def zernike(zernike_indexes, labels, indexes):
"""Compute the Zernike features for the labels with the label #s in indexes
returns the score per labels and an array of one image per zernike feature
"""
#
# "Reverse_indexes" is -1 if a label # is not to be processed. Otherwise
# reverse_index[label] gives you the index into indexes of the label
# and other similarly shaped vectors (like the results)
#
if indexes == None:
indexes = np.arange(1, nindexes + 1, dtype=np.int32)
else:
indexes = np.array(indexes, dtype=np.int32)
nindexes = len(indexes)
reverse_indexes = -np.ones((np.max(indexes) + 1, ), int)
reverse_indexes[indexes] = np.arange(indexes.shape[0], dtype=int)
mask = reverse_indexes[labels] != -1
centers, radii = minimum_enclosing_circle(labels, indexes)
y, x = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
xm = x[mask].astype(float)
ym = y[mask].astype(float)
lm = labels[mask]
#
# The Zernikes are inscribed in circles with points labeled by
# their fractional distance (-1 <= x,y <= 1) from the center.
# So we transform x and y by subtracting the center and
# dividing by the radius
#
ym = (ym - centers[reverse_indexes[lm], 0]) / radii[reverse_indexes[lm]]
xm = (xm - centers[reverse_indexes[lm], 1]) / radii[reverse_indexes[lm]]
#
# Blow up ym and xm into new x and y vectors
#
x = np.zeros(x.shape)
x[mask] = xm
y = np.zeros(y.shape)
y[mask] = ym
#
# Pass the resulting x and y through the rest of Zernikeland
#
score = np.zeros((nindexes, len(zernike_indexes)))
for i in range(len(zernike_indexes)):
zf = construct_zernike_polynomials(x, y, zernike_indexes[i:i + 1],
mask)
one_score = score_zernike(zf, radii, labels, indexes)
score[:, i] = one_score[:, 0]
return score
def zernike(zernike_indexes, labels, indexes):
"""Compute the Zernike features for the labels with the label #s in indexes
returns the score per labels and an array of one image per zernike feature
"""
#
# "Reverse_indexes" is -1 if a label # is not to be processed. Otherwise
# reverse_index[label] gives you the index into indexes of the label
# and other similarly shaped vectors (like the results)
#
if indexes == None:
indexes = np.arange(1, nindexes + 1, dtype=np.int32)
else:
indexes = np.array(indexes, dtype=np.int32)
nindexes = len(indexes)
reverse_indexes = -np.ones((np.max(indexes) + 1,), int)
reverse_indexes[indexes] = np.arange(indexes.shape[0], dtype=int)
mask = reverse_indexes[labels] != -1
centers, radii = minimum_enclosing_circle(labels, indexes)
y, x = np.mgrid[0 : labels.shape[0], 0 : labels.shape[1]]
xm = x[mask].astype(float)
ym = y[mask].astype(float)
lm = labels[mask]
#
# The Zernikes are inscribed in circles with points labeled by
# their fractional distance (-1 <= x,y <= 1) from the center.
# So we transform x and y by subtracting the center and
# dividing by the radius
#
ym = (ym - centers[reverse_indexes[lm], 0]) / radii[reverse_indexes[lm]]
xm = (xm - centers[reverse_indexes[lm], 1]) / radii[reverse_indexes[lm]]
#
# Blow up ym and xm into new x and y vectors
#
x = np.zeros(x.shape)
x[mask] = xm
y = np.zeros(y.shape)
y[mask] = ym
#
# Pass the resulting x and y through the rest of Zernikeland
#
score = np.zeros((nindexes, len(zernike_indexes)))
for i in range(len(zernike_indexes)):
zf = construct_zernike_polynomials(x, y, zernike_indexes[i : i + 1], mask)
one_score = score_zernike(zf, radii, labels, indexes)
score[:, i] = one_score[:, 0]
return score