Simplified ScipyDistances

Also enables kwargs to be passed to scipy's cdist

norfair/distances.py

@@ -1,5 +1,6 @@
 """Predefined distances"""
 from abc import ABC, abstractmethod
+from functools import partial
 from logging import warning
 from typing import TYPE_CHECKING, Callable, List, Optional, Sequence, Union
 
@@ -39,7 +40,6 @@ def get_distances(
         np.ndarray
             A matrix containing the distances between objects and candidates.
         """
-        pass
 
 
 class ScalarDistance(Distance):
@@ -219,37 +219,16 @@ class ScipyDistance(VectorizedDistance):
         Defines the specific Scipy metric to use to calculate the pairwise distances between
         new candidates and objects.
 
+    Other kwargs are passed through to cdist
+
     See Also
     --------
     [`scipy.spatial.distance.cdist`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html)
     """
 
-    def __init__(
-        self,
-        metric: str = "euclidean",
-    ):
+    def __init__(self, metric: str = "euclidean", **kwargs):
         self.metric = metric
-
-    def _compute_distance(
-        self, stacked_candidates: np.ndarray, stacked_objects: np.ndarray
-    ) -> np.ndarray:
-        """
-        Method that computes the pairwise distances between new candidates and objects.
-        It is intended to use the entire vectors to compare to each other in a single operation.
-
-        Parameters
-        ----------
-        stacked_candidates : np.ndarray
-            np.ndarray containing a stack of candidates to be compared with the stacked_objects.
-        stacked_objects : np.ndarray
-            np.ndarray containing a stack of objects to be compared with the stacked_objects.
-
-        Returns
-        -------
-        np.ndarray
-            A matrix containing the distances between objects and candidates.
-        """
-        return cdist(stacked_candidates, stacked_objects, metric=self.metric)
+        super().__init__(distance_function=partial(cdist, metric=self.metric, **kwargs))
 
 
 def frobenius(detection: "Detection", tracked_object: "TrackedObject") -> float:
@@ -363,9 +342,7 @@ def _validate_bboxes(bboxes: np.ndarray):
     ), f"Bounding boxes must be defined as np.array with (N, 4) shape, {bboxes} given"
 
     if not (all(bboxes[:, 0] < bboxes[:, 2]) and all(bboxes[:, 1] < bboxes[:, 3])):
-        warning(
-            f"Incorrect bounding boxes. Check that x_min < x_max and y_min < y_max."
-        )
+        warning("Incorrect bounding boxes. Check that x_min < x_max and y_min < y_max.")
 
 
 def iou(candidates: np.ndarray, objects: np.ndarray) -> np.ndarray: