Refactoring for NN, scenarios, laser tests

README.md

@@ -14,13 +14,13 @@ These are roughly the steps to go from datagathering to training
 1. Make sure to have some recordings with a fixed camera. [UPDATE: not needed anymore, except for calibration & homography footage]
     * Recording can be done with `ffmpeg  -rtsp_transport udp -i rtsp://USER:PASS@IP:554/Streaming/Channels/1.mp4   hof2-cam-$(date "+%Y%m%d-%H%M").mp4`
 2. Follow the steps in the auxilary  [traptools](https://git.rubenvandeven.com/security_vision/traptools) repository to obtain (1) camera matrix, lens distortion, image dimensions, and (2+3) homography
-3. Run the tracker, e.g. `poetry run tracker --detector ultralytics  --homography ../DATASETS/NAME/homography.json   --video-src ../DATASETS/NAME/*.mp4 --calibration ../DATASETS/NAME/calibration.json  --save-for-training EXPERIMENTS/raw/NAME/`
-    * Note: You can run this right of the camera stream: `poetry run tracker --eval_device cuda:0 --detector ultralytics   --video-src  rtsp://USER:PW@ADDRESS/STREAM --homography ../DATASETS/NAME/homography.json   --calibration ../DATASETS/NAME/calibration.json  --save-for-training EXPERIMENTS/raw/NAME/`, each recording adding a new file to the `raw` folder.
-4. Parse tracker data to Trajectron format: `poetry run process_data --src-dir EXPERIMENTS/raw/NAME --dst-dir EXPERIMENTS/trajectron-data/ --name NAME` Optionally, smooth tracks: `--smooth-tracks`
+3. Run the tracker, e.g. `uv run tracker --detector ultralytics  --homography ../DATASETS/NAME/homography.json   --video-src ../DATASETS/NAME/*.mp4 --calibration ../DATASETS/NAME/calibration.json  --save-for-training EXPERIMENTS/raw/NAME/`
+    * Note: You can run this right of the camera stream: `uv run tracker --eval_device cuda:0 --detector ultralytics   --video-src  rtsp://USER:PW@ADDRESS/STREAM --homography ../DATASETS/NAME/homography.json   --calibration ../DATASETS/NAME/calibration.json  --save-for-training EXPERIMENTS/raw/NAME/`, each recording adding a new file to the `raw` folder.
+4. Parse tracker data to Trajectron format: `uv run process_data --src-dir EXPERIMENTS/raw/NAME --dst-dir EXPERIMENTS/trajectron-data/ --name NAME` Optionally, smooth tracks: `--smooth-tracks`
     * Optionally, add a map: ideally a RGB png: 3 layers of 0-255
-        * `poetry 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 `poetry 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 `
+        * `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 poetry 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)
+    * 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`.

pyproject.toml

@@ -28,15 +28,19 @@ dependencies = [
     "qrcode~=8.0",
     "pyusb>=1.3.1,<2",
     "ipywidgets>=8.1.5,<9",
+    "foucault",
 ]
 
 [project.scripts]
+start = "trap.conductofconduct:run"
 trapserv = "trap.plumber:start"
 tracker = "trap.tools:tracker_preprocess"
 compare = "trap.tools:tracker_compare"
 process_data = "trap.process_data:main"
 blacklist = "trap.tools:blacklist_tracks"
 rewrite_tracks = "trap.tools:rewrite_raw_track_files"
+live_video_source = "trap.frame_emitter:run"
+live_tracker = "trap.tracker:run"
 
 [tool.uv]
 
@@ -47,6 +51,8 @@ torchvision = [{ url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.
 pandas-helper-calc = { git = "https://github.com/scls19fr/pandas-helper-calc" }
 bytetracker = { git = "https://github.com/rubenvandeven/bytetrack-pip" }
 baumer-neoapi = { path = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" }
+foucault = { git = "https://git.rubenvandeven.com/r/conductofconduct" }
+opencv-python = {path="./opencv_python-4.10.0.84-cp310-cp310-linux_x86_64.whl"}
 
 [build-system]
 requires = ["hatchling"]

trap/base.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import argparse
 from ast import List
 from enum import IntFlag
 from itertools import cycle
@@ -388,7 +389,9 @@ class Frame:
     def without_img(self):
         return Frame(self.index, None, self.time, self.tracks, self.H, self.camera, self.maps)
 
+
 def video_src_from_config(config) -> Iterable[UrlOrPath]:
+    """deprecated, now in video_source"""
     if config.video_loop:
         video_srcs: Iterable[UrlOrPath] = cycle(config.video_src)
     else:
@@ -405,4 +408,43 @@ class Trajectory:
 
     def __iter__(self):
         for d in self.points:
-            yield d
+            yield d
+
+
+class HomographyAction(argparse.Action):
+     def __init__(self, option_strings, dest, nargs=None, **kwargs):
+         if nargs is not None:
+             raise ValueError("nargs not allowed")
+         super().__init__(option_strings, dest, **kwargs)
+     def __call__(self, parser, namespace, values: Path, option_string=None):
+        if values.suffix == '.json':
+            with values.open('r') as fp:
+                H = np.array(json.load(fp))
+        else:
+            H = np.loadtxt(values, delimiter=',')
+            
+        setattr(namespace, self.dest, values)
+        setattr(namespace, 'H', H)
+
+class CameraAction(argparse.Action):
+     def __init__(self, option_strings, dest, nargs=None, **kwargs):
+         if nargs is not None:
+             raise ValueError("nargs not allowed")
+         super().__init__(option_strings, dest, **kwargs)
+     def __call__(self, parser, namespace, values, option_string=None):
+        if values is None:
+            setattr(namespace, self.dest, None)
+        else:
+            camera = Camera.from_calibfile(Path(values), namespace.H, namespace.camera_fps)
+            # values = Path(values)
+            # with values.open('r') as fp:
+            #     data = json.load(fp)
+            #     # print(data)
+            #     # print(data['camera_matrix'])
+            # # camera = {
+            # #     'camera_matrix': np.array(data['camera_matrix']), 
+            # #     'dist_coeff': np.array(data['dist_coeff']),
+            # # }
+            # camera = Camera(np.array(data['camera_matrix']), np.array(data['dist_coeff']), data['dim']['width'], data['dim']['height'], namespace.H, namespace.camera_fps)
+            
+            setattr(namespace, 'camera', camera)

trap/config.py

@@ -6,7 +6,7 @@ import json
 
 from trap.tracker import DETECTORS, TRACKER_BYTETRACK, TRACKERS
 from trap.frame_emitter import Camera
-
+from trap.base import CameraAction, HomographyAction
 from pyparsing import Optional
 from trap.frame_emitter import UrlOrPath
 
@@ -53,44 +53,6 @@ frame_emitter_parser = parser.add_argument_group('Frame emitter')
 tracker_parser = parser.add_argument_group('Tracker')
 render_parser = parser.add_argument_group('Renderer')
 
-class HomographyAction(argparse.Action):
-     def __init__(self, option_strings, dest, nargs=None, **kwargs):
-         if nargs is not None:
-             raise ValueError("nargs not allowed")
-         super().__init__(option_strings, dest, **kwargs)
-     def __call__(self, parser, namespace, values: Path, option_string=None):
-        if values.suffix == '.json':
-            with values.open('r') as fp:
-                H = np.array(json.load(fp))
-        else:
-            H = np.loadtxt(values, delimiter=',')
-            
-        setattr(namespace, self.dest, values)
-        setattr(namespace, 'H', H)
-
-class CameraAction(argparse.Action):
-     def __init__(self, option_strings, dest, nargs=None, **kwargs):
-         if nargs is not None:
-             raise ValueError("nargs not allowed")
-         super().__init__(option_strings, dest, **kwargs)
-     def __call__(self, parser, namespace, values, option_string=None):
-        if values is None:
-            setattr(namespace, self.dest, None)
-        else:
-            camera = Camera.from_calibfile(Path(values), namespace.H, namespace.camera_fps)
-            # values = Path(values)
-            # with values.open('r') as fp:
-            #     data = json.load(fp)
-            #     # print(data)
-            #     # print(data['camera_matrix'])
-            # # camera = {
-            # #     'camera_matrix': np.array(data['camera_matrix']), 
-            # #     'dist_coeff': np.array(data['dist_coeff']),
-            # # }
-            # camera = Camera(np.array(data['camera_matrix']), np.array(data['dist_coeff']), data['dim']['width'], data['dim']['height'], namespace.H, namespace.camera_fps)
-            
-            setattr(namespace, 'camera', camera)
-
 
 inference_parser.add_argument("--step-size", 
                               # TODO)) Make dataset/model metadata
@@ -279,6 +241,10 @@ frame_emitter_parser.add_argument("--video-offset",
                     help="Start playback from given frame. Note that when src is an array, this applies to all videos individually.",
                     default=None,
                     type=int)
+frame_emitter_parser.add_argument("--video-end",
+                    help="End (or loop) playback at given frame.",
+                    default=None,
+                    type=int)
 #TODO: camera as source
 
 frame_emitter_parser.add_argument("--video-loop",

trap/counter.py

@@ -0,0 +1,68 @@
+import collections
+import logging
+import statistics
+import time
+from typing import MutableSequence
+import zmq
+
+logger = logging.getLogger('counter')
+
+class CounterSender:
+    def __init__(self, address = "ipc:///tmp/trap-counters"):
+        # 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.sndhwm = 1
+        self.sock.bind(address)
+
+    def set(self, name:str, value:float):
+        try:
+            self.sock.send_multipart([name.encode('utf8'), str(value).encode("utf8")], flags=zmq.NOBLOCK)
+        except zmq.ZMQError as e:
+            logger.warning(f"No space in que to count {name} as {value}")
+
+class CounterLog():
+    def __init__(self, history = 20):
+        self.history: MutableSequence[(float, float)] = collections.deque(maxlen=history)
+    
+    def add(self, value):
+        self.history.append((time.perf_counter(), value))
+    
+    def value(self):
+        if not len(self.history):
+            return None
+        return self.history[-1][1]
+    
+    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"):
+        self.context = zmq.Context()
+        self.sock = self.context.socket(zmq.SUB)
+        self.sock.connect(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)
+        
+    
+    def get_latest(self):
+        self.snapshot()
+        return self.values
+
+    def to_string(self):
+        strs = [f"{k}: {v.value()} ({v.avg()})" for (k,v) in self.values.items()]
+        return " ".join(strs)
+            
+    
+
+

trap/face_detector.py

@@ -0,0 +1,170 @@
+from argparse import Namespace
+from collections import defaultdict
+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
+import jsonlines
+import numpy as np
+import torch
+import torchvision
+import ultralytics
+import zmq
+import cv2
+
+from facenet_pytorch import InceptionResnetV1, MTCNN
+
+from trap.base import Frame
+
+logger = logging.getLogger('trap.face_detector')
+
+class FaceDetector:
+    def __init__(self, config: Namespace):
+        self.config = config
+
+
+        self.context = zmq.Context()
+        self.frame_sock = self.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.face_socket = self.context.socket(zmq.PUB)
+        self.face_socket.setsockopt(zmq.CONFLATE, 1) # only keep latest frame
+        self.face_socket.bind(self.config.zmq_face_addr)
+
+
+        # # TODO: config device
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+
+    def track(self,  is_running: Event, timer_counter: int = 0):
+        """
+        Live tracking of frames coming in over zmq
+        """
+
+        self.is_running = is_running
+
+        prev_frame_i = -1
+
+        # For a model pretrained on CASIA-Webface
+        # model = InceptionResnetV1(pretrained='casia-webface').eval().to(self.device)
+        # mtcnn = MTCNN(
+        #     image_size=160, margin=0, min_face_size=10,
+        #     thresholds=[0.3, 0.3, 0.3], factor=0.709, post_process=True,
+        #     device=self.device, keep_all=True
+            
+        # )
+        # modelpath = Path("face_detection_yunet_2023mar_int8bq.onnx")
+        modelpath = Path("face_detection_yunet_2023mar_int8.onnx")
+        # model = YuNet(modelPath=args.model,
+        #           inputSize=[320, 320],
+        #           confThreshold=args.conf_threshold,
+        #           nmsThreshold=args.nms_threshold,
+        #           topK=args.top_k,
+        #           backendId=backend_id,
+        #           targetId=target_id)
+        detector = cv2.FaceDetectorYN.create(
+            str(modelpath),
+            "",
+            (320, 320),
+            .3,
+            .3,
+            5000,
+            cv2.dnn.DNN_BACKEND_CUDA,
+            target_id=cv2.dnn.DNN_TARGET_CUDA
+        )
+        
+        
+                
+        while self.is_running.is_set():
+            
+            with timer_counter.get_lock():
+                timer_counter.value += 1
+
+            poll_time = time.time()
+            zmq_ev = self.frame_sock.poll(timeout=2000)
+            if not zmq_ev:
+                logger.warning('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
+
+            # print(time.time()- frame.time)
+            
+            if frame.index > (prev_frame_i+1):
+                logger.warning(f"Dropped {frame.index - prev_frame_i - 1} frames ({frame.index=}, {prev_frame_i=}) -- poll time {start_time-poll_time:.5f}")
+            
+            height, width, channels = frame.img.shape
+
+            detector.setInputSize((width//2, height//2))
+
+            img = cv2.resize(frame.img, (width//2, height//2))
+ 
+            faces = detector.detect(img)
+            
+            prev_frame_i = frame.index
+            
+            # print(f"send to {self.trajectory_socket}, {self.config.zmq_trajectory_addr}")
+            self.face_socket.send_pyobj(faces) # ditch image for faster passthrough
+            
+            
+        logger.info('Stopping')
+
+    
+
+def run_detector(config: Namespace, is_running: Event, timer_counter):
+    router = FaceDetector(config)
+    router.track(is_running, timer_counter)
+
+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-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')
+    config = argparser.parse_args()
+    is_running = multiprocessing.Event()
+    is_running.set()
+    timer_counter = timer.Timer('frame_emitter')
+
+    router = Tracker(config)
+    router.track(is_running, timer_counter.iterations)
+    is_running.clear()
+

trap/frame_emitter.py

@@ -8,6 +8,7 @@ from itertools import cycle
 import json
 import logging
 from multiprocessing import Event
+import multiprocessing
 from pathlib import Path
 import pickle
 import sys
@@ -26,6 +27,7 @@ from trajectron.environment import Environment, Node, Scene
 from urllib.parse import urlparse
 
 from trap.base import *
+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
@@ -83,13 +85,13 @@ class FrameEmitter:
         logger.info(f"Connection socket {config.zmq_frame_addr}")
         logger.info(f"Connection socket {config.zmq_frame_noimg_addr}")
 
-        self.video_srcs = video_src_from_config(self.config)
+        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))
+        source = get_video_source(self.video_srcs, self.config.camera, int(self.config.video_offset), self.config.video_end, self.config.video_loop)
         for i, img in enumerate(source):
 
             with timer_counter.get_lock():
@@ -114,4 +116,61 @@ class FrameEmitter:
 def run_frame_emitter(config: Namespace, is_running: Event, timer_counter: int):
     router = FrameEmitter(config, is_running)
     router.emit_video(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/laser_renderer.py

@@ -40,6 +40,9 @@ class LaserFrame():
     def __init__(self, paths: list[LaserPath]):
         self.paths = paths
 
+    def point_count(self):
+        return sum([len(p.points) for p in self.paths])
+
     # def closest_path(cls, point, paths):
     #     distances = [min(p.last()-)]
     
@@ -54,6 +57,14 @@ class LaserFrame():
     #     last_point = endpoint
     #     pass
 
+    def as_cropped_to_projector(self):
+        paths = []
+        for path in self.paths:
+            p = path.as_cropped_to_projector()
+            if len(p.points):
+                paths.append(p)
+        return LaserFrame(paths)
+
     def get_points_interpolated_by_distance(self, point_interval, last_point: Optional[LaserPoint] = None) -> list[LaserPoint]:
         """
         Interpolate the gaps between paths (NOT THE PATHS THEMSELVES)
@@ -101,6 +112,12 @@ class LaserPath():
     def as_array(self):
         np.array([[p.x, p.y] for p in self.points])
     
+    def as_cropped_to_projector(self):
+        """Make sure all points fall within range of laser"""
+        points = [p for p in self.points if p.x >= 0 and p.y >= 0 and p.x < 0xFFF and p.y < 0xFFF ]
+        return LaserPath(points)
+         
+    
     def simplyfied_path(self, start_v= 10., max_v= 20., a = 2):
         """walk over the path with specific velocity,
         continuously accelerate (a) until max_v is reached
@@ -341,12 +358,18 @@ class LaserRenderer:
             paths = render_frame_to_pathlist( tracker_frame, prediction_frame, first_time, self.config, self.tracks, self.predictions, self.config.render_clusters)
             print(f"Paths: {len(paths)} ... points {sum([len(p.points) for p in paths])}")
             laserframe = LaserFrame(paths)
+            laserframe_cropped = laserframe.as_cropped_to_projector()
+            if laserframe.point_count() > laserframe_cropped.point_count():
+                logger.warning("Removed laser points out of frame!")
+                # print(laserframe.point_count(), laserframe_cropped.point_count())
+                laserframe = laserframe_cropped
             # pointlist=pointlist_test
             # print([(p.x, p.y) for p in pointlist])
             # pointlist.extend(pointlist_test)
 
             pointlist = laserframe.get_points_interpolated_by_distance(30, last_laser_point)
-            print(len(pointlist))
+            # pointlist_cropped = 
+            
             # pointlist = pointlist[::2]
             # print('decimated', len(pointlist))
 
@@ -463,9 +486,10 @@ def render_frame_to_pathlist(tracker_frame: Optional[Frame], prediction_frame: O
     #     cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
     # cv2.rectangle(img, (0,0), (img.shape[1],25), (0,0,0), -1)
 
-    intensity = 100 # range 0-255
+    intensity = 40 # range 0-255
     test_r = 100
     base_c = (0,0, intensity)
+    base_c = (0,intensity, intensity)
     track_c = (intensity,0,0)
     pred_c = (0,intensity,0)
 
@@ -508,6 +532,7 @@ def render_frame_to_pathlist(tracker_frame: Optional[Frame], prediction_frame: O
 
         for track_id, track in tracks.items():
             inv_H = np.linalg.pinv(tracker_frame.H)
+            # track = track.get_sampled(4)
             history = track.get_projected_history(camera=config.camera)
             history = world_points_to_laser_points(history)[0]
             
@@ -528,10 +553,11 @@ def render_frame_to_pathlist(tracker_frame: Optional[Frame], prediction_frame: O
             )
         # cv2.putText(img, f"Waiting for prediction...", (500,17), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
         # continue
-    # elif True:
-    #     pass
+    elif True:
+        pass
     else:
         for track_id, track in predictions.items():
+            
             inv_H = np.linalg.pinv(prediction_frame.H)
             # For debugging:
             # draw_trackjectron_history(img, track, int(track.track_id), convert_world_points_to_img_points)

trap/prediction_server.py

@@ -320,7 +320,7 @@ class PredictionServer:
                     if len(track.history) < 2:
                         continue
                     
-                    node = track.to_trajectron_node(self.config.camera, online_env)
+                    node = track.to_trajectron_node(frame.camera, online_env)
                     # print(node.data.data[-1])
                     input_dict[node] = np.array(object=node.data.data[-1])
                     # print("history", node.data.data[-10:])

trap/scenarios.py

@@ -0,0 +1,125 @@
+
+from enum import Enum
+import time
+from typing import Optional
+from statemachine import Event, State, StateMachine
+from statemachine.exceptions import TransitionNotAllowed
+
+from trap.base import Track
+
+
+class ScenarioScene(Enum):
+    DETECTED = 1
+    FIRST_PREDICTION = 2
+    CORRECTED_PREDICTION = 3
+    LOITERING = 4
+    PLAY = 4
+    LOST = -1
+
+class TrackScenario(StateMachine):
+    detected = State(initial=True)
+    substantial = State()
+    first_prediction = State()
+    corrected_prediction = State()
+    loitering = State()
+    play = State()
+    lost = State(final=True)
+    
+    receive_track = lost.from_(
+        detected, first_prediction, corrected_prediction, loitering, play, substantial, cond="track_is_lost"
+    ) | corrected_prediction.to(loitering, cond="track_is_loitering") | detected.to(substantial, cond="track_is_long")
+
+    receive_prediction = detected.to(first_prediction) | first_prediction.to(corrected_prediction, cond="prediction_is_stale") | corrected_prediction.to(play, cond="prediction_is_playing")
+
+    def __init__(self, track: Track):
+        self._track = track
+        self.first_prediction_track: Optional[Track] = None
+        self.prediction_track: Optional[Track] = None
+        super().__init__()
+
+    def track_is_long(self, track: Track):
+        return len(track.history) > 20
+
+    def track_is_lost(self, track: Track):
+        return track.lost
+
+    def track_is_loitering(self, track: Track):
+        # TODO)) Change to measure displacement over the last n seconds
+        return len(track.history) > (track.fps * 60) # seconds after which someone is loitering
+    
+    def prediction_is_stale(self, track: Track):
+        # TODO use displacement instead of time
+        return bool(self.prediction_track and self.prediction_track.created_at < (time.perf_counter() - 2))
+    
+    def prediction_is_playing(self, Track):
+        return False
+    
+    # @property
+    # def track(self):
+    #     return self._track
+    
+    def set_track(self, track: Track):
+        self._track = track
+        try:
+            self.receive_track(track)
+        except TransitionNotAllowed as e:
+            # state change is optional
+            pass
+    
+    def set_prediction(self, track: Track):
+        if not self.first_prediction_track:
+            self.first_prediction_track = track
+        
+        self.prediction_track = track
+        try:
+            self.receive_prediction(track)
+        except TransitionNotAllowed as e:
+            # state change is optional
+            pass
+    
+    def after_receive_track(self, track: Track):
+        print('change state')
+
+    def on_receive_track(self, track: Track):
+        # on event, because it happens for every receive, despite transition
+        print('updating track!')
+        # self.track = track
+
+    def on_receive_prediction(self, track: Track):
+        # on event, because it happens for every receive, despite transition
+        print('updating prediction!')
+        # self.track = track
+
+    def after_receive_prediction(self, track: Track):
+        # after 
+        self.prediction_track = track
+        if not self.first_prediction_track:
+            self.first_prediction_track = track
+
+    def on_enter_corrected_prediction(self):
+        print('corrected!')
+
+    def on_enter_detected(self):
+        print("DETECTED!")
+
+    def on_enter_first_prediction(self):
+        print("Hello!")
+
+    def on_enter_detected(self):
+        print(f"enter {self.current_state.id}")
+    def on_enter_substantial(self):
+        print(f"enter {self.current_state.id}")
+    def on_enter_first_prediction(self):
+        print(f"enter {self.current_state.id}")
+    def on_enter_corrected_prediction(self):
+        print(f"enter {self.current_state.id}")
+    def on_enter_loitering(self):
+        print(f"enter {self.current_state.id}")
+    def on_enter_play(self):
+        print(f"enter {self.current_state.id}")
+    def on_enter_lost(self):
+        print(f"enter {self.current_state.id}")
+    
+
+
+    

trap/tools.py

@@ -339,6 +339,8 @@ def track_predictions_to_lines(track: Track, camera:Camera, anim_position=.8):
         # print(pred_coords, current_point, line_points)
         line_points = transition_path_points(line_points, slide_t)
         lines.append(line_points)
+        # print("prediction line", len(line_points))
+        # break # TODO: only one
     return lines
 
 def draw_track_predictions(img: cv2.Mat, track: Track, color_index: int, camera:Camera, convert_points: Optional[Callable], anim_position=.8, as_clusters=False):

trap/tracker.py

@@ -6,6 +6,7 @@ import json
 import logging
 from math import nan
 from multiprocessing import Event
+import multiprocessing
 from pathlib import Path
 import pickle
 import time
@@ -25,6 +26,7 @@ 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 import timer
 from trap.frame_emitter import Camera, DataclassJSONEncoder, DetectionState, Frame, Detection, Track
 from bytetracker import BYTETracker
 
@@ -434,7 +436,7 @@ class Tracker:
 
         # homography = list(source.glob('*img2world.txt'))[0]
 
-        self.H = self.config.H
+        # self.H = self.config.H
 
         if self.config.smooth_tracks:
             logger.info("Smoother enabled")
@@ -455,7 +457,8 @@ class Tracker:
         for detection in detections:
             track = self.tracks[detection.track_id]
             track.track_id = detection.track_id # for new tracks
-            track.fps = self.config.camera.fps # for new tracks
+            track.fps = frame.camera.fps
+            # track.fps = self.config.camera.fps # for new tracks
 
             track.history.append(detection) # add to history
         
@@ -537,9 +540,9 @@ 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')
+                    raise RuntimeError('Tracker no longer configures H')
                     # fallback: load configured H
-                    frame.H = self.H
+                    # frame.H = self.H
 
                 # logger.info(f"Frame delivery delay = {time.time()-frame.time}s")
 
@@ -680,6 +683,44 @@ def run_tracker(config: Namespace, is_running: Event, timer_counter):
     router = Tracker(config)
     router.track(is_running, timer_counter)
 
+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-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')
+    config = argparser.parse_args()
+    is_running = multiprocessing.Event()
+    is_running.set()
+    timer_counter = timer.Timer('frame_emitter')
+
+    router = Tracker(config)
+    router.track(is_running, timer_counter.iterations)
+    is_running.clear()
 
 
 class Smoother:

trap/video_sources.py

@@ -1,3 +1,4 @@
+from itertools import cycle
 import logging
 import math
 from pathlib import Path
@@ -84,9 +85,9 @@ class RtspSource(SingleCvVideoSource):
         
 
 class FilelistSource(SingleCvVideoSource):
-    def __init__(self, video_sources: Iterable[UrlOrPath], camera: Camera = None, delay = True, offset = 0):
+    def __init__(self, video_sources: Iterable[UrlOrPath], camera: Camera = None, delay = True, offset = 0, end: Optional[int] = None, loop=False):
         # store current position
-        self.video_sources = video_sources
+        self.video_sources = video_sources if not loop else cycle(video_sources)
         self.camera = camera
         self.video_path = None
         self.video_nr = None
@@ -95,6 +96,7 @@ class FilelistSource(SingleCvVideoSource):
         self.n = 0
         self.delay_generation = delay
         self.offset = offset
+        self.end = end
 
     def recv(self):
         prev_time = time.time()
@@ -134,6 +136,10 @@ class FilelistSource(SingleCvVideoSource):
                 # frame = Frame(index=self.n, img=img, H=self.camera.H, camera=self.camera)
                 yield img
 
+                if self.end is not None and self.frame_idx >= self.end:
+                    logger.info(f"Reached frame {self.end}")
+                    break
+                
                 if self.delay_generation:
                     # defer next loop
                     now = time.time()
@@ -143,6 +149,7 @@ class FilelistSource(SingleCvVideoSource):
                         now += target_frame_duration - time_diff
                     
                     prev_time = now
+                
 
 class CameraSource(SingleCvVideoSource):
     def __init__(self, identifier: int, camera: Camera):
@@ -156,7 +163,8 @@ class CameraSource(SingleCvVideoSource):
         self.video.set(cv2.CAP_PROP_FPS, self.camera.fps)
         self.frame_idx = 0
  
-def get_video_source(video_sources: List[UrlOrPath], camera: Camera, frame_offset=0):
+def get_video_source(video_sources: List[UrlOrPath], camera: Camera, frame_offset=0, frame_end:Optional[int]=None, loop=False):
+    
     if str(video_sources[0]).isdigit():
         # numeric input is a CV camera
         if frame_offset:
@@ -172,6 +180,6 @@ def get_video_source(video_sources: List[UrlOrPath], camera: Camera, frame_offse
             logger.info("video-offset ignored for gige source")
         return GigE(video_sources[0].url.hostname)
     else:
-        return FilelistSource(video_sources, offset = frame_offset)
+        return FilelistSource(video_sources, offset = frame_offset, end=frame_end, loop=loop)
         # os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "fflags;nobuffer|flags;low_delay|avioflags;direct|rtsp_transport;udp"
         

uv.lock

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+version = "24.1.0"
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+[[package]]
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+    { name = "aiosignal" },
+    { name = "async-timeout" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
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