Merge branch 'roboflow:main' into feat/mot_datasets

Author: rolson24

.pre-commit-config.yaml

@@ -30,7 +30,7 @@ repos:
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.11.6
+      rev: v0.11.7
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

README.md

@@ -3,6 +3,7 @@
     <img width="200" src="https://raw.githubusercontent.com/roboflow/trackers/refs/heads/main/docs/assets/logo-trackers-violet.svg" alt="trackers logo">
 
 [![version](https://badge.fury.io/py/trackers.svg)](https://badge.fury.io/py/trackers)
+[![downloads](https://img.shields.io/pypi/dm/trackers)](https://pypistats.org/packages/trackers)
 [![license](https://img.shields.io/badge/license-Apache%202.0-blue)](https://github.com/roboflow/trackers/blob/main/LICENSE.md)
 [![python-version](https://img.shields.io/pypi/pyversions/trackers)](https://badge.fury.io/py/trackers)
 

trackers/core/sort/tracker.py

@@ -1,5 +1,6 @@
 import numpy as np
 import supervision as sv
+from scipy.optimize import linear_sum_assignment
 
 from trackers.core.base import BaseTracker
 from trackers.core.sort.kalman_box_tracker import SORTKalmanBoxTracker
@@ -76,25 +77,17 @@ def _get_associated_indices(
         unmatched_trackers = set(range(len(self.trackers)))
         unmatched_detections = set(range(len(detection_boxes)))
 
-        if iou_matrix.size > 0:
-            row_indices, col_indices = np.where(iou_matrix > self.minimum_iou_threshold)
-            # Sort in descending order of IOU. Higher = better match.
-            sorted_pairs = sorted(
-                zip(row_indices, col_indices),
-                key=lambda x: iou_matrix[x[0], x[1]],
-                reverse=True,
-            )
-            # keep each unique row/col pair at most once
-            used_rows = set()
-            used_cols = set()
-            for row, col in sorted_pairs:
-                if (row not in used_rows) and (col not in used_cols):
-                    used_rows.add(row)
-                    used_cols.add(col)
+        if len(self.trackers) > 0 and len(detection_boxes) > 0:
+            # Find optimal assignment using scipy.optimize.linear_sum_assignment.
+            # Note that it uses a a modified Jonker-Volgenant algorithm with no
+            # initialization instead of the Hungarian algorithm as mentioned in the
+            # SORT paper.
+            row_indices, col_indices = linear_sum_assignment(iou_matrix, maximize=True)
+            for row, col in zip(row_indices, col_indices):
+                if iou_matrix[row, col] >= self.minimum_iou_threshold:
                     matched_indices.append((row, col))
-
-            unmatched_trackers = unmatched_trackers - used_rows
-            unmatched_detections = unmatched_detections - used_cols
+                    unmatched_trackers.remove(row)
+                    unmatched_detections.remove(col)
 
         return matched_indices, unmatched_trackers, unmatched_detections