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# and Dorit S. Hochbaum, Department of Industrial Engineering and Operations Research,
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"""Postprocesser component for selecting the best segmentation in HNCcorr."""
import numpy as np
[docs]class SizePostprocessor:
"""Selects the best segmentation based on the number of selected pixels.
Discards all segmentations that contain more pixels than ``_max_size`` or less
pixels then ``_min_size``. If no segmentations remains, no cell was found and
``None`` is returned. Otherwise the segmentation is returned that minimizes
``|sqrt(x) - sqrt(_pref_size)|`` where x is the number of pixels in the
segmentation.
Attributes:
_min_size (int): Lower bound for the cell size in pixels.
_max_size (int): Upper bound for the cell size in pixels.
_pref_size (int): Preferred cell size in pixels.
"""
[docs] def __init__(self, min_size, max_size, pref_size):
"""Initializes a SizePostprocessor object."""
self._min_size = min_size
self._max_size = max_size
self._pref_size = pref_size
[docs] def _filter(self, segmentations):
"""Returns a list of segmentations with size between min_size and max_size."""
return [
s
for s in segmentations
if self._min_size <= len(s.selection) <= self._max_size
]
[docs] def select(self, segmentations):
"""Selects the best segmentation based on the number of selected pixels.
See class description for details.
Args:
segmentations (List[Segmentation]): List of candidate segmentations.
Returns:
Segmentation or None: Best segmentation or None if all are discarded.
"""
candidates = self._filter(segmentations)
if not candidates:
return None
size = np.array([len(c.selection) for c in candidates])
best_index = np.argmin(np.abs(np.sqrt(size) - np.sqrt(self._pref_size)))
return candidates[best_index]