Run as separate processes for simple auto restart of individual nodes

README.md

@@ -21,6 +21,7 @@ These are roughly the steps to go from datagathering to training
         * `uv run process_data --src-dir EXPERIMENTS/raw/NAME --dst-dir EXPERIMENTS/trajectron-data/ --name NAME --smooth-tracks --camera-fps 12 --homography ../DATASETS/NAME/homography.json --calibration ../DATASETS/NAME/calibration.json --filter-displacement 2 --map-img-path ../DATASETS/NAME/map.png`
 5. Train Trajectron model `uv run trajectron_train --eval_every 10 --vis_every 1 --train_data_dict NAME_train.pkl --eval_data_dict NAME_val.pkl --offline_scene_graph no --preprocess_workers 8 --log_dir EXPERIMENTS/models --log_tag _NAME --train_epochs 100 --conf EXPERIMENTS/config.json --batch_size 256 --data_dir EXPERIMENTS/trajectron-data `
 6. The run!
-    * On a video file (you can use a wildcard) `DISPLAY=:1 uv run trapserv --remote-log-addr 100.69.123.91 --eval_device cuda:0 --detector ultralytics  --homography ../DATASETS/NAME/homography.json --eval_data_dict EXPERIMENTS/trajectron-data/hof2s-m_test.pkl  --video-src ../DATASETS/NAME/*.mp4 --model_dir EXPERIMENTS/models/models_DATE_NAME/--smooth-predictions  --smooth-tracks --num-samples 3  --render-window --calibration ../DATASETS/NAME/calibration.json` (the DISPLAY environment variable is used here to running over SSH connection and display on local monitor)
+    * `uv run supervisord`
+    <!-- * On a video file (you can use a wildcard) `DISPLAY=:1 uv run trapserv --remote-log-addr 100.69.123.91 --eval_device cuda:0 --detector ultralytics  --homography ../DATASETS/NAME/homography.json --eval_data_dict EXPERIMENTS/trajectron-data/hof2s-m_test.pkl  --video-src ../DATASETS/NAME/*.mp4 --model_dir EXPERIMENTS/models/models_DATE_NAME/--smooth-predictions  --smooth-tracks --num-samples 3  --render-window --calibration ../DATASETS/NAME/calibration.json` (the DISPLAY environment variable is used here to running over SSH connection and display on local monitor)
     * or on the RTSP stream. Which uses gstreamer to substantially reduce latency compared to the default ffmpeg bindings in OpenCV.
-    * To just have a single trajectory pulled from distribution use `--full-dist`. Also try `--z_mode`.
+    * To just have a single trajectory pulled from distribution use `--full-dist`. Also try `--z_mode`. -->

pyproject.toml

@@ -33,6 +33,7 @@ dependencies = [
     "python-statemachine>=2.5.0",
     "facenet-pytorch>=2.5.3",
     "simplification>=0.7.12",
+    "supervisor>=4.2.5",
 ]
 
 [project.scripts]
@@ -43,8 +44,13 @@ compare = "trap.tools:tracker_compare"
 process_data = "trap.process_data:main"
 blacklist = "trap.tools:blacklist_tracks"
 rewrite_tracks = "trap.tools:rewrite_raw_track_files"
-live_video_source = "trap.frame_emitter:run"
-live_tracker = "trap.tracker:run"
+
+trap_video_source = "trap.frame_emitter:FrameEmitter.parse_and_start"
+trap_tracker = "trap.tracker:Tracker.parse_and_start"
+trap_stage = "trap.stage:Stage.parse_and_start"
+trap_prediction = "trap.prediction_server:PredictionServer.parse_and_start"
+trap_render_cv = "trap.cv_renderer:CvRenderer.parse_and_start"
+trap_monitor = "trap.monitor:Monitor.parse_and_start" # migrate timer
 
 [tool.uv]
 

supervisord.conf

@@ -0,0 +1,48 @@
+[inet_http_server]
+port = *:8293
+# username = user
+# password = 123
+
+[supervisord]
+nodaemon = True
+
+
+; The rpcinterface:supervisor section must remain in the config file for
+; RPC (supervisorctl/web interface) to work.  Additional interfaces may be
+; added by defining them in separate [rpcinterface:x] sections.
+[rpcinterface:supervisor]
+supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface
+
+[supervisorctl]
+serverurl = http://localhost:8293
+
+[program:monitor]
+command=uv run trap_monitor
+numprocs=1
+directory=%(here)s
+
+[program:video]
+command=uv run trap_video_source  --homography ../DATASETS/hof3/homography.json   --video-src ../DATASETS/hof3/hof3-cam-demo-twoperson.mp4  --calibration ../DATASETS/hof3/calibration.json --video-loop
+# command=uv run trap_video_source  --homography ../DATASETS/hof3-cam-baumer/homography.json   --video-src gige://../DATASETS/hof3-cam-baumer/gige_config.json --calibration ../DATASETS/hof3-cam-baumer/calibration.json
+directory=%(here)s
+directory=%(here)s
+
+[program:tracker]
+command=uv run trap_tracker
+directory=%(here)s
+
+[program:stage]
+command=uv run trap_stage
+directory=%(here)s
+
+[program:predictor]
+command=uv run trap_prediction --eval_device cuda:0  --model_dir EXPERIMENTS/models/models_20241229_21_35_13_hof3-m2-ud-split-conv12-f2.0-map-2024-12-29/    --num-samples 1      --map_encoding --eval_data_dict EXPERIMENTS/trajectron-data/hof3-m2-ud-split-nostep-conv12-f2.0-map-2024-12-29_val.pkl --prediction-horizon 120 --gmm-mode True --z-mode
+directory=%(here)s
+
+[program:render_cv]
+command=uv run trap_render_cv
+directory=%(here)s
+environment=DISPLAY=":0"
+autostart=false
+; can be long to quit if rendering to video file
+stopwaitsecs=60

trap/base.py

@@ -2,12 +2,14 @@ from __future__ import annotations
 
 from abc import ABC, abstractmethod
 import argparse
+from collections import defaultdict
 from enum import IntFlag
 from itertools import cycle
 import json
 import logging
 from pathlib import Path
 import time
+import types
 from typing import Iterable, Optional, Tuple, Union, List
 import cv2
 from dataclasses import dataclass, field
@@ -617,6 +619,7 @@ class Frame:
     H: Optional[np.array] = None
     camera: Optional[Camera] = None
     maps: Optional[List[cv2.Mat]] = None
+    log: dict = field(default_factory=lambda: {}) # settings used during processing. All intermediate nodes can store their config here
 
     def aslist(self) -> List[dict]:
         return { t.track_id:
@@ -724,3 +727,17 @@ class CameraAction(argparse.Action):
             # 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)
+
+class LambdaParser(argparse.ArgumentParser):
+    """Execute lambda functions
+    """
+    def parse_args(self, args=None, namespace=None):
+        args = super().parse_args(args, namespace)
+
+        for key in vars(args):
+            f = args.__dict__[key]
+            if type(f) == types.LambdaType:
+                print(f'Getting default value for {key}')
+                args.__dict__[key] = f()
+
+        return args

trap/config.py

@@ -6,23 +6,10 @@ import json
 
 from trap.tracker import DETECTORS, TRACKER_BYTETRACK, TRACKERS
 from trap.frame_emitter import Camera
-from trap.base import CameraAction, HomographyAction
+from trap.base import CameraAction, HomographyAction, LambdaParser
 from pyparsing import Optional
 from trap.frame_emitter import UrlOrPath
 
-class LambdaParser(argparse.ArgumentParser):
-    """Execute lambda functions
-    """
-    def parse_args(self, args=None, namespace=None):
-        args = super().parse_args(args, namespace)
-
-        for key in vars(args):
-            f = args.__dict__[key]
-            if type(f) == types.LambdaType:
-                print(f'Getting default value for {key}')
-                args.__dict__[key] = f()
-
-        return args
 
 parser = LambdaParser()
 # parser.parse_args()
@@ -261,7 +248,6 @@ frame_emitter_parser.add_argument("--video-loop",
 #TODO: camera as source
 
 
-# Tracker
 
 tracker_parser.add_argument("--camera-fps",
                     help="Camera FPS",
@@ -277,6 +263,8 @@ tracker_parser.add_argument("--calibration",
                     # type=Path,
                     default=None,
                     action=CameraAction)
+
+# Tracker
 tracker_parser.add_argument("--save-for-training",
                     help="Specify the path in which to save",
                     type=Path,

trap/counter.py

@@ -1,6 +1,8 @@
 import collections
+from gc import is_finalized
 import logging
 import statistics
+import threading
 import time
 from typing import MutableSequence
 import zmq
@@ -8,20 +10,59 @@ import zmq
 logger = logging.getLogger('counter')
 
 class CounterSender:
-    def __init__(self, address = "ipc:///tmp/trap-counters"):
+    def __init__(self, address = "ipc:///tmp/trap-counters2"):
         # self.name = name
         self.context = zmq.Context()
         self.sock = self.context.socket(zmq.PUB)
-        # self.sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame
+        self.sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame
         # self.sock.sndhwm = 1
-        self.sock.bind(address)
+        self.sock.connect(address)
 
     def set(self, name:str, value:float):
         try:
-            self.sock.send_multipart([name.encode('utf8'), str(value).encode("utf8")], flags=zmq.NOBLOCK)
+            # we cannot use send_multipart in combination with conflate
+            self.sock.send_pyobj([name, value], flags=zmq.NOBLOCK)
         except zmq.ZMQError as e:
             logger.warning(f"No space in que to count {name} as {value}")
 
+class CounterFpsSender():
+    def __init__(self, name:str , sender: CounterSender):
+        self.name = name
+        self.sender = sender
+        self.tocs: MutableSequence[(float, int)] = collections.deque(maxlen=5)
+        self.iterations: int = 0
+        # threading.Event.wait()
+        # TODO thread to daeomic loop so it automatically stops
+        self.thread = threading.Thread(target=self.interval, daemon=True)
+        self.is_finished = threading.Event()
+
+    def tick(self):
+        self.iterations += 1
+        self.snapshot()
+    
+    def snapshot(self):
+        self.tocs.append((time.perf_counter(), self.iterations))
+        self.sender.set(self.name, self.fps)
+
+    @property
+    def fps(self):
+        if len(self.tocs) < 2:
+            return 0
+        dt = self.tocs[-1][0] - self.tocs[0][0]
+        di = self.tocs[-1][1] - self.tocs[0][1]
+        return di/dt
+    
+    def interval(self):
+        while True:
+            self.is_finished.wait(.5)
+            if self.is_finished.is_set():
+                break
+
+            self.snapshot()
+            # timer = threading.Timer(.5, self.interval)
+            # timer.start()
+    
+
 class CounterLog():
     def __init__(self, history = 20):
         self.history: MutableSequence[(float, float)] = collections.deque(maxlen=history)
@@ -30,29 +71,37 @@ class CounterLog():
         self.history.append((time.perf_counter(), value))
     
     def value(self):
-        if not len(self.history):
-            return None
         return self.history[-1][1]
     
+    def has_value(self):
+        if not len(self.history):
+            return False
+        if (time.perf_counter() - self.history[-1][0]) > 4:
+            # no update in 4s: very slow. Dead thread?
+            return False
+        return True
+    
     def avg(self):
         if not len(self.history):
             return 0.
         return statistics.fmean([h[1] for h in self.history])
 
 class CounterListerner():
-    def __init__(self, address = "ipc:///tmp/trap-counters"):
+    def __init__(self, address = "ipc:///tmp/trap-counters2"):
         self.context = zmq.Context()
         self.sock = self.context.socket(zmq.SUB)
-        self.sock.connect(address)
+        self.sock.bind(address)
         self.sock.subscribe( b'')
         self.values: collections.defaultdict[str, CounterLog] = collections.defaultdict(lambda: CounterLog())
     
     def snapshot(self):
         messages = []
         while self.sock.poll(0) == zmq.POLLIN:
-            name, value = self.sock.recv_multipart()
-            name, value = name.decode('utf8'),float(value.decode('utf8'))
-            self.values[name].add(value)
+            msg = self.sock.recv_pyobj()
+            # print(msg)
+            name, value = msg
+            # name, value = name.decode('utf8'),float(value.decode('utf8'))
+            self.values[name].add(float(value))
         
     
     def get_latest(self):
@@ -60,7 +109,7 @@ class CounterListerner():
         return self.values
 
     def to_string(self):
-        strs = [f"{k}: {v.value()} ({v.avg()})" for (k,v) in self.values.items()]
+        strs = [(f"{k}: {v.value():.2f} ({v.avg():.2f})" if v.has_value() else f"{k}: --") for (k,v) in self.values.items()]
         return " ".join(strs)
             
     

trap/cv_renderer.py

@@ -1,74 +1,44 @@
 # used for "Forward Referencing of type annotations"
 from __future__ import annotations
 
-import time
-import ffmpeg
-from argparse import Namespace
 import datetime
 import logging
-from multiprocessing import Event
+import time
+from argparse import ArgumentParser, Namespace
 from multiprocessing.synchronize import Event as BaseEvent
+from typing import Dict
+
 import cv2
+import ffmpeg
 import numpy as np
-import json
 import pyglet
-import pyglet.event
 import zmq
-import tempfile
-from pathlib import Path
-import shutil
-import math
-from typing import Dict, Iterable, Optional
-
-
 from pyglet import shapes
-from PIL import Image
 
 from trap.counter import CounterListerner
-from trap.frame_emitter import DetectionState, Frame, Track, Camera
+from trap.frame_emitter import Frame, Track
+from trap.node import Node
 from trap.preview_renderer import FrameWriter
-from trap.tools import draw_track, draw_track_predictions, draw_track_projected, draw_trackjectron_history, to_point
-from trap.utils import convert_world_points_to_img_points, convert_world_space_to_img_space
-
-
+from trap.tools import draw_track_predictions, draw_track_projected, to_point
+from trap.utils import convert_world_points_to_img_points
 
 logger = logging.getLogger("trap.simple_renderer")
 
-class CvRenderer:
-    def __init__(self, config: Namespace, is_running: BaseEvent):
-        self.config = config
-        self.is_running = is_running
+class CvRenderer(Node):
+    def setup(self):
+        self.prediction_sock = self.sub(self.config.zmq_prediction_addr)
+        self.tracker_sock = self.sub(self.config.zmq_trajectory_addr)
+        self.frame_sock = self.sub(self.config.zmq_frame_addr)
 
-        self.counter_listener = CounterListerner()
-
-        context = zmq.Context()
-        self.prediction_sock = context.socket(zmq.SUB)
-        self.prediction_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. NB. make sure this comes BEFORE connect, otherwise it's ignored!!
-        self.prediction_sock.setsockopt(zmq.SUBSCRIBE, b'')
-        # self.prediction_sock.connect(config.zmq_prediction_addr if not self.config.bypass_prediction else config.zmq_trajectory_addr)
-        self.prediction_sock.connect(config.zmq_prediction_addr)
-        
-        self.tracker_sock = context.socket(zmq.SUB)
-        self.tracker_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. NB. make sure this comes BEFORE connect, otherwise it's ignored!!
-        self.tracker_sock.setsockopt(zmq.SUBSCRIBE, b'')
-        self.tracker_sock.connect(config.zmq_trajectory_addr)
-        
-        self.frame_sock = context.socket(zmq.SUB)
-        self.frame_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. NB. make sure this comes BEFORE connect, otherwise it's ignored!!
-        self.frame_sock.setsockopt(zmq.SUBSCRIBE, b'')
-        self.frame_sock.connect(config.zmq_frame_addr)
-    
-        
-        self.H = self.config.H
-            
-
-        self.inv_H = np.linalg.pinv(self.H)
+        # self.H = self.config.H
+        # self.inv_H = np.linalg.pinv(self.H)
 
         # TODO: get FPS from frame_emitter
         # self.out = cv2.VideoWriter(str(filename), fourcc, 23.97, (1280,720))
         self.fps = 60
-        self.frame_size = (self.config.camera.projected_w,self.config.camera.projected_h)
-        self.hide_stats = False
+        self.frame_size = None # configure on first frame recv
+        # self.frame_size = (self.config.camera.projected_w,self.config.camera.projected_h)
+        
         self.out_writer = self.start_writer() if self.config.render_file else None
         self.streaming_process = self.start_streaming() if self.config.render_url else None
 
@@ -80,81 +50,6 @@ class CvRenderer:
         self.tracks: Dict[str, Track] = {}
         self.predictions: Dict[str, Track] = {}
 
-        
-        # self.init_shapes()
-
-        # self.init_labels()
-
-    
-    def init_shapes(self):
-        '''
-        Due to error when running headless, we need to configure options before extending the shapes class
-        '''
-        class GradientLine(shapes.Line):
-            def __init__(self, x, y, x2, y2, width=1, color1=[255,255,255], color2=[255,255,255], batch=None, group=None):
-                # print('colors!', colors)
-                # assert len(colors) == 6
-                
-                r, g, b, *a = color1
-                self._rgba1 = (r, g, b, a[0] if a else 255)
-                r, g, b, *a = color2
-                self._rgba2 = (r, g, b, a[0] if a else 255)
-                
-                # print('rgba', self._rgba)
-
-                super().__init__(x, y, x2, y2, width, color1, batch=None, group=None)
-                # <pyglet.graphics.vertexdomain.VertexList
-                # pyglet.graphics.vertexdomain
-                # print(self._vertex_list)
-            
-            def _create_vertex_list(self):
-                '''
-                copy of super()._create_vertex_list but with additional colors'''
-                self._vertex_list = self._group.program.vertex_list(
-                    6, self._draw_mode, self._batch, self._group,
-                    position=('f', self._get_vertices()),
-                    colors=('Bn', self._rgba1+ self._rgba2 + self._rgba2 + self._rgba1 + self._rgba2 +self._rgba1 ),
-                    translation=('f', (self._x, self._y) * self._num_verts))
-            
-            def _update_colors(self):
-                self._vertex_list.colors[:] = self._rgba1+ self._rgba2 + self._rgba2 + self._rgba1 + self._rgba2 +self._rgba1
-            
-            def color1(self, color):
-                r, g, b, *a = color
-                self._rgba1 = (r, g, b, a[0] if a else 255)
-                self._update_colors()
-            
-            def color2(self, color):
-                r, g, b, *a = color
-                self._rgba2 = (r, g, b, a[0] if a else 255)
-                self._update_colors()
-                
-        self.gradientLine = GradientLine
-
-    def init_labels(self):
-        base_color = (255,)*4
-        color_predictor = (255,255,0, 255)
-        color_info = (255,0, 255, 255)
-        color_tracker = (0,255, 255, 255)
-
-        options = []
-        for option in ['prediction_horizon','num_samples','full_dist','gmm_mode','z_mode', 'model_dir']:
-            options.append(f"{option}: {self.config.__dict__[option]}")
-
-        self.labels = {
-            'waiting': pyglet.text.Label("Waiting for prediction"),
-            'frame_idx': pyglet.text.Label("", x=20, y=self.window.height - 17, color=base_color, batch=self.batch_overlay),
-            'tracker_idx': pyglet.text.Label("", x=90, y=self.window.height - 17, color=color_tracker, batch=self.batch_overlay),
-            'pred_idx': pyglet.text.Label("", x=110, y=self.window.height - 17, color=color_predictor, batch=self.batch_overlay),
-            'frame_time': pyglet.text.Label("t", x=140, y=self.window.height - 17, color=base_color, batch=self.batch_overlay),
-            'frame_latency': pyglet.text.Label("", x=235, y=self.window.height - 17, color=color_info, batch=self.batch_overlay),
-            'tracker_time': pyglet.text.Label("", x=300, y=self.window.height - 17, color=color_tracker, batch=self.batch_overlay),
-            'pred_time': pyglet.text.Label("", x=360, y=self.window.height - 17, color=color_predictor, batch=self.batch_overlay),
-            'track_len': pyglet.text.Label("", x=800, y=self.window.height - 17, color=color_tracker, batch=self.batch_overlay),
-            'options1': pyglet.text.Label(options.pop(-1), x=20, y=30, color=base_color, batch=self.batch_overlay),
-            'options2': pyglet.text.Label(" | ".join(options), x=20, y=10, color=base_color, batch=self.batch_overlay),
-        }
-    
     def refresh_labels(self, dt: float):
         """Every frame"""
         
@@ -173,130 +68,6 @@ class CvRenderer:
             self.labels['pred_time'].text = f"{self.prediction_frame.time - time.time():.3f}s"
             # self.labels['track_len'].text = f"{len(self.prediction_frame.tracks)} tracks"
 
-
-        # cv2.putText(img, f"{frame.index:06d}", (20,17), cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
-        # cv2.putText(img, f"{frame.time - first_time:.3f}s", (120,17), cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
-
-        # if prediction_frame:
-        #     # render Δt and Δ frames
-        #     cv2.putText(img, f"{prediction_frame.index - frame.index}", (90,17), cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
-        #     cv2.putText(img, f"{prediction_frame.time - time.time():.2f}s", (200,17), cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
-        #     cv2.putText(img, f"{len(prediction_frame.tracks)} tracks", (500,17), cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
-        #     cv2.putText(img, f"h: {np.average([len(t.history or []) for t in prediction_frame.tracks.values()]):.2f}", (580,17),  cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
-        #     cv2.putText(img, f"ph: {np.average([len(t.predictor_history or []) for t in prediction_frame.tracks.values()]):.2f}", (660,17),  cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
-        #     cv2.putText(img, f"p: {np.average([len(t.predictions or []) for t in prediction_frame.tracks.values()]):.2f}", (740,17),  cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
-        
-        # options = []
-        # for option in ['prediction_horizon','num_samples','full_dist','gmm_mode','z_mode', 'model_dir']:
-        #     options.append(f"{option}: {config.__dict__[option]}")
-        
-
-        # cv2.putText(img, options.pop(-1), (20,img.shape[0]-30),  cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
-        # cv2.putText(img, " | ".join(options), (20,img.shape[0]-10),  cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
-
-        
-
-    def check_frames(self, dt):
-        new_tracks = False
-        try:
-            self.frame: Frame = self.frame_sock.recv_pyobj(zmq.NOBLOCK)
-            if not self.first_time:
-                self.first_time = self.frame.time
-            img = cv2.GaussianBlur(self.frame.img, (15, 15), 0)
-            img = cv2.flip(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), 0)
-            img = pyglet.image.ImageData(self.frame_size[0], self.frame_size[1], 'RGB', img.tobytes())
-            # don't draw in batch, so that it is the background
-            self.video_sprite = pyglet.sprite.Sprite(img=img, batch=self.batch_bg)
-            self.video_sprite.opacity = 100
-        except zmq.ZMQError as e:
-            # idx = frame.index if frame else "NONE"
-            # logger.debug(f"reuse video frame {idx}")
-            pass
-        try:
-            self.prediction_frame: Frame = self.prediction_sock.recv_pyobj(zmq.NOBLOCK)
-            new_tracks = True
-        except zmq.ZMQError as e:
-            pass
-        try:
-            self.tracker_frame: Frame = self.tracker_sock.recv_pyobj(zmq.NOBLOCK)
-            new_tracks = True
-        except zmq.ZMQError as e:
-            pass
-        
-
-    
-    def on_key_press(self, symbol, modifiers):
-        print('A key was pressed, use f to hide')
-        if symbol == ord('f'):
-            self.window.set_fullscreen(not self.window.fullscreen)
-        if symbol == ord('h'):
-            self.hide_stats = not self.hide_stats
-    
-    def check_running(self, dt):
-        if not self.is_running.is_set():
-            self.window.close()
-            self.event_loop.exit()
-    
-    def on_close(self):
-        self.is_running.clear()
-    
-    def on_refresh(self, dt: float):
-        # update shapes
-        # self.bg =
-        for track_id, track in self.drawn_tracks.items():
-            track.update_drawn_positions(dt)
-
-
-        self.refresh_labels(dt)
-
-        # self.shape1 = shapes.Circle(700, 150, 100, color=(50, 0, 30), batch=self.batch_anim)
-        # self.shape3 = shapes.Circle(800, 150, 100, color=(100, 225, 30), batch=self.batch_anim)
-        pass
-    
-    def on_draw(self):
-        self.window.clear()
-
-        self.batch_bg.draw()
-        
-        for track in self.drawn_tracks.values():
-            for shape in track.shapes:
-                shape.draw() # for some reason the batches don't work
-        for track in self.drawn_tracks.values():
-            for shapes in track.pred_shapes:
-                for shape in shapes:
-                    shape.draw()
-        # self.batch_anim.draw()
-        self.batch_overlay.draw()
-        
-
-        # pyglet.graphics.draw(3, pyglet.gl.GL_LINE, ("v2i", (100,200, 600,800)), ('c3B', (255,255,255, 255,255,255)))
-
-
-
-        if not self.hide_stats:
-            self.fps_display.draw()
-
-        # if streaming, capture buffer and send
-        try:
-            if self.streaming_process or self.out_writer:
-                buf = pyglet.image.get_buffer_manager().get_color_buffer()
-                img_data = buf.get_image_data()
-                data = img_data.get_data() # alternative: .get_data("RGBA", image_data.pitch)
-                img = np.asanyarray(data).reshape((img_data.height, img_data.width, 4))
-                img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
-                img = np.flip(img, 0)
-                # img = cv2.flip(img, cv2.0)
-
-                # cv2.imshow('frame', img)
-                # cv2.waitKey(1)
-                if self.streaming_process:
-                    self.streaming_process.stdin.write(img.tobytes())
-                if self.out_writer:
-                    self.out_writer.write(img)
-        except Exception as e:
-            logger.exception(e)
-
-
     def start_writer(self):
         if not self.config.output_dir.exists():
             raise FileNotFoundError("Path does not exist")
@@ -305,16 +76,16 @@ class CvRenderer:
         filename = self.config.output_dir / f"render_predictions-{date_str}-{self.config.detector}.mp4"
         logger.info(f"Write to {filename}")
 
-        return FrameWriter(str(filename), self.fps, self.frame_size)
+        return FrameWriter(str(filename), self.fps, None)
 
-        fourcc = cv2.VideoWriter_fourcc(*'vp09')
+        # fourcc = cv2.VideoWriter_fourcc(*'vp09')
 
-        return cv2.VideoWriter(str(filename), fourcc, self.fps, self.frame_size)
+        # return cv2.VideoWriter(str(filename), fourcc, self.fps, self.frame_size)
 
-    def start_streaming(self):
+    def start_streaming(self, frame_size=(1920,1080)):
         return (
             ffmpeg
-            .input('pipe:', format='rawvideo',codec="rawvideo", pix_fmt='bgr24', s='{}x{}'.format(*self.frame_size))
+            .input('pipe:', format='rawvideo',codec="rawvideo", pix_fmt='bgr24', s='{}x{}'.format(*frame_size))
             .output(
                 self.config.render_url,
                 #codec = "copy", # use same codecs of the original video
@@ -334,10 +105,7 @@ class CvRenderer:
         )
         # return process
 
-        
-
-    
-    def run(self, timer_counter):
+    def run(self):
         frame = None
         prediction_frame = None
         tracker_frame = None
@@ -348,14 +116,12 @@ class CvRenderer:
         cv2.namedWindow("frame", cv2.WINDOW_NORMAL)
         # https://gist.github.com/ronekko/dc3747211543165108b11073f929b85e
         cv2.moveWindow("frame", 1920, -1)
-        cv2.setWindowProperty("frame",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
-        bgsub = cv2.createBackgroundSubtractorMOG2(120, 50, detectShadows=True)
-
-        while self.is_running.is_set():
-            i+=1
-            with timer_counter.get_lock():
-                timer_counter.value+=1
+        if self.config.full_screen:
+            cv2.setWindowProperty("frame",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
+        # bgsub = cv2.createBackgroundSubtractorMOG2(120, 50, detectShadows=True)
 
+        while self.run_loop():
+            i += 1
 
             # zmq_ev = self.frame_sock.poll(timeout=2000)
             # if not zmq_ev:
@@ -397,7 +163,7 @@ class CvRenderer:
                     first_time = frame.time
 
             # img = frame.img
-            img = decorate_frame(frame, tracker_frame, prediction_frame, first_time, self.config, self.tracks, self.predictions, self.config.render_clusters, self.counter_listener,bgsub)
+            img = decorate_frame(frame, tracker_frame, prediction_frame, first_time, self.config, self.tracks, self.predictions, self.config.render_clusters)
 
             logger.debug(f"write frame {frame.time - first_time:.3f}s")
             if self.out_writer:
@@ -431,6 +197,50 @@ class CvRenderer:
             self.streaming_process.wait()
 
         logger.info('stopped')
+
+
+    @classmethod
+    def arg_parser(cls):
+        render_parser = ArgumentParser()
+        render_parser.add_argument('--zmq-frame-addr',
+                    help='Manually specity communication addr for the frame messages',
+                    type=str,
+                    default="ipc:///tmp/feeds_frame") 
+        render_parser.add_argument('--zmq-trajectory-addr',
+                    help='Manually specity communication addr for the trajectory messages',
+                    type=str,
+                    default="ipc:///tmp/feeds_traj")
+        render_parser.add_argument('--zmq-prediction-addr',
+                            help='Manually specity communication addr for the prediction messages',
+                            type=str,
+                            default="ipc:///tmp/feeds_preds")
+        
+        render_parser.add_argument("--render-file",
+                    help="Render a video file previewing the prediction, and its delay compared to the current frame",
+                    action='store_true')
+        render_parser.add_argument("--render-window",
+                            help="Render a previewing to a window",
+                            action='store_true')
+        
+        render_parser.add_argument("--full-screen",
+                            help="Set Window full screen",
+                            action='store_true')
+        render_parser.add_argument("--render-clusters",
+                            help="renders arrowd clusters instead of individual predictions",
+                            action='store_true')
+
+        render_parser.add_argument("--render-url",
+                            help="""Stream renderer on given URL. Two easy approaches:
+                            - using zmq wrapper one can specify the LISTENING ip. To listen to any incoming connection: zmq:tcp://0.0.0.0:5556
+                            - alternatively, using e.g. UDP one needs to specify the IP of the client. E.g. udp://100.69.123.91:5556/stream
+                            Note that with ZMQ you can have multiple clients connecting simultaneously. E.g. using `ffplay zmq:tcp://100.109.175.82:5556` 
+                            When using udp, connecting can be done using `ffplay udp://100.109.175.82:5556/stream`
+                            """,
+                            type=str,
+                            default=None)
+
+        return render_parser
+    
 # colorset = itertools.product([0,255], repeat=3) # but remove white
 # colorset = [(0, 0, 0),
 #  (0, 0, 255),
@@ -460,7 +270,7 @@ def get_animation_position(track: Track, current_frame: Frame):
 
 
 
-def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame, first_time: float, config: Namespace, tracks: Dict[str, Track], predictions: Dict[str, Track], as_clusters = True, counter_listener: CounterListerner|None = None, bg_subtractor = None) -> np.array:
+def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame, first_time: float, config: Namespace, tracks: Dict[str, Track], predictions: Dict[str, Track], as_clusters = True) -> np.array:
     scale =  100
     # TODO: replace opencv with QPainter to support alpha? https://doc.qt.io/qtforpython-5/PySide2/QtGui/QPainter.html#PySide2.QtGui.PySide2.QtGui.QPainter.drawImage
     # or https://github.com/pygobject/pycairo?tab=readme-ov-file
@@ -503,7 +313,7 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
     else:
         for track_id, track in tracks.items():
             inv_H = np.linalg.pinv(tracker_frame.H)
-            draw_track_projected(img, track, int(track_id), config.camera, conversion)
+            draw_track_projected(img, track, int(track_id), frame.camera, conversion)
     
     if not prediction_frame:
         cv2.putText(img, f"Waiting for prediction...", (500,17), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
@@ -514,7 +324,7 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
             # For debugging:
             # draw_trackjectron_history(img, track, int(track.track_id), conversion)
             anim_position = get_animation_position(track, frame)
-            draw_track_predictions(img, track, int(track.track_id)+1, config.camera, conversion, anim_position=anim_position, as_clusters=as_clusters)
+            draw_track_predictions(img, track, int(track.track_id)+1, frame.camera, conversion, anim_position=anim_position, as_clusters=as_clusters)
             cv2.putText(img, f"{len(track.predictor_history) if track.predictor_history else 'none'}", to_point(track.history[0].get_foot_coords()), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 1)
         if prediction_frame.maps:
             for i, m in enumerate(prediction_frame.maps):
@@ -544,6 +354,8 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
     cv2.putText(img, f"{frame.time - first_time: >10.2f}s", (150,17), cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
     cv2.putText(img, f"{frame.time - time.time():.2f}s", (250,17), cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
 
+    options = []
+    
     if prediction_frame:
         # render Δt and Δ frames
         cv2.putText(img, f"{tracker_frame.index - frame.index}", (90,17), cv2.FONT_HERSHEY_PLAIN, 1, tracker_color, 1)
@@ -554,19 +366,15 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
         cv2.putText(img, f"h: {np.average([len(t.history or []) for t in prediction_frame.tracks.values()]):.2f}", (700,17),  cv2.FONT_HERSHEY_PLAIN, 1, tracker_color, 1)
         cv2.putText(img, f"ph: {np.average([len(t.predictor_history or []) for t in prediction_frame.tracks.values()]):.2f}", (780,17),  cv2.FONT_HERSHEY_PLAIN, 1, predictor_color, 1)
         cv2.putText(img, f"p: {np.average([len(t.predictions or []) for t in prediction_frame.tracks.values()]):.2f}", (860,17),  cv2.FONT_HERSHEY_PLAIN, 1, predictor_color, 1)
-    
-    options = []
-    for option in ['prediction_horizon','num_samples','full_dist','gmm_mode','z_mode', 'model_dir']:
-        options.append(f"{option}: {config.__dict__[option]}")
-    
+
+        
+        for option, value in prediction_frame.log['predictor'].items():
+            options.append(f"{option}: {value}")
+        
 
     cv2.putText(img, options.pop(-1), (20,img.shape[0]-30),  cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
     cv2.putText(img, " | ".join(options), (20,img.shape[0]-10),  cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
 
-    # for i, (k, v) in enumerate(counter_listener.get_latest().items()):
-    #     cv2.putText(img, f"{k} {v.value()}", (20,img.shape[0]-(40*i)-40),  cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
-
-
     return img
 
     

trap/frame_emitter.py

@@ -1,75 +1,51 @@
 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
 import multiprocessing
-from pathlib import Path
 import pickle
-import sys
-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 argparse import ArgumentParser, Namespace
+from multiprocessing import Event
+from pathlib import Path
 
+import zmq
+
+from trap import node
 from trap.base import *
+from trap.base import LambdaParser
 from trap.timer import Timer
-from trap.utils import get_bins
-from trap.utils import inv_lerp, lerp
 from trap.video_sources import get_video_source
 
 logger = logging.getLogger('trap.frame_emitter')
-
-
     
 
-class FrameEmitter:
+class FrameEmitter(node.Node):
     '''
     Emit frame in a separate threat so they can be throttled,
     or thrown away when the rest of the system cannot keep up
     '''
-    def __init__(self, config: Namespace, is_running: Event) -> None:
-        self.config = config
-        self.is_running = is_running
-
-        context = zmq.Context()
-        # TODO: to make things faster, a multiprocessing.Array might be a tad faster: https://stackoverflow.com/a/65201859
-        self.frame_sock = context.socket(zmq.PUB)
-        self.frame_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. make sure to set BEFORE connect/bind
-        self.frame_sock.bind(config.zmq_frame_addr)
-
-        self.frame_noimg_sock = context.socket(zmq.PUB)
-        self.frame_noimg_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. make sure to set BEFORE connect/bind
-        self.frame_noimg_sock.bind(config.zmq_frame_noimg_addr)
+    def setup(self) -> None:
+        self.frame_sock = self.pub(self.config.zmq_frame_addr)
+        self.frame_noimg_sock = self.pub(self.config.zmq_frame_noimg_addr)
         
-        logger.info(f"Connection socket {config.zmq_frame_addr}")
-        logger.info(f"Connection socket {config.zmq_frame_noimg_addr}")
+        logger.info(f"Connection socket {self.config.zmq_frame_addr}")
+        logger.info(f"Connection socket {self.config.zmq_frame_noimg_addr}")
 
         self.video_srcs = self.config.video_src
 
+    
 
-    def emit_video(self, timer_counter):
-        i = 0
 
-        source = get_video_source(self.video_srcs, self.config.camera, int(self.config.video_offset or 0), self.config.video_end, self.config.video_loop)
-        for i, img in enumerate(source):
+    def run(self):
+        offset = int(self.config.video_offset or 0)
+        source = get_video_source(self.video_srcs, self.config.camera, offset, self.config.video_end, self.config.video_loop)
+        video_gen = enumerate(source, start = offset)
+        while self.run_loop():
 
-            with timer_counter.get_lock():
-                timer_counter.value += 1
+            try:
+                i, img = next(video_gen)
+            except StopIteration as e:
+                logger.info("Video source ended")
+                break
 
             frame = Frame(i, img=img, H=self.config.camera.H, camera=self.config.camera)
 
@@ -78,73 +54,62 @@ class FrameEmitter:
             self.frame_noimg_sock.send(pickle.dumps(frame.without_img()))
             self.frame_sock.send(pickle.dumps(frame))
 
-            if not self.is_running.is_set():
-                # if not running, also break out of infinite generator loop
-                break
-
 
         logger.info("Stopping")
+    
+
+    @classmethod
+    def arg_parser(cls) -> ArgumentParser:
+        argparser = LambdaParser()
+        argparser.add_argument('--zmq-frame-addr',
+                    help='Manually specity communication addr for the frame messages',
+                    type=str,
+                    default="ipc:///tmp/feeds_frame") 
+
+        argparser.add_argument('--zmq-frame-noimg-addr',
+                            help='Manually specity communication addr for the frame messages',
+                            type=str,
+                            default="ipc:///tmp/feeds_frame2")
+
+        argparser.add_argument("--video-src",
+                            help="source video to track from can be either a relative or absolute path, or a url, like an RTSP resource, or use gige://RELATIVE_PATH_TO_GIGE_CONFIG_JSON",
+                            type=UrlOrPath,
+                            nargs='+',
+                            default=lambda: [UrlOrPath(p) for p in Path('../DATASETS/VIRAT_subset_0102x/').glob('*.mp4')])
+        argparser.add_argument("--video-offset",
+                            help="Start playback from given frame. Note that when src is an array, this applies to all videos individually.",
+                            default=0,
+                            type=int)
+        argparser.add_argument("--video-end",
+                    help="End (or loop) playback at given frame.",
+                    default=None,
+                    type=int)
+
+        argparser.add_argument("--video-loop",
+                            help="By default it emitter will run only once. This allows it to loop the video file to keep testing.",
+                            action='store_true')
+
+        argparser.add_argument("--camera-fps",
+                            help="Camera FPS",
+                            type=int,
+                            default=12)
+        argparser.add_argument("--homography", 
+                            help="File with homography params [Deprecated]",
+                            type=Path,
+                            default='../DATASETS/VIRAT_subset_0102x/VIRAT_0102_homography_img2world.txt',
+                            action=HomographyAction)
+        argparser.add_argument("--calibration",
+                            help="File with camera intrinsics and lens distortion params (calibration.json)",
+                            # type=Path,
+                            required=True,
+                            # default=None,
+                            action=CameraAction)
+        return argparser
+
 
       
 
 def run_frame_emitter(config: Namespace, is_running: Event, timer_counter: int):
     router = FrameEmitter(config, is_running)
-    router.emit_video(timer_counter)
+    router.run(timer_counter)
     is_running.clear()
-
-def run():
-    # Frame emitter
-    import argparse
-    argparser = argparse.ArgumentParser()
-    argparser.add_argument('--zmq-frame-addr',
-                    help='Manually specity communication addr for the frame messages',
-                    type=str,
-                    default="ipc:///tmp/feeds_frame") 
-
-    argparser.add_argument('--zmq-frame-noimg-addr',
-                        help='Manually specity communication addr for the frame messages',
-                        type=str,
-                        default="ipc:///tmp/feeds_frame2")
-
-    argparser.add_argument("--video-src",
-                        help="source video to track from can be either a relative or absolute path, or a url, like an RTSP resource",
-                        type=UrlOrPath,
-                        nargs='+',
-                        default=lambda: [UrlOrPath(p) for p in Path('../DATASETS/VIRAT_subset_0102x/').glob('*.mp4')])
-    argparser.add_argument("--video-offset",
-                        help="Start playback from given frame. Note that when src is an array, this applies to all videos individually.",
-                        default=0,
-                        type=int)
-    #TODO: camera as source
-
-    argparser.add_argument("--video-loop",
-                        help="By default it emitter will run only once. This allows it to loop the video file to keep testing.",
-                        action='store_true')
-    #TODO: camera as source
-
-
-    # Tracker
-
-    argparser.add_argument("--camera-fps",
-                        help="Camera FPS",
-                        type=int,
-                        default=12)
-    argparser.add_argument("--homography",
-                        help="File with homography params",
-                        type=Path,
-                        default='../DATASETS/VIRAT_subset_0102x/VIRAT_0102_homography_img2world.txt',
-                        action=HomographyAction)
-    argparser.add_argument("--calibration",
-                        help="File with camera intrinsics and lens distortion params (calibration.json)",
-                        # type=Path,
-                        required=True,
-                        # default=None,
-                        action=CameraAction)
-    config = argparser.parse_args()
-    is_running = multiprocessing.Event()
-    print(is_running.set())
-    timer_counter = Timer('frame_emitter')
-
-    router = FrameEmitter(config, is_running)
-    router.emit_video(timer_counter.iterations)
-    is_running.clear()

trap/node.py

@@ -1,18 +1,20 @@
 import logging
+import multiprocessing
 from multiprocessing.synchronize import Event as BaseEvent
-from argparse import Namespace
+from argparse import ArgumentParser, Namespace
 from typing import Optional
 
 import zmq
 
+from trap.counter import CounterFpsSender, CounterSender
 from trap.timer import Timer
 
 
 class Node():
-    def __init__(self, config: Namespace, is_running: BaseEvent, timer_counter: Timer):
+    def __init__(self, config: Namespace, is_running: BaseEvent, fps_counter: CounterFpsSender):
         self.config = config
         self.is_running = is_running
-        self.timer_counter = timer_counter
+        self.fps_counter = fps_counter
         self.zmq_context = zmq.Context()
         self.logger = self._logger()
 
@@ -23,8 +25,9 @@ class Node():
         return logging.getLogger(f"trap.{cls.__name__}")
 
     def tick(self):
-        with self.timer_counter.get_lock():
-            self.timer_counter.value+=1
+        self.fps_counter.tick()
+        # with self.fps_counter.get_lock():
+        #     self.fps_counter.value+=1
     
     def setup(self):
         raise RuntimeError("Not implemented setup()")
@@ -32,6 +35,17 @@ class Node():
     def run(self):
         raise RuntimeError("Not implemented run()")
     
+    def run_loop(self):
+        """Use in run(), to check if it should keep looping
+        Takes care of tick()'ing the iterations/second counter
+        """
+        self.tick()
+        return self.is_running.is_set()
+    
+    @classmethod
+    def arg_parser(cls) -> ArgumentParser:
+        raise RuntimeError("Not implemented arg_parser()")
+        
     def sub(self, addr: str):
         "Default zmq sub configuration"
         sock = self.zmq_context.socket(zmq.SUB)
@@ -51,4 +65,15 @@ class Node():
     def start(cls, config: Namespace, is_running: BaseEvent, timer_counter: Optional[Timer]):
         instance = cls(config, is_running, timer_counter)
         instance.run()
-        instance.logger.info("Stopping")
+        instance.logger.info("Stopping")
+    
+    @classmethod
+    def parse_and_start(cls):
+        config = cls.arg_parser().parse_args()
+        is_running = multiprocessing.Event()
+        is_running.set()
+        statsender = CounterSender()
+        counter = CounterFpsSender(f"trap.{cls.__name__}", statsender)
+        # timer_counter = Timer(cls.__name__)
+
+        cls.start(config, is_running, counter)

trap/prediction_server.py

@@ -1,33 +1,27 @@
 # adapted from Trajectron++ online_server.py
-from argparse import Namespace
-import logging
-from multiprocessing import Event, Queue
-import os
-import pickle
-import sys
-import time
 import json
-import traceback
-import warnings
-import pandas as pd
-import torch
-import dill
-import random
+import logging
+import os
 import pathlib
-import numpy as np
-from trajectron.environment.data_utils import derivative_of
-from trajectron.utils import prediction_output_to_trajectories
-from trajectron.model.online.online_trajectron import OnlineTrajectron
-from trajectron.model.model_registrar import ModelRegistrar
-from trajectron.environment import Environment, Scene
-from trajectron.environment.node import Node
-from trajectron.environment.node_type import NodeType
-import matplotlib.pyplot as plt
+import pickle
+import random
+import time
+import warnings
+from argparse import ArgumentParser, Namespace
+from multiprocessing import Event
 
+import dill
+import numpy as np
+import torch
 import zmq
+from trajectron.environment import Environment, Scene
+from trajectron.model.model_registrar import ModelRegistrar
+from trajectron.model.online.online_trajectron import OnlineTrajectron
+from trajectron.utils import prediction_output_to_trajectories
 
 from trap.frame_emitter import DataclassJSONEncoder, Frame
-from trap.tracker import Track, Smoother
+from trap.node import Node
+from trap.tracker import Smoother
 
 logger = logging.getLogger("trap.prediction")
 
@@ -146,27 +140,18 @@ def offset_trajectron_dict(source, x, y):
             source[t][node][:,1] += y
     return source
 
-class PredictionServer:
-    def __init__(self, config: Namespace, is_running: Event):
-        self.config = config
-        self.is_running = is_running
-
+class PredictionServer(Node):
+    def setup(self):
         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(window_len=12, convolution=True) # convolution seems fine for predictions
         
-        context = zmq.Context()
-        self.trajectory_socket: zmq.Socket = context.socket(zmq.SUB)
-        self.trajectory_socket.setsockopt(zmq.SUBSCRIBE, b'')
-        self.trajectory_socket.setsockopt(zmq.CONFLATE, 1) # only keep last msg. Set BEFORE connect!
-        self.trajectory_socket.connect(config.zmq_trajectory_addr)
-        
-        self.prediction_socket: zmq.Socket = context.socket(zmq.PUB)
-        self.prediction_socket.bind(config.zmq_prediction_addr)
+        self.trajectory_socket = self.sub(self.config.zmq_trajectory_addr)
+        self.prediction_socket = self.pub(self.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:
@@ -175,8 +160,7 @@ class PredictionServer:
         else:
             self.prediction_socket.send_pyobj(frame)
 
-    def run(self, timer_counter):
-        print(self.config)
+    def run(self):
         if self.config.seed is not None:
             random.seed(self.config.seed)
             np.random.seed(self.config.seed)
@@ -250,17 +234,9 @@ class PredictionServer:
         trajectron.set_environment(online_env, init_timestep)
 
         timestep = init_timestep + 1
-        prev_run_time = 0
-        while self.is_running.is_set():
-            timestep += 1
-            with timer_counter.get_lock():
-                timer_counter.value+=1
-            
-            # this_run_time = time.time()
-            # logger.debug(f'test {prev_run_time - this_run_time}')
-            # time.sleep(max(0, prev_run_time - this_run_time + .5))
-            # prev_run_time = time.time()
 
+        while self.run_loop():
+            
 
             # TODO: see process_data.py on how to create a node, the provide nodes + incoming data columns
             # data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
@@ -283,7 +259,6 @@ 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
@@ -295,6 +270,12 @@ class PredictionServer:
                 # print('recv tracker frame')
                 frame: Frame = pickle.loads(data)
 
+                # add settings to log
+                frame.log['predictor'] = {}
+                for option in ['prediction_horizon','num_samples','full_dist','gmm_mode','z_mode', 'model_dir']:
+                    frame.log['predictor'][option] = self.config.__dict__[option]
+                
+
                 # print('indexrecv', [frame.tracks[t].frame_index for t in frame.tracks])
                 # trajectory_data = {t.track_id: t.get_projected_history_as_dict(frame.H) for t in frame.tracks.values()}
                 # trajectory_data = json.loads(data)
@@ -489,6 +470,160 @@ class PredictionServer:
             self.send_frame(frame)
             
         logger.info('Stopping')
+    
+    @classmethod
+    def arg_parser(cls) -> ArgumentParser:
+        inference_parser = ArgumentParser()
+        inference_parser.add_argument('--zmq-trajectory-addr',
+                    help='Manually specity communication addr for the trajectory messages',
+                    type=str,
+                    default="ipc:///tmp/feeds_traj")
+        inference_parser.add_argument('--zmq-prediction-addr',
+                    help='Manually specity communication addr for the prediction messages',
+                    type=str,
+                    default="ipc:///tmp/feeds_preds")
+        
+
+        inference_parser.add_argument("--step-size", 
+                              # TODO)) Make dataset/model metadata
+                              help="sample step size (should be the same as for data processing and augmentation)",
+                              type=int,
+                              default=1,
+                              )
+        inference_parser.add_argument("--model_dir",
+                            help="directory with the model to use for inference",
+                            type=str, # TODO: make into Path
+                            default='../Trajectron-plus-plus/experiments/trap/models/models_18_Oct_2023_19_56_22_virat_vel_ar3/')
+                            # default='../Trajectron-plus-plus/experiments/pedestrians/models/models_04_Oct_2023_21_04_48_eth_vel_ar3')
+
+        inference_parser.add_argument("--conf",
+                            help="path to json config file for hyperparameters, relative to model_dir",
+                            type=str,
+                            default='config.json')
+
+        # Model Parameters (hyperparameters)
+        inference_parser.add_argument("--offline_scene_graph",
+                            help="whether to precompute the scene graphs offline, options are 'no' and 'yes'",
+                            type=str,
+                            default='yes')
+
+        inference_parser.add_argument("--dynamic_edges",
+                            help="whether to use dynamic edges or not, options are 'no' and 'yes'",
+                            type=str,
+                            default='yes')
+
+        inference_parser.add_argument("--edge_state_combine_method",
+                            help="the method to use for combining edges of the same type",
+                            type=str,
+                            default='sum')
+
+        inference_parser.add_argument("--edge_influence_combine_method",
+                            help="the method to use for combining edge influences",
+                            type=str,
+                            default='attention')
+
+        inference_parser.add_argument('--edge_addition_filter',
+                            nargs='+',
+                            help="what scaling to use for edges as they're created",
+                            type=float,
+                            default=[0.25, 0.5, 0.75, 1.0]) # We don't automatically pad left with 0.0, if you want a sharp
+                                                            # and short edge addition, then you need to have a 0.0 at the
+                                                            # beginning, e.g. [0.0, 1.0].
+
+        inference_parser.add_argument('--edge_removal_filter',
+                            nargs='+',
+                            help="what scaling to use for edges as they're removed",
+                            type=float,
+                            default=[1.0, 0.0])  # We don't automatically pad right with 0.0, if you want a sharp drop off like
+                                                # the default, then you need to have a 0.0 at the end.
+
+
+        inference_parser.add_argument('--incl_robot_node',
+                            help="whether to include a robot node in the graph or simply model all agents",
+                            action='store_true')
+
+        inference_parser.add_argument('--map_encoding',
+                            help="Whether to use map encoding or not",
+                            action='store_true')
+
+        inference_parser.add_argument('--no_edge_encoding',
+                            help="Whether to use neighbors edge encoding",
+                            action='store_true')
+
+
+        inference_parser.add_argument('--batch_size',
+                            help='training batch size',
+                            type=int,
+                            default=256)
+
+        inference_parser.add_argument('--k_eval',
+                            help='how many samples to take during evaluation',
+                            type=int,
+                            default=25)
+
+        # Data Parameters
+        inference_parser.add_argument("--eval_data_dict",
+                            help="what file to load for evaluation data (WHEN NOT USING LIVE DATA)",
+                            type=str,
+                            default='../Trajectron-plus-plus/experiments/processed/eth_test.pkl')
+
+        inference_parser.add_argument("--output_dir",
+                            help="what dir to save output (i.e., saved models, logs, etc) (WHEN NOT USING LIVE OUTPUT)",
+                            type=pathlib.Path,
+                            default='./OUT/test_inference')
+
+
+        # inference_parser.add_argument('--device',
+        #                     help='what device to perform training on',
+        #                     type=str,
+        #                     default='cuda:0')
+
+        inference_parser.add_argument("--eval_device",
+                            help="what device to use during inference",
+                            type=str,
+                            default="cpu")
+
+
+        inference_parser.add_argument('--seed',
+                            help='manual seed to use, default is 123',
+                            type=int,
+                            default=123)
+
+        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')
+
+        inference_parser.add_argument('--prediction-horizon',
+                            help='Trajectron.incremental_forward parameter',
+                            type=int,
+                            default=30)
+        inference_parser.add_argument('--num-samples',
+                            help='Trajectron.incremental_forward parameter',
+                            type=int,
+                            default=5)
+        inference_parser.add_argument("--full-dist",
+                            help="Trajectron.incremental_forward parameter",
+                            action='store_true')
+        inference_parser.add_argument("--gmm-mode",
+                            help="Trajectron.incremental_forward parameter",
+                            type=bool,
+                            default=True)
+        inference_parser.add_argument("--z-mode",
+                            help="Trajectron.incremental_forward parameter",
+                            action='store_true')
+        inference_parser.add_argument('--cm-to-m',
+                                    help="Correct for homography that is in cm (i.e. {x,y}/100). Should also be used when processing data",
+                                    action='store_true')
+        inference_parser.add_argument('--center-data',
+                                    help="Center data around cx and cy. Should also be used when processing data",
+                                    action='store_true')
+        
+
+        return inference_parser
 
             
 

trap/preview_renderer.py

@@ -298,7 +298,7 @@ class FrameWriter:
     framerate.
     See https://video.stackexchange.com/questions/25811/ffmpeg-make-video-with-non-constant-framerate-from-image-filenames
     """
-    def __init__(self, filename: str, fps: float, frame_size: tuple) -> None:
+    def __init__(self, filename: str, fps: float, frame_size: Optional[tuple] = None) -> None:
         self.filename = filename
         self.fps = fps
         self.frame_size = frame_size

trap/stage.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from argparse import ArgumentParser
 from collections import defaultdict
 from dataclasses import dataclass
 from enum import Enum
@@ -870,4 +871,21 @@ class Stage(Node):
         
         # print(json.dumps(rl, cls=DataclassJSONEncoder))
 
+    @classmethod
+    def arg_parser(cls) -> ArgumentParser:
+        argparser = ArgumentParser()
+        argparser.add_argument('--zmq-trajectory-addr',
+                    help='Manually specity communication addr for the trajectory messages',
+                    type=str,
+                    default="ipc:///tmp/feeds_traj")
+        argparser.add_argument('--zmq-prediction-addr',
+                            help='Manually specity communication addr for the prediction messages',
+                            type=str,
+                            default="ipc:///tmp/feeds_preds")
+        argparser.add_argument('--zmq-stage-addr',
+                            help='Manually specity communication addr for the stage  messages (the rendered lines)',
+                            type=str,
+                            default="tcp://0.0.0.0:99174")
+        return argparser
+
     

trap/tracker.py

@@ -1,39 +1,40 @@
-from argparse import Namespace
-from collections import defaultdict
+import argparse
 import csv
-from dataclasses import dataclass, field
 import json
 import logging
-from math import nan
-from multiprocessing import Event
 import multiprocessing
-from pathlib import Path
 import pickle
 import time
-from typing import DefaultDict, Dict, Optional, List
+from argparse import Namespace
+from collections import defaultdict
+from datetime import datetime, timedelta
+from multiprocessing import Event
+from pathlib import Path
+from typing import DefaultDict, Dict, List, Optional
+
+import cv2
 import jsonlines
 import numpy as np
 import torch
 import torchvision
-import ultralytics
 import zmq
-import cv2
-
-from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights, keypointrcnn_resnet50_fpn, KeypointRCNN_ResNet50_FPN_Weights, maskrcnn_resnet50_fpn_v2, MaskRCNN_ResNet50_FPN_V2_Weights, FasterRCNN_ResNet50_FPN_V2_Weights, fasterrcnn_resnet50_fpn_v2
-from deep_sort_realtime.deepsort_tracker import DeepSort
-from torchvision.models import ResNet50_Weights
+from bytetracker import BYTETracker
 from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
-
+from deep_sort_realtime.deepsort_tracker import DeepSort
+from torchvision.models.detection import (FasterRCNN_ResNet50_FPN_V2_Weights,
+                                          KeypointRCNN_ResNet50_FPN_Weights,
+                                          MaskRCNN_ResNet50_FPN_V2_Weights,
+                                          fasterrcnn_resnet50_fpn_v2,
+                                          keypointrcnn_resnet50_fpn,
+                                          maskrcnn_resnet50_fpn_v2)
+from tsmoothie.smoother import ConvolutionSmoother, KalmanSmoother
 from ultralytics import YOLO
 from ultralytics.engine.results import Results as YOLOResult
 
 from trap import timer
-from trap.frame_emitter import Camera, DataclassJSONEncoder, DetectionState, Frame, Detection, Track
-from bytetracker import BYTETracker
-
-from tsmoothie.smoother import KalmanSmoother, ConvolutionSmoother
-import tsmoothie.smoother
-from datetime import datetime, timedelta
+from trap.frame_emitter import (Camera, DataclassJSONEncoder, Detection,
+                                DetectionState, Frame, Track)
+from trap.node import Node
 
 # Detection = [int, int, int, int, float, int]
 # Detections = [Detection]
@@ -387,9 +388,8 @@ class ByteTrackWrapper(TrackerWrapper):
 
         
 
-class Tracker:
-    def __init__(self, config: Namespace):
-        self.config = config
+class Tracker(Node):
+    def setup(self):
 
         
         # # TODO: config device
@@ -453,6 +453,9 @@ class Tracker:
             logger.info("Smoother Disabled (enable with --smooth-tracks)")
 
 
+        self.frame_sock = self.sub(self.config.zmq_frame_addr)
+        self.trajectory_socket = self.pub(self.config.zmq_trajectory_addr)
+
         logger.debug("Set up tracker")
 
     def track_frame(self, frame: Frame):
@@ -474,42 +477,11 @@ class Tracker:
         return detections
         
 
-    def track(self,  is_running: Event, timer_counter: int = 0):
+    def run(self):
         """
         Live tracking of frames coming in over zmq
         """
 
-        self.is_running = is_running
-
-
-        context = zmq.Context()
-        self.frame_sock = context.socket(zmq.SUB)
-        self.frame_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. NB. make sure this comes BEFORE connect, otherwise it's ignored!!
-        self.frame_sock.setsockopt(zmq.SUBSCRIBE, b'')
-        self.frame_sock.connect(self.config.zmq_frame_addr)
-
-        self.trajectory_socket = context.socket(zmq.PUB)
-        self.trajectory_socket.setsockopt(zmq.CONFLATE, 1) # only keep latest frame
-        self.trajectory_socket.bind(self.config.zmq_trajectory_addr)
-
-        prev_run_time = 0
-
-        # training_fp = None
-        # training_csv = None
-        # training_frames = 0
-
-        # if self.config.save_for_training is not None:
-        #     if not isinstance(self.config.save_for_training, Path):
-        #         raise ValueError("save-for-training should be a path")
-        #     if not self.config.save_for_training.exists():
-        #         logger.info(f"Making path for training data: {self.config.save_for_training}")
-        #         self.config.save_for_training.mkdir(parents=True, exist_ok=False)
-        #     else:
-        #         logger.warning(f"Path for training-data exists: {self.config.save_for_training}. Continuing assuming that's ok.")
-        #     training_fp = open(self.config.save_for_training / 'all.txt', 'w')
-        #     # following https://github.com/StanfordASL/Trajectron-plus-plus/blob/master/experiments/pedestrians/process_data.py
-        #     training_csv = csv.DictWriter(training_fp, fieldnames=['frame_id', 'track_id', 'l', 't', 'w', 'h', 'x', 'y', 'state'], delimiter='\t', quoting=csv.QUOTE_NONE)
-
         prev_frame_i = -1
         
         with TrainingDataWriter(self.config.save_for_training) as writer:
@@ -518,9 +490,6 @@ class Tracker:
             w_time = None
             displacement_filter = FinalDisplacementFilter(.8)
             while self.is_running.is_set():
-                
-                with timer_counter.get_lock():
-                    timer_counter.value += 1
                 # this waiting for target_dt causes frame loss. E.g. with target_dt at .1, it
                 # skips exactly 1 frame on a 10 fps video (which, it obviously should not do)
                 # so for now, timing should move to emitter
@@ -532,10 +501,12 @@ class Tracker:
                 poll_time = time.time()
                 zmq_ev = self.frame_sock.poll(timeout=2000)
                 if not zmq_ev:
-                    logger.warning('skip poll after 2000ms')
+                    logger.warning('no frame for 2000ms')
                     # when there's no data after timeout, loop so that is_running is checked
                     continue
 
+                self.tick() # only tick if something is actually received
+
                 start_time = time.time()
                 frame: Frame = self.frame_sock.recv_pyobj() # frame delivery in current setup: 0.012-0.03s
 
@@ -696,11 +667,42 @@ class Tracker:
         different nesting
         """
         return [([d[0], d[1], d[2]-d[0], d[3]-d[1]], d[4], d[5]) for d in detections]
+    
+    @classmethod
+    def arg_parser(cls):
+        argparser = argparse.ArgumentParser()
+        argparser.add_argument('--zmq-frame-addr',
+                        help='Manually specity communication addr for the frame messages',
+                        type=str,
+                        default="ipc:///tmp/feeds_frame") 
+        argparser.add_argument('--zmq-trajectory-addr',
+                        help='Manually specity communication addr for the trajectory messages',
+                        type=str,
+                        default="ipc:///tmp/feeds_traj")
+
+        argparser.add_argument("--save-for-training",
+                        help="Specify the path in which to save",
+                        type=Path,
+                        default=None)
+        argparser.add_argument("--detector",
+                            help="Specify the detector to use",
+                            type=str,
+                            default=DETECTOR_YOLOv8,
+                            choices=DETECTORS)
+        argparser.add_argument("--tracker",
+                            help="Specify the detector to use",
+                            type=str,
+                            default=TRACKER_BYTETRACK,
+                            choices=TRACKERS)
+        argparser.add_argument("--smooth-tracks",
+                            help="Smooth the tracker tracks before sending them to the predictor",
+                            action='store_true')
+        return argparser
 
 
 def run_tracker(config: Namespace, is_running: Event, timer_counter):
     router = Tracker(config)
-    router.track(is_running, timer_counter)
+    router.run(is_running, timer_counter)
 
 def run():
     # Frame emitter
@@ -738,7 +740,7 @@ def run():
     timer_counter = timer.Timer('frame_emitter')
 
     router = Tracker(config)
-    router.track(is_running, timer_counter.iterations)
+    router.run(is_running, timer_counter.iterations)
     is_running.clear()
 
 

uv.lock

@@ -2191,6 +2191,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a0/4b/528ccf7a982216885a1ff4908e886b8fb5f19862d1962f56a3fce2435a70/starlette-0.46.1-py3-none-any.whl", hash = "sha256:77c74ed9d2720138b25875133f3a2dae6d854af2ec37dceb56aef370c1d8a227", size = 71995 },
 ]
 
+[[package]]
+name = "supervisor"
+version = "4.2.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "setuptools" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ce/37/517989b05849dd6eaa76c148f24517544704895830a50289cbbf53c7efb9/supervisor-4.2.5.tar.gz", hash = "sha256:34761bae1a23c58192281a5115fb07fbf22c9b0133c08166beffc70fed3ebc12", size = 466073 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2c/7a/0ad3973941590c040475046fef37a2b08a76691e61aa59540828ee235a6e/supervisor-4.2.5-py2.py3-none-any.whl", hash = "sha256:2ecaede32fc25af814696374b79e42644ecaba5c09494c51016ffda9602d0f08", size = 319561 },
+]
+
 [[package]]
 name = "tensorboard"
 version = "2.19.0"
@@ -2495,6 +2507,7 @@ dependencies = [
     { name = "setproctitle" },
     { name = "shapely" },
     { name = "simplification" },
+    { name = "supervisor" },
     { name = "tensorboardx" },
     { name = "torch", version = "1.12.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux'" },
     { name = "torch", version = "1.12.1+cu113", source = { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl" }, marker = "sys_platform == 'linux'" },
@@ -2527,6 +2540,7 @@ requires-dist = [
     { name = "setproctitle", specifier = ">=1.3.3,<2" },
     { name = "shapely", specifier = ">=2.1" },
     { name = "simplification", specifier = ">=0.7.12" },
+    { name = "supervisor", specifier = ">=4.2.5" },
     { name = "tensorboardx", specifier = ">=2.6.2.2,<3" },
     { name = "torch", marker = "python_full_version < '3.10' or python_full_version >= '4' or sys_platform != 'linux'", specifier = "==1.12.1" },
     { name = "torch", marker = "python_full_version >= '3.10' and python_full_version < '4' and sys_platform == 'linux'", url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl" },