def test_local_search_blob_detector_jitter_code():
stack = jitter_code()
bd = blob_detector()
spot_results = bd.run(stack)
intensity_table = build_traces_nearest_neighbors(spot_results=spot_results,
anchor_round=1,
search_radius=3)
assert intensity_table.shape == (1, 2, 3)
f, c, r = np.where(~intensity_table.isnull())
assert np.all(f == np.array([0, 0, 0]))
assert np.all(c == np.array([0, 0, 1]))
assert np.all(r == np.array([0, 2, 1]))
# test again with smaller search radius
bd = BlobDetector(min_sigma=1, max_sigma=4, num_sigma=30, threshold=.1)
per_tile_spot_results = bd.run(stack)
intensity_table = build_traces_nearest_neighbors(
spot_results=per_tile_spot_results, anchor_round=0, search_radius=1)
assert intensity_table.shape == (1, 2, 3)
f, c, r = np.where(~intensity_table.isnull())
assert np.all(f == 0)
assert np.all(c == 0)
assert np.all(r == 0)
def testExactMatches():
codebook = seqfishCodebook(5, 3, 20)
img, trueTargets = syntheticSeqfish(100, 100, 20, codebook, 5, 0, False)
bd = BlobDetector(min_sigma=1,
max_sigma=4,
num_sigma=30,
threshold=.1,
exclude_border=False)
spots = bd.run(image_stack=img)
assert spots.count_total_spots(
) == 5 * 5, 'Spot detector did not find all spots'
decoder = CheckAll(codebook=codebook, search_radius=1, error_rounds=0)
hits = decoder.run(spots=spots, n_processes=4)
testTargets = []
for i in range(len(hits)):
testTargets.append(
(int(hits[i]['target'].data), (int(hits[i]['x'].data),
int(hits[i]['y'].data),
int(hits[i]['z'].data))))
matches = 0
for true in trueTargets:
for test in testTargets:
if true[0] == test[0]:
if test[1][0] + 1 >= true[1][0] >= test[1][0] - 1 and \
test[1][1] + 1 >= true[1][1] >= test[1][1] - 1:
matches += 1
assert matches == len(trueTargets)
def simple_gaussian_spot_detector() -> BlobDetector:
"""create a basic gaussian spot detector"""
return BlobDetector(min_sigma=1,
max_sigma=4,
num_sigma=5,
threshold=0,
measurement_type='max')
def synthetic_dataset_with_truth_values_and_called_spots():
codebook, true_intensities, image = synthetic_dataset_with_truth_values()
wth = WhiteTophat(masking_radius=15)
filtered = wth.run(image, in_place=False)
min_sigma = 1.5
max_sigma = 4
num_sigma = 10
threshold = 1e-4
gsd = BlobDetector(min_sigma=min_sigma,
max_sigma=max_sigma,
num_sigma=num_sigma,
threshold=threshold,
measurement_type='max')
spots = gsd.run(image_stack=filtered)
decoder = MetricDistance(codebook=codebook, max_distance=1, min_intensity=0, norm_order=2)
decoded_intensities = decoder.run(spots)
assert decoded_intensities.shape[0] == 5
return codebook, true_intensities, image, decoded_intensities
def test_blob_doh_error_handling():
"""
Test that BlobDetector throws a Value error if a user tries to use blob_doh on 3d data.
`skimage.blob_doh` only supports 2d data.
"""
stack = ImageStack.from_numpy(np.zeros((4, 2, 10, 100, 100), dtype=np.float32))
blob_doh = BlobDetector(
min_sigma=1,
max_sigma=4,
num_sigma=5,
threshold=0,
detector_method='blob_doh',
measurement_type='max',
is_volume=True)
# Check that Value error gets raised when blob_doh and is_volume=True provided
with pytest.raises(ValueError):
blob_doh.run(stack)
ref_image = stack.reduce({Axes.ROUND, Axes.CH}, func='max')
# Check that Value error gets raised when blob_doh and reference image is 3d
with pytest.raises(ValueError):
blob_doh.run(stack, reference_image=ref_image)
def blob_detector():
return BlobDetector(min_sigma=1, max_sigma=4, num_sigma=30, threshold=.1)