def test_run_refine_fastmatch(lt_ctx):
shape = np.array([128, 128])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
drop = np.random.choice([True, False], size=len(indices), p=[0.9, 0.1])
indices = indices[drop]
radius = 10
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
matcher = grm.Matcher()
template = m.radial_gradient(centerX=radius + 1,
centerY=radius + 1,
imageSizeX=2 * radius + 2,
imageSizeY=2 * radius + 2,
radius=radius)
match_patterns = [
blobfinder.RadialGradient(radius=radius),
blobfinder.Circular(radius=radius),
blobfinder.BackgroundSubtraction(radius=radius),
blobfinder.RadialGradientBackgroundSubtraction(radius=radius),
blobfinder.UserTemplate(template=template)
]
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
for match_pattern in match_patterns:
print("refining using template %s" % type(match_pattern))
(res, real_indices) = blobfinder.run_refine(
ctx=lt_ctx,
dataset=dataset,
zero=zero + np.random.uniform(-1, 1, size=2),
a=a + np.random.uniform(-1, 1, size=2),
b=b + np.random.uniform(-1, 1, size=2),
matcher=matcher,
match_pattern=match_pattern)
print(peaks - grm.calc_coords(res['zero'].data[0], res['a'].data[0],
res['b'].data[0], indices))
assert np.allclose(res['zero'].data[0], zero, atol=0.5)
assert np.allclose(res['a'].data[0], a, atol=0.2)
assert np.allclose(res['b'].data[0], b, atol=0.2)
def test_run_refine_affinematch(lt_ctx):
for i in range(1):
try:
shape = np.array([128, 128])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
radius = 10
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices,
radius)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
matcher = grm.Matcher()
match_pattern = blobfinder.RadialGradient(radius=radius)
affine_indices = peaks - zero
for j in range(5):
zzero = zero + np.random.uniform(-1, 1, size=2)
aa = np.array([1, 0]) + np.random.uniform(-0.05, 0.05, size=2)
bb = np.array([0, 1]) + np.random.uniform(-0.05, 0.05, size=2)
(res, real_indices) = blobfinder.run_refine(
ctx=lt_ctx,
dataset=dataset,
zero=zzero,
a=aa,
b=bb,
indices=affine_indices,
matcher=matcher,
match_pattern=match_pattern,
match='affine')
assert np.allclose(res['zero'].data[0], zero, atol=0.5)
assert np.allclose(res['a'].data[0], [1, 0], atol=0.05)
assert np.allclose(res['b'].data[0], [0, 1], atol=0.05)
except Exception:
print("zero = np.array([%s, %s])" % tuple(zero))
print("a = np.array([%s, %s])" % tuple(a))
print("b = np.array([%s, %s])" % tuple(b))
print("zzero = np.array([%s, %s])" % tuple(zzero))
print("aa = np.array([%s, %s])" % tuple(aa))
print("bb = np.array([%s, %s])" % tuple(bb))
raise
def test_run_refine_fullframe(lt_ctx):
shape = np.array([128, 128])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
radius = 10
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
matcher = grm.Matcher()
match_pattern = blobfinder.RadialGradient(radius=radius)
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
(res, real_indices) = blobfinder.run_refine(
ctx=lt_ctx,
dataset=dataset,
zero=zero + np.random.uniform(-0.5, 0.5, size=2),
a=a + np.random.uniform(-0.5, 0.5, size=2),
b=b + np.random.uniform(-0.5, 0.5, size=2),
matcher=matcher,
match_pattern=match_pattern,
correlation='fullframe',
)
print(peaks - grm.calc_coords(res['zero'].data[0], res['a'].data[0],
res['b'].data[0], indices))
assert np.allclose(res['zero'].data[0], zero, atol=0.5)
assert np.allclose(res['a'].data[0], a, atol=0.2)
assert np.allclose(res['b'].data[0], b, atol=0.2)
def test_run_refine_affinematch(lt_ctx):
shape = np.array([256, 256])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-3:4, -3:4]
indices = np.concatenate(indices.T)
radius = 10
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
matcher = grm.Matcher()
match_pattern = blobfinder.RadialGradient(radius=radius)
affine_indices = peaks - zero
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
(res, real_indices) = blobfinder.run_refine(
ctx=lt_ctx,
dataset=dataset,
zero=zero + np.random.uniform(-1, 1, size=2),
a=np.array([1, 0]) + np.random.uniform(-0.05, 0.05, size=2),
b=np.array([0, 1]) + np.random.uniform(-0.05, 0.05, size=2),
indices=affine_indices,
matcher=matcher,
match_pattern=match_pattern,
match='affine')
assert np.allclose(res['zero'].data[0], zero, atol=0.5)
assert np.allclose(res['a'].data[0], [1, 0], atol=0.05)
assert np.allclose(res['b'].data[0], [0, 1], atol=0.05)