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 get_mask(self, sig_shape):
return masks.background_subtraction(centerY=sig_shape[0] // 2,
centerX=sig_shape[1] // 2,
imageSizeY=sig_shape[0],
imageSizeX=sig_shape[1],
radius=self.radius_outer,
radius_inner=self.radius,
antialiased=True)
def test_background_subtraction():
mask = m.background_subtraction(10, 10, 20, 20, 5, 3)
assert (np.allclose(np.sum(mask), 0))
