Tools for blacklisting tracks

.gitignore

@@ -1,6 +1,7 @@
 .idea/
 OUT/
 EXPERIMENTS/
+runs/
 
 ## Core latex/pdflatex auxiliary files:
 *.aux

poetry.lock

@@ -1015,6 +1015,20 @@ files = [
 [package.extras]
 dev = ["hypothesis"]
 
+[[package]]
+name = "jsonlines"
+version = "4.0.0"
+description = "Library with helpers for the jsonlines file format"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jsonlines-4.0.0-py3-none-any.whl", hash = "sha256:185b334ff2ca5a91362993f42e83588a360cf95ce4b71a73548502bda52a7c55"},
+    {file = "jsonlines-4.0.0.tar.gz", hash = "sha256:0c6d2c09117550c089995247f605ae4cf77dd1533041d366351f6f298822ea74"},
+]
+
+[package.dependencies]
+attrs = ">=19.2.0"
+
 [[package]]
 name = "jsonpointer"
 version = "2.4"
@@ -3725,4 +3739,4 @@ watchdog = ["watchdog (>=2.3)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10,<3.12,"
-content-hash = "bf4feafd4afa6ceb39a1c599e3e7cdc84afbe11ab1672b49e5de99ad44568b08"
+content-hash = "71868029d1943c412082bcb705dd76711f105afa8a93326a45e129230de8ffa9"

pyproject.toml

@@ -10,6 +10,8 @@ trapserv = "trap.plumber:start"
 tracker = "trap.tools:tracker_preprocess"
 compare = "trap.tools:tracker_compare"
 process_data = "trap.process_data:main"
+blacklist = "trap.tools:blacklist_tracks"
+rewrite_tracks = "trap.tools:rewrite_raw_track_files"
 
 
 [tool.poetry.dependencies]
@@ -39,6 +41,7 @@ pyglet-cornerpin = "^0.3.0"
 opencv-python = {file="./opencv_python-4.10.0.84-cp310-cp310-linux_x86_64.whl"}
 setproctitle = "^1.3.3"
 bytetracker =  { git = "https://github.com/rubenvandeven/bytetrack-pip" }
+jsonlines = "^4.0.0"
 
 [build-system]
 requires = ["poetry-core"]

trap/config.py

@@ -86,7 +86,7 @@ class CameraAction(argparse.Action):
             #     'camera_matrix': np.array(data['camera_matrix']), 
             #     'dist_coeff': np.array(data['dist_coeff']),
             # }
-            camera = Camera(np.array(data['camera_matrix']), np.array(data['dist_coeff']), data['dim']['width'], data['dim']['height'], namespace.H)
+            camera = Camera(np.array(data['camera_matrix']), np.array(data['dist_coeff']), data['dim']['width'], data['dim']['height'], namespace.H, namespace.camera_fps)
             
             setattr(namespace, 'camera', camera)
 
@@ -276,6 +276,10 @@ frame_emitter_parser.add_argument("--video-loop",
 
 # Tracker
 
+tracker_parser.add_argument("--camera-fps",
+                    help="Camera FPS",
+                    type=int,
+                    default=12)
 tracker_parser.add_argument("--homography",
                     help="File with homography params",
                     type=Path,
@@ -314,6 +318,9 @@ tracker_parser.add_argument("--smooth-tracks",
 
 
 # Renderer
+# render_parser.add_argument("--disable-renderer",
+#                            help="Disable the renderer all together. Usefull when using an external renderer",
+#                            action="store_true")
 
 render_parser.add_argument("--render-file",
                     help="Render a video file previewing the prediction, and its delay compared to the current frame",

trap/frame_emitter.py

@@ -1,7 +1,11 @@
+from __future__ import annotations
+
 from argparse import Namespace
 from dataclasses import dataclass, field
+import dataclasses
 from enum import IntFlag
 from itertools import cycle
+import json
 import logging
 from multiprocessing import Event
 from pathlib import Path
@@ -11,21 +15,36 @@ import time
 from typing import Iterable, List, Optional
 import numpy as np
 import cv2
+import pandas as pd
 import zmq
 import os
 from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
 from deep_sort_realtime.deep_sort.track import TrackState as DeepsortTrackState
 from bytetracker.byte_tracker import STrack as ByteTrackTrack
 from bytetracker.basetrack import TrackState as ByteTrackTrackState
-
+from trajectron.environment import Environment, Node, Scene
 from urllib.parse import urlparse
 
+from trap.utils import lerp
+
 logger = logging.getLogger('trap.frame_emitter')
 
+class DataclassJSONEncoder(json.JSONEncoder):
+        def default(self, o):
+            if isinstance(o, np.ndarray):
+                return o.tolist()
+            if dataclasses.is_dataclass(o):
+                d = dataclasses.asdict(o)
+                if isinstance(o, Frame):
+                    # Don't send images over JSON
+                    del d['img']
+                return d
+            return super().default(o)
+
 
 class UrlOrPath():
-    def __init__(self, str):
-        self.url = urlparse(str)
+    def __init__(self, string):
+        self.url = urlparse(str(string))
 
     def __str__(self) -> str:
         return self.url.geturl()
@@ -64,14 +83,29 @@ class DetectionState(IntFlag):
             return cls.Confirmed
         raise RuntimeError("Should not run into Deleted entries here")
 
+@dataclass
 class Camera:
-    def __init__(self, mtx, dist, w, h, H):
-        self.mtx = mtx
-        self.dist = dist
-        self.w = w
-        self.h = h
-        self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
-        self.H = H # homography
+    mtx: cv2.Mat
+    dist: cv2.Mat
+    w: float
+    h: float
+    H: cv2.Mat # homography
+
+    newcameramtx: cv2.Mat = field(init=False)
+    roi: cv2.typing.Rect = field(init=False)
+    fps: float
+
+    def __post_init__(self):
+        self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(self.mtx, self.dist, (self.w,self.h), 1, (self.w,self.h))
+
+
+    # def __init__(self, mtx, dist, w, h, H):
+    #     self.mtx = mtx
+    #     self.dist = dist
+    #     self.w = w
+    #     self.h = h
+    #     self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
+    #     self.H = H # homography
 
 
 @dataclass
@@ -131,6 +165,7 @@ class Track:
     history: List[Detection] = field(default_factory=lambda: [])
     predictor_history: Optional[list] = None # in image space
     predictions: Optional[list] = None
+    fps: int = 12
 
     def get_projected_history(self, H, camera: Optional[Camera]= None) -> np.array:
         foot_coordinates = [d.get_foot_coords() for d in self.history]
@@ -138,7 +173,7 @@ class Track:
         if len(foot_coordinates):
             if camera:
                 coords = cv2.undistortPoints(np.array([foot_coordinates]).astype('float32'), camera.mtx, camera.dist, None, camera.newcameramtx)
-                coords = cv2.perspectiveTransform(np.array(coords),H)
+                coords = cv2.perspectiveTransform(np.array(coords),camera.H)
                 return coords.reshape((coords.shape[0],2))
             else:
                 coords = cv2.perspectiveTransform(np.array([foot_coordinates]),H)
@@ -149,8 +184,64 @@ class Track:
         coords = self.get_projected_history(H, camera)
         return [{"x":c[0], "y":c[1]} for c in coords]
 
+    def get_with_interpolated_history(self) -> Track:
+        # new_history = [Detection(d.track_id, l, t, w, h, d.conf, d.state, d.frame_nr, d.det_class) 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_history = []
+        for j in range(len(self.history)-1):
+            a = self.history[j]
+            b = self.history[j+1]
+            gap = b.frame_nr - a.frame_nr
+            new_history.append(Detection(a.track_id, a.l, a.t, a.w, a.h, a.conf, a.state, a.frame_nr, a.det_class))
+            if gap < 1:
+                logger.error(f"WARNING, gap between frames {a.frame_nr} -> {b.frame_nr} is negative?")
+            if gap > 1:
+                for g in range(1, gap):
+                    l = lerp(a.l, b.l, g/gap)
+                    t = lerp(a.t, b.t, g/gap)
+                    w = lerp(a.w, b.w, g/gap)
+                    h = lerp(a.h, b.h, g/gap)
+                    conf = 0
+                    state = DetectionState.Lost
+                    frame_nr = a.frame_nr + g
+                    new_history.append(Detection(a.track_id, l, t, w, h, conf, state, frame_nr, a.det_class))
+
+        return Track(
+            self.track_id,
+            new_history,
+            self.predictor_history,
+            self.predictions,
+            self.fps)
 
 
+    def to_trajectron_node(self, camera: Camera, env: Environment) -> Node:
+        positions = self.get_projected_history(None, camera)
+        velocity = np.gradient(positions, self.fps, axis=0)
+        acceleration = np.gradient(velocity, self.fps, axis=0)
+
+        new_first_idx = self.history[0].frame_nr
+
+        data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
+        
+
+        # vx = derivative_of(x, scene.dt)
+        # vy = derivative_of(y, scene.dt)
+        # ax = derivative_of(vx, scene.dt)
+        # ay = derivative_of(vy, scene.dt)
+
+        data_dict = {
+            ('position', 'x'): positions[:,0],
+            ('position', 'y'): positions[:,1],
+            ('velocity', 'x'): velocity[:,0],
+            ('velocity', 'y'): velocity[:,1],
+            ('acceleration', 'x'): acceleration[:,0],
+            ('acceleration', 'y'): acceleration[:,1]
+            }
+
+        node_data = pd.DataFrame(data_dict, columns=data_columns)
+        
+        return Node(node_type=env.NodeType.PEDESTRIAN, node_id=self.track_id, data=node_data, first_timestep=new_first_idx)
+        
 
     
 

trap/plumber.py

@@ -86,7 +86,7 @@ def start():
 
     # instantiating process with arguments
     procs = [
-        ExceptionHandlingProcess(target=run_ws_forwarder, kwargs={'config': args, 'is_running': isRunning}, name='forwarder'),
+        # ExceptionHandlingProcess(target=run_ws_forwarder, kwargs={'config': args, 'is_running': isRunning}, name='forwarder'),
         ExceptionHandlingProcess(target=run_frame_emitter, kwargs={'config': args, 'is_running': isRunning}, name='frame_emitter'),
         ExceptionHandlingProcess(target=run_tracker, kwargs={'config': args, 'is_running': isRunning}, name='tracker'),
     ]

trap/prediction_server.py

@@ -26,7 +26,7 @@ import matplotlib.pyplot as plt
 
 import zmq
 
-from trap.frame_emitter import Frame
+from trap.frame_emitter import DataclassJSONEncoder, Frame
 from trap.tracker import Track, Smoother
 
 logger = logging.getLogger("trap.prediction")
@@ -160,8 +160,16 @@ class PredictionServer:
         
         self.prediction_socket: zmq.Socket = context.socket(zmq.PUB)
         self.prediction_socket.bind(config.zmq_prediction_addr)
+        self.external_predictions = not self.config.zmq_prediction_addr.startswith("ipc://")
         # print(self.prediction_socket)
 
+    def send_frame(self, frame: Frame):
+        if self.external_predictions:
+            # data = json.dumps(frame, cls=DataclassJSONEncoder)
+            self.prediction_socket.send_json(frame, cls=DataclassJSONEncoder)
+        else:
+            self.prediction_socket.send_pyobj(frame)
+
     def run(self):
 
         if self.config.seed is not None:
@@ -275,12 +283,14 @@ class PredictionServer:
             if self.config.predict_training_data:
                 input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
             else:
+                # print('await', self.config.zmq_trajectory_addr)
                 zmq_ev = self.trajectory_socket.poll(timeout=2000)
                 if not zmq_ev:
                     # on no data loop so that is_running is checked
                     continue
 
                 data = self.trajectory_socket.recv()
+                # print('recv tracker frame')
                 frame: Frame = pickle.loads(data)
                 # trajectory_data = {t.track_id: t.get_projected_history_as_dict(frame.H) for t in frame.tracks.values()}
                 # trajectory_data = json.loads(data)
@@ -330,7 +340,7 @@ class PredictionServer:
                         first_timestep=timestep
                         )
 
-                    input_dict[node] = np.array([x[-1],y[-1],vx[-1],vy[-1],ax[-1],ay[-1]])
+                    input_dict[node] = np.array(object=[x[-1],y[-1],vx[-1],vy[-1],ax[-1],ay[-1]])
 
                 # print(input_dict)
 
@@ -340,7 +350,7 @@ class PredictionServer:
                     # And want to update the network
 
                     # data = json.dumps({})
-                    self.prediction_socket.send_pyobj(frame)
+                    self.send_frame(frame)
                     
                     continue
 
@@ -444,7 +454,7 @@ class PredictionServer:
             if self.config.smooth_predictions:
                 frame = self.smoother.smooth_frame_predictions(frame)
 
-            self.prediction_socket.send_pyobj(frame)
+            self.send_frame(frame)
             time.sleep(.5)
         logger.info('Stopping')
 

trap/tools.py

@@ -1,15 +1,17 @@
 from argparse import Namespace
+import json
 import math
 from pathlib import Path
 import pickle
 from tempfile import mktemp
 
+import jsonlines
 import numpy as np
 import pandas as pd
 import trap.tracker
 from trap.config import parser
 from trap.frame_emitter import Detection, DetectionState, video_src_from_config, Frame
-from trap.tracker import DETECTOR_YOLOv8, Smoother, _yolov8_track, Track, TrainingDataWriter, Tracker
+from trap.tracker import DETECTOR_YOLOv8, Smoother, _yolov8_track, Track, TrainingDataWriter, Tracker, read_tracks_json
 from collections import defaultdict
 
 import logging
@@ -20,6 +22,8 @@ from ultralytics import YOLO
 from ultralytics.engine.results import Results as YOLOResult
 import tqdm
 
+from trap.utils import lerp
+
 
 
 logger = logging.getLogger('tools')
@@ -98,6 +102,7 @@ def tracker_preprocess():
             
             total = 0
             frames = FrameGenerator(config)
+            total_tracks = set()
             for frame in frames:
                 bar.update()
                 
@@ -105,7 +110,6 @@ def tracker_preprocess():
                 total += len(detections)
                 # detections = _yolov8_track(frame, model, imgsz=1440, classes=[0])
 
-                bar.set_description(f"{frames.video_nr}/{len(frames.video_srcs)} [{frames.frame_idx}/{frames.frame_count}] {marquee_string(str(frames.video_path), 10, frames.n//2)} | dets {len(detections)}: {[d.track_id for d in detections]} (∑{total})")
             
                 for detection in detections:
                     track = tracks[detection.track_id]
@@ -114,6 +118,9 @@ def tracker_preprocess():
 
                 active_track_ids = [d.track_id for d in detections]
                 active_tracks = {t.track_id: t for t in tracks.values() if t.track_id in active_track_ids}
+                total_tracks.update(active_track_ids)
+
+                bar.set_description(f"{frames.video_nr}/{len(frames.video_srcs)} [{frames.frame_idx}/{frames.frame_count}] {marquee_string(str(frames.video_path), 10, frames.n//2)} | dets {len(detections)}: {[d.track_id for d in detections]} (∑{total} → {len(total_tracks)})")
 
                 writer.add(frame, active_tracks.values())
                 
@@ -122,12 +129,13 @@ def tracker_preprocess():
 bgr_colors = [
     (255, 0, 0),
     (0, 255, 0),
-    (0, 0, 255),
+    # (0, 0, 255),# red used for missing waypoints
     (0, 255, 255),
 ]
 
-def detection_color(detection: Detection, i):
-    return bgr_colors[i % len(bgr_colors)] if detection.state != DetectionState.Lost else (100,100,100)
+def detection_color(detection: Detection, i, prev_detection: Optional[Detection] = None):
+    vague = detection.state == DetectionState.Lost or (prev_detection and detection.frame_nr - prev_detection.frame_nr > 1)
+    return bgr_colors[i % len(bgr_colors)] if not vague else (0,0,255)
 
 def to_point(coord):
     return (int(coord[0]), int(coord[1]))
@@ -164,13 +172,7 @@ def tracker_compare():
         for i, (tracker, detections) in enumerate(trackers_detections):
             
             for track_id in tracker.tracks:
-                history = tracker.tracks[track_id].history
-                cv2.putText(frame.img, f"{track_id}", to_point(history[0].get_foot_coords()), cv2.FONT_HERSHEY_DUPLEX, 1, color=bgr_colors[i % len(bgr_colors)])
-                for j in range(len(history)-1):
-                    a = history[j]
-                    b = history[j+1]
-                    color = detection_color(b, i)
-                    cv2.line(frame.img, to_point(a.get_foot_coords()), to_point(b.get_foot_coords()), color, 1)
+                draw_track(frame.img, tracker.tracks[track_id], i)
             for detection in detections:
                 color = color = detection_color(detection, i)
                 l, t, r, b = detection.to_ltrb()
@@ -183,6 +185,97 @@ def tracker_compare():
 
         bar.set_description(f"[{frames.video_nr}/{len(frames.video_srcs)}] [{frames.frame_idx}/{frames.frame_count}] {str(frames.video_path)}")
 
+
+def draw_track(img: cv2.Mat, track: Track, color_index: int):
+    history = track.history
+    cv2.putText(img, f"{track.track_id} ({len(history)})", to_point(history[0].get_foot_coords()), cv2.FONT_HERSHEY_DUPLEX, 1, color=bgr_colors[color_index % len(bgr_colors)])
+    
+    point_color = detection_color(history[0], color_index)
+    cv2.circle(img, to_point(history[0].get_foot_coords()), 3, point_color, 2)
+    
+    for j in range(len(history)-1):
+        a = history[j]
+        b = history[j+1]
+        # TODO)) replace with Track.get_with_interpolated_history()
+        gap = b.frame_nr - a.frame_nr - 1
+        if gap < 0:
+            print(f"WARNING, gap between frames {a.frame_nr} -> {b.frame_nr} is negative?")
+        if gap > 0:
+            for g in range(gap):
+                p1 = a.get_foot_coords()
+                p2 = b.get_foot_coords()
+                point = (lerp(p1[0], p2[0], g/gap), lerp(p1[1], p2[1], g/gap))
+
+                cv2.circle(img, to_point(point), 3, (0,0,255), 1)
+
+        color = detection_color(b, color_index, a)
+        cv2.line(img, to_point(a.get_foot_coords()), to_point(b.get_foot_coords()), color, 1)
+        point_color = detection_color(b, color_index)
+        cv2.circle(img, to_point(b.get_foot_coords()), 3, point_color, 2)
+
+def blacklist_tracks():
+    config = parser.parse_args()
+
+    cv2.namedWindow("frame", cv2.WND_PROP_FULLSCREEN)
+    cv2.setWindowProperty("frame",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
+    
+    backdrop = cv2.imread('../DATASETS/hof3/output.png')
+    blacklist = []
+    path: Path = config.save_for_training
+    blacklist_file = path / "blacklist.jsonl"
+    whitelist_file = path / "whitelist.jsonl" # for skipping
+    tracks_file = path / "tracks.json"
+
+    FPS = 12 # TODO)) From config
+
+    if whitelist_file.exists():
+        # with whitelist_file.open('r') as fp:
+        with jsonlines.open(whitelist_file, 'r') as reader:
+            whitelist = [l for l in reader.iter(type=str)]
+    else:
+        whitelist = []
+    
+    try:
+        for track in read_tracks_json(tracks_file, blacklist_file, FPS):
+            if track.track_id in whitelist:
+                logger.info(f'skip whitelisted {track.track_id}')
+                continue
+            
+            img = backdrop.copy()
+            draw_track(img, track, 0)
+
+            imgS = cv2.resize(img, (1920, 1080))
+            cv2.imshow('frame', imgS)
+            while True:
+                k = cv2.waitKey(0)
+                if k==27:    # Esc key to stop
+                    raise StopIteration
+                elif k == ord('s'):
+                    break # skip for now
+                elif k == ord('y'):
+                    with jsonlines.open(whitelist_file, mode='a') as writer:
+                        # skip next time around
+                        writer.write(track.track_id)
+                    break
+                elif k == ord('n'):
+                    print('blacklist', track.track_id)
+                    logger.info(f"Append {len(blacklist)} items to {str(blacklist_file)}")
+                    with jsonlines.open(blacklist_file, mode='a') as writer:
+                        writer.write(track.track_id)
+                    break
+                else:
+                    # ignore all other keypresses
+                    print(k) # else print its value
+                    continue
+    except StopIteration as e:
+        pass
+        
+
+def rewrite_raw_track_files():
+    logging.basicConfig(level=logging.DEBUG)
+    config = parser.parse_args()
+    trap.tracker.rewrite_raw_track_files(config.save_for_training)
+
     
 
 def interpolate_missing_frames(data: pd.DataFrame):

trap/tracker.py

@@ -4,11 +4,13 @@ import csv
 from dataclasses import dataclass, field
 import json
 import logging
+from math import nan
 from multiprocessing import Event
 from pathlib import Path
 import pickle
 import time
 from typing import Optional, List
+import jsonlines
 import numpy as np
 import torch
 import torchvision
@@ -28,7 +30,7 @@ from bytetracker import BYTETracker
 
 from tsmoothie.smoother import KalmanSmoother, ConvolutionSmoother
 import tsmoothie.smoother
-from datetime import datetime
+from datetime import datetime, timedelta
 
 # Detection = [int, int, int, int, float, int]
 # Detections = [Detection]
@@ -89,6 +91,43 @@ class Multifile():
     def readline(self):
         return self.g.__next__()
 
+FIELDNAMES = ['frame_id', 'track_id', 'l', 't', 'w', 'h', 'x', 'y', 'state']
+
+def read_tracks_json(path: Path, blacklist_path: Path, fps):
+    """
+    Reader for tracks.json produced by TrainingDataWriter
+    """
+    with path.open('r') as fp:
+        tracks_dict: dict = json.load(fp)
+    
+    if blacklist_path.exists():
+        with jsonlines.open(blacklist_path, 'r') as reader:
+            blacklist = [track_id for track_id in reader.iter(type=str)]
+        
+    else:
+        blacklist = []
+
+    for track_id, detection_values in tracks_dict.items():
+        if track_id in blacklist:
+            continue
+        
+        history = []
+        # for detection_values in 
+        for detection_items in detection_values:
+            d = dict(zip(FIELDNAMES, detection_items))
+            history.append(Detection(
+                d['track_id'],
+                d['l'],
+                d['t'],
+                d['w'],
+                d['h'],
+                nan,
+                d['state'],
+                d['frame_id'],
+                1
+            ))
+
+        yield Track(track_id, history, fps=fps)
 
 class TrainingDataWriter:
     def __init__(self, training_path: Optional[Path]):
@@ -114,7 +153,7 @@ class TrainingDataWriter:
             self.training_fp = open(self.path / f'all-{d}.txt', 'w')
             logger.debug(f"Writing tracker data to {self.training_fp.name}")
             # following https://github.com/StanfordASL/Trajectron-plus-plus/blob/master/experiments/pedestrians/process_data.py
-            self.csv = csv.DictWriter(self.training_fp, fieldnames=['frame_id', 'track_id', 'l', 't', 'w', 'h', 'x', 'y', 'state'], delimiter='\t', quoting=csv.QUOTE_NONE)
+            self.csv = csv.DictWriter(self.training_fp, fieldnames=FIELDNAMES, delimiter='\t', quoting=csv.QUOTE_NONE)
             self.count = 0
         return self
     
@@ -146,58 +185,80 @@ class TrainingDataWriter:
             return
         
         self.training_fp.close()
-
-        source_files = list(self.path.glob("*.txt")) # we loop twice, so need a list instead of generator
-        total = 0
-        sources = Multifile(source_files)
-        for line in sources:
-            if len(line) > 3: # make sure not to count empty lines
-                total += 1 
-
-
-        lines = {
-            'train': int(total * .8),
-            'val': int(total * .12),
-            'test': int(total * .08),
-        }
-
-        logger.info(f"Splitting gathered data from {sources.name}")
-        # for source_file in source_files:
-
-        tracks_file = self.path / 'tracks.json'
-        tracks = defaultdict(lambda: [])
+        rewrite_raw_track_files(self.path)
         
-        for name, line_nrs in lines.items():
-            dir_path = self.path / name
-            dir_path.mkdir(exist_ok=True)
-            file = dir_path / 'tracked.txt'
-            logger.debug(f"- Write {line_nrs} lines to {file}")
-            with file.open('w') as target_fp:
-                max_track_id = 0
-                offset = 0
-                prev_file = None
-                for i in range(line_nrs):
-                    line = sources.readline()
-                    current_file = sources.current_file
-                    if prev_file != current_file:
-                        offset = max_track_id
-                        
-                        logger.debug(f'{name} - update offset {offset} ({sources.current_file})')
-                        prev_file = current_file
+                    
 
-                    parts = line.split('\t')
-                    track_id = int(parts[1]) + offset
-                    
-                    if track_id > max_track_id:
-                        max_track_id = track_id
-                    
-                    parts[1] = str(track_id)
-                    target_fp.write("\t".join(parts))
-                    tracks[track_id].append(parts)
-        
-        with tracks_file.open('w') as fp:
-            json.dump(tracks, fp)
+def rewrite_raw_track_files(path: Path):
+    source_files = list(sorted(path.glob("*.txt"))) # we loop twice, so need a list instead of generator
+    total = 0
+    sources = Multifile(source_files)
+    for line in sources:
+        if len(line) > 3: # make sure not to count empty lines
+            total += 1 
+
+    
+    destinations = {
+        'train': int(total * .8),
+        'val': int(total * .12),
+        'test': int(total * .08),
+    }
+
+    logger.info(f"Splitting gathered data from {source_files}")
+    # for source_file in source_files:
+
+    tracks_file = path / 'tracks.json'
+    tracks = defaultdict(lambda: [])
+    
+
+    offset = 0
+    max_track_id = 0
+    prev_file = None
+
+    # all-2024-11-12T13:30.txt
+    file_date = None
+
+    for name, line_nrs in destinations.items():
+        dir_path = path / name
+        dir_path.mkdir(exist_ok=True)
+        file = dir_path / 'tracked.txt'
+        logger.debug(f"- Write {line_nrs} lines to {file}")
+        with file.open('w') as target_fp:
+            
+            for i in range(line_nrs):
+                line = sources.readline()
+                current_file = sources.current_file
+                if prev_file != current_file:
+                    offset: int = max_track_id
                     
+                    logger.info(f'{name} - update offset {offset} ({sources.current_file})')
+                    prev_file = current_file
+
+                    file_date = datetime.strptime(current_file.name, 'all-%Y-%m-%dT%H:%M.txt')
+
+                if file_date:
+                    frame_date = file_date + timedelta(seconds = int(parts[0])//10)
+                else:
+                    frame_date = None
+
+                parts = line.split('\t')
+                track_id = int(parts[1]) + offset
+                
+                if track_id > max_track_id:
+                    max_track_id = track_id
+                
+                parts[1] = str(track_id)
+                target_fp.write("\t".join(parts))
+                
+                parts = [float(p) for p in parts]
+                tracks[track_id].append([
+                    int(parts[0] / 10),
+                    track_id,
+                    ] + parts[2:8] + [int(parts[8])])
+    
+    with tracks_file.open('w') as fp:
+        logger.info(f"Write {len(tracks)} tracks to {str(tracks_file)}")
+        json.dump(tracks, fp)
 
 
 class TrackerWrapper():
@@ -317,6 +378,7 @@ class Tracker:
         for detection in detections:
             track = self.tracks[detection.track_id]
             track.track_id = detection.track_id # for new tracks
+            track.fps = self.config.camera.fps # for new tracks
 
             track.history.append(detection) # add to history
         
@@ -441,6 +503,7 @@ class Tracker:
                 if self.config.smooth_tracks:
                     frame = self.smoother.smooth_frame_tracks(frame)
 
+                # print(f"send to {self.trajectory_socket}, {self.config.zmq_trajectory_addr}")
                 self.trajectory_socket.send_pyobj(frame)
                     
                 end_time = time.time()
@@ -549,25 +612,27 @@ class Smoother:
     def smooth(self, points: List[float]):
         self.smoother.smooth(points)
         return self.smoother.smooth_data[0]
-        
+    
+    def smooth_track(self, track: Track) -> Track:
+        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, d.det_class) 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, track.fps)
     
     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, d.det_class) 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_track = self.smooth_track(track)
             new_tracks.append(new_track)
         frame.tracks = {t.track_id: t for t in new_tracks}
         return frame

trap/utils.py

@@ -0,0 +1,8 @@
+def lerp(a: float, b: float, t: float) -> float:
+    """Linear interpolate on the scale given by a to b, using t as the point on that scale.
+    Examples
+    --------
+        50 == lerp(0, 100, 0.5)
+        4.2 == lerp(1, 5, 0.8)
+    """
+    return (1 - t) * a + t * b