Run tracker with smoother enabled

trap/config.py

@@ -152,6 +152,9 @@ inference_parser.add_argument('--predict_training_data',
                     help='Ignore tracker and predict data from the training dataset',
                     action='store_true')
 
+inference_parser.add_argument("--smooth-predictions",
+                    help="Smooth the predicted tracks",
+                    action='store_true')
 
 # Internal connections.
 
@@ -214,6 +217,9 @@ tracker_parser.add_argument("--detector",
                     help="Specify the detector to use",
                     type=str,
                     choices=DETECTORS)
+tracker_parser.add_argument("--smooth-tracks",
+                    help="Smooth the tracker tracks before sending them to the predictor",
+                    action='store_true')
 
 # Renderer
 

trap/frame_emitter.py

@@ -42,6 +42,7 @@ class Detection:
     h: int # height - image space
     conf: float # object detector probablity
     state: DetectionState
+    frame_nr: int
 
     def get_foot_coords(self):
         return [self.l + 0.5 * self.w, self.t+self.h]

trap/prediction_server.py

@@ -27,7 +27,7 @@ import matplotlib.pyplot as plt
 import zmq
 
 from trap.frame_emitter import Frame
-from trap.tracker import Track
+from trap.tracker import Track, Smoother
 
 logger = logging.getLogger("trap.prediction")
 
@@ -120,6 +120,9 @@ class PredictionServer:
 
         if self.config.eval_device == 'cpu':
             logger.warning("Running on CPU. Specifying --eval_device cuda:0 should dramatically speed up prediction")
+
+        if self.config.smooth_predictions:
+            self.smoother = Smoother()
         
         context = zmq.Context()
         self.trajectory_socket: zmq.Socket = context.socket(zmq.SUB)
@@ -387,6 +390,10 @@ class PredictionServer:
                 logger.info(f"Frame prediction: {len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s")
             else:
                 logger.info(f"Total frame delay = {time.time()-frame.time}s ({len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s)")
+            
+            if self.config.smooth_predictions:
+                frame = self.smoother.smooth_frame_predictions(frame)
+
             self.prediction_socket.send_pyobj(frame)
         logger.info('Stopping')
 

trap/renderer.py

@@ -107,9 +107,9 @@ class Renderer:
             
             logger.debug(f"write frame {frame.time - first_time:.3f}s")
             if self.out_writer:
-                self.out_writer.write(img)
+                self.out_writer.write(frame.img)
             if self.streaming_process:
-                self.streaming_process.stdin.write(img.tobytes())
+                self.streaming_process.stdin.write(frame.img.tobytes())
         logger.info('Stopping')
 
         if i>2:

trap/tracker.py

@@ -24,6 +24,9 @@ from ultralytics.engine.results import Results as YOLOResult
 
 from trap.frame_emitter import DetectionState, Frame, Detection, Track
 
+
+from tsmoothie.smoother import KalmanSmoother, ConvolutionSmoother
+
 # Detection = [int, int, int, int, float, int]
 # Detections = [Detection]
 
@@ -103,6 +106,13 @@ class Tracker:
 
         self.H = np.loadtxt(self.config.homography, delimiter=',')
 
+        if self.config.smooth_tracks:
+            logger.info("Smoother enabled")
+            self.smoother = Smoother()
+        else:
+            logger.info("Smoother Disabled (enable with --smooth-tracks)")
+
+
         logger.debug("Set up tracker")
         
 
@@ -160,7 +170,7 @@ class Tracker:
 
 
             if self.config.detector == DETECTOR_YOLOv8:
-                detections: [Detection]  = self._yolov8_track(frame.img)
+                detections: [Detection]  = self._yolov8_track(frame)
             else :
                 detections: [Detection] = self._resnet_track(frame.img, scale = 1)
             
@@ -199,6 +209,9 @@ class Tracker:
             #     self.trajectory_socket.send_string(json.dumps(trajectories))
             # else:
             #     self.trajectory_socket.send(pickle.dumps(frame))
+            if self.config.smooth_tracks:
+                frame = self.smoother.smooth_frame_tracks(frame)
+
             self.trajectory_socket.send_pyobj(frame)
                 
             current_time = time.time()
@@ -249,12 +262,12 @@ class Tracker:
                     
         logger.info('Stopping')
 
-    def _yolov8_track(self, img) -> [Detection]:
+    def _yolov8_track(self, frame: Frame,) -> [Detection]:
-        results: [YOLOResult] = self.model.track(img, persist=True)
+        results: [YOLOResult] = self.model.track(frame.img, persist=True, tracker="bytetrack.yaml", verbose=False)
         if results[0].boxes is None or results[0].boxes.id is None:
             # work around https://github.com/ultralytics/ultralytics/issues/5968
             return []
-        return [Detection(track_id, *bbox) for bbox, track_id in zip(results[0].boxes.xywh.cpu(), results[0].boxes.id.int().cpu().tolist())]
+        return [Detection(track_id, bbox[0]-.5*bbox[2], bbox[1]-.5*bbox[3], bbox[2], bbox[3], 1, DetectionState.Confirmed, frame.index) for bbox, track_id in zip(results[0].boxes.xywh.cpu(), results[0].boxes.id.int().cpu().tolist())]
         
     def _resnet_track(self, img, scale: float = 1) -> [Detection]:
         if scale != 1:
@@ -303,4 +316,55 @@ class Tracker:
 
 def run_tracker(config: Namespace, is_running: Event):
     router = Tracker(config, is_running)
-    router.track()
+    router.track()
+
+
+
+class Smoother:
+
+    def __init__(self):
+        self.smoother = ConvolutionSmoother(window_len=20, window_type='ones', copy=None)
+    
+    def smooth_frame_tracks(self, frame: Frame) -> Frame:
+        new_tracks = []
+        for track in frame.tracks.values():
+            ls = [d.l for d in track.history]
+            ts = [d.t for d in track.history]
+            ws = [d.w for d in track.history]
+            hs = [d.h for d in track.history]
+            self.smoother.smooth(ls)
+            ls = self.smoother.smooth_data[0]
+            self.smoother.smooth(ts)
+            ts = self.smoother.smooth_data[0]
+            self.smoother.smooth(ws)
+            ws = self.smoother.smooth_data[0]
+            self.smoother.smooth(hs)
+            hs = self.smoother.smooth_data[0]
+            new_history = [Detection(d.track_id, l, t, w, h, d.conf, d.state, d.frame_nr) for l, t, w, h, d in zip(ls,ts,ws,hs, track.history)]
+            new_track = Track(track.track_id, new_history, track.predictor_history, track.predictions)
+            new_tracks.append(new_track)
+        frame.tracks = {t.track_id: t for t in new_tracks}
+        return frame
+    
+    def smooth_frame_predictions(self, frame) -> Frame:
+        
+        for track in frame.tracks.values():
+            new_predictions = []
+            if not track.predictions:
+                continue
+            
+            for prediction in track.predictions:
+                xs = [d[0] for d in prediction]
+                ys = [d[1] for d in prediction]
+            
+                self.smoother.smooth(xs)
+                xs = self.smoother.smooth_data[0]
+                self.smoother.smooth(ys)
+                ys = self.smoother.smooth_data[0]
+                
+                smooth_prediction = [[x,y] for x, y in zip(xs, ys)]
+                
+                new_predictions.append(smooth_prediction)
+            track.predictions = new_predictions
+        
+        return frame