elektronn2.data.image module

elektronn2.data.image.make_affinities(labels, nhood=None, size_thresh=1)

Construct an affinity graph from a segmentation (IDs)

Segments with ID 0 are regarded as disconnected The spatial shape of the affinity graph is the same as of seg_gt. This means that some edges are are undefined and therefore treated as disconnected. If the offsets in nhood are positive, the edges with largest spatial index are undefined.

Connected components is run on the affgraph to relabel the IDs locally.

Parameters:

• labels (4d np.ndarray, int (any precision)) – Volumes of segmentation IDs (bs, z, y, x)
• nhood (2d np.ndarray, int) – Neighbourhood pattern specifying the edges in the affinity graph Shape: (#edges, ndim) nhood[i] contains the displacement coordinates of edge i The number and order of edges is arbitrary
• size_thresh (int) – Size filters for connected components, smaller objects are mapped to BG

Returns:

• aff (5d np.ndarray int16) – Affinity graph of shape (bs, #edges, x, y, z) 1: connected, 0: disconnected
• seg_gt – 4d np.ndarray int16 Affinity graph of shape (bs, x, y, z) Relabelling of components

elektronn2.data.image.downsample_xy(d, l, factor)

Downsample by averaging :param d: data :param l: label :param factor: :return:

elektronn2.data.image.ids2barriers(ids, dilute=[True, True, True], connectivity=[True, True, True], ecs_as_barr=True, smoothen=False)

elektronn2.data.image.smearbarriers(barriers, kernel=None)

barriers: 3d volume (z,x,y)

elektronn2.data.image.center_cubes(cube1, cube2, crop=True)

shapes (ch,x,y,z) or (x,y,z)