def test_compute_haralick_features(self):
f = [
'Haralick.ASM',
'Haralick.Contrast',
'Haralick.Correlation',
'Haralick.SumOfSquares',
'Haralick.IDM',
'Haralick.SumAverage',
'Haralick.SumVariance',
'Haralick.SumEntropy',
'Haralick.Entropy',
'Haralick.DifferenceVariance',
'Haralick.DifferenceEntropy',
'Haralick.IMC1',
'Haralick.IMC2',
]
expected_feature_list = []
for col in f:
expected_feature_list.append(col + '.Mean')
expected_feature_list.append(col + '.Range')
fdata = htk_features.compute_haralick_features(
self.im_nuclei_seg_mask, self.im_nuclei_stain)
self.check_fdata_sanity(fdata, expected_feature_list)
self.check_fdata_sanity(self.fdata_nuclei, expected_feature_list,
prefix='Nucleus.',
match_feature_count=False)
self.check_fdata_sanity(self.fdata_nuclei, expected_feature_list,
prefix='Cytoplasm.',
match_feature_count=False)
def test_compute_haralick_features(self):
f = [
'Haralick.ASM',
'Haralick.Contrast',
'Haralick.Correlation',
'Haralick.SumOfSquares',
'Haralick.IDM',
'Haralick.SumAverage',
'Haralick.SumVariance',
'Haralick.SumEntropy',
'Haralick.Entropy',
'Haralick.DifferenceVariance',
'Haralick.DifferenceEntropy',
'Haralick.IMC1',
'Haralick.IMC2',
]
expected_feature_list = []
for col in f:
expected_feature_list.append(col + '.Mean')
expected_feature_list.append(col + '.Range')
fdata = htk_features.compute_haralick_features(cfg.im_nuclei_seg_mask,
cfg.im_nuclei_stain)
check_fdata_sanity(fdata, expected_feature_list)
check_fdata_sanity(cfg.fdata_nuclei,
expected_feature_list,
prefix='Nucleus.',
match_feature_count=False)
check_fdata_sanity(cfg.fdata_nuclei,
expected_feature_list,
prefix='Cytoplasm.',
match_feature_count=False)
if GENERATE_GROUNDTRUTH:
fdata.to_csv(os.path.join(tempfile.gettempdir(),
'Easy1_nuclei_haralick_features.csv'),
index=False)
fdata_gtruth = pd.read_csv(
utilities.getTestFilePath('Easy1_nuclei_haralick_features.csv'))
pd.testing.assert_frame_equal(fdata,
fdata_gtruth,
check_less_precise=2)
def test_compute_haralick_features(self):
f = [
'Haralick.ASM',
'Haralick.Contrast',
'Haralick.Correlation',
'Haralick.SumOfSquares',
'Haralick.IDM',
'Haralick.SumAverage',
'Haralick.SumVariance',
'Haralick.SumEntropy',
'Haralick.Entropy',
'Haralick.DifferenceVariance',
'Haralick.DifferenceEntropy',
'Haralick.IMC1',
'Haralick.IMC2',
]
expected_feature_list = []
for col in f:
expected_feature_list.append(col + '.Mean')
expected_feature_list.append(col + '.Range')
fdata = htk_features.compute_haralick_features(self.im_nuclei_seg_mask,
self.im_nuclei_stain)
self.check_fdata_sanity(fdata, expected_feature_list)
self.check_fdata_sanity(self.fdata_nuclei,
expected_feature_list,
prefix='Nucleus.',
match_feature_count=False)
self.check_fdata_sanity(self.fdata_nuclei,
expected_feature_list,
prefix='Cytoplasm.',
match_feature_count=False)
# Uncomment to generate ground truth
# fdata.to_csv(os.path.join(
# TEST_DATA_DIR, 'Easy1_nuclei_haralick_features.csv'),
# index=False)
fdata_gtruth = pd.read_csv(
os.path.join(TEST_DATA_DIR, 'Easy1_nuclei_haralick_features.csv'))
pd.testing.assert_frame_equal(fdata, fdata_gtruth)