def run_on_image_setting(self, workspace, image):
assert isinstance(workspace, cpw.Workspace)
image_set = workspace.image_set
measurements = workspace.measurements
im = image_set.get_image(image.image_name.value,
must_be_grayscale=True)
#
# Downsample the image and mask
#
new_shape = np.array(im.pixel_data.shape)
if image.subsample_size.value < 1:
new_shape = new_shape * image.subsample_size.value
i,j = (np.mgrid[0:new_shape[0],0:new_shape[1]].astype(float) /
image.subsample_size.value)
pixels = scind.map_coordinates(im.pixel_data,(i,j),order=1)
mask = scind.map_coordinates(im.mask.astype(float), (i,j)) > .9
else:
pixels = im.pixel_data
mask = im.mask
#
# Remove background pixels using a greyscale tophat filter
#
if image.image_sample_size.value < 1:
back_shape = new_shape * image.image_sample_size.value
i,j = (np.mgrid[0:back_shape[0],0:back_shape[1]].astype(float) /
image.image_sample_size.value)
back_pixels = scind.map_coordinates(pixels,(i,j), order=1)
back_mask = scind.map_coordinates(mask.astype(float), (i,j)) > .9
else:
back_pixels = pixels
back_mask = mask
radius = image.element_size.value
back_pixels = morph.grey_erosion(back_pixels, radius, back_mask)
back_pixels = morph.grey_dilation(back_pixels, radius, back_mask)
if image.image_sample_size.value < 1:
i,j = np.mgrid[0:new_shape[0],0:new_shape[1]].astype(float)
#
# Make sure the mapping only references the index range of
# back_pixels.
#
i *= float(back_shape[0]-1)/float(new_shape[0]-1)
j *= float(back_shape[1]-1)/float(new_shape[1]-1)
back_pixels = scind.map_coordinates(back_pixels,(i,j), order=1)
pixels -= back_pixels
pixels[pixels < 0] = 0
#
# For each object, build a little record
#
class ObjectRecord(object):
def __init__(self, name):
self.name = name
self.labels = workspace.object_set.get_objects(name).segmented
self.nobjects = np.max(self.labels)
if self.nobjects != 0:
self.range = np.arange(1, np.max(self.labels)+1)
self.labels = self.labels.copy()
self.labels[~ im.mask] = 0
self.current_mean = fix(
scind.mean(im.pixel_data,
self.labels,
self.range))
self.start_mean = np.maximum(
self.current_mean, np.finfo(float).eps)
object_records = [ObjectRecord(ob.objects_name.value)
for ob in image.objects ]
#
# Transcribed from the Matlab module: granspectr function
#
# CALCULATES GRANULAR SPECTRUM, ALSO KNOWN AS SIZE DISTRIBUTION,
# GRANULOMETRY, AND PATTERN SPECTRUM, SEE REF.:
# J.Serra, Image Analysis and Mathematical Morphology, Vol. 1. Academic Press, London, 1989
# Maragos,P. "Pattern spectrum and multiscale shape representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, N 7, pp. 701-716, 1989
# L.Vincent "Granulometries and Opening Trees", Fundamenta Informaticae, 41, No. 1-2, pp. 57-90, IOS Press, 2000.
# L.Vincent "Morphological Area Opening and Closing for Grayscale Images", Proc. NATO Shape in Picture Workshop, Driebergen, The Netherlands, pp. 197-208, 1992.
# I.Ravkin, V.Temov "Bit representation techniques and image processing", Applied Informatics, v.14, pp. 41-90, Finances and Statistics, Moskow, 1988 (in Russian)
# THIS IMPLEMENTATION INSTEAD OF OPENING USES EROSION FOLLOWED BY RECONSTRUCTION
#
ng = image.granular_spectrum_length.value
startmean = np.mean(pixels[mask])
ero = pixels.copy()
# Mask the test image so that masked pixels will have no effect
# during reconstruction
#
ero[~mask] = 0
currentmean = startmean
startmean = max(startmean, np.finfo(float).eps)
footprint = np.array([[False,True,False],
[True ,True,True],
[False,True,False]])
statistics = [ image.image_name.value]
for i in range(1,ng+1):
prevmean = currentmean
ero = morph.grey_erosion(ero, mask = mask, footprint=footprint)
rec = morph.grey_reconstruction(ero, pixels, footprint)
currentmean = np.mean(rec[mask])
gs = (prevmean - currentmean) * 100 / startmean
statistics += [ "%.2f"%gs]
feature = image.granularity_feature(i)
measurements.add_image_measurement(feature, gs)
#
# Restore the reconstructed image to the shape of the
# original image so we can match against object labels
#
orig_shape = im.pixel_data.shape
i,j = np.mgrid[0:orig_shape[0],0:orig_shape[1]].astype(float)
#
# Make sure the mapping only references the index range of
# back_pixels.
#
i *= float(new_shape[0]-1)/float(orig_shape[0]-1)
j *= float(new_shape[1]-1)/float(orig_shape[1]-1)
rec = scind.map_coordinates(rec,(i,j), order=1)
#
# Calculate the means for the objects
#
for object_record in object_records:
assert isinstance(object_record, ObjectRecord)
if object_record.nobjects > 0:
new_mean = fix(scind.mean(rec, object_record.labels,
object_record.range))
gss = ((object_record.current_mean - new_mean) * 100 /
object_record.start_mean)
object_record.current_mean = new_mean
else:
gss = np.zeros((0,))
measurements.add_measurement(object_record.name, feature, gss)
return statistics
def run_on_image_setting(self, workspace, image):
assert isinstance(workspace, cpw.Workspace)
image_set = workspace.image_set
measurements = workspace.measurements
im = image_set.get_image(image.image_name.value,
must_be_grayscale=True)
#
# Downsample the image and mask
#
new_shape = np.array(im.pixel_data.shape)
if image.subsample_size.value < 1:
new_shape = new_shape * image.subsample_size.value
i, j = (np.mgrid[0:new_shape[0], 0:new_shape[1]].astype(float) /
image.subsample_size.value)
pixels = scind.map_coordinates(im.pixel_data, (i, j), order=1)
mask = scind.map_coordinates(im.mask.astype(float), (i, j)) > .9
else:
pixels = im.pixel_data
mask = im.mask
#
# Remove background pixels using a greyscale tophat filter
#
if image.image_sample_size.value < 1:
back_shape = new_shape * image.image_sample_size.value
i, j = (np.mgrid[0:back_shape[0], 0:back_shape[1]].astype(float) /
image.image_sample_size.value)
back_pixels = scind.map_coordinates(pixels, (i, j), order=1)
back_mask = scind.map_coordinates(mask.astype(float), (i, j)) > .9
else:
back_pixels = pixels
back_mask = mask
radius = image.element_size.value
back_pixels = morph.grey_erosion(back_pixels, radius, back_mask)
back_pixels = morph.grey_dilation(back_pixels, radius, back_mask)
if image.image_sample_size.value < 1:
i, j = np.mgrid[0:new_shape[0], 0:new_shape[1]].astype(float)
#
# Make sure the mapping only references the index range of
# back_pixels.
#
i *= float(back_shape[0] - 1) / float(new_shape[0] - 1)
j *= float(back_shape[1] - 1) / float(new_shape[1] - 1)
back_pixels = scind.map_coordinates(back_pixels, (i, j), order=1)
pixels -= back_pixels
pixels[pixels < 0] = 0
#
# For each object, build a little record
#
class ObjectRecord(object):
def __init__(self, name):
self.name = name
self.labels = workspace.object_set.get_objects(name).segmented
self.nobjects = np.max(self.labels)
if self.nobjects != 0:
self.range = np.arange(1, np.max(self.labels) + 1)
self.labels = self.labels.copy()
self.labels[~im.mask] = 0
self.current_mean = fix(
scind.mean(im.pixel_data, self.labels, self.range))
self.start_mean = np.maximum(self.current_mean,
np.finfo(float).eps)
object_records = [
ObjectRecord(ob.objects_name.value) for ob in image.objects
]
#
# Transcribed from the Matlab module: granspectr function
#
# CALCULATES GRANULAR SPECTRUM, ALSO KNOWN AS SIZE DISTRIBUTION,
# GRANULOMETRY, AND PATTERN SPECTRUM, SEE REF.:
# J.Serra, Image Analysis and Mathematical Morphology, Vol. 1. Academic Press, London, 1989
# Maragos,P. "Pattern spectrum and multiscale shape representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, N 7, pp. 701-716, 1989
# L.Vincent "Granulometries and Opening Trees", Fundamenta Informaticae, 41, No. 1-2, pp. 57-90, IOS Press, 2000.
# L.Vincent "Morphological Area Opening and Closing for Grayscale Images", Proc. NATO Shape in Picture Workshop, Driebergen, The Netherlands, pp. 197-208, 1992.
# I.Ravkin, V.Temov "Bit representation techniques and image processing", Applied Informatics, v.14, pp. 41-90, Finances and Statistics, Moskow, 1988 (in Russian)
# THIS IMPLEMENTATION INSTEAD OF OPENING USES EROSION FOLLOWED BY RECONSTRUCTION
#
ng = image.granular_spectrum_length.value
startmean = np.mean(pixels[mask])
ero = pixels.copy()
# Mask the test image so that masked pixels will have no effect
# during reconstruction
#
ero[~mask] = 0
currentmean = startmean
startmean = max(startmean, np.finfo(float).eps)
footprint = np.array([[False, True, False], [True, True, True],
[False, True, False]])
statistics = [image.image_name.value]
for i in range(1, ng + 1):
prevmean = currentmean
ero = morph.grey_erosion(ero, mask=mask, footprint=footprint)
rec = morph.grey_reconstruction(ero, pixels, footprint)
currentmean = np.mean(rec[mask])
gs = (prevmean - currentmean) * 100 / startmean
statistics += ["%.2f" % gs]
feature = image.granularity_feature(i)
measurements.add_image_measurement(feature, gs)
#
# Restore the reconstructed image to the shape of the
# original image so we can match against object labels
#
orig_shape = im.pixel_data.shape
i, j = np.mgrid[0:orig_shape[0], 0:orig_shape[1]].astype(float)
#
# Make sure the mapping only references the index range of
# back_pixels.
#
i *= float(new_shape[0] - 1) / float(orig_shape[0] - 1)
j *= float(new_shape[1] - 1) / float(orig_shape[1] - 1)
rec = scind.map_coordinates(rec, (i, j), order=1)
#
# Calculate the means for the objects
#
for object_record in object_records:
assert isinstance(object_record, ObjectRecord)
if object_record.nobjects > 0:
new_mean = fix(
scind.mean(rec, object_record.labels,
object_record.range))
gss = ((object_record.current_mean - new_mean) * 100 /
object_record.start_mean)
object_record.current_mean = new_mean
else:
gss = np.zeros((0, ))
measurements.add_measurement(object_record.name, feature, gss)
return statistics