def _slow_zernike(img, radius, D):
zvalues = []
Y,X = np.where(img > 0)
P = img[Y,X].ravel()
cofy,cofx = center_of_mass(img)
Yn = ( (Y -cofy)/radius).ravel()
Xn = ( (X -cofx)/radius).ravel()
k = (np.sqrt(Xn**2 + Yn**2) <= 1.)
frac_center = np.array(P[k], np.double)
frac_center /= frac_center.sum()
Yn = Yn[k]
Xn = Xn[k]
frac_center = frac_center.ravel()
for n in xrange(D+1):
for l in xrange(n+1):
if (n-l)%2 == 0:
z = _slow_znl(Yn, Xn, frac_center, float(n), float(l))
zvalues.append(abs(z))
return np.array(zvalues)
def imgfeaturesdna(imageproc, dnaproc, isfield=False):
"""
values = imgfeaturesdna(imageproc, dnaproc, isfield=False)
calculates object features for imageproc
where imageproc contains the pre-processed fluorescence image,
and dnaproc the pre-processed dna fluorescence image.
Pre-processed means that the image has been cropped and had
pixels of interest selected (via a threshold, for instance).
use dnaproc=None to exclude features based on the dna image.
Parameters
----------
* imageproc: pre-processed image
* dnaproc: pre-processed DNA image
* isfield: whether to calculate only field level features
(default: False)
Features calculated include:
- Number of objects
- Euler number of the image (# of objects - # of holes)
- Average of the object sizes
- Variance of the object sizes
- Ratio of the largest object to the smallest
- Average of the object distances from the COF [unless isfield]
- Variance of the object distances from the COF [unless isfield]
- Ratio of the largest object distance to the smallest
- DNA: average of the object distances to the DNA COF [unless isfield]
- DNA: variance of the object distances to the DNA COF [unless isfield]
- DNA: ratio of the largest object distance to the smallest
- DNA/Image: distance of the DNA COF to the image COF [unless isfield]
- DNA/Image: ratio of the DNA image area to the image area
- DNA/Image: fraction of image that overlaps with DNA
"""
bwimage = (imageproc > 0)
imagelabeled,nr_objs = ndimage.label(bwimage)
if not nr_objs:
nfeatures = 5
if not isfield:
nfeatures += 3
if dnaproc is not None:
nfeatures += 2
if (not isfield and (dnaproc is not None)):
nfeatures += 1
return np.zeros(nfeatures)
values = [nr_objs]
dnacof = None
euler_nr = euler(bwimage)
values.append(euler_nr)
cof = None
if not isfield:
cof = center_of_mass(imageproc.astype(np.uint32))
obj_sizes = fullhistogram(imagelabeled.view(np.uint32))[1:]
obj_size_avg = obj_sizes.mean()
obj_size_var = obj_sizes.var()
obj_size_ratio = obj_sizes.max()/obj_sizes.min()
values.append(obj_size_avg)
values.append(obj_size_var)
values.append(obj_size_ratio)
if not isfield:
obj_cms = center_of_mass(imageproc.astype(np.uint32), imagelabeled)
obj_cms = obj_cms[1:]
obj_distances = []
obj_dnadistances = []
if dnaproc is not None:
dnacof = center_of_mass(dnaproc.astype(np.uint32))
for obj_center in obj_cms:
obj_distance = _norm2(obj_center - cof)
obj_distances.append(obj_distance)
if dnaproc is not None:
obj_dnadistance = _norm2(obj_center - dnacof)
obj_dnadistances.append(obj_dnadistance)
obj_distances = np.array(obj_distances)
obj_dist_avg = obj_distances.mean()
obj_dist_var = obj_distances.var()
mindist = obj_distances.min()
if mindist != 0:
obj_dist_ratio = obj_distances.max()/mindist
else:
obj_dist_ratio = 0
values.append(obj_dist_avg)
values.append(obj_dist_var)
values.append(obj_dist_ratio)
if dnaproc is not None:
obj_dnadistances = np.array(obj_dnadistances)
obj_dnadist_avg = obj_dnadistances.mean()
obj_dnadist_var = obj_dnadistances.var()
obj_dnamindist=obj_dnadistances.min()
if obj_dnamindist != 0:
obj_dnadist_ratio = obj_dnadistances.max()/obj_dnamindist
else:
obj_dnadist_ratio = 0 ;
values.append(obj_dnadist_avg)
values.append(obj_dnadist_var)
values.append(obj_dnadist_ratio)
if dnaproc is not None:
dna_area = (dnaproc > 0).sum()
image_area = (imageproc > 0).sum()
# what fraction of the image fluorescence area overlaps the dna image?
image_overlap = ( (imageproc > 0) & (dnaproc > 0) ).sum()
if image_area == 0:
dna_image_area_ratio = 0
image_dna_overlap = 0
else:
dna_image_area_ratio = dna_area/image_area
image_dna_overlap = image_overlap/image_area
if dnacof is not None:
dna_image_distance = _norm2(cof-dnacof)
values.append(dna_image_distance)
values.append(dna_image_area_ratio)
values.append(image_dna_overlap)
return values
