def test_custom_template_fuzz():
for i in range(10):
integers = np.arange(1, 15)
center_y = np.random.choice(integers)
center_x = np.random.choice(integers)
size_y = np.random.choice(integers)
size_x = np.random.choice(integers)
radius = np.random.choice(integers)
search = np.random.choice(integers)
mask_y = np.random.choice(integers)
mask_x = np.random.choice(integers)
print("center_y:", center_y)
print("center_x:", center_x)
print("size_y:", size_y)
print("size_x:", size_x)
print("radius:", radius)
print("search:", search)
print("mask_y:", mask_y)
print("mask_x:", mask_x)
template = m.radial_gradient(centerX=center_x,
centerY=center_y,
imageSizeX=size_x,
imageSizeY=size_y,
radius=radius)
custom = blobfinder.UserTemplate(template=template, search=search)
mask = custom.get_mask((mask_y, mask_x)) # noqa
def get_mask(self, sig_shape):
return radial_gradient(
centerY=sig_shape[0] // 2,
centerX=sig_shape[1] // 2,
imageSizeY=sig_shape[0],
imageSizeX=sig_shape[1],
radius=self.radius,
)
def get_mask(self, sig_shape):
return masks.radial_gradient(
centerY=sig_shape[0] // 2,
centerX=sig_shape[1] // 2,
imageSizeY=sig_shape[0],
imageSizeX=sig_shape[1],
radius=self.radius,
antialiased=True,
)
def test_correlation_methods(lt_ctx, cls, dtype, kwargs):
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 = 8
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
dataset = MemoryDataSet(data=data, tileshape=(1, *shape),
num_partitions=1, sig_dims=2)
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))
if cls is udf.correlation.SparseCorrelationUDF and kwargs.get('zero_shift'):
with pytest.raises(ValueError):
m_udf = cls(match_pattern=match_pattern, peaks=peaks.astype(dtype), **kwargs)
else:
m_udf = cls(match_pattern=match_pattern, peaks=peaks.astype(dtype), **kwargs)
res = lt_ctx.run_udf(dataset=dataset, udf=m_udf)
print(peaks)
print(res['refineds'].data[0])
print(peaks - res['refineds'].data[0])
print(res['peak_values'].data[0])
print(res['peak_elevations'].data[0])
# import matplotlib.pyplot as plt
# fig, ax = plt.subplots()
# plt.imshow(data[0])
# for p in np.flip(res['refineds'].data[0], axis=-1):
# ax.add_artist(plt.Circle(p, radius, fill=False, color='y'))
# plt.show()
assert np.allclose(res['refineds'].data[0], peaks, atol=0.5)
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_correlation_method_fullframe(lt_ctx, cls, dtype, kwargs):
shape = np.array([128, 128])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([34.3, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 42.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
radius = 8
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
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, search=radius * 1.5),
blobfinder.BackgroundSubtraction(radius=radius,
radius_outer=radius * 1.5,
search=radius * 1.8),
blobfinder.RadialGradientBackgroundSubtraction(radius=radius,
radius_outer=radius *
1.5,
search=radius * 1.8),
blobfinder.UserTemplate(template=template, search=radius * 1.5)
]
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
for match_pattern in match_patterns:
print("refining using template %s" % type(match_pattern))
udf = cls(match_pattern=match_pattern,
peaks=peaks.astype(dtype),
**kwargs)
res = lt_ctx.run_udf(dataset=dataset, udf=udf)
print(peaks - res['refineds'].data[0])
# import matplotlib.pyplot as plt
# fig, ax = plt.subplots()
# plt.imshow(data[0])
# for p in np.flip(res['refineds'].data[0], axis=-1):
# ax.add_artist(plt.Circle(p, radius, fill=False, color='y'))
# plt.show()
assert np.allclose(res['refineds'].data[0], peaks, atol=0.5)
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_custom_template():
template = m.radial_gradient(centerX=10,
centerY=10,
imageSizeX=21,
imageSizeY=23,
radius=7)
custom = blobfinder.UserTemplate(template=template, search=18)
assert custom.get_crop_size() == 18
same = custom.get_mask((23, 21))
larger = custom.get_mask((25, 23))
smaller = custom.get_mask((10, 10))
assert np.allclose(same, template)
assert np.allclose(larger[1:-1, 1:-1], template)
assert np.allclose(template[6:-7, 5:-6], smaller)
def test_standalone_full():
shape = np.array([128, 128])
zero = shape / 2 + np.random.uniform(-1, 1, size=2)
a = np.array([34.3, 0.]) + np.random.uniform(-1, 1, size=2)
b = np.array([0., 42.19]) + np.random.uniform(-1, 1, size=2)
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
radius = 8
data, indices, peaks = cbed_frame(*shape, zero, a, b, indices, radius)
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, search=radius * 1.5),
common.patterns.BackgroundSubtraction(radius=radius,
radius_outer=radius * 1.5,
search=radius * 1.8),
common.patterns.RadialGradientBackgroundSubtraction(
radius=radius, radius_outer=radius * 1.5, search=radius * 1.8),
common.patterns.UserTemplate(template=template, search=radius * 1.5)
]
print("zero: ", zero)
print("a: ", a)
print("b: ", b)
for match_pattern in match_patterns:
print("refining using template %s" % type(match_pattern))
(centers, refineds, heights,
elevations) = common.correlation.process_frames_full(
pattern=match_pattern,
frames=data,
peaks=peaks.astype(np.int32),
)
print(peaks - refineds)
assert np.allclose(refineds[0], peaks, atol=0.5)
def test_radial_gradient(antialiased):
template = m.radial_gradient(centerX=30,
centerY=33,
imageSizeX=65,
imageSizeY=66,
radius=17,
antialiased=antialiased)
assert template.shape == (66, 65)
for i in range(17):
assert np.allclose(template[33, 30 + i], i / 17)
assert np.allclose(template[33, 30 - i], i / 17)
assert np.allclose(template[33 + i, 30], i / 17)
assert np.allclose(template[33 - i, 30], i / 17)
for i in range(18, 30):
assert np.allclose(template[33, 30 + i], 0)
assert np.allclose(template[33, 30 - i], 0)
assert np.allclose(template[33 + i, 30], 0)
assert np.allclose(template[33 - i, 30], 0)
