def get_image_from_features(radius, feature_dictionary):
'''Reconstruct the intensity image from the zernike features
radius - the radius of the minimum enclosing circle
feature_dictionary - keys are (n, m) tuples and values are the
magnitudes.
returns a greyscale image based on the feature dictionary.
'''
i, j = np.mgrid[-radius:(radius + 1),
-radius:(radius + 1)].astype(float) / radius
mask = (i * i + j * j) <= 1
zernike_indexes = np.array(feature_dictionary.keys())
zernike_features = np.array(feature_dictionary.values())
z = construct_zernike_polynomials(j,
i,
np.abs(zernike_indexes),
mask=mask)
zn = (2 * zernike_indexes[:, 0] + 2) / (
(zernike_indexes[:, 1] == 0) + 1) / np.pi
z = z * zn[np.newaxis, np.newaxis, :]
z = z.real * (zernike_indexes[:, 1] >= 0)[np.newaxis, np.newaxis, :] +\
z.imag * (zernike_indexes[:, 1] <= 0)[np.newaxis, np.newaxis, :]
return np.sum(z * zernike_features[np.newaxis, np.newaxis, :], 2)
def test_00_00_zeros(self):
"""Test construct_zernike_polynomials on an empty image"""
zi = self.make_zernike_indexes()
zf = z.construct_zernike_polynomials(np.zeros((100, 100)),
np.zeros((100, 100)), zi)
# All zernikes with m!=0 should be zero
m_ne_0 = np.array([i for i in range(zi.shape[0]) if zi[i, 1]])
m_eq_0 = np.array([i for i in range(zi.shape[0]) if zi[i, 1] == 0])
self.assertTrue(np.all(zf[:, :, m_ne_0] == 0))
self.assertTrue(np.all(zf[:, :, m_eq_0] != 0))
scores = z.score_zernike(zf, np.array([]), np.zeros((100, 100), int))
self.assertEqual(np.product(scores.shape), 0)
def test_00_00_zeros(self):
"""Test construct_zernike_polynomials on an empty image"""
zi = self.make_zernike_indexes()
zf = z.construct_zernike_polynomials(np.zeros((100,100)),
np.zeros((100,100)),
zi)
# All zernikes with m!=0 should be zero
m_ne_0 = np.array([i for i in range(zi.shape[0]) if zi[i,1]])
m_eq_0 = np.array([i for i in range(zi.shape[0]) if zi[i,1]==0])
self.assertTrue(np.all(zf[:,:,m_ne_0]==0))
self.assertTrue(np.all(zf[:,:,m_eq_0]!=0))
scores = z.score_zernike(zf, np.array([]), np.zeros((100,100),int))
self.assertEqual(np.product(scores.shape), 0)
def test_01_01_one_object(self):
"""Test Zernike on one single circle"""
zi = self.make_zernike_indexes()
y,x = np.mgrid[-50:51,-50:51].astype(float)/50
labels = x**2+y**2 <=1
x[labels==0]=0
y[labels==0]=0
zf = z.construct_zernike_polynomials(x, y, zi)
scores = z.score_zernike(zf,[50], labels)
# Zernike 0,0 should be 1 and others should be zero within
# an approximation of 1/radius
epsilon = 1.0/50.0
self.assertTrue(abs(scores[0,0]-1) < epsilon )
self.assertTrue(np.all(scores[0,1:] < epsilon))
def test_01_01_one_object(self):
"""Test Zernike on one single circle"""
zi = self.make_zernike_indexes()
y, x = np.mgrid[-50:51, -50:51].astype(float) / 50
labels = x**2 + y**2 <= 1
x[labels == 0] = 0
y[labels == 0] = 0
zf = z.construct_zernike_polynomials(x, y, zi)
scores = z.score_zernike(zf, [50], labels)
# Zernike 0,0 should be 1 and others should be zero within
# an approximation of 1/radius
epsilon = 1.0 / 50.0
self.assertTrue(abs(scores[0, 0] - 1) < epsilon)
self.assertTrue(np.all(scores[0, 1:] < epsilon))
def score_rotations(self, labels, n):
"""Score the result of n rotations of the label matrix then test for equality"""
self.assertEqual(labels.shape[0], labels.shape[1],
"Must use square matrix for test")
self.assertEqual(labels.shape[0] & 1, 1, "Must be odd width/height")
zi = self.make_zernike_indexes()
test_labels = np.zeros((labels.shape[0] * n, labels.shape[0]))
test_x = np.zeros((labels.shape[0] * n, labels.shape[0]))
test_y = np.zeros((labels.shape[0] * n, labels.shape[0]))
diameter = labels.shape[0]
radius = labels.shape[0] / 2
y, x = np.mgrid[-radius:radius + 1,
-radius:radius + 1].astype(float) / radius
anti_mask = x**2 + y**2 > 1
x[anti_mask] = 0
y[anti_mask] = 0
min_pixels = 100000
max_pixels = 0
for i in range(0, n):
angle = 360 * i / n # believe it or not, in degrees!
off_x = labels.shape[0] * i
off_y = 0
rotated_labels = scind.rotate(labels,
angle,
order=0,
reshape=False)
pixels = np.sum(rotated_labels)
min_pixels = min(min_pixels, pixels)
max_pixels = max(max_pixels, pixels)
x_mask = x.copy()
y_mask = y.copy()
x_mask[rotated_labels == 0] = 0
y_mask[rotated_labels == 0] = 0
test_labels[off_x:off_x + diameter,
off_y:off_y + diameter] = rotated_labels * (i + 1)
test_x[off_x:off_x + diameter, off_y:off_y + diameter] = x_mask
test_y[off_x:off_x + diameter, off_y:off_y + diameter] = y_mask
zf = z.construct_zernike_polynomials(test_x, test_y, zi)
scores = z.score_zernike(zf, np.ones((n, )) * radius, test_labels)
score_0 = scores[0]
epsilon = 2.0 * (max(1, max_pixels - min_pixels)) / max_pixels
for score in scores[1:, :]:
self.assertTrue(np.all(np.abs(score - score_0) < epsilon))
def score_rotations(self,labels,n):
"""Score the result of n rotations of the label matrix then test for equality"""
self.assertEqual(labels.shape[0],labels.shape[1],"Must use square matrix for test")
self.assertEqual(labels.shape[0] & 1,1,"Must be odd width/height")
zi = self.make_zernike_indexes()
test_labels = np.zeros((labels.shape[0]*n,labels.shape[0]))
test_x = np.zeros((labels.shape[0]*n,labels.shape[0]))
test_y = np.zeros((labels.shape[0]*n,labels.shape[0]))
diameter = labels.shape[0]
radius = labels.shape[0]/2
y,x=np.mgrid[-radius:radius+1,-radius:radius+1].astype(float)/radius
anti_mask = x**2+y**2 > 1
x[anti_mask] = 0
y[anti_mask] = 0
min_pixels = 100000
max_pixels = 0
for i in range(0,n):
angle = 360*i / n # believe it or not, in degrees!
off_x = labels.shape[0]*i
off_y = 0
rotated_labels = scind.rotate(labels,angle,order=0,reshape=False)
pixels = np.sum(rotated_labels)
min_pixels = min(min_pixels,pixels)
max_pixels = max(max_pixels,pixels)
x_mask = x.copy()
y_mask = y.copy()
x_mask[rotated_labels==0]=0
y_mask[rotated_labels==0]=0
test_labels[off_x:off_x+diameter,
off_y:off_y+diameter] = rotated_labels * (i+1)
test_x[off_x:off_x+diameter,
off_y:off_y+diameter] = x_mask
test_y[off_x:off_x+diameter,
off_y:off_y+diameter] = y_mask
zf = z.construct_zernike_polynomials(test_x,test_y,zi)
scores = z.score_zernike(zf,np.ones((n,))*radius,test_labels)
score_0=scores[0]
epsilon = 2.0*(max(1,max_pixels-min_pixels))/max_pixels
for score in scores[1:,:]:
self.assertTrue(np.all(np.abs(score-score_0)<epsilon))
def get_image_from_features(radius, feature_dictionary):
'''Reconstruct the intensity image from the zernike features
radius - the radius of the minimum enclosing circle
feature_dictionary - keys are (n, m) tuples and values are the
magnitudes.
returns a greyscale image based on the feature dictionary.
'''
i, j = np.mgrid[-radius:(radius+1), -radius:(radius+1)].astype(float) / radius
mask = (i*i + j*j) <= 1
zernike_indexes = np.array(feature_dictionary.keys())
zernike_features = np.array(feature_dictionary.values())
z = construct_zernike_polynomials(
j, i, np.abs(zernike_indexes), mask=mask)
zn = (2*zernike_indexes[:, 0] + 2) / ((zernike_indexes[:, 1] == 0) + 1) / np.pi
z = z * zn[np.newaxis, np.newaxis, :]
z = z.real * (zernike_indexes[:, 1] >= 0)[np.newaxis, np.newaxis, :] +\
z.imag * (zernike_indexes[:, 1] <= 0)[np.newaxis, np.newaxis, :]
return np.sum(z * zernike_features[np.newaxis, np.newaxis, :], 2)
def score_scales(self,labels,n):
"""Score the result of n 3x scalings of the label matrix then test for equality"""
self.assertEqual(labels.shape[0],labels.shape[1],"Must use square matrix for test")
self.assertEqual(labels.shape[0] & 1,1,"Must be odd width/height")
width = labels.shape[0] * 3**n
height = width * (n+1)
zi = self.make_zernike_indexes()
test_labels = np.zeros((height,width))
test_x = np.zeros((height,width))
test_y = np.zeros((height,width))
radii = []
for i in range(n+1):
scaled_labels = scind.zoom(labels,3**i,order=0)
diameter = scaled_labels.shape[0]
radius = scaled_labels.shape[0]/2
radii.append(radius)
y,x=np.mgrid[-radius:radius+1,-radius:radius+1].astype(float)/radius
anti_mask = x**2+y**2 > 1
x[anti_mask] = 0
y[anti_mask] = 0
off_x = width*i
off_y = 0
x[scaled_labels==0]=0
y[scaled_labels==0]=0
test_labels[off_x:off_x+diameter,
off_y:off_y+diameter] = scaled_labels * (i+1)
test_x[off_x:off_x+diameter,
off_y:off_y+diameter] = x
test_y[off_x:off_x+diameter,
off_y:off_y+diameter] = y
zf = z.construct_zernike_polynomials(test_x,test_y,zi)
scores = z.score_zernike(zf,np.array(radii),test_labels)
score_0=scores[0]
epsilon = .02
for score in scores[1:,:]:
self.assertTrue(np.all(np.abs(score-score_0)<epsilon))
def score_scales(self, labels, n):
"""Score the result of n 3x scalings of the label matrix then test for equality"""
self.assertEqual(labels.shape[0], labels.shape[1],
"Must use square matrix for test")
self.assertEqual(labels.shape[0] & 1, 1, "Must be odd width/height")
width = labels.shape[0] * 3**n
height = width * (n + 1)
zi = self.make_zernike_indexes()
test_labels = np.zeros((height, width))
test_x = np.zeros((height, width))
test_y = np.zeros((height, width))
radii = []
for i in range(n + 1):
scaled_labels = scind.zoom(labels, 3**i, order=0)
diameter = scaled_labels.shape[0]
radius = scaled_labels.shape[0] / 2
radii.append(radius)
y, x = np.mgrid[-radius:radius + 1,
-radius:radius + 1].astype(float) / radius
anti_mask = x**2 + y**2 > 1
x[anti_mask] = 0
y[anti_mask] = 0
off_x = width * i
off_y = 0
x[scaled_labels == 0] = 0
y[scaled_labels == 0] = 0
test_labels[off_x:off_x + diameter,
off_y:off_y + diameter] = scaled_labels * (i + 1)
test_x[off_x:off_x + diameter, off_y:off_y + diameter] = x
test_y[off_x:off_x + diameter, off_y:off_y + diameter] = y
zf = z.construct_zernike_polynomials(test_x, test_y, zi)
scores = z.score_zernike(zf, np.array(radii), test_labels)
score_0 = scores[0]
epsilon = .02
for score in scores[1:, :]:
self.assertTrue(np.all(np.abs(score - score_0) < epsilon))
def measure_zernike(self, pixels, labels, indexes, centers, radius, n, m):
# I'll put some documentation in here to explain what it does.
# If someone ever wants to call it, their editor might display
# the documentation.
'''Measure the intensity of the image with Zernike (N, M)
pixels - the intensity image to be measured
labels - the labels matrix that labels each object with an integer
indexes - the label #s in the image
centers - the centers of the minimum enclosing circle for each object
radius - the radius of the minimum enclosing circle for each object
n, m - the Zernike coefficients.
See http://en.wikipedia.org/wiki/Zernike_polynomials for an
explanation of the Zernike polynomials
'''
#
# The strategy here is to operate on the whole array instead
# of operating on one object at a time. The most important thing
# is to avoid having to run the Python interpreter once per pixel
# in the image and the second most important is to avoid running
# it per object in case there are hundreds of objects.
#
# We play lots of indexing tricks here to operate on the whole image.
# I'll try to explain some - hopefully, you can reuse.
center_x = centers[:, 1]
center_y = centers[:, 0]
#
# Make up fake values for 0 (the background). This lets us do our
# indexing tricks. Really, we're going to ignore the background,
# but we want to do the indexing without ignoring the background
# because that's easier.
#
center_x = np.hstack([[0], center_x])
center_y = np.hstack([[0], center_y])
radius = np.hstack([[1], radius])
#
# Now get one array that's the y coordinate of each pixel and one
# that's the x coordinate. This might look stupid and wasteful,
# but these "arrays" are never actually realized and made into
# real memory.
#
y, x = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
#
# Get the x and y coordinates relative to the object centers.
# This uses Numpy broadcasting. For each pixel, we use the
# value in the labels matrix as an index into the appropriate
# one-dimensional array. So we get the value for that object.
#
y -= center_y[labels]
x -= center_x[labels]
#
# Zernikes take x and y values from zero to one. We scale the
# integer coordinate values by dividing them by the radius of
# the circle. Again, we use the indexing trick to look up the
# values for each object.
#
y = y.astype(float) / radius[labels]
x = x.astype(float) / radius[labels]
#
#################################
#
# ZERNIKE POLYNOMIALS
#
# Now we can get Zernike polynomials per-pixel where each pixel
# value is calculated according to its object's MEC.
#
# We use a mask of all of the non-zero labels so the calculation
# runs a little faster.
#
zernike_polynomial = construct_zernike_polynomials(
x, y, np.array([ [ n, m ]]), labels > 0)
#
# For historical reasons, CellProfiler didn't multiply by the per/zernike
# normalizing factor: 2*n + 2 / E / pi where E is 2 if m is zero and 1
# if m is one. We do it here to aid with the reconstruction
#
zernike_polynomial *= (2*n + 2) / (2 if m == 0 else 1) / np.pi
#
# Multiply the Zernike polynomial by the image to dissect
# the image by the Zernike basis set.
#
output_pixels = pixels * zernike_polynomial[:,:,0]
#
# Finally, we use Scipy to sum the intensities. Scipy has different
# versions with different quirks. The "fix" function takes all
# of that into account.
#
# The sum function calculates the sum of the pixel values for
# each pixel in an object, using the labels matrix to name
# the pixels in an object
#
zr = fix(scind.sum(output_pixels.real, labels, indexes))
zi = fix(scind.sum(output_pixels.imag, labels, indexes))
#
# And we're done! Did you like it? Did you get it?
#
return zr, zi
def measure_zernike(self, pixels, labels, n, m):
# I'll put some documentation in here to explain what it does.
# If someone ever wants to call it, their editor might display
# the documentation.
'''Measure the intensity of the image with Zernike (N, M)
pixels - the intensity image to be measured
labels - the labels matrix that labels each object with an integer
n, m - the Zernike coefficients.
See http://en.wikipedia.org/wiki/Zernike_polynomials for an
explanation of the Zernike polynomials
'''
#
# The strategy here is to operate on the whole array instead
# of operating on one object at a time. The most important thing
# is to avoid having to run the Python interpreter once per pixel
# in the image and the second most important is to avoid running
# it per object in case there are hundreds of objects.
#
# We play lots of indexing tricks here to operate on the whole image.
# I'll try to explain some - hopefully, you can reuse.
#
# You could move the calculation of the minimum enclosing circle
# outside of this function. The function gets called more than
# 10 times, so the same calculation is performed 10 times.
# It would make the code a little more confusing, so I'm leaving
# it as-is.
###########################################
#
# The minimum enclosing circle (MEC) is the smallest circle that
# will fit around the object. We get the centers and radii of
# all of the objects at once. You'll see how that lets us
# compute the X and Y position of each pixel in a label all at
# one go.
#
# First, get an array that lists the whole range of indexes in
# the labels matrix.
#
indexes = np.arange(1, np.max(labels)+1,dtype=np.int32)
#
# Then ask for the minimum_enclosing_circle for each object named
# in those indexes. MEC returns the i and j coordinate of the center
# and the radius of the circle and that defines the circle entirely.
#
centers, radius = minimum_enclosing_circle(labels, indexes)
center_x = centers[:, 1]
center_y = centers[:, 0]
#
# Make up fake values for 0 (the background). This lets us do our
# indexing tricks. Really, we're going to ignore the background,
# but we want to do the indexing without ignoring the background
# because that's easier.
#
center_x = np.hstack([[0], center_x])
center_y = np.hstack([[0], center_y])
radius = np.hstack([[1], radius])
#
# Now get one array that's the y coordinate of each pixel and one
# that's the x coordinate. This might look stupid and wasteful,
# but these "arrays" are never actually realized and made into
# real memory.
#
y, x = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
#
# Get the x and y coordinates relative to the object centers.
# This uses Numpy broadcasting. For each pixel, we use the
# value in the labels matrix as an index into the appropriate
# one-dimensional array. So we get the value for that object.
#
y -= center_y[labels]
x -= center_x[labels]
#
# Zernikes take x and y values from zero to one. We scale the
# integer coordinate values by dividing them by the radius of
# the circle. Again, we use the indexing trick to look up the
# values for each object.
#
y = y.astype(float) / radius[labels]
x = x.astype(float) / radius[labels]
#
#################################
#
# ZERNIKE POLYNOMIALS
#
# Now we can get Zernike polynomials per-pixel where each pixel
# value is calculated according to its object's MEC.
#
# We use a mask of all of the non-zero labels so the calculation
# runs a little faster.
#
zernike_polynomial = construct_zernike_polynomials(
x, y, np.array([ [ n, m ]]))
#
# Multiply the Zernike polynomial by the image to dissect
# the image by the Zernike basis set.
#
output_pixels = pixels * zernike_polynomial[:,:,0]
#
# The zernike polynomial is a complex number. We get a power
# spectrum here to combine the real and imaginary parts
#
output_pixels = np.sqrt(output_pixels * output_pixels.conjugate())
#
# Finally, we use Scipy to sum the intensities. Scipy has different
# versions with different quirks. The "fix" function takes all
# of that into account.
#
# The sum function calculates the sum of the pixel values for
# each pixel in an object, using the labels matrix to name
# the pixels in an object
#
result = fix(scind.sum(output_pixels.real, labels, indexes))
#
# And we're done! Did you like it? Did you get it?
#
return result
def measure_zernike(self, pixels, labels, n, m):
# I'll put some documentation in here to explain what it does.
# If someone ever wants to call it, their editor might display
# the documentation.
'''Measure the intensity of the image with Zernike (N, M)
pixels - the intensity image to be measured
labels - the labels matrix that labels each object with an integer
n, m - the Zernike coefficients.
See http://en.wikipedia.org/wiki/Zernike_polynomials for an
explanation of the Zernike polynomials
'''
#
# The strategy here is to operate on the whole array instead
# of operating on one object at a time. The most important thing
# is to avoid having to run the Python interpreter once per pixel
# in the image and the second most important is to avoid running
# it per object in case there are hundreds of objects.
#
# We play lots of indexing tricks here to operate on the whole image.
# I'll try to explain some - hopefully, you can reuse.
#
# You could move the calculation of the minimum enclosing circle
# outside of this function. The function gets called more than
# 10 times, so the same calculation is performed 10 times.
# It would make the code a little more confusing, so I'm leaving
# it as-is.
###########################################
#
# The minimum enclosing circle (MEC) is the smallest circle that
# will fit around the object. We get the centers and radii of
# all of the objects at once. You'll see how that lets us
# compute the X and Y position of each pixel in a label all at
# one go.
#
# First, get an array that lists the whole range of indexes in
# the labels matrix.
#
indexes = np.arange(1, np.max(labels) + 1, dtype=np.int32)
#
# Then ask for the minimum_enclosing_circle for each object named
# in those indexes. MEC returns the i and j coordinate of the center
# and the radius of the circle and that defines the circle entirely.
#
centers, radius = minimum_enclosing_circle(labels, indexes)
center_x = centers[:, 1]
center_y = centers[:, 0]
#
# Make up fake values for 0 (the background). This lets us do our
# indexing tricks. Really, we're going to ignore the background,
# but we want to do the indexing without ignoring the background
# because that's easier.
#
center_x = np.hstack([[0], center_x])
center_y = np.hstack([[0], center_y])
radius = np.hstack([[1], radius])
#
# Now get one array that's the y coordinate of each pixel and one
# that's the x coordinate. This might look stupid and wasteful,
# but these "arrays" are never actually realized and made into
# real memory.
#
y, x = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
#
# Get the x and y coordinates relative to the object centers.
# This uses Numpy broadcasting. For each pixel, we use the
# value in the labels matrix as an index into the appropriate
# one-dimensional array. So we get the value for that object.
#
y -= center_y[labels]
x -= center_x[labels]
#
# Zernikes take x and y values from zero to one. We scale the
# integer coordinate values by dividing them by the radius of
# the circle. Again, we use the indexing trick to look up the
# values for each object.
#
y = y.astype(float) / radius[labels]
x = x.astype(float) / radius[labels]
#
#################################
#
# ZERNIKE POLYNOMIALS
#
# Now we can get Zernike polynomials per-pixel where each pixel
# value is calculated according to its object's MEC.
#
# We use a mask of all of the non-zero labels so the calculation
# runs a little faster.
#
zernike_polynomial = construct_zernike_polynomials(
x, y, np.array([[n, m]]))
#
# Multiply the Zernike polynomial by the image to dissect
# the image by the Zernike basis set.
#
output_pixels = pixels * zernike_polynomial[:, :, 0]
#
# The zernike polynomial is a complex number. We get a power
# spectrum here to combine the real and imaginary parts
#
output_pixels = np.sqrt(output_pixels * output_pixels.conjugate())
#
# Finally, we use Scipy to sum the intensities. Scipy has different
# versions with different quirks. The "fix" function takes all
# of that into account.
#
# The sum function calculates the sum of the pixel values for
# each pixel in an object, using the labels matrix to name
# the pixels in an object
#
result = fix(scind.sum(output_pixels.real, labels, indexes))
#
# And we're done! Did you like it? Did you get it?
#
return result
def measure_zernike(self, pixels, labels, indexes, centers, radius, n, m):
# I'll put some documentation in here to explain what it does.
# If someone ever wants to call it, their editor might display
# the documentation.
'''Measure the intensity of the image with Zernike (N, M)
pixels - the intensity image to be measured
labels - the labels matrix that labels each object with an integer
indexes - the label #s in the image
centers - the centers of the minimum enclosing circle for each object
radius - the radius of the minimum enclosing circle for each object
n, m - the Zernike coefficients.
See http://en.wikipedia.org/wiki/Zernike_polynomials for an
explanation of the Zernike polynomials
'''
#
# The strategy here is to operate on the whole array instead
# of operating on one object at a time. The most important thing
# is to avoid having to run the Python interpreter once per pixel
# in the image and the second most important is to avoid running
# it per object in case there are hundreds of objects.
#
# We play lots of indexing tricks here to operate on the whole image.
# I'll try to explain some - hopefully, you can reuse.
center_x = centers[:, 1]
center_y = centers[:, 0]
#
# Make up fake values for 0 (the background). This lets us do our
# indexing tricks. Really, we're going to ignore the background,
# but we want to do the indexing without ignoring the background
# because that's easier.
#
center_x = np.hstack([[0], center_x])
center_y = np.hstack([[0], center_y])
radius = np.hstack([[1], radius])
#
# Now get one array that's the y coordinate of each pixel and one
# that's the x coordinate. This might look stupid and wasteful,
# but these "arrays" are never actually realized and made into
# real memory.
#
y, x = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
#
# Get the x and y coordinates relative to the object centers.
# This uses Numpy broadcasting. For each pixel, we use the
# value in the labels matrix as an index into the appropriate
# one-dimensional array. So we get the value for that object.
#
y -= center_y[labels]
x -= center_x[labels]
#
# Zernikes take x and y values from zero to one. We scale the
# integer coordinate values by dividing them by the radius of
# the circle. Again, we use the indexing trick to look up the
# values for each object.
#
y = y.astype(float) / radius[labels]
x = x.astype(float) / radius[labels]
#
#################################
#
# ZERNIKE POLYNOMIALS
#
# Now we can get Zernike polynomials per-pixel where each pixel
# value is calculated according to its object's MEC.
#
# We use a mask of all of the non-zero labels so the calculation
# runs a little faster.
#
zernike_polynomial = construct_zernike_polynomials(
x, y, np.array([ [ n, m ]]), labels > 0)
#
# For historical reasons, CellProfiler didn't multiply by the per/zernike
# normalizing factor: 2*n + 2 / E / pi where E is 2 if m is zero and 1
# if m is one. We do it here to aid with the reconstruction
#
zernike_polynomial *= (2*n + 2) / (2 if m == 0 else 1) / np.pi
#
# Multiply the Zernike polynomial by the image to dissect
# the image by the Zernike basis set.
#
output_pixels = pixels * zernike_polynomial[:,:,0]
#
# Finally, we use Scipy to sum the intensities. Scipy has different
# versions with different quirks. The "fix" function takes all
# of that into account.
#
# The sum function calculates the sum of the pixel values for
# each pixel in an object, using the labels matrix to name
# the pixels in an object
#
zr = fix(scind.sum(output_pixels.real, labels, indexes))
zi = fix(scind.sum(output_pixels.imag, labels, indexes))
#
# And we're done! Did you like it? Did you get it?
#
return zr, zi