def test_run_refine_fastmatch_zeroshift(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
# Exactly between peaks, worst case
shift = (a + b) / 2
data_0, indices_0, peaks_0 = cbed_frame(*shape, zero, a, b, indices,
radius)
data_1, indices_1, peaks_1 = cbed_frame(*shape, zero + shift, a, b,
indices, radius)
data = np.concatenate((data_0, data_1), axis=0)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
matcher = grm.Matcher()
match_patterns = [
# Least reliable pattern
common.patterns.Circular(radius=radius),
]
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
for match_pattern in match_patterns:
print("refining using template %s" % type(match_pattern))
zero_shift = np.array([(0., 0.), shift]).astype(np.float32)
(res, real_indices) = udf.refinement.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,
zero_shift=UDF.aux_data(zero_shift, kind='nav', extra_shape=(2, )))
print(peaks_0 - grm.calc_coords(res['zero'].data[0], res['a'].data[0],
res['b'].data[0], indices_0))
print(peaks_1 - grm.calc_coords(res['zero'].data[1], res['a'].data[1],
res['b'].data[1], indices_1))
assert np.allclose(res['zero'].data[0], zero, atol=0.5)
assert np.allclose(res['zero'].data[1], zero + shift, atol=0.5)
assert np.allclose(res['a'].data, a, atol=0.2)
assert np.allclose(res['b'].data, b, atol=0.2)
def test_fastmatch(zero, a, b):
grid = _fullgrid(zero, a, b, 5)
matcher = grm.Matcher()
match = matcher.fastmatch(centers=grid, zero=zero, a=a, b=b)
assert (np.allclose(zero, match.zero))
assert (np.allclose(a, match.a))
assert (np.allclose(b, match.b))
assert (len(match) == len(grid))
assert (np.allclose(match.calculated_refineds, grid))
def test_run_refine_fastmatch(lt_ctx, progress):
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 = [
common.patterns.RadialGradient(radius=radius),
common.patterns.Circular(radius=radius),
common.patterns.BackgroundSubtraction(radius=radius),
common.patterns.RadialGradientBackgroundSubtraction(radius=radius),
common.patterns.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) = udf.refinement.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,
progress=progress)
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_affinematch(zero, a, b):
grid = _fullgrid(zero, a, b, 5)
indices = grm.get_indices(grid, zero, a, b)
matcher = grm.Matcher()
match = matcher.affinematch(centers=grid, indices=indices)
assert (np.allclose(zero, match.zero))
assert (np.allclose(a, match.a))
assert (np.allclose(b, match.b))
assert (len(match) == len(grid))
assert (np.allclose(match.calculated_refineds, grid))
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 = common.patterns.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) = udf.refinement.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_visualize_smoke(navshape, 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)
data = data.reshape((*navshape, *shape))
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
matcher = grm.Matcher()
match_pattern = common.patterns.RadialGradientBackgroundSubtraction(
radius=radius)
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
(res, real_indices) = udf.refinement.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)
fig, axes = plt.subplots()
if len(navshape) == 1:
y = None
elif len(navshape) == 2:
y = 0
else:
raise ValueError(
f"Nav shape too long, supported are 1D and 2D: {navshape}")
udf.utils.visualize_frame(ctx=lt_ctx,
ds=dataset,
result=res,
indices=real_indices,
r=radius,
y=y,
x=0,
axes=axes)
def test_run_refine_fastmatch(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()
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.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_sparse(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 = common.patterns.RadialGradient(radius=radius)
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
(res, real_indices) = udf.refinement.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='sparse',
steps=3
)
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)
