refactor: Enhance tracking state management and velocity filter integration

- Introduced `TrackingState` to encapsulate the state of tracking, improving data organization and immutability.
- Updated the `Tracking` class to utilize `TrackingState`, enhancing clarity in state management.
- Refactored methods to access keypoints and timestamps through the new state structure, ensuring consistency across the codebase.
- Added a `DummyVelocityFilter` for cases where no velocity estimation is needed, improving flexibility in tracking implementations.
- Cleaned up imports and improved type hints for better code organization.

playground.py

@@ -58,7 +58,12 @@ from app.camera import (
     classify_by_camera,
 )
 from app.solver._old import GLPKSolver
-from app.tracking import AffinityResult, LastDifferenceVelocityFilter, Tracking
+from app.tracking import (
+    AffinityResult,
+    LastDifferenceVelocityFilter,
+    Tracking,
+    TrackingState,
+)
 from app.visualize.whole_body import visualize_whole_body
 
 NDArray: TypeAlias = np.ndarray
@@ -543,11 +548,14 @@ class GlobalTrackingState:
     def add_tracking(self, cluster: Sequence[Detection]) -> Tracking:
         kps_3d, latest_timestamp = triangle_from_cluster(cluster)
         next_id = self._last_id + 1
-        tracking = Tracking(
-            id=next_id,
+        tracking_state = TrackingState(
             keypoints=kps_3d,
             last_active_timestamp=latest_timestamp,
             historical_detections=v(*cluster),
+        )
+        tracking = Tracking(
+            id=next_id,
+            state=tracking_state,
             velocity_filter=LastDifferenceVelocityFilter(kps_3d, latest_timestamp),
         )
         self._trackings[next_id] = tracking
@@ -753,12 +761,12 @@ def calculate_tracking_detection_affinity(
         Combined affinity score
     """
     camera = detection.camera
-    delta_t_raw = detection.timestamp - tracking.last_active_timestamp
+    delta_t_raw = detection.timestamp - tracking.state.last_active_timestamp
     # Clamp delta_t to avoid division-by-zero / exploding affinity.
     delta_t = max(delta_t_raw, DELTA_T_MIN)
 
     # Calculate 2D affinity
-    tracking_2d_projection = camera.project(tracking.keypoints)
+    tracking_2d_projection = camera.project(tracking.state.keypoints)
     w, h = camera.params.image_size
     distance_2d = calculate_distance_2d(
         tracking_2d_projection,
@@ -838,7 +846,7 @@ def calculate_camera_affinity_matrix_jax(
 
     # === Tracking-side tensors ===
     kps3d_trk: Float[Array, "T J 3"] = jnp.stack(
-        [trk.keypoints for trk in trackings]
+        [trk.state.keypoints for trk in trackings]
     )  # (T, J, 3)
     J = kps3d_trk.shape[1]
     # === Detection-side tensors ===
@@ -855,12 +863,12 @@ def calculate_camera_affinity_matrix_jax(
     # --- timestamps ----------
     t0 = min(
         chain(
-            (trk.last_active_timestamp for trk in trackings),
+            (trk.state.last_active_timestamp for trk in trackings),
             (det.timestamp for det in camera_detections),
         )
     ).timestamp()  # common origin (float)
     ts_trk = jnp.array(
-        [trk.last_active_timestamp.timestamp() - t0 for trk in trackings],
+        [trk.state.last_active_timestamp.timestamp() - t0 for trk in trackings],
         dtype=jnp.float32,  # now small, ms-scale fits in fp32
     )
     ts_det = jnp.array(
@@ -1032,7 +1040,5 @@ display(affinities)
 
 # %%
 def update_tracking(tracking: Tracking, detection: Detection):
-    delta_t_ = detection.timestamp - tracking.last_active_timestamp
-    delta_t = max(delta_t_, DELTA_T_MIN)
-
-    return tracking
+    delta_t_ = detection.timestamp - tracking.state.last_active_timestamp
+    raise NotImplementedError