def test_outputs(self):
data = FMDLData()
data.postProcessed_roi_actualSize = self.postProcessedRoi
data.config = Config(
{'minContourArea': 8000,'roiBoundaryPointsReductionFactor': 0.01},
[('minContourArea', 10, 100000, 'integer'),('roiBoundaryPointsReductionFactor',\
0.001, 0.1, 'float'),])
validResults = getRoiContour(data)
self.assertTrue(validResults)
testroiBoundaryContour = np.load(
'tests/networksAndImages-forUnitTesting/roiBoundaryContour_FMDL_2018.04.30_19.11.20.npy'
)
print("\nthis is the predicted roi_boundary_contour point\n")
print(data.roi_boundary_contour)
print("\nthis is the groundTruth testroiBoundaryContour point\n")
print(testroiBoundaryContour)
self.assertTrue(
np.allclose(data.roi_boundary_contour,
testroiBoundaryContour,
atol=1))
def test_missingConfigItems(self):
data = FMDLData()
data.postProcessed_roi_actualSize = self.postProcessedRoi
validResults = getRoiContour(data)
self.assertFalse(validResults)
self.assertEqual(len(data.approximated_roi_boundary), 0)
def test_badPostProcessedRoi(self):
data = FMDLData()
data.postProcessed_roi_actualSize = np.zeros((100, 1), int)
data.config = Config(
{
'minContourArea': 8000,
'roiBoundaryPointsReductionFactor': 0.01
},
[('minContourArea', 10, 100000, 'integer'),('roiBoundaryPointsReductionFactor',\
0.001, 0.1, 'float'),])
validResults = getRoiContour(data)
self.assertFalse(validResults)
self.assertEqual(len(data.approximated_roi_boundary), 0)