def test_stackio_image_iterator():
fname = data.CZT_peaks()
st = StackIO(fname, json_discovery=False)
arr = st.get_tif().asarray(memmap=True)
iterator = st.image_iterator(channel_index=0, memmap=True)
for a in iterator():
assert a.shape == arr.shape[-2:]
# Test with channels index
fname = data.CZT_peaks()
st = StackIO(fname, json_discovery=False)
assert_raises(TypeError,
st.image_iterator,
hannel_index='GFP',
memmap=True)
st.metadata['Channels'] = ['GFP']
it = st.image_iterator(channel_index='GFP',
memmap=False,
z_projection=True)
assert list(it())[0].shape == arr.shape[-2:]
def test_cell_boundaries_detector():
# Fake to only detect the first time stamp
# to make tests faster
st = StackIO(data.TC_BF_cells())
data_iterator = [list(st.image_iterator(position=-3)())[0]]
metadata = st.metadata
metadata['SizeT'] = 1
parameters = {'object_height': 3, 'minimal_area': 160}
shapes = cell_boundaries_detector(data_iterator,
st.metadata,
show_progress=True,
parameters=parameters)
real_shapes = np.array([[
5.529290429042903909e+00, 7.725527184297377836e+00,
-1.109566958016608318e+00, 1.057975329135689790e+01,
7.965464600887037783e+00, 0.000000000000000000e+00
]])
assert_array_almost_equal(shapes, real_shapes)
def test_peak_detector_no_peaks():
fname = data.CZT_peaks()
st = StackIO(fname, json_discovery=False)
data_iterator = st.image_iterator(channel_index=0)
parameters = {'w_s': 0.7,
'peak_radius': 0.2,
'threshold': 300,
'max_peaks': 4}
peaks = peak_detector(data_iterator(),
st.metadata,
parallel=True,
show_progress=False,
parameters=parameters)
assert peaks.empty is True
def test_cell_boundaries_detector_no_shapes():
# Fake to only detect the first time stamp
# to make tests faster
st = StackIO(data.CZT_peaks())
data_iterator = [list(st.image_iterator(position=-3, channel_index=0)())[0]]
metadata = st.metadata
metadata['SizeT'] = 1
parameters = {'object_height': 1e-6,
'minimal_area': 160}
shapes = cell_boundaries_detector(data_iterator, metadata,
show_progress=True,
parameters=parameters)
assert shapes.empty is True
def test_cell_boundaries_detector_no_shapes():
# Fake to only detect the first time stamp
# to make tests faster
st = StackIO(data.CZT_peaks())
data_iterator = [
list(st.image_iterator(position=-3, channel_index=0)())[0]
]
metadata = st.metadata
metadata['SizeT'] = 1
parameters = {'object_height': 1e-6, 'minimal_area': 160}
shapes = cell_boundaries_detector(data_iterator,
metadata,
show_progress=True,
parameters=parameters)
assert shapes.empty is True
def test_cell_boundaries_detector():
# Fake to only detect the first time stamp
# to make tests faster
st = StackIO(data.TC_BF_cells())
data_iterator = [list(st.image_iterator(position=-3)())[0]]
metadata = st.metadata
metadata['SizeT'] = 1
parameters = {'object_height': 3,
'minimal_area': 160}
shapes = cell_boundaries_detector(data_iterator, st.metadata,
show_progress=True,
parameters=parameters)
real_shapes = np.array([[5.529290429042903909e+00, 7.725527184297377836e+00,
-1.109566958016608318e+00, 1.057975329135689790e+01,
7.965464600887037783e+00, 0.000000000000000000e+00]])
assert_array_almost_equal(shapes, real_shapes)