:orphan:

:py:mod:`traccuracy._run_metrics`
=================================

.. py:module:: traccuracy._run_metrics


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   traccuracy._run_metrics.run_metrics



.. py:function:: run_metrics(gt_data: traccuracy._tracking_graph.TrackingGraph, pred_data: traccuracy._tracking_graph.TrackingGraph, matcher: traccuracy.matchers._base.Matcher, metrics: list[traccuracy.metrics._base.Metric], relax_skips_gt: bool = False, relax_skips_pred: bool = False) -> tuple[list[dict], traccuracy.matchers._matched.Matched]

   Compute given metrics on data using the given matcher.

   The returned result dictionary will contain all metrics computed by
   the given Metric classes, as well as general summary numbers
   e.g. false positive/false negative detection and edge counts.

   :param gt_data: ground truth graph and optionally segmentation
   :type gt_data: traccuracy.TrackingGraph
   :param pred_data: predicted graph and optionally segmentation
   :type pred_data: traccuracy.TrackingGraph
   :param matcher: instantiated matcher object
   :type matcher: traccuracy.matchers._base.Matcher
   :param metrics: list of instantiated metrics objects
                   to compute
   :type metrics: List[traccuracy.metrics._base.Metric]
   :param relax_skips_gt: If True, the metric will check if skips in the ground truth
                          graph have an equivalent multi-edge path in predicted graph
   :type relax_skips_gt: bool
   :param relax_skips_pred: If True, the metric will check if skips in the predicted
                            graph have an equivalent multi-edge path in ground truth graph
   :type relax_skips_pred: bool

   :returns: List of dictionaries with one dictionary per Metric object
             Matched: Matched data which includes annotated graphs
   :rtype: List[Dict]