def eccentricity(im):
''' im should be a binary image with a single region '''
c = mahotas.center_of_mass(im)
m11 = mahotas.moments(im, 1, 1, c)
m02 = mahotas.moments(im, 2, 0, c)
m20 = mahotas.moments(im, 0, 2, c)
l1 = (m20 + m02) / 2 + np.sqrt(4*m11**2 + (m20-m02)**2) / 2
l2 = (m20 + m02) / 2 - np.sqrt(4*m11**2 + (m20-m02)**2) / 2
e = np.sqrt(1 - l2 / l1)
return e
def _calc_orientation(img):
com = mh.center_of_mass(img)
mu00 = mh.moments(img, 0, 0, com)
mu11 = mh.moments(img, 1, 1, com)
mu20 = mh.moments(img, 2, 0, com)
mu02 = mh.moments(img, 0, 2, com)
mup_20 = mu20 / mu00
mup_02 = mu02 / mu00
mup_11 = mu11 / mu00
theta_rad = 0.5 * math.atan(2 * mup_11 /
(mup_20 - mup_02)) # todo math -> numpy
theta = theta_rad * (180 / math.pi)
if (mup_20 - mup_02) > 0:
theta += 90
return theta
def test_expression():
import mahotas as mh
import numpy as np
im = (np.zeros((12, 16)) + np.arange(16))
im -= im.mean()
c0, c1 = 4, 7
p0, p1 = 2, 3
for p0, p1 in [(0, 1), (1, 0), (1, 2), (3, 2), (2, 0)]:
sum_r = sum((im[i, j] * (i - c0)**p0 * (j - c1)**p1)
for i in range(im.shape[0]) for j in range(im.shape[1]))
mm_r = mh.moments(im, p0, p1, cm=(c0, c1))
assert np.abs(mm_r - sum_r) < 1.
def _process_image(self, im):
thresh = skimage.filters.threshold_otsu(im)
labeled, seeds = mahotas.label(im > thresh)
labeled = mahotas.labeled.remove_bordering(labeled)
features = []
for seed in range(1, seeds):
precipitates = (labeled == seed).astype('int')
m00 = mahotas.moments(precipitates, 0, 0)
m01 = mahotas.moments(precipitates, 0, 1)
m10 = mahotas.moments(precipitates, 1, 0)
m11 = mahotas.moments(precipitates, 1, 1)
m02 = mahotas.moments(precipitates, 0, 2)
m20 = mahotas.moments(precipitates, 2, 0)
xm = m10 / m00
ym = m01 / m00
u00 = 1.0
u11 = m11 / m00 - xm * ym
u20 = m20 / m00 - xm ** 2
u02 = m02 / m00 - ym ** 2
w1 = u00**2 / (2.0 * numpy.pi * (u20 + u02))
w2 = u00**4 / (16.0 * numpy.pi * numpy.pi * (u20 * u02 - u11**2))
if numpy.isnan(w1) or numpy.isnan(w2) or numpy.isinf(w1) or numpy.isinf(w2):
continue
features.append(((xm, ym), [numpy.sqrt(m00), w1, w2, u20, u02]))#
return features
def test_expression():
import mahotas as mh
import numpy as np
im = (np.zeros((12,16)) + np.arange(16))
im -= im.mean()
c0,c1 = 4,7
p0,p1 = 2, 3
for p0,p1 in [(0,1),
(1,0),
(1,2),
(3,2),
(2,0)]:
sum_r = sum((im[i,j] * (i - c0)**p0 * (j - c1)**p1) for i in range(im.shape[0]) for j in range(im.shape[1]))
mm_r = mh.moments(im , p0, p1, cm=(c0,c1))
assert np.abs(mm_r - sum_r) < 1.
def moment_feats(im):
thresh = skimage.filters.threshold_otsu(im)
#plt.imshow(im > thresh)
#plt.show()
labeled, seeds = mahotas.label(im > thresh)
labeled = mahotas.labeled.remove_bordering(labeled)
features = []
for seed in range(1, seeds):
precipitates = (labeled == seed).astype('int')
m00 = mahotas.moments(precipitates, 0, 0)
m01 = mahotas.moments(precipitates, 0, 1)
m10 = mahotas.moments(precipitates, 1, 0)
m11 = mahotas.moments(precipitates, 1, 1)
m02 = mahotas.moments(precipitates, 0, 2)
m20 = mahotas.moments(precipitates, 2, 0)
#m12 = mahotas.moments(precipitates, 1, 2)
#m21 = mahotas.moments(precipitates, 2, 1)
#m22 = mahotas.moments(precipitates, 2, 2)
#plt.imshow(precipitates)
#plt.show()
xm = m10 / m00
ym = m01 / m00
#if m00 < 1e-5:
# continue
#u00 = m00
u00 = 1.0
u11 = m11 / m00 - xm * ym
u20 = m20 / m00 - xm ** 2
u02 = m02 / m00 - ym ** 2
w1 = u00**2 / (2.0 * numpy.pi * (u20 + u02))
w2 = u00**4 / (16.0 * numpy.pi * numpy.pi * (u20 * u02 - u11**2))
if w1 < 0.0 or w1 > 1.0 or w2 < 0.0 or w2 > 1.0 or numpy.isnan(w1) or numpy.isnan(w2):
continue
print m00, m01, m10, m11, m02, m20
print xm
print ym
print u00, u11, u20, u02
print w1, w2
1/0
features.append([numpy.sqrt(m00)])#w1, w2, , u20, u02
features = numpy.array(features)
return features
def get_mahotas_features(image, bin_image):
"""
"""
# pdb.set_trace()
zer = zernike.zernike_moments(image, len(image)/2) #adding 25
mm = []
for i in range(7):
for j in range(7):
mm.append(np.log10(moments(image, i,j))) #adding 49
rou = features.roundness(bin_image) #adding 1
el1 = features.ellipse_axes(bin_image)[0]
el2 = features.ellipse_axes(bin_image)[0]#adding 2
exe = features.eccentricity(bin_image) #adding 1
pft = features.pftas(image) #adding 54
hara = mahotas.features.haralick(image).reshape(-1) #adding 52
# return np.concatenate((zer, np.array([rou]), np.array(ellip), hara.reshape(-1), lbps.reshape(-1)))
#adding in total 184 features
return np.concatenate((zer, np.array(mm), np.array([rou, el1, el2, exe]), pft, hara))
def test_moments01():
im = np.zeros((16,16))
im += np.arange(16)
im -= im.mean()
assert np.abs(mh.moments(im , 1, 0)) < 0.1
def test_moments01():
im = np.zeros((16, 16))
im += np.arange(16)
im -= im.mean()
assert np.abs(mh.moments(im, 1, 0)) < 0.1
