def test_graphcut(self):
if _has_graphcut:
data = self.data
# Core labels don't matter in this graphcut test
# core_binary = (data == 5)
core_labels = np.empty_like(data, dtype=np.uint32)
# core_labels[0,...,0] = vigra.analysis.labelMultiArrayWithBackground(core_binary[0,...,0].astype(np.uint8))
op = OpLabeledThreshold(graph=Graph())
op.Method.setValue(ThresholdMethod.GRAPHCUT)
op.FinalThreshold.setValue(0.5)
op.Input.setValue(data.copy())
op.CoreLabels.setValue(core_labels)
op.GraphcutBeta.setValue(
0.5) # High enough to fill the hole in the third segment
result = op.Output[:].wait()
result_yx = result[0, :, :, 0, 0]
# print result_yx
label_values = np.unique(result)[1:]
assert len(label_values) == 3
assert len(np.unique(result_yx[1:12,
1:5])) == 1 # Same as 'simple'
assert len(
np.unique(result_yx[7:10, 6:9])
) == 1 # Almost same as simple, but with the hole filled in.
def test_simple(self):
data = self.data
core_labels = np.zeros_like(data) # core_labels aren't used by 'simple' method
op = OpLabeledThreshold(graph=Graph())
op.Method.setValue(ThresholdMethod.SIMPLE)
op.FinalThreshold.setValue(0.5)
op.Input.setValue(data.copy())
op.CoreLabels.setValue(core_labels)
result = op.Output[:].wait()
assert result.max() == 3
assert (result.astype(bool) == data.astype(bool)).all()
def test_ipht(self):
data = self.data
core_binary = (data == 5)
core_labels = np.empty_like(data, dtype=np.uint32)
core_labels[0,...,0] = vigra.analysis.labelMultiArrayWithBackground(core_binary[0,...,0].astype(np.uint8))
op = OpLabeledThreshold(graph=Graph())
op.Method.setValue(ThresholdMethod.IPHT)
op.FinalThreshold.setValue(0.5)
op.Input.setValue(data.copy())
op.CoreLabels.setValue(core_labels)
result = op.Output[:].wait()
result_yx = result[0,:,:,0,0]
#print result_yx
label_values = np.unique(result)[1:]
assert len(label_values) == 2
assert len(np.unique(result_yx[1:6, 1:5])) == 1
assert len(np.unique(result_yx[7:12, 1:5])) == 1
def test_hysteresis(self):
data = self.data
core_binary = (data == 5)
core_labels = np.empty_like(data, dtype=np.uint32)
core_labels[0,...,0] = vigra.analysis.labelMultiArrayWithBackground(core_binary[0,...,0].astype(np.uint8))
op = OpLabeledThreshold(graph=Graph())
op.Method.setValue(ThresholdMethod.HYSTERESIS)
op.FinalThreshold.setValue(0.5)
op.Input.setValue(data.copy())
op.CoreLabels.setValue(core_labels)
result = op.Output[:].wait()
label_values = np.unique(result)[1:]
assert len(label_values) == 1
expected_result = np.where(data, label_values[0], 0)
expected_result[0,:,5:,0,0] = 0 # Right half won't survive
#print expected_result[0,:,:,0,0]
#print result[0,:,:,0,0]
assert (result == expected_result).all()