slic(mask=..., enforce_connectivity=True, min_size_factor=...) can assign the same label to two non-connected supervoxels #8295

import numpy as np
from scipy import ndimage as ndi
from scipy.spatial.distance import cdist
from skimage.segmentation import slic

CONNECTIVITY_6 = ndi.generate_binary_structure(3, 1)

def connected_pieces(label_mask):
    labeled, n_pieces = ndi.label(label_mask, structure=CONNECTIVITY_6)
    return [np.argwhere(labeled == i) for i in range(1, n_pieces + 1)]

# Volume + mask with several small excluded ("below threshold") pockets.
shape = (24, 24, 24)
rng = np.random.default_rng(27)
volume = rng.integers(0, 255, size=shape + (3,)).astype(np.float64)
volume = ndi.uniform_filter(volume, size=(3, 3, 3, 1))

mask_rng = np.random.default_rng(27 + 1000)
mask = np.ones(shape, dtype=bool)
for _ in range(6):
    z, y, x = (mask_rng.integers(0, shape[i] - 2) for i in range(3))
    mask[z:z+2, y:y+2, x:x+2] = False

start_label = 1
segments = slic(
    volume, mask=mask, n_segments=80, compactness=5,
    enforce_connectivity=True, min_size_factor=0.3, max_size_factor=3,
    start_label=start_label, channel_axis=-1,
)

bg_label = start_label - 1
for lbl in np.unique(segments):
    if lbl == bg_label:
        continue
    pieces = connected_pieces(segments == lbl)
    if len(pieces) > 1:
        dist = min(
            cdist(pieces[i], pieces[j]).min()
            for i in range(len(pieces))
            for j in range(i + 1, len(pieces))
        )
        sizes = [len(p) for p in pieces]
        print(f"label {lbl}: {len(pieces)} disconnected pieces, sizes={sizes}, "
              f"min distance between pieces={dist:.2f}")