def test_save_trks(self):
X = _get_image(30, 30)
y = np.random.randint(low=0, high=10, size=X.shape)
lineage = [dict()]
try:
tempdir = tempfile.mkdtemp() # create dir
with pytest.raises(ValueError):
badfilename = os.path.join(tempdir, 'x.trk')
utils.save_trks(badfilename, lineage, X, y)
filename = os.path.join(tempdir, 'x.trks')
utils.save_trks(filename, lineage, X, y)
assert os.path.isfile(filename)
# test saved tracks can be loaded
loaded = utils.load_trks(filename)
assert loaded['lineages'] == lineage
np.testing.assert_array_equal(X, loaded['X'])
np.testing.assert_array_equal(y, loaded['y'])
finally:
try:
shutil.rmtree(tempdir) # delete directory
except OSError as exc:
if exc.errno != errno.ENOENT: # no such file or directory
raise # re-raise exception
def benchmark_division_performance(trk_gt, trk_res, path_gt, path_res):
"""Compare two related .trk files (one being the GT of the other) and meaasure
performance on the the divisions in the GT file. This function produces two .txt
documents as a by-product (ISBI-style lineage documents)
# TODO: there should be an option to not write the files but compare in memory
Args:
trk_gt (path): Path to the ground truth .trk file.
trk_res (path): Path to the predicted results .trk file.
path_gt (path): Desired destination path for the GT ISBI-style .txt file.
path_res (path): Desired destination path for the result ISBI-style .txt file.
Returns:
dict: Diciontary of all division statistics.
"""
# Identify nodes with parent attribute
# Load both .trk
trks = load_trks(trk_gt)
lineage_gt, _, y_gt = trks['lineages'][0], trks['X'], trks['y']
trks = load_trks(trk_res)
lineage_res, _, y_res = trks['lineages'][0], trks['X'], trks['y']
# Produce ISBI style text doc to work with
trk_to_isbi(lineage_gt, path_gt)
trk_to_isbi(lineage_res, path_res)
# Match up labels in GT to Results to allow for direct comparisons
cells_gt, cells_res = match_nodes(y_gt, y_res)
if len(np.unique(cells_res)) < len(np.unique(cells_gt)):
node_key = {r: g for g, r in zip(cells_gt, cells_res)}
# node_key maps gt nodes onto resnodes so must be applied to gt
G_res = txt_to_graph(path_res, node_key=node_key)
G_gt = txt_to_graph(path_gt)
div_results = classify_divisions(G_gt, G_res)
else:
node_key = {g: r for g, r in zip(cells_gt, cells_res)}
G_res = txt_to_graph(path_res)
G_gt = txt_to_graph(path_gt, node_key=node_key)
div_results = classify_divisions(G_gt, G_res)
return div_results
def __init__(self, path=None, tracked_data=None,
appearance_dim=32, distance_threshold=64):
if tracked_data:
training_data = tracked_data
elif path:
training_data = load_trks(path)
else:
raise ValueError('One of `tracked_data` or `path` is required')
self.X = training_data['X'].astype('float32')
self.y = training_data['y'].astype('int32')
self.lineages = training_data['lineages']
if not len(self.X) == len(self.y) == len(self.lineages):
raise ValueError(
'The data do not share the same batch size. '
'Please make sure you are using a valid .trks file')
self.appearance_dim = appearance_dim
self.distance_threshold = distance_threshold
# Correct lineages and remove bad batches
self._correct_lineages()
# Create feature dictionaries
features_dict = self._get_features()
self.appearances = features_dict['appearances']
self.morphologies = features_dict['morphologies']
self.centroids = features_dict['centroids']
# Convert adj matrices to sparse
self.adj_matrices = self._get_sparse(
features_dict['adj_matrix'])
self.norm_adj_matrices = self._get_sparse(
normalize_adj_matrix(features_dict['adj_matrix']))
self.temporal_adj_matrices = self._get_sparse(
features_dict['temporal_adj_matrix'])
self.mask = features_dict['mask']
self.track_length = features_dict['track_length']
def test_track_cells(self):
frames = 10
track_length = 3
labels_per_frame = 3
# TODO: test detected divisions
# TODO: test creating new track
# TODO: Fix for channels_first
for data_format in ('channels_last', ): # 'channels_first'):
y1 = get_annotated_movie(img_size=256,
labels_per_frame=labels_per_frame,
frames=frames,
mov_type='sequential',
seed=1,
data_format=data_format)
y2 = get_annotated_movie(img_size=256,
labels_per_frame=labels_per_frame * 2,
frames=frames,
mov_type='sequential',
seed=2,
data_format=data_format)
y3 = get_annotated_movie(img_size=256,
labels_per_frame=labels_per_frame,
frames=frames,
mov_type='sequential',
seed=3,
data_format=data_format)
y = np.concatenate((y1, y2, y3))
x = np.random.random(y.shape)
tracker = tracking.CellTracker(x,
y,
tracking_model=DummyModel(),
neighborhood_encoder=DummyEncoder(),
track_length=track_length,
data_format=data_format)
tracker.track_cells()
# test tracker.dataframe
df = tracker.dataframe(cell_type='test-value')
assert isinstance(df, pd.DataFrame)
assert 'cell_type' in df.columns # pylint: disable=E1135
# test incorrect values in tracker.dataframe
with pytest.raises(ValueError):
tracker.dataframe(bad_value=-1)
try:
# test tracker.postprocess
tempdir = tempfile.mkdtemp() # create dir
path = os.path.join(tempdir, 'postprocess.xyz')
tracker.postprocess(filename=path)
post_saved_path = os.path.join(tempdir, 'postprocess.trk')
assert os.path.isfile(post_saved_path)
# test tracker.dump
path = os.path.join(tempdir, 'test.xyz')
tracker.dump(path)
dump_saved_path = os.path.join(tempdir, 'test.trk')
assert os.path.isfile(dump_saved_path)
# utility tests for loading trk files
# TODO: move utility tests into utils_test.py
# test trk_folder_to_trks
utils.trk_folder_to_trks(tempdir,
os.path.join(tempdir, 'all.trks'))
assert os.path.isfile(os.path.join(tempdir, 'all.trks'))
# test load_trks
data = utils.load_trks(post_saved_path)
assert isinstance(data['lineages'], list)
assert all(isinstance(d, dict) for d in data['lineages'])
np.testing.assert_equal(data['X'], tracker.X)
np.testing.assert_equal(data['y'], tracker.y_tracked)
# load trks instead of trk
data = utils.load_trks(os.path.join(tempdir, 'all.trks'))
# test trks_stats
utils.trks_stats(os.path.join(tempdir, 'test.trk'))
finally:
try:
shutil.rmtree(tempdir) # delete directory
except OSError as exc:
if exc.errno != errno.ENOENT: # no such file or directory
raise # re-raise exception
def test_track_cells(self):
length = 128
frames = 5
track_length = 2
features = ['appearance', 'neighborhood', 'regionprop', 'distance']
# TODO: Fix for channels_first
for data_format in ('channels_last', ): # 'channels_first'):
x, y = _get_dummy_tracking_data(length,
frames=frames,
data_format=data_format)
tracker = tracking.CellTracker(x,
y,
model=DummyModel(),
track_length=track_length,
data_format=data_format,
features=features)
tracker.track_cells()
# test tracker.dataframe
df = tracker.dataframe(cell_type='test-value')
assert isinstance(df, pd.DataFrame)
assert 'cell_type' in df.columns # pylint: disable=E1135
# test incorrect values in tracker.dataframe
with pytest.raises(ValueError):
tracker.dataframe(bad_value=-1)
try:
# test tracker.postprocess
tempdir = tempfile.mkdtemp() # create dir
path = os.path.join(tempdir, 'postprocess.xyz')
tracker.postprocess(filename=path)
post_saved_path = os.path.join(tempdir, 'postprocess.trk')
assert os.path.isfile(post_saved_path)
# test tracker.dump
path = os.path.join(tempdir, 'test.xyz')
tracker.dump(path)
dump_saved_path = os.path.join(tempdir, 'test.trk')
assert os.path.isfile(dump_saved_path)
# utility tests for loading trk files
# TODO: move utility tests into utils_test.py
# test trk_folder_to_trks
utils.trk_folder_to_trks(tempdir,
os.path.join(tempdir, 'all.trks'))
assert os.path.isfile(os.path.join(tempdir, 'all.trks'))
# test load_trks
data = utils.load_trks(post_saved_path)
assert isinstance(data['lineages'], list)
assert all(isinstance(d, dict) for d in data['lineages'])
np.testing.assert_equal(data['X'], tracker.x)
np.testing.assert_equal(data['y'], tracker.y_tracked)
# load trks instead of trk
data = utils.load_trks(os.path.join(tempdir, 'all.trks'))
# test trks_stats
utils.trks_stats(os.path.join(tempdir, 'test.trk'))
finally:
try:
shutil.rmtree(tempdir) # delete directory
except OSError as exc:
if exc.errno != errno.ENOENT: # no such file or directory
raise # re-raise exception