Trajectory prediction - test in browser

run_server.py

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+from trajpred import plumber
+
+if __name__ == "__main__":
+    plumber.start()
+

trajpred/config.py

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+import argparse
+
+parser = argparse.ArgumentParser()
+
+
+parser.add_argument(
+    '--verbose',
+    '-v',
+    help="Increase verbosity. Add multiple times to increase further.",
+    action='count', default=0
+)
+
+# parser.add_argument('--foo')
+inference_parser = parser.add_argument_group('inference server')
+connection_parser = parser.add_argument_group('connection')
+
+inference_parser.add_argument("--model_dir",
+                    help="directory with the model to use for inference",
+                    type=str, # TODO: make into Path
+                    default='./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='./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=str,
+                    default='../experiments/pedestrians/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)
+
+
+# Internal connections.
+
+connection_parser.add_argument('--zmq-trajectory-addr',
+                    help='Manually specity communication addr for the trajectory messages',
+                    type=str,
+                    default="ipc:///tmp/feeds/traj")
+
+connection_parser.add_argument('--zmq-camera-stream-addr',
+                    help='Manually specity communication addr for the camera stream messages',
+                    type=str,
+                    default="ipc:///tmp/feeds/img")
+
+connection_parser.add_argument('--zmq-prediction-addr',
+                    help='Manually specity communication addr for the prediction messages',
+                    type=str,
+                    default="ipc:///tmp/feeds/preds")
+
+
+connection_parser.add_argument('--ws-port',
+                    help='Port to listen for incomming websocket connections. Also serves the testing html-page.',
+                    type=int,
+                    default=8888)
+
+connection_parser.add_argument('--bypass-prediction',
+                    help='For debugging purpose: websocket input immediately to output',
+                    action='store_true')
+

trajpred/plumber.py

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+import logging
+from multiprocessing import Process, Queue
+from trajpred.config import parser
+from trajpred.prediction_server import InferenceServer, run_inference_server
+from trajpred.socket_forwarder import run_ws_forwarder
+
+
+logger = logging.getLogger("trajpred.plumbing")
+
+def start():
+    args = parser.parse_args()
+    loglevel = logging.NOTSET if args.verbose > 1 else logging.DEBUG if args.verbose > 0  else logging.INFO
+    logging.basicConfig(
+        level=loglevel,
+    )
+    # rootLogger = logging.getLogger()
+    # rootLogger.setLevel(loglevel)
+
+    movement_q = Queue()
+    prediction_q = Queue()
+
+
+    # instantiating process with arguments
+    procs = [
+        Process(target=run_ws_forwarder, args=(args,))
+    ]
+    if not args.bypass_prediction:
+        procs.append(
+            Process(target=run_inference_server, args=(args, movement_q, prediction_q)),
+        )
+
+    logger.info("start")
+    for proc in procs:
+        proc.start()
+
+    for proc in procs:
+        proc.join()
+

trajpred/prediction_server.py

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+# adapted from Trajectron++ online_server.py
+import logging
+from multiprocessing import Queue
+import os
+import time
+import json
+import torch
+import dill
+import random
+import pathlib
+import numpy as np
+import trajectron.visualization as vis
+from trajectron.model.online.online_trajectron import OnlineTrajectron
+from trajectron.model.model_registrar import ModelRegistrar
+from trajectron.environment import Environment, Scene
+import matplotlib.pyplot as plt
+
+
+
+logger = logging.getLogger("trajpred.inference")
+
+
+# if not torch.cuda.is_available() or self.config.device == 'cpu':
+#     self.config.device = torch.device('cpu')
+# else:
+#     if torch.cuda.device_count() == 1:
+#         # If you have CUDA_VISIBLE_DEVICES set, which you should,
+#         # then this will prevent leftover flag arguments from
+#         # messing with the device allocation.
+#         self.config.device = 'cuda:0'
+
+#     self.config.device = torch.device(self.config.device)
+
+
+
+def create_online_env(env, hyperparams, scene_idx, init_timestep):
+    test_scene = env.scenes[scene_idx]
+
+    online_scene = Scene(timesteps=init_timestep + 1,
+                         map=test_scene.map,
+                         dt=test_scene.dt)
+    online_scene.nodes = test_scene.get_nodes_clipped_at_time(
+        timesteps=np.arange(init_timestep - hyperparams['maximum_history_length'],
+                            init_timestep + 1),
+        state=hyperparams['state'])
+    online_scene.robot = test_scene.robot
+    online_scene.calculate_scene_graph(attention_radius=env.attention_radius,
+                                       edge_addition_filter=hyperparams['edge_addition_filter'],
+                                       edge_removal_filter=hyperparams['edge_removal_filter'])
+
+    return Environment(node_type_list=env.node_type_list,
+                       standardization=env.standardization,
+                       scenes=[online_scene],
+                       attention_radius=env.attention_radius,
+                       robot_type=env.robot_type)
+
+
+def get_maps_for_input(input_dict, scene, hyperparams):
+    scene_maps = list()
+    scene_pts = list()
+    heading_angles = list()
+    patch_sizes = list()
+    nodes_with_maps = list()
+    for node in input_dict:
+        if node.type in hyperparams['map_encoder']:
+            x = input_dict[node]
+            me_hyp = hyperparams['map_encoder'][node.type]
+            if 'heading_state_index' in me_hyp:
+                heading_state_index = me_hyp['heading_state_index']
+                # We have to rotate the map in the opposit direction of the agent to match them
+                if type(heading_state_index) is list:  # infer from velocity or heading vector
+                    heading_angle = -np.arctan2(x[-1, heading_state_index[1]],
+                                                x[-1, heading_state_index[0]]) * 180 / np.pi
+                else:
+                    heading_angle = -x[-1, heading_state_index] * 180 / np.pi
+            else:
+                heading_angle = None
+
+            scene_map = scene.map[node.type]
+            map_point = x[-1, :2]
+
+            patch_size = hyperparams['map_encoder'][node.type]['patch_size']
+
+            scene_maps.append(scene_map)
+            scene_pts.append(map_point)
+            heading_angles.append(heading_angle)
+            patch_sizes.append(patch_size)
+            nodes_with_maps.append(node)
+
+    if heading_angles[0] is None:
+        heading_angles = None
+    else:
+        heading_angles = torch.Tensor(heading_angles)
+
+    maps = scene_maps[0].get_cropped_maps_from_scene_map_batch(scene_maps,
+                                                               scene_pts=torch.Tensor(scene_pts),
+                                                               patch_size=patch_sizes[0],
+                                                               rotation=heading_angles)
+
+    maps_dict = {node: maps[[i]] for i, node in enumerate(nodes_with_maps)}
+    return maps_dict
+
+
+class InferenceServer:
+    def __init__(self, config: dict, movement_q: Queue, prediction_q: Queue):
+        self.config = config
+        self.movement_q = movement_q
+        self.prediction_q = prediction_q
+
+    def run(self):
+
+        if self.config.seed is not None:
+            random.seed(self.config.seed)
+            np.random.seed(self.config.seed)
+            torch.manual_seed(self.config.seed)
+            if torch.cuda.is_available():
+                torch.cuda.manual_seed_all(self.config.seed)
+        
+        # Choose one of the model directory names under the experiment/*/models folders.
+        # Possibilities are 'vel_ee', 'int_ee', 'int_ee_me', or 'robot'
+        # model_dir = os.path.join(self.config.log_dir, 'int_ee')
+        # model_dir = 'models/models_04_Oct_2023_21_04_48_eth_vel_ar3'
+
+        # Load hyperparameters from json
+        config_file = os.path.join(self.config.model_dir, self.config.conf)
+        if not os.path.exists(config_file):
+            raise ValueError('Config json not found!')
+        with open(config_file, 'r') as conf_json:
+            hyperparams = json.load(conf_json)
+
+        # Add hyperparams from arguments
+        hyperparams['dynamic_edges'] = self.config.dynamic_edges
+        hyperparams['edge_state_combine_method'] = self.config.edge_state_combine_method
+        hyperparams['edge_influence_combine_method'] = self.config.edge_influence_combine_method
+        hyperparams['edge_addition_filter'] = self.config.edge_addition_filter
+        hyperparams['edge_removal_filter'] = self.config.edge_removal_filter
+        hyperparams['batch_size'] = self.config.batch_size
+        hyperparams['k_eval'] = self.config.k_eval
+        hyperparams['offline_scene_graph'] = self.config.offline_scene_graph
+        hyperparams['incl_robot_node'] = self.config.incl_robot_node
+        hyperparams['edge_encoding'] = not self.config.no_edge_encoding
+        hyperparams['use_map_encoding'] = self.config.map_encoding
+
+        output_save_dir = os.path.join(self.config.output_dir, 'pred_figs')
+        pathlib.Path(output_save_dir).mkdir(parents=True, exist_ok=True)
+
+
+        with open(self.config.eval_data_dict, 'rb') as f:
+            eval_env = dill.load(f, encoding='latin1')
+
+        if eval_env.robot_type is None and hyperparams['incl_robot_node']:
+            eval_env.robot_type = eval_env.NodeType[0]  # TODO: Make more general, allow the user to specify?
+            for scene in eval_env.scenes:
+                scene.add_robot_from_nodes(eval_env.robot_type)
+
+        print('Loaded data from %s' % (self.config.eval_data_dict,))
+
+        # Creating a dummy environment with a single scene that contains information about the world.
+        # When using this code, feel free to use whichever scene index or initial timestep you wish.
+        scene_idx = 0
+
+        # You need to have at least acceleration, so you want 2 timesteps of prior data, e.g. [0, 1],
+        # so that you can immediately start incremental inference from the 3rd timestep onwards.
+        init_timestep = 1
+
+        eval_scene = eval_env.scenes[scene_idx]
+        online_env = create_online_env(eval_env, hyperparams, scene_idx, init_timestep)
+
+        model_registrar = ModelRegistrar(self.config.model_dir, self.config.eval_device)
+        model_registrar.load_models(iter_num=100)
+
+        trajectron = OnlineTrajectron(model_registrar,
+                                    hyperparams,
+                                    self.config.eval_device)
+
+        # If you want to see what different robot futures do to the predictions, uncomment this line as well as
+        # related "... += adjustment" lines below.
+        # adjustment = np.stack([np.arange(13)/float(i*2.0) for i in range(6, 12)], axis=1)
+
+        # Here's how you'd incrementally run the model, e.g. with streaming data.
+        trajectron.set_environment(online_env, init_timestep)
+
+        for timestep in range(init_timestep + 1, eval_scene.timesteps):
+            input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
+
+            maps = None
+            if hyperparams['use_map_encoding']:
+                maps = get_maps_for_input(input_dict, eval_scene, hyperparams)
+
+            robot_present_and_future = None
+            if eval_scene.robot is not None and hyperparams['incl_robot_node']:
+                robot_present_and_future = eval_scene.robot.get(np.array([timestep,
+                                                                        timestep + hyperparams['prediction_horizon']]),
+                                                                hyperparams['state'][eval_scene.robot.type],
+                                                                padding=0.0)
+                robot_present_and_future = np.stack([robot_present_and_future, robot_present_and_future], axis=0)
+                # robot_present_and_future += adjustment
+
+            start = time.time()
+            dists, preds = trajectron.incremental_forward(input_dict,
+                                                        maps,
+                                                        prediction_horizon=6,
+                                                        num_samples=51,
+                                                        robot_present_and_future=robot_present_and_future,
+                                                        full_dist=True)
+            end = time.time()
+            print("t=%d: took %.2f s (= %.2f Hz) w/ %d nodes and %d edges" % (timestep, end - start,
+                                                                            1. / (end - start), len(trajectron.nodes),
+                                                                            trajectron.scene_graph.get_num_edges()))
+
+            detailed_preds_dict = dict()
+            for node in eval_scene.nodes:
+                if node in preds:
+                    detailed_preds_dict[node] = preds[node]
+
+            fig = plt.figure(figsize=(10,10))
+            ax = fig.gca()
+            # fig, ax = plt.subplots()
+            
+            # vis.visualize_distribution(ax,
+            #                            dists)
+            vis.visualize_prediction(ax,
+                                    {timestep: preds},
+                                    eval_scene.dt,
+                                    hyperparams['maximum_history_length'],
+                                    hyperparams['prediction_horizon'])
+
+            if eval_scene.robot is not None and hyperparams['incl_robot_node']:
+                robot_for_plotting = eval_scene.robot.get(np.array([timestep,
+                                                                    timestep + hyperparams['prediction_horizon']]),
+                                                        hyperparams['state'][eval_scene.robot.type])
+                # robot_for_plotting += adjustment
+
+                ax.plot(robot_for_plotting[1:, 1], robot_for_plotting[1:, 0],
+                        color='r',
+                        linewidth=1.0, alpha=1.0)
+
+                # Current Node Position
+                circle = plt.Circle((robot_for_plotting[0, 1],
+                                    robot_for_plotting[0, 0]),
+                                    0.3,
+                                    facecolor='r',
+                                    edgecolor='k',
+                                    lw=0.5,
+                                    zorder=3)
+                ax.add_artist(circle)
+
+            ax.set_xlim(-10,10)
+            ax.set_ylim(-10,10)
+            fig.suptitle(f"frame {timestep:04d}")
+            fig.savefig(os.path.join(output_save_dir, f'pred_{timestep:04d}.png'))
+            plt.close(fig)
+
+def run_inference_server(config, movement_q: Queue, prediction_q: Queue):
+    s = InferenceServer(config, movement_q, prediction_q)
+    s.run()

trajpred/socket_forwarder.py

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+
+from argparse import Namespace
+import asyncio
+import logging
+from typing import Set, Union, Dict, Any
+from typing_extensions import Self
+
+from urllib.error import HTTPError
+import tornado.ioloop
+import tornado.web
+import tornado.websocket
+import zmq
+import zmq.asyncio
+
+
+logger = logging.getLogger("trajpred.forwarder")
+
+
+class WebSocketTrajectoryHandler(tornado.websocket.WebSocketHandler):
+    def initialize(self, zmq_socket: zmq.asyncio.Socket):
+        self.zmq_socket = zmq_socket
+
+    async def on_message(self, message):
+        logger.info(f"recieve: {message}")
+
+        try:
+            await self.zmq_socket.send_string(message)
+            # msg = json.loads(message)
+        except Exception as e:
+            # self.send({'alert': 'Invalid request: {}'.format(e)})
+            logger.exception(e)
+        # self.write_message(u"You said: " + message)
+
+    def open(self, p=None):
+        logger.info(f"connected  {self.request.remote_ip}")
+
+    # client disconnected
+    def on_close(self):
+        logger.info(f"Client disconnected: {self.request.remote_ip}")
+
+
+
+class WebSocketPredictionHandler(tornado.websocket.WebSocketHandler):
+    connections: Set[Self] = set()
+
+    def initialize(self, config):
+        self.config = config
+
+    def on_message(self, message):
+        logger.warning(f"Receiving message on send-only ws handler: {message}")
+
+    def open(self, p=None):
+        logger.info(f"Prediction WS connected {self.request.remote_ip}")
+        self.__class__.connections.add(self)
+
+    # client disconnected
+    def on_close(self):
+        self.__class__.rmConnection(self)
+
+        logger.info(f"Client disconnected: {self.request.remote_ip}")
+
+    @classmethod
+    def rmConnection(cls, client):
+        if client not in cls.connections:
+            return
+        cls.connections.remove(client)
+
+    @classmethod
+    def hasConnection(cls, client):
+        return client in cls.connections
+    
+    @classmethod
+    def write_to_clients(cls, msg: Union[bytes, str, Dict[str, Any]]):
+        if msg is None:
+            logger.critical("Tried to send 'none'")
+            return
+
+        toRemove = []
+        for client in cls.connections:
+            try:
+                client.write_message(msg)
+            except tornado.websocket.WebSocketClosedError as e:
+                logger.warning(f"Not properly closed websocket connection")
+                toRemove.append(client) # If we remove it here from the set we get an exception about changing set size during iteration
+
+        for client in toRemove:
+            cls.rmConnection(client)
+
+class DemoHandler(tornado.web.RequestHandler):
+    def initialize(self, config: Namespace):
+        self.config = config
+
+    def get(self):
+        self.render("index.html", ws_port=self.config.ws_port)
+
+class StaticFileWithHeaderHandler(tornado.web.StaticFileHandler):
+    def set_extra_headers(self, path):
+        """For subclass to add extra headers to the response"""
+        if path[-5:] == ".html":
+            self.set_header("Access-Control-Allow-Origin", "*")
+        if path[-4:] == ".svg":
+            self.set_header("Content-Type", "image/svg+xml")
+
+
+class WsRouter:
+    def __init__(self, config: Namespace):
+        self.config = config
+
+        context = zmq.asyncio.Context()
+        self.trajectory_socket = context.socket(zmq.PUB)
+        self.trajectory_socket.bind(config.zmq_prediction_addr if config.bypass_prediction else config.zmq_trajection_addr)
+        
+        self.prediction_socket = context.socket(zmq.SUB)
+        self.prediction_socket.connect(config.zmq_prediction_addr)
+        self.prediction_socket.setsockopt(zmq.SUBSCRIBE, b'')
+
+        self.application = tornado.web.Application(
+            [
+                (
+                    r"/ws/trajectory",
+                    WebSocketTrajectoryHandler,
+                    {
+                        "zmq_socket": self.trajectory_socket
+                    },
+                ),
+                (
+                    r"/ws/prediction",
+                    WebSocketPredictionHandler,
+                    {
+                        "config": config,
+                    },
+                ),
+                (r"/", DemoHandler, {"config": config}),
+                # (r"/(.*)", StaticFileWithHeaderHandler, {"config": config, "index": 'index.html'}),
+            ],
+            template_path = 'trajpred/web/',
+            compiled_template_cache=False)
+
+    def start(self):
+
+        evt_loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(evt_loop)
+
+        # loop = tornado.ioloop.IOLoop.current()
+        logger.info(f"Listen on {self.config.ws_port}")
+        self.application.listen(self.config.ws_port)
+        loop = asyncio.get_event_loop()
+
+        task = evt_loop.create_task(self.prediction_forwarder())
+
+        evt_loop.run_forever()
+    
+    async def prediction_forwarder(self):
+        logger.info("Starting prediction forwarder")
+        while True:
+            msg = await self.prediction_socket.recv_string()
+            logger.info("Forward: ")
+            WebSocketPredictionHandler.write_to_clients(msg)
+
+def run_ws_forwarder(config: Namespace):
+    router = WsRouter(config)
+    router.start()

trajpred/web/index.html

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+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Trajectory Prediction Browser Test</title>
+    <style>
+        body {
+            background: black;
+        }
+
+        #field {
+            background: white;
+            width: 100%;
+            height: 100%;
+        }
+    </style>
+</head>
+
+<body>
+    <canvas id="field" width="1500" height="1500">
+
+    </canvas>
+
+    <script>
+        // map the field to coordinates of our dummy tracker
+        const field_range = { x: [-10, 10], y: [-10, 10] }
+
+        // Create WebSocket connection.
+        const trajectory_socket = new WebSocket(`ws://${window.location.hostname}:{{ ws_port }}/ws/trajectory`);
+        const prediction_socket = new WebSocket(`ws://${window.location.hostname}:{{ ws_port }}/ws/prediction`);
+        let is_moving = false;
+        const fieldEl = document.getElementById('field');
+
+        let current_data = {}
+        // Listen for messages
+        prediction_socket.addEventListener("message", (event) => {
+            // console.log("Message from server ", event.data);
+            current_data = JSON.parse(event.data);
+        });
+
+        function getMousePos(canvas, evt) {
+            const rect = canvas.getBoundingClientRect();
+            return {
+                x: evt.clientX - rect.left,
+                y: evt.clientY - rect.top
+            };
+        }
+        function mouse_coordinates_to_position(coordinates) {
+            const x_range = field_range.x[1] - field_range.x[0]
+            const x = (coordinates.x / fieldEl.clientWidth) * x_range + field_range.x[0]
+            const y_range = field_range.y[1] - field_range.y[0]
+            const y = (coordinates.y / fieldEl.clientWidth) * y_range + field_range.y[0]
+            return { x: x, y: y }
+        }
+        function position_to_canvas_coordinate(position) {
+            const x_range = field_range.x[1] - field_range.x[0]
+            const y_range = field_range.y[1] - field_range.y[0]
+            return {
+                x: (position.x - field_range.x[0]) * fieldEl.width / x_range,
+                y: (position.y - field_range.y[0]) * fieldEl.width / y_range,
+            }
+        }
+
+        // helper function so we can spread
+        function coord_as_list(coord) {
+            return [coord.x, coord.y]
+        }
+
+
+        let tracker = {}
+        let person_counter = 0
+
+        class Person {
+            constructor(id) {
+                this.id = id;
+                this.history = [];
+                this.prediction = []
+            }
+
+            addToHistory(position) {
+                this.history.push(position);
+            }
+        }
+
+        fieldEl.addEventListener('mousedown', (event) => {
+            person_counter++;
+            tracker[person_counter] = new Person(person_counter);
+            is_moving = true;
+
+            const mousePos = getMousePos(fieldEl, event);
+            const position = mouse_coordinates_to_position(mousePos)
+            tracker[person_counter].addToHistory(position);
+            trajectory_socket.send(JSON.stringify(tracker))
+        });
+        fieldEl.addEventListener('mousemove', (event) => {
+            if (!is_moving) return;
+            const mousePos = getMousePos(fieldEl, event);
+            const position = mouse_coordinates_to_position(mousePos)
+            tracker[person_counter].addToHistory(position);
+            trajectory_socket.send(JSON.stringify(tracker))
+        });
+        document.addEventListener('mouseup', (e) => {
+            is_moving = false;
+            tracker = {}
+        })
+
+        const ctx = fieldEl.getContext("2d");
+        function drawFrame() {
+            ctx.clearRect(0, 0, fieldEl.width, fieldEl.height);
+            ctx.save();
+            for (let id in current_data) {
+                const person = current_data[id];
+                if (person.history.length > 1) {
+                    const hist = structuredClone(person.history)
+                    // draw current position:
+                    ctx.beginPath()
+                    ctx.arc(
+                        ...coord_as_list(position_to_canvas_coordinate(hist[hist.length - 1])),
+                        5, //radius
+                        0, 2 * Math.PI);
+                    ctx.fill()
+
+                    ctx.beginPath()
+                    ctx.lineWidth = 3;
+                    ctx.strokeStyle = "#325FA2";
+
+                    ctx.moveTo(...coord_as_list(position_to_canvas_coordinate(hist.shift())));
+                    for (const position of hist) {
+                        ctx.lineTo(...coord_as_list(position_to_canvas_coordinate(position)))
+                    }
+                    ctx.stroke();
+                }
+            }
+            ctx.restore();
+
+            window.requestAnimationFrame(drawFrame);
+        }
+
+        window.requestAnimationFrame(drawFrame);
+    </script>
+</body>
+
+</html>