def test_distance_neighbor_dist2_repeated():
# A constant pattern
a = np.ones((100, 100, 3))
# Let's get some translated slices
w = 80
N = 10
A = []
for i in range(N):
A.append(a[i:i + w, 0:0 + w, ...])
print()
areas = [0.01, 0.03, 0.05, 0.07]
Ds = [DistanceNeighborDist((a, a)) for a in areas]
L2 = DistanceNorm(2)
for i in range(N):
y0 = UncertainImage(A[0])
y1 = UncertainImage(A[i])
vl2 = L2.distance(y0, y1)
ds = [x.distance(y0, y1) for x in Ds]
assert_allclose(ds, 0) # < this must be zero
s = ";".join("%1.5f" % x for x in ds)
print('- step %d L2: %1.5f %s' % (i, vl2, s))
def test_distance_neighbor_dist2_repeated():
# A constant pattern
a = np.ones((100, 100, 3))
# Let's get some translated slices
w = 80
N = 10
A = []
for i in range(N):
A.append(a[i:i + w, 0:0 + w, ...])
print()
areas = [0.01, 0.03, 0.05, 0.07]
Ds = [DistanceNeighborDist((a, a)) for a in areas]
L2 = DistanceNorm(2)
for i in range(N):
y0 = UncertainImage(A[0])
y1 = UncertainImage(A[i])
vl2 = L2.distance(y0, y1)
ds = [x.distance(y0, y1) for x in Ds]
assert_allclose(ds, 0) # < this must be zero
s = ";".join("%1.5f" % x for x in ds)
print('- step %d L2: %1.5f %s' % (i, vl2, s))
def test_distance_neighbor_dist2_rgb():
# A random pattern
a = np.random.rand(100, 100, 3)
# Let's get some translated slices
w = 80
N = 10
A = []
for i in range(N):
A.append(a[i:i + w, 0:0 + w, ...])
print()
areas = [0.01, 0.03, 0.05, 0.07]
Ds = [DistanceNeighborDist((a, a)) for a in areas]
L2 = DistanceNorm(2)
for i in range(N):
y0 = UncertainImage(A[0])
y1 = UncertainImage(A[i])
vl2 = L2.distance(y0, y1)
ds = [x.distance(y0, y1) for x in Ds]
s = ";".join("%1.5f" % x for x in ds)
print('- step %d L2: %1.5f %s' % (i, vl2, s))
def test_distance_neighbor_dist2_rgb():
# A random pattern
a = np.random.rand(100, 100, 3)
# Let's get some translated slices
w = 80
N = 10
A = []
for i in range(N):
A.append(a[i:i + w, 0:0 + w, ...])
print()
areas = [0.01, 0.03, 0.05, 0.07]
Ds = [DistanceNeighborDist((a, a)) for a in areas
]
L2 = DistanceNorm(2)
for i in range(N):
y0 = UncertainImage(A[0])
y1 = UncertainImage(A[i])
vl2 = L2.distance(y0, y1)
ds = [x.distance(y0, y1) for x in Ds]
s = ";".join("%1.5f" % x for x in ds)
print('- step %d L2: %1.5f %s' % (i, vl2, s))
