elektronn2.malis.malis_utils module

elektronn2.malis.malis_utils.compute_V_rand_N2(seg_true, seg_pred)

Computes Rand index of seg_pred w.r.t. seg_true. Small is better!!! The input arrays both contain label IDs and may be of arbitrary, but equal, shape.

Pixels which are have ID in the true segmentation are not counted!

Parameters:

seg_true: np.ndarray

True segmentation, IDs

seg_pred: np.ndarray

Predicted segmentation

Returns:ri Return type:???

elektronn2.malis.malis_utils.mknhood2d(radius=1)

Makes nhood structures for some most used dense graphs

elektronn2.malis.malis_utils.mknhood3d(radius=1)

Makes nhood structures for some most used dense graphs. The neighbourhood reference for the dense graph representation we use nhood(1,:) is a 3 vector that describe the node that conn(:,:,:,1) connects to so to use it: conn(23,12,42,3) is the edge between node [23 12 42] and [23 12 42]+nhood(3,:) See? It’s simple! nhood is just the offset vector that the edge corresponds to.

elektronn2.malis.malis_utils.mknhood3d_aniso(radiusxy=1, radiusxy_zminus1=1.8)

Makes nhood structures for some most used dense graphs.

elektronn2.malis.malis_utils.bmap_to_affgraph(bmap, nhood)

Construct an affinity graph from a boundary map

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.

Parameters:

• bmap (3d np.ndarray, int) – Volume of boundaries 0: object interior, 1: boundaries / ECS
• 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

Returns:

aff – Affinity graph of shape (#edges, x, y, z) 1: connected, 0: disconnected

Return type:

4d np.ndarray int32

elektronn2.malis.malis_utils.seg_to_affgraph(seg_gt, nhood)

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.

Parameters:

• seg_gt (3d np.ndarray, int (any precision)) – Volume of segmentation IDs
• 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

Returns:

aff – Affinity graph of shape (#edges, x, y, z) 1: connected, 0: disconnected

Return type:

4d np.ndarray int16

elektronn2.malis.malis_utils.watershed_from_affgraph(aff, seeds, nhood)

elektronn2.malis.malis_utils.bmappred_to_affgraph(pred, nhood)

Construct an affinity graph from boundary predictions

Parameters:

• pred (3d np.ndarray) – Volume of boundary predictions
• 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

Returns:

aff – Affinity graph of shape (#edges, x, y, z) 1: connected, 0: disconnected

Return type:

4d np.ndarray int16