class DistanceNeighborEff(UncertainImageDistance):
""" A more efficient version. """
@contract(size='seq[2](int)', neighborarea='seq[2](int)')
def __init__(self, size=[160, 120], neighborarea=[8, 8]):
self.fs = FlatStructure(shape=tuple(size),
neighborarea=tuple(neighborarea))
self.size = size
def distance(self, y0, y1):
"""
Compare Y1(s) with best matching pixel in Y2(s_n)
where s_s \in (the neighbourhood of s)
"""
Y1 = y0.resize(self.size).get_values()
Y2 = y1.resize(self.size).get_values()
Y2_unrolled = self.fs.image2unrolledneighbors(Y2)
Y1_repeated = self.fs.image2repeated(Y1)
assert_allclose(Y2_unrolled.shape, Y1_repeated.shape)
diff1 = np.abs(Y2_unrolled - Y1_repeated)
myres = np.mean(np.min(diff1, axis=1))
if False:
# old method, equivalent
neighbor_indices_flat = self.fs.neighbor_indices_flat
nchannels = Y1.shape[2]
nsensel = Y1[:, :, 0].size
best = np.zeros((nsensel, Y1.shape[2]))
for c in range(nchannels):
y1_flat = Y1[:, :, c].astype(np.int16).flat
y2_flat = Y2[:, :, c].astype(np.int16).flat
for k in range(nsensel):
a = y1_flat[k].astype(np.float)
b = y2_flat[neighbor_indices_flat[k]]
diff = np.abs(a - b)
best[k, c] = np.min(diff)
res = np.mean(best) # /self.maxval_distance_neighborhood_bestmatch
assert_allclose(res, myres)
return myres
class DistanceNeighborEff(UncertainImageDistance):
""" A more efficient version. """
@contract(size='seq[2](int)', neighborarea='seq[2](int)')
def __init__(self, size=[160, 120], neighborarea=[8, 8]):
self.fs = FlatStructure(shape=tuple(size),
neighborarea=tuple(neighborarea))
self.size = size
def distance(self, y0, y1):
"""
Compare Y1(s) with best matching pixel in Y2(s_n)
where s_s \in (the neighbourhood of s)
"""
Y1 = y0.resize(self.size).get_values()
Y2 = y1.resize(self.size).get_values()
Y2_unrolled = self.fs.image2unrolledneighbors(Y2)
Y1_repeated = self.fs.image2repeated(Y1)
assert_allclose(Y2_unrolled.shape, Y1_repeated.shape)
diff1 = np.abs(Y2_unrolled - Y1_repeated)
myres = np.mean(np.min(diff1, axis=1))
if False:
# old method, equivalent
neighbor_indices_flat = self.fs.neighbor_indices_flat
nchannels = Y1.shape[2]
nsensel = Y1[:, :, 0].size
best = np.zeros((nsensel, Y1.shape[2]))
for c in range(nchannels):
y1_flat = Y1[:, :, c].astype(np.int16).flat
y2_flat = Y2[:, :, c].astype(np.int16).flat
for k in range(nsensel):
a = y1_flat[k].astype(np.float)
b = y2_flat[neighbor_indices_flat[k]]
diff = np.abs(a - b)
best[k, c] = np.min(diff)
res = np.mean(best) # /self.maxval_distance_neighborhood_bestmatch
assert_allclose(res, myres)
return myres
def _get_flat_structure(self, shape):
if self.fs is None:
def make_size(x):
x = int(np.ceil(x))
if x % 2 == 0:
x += 1
return x
ax = make_size(shape[0] * self.ratios[0])
ay = make_size(shape[1] * self.ratios[1])
area = (ax, ay)
if np.min(area) == 1:
logger.warning(
'Area is too small. Ratios: %s shape: %s area: %s' %
(self.ratios, shape, area))
logger.info('ratios: %s shape: %s area: %s' %
(self.ratios, shape, area))
self.fs = FlatStructure(shape=tuple(shape),
neighborarea=tuple(area))
return self.fs
def __init__(self, size=[160, 120], neighborarea=[8, 8]):
self.fs = FlatStructure(shape=tuple(size),
neighborarea=tuple(neighborarea))
self.size = size
def test_distance_neighbor_dist1():
gs = FlatStructure((100, 100), (1, 1))
D = gs.get_distances()
assert_allclose(D, 0)
def test_distance_neighbor_dist3():
gs = FlatStructure((100, 100), (3, 3))
D = gs.get_distances()
assert_allclose(np.max(D), np.sqrt(2))
def test_distance_neighbor_dist1():
gs = FlatStructure((100, 100), (1, 1))
D = gs.get_distances()
assert_allclose(D, 0)
def test_distance_neighbor_dist3():
gs = FlatStructure((100, 100), (3, 3))
D = gs.get_distances()
assert_allclose(np.max(D), np.sqrt(2))
def _create_flat_structure(self):
fs = FlatStructure(shape=self.shape,
neighborarea=self.area,
centers=self.guess)
return fs
def __init__(self, size=[160, 120], neighborarea=[8, 8]):
self.fs = FlatStructure(shape=tuple(size),
neighborarea=tuple(neighborarea))
self.size = size