testing the tracker

poetry.lock

@@ -1817,9 +1817,9 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
 ]
 
 [[package]]
@@ -1939,8 +1939,8 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
     {version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -1970,6 +1970,21 @@ sql-other = ["SQLAlchemy (>=1.4.36)"]
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.8.0)"]
 
+[[package]]
+name = "pandas_helper_calc"
+version = "0.0.1"
+description = ""
+optional = false
+python-versions = "*"
+files = []
+develop = false
+
+[package.source]
+type = "git"
+url = "https://github.com/scls19fr/pandas-helper-calc"
+reference = "HEAD"
+resolved_reference = "22df480f09c0fa96548833f9dee8f9128512641b"
+
 [[package]]
 name = "pandocfilters"
 version = "1.5.0"
@@ -2961,6 +2976,24 @@ all = ["numpy", "pytest", "pytest-cov"]
 test = ["pytest", "pytest-cov"]
 vectorized = ["numpy"]
 
+[[package]]
+name = "simdkalman"
+version = "1.0.4"
+description = "Kalman filters vectorized as Single Instruction, Multiple Data"
+optional = false
+python-versions = "*"
+files = [
+    {file = "simdkalman-1.0.4-py2.py3-none-any.whl", hash = "sha256:fc2c6b9e540e0a26b39d087e78623d3c1e8c6677abf5d91111f5d49e328e1668"},
+]
+
+[package.dependencies]
+numpy = ">=1.9.0"
+
+[package.extras]
+dev = ["check-manifest"]
+docs = ["sphinx"]
+test = ["pylint"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3300,6 +3333,22 @@ tqdm = "^4.65.0"
 type = "directory"
 url = "../Trajectron-plus-plus"
 
+[[package]]
+name = "tsmoothie"
+version = "1.0.5"
+description = "A python library for timeseries smoothing and outlier detection in a vectorized way."
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "tsmoothie-1.0.5-py3-none-any.whl", hash = "sha256:dedf8d8e011562824abe41783bf33e1b9ee1424bc572853bb82408743316a90e"},
+    {file = "tsmoothie-1.0.5.tar.gz", hash = "sha256:d83fa0ccae32bde7b904d9581ebf137e8eb18629cc3563d7379ca5f92461f6f5"},
+]
+
+[package.dependencies]
+numpy = "*"
+scipy = "*"
+simdkalman = "*"
+
 [[package]]
 name = "types-python-dateutil"
 version = "2.8.19.14"
@@ -3468,4 +3517,4 @@ watchdog = ["watchdog (>=2.3)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10,<3.12,"
-content-hash = "c9d4fe6a1d054a835a689cee011753b900b696aa8a06b81aa7a10afc24a8bc70"
+content-hash = "66f062f9db921cfa83e576288d09fd9b959780eb189d95765934ae9a6769f200"

pyproject.toml

@@ -29,6 +29,8 @@ ultralytics = "^8.0.200"
 ffmpeg-python = "^0.2.0"
 torchreid = "^0.2.5"
 gdown = "^4.7.1"
+pandas-helper-calc = {git = "https://github.com/scls19fr/pandas-helper-calc"}
+tsmoothie = "^1.0.5"
 
 [build-system]
 requires = ["poetry-core"]

trap/frame_emitter.py

@@ -1,5 +1,6 @@
 from argparse import Namespace
 from dataclasses import dataclass, field
+from enum import IntFlag
 from itertools import cycle
 import logging
 from multiprocessing import Event
@@ -12,9 +13,25 @@ import numpy as np
 import cv2
 import zmq
 from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
+from deep_sort_realtime.deep_sort.track import TrackState as DeepsortTrackState
 
 logger = logging.getLogger('trap.frame_emitter')
 
+class DetectionState(IntFlag):
+    Tentative = 1 # state before n_init (see DeepsortTrack)
+    Confirmed = 2 # after tentative
+    Lost = 4 # lost when DeepsortTrack.time_since_update > 0 but not Deleted
+
+    @classmethod
+    def from_deepsort_track(cls, track: DeepsortTrack):
+        if track.state == DeepsortTrackState.Tentative:
+            return cls.Tentative
+        if track.state == DeepsortTrackState.Confirmed:
+            if track.time_since_update > 0:
+                return cls.Lost
+            return cls.Confirmed
+        raise RuntimeError("Should not run into Deleted entries here")
+
 
 @dataclass
 class Detection:
@@ -24,13 +41,27 @@ class Detection:
     w: int # width - image space
     h: int # height - image space
     conf: float # object detector probablity
+    state: DetectionState
 
     def get_foot_coords(self):
         return [self.l + 0.5 * self.w, self.t+self.h]
 
     @classmethod
     def from_deepsort(cls, dstrack: DeepsortTrack):
-        return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf)
+        return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf, DetectionState.from_deepsort_track(dstrack))
+    
+    def get_scaled(self, scale: float = 1):
+        if scale == 1:
+            return self
+        
+        return Detection(
+            self.track_id,
+            self.l*scale,
+            self.t*scale,
+            self.w*scale,
+            self.h*scale,
+            self.conf,
+            self.state)
     
     def to_ltwh(self):
         return (int(self.l), int(self.t), int(self.w), int(self.h))
@@ -39,6 +70,7 @@ class Detection:
         return (int(self.l), int(self.t), int(self.l+self.w), int(self.t+self.h))
 
 
+    
 @dataclass
 class Track:
     """A bit of an haphazardous wrapper around the 'real' tracker to provide 
@@ -63,6 +95,7 @@ class Track:
         return [{"x":c[0], "y":c[1]} for c in coords]
 
     
+
 @dataclass
 class Frame:
     index: int
@@ -71,6 +104,19 @@ class Frame:
     tracks: Optional[dict[str, Track]] = None
     H: Optional[np.array] = None
 
+    def aslist(self) -> [dict]:
+        return { t.track_id:
+            {
+                'id': t.track_id,
+                'history': t.get_projected_history(self.H).tolist(),
+                'det_conf': t.history[-1].conf,
+                #     'det_conf': trajectory_data[node.id]['det_conf'],
+                #     'bbox': trajectory_data[node.id]['bbox'],
+                #     'history': history.tolist(),
+                'predictions': t.predictions
+            } for t in self.tracks.values()
+        }
+
 class FrameEmitter:
     '''
     Emit frame in a separate threat so they can be throttled,
@@ -95,6 +141,7 @@ class FrameEmitter:
 
 
     def emit_video(self):
+        i = 0
         for video_path in self.video_srcs:
             logger.info(f"Play from '{str(video_path)}'")
             video = cv2.VideoCapture(str(video_path))
@@ -102,8 +149,21 @@ class FrameEmitter:
             target_frame_duration = 1./fps
             logger.info(f"Emit frames at {fps} fps")
 
+            if '-' in video_path.stem:
+                path_stem = video_path.stem[:video_path.stem.rfind('-')]
+            else:
+                path_stem = video_path.stem
+            path_stem += "-homography"
+            homography_path = video_path.with_stem(path_stem).with_suffix('.txt')
+            logger.info(f'check homography file {homography_path}')
+            if homography_path.exists():
+                logger.info(f'Found custom homography file! Using {homography_path}')
+                video_H = np.loadtxt(homography_path, delimiter=',')
+            else:
+                video_H = None
+
             prev_time = time.time()
-            i = 0
+            
             while self.is_running.is_set():
                 ret, img = video.read()
 
@@ -120,7 +180,7 @@ class FrameEmitter:
                     # hack to mask out area
                     cv2.rectangle(img, (0,0), (800,200), (0,0,0), -1)
 
-                frame = Frame(index=i, img=img)
+                frame = Frame(index=i, img=img, H=video_H)
                 # TODO: this is very dirty, need to find another way.
                 # perhaps multiprocessing Array?
                 self.frame_sock.send(pickle.dumps(frame))

trap/prediction_server.py

@@ -243,7 +243,7 @@ class PredictionServer:
             if self.config.predict_training_data:
                 input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
             else:
-                zmq_ev = self.trajectory_socket.poll(timeout=3)
+                zmq_ev = self.trajectory_socket.poll(timeout=2000)
                 if not zmq_ev:
                     # on no data loop so that is_running is checked
                     continue
@@ -252,7 +252,7 @@ class PredictionServer:
                 frame: Frame = pickle.loads(data)
                 # trajectory_data = {t.track_id: t.get_projected_history_as_dict(frame.H) for t in frame.tracks.values()}
                 # trajectory_data = json.loads(data)
-                logger.debug(f"Receive {frame.index}")
+                # logger.debug(f"Receive {frame.index}")
 
                 # class FakeNode:
                 #     def __init__(self, node_type: NodeType):
@@ -276,12 +276,12 @@ class PredictionServer:
                     ax = derivative_of(vx, 0.1)
                     ay = derivative_of(vy, 0.1)
 
-                    data_dict = {('position', 'x'): x[:],
+                    data_dict = {('position', 'x'): x[:], # [-10:-1]
-                                ('position', 'y'): y[:],
+                                ('position', 'y'): y[:], # [-10:-1]
-                                ('velocity', 'x'): vx[:],
+                                ('velocity', 'x'): vx[:], # [-10:-1]
-                                ('velocity', 'y'): vy[:],
+                                ('velocity', 'y'): vy[:], # [-10:-1]
-                                ('acceleration', 'x'): ax[:],
+                                ('acceleration', 'x'): ax[:], # [-10:-1]
-                                ('acceleration', 'y'): ay[:]}
+                                ('acceleration', 'y'): ay[:]} # [-10:-1]
                     data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
 
                     node_data = pd.DataFrame(data_dict, columns=data_columns)
@@ -301,7 +301,7 @@ class PredictionServer:
                     # TODO: we want to send out empty result...
                     # And want to update the network
 
-                    data = json.dumps({})
+                    # data = json.dumps({})
                     self.prediction_socket.send_pyobj(frame)
                     
                     continue
@@ -325,7 +325,7 @@ class PredictionServer:
                 warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
                 dists, preds = trajectron.incremental_forward(input_dict,
                                                             maps,
-                                                            prediction_horizon=25, # TODO: make variable
+                                                            prediction_horizon=125, # TODO: make variable
                                                             num_samples=5, # TODO: make variable
                                                             robot_present_and_future=robot_present_and_future,
                                                             full_dist=True)

trap/renderer.py

@@ -1,3 +1,4 @@
+import time
 import ffmpeg
 from argparse import Namespace
 import datetime
@@ -8,7 +9,7 @@ import numpy as np
 
 import zmq
 
-from trap.frame_emitter import Frame
+from trap.frame_emitter import DetectionState, Frame
 
 
 logger = logging.getLogger("trap.renderer")
@@ -84,7 +85,7 @@ class Renderer:
         while self.is_running.is_set():
             i+=1
 
-            zmq_ev = self.frame_sock.poll(timeout=3)
+            zmq_ev = self.frame_sock.poll(timeout=2000)
             if not zmq_ev:
                 # when no data comes in, loop so that is_running is checked
                 continue
@@ -95,6 +96,32 @@ class Renderer:
             except zmq.ZMQError as e:
                 logger.debug(f'reuse prediction')
 
+            if first_time is None:
+                    first_time = frame.time
+
+            decorate_frame(frame, prediction_frame, first_time)
+
+            img_path = (self.config.output_dir / f"{i:05d}.png").resolve()
+
+            # cv2.imwrite(str(img_path), img)
+            
+            logger.debug(f"write frame {frame.time - first_time:.3f}s")
+            if self.out_writer:
+                self.out_writer.write(img)
+            if self.streaming_process:
+                self.streaming_process.stdin.write(img.tobytes())
+        logger.info('Stopping')
+
+        if i>2:
+            if self.streaming_process:
+                self.streaming_process.stdin.close()
+            if self.out_writer:
+                self.out_writer.release() 
+            if self.streaming_process:
+                # oddly wrapped, because both close and release() take time.
+                self.streaming_process.wait()
+
+def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.array:
     img = frame.img
 
     # all not working:
@@ -108,27 +135,31 @@ class Renderer:
 
     if not prediction_frame:
         cv2.putText(img, f"Waiting for prediction...", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
-                continue
+        # continue
     else:
+        inv_H = np.linalg.pinv(prediction_frame.H)
         for track_id, track in prediction_frame.tracks.items():
             if not len(track.history):
                 continue
             
             # coords = cv2.perspectiveTransform(np.array([prediction['history']]), self.inv_H)[0]
             coords = [d.get_foot_coords() for d in track.history]
+            confirmations = [d.state == DetectionState.Confirmed for d in track.history]
+
             # logger.warning(f"{coords=}")
             
             for ci in range(1, len(coords)):
                 start = [int(p) for p in coords[ci-1]]
                 end = [int(p) for p in coords[ci]]
-                        cv2.line(img, start, end, (255,255,255), 2, lineType=cv2.LINE_AA)
+                color = (255,255,255) if confirmations[ci] else (100,100,100)
+                cv2.line(img, start, end, color, 2, lineType=cv2.LINE_AA)
 
             if not track.predictions or not len(track.predictions):
                 continue
 
             for pred_i, pred in enumerate(track.predictions):
-                        pred_coords = cv2.perspectiveTransform(np.array([pred]), self.inv_H)[0]
+                pred_coords = cv2.perspectiveTransform(np.array([pred]), inv_H)[0]
-                        color = (0,0,255) if pred_i == 1 else (100,100,100)
+                color = (0,0,255) if pred_i else (100,100,100)
                 for ci in range(1, len(pred_coords)):
                     start = [int(p) for p in pred_coords[ci-1]]
                     end = [int(p) for p in pred_coords[ci]]
@@ -148,36 +179,20 @@ class Renderer:
             cv2.rectangle(img, p1, p2, (255,0,0), 1)
             cv2.putText(img, f"{track_id} ({(track.history[-1].conf or 0):.2f})", (center[0]+8, center[1]), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.7, thickness=2, color=(0,255,0), lineType=cv2.LINE_AA)
 
-            if first_time is None:
-                first_time = frame.time
        
     cv2.putText(img, f"{frame.index:06d}", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
     cv2.putText(img, f"{frame.time - first_time:.3f}s", (120,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
 
     if prediction_frame:
+        # render Δt and Δ frames
         cv2.putText(img, f"{prediction_frame.index - frame.index}", (90,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
+        cv2.putText(img, f"{prediction_frame.time - time.time():.2f}s", (200,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
+        cv2.putText(img, f"{len(prediction_frame.tracks)} tracks", (500,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
+        cv2.putText(img, f"h: {np.average([len(t.history or []) for t in prediction_frame.tracks.values()])}", (580, 50),  cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
+        cv2.putText(img, f"ph: {np.average([len(t.predictor_history or []) for t in prediction_frame.tracks.values()])}", (660, 50),  cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
+        cv2.putText(img, f"p: {np.average([len(t.predictions or []) for t in prediction_frame.tracks.values()])}", (740, 50),  cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
 
-    
+    return img    
-
-            img_path = (self.config.output_dir / f"{i:05d}.png").resolve()
-
-            # cv2.imwrite(str(img_path), img)
-            logger.info(f"write frame {frame.time - first_time:.3f}s")
-            if self.out_writer:
-                self.out_writer.write(img)
-            if self.streaming_process:
-                self.streaming_process.stdin.write(img.tobytes())
-        logger.info('Stopping')
-
-        if i>2:
-            if self.streaming_process:
-                self.streaming_process.stdin.close()
-            if self.out_writer:
-                self.out_writer.release() 
-            if self.streaming_process:
-                # oddly wrapped, because both close and release() take time.
-                self.streaming_process.wait()
-
 
     
 def run_renderer(config: Namespace, is_running: Event):

trap/socket_forwarder.py

@@ -28,7 +28,7 @@ class WebSocketTrajectoryHandler(tornado.websocket.WebSocketHandler):
         self.zmq_socket = zmq_socket
 
     async def on_message(self, message):
-        logger.debug(f"recieve msg")
+        logger.debug(f"receive msg")
 
         try:
             await self.zmq_socket.send_string(message)
@@ -116,6 +116,7 @@ class WsRouter:
 
         context = zmq.asyncio.Context()
         self.trajectory_socket = context.socket(zmq.PUB)
+        logger.info(f'Publish trajectories on {config.zmq_trajectory_addr}')
         self.trajectory_socket.bind(config.zmq_trajectory_addr)
         
         self.prediction_socket = context.socket(zmq.SUB)

trap/tracker.py

@@ -22,7 +22,7 @@ from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
 from ultralytics import YOLO
 from ultralytics.engine.results import Results as YOLOResult
 
-from trap.frame_emitter import Frame, Detection, Track
+from trap.frame_emitter import DetectionState, Frame, Detection, Track
 
 # Detection = [int, int, int, int, float, int]
 # Detections = [Detection]
@@ -66,6 +66,9 @@ class Tracker:
         # TODO: support removal
         self.tracks = defaultdict(lambda: Track())
 
+
+        logger.debug(f"Load tracker: {self.config.detector}")
+
         if self.config.detector == DETECTOR_RETINANET:
             # weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
             # self.model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.2)
@@ -76,7 +79,7 @@ class Tracker:
             self.model.eval()
             # Get the transforms for the model's weights
             self.preprocess = weights.transforms().to(self.device)
-            self.mot_tracker = DeepSort(max_iou_distance=1, max_cosine_distance=0.5, max_age=12, nms_max_overlap=0.9,
+            self.mot_tracker = DeepSort(max_iou_distance=1, max_cosine_distance=0.5, max_age=15, nms_max_overlap=0.9,
                                     #   embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
                                     )
         elif self.config.detector == DETECTOR_MASKRCNN:
@@ -87,7 +90,7 @@ class Tracker:
             self.model.eval()
             # Get the transforms for the model's weights
             self.preprocess = weights.transforms().to(self.device)
-            self.mot_tracker = DeepSort(n_init=5, max_iou_distance=1, max_cosine_distance=0.5, max_age=12, nms_max_overlap=0.9,
+            self.mot_tracker = DeepSort(n_init=5, max_iou_distance=1, max_cosine_distance=0.5, max_age=15, nms_max_overlap=0.9,
                                     #   embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
                                     )
         elif self.config.detector == DETECTOR_YOLOv8:
@@ -120,7 +123,7 @@ class Tracker:
                 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', 'x', 'y'], delimiter='\t', quoting=csv.QUOTE_NONE)
+            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
         
@@ -133,6 +136,12 @@ class Tracker:
             # time.sleep(max(0, prev_run_time - this_run_time + TARGET_DT))
             # prev_run_time = time.time()
 
+            zmq_ev = self.frame_sock.poll(timeout=2000)
+            if not zmq_ev:
+                logger.warn('skip poll after 2000ms')
+                # when there's no data after timeout, loop so that is_running is checked
+                continue
+
             start_time = time.time()
             frame: Frame = self.frame_sock.recv_pyobj() # frame delivery in current setup: 0.012-0.03s
 
@@ -142,6 +151,9 @@ class Tracker:
             
             prev_frame_i = frame.index
             # load homography into frame (TODO: should this be done in emitter?)
+            if frame.H is None:
+                # logger.warning('Falling back to default H')
+                # fallback: load configured H
                 frame.H = self.H
 
             # logger.info(f"Frame delivery delay = {time.time()-frame.time}s")
@@ -150,7 +162,7 @@ class Tracker:
             if self.config.detector == DETECTOR_YOLOv8:
                 detections: [Detection]  = self._yolov8_track(frame.img)
             else :
-                detections: [Detection] = self._resnet_track(frame.img)
+                detections: [Detection] = self._resnet_track(frame.img, scale = 1)
             
                 
             # Store detections into tracklets
@@ -201,10 +213,18 @@ class Tracker:
             if training_csv:
                 training_csv.writerows([{
                     'frame_id': round(frame.index * 10., 1), # not really time
-                    'track_id': t['id'],
+                    'track_id': t.track_id,
-                    'x': t['history'][-1]['x' if not self.config.bypass_prediction else 0],
+                    'l': t.history[-1].l,
-                    'y': t['history'][-1]['y' if not self.config.bypass_prediction else 1],
+                    't': t.history[-1].t,
-                } for t in active_tracks.values()])
+                    'w': t.history[-1].w,
+                    'h': t.history[-1].h,
+                    'x': t.get_projected_history(frame.H)[-1][0],
+                    'y': t.get_projected_history(frame.H)[-1][1],
+                    'state': t.history[-1].state.value
+                    # only keep _actual_detections, no lost entries
+                    } for t in active_tracks.values() 
+                    # if t.history[-1].state != DetectionState.Lost
+                    ])
                 training_frames += len(active_tracks)
             # print(time.time() - start_time)
             
@@ -236,10 +256,13 @@ class Tracker:
             return []
         return [Detection(track_id, *bbox) for bbox, track_id in zip(results[0].boxes.xywh.cpu(), results[0].boxes.id.int().cpu().tolist())]
         
-    def _resnet_track(self, img) -> [Detection]:
+    def _resnet_track(self, img, scale: float = 1) -> [Detection]:
+        if scale != 1:
+            dsize = (int(img.shape[1] * scale), int(img.shape[0] * scale))
+            img = cv2.resize(img, dsize)
         detections = self._resnet_detect_persons(img)
         tracks: [DeepsortTrack] = self.mot_tracker.update_tracks(detections, frame=img)
-        return [Detection.from_deepsort(t) for t in tracks]
+        return [Detection.from_deepsort(t).get_scaled(1/scale) for t in tracks]
 
     def _resnet_detect_persons(self, frame) -> [Detection]:
         t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

trap/web/index.html

@@ -30,7 +30,8 @@
 
     <script>
         // map the field to coordinates of our dummy tracker
-        const field_range = { x: [-30, 10], y: [-10, 10] }
+        // see test_homography.ipynb for the logic behind these values
+        const field_range = { x: [-13.092, 15.37], y: [-4.66, 10.624] }
 
         // Create WebSocket connection.
         const trajectory_socket = new WebSocket(`ws://${window.location.hostname}:{{ ws_port }}/ws/trajectory`);
@@ -125,7 +126,7 @@
             const mousePos = getMousePos(fieldEl, event);
             const position = mouse_coordinates_to_position(mousePos)
             current_pos = position;
-            // tracker[person_counter].addToHistory(current_pos);
+            tracker[person_counter].addToHistory(current_pos);
             // trajectory_socket.send(JSON.stringify(tracker))
 
         });
@@ -134,8 +135,8 @@
             const mousePos = getMousePos(fieldEl, event);
             const position = mouse_coordinates_to_position(mousePos)
             current_pos = position;
-            // tracker[person_counter].addToHistory(current_pos);
+            tracker[person_counter].addToHistory(current_pos);
-            // trajectory_socket.send(JSON.stringify(tracker))
+            trajectory_socket.send(JSON.stringify(tracker))
         });
         document.addEventListener('mouseup', (e) => {
             person_counter++;
@@ -174,8 +175,9 @@
                     // multiple predictions can be sampled
                     person.predictions.forEach((prediction, i) => {
                         ctx.beginPath()
-                        ctx.lineWidth = i === 1 ? 3 : 0.2;
+                        ctx.lineWidth = 0.2;
                         ctx.strokeStyle = i === 1 ? "#ff0000" : "#ccaaaa";
+                        ctx.strokeStyle = "#ccaaaa";
 
                         // start from current position:
                         ctx.moveTo(...coord_as_list(position_to_canvas_coordinate(person.history[person.history.length - 1])));
@@ -184,6 +186,33 @@
                         }
                         ctx.stroke();
                     });
+
+                    // average stroke:
+                    ctx.beginPath()
+                    ctx.lineWidth = 3;
+                    ctx.strokeStyle = "#ff0000";
+
+                    // start from current position:
+                    ctx.moveTo(...coord_as_list(position_to_canvas_coordinate(person.history[person.history.length - 1])));
+                    for (let index = 0; index < person.predictions[0].length; index++) {
+
+                        sum = person.predictions.reduce(
+                            (accumulator, prediction) => ({
+                                "x": accumulator.x + prediction[index][0],
+                                "y": accumulator.y + prediction[index][1],
+                            }),
+                            { x: 0, y: 0 },
+                        );
+                        avg = { x: sum.x / person.predictions.length, y: sum.y / person.predictions.length }
+                                // console.log(sum, avg)
+                        ctx.lineTo(...coord_as_list(position_to_canvas_coordinate(avg)))
+
+
+                    }
+                    // for (const position of ) {
+                    // }
+
+                    ctx.stroke();
                 }
             }
             ctx.restore();