def test_cooccurence():
np.random.seed(222)
f = np.random.rand(32, 32)
f = (f * 255).astype(np.int32)
assert np.all(
texture.cooccurence(f, 0, distance=1, symmetric=False) == brute_force(
f, 0, 1))
assert np.all(
texture.cooccurence(f, 1, distance=1, symmetric=False) == brute_force(
f, 1, 1))
assert np.all(
texture.cooccurence(f, 2, distance=1, symmetric=False) == brute_force(
f, 1, 0))
assert np.all(
texture.cooccurence(f, 3, distance=1, symmetric=False) == brute_force(
f, 1, -1))
assert np.all(
texture.cooccurence(f, 0, distance=1, symmetric=1) == brute_force_sym(
f, 0, 1))
assert np.all(
texture.cooccurence(f, 1, distance=1, symmetric=1) == brute_force_sym(
f, 1, 1))
assert np.all(
texture.cooccurence(f, 2, distance=1, symmetric=1) == brute_force_sym(
f, 1, 0))
assert np.all(
texture.cooccurence(f, 3, distance=1, symmetric=1) == brute_force_sym(
f, 1, -1))
def computeTexture(self, p, imag, center, distances=1):
'''
Computes the texture around vector p
'''
from mahotas.features import texture
origins = self._get_points_centering(center, p[1, :])
puntos = numpy.array([p + cent for cent in origins])
select = (puntos < numpy.array(imag.shape) - 1).all(axis=2).all(axis=0)
p_real = puntos[:, select, :]
colors = self._interp_gray_level(p_real, imag)
ndarray = self._posterizeImage(colors)
# Prevent errors with iterative list:
if (type(distances) is not list) and (type(distances)
is not numpy.ndarray):
distances = [distances]
glcm = []
for dis in distances:
glcm.append(
texture.cooccurence(ndarray.astype(int) - 1,
0,
distance=dis,
symmetric=False))
features = texture.haralick_features(glcm)
labels = texture.haralick_labels
return features, labels
def test_cooccurence3():
np.random.seed(222)
f = np.random.rand(32, 32, 8)
f = (f * 255).astype(np.int32)
for di, (d0,d1,d2) in enumerate(texture._3d_deltas):
assert np.all(texture.cooccurence(f, di, distance=1, symmetric=False) == brute_force3(f, d0, d1, d2))
def test_cooccurence3():
np.random.seed(222)
f = np.random.rand(32, 32, 8)
f = (f * 255).astype(np.int32)
for di, (d0,d1,d2) in enumerate(texture._3d_deltas):
assert np.all(texture.cooccurence(f, di, symmetric=False) == brute_force3(f, d0, d1, d2))
def computeTexture(self, p, imag, center, distances=1):
'''
Computes the texture around vector p
'''
from mahotas.features import texture
origins = self._get_points_centering(center, p[1,:])
puntos = numpy.array([p+cent for cent in origins])
select = (puntos<numpy.array(imag.shape)-1).all(axis=2).all(axis=0)
p_real = puntos[:,select,:]
colors = self._interp_gray_level(p_real, imag)
ndarray = self._posterizeImage(colors)
# Prevent errors with iterative list:
if (type(distances) is not list) and (type(distances) is not numpy.ndarray):
distances = [distances]
glcm=[]
for dis in distances:
glcm.append(texture.cooccurence(ndarray.astype(int)-1, 0, distance=dis, symmetric=False))
features = texture.haralick_features(glcm)
labels = texture.haralick_labels
return features, labels
def test_cooccurence_errors():
f2 = np.zeros((6, 6), np.uint8)
f3 = np.zeros((6, 6, 6), np.uint8)
f4 = np.zeros((6, 6, 6, 6), np.uint8)
with pytest.raises(ValueError):
texture.cooccurence(f2, -2, distance=1)
with pytest.raises(ValueError):
texture.cooccurence(f3, -2, distance=1)
with pytest.raises(ValueError):
texture.cooccurence(f2, 10, distance=1)
with pytest.raises(ValueError):
texture.cooccurence(f3, 17, distance=1)
with pytest.raises(ValueError):
texture.cooccurence(f4, 1, distance=1)
def test_cooccurence():
np.random.seed(222)
f = np.random.rand(32, 32)
f = (f * 255).astype(np.int32)
assert np.all(texture.cooccurence(f, 0, distance=1, symmetric=False) == brute_force(f, 0, 1))
assert np.all(texture.cooccurence(f, 1, distance=1, symmetric=False) == brute_force(f, 1, 1))
assert np.all(texture.cooccurence(f, 2, distance=1, symmetric=False) == brute_force(f, 1, 0))
assert np.all(texture.cooccurence(f, 3, distance=1, symmetric=False) == brute_force(f, 1, -1))
assert np.all(texture.cooccurence(f, 0, distance=1, symmetric=1) == brute_force_sym(f, 0, 1))
assert np.all(texture.cooccurence(f, 1, distance=1, symmetric=1) == brute_force_sym(f, 1, 1))
assert np.all(texture.cooccurence(f, 2, distance=1, symmetric=1) == brute_force_sym(f, 1, 0))
assert np.all(texture.cooccurence(f, 3, distance=1, symmetric=1) == brute_force_sym(f, 1, -1))
def computeTexture(self, p, imag, origin, distances=1):
'''
Computes the texture around vector p
:param p: The mapping or rojecting vector :math:`\mathbf{v}_{{\\theta},{\\varphi}}`
:param imag: Three-dimensional intensity array corresponding to a 3D registered brain image.
:type imag: 3D numpy array
:param origin: Specifies the position of the origin of mapping vectors. If not specified, defaults to the geometrical center of the image.
:type origin: 3D coordinates in numpy array
:param distances: It helds the distances at which the GLCM is going to be computed.
:type distances: integer or list of integers
'''
from mahotas.features import texture
# set the originso of each vector (the central one and surrounding 8)
origins = self._get_points_centering(origin, p[1, :])
puntos = numpy.array([p + cent for cent in origins])
select = (puntos < numpy.array(imag.shape) - 1).all(axis=2).all(axis=0)
p_real = puntos[:, select, :]
colors = self._interp_gray_level(p_real, imag)
ndarray = self._posterizeImage(colors)
# Prevent errors with iterative list, adding support for
# several distances in the computation of the GLCM
if (type(distances) is not list) and (type(distances)
is not numpy.ndarray):
distances = [distances]
glcm = []
for dis in distances:
glcm.append(
texture.cooccurence(ndarray.astype(int) - 1,
0,
distance=dis,
symmetric=False))
features = texture.haralick_features(glcm)
labels = texture.haralick_labels
return features, labels
def test_single_point():
A = np.zeros((5,5), np.uint8)
A[2,2]=12
assert not np.any(np.isnan(texture.cooccurence(A, 0, distance=1)))
def c_1():
texture.cooccurence(f3, -2, distance=1)
def c_1():
texture.cooccurence(f3, -2)
def c_2_10():
texture.cooccurence(f2, 10, distance=1)
def c_2_10():
texture.cooccurence(f2, 10, distance=1)
def c_4_1():
texture.cooccurence(f4, 1, distance=1)
def c_4_1():
texture.cooccurence(f4, 1, distance=1)
def c_4_1():
texture.cooccurence(f4, 1)
def c_1():
texture.cooccurence(f3, -2)
def c_2_10():
texture.cooccurence(f2, 10)
def c_4_1():
texture.cooccurence(f4, 1)
def c_3_17():
texture.cooccurence(f3, 17)
def c_2_10():
texture.cooccurence(f2, 10)
def test_float_cooccurence():
A = np.zeros((5,5), np.float32)
A[2,2]=12
texture.cooccurence(A, 0, distance=1)
def test_single_point():
A = np.zeros((5, 5), np.uint8)
A[2, 2] = 12
assert not np.any(np.isnan(texture.cooccurence(A, 0, distance=1)))
def c_1():
texture.cooccurence(f3, -2, distance=1)
def test_float_cooccurence():
A = np.zeros((5, 5), np.float32)
A[2, 2] = 12
with pytest.raises(TypeError):
texture.cooccurence(A, 0, distance=1)
def c_3_17():
texture.cooccurence(f3, 17, distance=1)
def c_3_17():
texture.cooccurence(f3, 17, distance=1)
def c_3_17():
texture.cooccurence(f3, 17)
def test_float_cooccurence():
A = np.zeros((5,5), np.float32)
A[2,2]=12
texture.cooccurence(A,0)