def test_get_axon_morphometrics_with_myelin_mask(self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
pred_myelin = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-myelin.png'),
flatten=True)
stats_array = get_axon_morphometrics(pred_axon,
path_folder,
im_myelin=pred_myelin)
assert stats_array[1]['gratio'] == pytest.approx(0.74, rel=0.01)
def test_get_aggregate_morphometrics_returns_expected_type(self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
pred_myelin = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-myelin.png'),
flatten=True)
aggregate_metrics = get_aggregate_morphometrics(
pred_axon, pred_myelin, path_folder)
assert isinstance(aggregate_metrics, dict)
def test_get_axon_morphometrics_with_myelin_mask_simulated_axons(self):
path_pred = (self.testPath / '__test_files__' /
'__test_simulated_axons__' / 'SimulatedAxons.png')
gratio_sim = np.array([0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1])
axon_diam_sim = np.array([100, 90, 80, 70, 60, 46, 36, 24, 12])
myelin_thickness_sim = (axon_diam_sim / 2) * (1 / gratio_sim - 1)
# Read paths and compute axon/myelin masks
pred = scipy_imread(path_pred)
pred_axon = pred > 200
pred_myelin = np.logical_and(pred >= 50, pred <= 200)
# Compute axon morphometrics
stats_array = get_axon_morphometrics(pred_axon,
str(path_pred.parent),
im_myelin=pred_myelin)
for ii in range(0, 9):
assert stats_array[ii]['gratio'] == pytest.approx(gratio_sim[ii],
rel=0.1)
assert stats_array[ii]['axon_diam'] == pytest.approx(
axon_diam_sim[ii], rel=0.1)
assert stats_array[ii]['myelin_thickness'] == pytest.approx(
myelin_thickness_sim[ii], rel=0.1)
def setup(self):
# Get the directory where this current file is saved
self.fullPath = Path(__file__).resolve().parent
# Move up to the test directory, "test/"
self.testPath = self.fullPath.parent
self.test_folder_path = (self.testPath / '__test_files__' /
'__test_demo_files__')
self.pixelsizeValue = 0.07 # For current demo data.
pred_axon_path = self.test_folder_path / 'AxonDeepSeg_seg-axon.png'
self.pred_axon = scipy_imread(pred_axon_path, flatten=True)
pred_myelin_path = self.test_folder_path / 'AxonDeepSeg_seg-myelin.png'
self.pred_myelin = scipy_imread(pred_myelin_path, flatten=True)
self.tmpDir = self.fullPath / '__tmp__'
if not self.tmpDir.exists():
self.tmpDir.mkdir()
def test_get_axon_morphometrics_returns_expected_type(self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
stats_array = get_axon_morphometrics(pred_axon, path_folder)
assert isinstance(stats_array, np.ndarray)
def test_write_aggregate_morphometrics_throws_error_if_folder_doesnt_exist(
self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
pred_myelin = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-myelin.png'),
flatten=True)
aggregate_metrics = get_aggregate_morphometrics(
pred_axon, pred_myelin, path_folder)
nonExistingFolder = ''.join(
random.choice(string.ascii_lowercase) for i in range(16))
with pytest.raises(IOError):
write_aggregate_morphometrics(nonExistingFolder, aggregate_metrics)
def readImage(fname_or_file='testImage_ml.tif'):
testFile = findTestFile(fname_or_file)
try:
in_array = imread(testFile)
except (ValueError, Image.DecompressionBombWarning):
warn(
"Error in reading image with tifffile.imread()"
"\n-> Assuming image is logical; opening with scipy.misc.imread() and casting array to np.bool"
)
in_array = scipy_imread(testFile).astype(np.bool)
return in_array
def test_draw_axon_diameter_creates_file_in_expected_location(self):
img = scipy_imread(self.test_folder_path / 'image.png')
path_prediction = self.test_folder_path / 'AxonDeepSeg_seg-axonmyelin.png'
result_path = self.test_folder_path / 'AxonDeepSeg_map-axondiameter.png'
fig = draw_axon_diameter(img, str(path_prediction), self.pred_axon,
self.pred_myelin)
assert fig.axes
fig.savefig(result_path)
assert result_path.is_file()
result_path.unlink()
def test_write_aggregate_morphometrics_creates_file_in_expected_location(
self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
pred_myelin = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-myelin.png'),
flatten=True)
aggregate_metrics = get_aggregate_morphometrics(
pred_axon, pred_myelin, path_folder)
expectedFilePath = os.path.join(self.tmpDir,
'aggregate_morphometrics.txt')
write_aggregate_morphometrics(self.tmpDir, aggregate_metrics)
assert os.path.isfile(expectedFilePath)
def test_get_aggregate_morphometrics_returns_returns_expected_keys(self):
expectedKeys = {
'avf', 'mvf', 'gratio_aggr', 'mean_axon_diam', 'mean_myelin_diam',
'mean_myelin_thickness', 'axon_density_mm2'
}
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
pred_myelin = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-myelin.png'),
flatten=True)
aggregate_metrics = get_aggregate_morphometrics(
pred_axon, pred_myelin, path_folder)
for key in list(aggregate_metrics.keys()):
assert key in expectedKeys
def test_draw_axon_diameter_creates_file_in_expected_location(self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
img = scipy_imread(os.path.join(path_folder, 'image.png'))
path_prediction = os.path.join(path_folder,
'AxonDeepSeg_seg-axonmyelin.png')
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
pred_myelin = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-myelin.png'),
flatten=True)
result_path = os.path.join(path_folder,
'AxonDeepSeg_map-axondiameter.png')
fig = draw_axon_diameter(img, path_prediction, pred_axon, pred_myelin)
assert fig.axes
fig.savefig(result_path)
assert os.path.isfile(result_path)
os.remove(result_path)
def test_save_axon_morphometrics_creates_file_in_expected_location(self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
stats_array = get_axon_morphometrics(pred_axon, path_folder)
save_axon_morphometrics(self.tmpDir, stats_array)
# Filename 'axonlist.npy' is hardcoded in function.
expectedFilePath = os.path.join(self.tmpDir, 'axonlist.npy')
assert os.path.isfile(expectedFilePath)
def test_get_axon_morphometrics_returns_expected_keys(self):
expectedKeys = {
'y0', 'x0', 'axon_diam', 'axon_area', 'solidity', 'eccentricity',
'orientation'
}
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
stats_array = get_axon_morphometrics(pred_axon, path_folder)
for key in list(stats_array[0].keys()):
assert key in expectedKeys
def test_load_axon_morphometrics_returns_identical_var_as_was_saved(self):
path_folder = self.pixelsizeFileName.split(
'pixel_size_in_micrometer.txt')[0]
pred_axon = scipy_imread(os.path.join(path_folder,
'AxonDeepSeg_seg-axon.png'),
flatten=True)
original_stats_array = get_axon_morphometrics(pred_axon, path_folder)
save_axon_morphometrics(self.tmpDir, original_stats_array)
# Load method only takes in a directory as an argument, expects that
# 'axonlist.npy' will be in directory.
loaded_stats_array = load_axon_morphometrics(self.tmpDir)
assert np.array_equal(loaded_stats_array, original_stats_array)
def test_get_axon_morphometrics_with_unexpected_myelin_mask_simulated_axons(
self):
path_pred = (self.testPath / '__test_files__' /
'__test_simulated_axons__' / 'SimulatedAxons.png')
prediction = scipy_imread(path_pred, flatten=True)
pred_axon = prediction > 200
unexpected_pred_myelin = np.zeros(prediction.shape)
stats_array = get_axon_morphometrics(pred_axon,
str(path_pred.parent),
im_myelin=unexpected_pred_myelin)
for axon_prop in stats_array:
assert axon_prop['myelin_thickness'] == pytest.approx(0.0,
rel=0.01)
assert axon_prop['myelin_area'] == pytest.approx(0.0, rel=0.01)
assert axon_prop['gratio'] == pytest.approx(1.0, rel=0.01)
def test_get_axon_morphometrics_with_unexpected_myelin_mask_simulated_axons(
self):
path_pred = os.path.join(self.testPath, '__test_files__',
'__test_simulated_axons__',
'SimulatedAxons.png')
# Read paths and compute axon/myelin masks
pred = scipy_imread(path_pred, flatten=True)
pred_axon = pred > 200
unexpected_pred_myelin = np.zeros(pred.shape)
path_folder, file_name = os.path.split(path_pred)
# Compute axon morphometrics
stats_array = get_axon_morphometrics(pred_axon,
path_folder,
im_myelin=unexpected_pred_myelin)
for axon_prop in stats_array:
assert axon_prop['myelin_thickness'] == pytest.approx(0.0,
rel=0.01)
assert axon_prop['myelin_area'] == pytest.approx(0.0, rel=0.01)
assert axon_prop['gratio'] == pytest.approx(1.0, rel=0.01)