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 test_featurevector(lt_ctx):
shape = np.array([128, 128])
zero = shape // 2
a = np.array([24, 0.])
b = np.array([0., 30])
indices = np.mgrid[-2:3, -2:3]
indices = np.concatenate(indices.T)
radius = 5
radius_outer = 10
template = m.background_subtraction(centerX=radius_outer + 1,
centerY=radius_outer + 1,
imageSizeX=radius_outer * 2 + 2,
imageSizeY=radius_outer * 2 + 2,
radius=radius_outer,
radius_inner=radius + 1,
antialiased=False)
data, indices, peaks = cbed_frame(*shape,
zero,
a,
b,
indices,
radius,
all_equal=True)
dataset = MemoryDataSet(data=data,
tileshape=(1, *shape),
num_partitions=1,
sig_dims=2)
match_pattern = blobfinder.UserTemplate(template=template)
stack = functools.partial(
blobfinder.feature_vector,
imageSizeX=shape[1],
imageSizeY=shape[0],
peaks=peaks,
match_pattern=match_pattern,
)
job = lt_ctx.create_mask_job(dataset=dataset,
factories=stack,
mask_count=len(peaks),
mask_dtype=np.float32)
res = lt_ctx.run(job)
peak_data, _, _ = cbed_frame(*shape,
zero,
a,
b,
np.array([(0, 0)]),
radius,
all_equal=True)
peak_sum = peak_data.sum()
assert np.allclose(res.sum(), data.sum())
assert np.allclose(res, peak_sum)
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([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_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)