Send history of track to predictor

trap/tracker.py

@@ -40,9 +40,21 @@ DETECTORS = [DETECTOR_RESNET, DETECTOR_YOLOv8]
 
 @dataclass
 class Track:
+    """A bit of an haphazardous wrapper around the 'real' tracker to provide 
+    a history, with which the predictor can work, as we then can deduce velocity
+    and acceleration.
+    """
     track_id: str = None
     history: [Detection]= field(default_factory=lambda: [])
 
+    def get_projected_history(self, H):
+        foot_coordinates = [d.get_foot_coords() for d in self.history]
+        
+        if len(foot_coordinates):
+            return cv2.perspectiveTransform(np.array([foot_coordinates]),H)
+        return np.array([])
+
+
 @dataclass
 class Detection:
     track_id: str
@@ -144,27 +156,27 @@ class Tracker:
             
                 
             # Store detections into tracklets
+            projected_coordinates = []
             for detection in detections:
                 track = self.tracks[detection.track_id]
                 track.track_id = detection.track_id # for new tracks
 
-                track.history.append(detection) 
+                track.history.append(detection) # add to history
+                # projected_coordinates.append(track.get_projected_history(self.H)) # then get full history
+                
+                # TODO: hadle occlusions, and dissappearance
                 # if len(track.history) > 30:  # retain 90 tracks for 90 frames
                 #     track.history.pop(0)
 
-            foot_coordinates = np.array([[t.get_foot_coords()] for t in detections])
-            if len(foot_coordinates):
-                projected_coordinates = cv2.perspectiveTransform(foot_coordinates,self.H)
-            else:
-                projected_coordinates = []
-
-            # print(TEMP_proj_coords)
+          
             trajectories = {}
-            for detection, coords in zip(detections, projected_coordinates):
+            for detection in detections:                 
                  tid = str(detection.track_id)
+                 track = self.tracks[detection.track_id]
+                 coords = track.get_projected_history(self.H) # get full history
                  trajectories[tid] = {
                      "id": tid,
-                     "history": [{"x":c[0], "y":c[1]} for c in coords] if not self.config.bypass_prediction else coords.tolist() # already doubles nested, fine for test
+                     "history": [{"x":c[0], "y":c[1]} for c in coords[0]] if not self.config.bypass_prediction else coords[0].tolist() # already doubles nested, fine for test
                  }
             # logger.info(f"{trajectories}")
             frame.trajectories = trajectories