def test_clean_up_annotations(self):
img = sk.measure.label(sk.data.binary_blobs(length=256, n_dim=2)) * 3
img = np.expand_dims(img, axis=-1)
img = np.expand_dims(img, axis=0) # time axis
uid = 100
cleaned = utils.clean_up_annotations(img,
uid=uid,
data_format='channels_last')
unique = np.unique(cleaned)
assert len(np.unique(img)) == len(unique)
expected = np.arange(len(unique)) + uid - 1
expected[0] = 0 # background shouldn't get added
np.testing.assert_equal(expected, unique)
img = sk.measure.label(sk.data.binary_blobs(length=256, n_dim=2)) * 3
img = np.expand_dims(img, axis=0)
img = np.expand_dims(img, axis=1) # time axis
cleaned = utils.clean_up_annotations(img,
uid=uid,
data_format='channels_first')
unique = np.unique(cleaned)
assert len(np.unique(img)) == len(unique)
expected = np.arange(len(unique)) + uid - 1
expected[0] = 0 # background shouldn't get added
np.testing.assert_equal(expected, unique)
def __init__(self,
movie,
annotation,
tracking_model,
neighborhood_encoder=None,
distance_threshold=64,
appearance_dim=32,
death=0.99,
birth=0.99,
division=0.9,
track_length=5,
embedding_axis=0,
dtype='float32',
data_format='channels_last'):
if not len(movie.shape) == 4 or not len(annotation.shape) == 4:
raise ValueError(
'Input data and labels but be rank 4 '
'(frames, x, y, channels). Got {} and {}.'.format(
len(movie.shape), len(annotation.shape)))
if not movie.shape[:-1] == annotation.shape[:-1]:
raise ValueError('Input data and labels should have the same shape'
' except for the channel dimension. Got {} and '
'{}'.format(movie.shape, annotation.shape))
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('The `data_format` argument must be one of '
'"channels_first", "channels_last". Received: ' +
str(data_format))
self.X = copy.copy(movie)
self.y = copy.copy(annotation)
self.tracks = {}
self.neighborhood_encoder = neighborhood_encoder
self.tracking_model = tracking_model
self.distance_threshold = distance_threshold
self.appearance_dim = appearance_dim
self.death = death
self.birth = birth
self.division = division
self.dtype = dtype
self.track_length = track_length
self.embedding_axis = embedding_axis
self.a_matrix = []
self.c_matrix = []
self.assignments = []
self.data_format = data_format
self.channel_axis = 0 if data_format == 'channels_first' else -1
self.time_axis = 1 if data_format == 'channels_first' else 0
self.logger = logging.getLogger(str(self.__class__.__name__))
# Clean up annotations
self.y = clean_up_annotations(self.y, data_format=self.data_format)
# Accounting for 0 (background) label with 0-indexing for tracks
self.id_to_idx = {} # int: int mapping
self.idx_to_id = {} # (frame, cell_idx): cell_id mapping
# Establish features for every instance of every cell in the movie
adj_matrices, appearances, morphologies, centroids = self._est_feats()
# Compute embeddings for every instance of every cell in the movie
embeddings = self._get_neighborhood_embeddings(
appearances=appearances,
morphologies=morphologies,
centroids=centroids,
adj_matrices=adj_matrices)
# TODO: immutable dict for safety? these values should never change.
self.features = {
'embedding': embeddings,
'centroid': centroids,
}
def __init__(self,
movie,
annotation,
model,
features={'appearance', 'distance', 'neighborhood', 'regionprop'},
crop_dim=32,
death=0.95,
birth=0.95,
division=0.9,
max_distance=50,
track_length=7,
neighborhood_scale_size=30,
neighborhood_true_size=100,
dtype='float32',
data_format='channels_last'):
if not len(movie.shape) == 4 or not len(annotation.shape) == 4:
raise ValueError('Input data and labels but be rank 4 '
'(frames, x, y, channels). Got {} and {}.'.format(
len(movie.shape), len(annotation.shape)))
if not movie.shape[:-1] == annotation.shape[:-1]:
raise ValueError('Input data and labels should have the same shape'
' except for the channel dimension. Got {} and '
'{}'.format(movie.shape, annotation.shape))
if not features:
raise ValueError('`features` is empty but should be a list with any'
' or all of the following values: "appearance", '
'"distance", "neighborhood" or "regionprop".')
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('The `data_format` argument must be one of '
'"channels_first", "channels_last". Received: ' +
str(data_format))
self.x = copy.copy(movie)
self.y = copy.copy(annotation)
self.tracks = {}
# TODO: Use a model that is served by tf-serving, not one on a local machine
self.model = model
self.crop_dim = crop_dim
self.death = death
self.birth = birth
self.division = division
self.max_distance = max_distance
self.neighborhood_scale_size = neighborhood_scale_size
self.neighborhood_true_size = neighborhood_true_size
self.dtype = dtype
self.data_format = data_format
self.track_length = track_length
self.channel_axis = 0 if data_format == 'channels_first' else -1
self.time_axis = 1 if data_format == 'channels_first' else 0
self.logger = logging.getLogger(str(self.__class__.__name__))
self._track_cells = self.track_cells # backwards compatibility
self.features = sorted(features)
# Clean up annotations
self.y = clean_up_annotations(self.y, data_format=self.data_format)