refactor: Optimize affinity matrix calculation with parallel processing

- Introduced parallel processing using `ThreadPoolExecutor` to enhance the performance of the `calculate_affinity_matrix` function, allowing simultaneous processing of multiple cameras.
- Added a new helper function `_single_camera_job` to encapsulate the logic for processing individual camera detections, improving code organization and readability.
- Updated the function signature to include an optional `max_workers` parameter for controlling the number of threads used in parallel execution.
- Enhanced documentation to clarify the purpose and parameters of the new parallel processing implementation.

playground.py

@@ -14,7 +14,11 @@
 
 
 # %%
+## imports
+
+import time
 from collections import OrderedDict
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from copy import copy as shallow_copy
 from copy import deepcopy as deep_copy
 from dataclasses import dataclass
@@ -960,9 +964,11 @@ def calculate_affinity_matrix(
     w_3d: float,
     alpha_3d: float,
     lambda_a: float,
+    max_workers: int | None = None,
 ) -> dict[CameraID, AffinityResult]:
     """
     Calculate the affinity matrix between a set of trackings and detections.
+    Uses parallel processing with ThreadPoolExecutor to handle multiple cameras concurrently.
 
     Args:
         trackings: Sequence of tracking objects
@@ -972,6 +978,7 @@ def calculate_affinity_matrix(
         w_3d: Weight for 3D affinity
         alpha_3d: Normalization factor for 3D distance
         lambda_a: Decay rate for time difference
+        max_workers: Maximum number of parallel workers (threads), None = auto
     Returns:
         A dictionary mapping camera IDs to affinity results.
     """
@@ -980,8 +987,30 @@ def calculate_affinity_matrix(
     else:
         detection_by_camera = classify_by_camera(detections)
 
-    res: dict[CameraID, AffinityResult] = {}
-    for camera_id, camera_detections in detection_by_camera.items():
+    def _single_camera_job(
+        camera_id: CameraID,
+        camera_detections: list[Detection],
+        trackings: Sequence[Tracking],
+        w_2d: float,
+        alpha_2d: float,
+        w_3d: float,
+        alpha_3d: float,
+        lambda_a: float,
+    ) -> tuple[CameraID, AffinityResult]:
+        """
+        Process a single camera's detections to calculate affinity and assignment.
+        This function is designed to be run in parallel for each camera.
+
+        Args:
+            camera_id: The camera ID
+            camera_detections: List of detections from this camera
+            trackings: Sequence of tracking objects
+            w_2d, alpha_2d, w_3d, alpha_3d, lambda_a: Affinity parameters
+
+        Returns:
+            A tuple of (camera_id, AffinityResult)
+        """
+        # 1) Calculate affinity matrix
         affinity_matrix = calculate_camera_affinity_matrix_jax(
             trackings,
             camera_detections,
@@ -991,16 +1020,37 @@ def calculate_affinity_matrix(
             alpha_3d,
             lambda_a,
         )
-        # row, col
+        # 2) Calculate assignment
         indices_T, indices_D = linear_sum_assignment(affinity_matrix)
-        affinity_result = AffinityResult(
+        return camera_id, AffinityResult(
             matrix=affinity_matrix,
             trackings=trackings,
             detections=camera_detections,
             indices_T=indices_T,
             indices_D=indices_D,
         )
-        res[camera_id] = affinity_result
+
+    # Run cameras in parallel
+    res: dict[CameraID, AffinityResult] = {}
+    job = partial(
+        _single_camera_job,
+        trackings=trackings,
+        w_2d=w_2d,
+        alpha_2d=alpha_2d,
+        w_3d=w_3d,
+        alpha_3d=alpha_3d,
+        lambda_a=lambda_a,
+    )
+
+    with ThreadPoolExecutor(max_workers=max_workers) as pool:
+        futures = [
+            pool.submit(job, cam_id, cam_dets)
+            for cam_id, cam_dets in detection_by_camera.items()
+        ]
+        for fut in as_completed(futures):
+            cam_id, out = fut.result()
+            res[cam_id] = out
+
     return res
 
 
@@ -1016,6 +1066,7 @@ trackings = sorted(global_tracking_state.trackings.values(), key=lambda x: x.id)
 unmatched_detections = shallow_copy(next_group)
 camera_detections = classify_by_camera(unmatched_detections)
 
+t0 = time.perf_counter()
 affinities = calculate_affinity_matrix(
     trackings,
     unmatched_detections,
@@ -1026,5 +1077,7 @@ affinities = calculate_affinity_matrix(
     lambda_a=LAMBDA_A,
 )
 display(affinities)
+t1 = time.perf_counter()
+print(f"Time taken: {t1 - t0} seconds")
 
 # %%