if not save is None:
writeSubStack(save, img, subStack = subStack);
if verbose:
out.write(timer.elapsedTime(head = 'Linear Filter') + '\n');
return img