change(track): add utils.py

src/aliby/track/__init__.py

+"""
+Classes that link outlines within and between time-points.
+"""

src/aliby/track/utils.py

+# If you publish results that make use of this software or the Birth Annotator
+# for Budding Yeast algorithm, please cite:
+# Julian M J Pietsch, Alán Muñoz, Diane Adjavon, Ivan B N Clark, Peter S
+# Swain, 2021, Birth Annotator for Budding Yeast (in preparation).
+#
+#
+# The MIT License (MIT)
+#
+# Copyright (c) Julian Pietsch, Alán Muñoz and Diane Adjavon 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to
+# deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+# sell copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+# IN THE SOFTWARE.
+import typing as t
+
+import numpy as np
+
+
+# Calculate barycentre
+def calc_barycentre(centres, weights=None, **kwargs):
+    """
+    :centres: ndarray containing the (x,y) centres of each cell
+    :weights: (optional) list of weights to consider for each cell
+    """
+    if weights is None:
+        weights = np.ones_like(centres)
+
+    barycentre = np.average(centres, axis=0, weights=weights)
+    return barycentre
+
+
+# Calculate distance to center
+def calc_barydists(centres, bary, **kwargs):
+    """
+    Calculate distances to the barycentre
+    :centre: int (2,) tuple. Centre of cell
+    :bary: float (2,) tuple. Barycentre of image
+    """
+    vec2bary = centres - bary
+    dists = np.sqrt(np.sum(vec2bary**2, axis=1))
+
+    return dists
+
+
+# Calculate angle to center
+def calc_baryangles(centres, bary, areas=None, **kwargs):
+    """
+    Calculate angle using centre of cell and barycentre
+    :centre: int (2,) tuple. Centre of cell
+    :bary: float (2,) tuple. Barycentre of image
+    :anchor_cell: int Cell id to use as angle 0.
+    """
+
+    angles = []
+    vec2bary = centres - bary
+    angles = np.apply_along_axis(lambda x: np.arctan2(*x), 1, vec2bary)
+    if areas is not None:
+        anchor_cell = np.argmax(areas)
+        angles -= angles[anchor_cell]
+
+    return angles
+
+
+def pick_baryfun(key):
+    baryfuns = {"barydist": calc_barydists, "baryangle": calc_baryangles}
+    return baryfuns[key]
+
+
+##  Tracking benchmark utils
+
+
+def lol_to_adj(cell_ids: t.List[t.List[int]]):
+    """
+    Convert a series list of lists with cell ids into a matrix
+    representing a graph.
+
+    Note that information is lost in the process, and a matrix can't be
+    turned back into a list of list by itself.
+
+    input
+
+    :lol: list of lists with cell ids
+
+    returns
+
+    :adj_matrix: (n, n) ndarray where n is the number of cells
+
+    """
+    n = len([y for x in cell_ids for y in x])
+    adj_mat = np.zeros((n, n))
+
+    prev = None
+    cur = 0
+    for c_ids_single_lst in cell_ids:
+        if not prev:
+            prev = c_ids_single_lst
+        else:
+            for i, el in enumerate(c_ids_single_lst):
+                prev_idx = prev.index(el) if el in prev else None
+                if prev_idx is not None:
+                    adj_mat[cur + len(prev) + i, cur + prev_idx] = True
+            cur += len(c_ids_single_lst)
+
+    return adj_mat
+
+
+def compare_pred_truth_lols(prediction, truth):
+    """
+    input
+
+    :prediction: list of lists with predicted cell ids
+    :truth: list of lists with real cell ids
+
+    returns
+
+    number of diferences between equivalent truth matrices
+
+    """
+    adj_pred = lol_to_adj(prediction)
+    adj_truth = lol_to_adj(truth)
+
+    return int(((adj_pred - adj_truth) != 0).sum())