trap/frame_emitter.py

@@ -290,49 +290,49 @@ class Track:
             t.predictions,
             t.fps/step_size)
     
-    # def get_binned(self, bin_size=.5, remove_overlap=True):
-    #     """
-    #     For an experiment: what if we predict using only concrete positions, by mapping 
-    #     dx,dy to a grid. Thus prediction can be for 8 moves, or rather headings
-    #     see ~/notes/attachments example svg
-    #     """
+    def get_binned(self, bin_size=.5, remove_overlap=True):
+        """
+        For an experiment: what if we predict using only concrete positions, by mapping 
+        dx,dy to a grid. Thus prediction can be for 8 moves, or rather headings
+        see ~/notes/attachments example svg
+        """
         
-    #     new_history: List[Detection] = []
-    #     for i, (det0, det1) in enumerate(zip(self.history[:-1], self.history[1:]):
-    #         if i == 0:
-    #             new_history.append(det0)
-    #             continue
-    #         if abs(det1.x - new_history[-1].x) < bin_size or abs(det1.y - new_history[-1].y) < bin_size:
-    #             continue
+        new_history: List[Detection] = []
+        for i, (det0, det1) in enumerate(zip(self.history[:-1], self.history[1:]):
+            if i == 0:
+                new_history.append(det0)
+                continue
+            if abs(det1.x - new_history[-1].x) < bin_size or abs(det1.y - new_history[-1].y) < bin_size:
+                continue
             
-    #         # det1 falls outside of the box [-bin_size:+bin_size] around last detection
+            # det1 falls outside of the box [-bin_size:+bin_size] around last detection
             
-    #         # 1. Interpolate exact point between det0 and det1 that this happens
-    #         if abs(det1.x - new_history[-1].x) >= bin_size:
-    #             if det1.x - new_history[-1].x >= bin_size:
-    #                 # det1 left of last
-    #                 x = new_history[-1].x + bin_size
-    #                 f = inv_lerp(det0.x, det1.x, x)
-    #             elif new_history[-1].x - det1.x >= bin_size:
-    #                 # det1 left of last
-    #                 x = new_history[-1].x - bin_size
-    #                 f = inv_lerp(det0.x, det1.x, x)
-    #             y = lerp(det0.y, det1.y, f)
-    #         if abs(det1.y - new_history[-1].y) >= bin_size:
-    #             if det1.y - new_history[-1].y >= bin_size:
-    #                 # det1 left of last
-    #                 y = new_history[-1].y + bin_size
-    #                 f = inv_lerp(det0.y, det1.y, x)
-    #             elif new_history[-1].y - det1.y >= bin_size:
-    #                 # det1 left of last
-    #                 y = new_history[-1].y - bin_size
-    #                 f = inv_lerp(det0.y, det1.y, x)
-    #             x = lerp(det0.x, det1.x, f)
+            # 1. Interpolate exact point between det0 and det1 that this happens
+            if abs(det1.x - new_history[-1].x) >= bin_size:
+                if det1.x - new_history[-1].x >= bin_size:
+                    # det1 left of last
+                    x = new_history[-1].x + bin_size
+                    f = inv_lerp(det0.x, det1.x, x)
+                elif new_history[-1].x - det1.x >= bin_size:
+                    # det1 left of last
+                    x = new_history[-1].x - bin_size
+                    f = inv_lerp(det0.x, det1.x, x)
+                y = lerp(det0.y, det1.y, f)
+            if abs(det1.y - new_history[-1].y) >= bin_size:
+                if det1.y - new_history[-1].y >= bin_size:
+                    # det1 left of last
+                    y = new_history[-1].y + bin_size
+                    f = inv_lerp(det0.y, det1.y, x)
+                elif new_history[-1].y - det1.y >= bin_size:
+                    # det1 left of last
+                    y = new_history[-1].y - bin_size
+                    f = inv_lerp(det0.y, det1.y, x)
+                x = lerp(det0.x, det1.x, f)
                 
 
-    #         # 2. Find closest point on rectangle (rectangle's four corners, or 4 midpoints)
-    #         points = [[bin_size, 0], [bin_size, bin_size], [0, bin_size], [-bin_size, bin_size], [-bin_size, 0], [-bin_size, -bin_size], [0, -bin_size], [bin_size, -bin_size]]
-    #         # todo Offsets to points:[ history for in points]
+            # 2. Find closest point on rectangle (rectangle's four corners, or 4 midpoints)
+            points = [[bin_size, 0], [bin_size, bin_size], [0, bin_size], [-bin_size, bin_size], [-bin_size, 0], [-bin_size, -bin_size], [0, -bin_size], [bin_size, -bin_size]]
+            # todo Offsets to points:[ history for in points]
 
 
     def to_trajectron_node(self, camera: Camera, env: Environment) -> Node:

trap/tools.py

@@ -11,7 +11,7 @@ import pandas as pd
 import trap.tracker
 from trap.config import parser
 from trap.frame_emitter import Camera, Detection, DetectionState, video_src_from_config, Frame
-from trap.tracker import DETECTOR_YOLOv8, FinalDisplacementFilter, Smoother, TrackReader, _yolov8_track, Track, TrainingDataWriter, Tracker, read_tracks_json
+from trap.tracker import DETECTOR_YOLOv8, Smoother, TrackReader, _yolov8_track, Track, TrainingDataWriter, Tracker, read_tracks_json
 from collections import defaultdict
 
 import logging
@@ -297,9 +297,6 @@ def blacklist_tracks():
     path: Path = config.save_for_training
 
     reader = TrackReader(path, config.camera.fps, exclude_whitelisted = True)
-    tracks = [t for t in reader]
-    filter = FinalDisplacementFilter(2.0)
-    tracks = filter.apply(tracks, config.camera)
     # blacklist_file = path / "blacklist.jsonl"
     # whitelist_file = path / "whitelist.jsonl" # for skipping
     # tracks_file = path / "tracks.json"
@@ -314,7 +311,7 @@ def blacklist_tracks():
     #     whitelist = []
     smoother = Smoother()
     try:
-        for track in tqdm.tqdm(tracks):
+        for track in reader:
 
             if len(track.history) < 5:
                 continue