pyproject.toml

@@ -26,9 +26,6 @@ torchvision = [
 ]
 deep-sort-realtime = "^1.3.2"
 ultralytics = "^8.0.200"
-ffmpeg-python = "^0.2.0"
-torchreid = "^0.2.5"
-gdown = "^4.7.1"
 
 [build-system]
 requires = ["poetry-core"]

trap/config.py

@@ -194,8 +194,8 @@ frame_emitter_parser.add_argument("--video-src",
                     default=lambda: list(Path('../DATASETS/VIRAT_subset_0102x/').glob('*.mp4')))
 #TODO: camera as source
 
-frame_emitter_parser.add_argument("--video-loop",
-                    help="By default it emitter will run only once. This allows it to loop the video file to keep testing.",
+frame_emitter_parser.add_argument("--video-no-loop",
+                    help="By default it emitter will run indefiniately. This prevents that and plays every video only once.",
                     action='store_true')
 #TODO: camera as source
 
@@ -217,17 +217,7 @@ tracker_parser.add_argument("--detector",
 
 # Renderer
 
-render_parser.add_argument("--render-file",
+render_parser.add_argument("--render-preview",
                     help="Render a video file previewing the prediction, and its delay compared to the current frame",
                     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)
-

trap/frame_emitter.py

@@ -3,73 +3,21 @@ from dataclasses import dataclass, field
 from itertools import cycle
 import logging
 from multiprocessing import Event
-from pathlib import Path
 import pickle
 import sys
 import time
-from typing import Iterable, Optional
+from typing import Optional
 import numpy as np
 import cv2
 import zmq
-from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
 
 logger = logging.getLogger('trap.frame_emitter')
 
-
-@dataclass
-class Detection:
-    track_id: str # deepsort track id association
-    l: int # left - image space
-    t: int # top - image space
-    w: int # width - image space
-    h: int # height - image space
-    conf: float # object detector probablity
-
-    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)
-    
-    def to_ltwh(self):
-        return (int(self.l), int(self.t), int(self.w), int(self.h))
-    
-    def to_ltrb(self):
-        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 
-    a history, with which the predictor can work, as we then can deduce velocity
-    and acceleration.
-    """
-    track_id: str = None
-    history: [Detection] = field(default_factory=lambda: [])
-    predictor_history: Optional[list] = None # in image space
-    predictions: Optional[list] = None
-
-    def get_projected_history(self, H) -> np.array:
-        foot_coordinates = [d.get_foot_coords() for d in self.history]
-        
-        if len(foot_coordinates):
-            coords = cv2.perspectiveTransform(np.array([foot_coordinates]),H)
-            return coords[0]
-        return np.array([])
-    
-    def get_projected_history_as_dict(self, H) -> dict:
-        coords = self.get_projected_history(H)
-        return [{"x":c[0], "y":c[1]} for c in coords]
-    
-
 @dataclass
 class Frame:
-    index: int
     img: np.array
     time: float= field(default_factory=lambda: time.time())
-    tracks: Optional[dict[str, Track]] = None
-    H: Optional[np.array] = None
+    trajectories: Optional[dict] = None
 
 class FrameEmitter:
     '''
@@ -88,10 +36,10 @@ class FrameEmitter:
         
         logger.info(f"Connection socket {config.zmq_frame_addr}")
 
-        if self.config.video_loop:
-            self.video_srcs: Iterable[Path] = cycle(self.config.video_src)
+        if self.config.video_no_loop:
+            self.video_srcs = self.config.video_src
         else:
-            self.video_srcs: [Path] = self.config.video_src
+            self.video_srcs = cycle(self.config.video_src)
 
 
     def emit_video(self):
@@ -103,7 +51,6 @@ class FrameEmitter:
             logger.info(f"Emit frames at {fps} fps")
 
             prev_time = time.time()
-            i = 0
             while self.is_running.is_set():
                 ret, img = video.read()
 
@@ -114,13 +61,7 @@ class FrameEmitter:
                     # video.set(cv2.CAP_PROP_POS_FRAMES, 0)
                     # ret, img = video.read()
                     # assert ret is not False # not really error proof...
-
-                
-                if "DATASETS/hof/" in str(video_path):
-                    # hack to mask out area
-                    cv2.rectangle(img, (0,0), (800,200), (0,0,0), -1)
-
-                frame = Frame(index=i, img=img)
+                frame = Frame(img=img)
                 # TODO: this is very dirty, need to find another way.
                 # perhaps multiprocessing Array?
                 self.frame_sock.send(pickle.dumps(frame))
@@ -134,13 +75,10 @@ class FrameEmitter:
                 else:
                     prev_time = new_frame_time
 
-                i += 1
-
             if not self.is_running.is_set():
                 # if not running, also break out of infinite generator loop
                 break
 
-
         logger.info("Stopping")
 
       

trap/plumber.py

@@ -73,7 +73,7 @@ def start():
         ExceptionHandlingProcess(target=run_tracker, kwargs={'config': args, 'is_running': isRunning}, name='tracker'),
     ]
     
-    if args.render_file or args.render_url:
+    if args.render_preview:
         procs.append(
             ExceptionHandlingProcess(target=run_renderer, kwargs={'config': args, 'is_running': isRunning}, name='renderer')
         )

trap/prediction_server.py

@@ -27,7 +27,6 @@ import matplotlib.pyplot as plt
 import zmq
 
 from trap.frame_emitter import Frame
-from trap.tracker import Track
 
 logger = logging.getLogger("trap.prediction")
 
@@ -243,38 +242,33 @@ 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)
-                if not zmq_ev:
-                    # on no data loop so that is_running is checked
-                    continue
-
                 data = self.trajectory_socket.recv()
                 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 = frame.trajectories # TODO: properly refractor
                 # trajectory_data = json.loads(data)
-                logger.debug(f"Receive {frame.index}")
+                logger.debug(f"Receive {trajectory_data}")
 
                 # class FakeNode:
                 #     def __init__(self, node_type: NodeType):
                 #         self.type = node_type
 
                 input_dict = {}
-                for identifier, track in frame.tracks.items():
+                for identifier, trajectory in trajectory_data.items():
                     # if len(trajectory['history']) < 7:
                     #     # TODO: these trajectories should still be in the output, but without predictions
                     #     continue
 
                     # TODO: modify this into a mapping function between JS data an the expected Node format
                     # node = FakeNode(online_env.NodeType.PEDESTRIAN)
-                    history = [[h['x'], h['y']] for h in track.get_projected_history_as_dict(frame.H)]
+                    history = [[h['x'], h['y']] for h in trajectory['history']]
                     history = np.array(history)
                     x = history[:, 0]
                     y = history[:, 1]
                     # TODO: calculate dt based on input
-                    vx = derivative_of(x, 0.1) #eval_scene.dt
-                    vy = derivative_of(y, 0.1)
-                    ax = derivative_of(vx, 0.1)
-                    ay = derivative_of(vy, 0.1)
+                    vx = derivative_of(x, 0.2) #eval_scene.dt
+                    vy = derivative_of(y, 0.2)
+                    ax = derivative_of(vx, 0.2)
+                    ay = derivative_of(vy, 0.2)
 
                     data_dict = {('position', 'x'): x[:],
                                 ('position', 'y'): y[:],
@@ -302,7 +296,7 @@ class PredictionServer:
                     # And want to update the network
 
                     data = json.dumps({})
-                    self.prediction_socket.send_pyobj(frame)
+                    self.prediction_socket.send_string(data)
                     
                     continue
 
@@ -326,7 +320,7 @@ class PredictionServer:
                 dists, preds = trajectron.incremental_forward(input_dict,
                                                             maps,
                                                             prediction_horizon=25, # TODO: make variable
-                                                            num_samples=5, # TODO: make variable
+                                                            num_samples=20, # TODO: make variable
                                                             robot_present_and_future=robot_present_and_future,
                                                             full_dist=True)
             end = time.time()
@@ -344,7 +338,6 @@ class PredictionServer:
             # prediction_dict provides the actual predictions
             # histories_dict provides the trajectory used for prediction
             # futures_dict is the Ground Truth, which is unvailable in an online setting
-            
             prediction_dict, histories_dict, futures_dict = prediction_output_to_trajectories({timestep: preds},
                                                                                       eval_scene.dt,
                                                                                       hyperparams['maximum_history_length'],
@@ -365,29 +358,24 @@ class PredictionServer:
             
             for node in histories_dict:
                 history = histories_dict[node]
-                # future = futures_dict[node] # ground truth dict
+                # future = futures_dict[node]
                 predictions = prediction_dict[node]
 
                 if not len(history) or np.isnan(history[-1]).any():
                     continue
 
-                # response[node.id] = {
-                #     'id': node.id,
-                #     'det_conf': trajectory_data[node.id]['det_conf'],
-                #     'bbox': trajectory_data[node.id]['bbox'],
-                #     'history': history.tolist(),
-                #     'predictions': predictions[0].tolist() # use batch 0
-                # }
+                response[node.id] = {
+                    'id': node.id,
+                    'history': history.tolist(),
+                    'predictions': predictions[0].tolist() # use batch 0
+                }
 
-                frame.tracks[node.id].predictor_history = history.tolist()
-                frame.tracks[node.id].predictions = predictions[0].tolist() # use batch 0
-
-            # data = json.dumps(response)
+            data = json.dumps(response)
             if self.config.predict_training_data:
                 logger.info(f"Frame prediction: {len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s")
             else:
                 logger.info(f"Total frame delay = {time.time()-frame.time}s ({len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s)")
-            self.prediction_socket.send_pyobj(frame)
+            self.prediction_socket.send_string(data)
         logger.info('Stopping')
 
             

trap/renderer.py

@@ -1,4 +1,4 @@
-import ffmpeg
+
 from argparse import Namespace
 import datetime
 import logging
@@ -33,65 +33,27 @@ class Renderer:
 
         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 = 10
-        self.frame_size = (1280,720)
-        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
-
-    def start_writer(self):
         if not self.config.output_dir.exists():
             raise FileNotFoundError("Path does not exist")
     
         date_str = datetime.datetime.now().isoformat(timespec="minutes")
-        filename = self.config.output_dir / f"render_predictions-{date_str}-{self.config.detector}.mp4"
+        filename = self.config.output_dir / f"render_predictions-{date_str}.mp4"
         logger.info(f"Write to {filename}")
 
         fourcc = cv2.VideoWriter_fourcc(*'vp09')
-
-        return cv2.VideoWriter(str(filename), fourcc, self.fps, self.frame_size)
-
-    def start_streaming(self):
-        return (
-            ffmpeg
-            .input('pipe:', format='rawvideo',codec="rawvideo", pix_fmt='bgr24', s='{}x{}'.format(*self.frame_size))
-            .output(
-                self.config.render_url,
-                #codec = "copy", # use same codecs of the original video
-                codec='libx264',
-                listen=1, # enables HTTP server
-                pix_fmt="yuv420p",
-                preset="ultrafast",
-                tune="zerolatency",
-                g=f"{self.fps*2}",
-                analyzeduration="2000000",
-                probesize="1000000",
-                f='mpegts'
-            )
-            .overwrite_output()
-            .run_async(pipe_stdin=True)
-        )
-        # return process
-
-        
+        # TODO: get FPS from frame_emitter
+        self.out = cv2.VideoWriter(str(filename), fourcc, 23.97, (1280,720))
 
     
     def run(self):
-        prediction_frame = None
+        predictions = {}
         i=0
         first_time = None
         while self.is_running.is_set():
             i+=1
-
-            zmq_ev = self.frame_sock.poll(timeout=3)
-            if not zmq_ev:
-                # when no data comes in, loop so that is_running is checked
-                continue
-
             frame: Frame = self.frame_sock.recv_pyobj()
             try:
-                prediction_frame: Frame = self.prediction_sock.recv_pyobj(zmq.NOBLOCK)
+                predictions = self.prediction_sock.recv_json(zmq.NOBLOCK)
             except zmq.ZMQError as e:
                 logger.debug(f'reuse prediction')
 
@@ -106,77 +68,47 @@ class Renderer:
             #     warpedFrame = cv2.warpPerspective(img, new_H, (1000,1000))
             #     cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
 
-            if not prediction_frame:
-                cv2.putText(img, f"Waiting for prediction...", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
-                continue
-            else:
-                for track_id, track in prediction_frame.tracks.items():
-                    if not len(track.history):
+            
+            for track_id, prediction in predictions.items():
+                if not 'history' in prediction or not len(prediction['history']):
                     continue
                 
-                    # coords = cv2.perspectiveTransform(np.array([prediction['history']]), self.inv_H)[0]
-                    coords = [d.get_foot_coords() for d in track.history]
+                coords = cv2.perspectiveTransform(np.array([prediction['history']]), self.inv_H)[0]
                 # logger.warning(f"{coords=}")
+                center = [int(p) for p in coords[-1]]
+                cv2.circle(img, center, 5, (0,255,0))
+                cv2.putText(img, track_id, (center[0]+8, center[1]), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(0,255,0))
 
                 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)
+                    cv2.line(img, start, end, (255,255,255), 2)
 
-                    if not track.predictions or not len(track.predictions):
+                if not 'predictions' in prediction or not len(prediction['predictions']):
                     continue
 
-                    for pred_i, pred in enumerate(track.predictions):
+                for pred in prediction['predictions']:
                     pred_coords = cv2.perspectiveTransform(np.array([pred]), self.inv_H)[0]
-                        color = (0,0,255) if pred_i == 1 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]]
-                            cv2.line(img, start, end, color, 1, lineType=cv2.LINE_AA)
+                        cv2.line(img, start, end, (0,0,255), 1)
 
-                for track_id, track in prediction_frame.tracks.items():
-                    # draw tracker marker and track id last so it lies over the trajectories
-                    # this goes is a second loop so it overlays over _all_ trajectories
-                    # coords = cv2.perspectiveTransform(np.array([[track.history[-1].get_foot_coords()]]), self.inv_H)[0]
-                    coords = track.history[-1].get_foot_coords()
 
-                    center = [int(p) for p in coords]
-                    cv2.circle(img, center, 5, (0,255,0))
-                    (l, t, r, b) = track.history[-1].to_ltrb()
-                    p1 = (l, t)
-                    p2 = (r, b)
-                    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:
-                cv2.putText(img, f"{prediction_frame.index - frame.index}", (90,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
-
-    
+            cv2.putText(img, f"{frame.time - first_time:.3f}s", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
 
             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())
+            self.out.write(img)
         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()
+            self.out.release() 
 
         
     

trap/socket_forwarder.py

@@ -1,10 +1,8 @@
 
 from argparse import Namespace
 import asyncio
-import errno
 import logging
 from multiprocessing import Event
-import subprocess
 from typing import Set, Union, Dict, Any
 from typing_extensions import Self
 
@@ -147,14 +145,7 @@ class WsRouter:
 
         # loop = tornado.ioloop.IOLoop.current()
         logger.info(f"Listen on {self.config.ws_port}")
-        try:
         self.application.listen(self.config.ws_port)
-        except OSError as e:
-            if e.errno == errno.EADDRINUSE:
-                logger.critical("Address already in use by process")
-                subprocess.run(["lsof", "-i", f"tcp:{self.config.ws_port:d}"])
-            raise
-
         loop = asyncio.get_event_loop()
 
         task = self.evt_loop.create_task(self.prediction_forwarder())
@@ -165,9 +156,6 @@ class WsRouter:
     async def prediction_forwarder(self):
         logger.info("Starting prediction forwarder")
         while self.is_running.is_set():
-            # timeout so that if no events occur, loop can still stop on is_running
-            has_event = await self.prediction_socket.poll(timeout=1)
-            if has_event:
             msg = await self.prediction_socket.recv_string()
             logger.debug(f"Forward prediction message of {len(msg)} chars")
             WebSocketPredictionHandler.write_to_clients(msg)

trap/tracker.py

@@ -8,24 +8,22 @@ from multiprocessing import Event
 from pathlib import Path
 import pickle
 import time
-from typing import Optional
 import numpy as np
 import torch
 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
+from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights, keypointrcnn_resnet50_fpn, KeypointRCNN_ResNet50_FPN_Weights
 from deep_sort_realtime.deepsort_tracker import DeepSort
-from torchvision.models import ResNet50_Weights
 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 Frame
 
-# Detection = [int, int, int, int, float, int]
-# Detections = [Detection]
+Detection = [int, int, int, int, float, int]
+Detections = [Detection]
 
 # This is the dt that is also used by the scene. 
 # as this needs to be rather stable, try to adhere
@@ -35,13 +33,30 @@ TARGET_DT = .1
 
 logger = logging.getLogger("trap.tracker")
 
-DETECTOR_RETINANET = 'retinanet'
-DETECTOR_MASKRCNN = 'maskrcnn'
-DETECTOR_FASTERRCNN = 'fasterrcnn'
+DETECTOR_RESNET = 'resnet'
 DETECTOR_YOLOv8 = 'ultralytics'
 
-DETECTORS = [DETECTOR_RETINANET, DETECTOR_MASKRCNN, DETECTOR_FASTERRCNN, DETECTOR_YOLOv8]
+DETECTORS = [DETECTOR_RESNET, DETECTOR_YOLOv8]
 
+@dataclass
+class Track:
+    track_id: str = None
+    history: [Detection]= field(default_factory=lambda: [])
+
+@dataclass
+class Detection:
+    track_id: str
+    l: int # left
+    t: int # top
+    w: int # width
+    h: int # height
+
+    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())
 
     
 
@@ -66,40 +81,27 @@ class Tracker:
         # TODO: support removal
         self.tracks = defaultdict(lambda: Track())
 
-        if self.config.detector == DETECTOR_RETINANET:
+        if self.config.detector == DETECTOR_RESNET:
             # weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
             # self.model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.2)
             weights = KeypointRCNN_ResNet50_FPN_Weights.DEFAULT
-            self.model = keypointrcnn_resnet50_fpn(weights=weights, box_score_thresh=0.35)
+            self.model = keypointrcnn_resnet50_fpn(weights=weights, box_score_thresh=0.20)
             self.model.to(self.device)
             # Put the model in inference mode
             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,
-                                    #  embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
-                                    )
-        elif self.config.detector == DETECTOR_MASKRCNN:
-            weights = MaskRCNN_ResNet50_FPN_V2_Weights.COCO_V1
-            self.model = maskrcnn_resnet50_fpn_v2(weights=weights, box_score_thresh=0.7)
-            self.model.to(self.device)
-            # Put the model in inference mode
-            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,
-                                    #  embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
-                                    )
         elif self.config.detector == DETECTOR_YOLOv8:
             self.model = YOLO('EXPERIMENTS/yolov8x.pt')
         else: 
-            raise RuntimeError(f"{self.config.detector} is not implemented yet. See --help")
+            raise RuntimeError("No valid detector specified. See --help")
 
 
         # homography = list(source.glob('*img2world.txt'))[0]
 
         self.H = np.loadtxt(self.config.homography, delimiter=',')
 
+        self.mot_tracker = DeepSort(max_age=30, nms_max_overlap=0.9)
         logger.debug("Set up tracker")
         
 
@@ -122,29 +124,17 @@ class Tracker:
             # 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)
 
-        prev_frame_i = -1
-        
+        frame_i = 0
         while self.is_running.is_set():
-            # 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
-            # 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 + TARGET_DT))
-            # prev_run_time = time.time()
-
-            start_time = time.time()
-            frame: Frame = self.frame_sock.recv_pyobj() # frame delivery in current setup: 0.012-0.03s
-
-            if frame.index > (prev_frame_i+1):
-                logger.warn(f"Dropped {frame.index - prev_frame_i - 1} frames ({frame.index=}, {prev_frame_i=})")
-
-            
-            prev_frame_i = frame.index
-            # load homography into frame (TODO: should this be done in emitter?)
-            frame.H = self.H
+            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 + TARGET_DT))
+            prev_run_time = time.time()
 
+            msg = self.frame_sock.recv()
+            frame: Frame = pickle.loads(msg) # frame delivery in current setup: 0.012-0.03s
             # logger.info(f"Frame delivery delay = {time.time()-frame.time}s")
+            start_time = time.time()
 
 
             if self.config.detector == DETECTOR_YOLOv8:
@@ -154,43 +144,37 @@ class Tracker:
             
                 
             # Store detections into tracklets
-            projected_coordinates = []
             for detection in detections:
                 track = self.tracks[detection.track_id]
                 track.track_id = detection.track_id # for new tracks
 
-                track.history.append(detection) # add to history
-                # projected_coordinates.append(track.get_projected_history(self.H)) # then get full history
-                
-                # TODO: hadle occlusions, and dissappearance
+                track.history.append(detection) 
                 # if len(track.history) > 30:  # retain 90 tracks for 90 frames
                 #     track.history.pop(0)
 
+            foot_coordinates = np.array([[t.get_foot_coords()] for t in detections])
+            if len(foot_coordinates):
+                projected_coordinates = cv2.perspectiveTransform(foot_coordinates,self.H)
+            else:
+                projected_coordinates = []
 
-            # trajectories = {}
-            # for detection in detections:                 
-            #      tid = str(detection.track_id)
-            #      track = self.tracks[detection.track_id]
-            #      coords = track.get_projected_history(self.H) # get full history
-            #      trajectories[tid] = {
-            #          "id": tid,
-            #          "det_conf": detection.conf,
-            #          "bbox": detection.to_ltwh(),
-            #          "history": [{"x":c[0], "y":c[1]} for c in coords[0]] if not self.config.bypass_prediction else coords[0].tolist() # already doubles nested, fine for test
-            #      }
-            active_track_ids = [d.track_id for d in detections]
-            active_tracks = {t.track_id: t for t in self.tracks.values() if t.track_id in active_track_ids}
+            # print(TEMP_proj_coords)
+            trajectories = {}
+            for detection, coords in zip(detections, projected_coordinates):
+                 tid = str(detection.track_id)
+                 trajectories[tid] = {
+                     "id": tid,
+                     "history": [{"x":c[0], "y":c[1]} for c in coords] if not self.config.bypass_prediction else coords.tolist() # already doubles nested, fine for test
+                 }
             # logger.info(f"{trajectories}")
-            frame.tracks = active_tracks
-
-            # if self.config.bypass_prediction:
-            #     self.trajectory_socket.send_string(json.dumps(trajectories))
-            # else:
-            #     self.trajectory_socket.send(pickle.dumps(frame))
-            self.trajectory_socket.send_pyobj(frame)
+            frame.trajectories = trajectories
 
             current_time = time.time()
-            logger.debug(f"Trajectories: {len(active_tracks)}. Current frame delay = {current_time-frame.time}s (trajectories: {current_time - start_time}s)")
+            logger.debug(f"Trajectories: {len(trajectories)}. Current frame delay = {current_time-frame.time}s (trajectories: {current_time - start_time}s)")
+            if self.config.bypass_prediction:
+                self.trajectory_socket.send_string(json.dumps(trajectories))
+            else:
+                self.trajectory_socket.send(pickle.dumps(frame))
                 
             # self.trajectory_socket.send_string(json.dumps(trajectories))
             # provide a {ID: {id: ID, history: [[x,y],[x,y],...]}}
@@ -200,14 +184,13 @@ class Tracker:
             # fpsfilter=fpsfilter*.9+(1/dt)*.1    #trust value in order to stabilize fps display
             if training_csv:
                 training_csv.writerows([{
-                    'frame_id': round(frame.index * 10., 1), # not really time
+                    'frame_id': round(frame_i * 10., 1), # not really time
                     'track_id': t['id'],
-                    'x': t['history'][-1]['x' if not self.config.bypass_prediction else 0],
-                    'y': t['history'][-1]['y' if not self.config.bypass_prediction else 1],
-                } for t in active_tracks.values()])
-                training_frames += len(active_tracks)
-            # print(time.time() - start_time)
-            
+                    'x': t['history'][-1]['x'],
+                    'y': t['history'][-1]['y'],
+                } for t in trajectories.values()])
+                training_frames += len(trajectories)
+            frame_i += 1
         
         if training_fp:
             training_fp.close()
@@ -231,9 +214,6 @@ class Tracker:
 
     def _yolov8_track(self, img) -> [Detection]:
         results: [YOLOResult] = self.model.track(img, persist=True)
-        if results[0].boxes is None or results[0].boxes.id is None:
-            # work around https://github.com/ultralytics/ultralytics/issues/5968
-            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]:
@@ -241,7 +221,7 @@ class Tracker:
         tracks: [DeepsortTrack] = self.mot_tracker.update_tracks(detections, frame=img)
         return [Detection.from_deepsort(t) for t in tracks]
 
-    def _resnet_detect_persons(self, frame) -> [Detection]:
+    def _resnet_detect_persons(self, frame) -> Detections:
         t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
         # change axes of image loaded image to be compatilbe with torch.io.read_image (which has C,W,H format instead of W,H,C)
         t = t.permute(2, 0, 1)