testing the tracker
This commit is contained in:
Ruben van de Ven
parent
ba4d2f7909
commit
7c05c060c3
10 changed files with 1016 additions and 217 deletions
55
poetry.lock
generated
55
poetry.lock
generated
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@ -1817,9 +1817,9 @@ files = [
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[package.dependencies]
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numpy = [
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{version = ">=1.23.5", markers = "python_version >= \"3.11\""},
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{version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
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{version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
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{version = ">=1.23.5", markers = "python_version >= \"3.11\""},
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]
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[[package]]
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@ -1939,8 +1939,8 @@ files = [
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[package.dependencies]
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numpy = [
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{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
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{version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
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{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
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]
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python-dateutil = ">=2.8.2"
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pytz = ">=2020.1"
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@ -1970,6 +1970,21 @@ sql-other = ["SQLAlchemy (>=1.4.36)"]
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test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
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xml = ["lxml (>=4.8.0)"]
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[[package]]
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name = "pandas_helper_calc"
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version = "0.0.1"
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description = ""
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optional = false
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python-versions = "*"
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files = []
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develop = false
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[package.source]
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type = "git"
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url = "https://github.com/scls19fr/pandas-helper-calc"
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reference = "HEAD"
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resolved_reference = "22df480f09c0fa96548833f9dee8f9128512641b"
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[[package]]
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name = "pandocfilters"
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version = "1.5.0"
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@ -2961,6 +2976,24 @@ all = ["numpy", "pytest", "pytest-cov"]
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test = ["pytest", "pytest-cov"]
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vectorized = ["numpy"]
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[[package]]
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name = "simdkalman"
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version = "1.0.4"
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description = "Kalman filters vectorized as Single Instruction, Multiple Data"
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optional = false
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python-versions = "*"
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files = [
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{file = "simdkalman-1.0.4-py2.py3-none-any.whl", hash = "sha256:fc2c6b9e540e0a26b39d087e78623d3c1e8c6677abf5d91111f5d49e328e1668"},
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]
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[package.dependencies]
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numpy = ">=1.9.0"
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[package.extras]
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dev = ["check-manifest"]
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docs = ["sphinx"]
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test = ["pylint"]
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[[package]]
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name = "six"
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version = "1.16.0"
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@ -3300,6 +3333,22 @@ tqdm = "^4.65.0"
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type = "directory"
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url = "../Trajectron-plus-plus"
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[[package]]
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name = "tsmoothie"
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version = "1.0.5"
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description = "A python library for timeseries smoothing and outlier detection in a vectorized way."
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optional = false
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python-versions = ">=3"
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files = [
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{file = "tsmoothie-1.0.5-py3-none-any.whl", hash = "sha256:dedf8d8e011562824abe41783bf33e1b9ee1424bc572853bb82408743316a90e"},
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{file = "tsmoothie-1.0.5.tar.gz", hash = "sha256:d83fa0ccae32bde7b904d9581ebf137e8eb18629cc3563d7379ca5f92461f6f5"},
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]
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[package.dependencies]
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numpy = "*"
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scipy = "*"
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simdkalman = "*"
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[[package]]
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name = "types-python-dateutil"
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version = "2.8.19.14"
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@ -3468,4 +3517,4 @@ watchdog = ["watchdog (>=2.3)"]
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[metadata]
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lock-version = "2.0"
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python-versions = "^3.10,<3.12,"
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content-hash = "c9d4fe6a1d054a835a689cee011753b900b696aa8a06b81aa7a10afc24a8bc70"
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content-hash = "66f062f9db921cfa83e576288d09fd9b959780eb189d95765934ae9a6769f200"
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@ -29,6 +29,8 @@ ultralytics = "^8.0.200"
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ffmpeg-python = "^0.2.0"
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torchreid = "^0.2.5"
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gdown = "^4.7.1"
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pandas-helper-calc = {git = "https://github.com/scls19fr/pandas-helper-calc"}
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tsmoothie = "^1.0.5"
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[build-system]
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requires = ["poetry-core"]
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File diff suppressed because one or more lines are too long
574
test_tracker.ipynb
Normal file
574
test_tracker.ipynb
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File diff suppressed because one or more lines are too long
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@ -1,5 +1,6 @@
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from argparse import Namespace
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from dataclasses import dataclass, field
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from enum import IntFlag
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from itertools import cycle
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import logging
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from multiprocessing import Event
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@ -12,9 +13,25 @@ import numpy as np
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import cv2
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import zmq
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from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
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from deep_sort_realtime.deep_sort.track import TrackState as DeepsortTrackState
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logger = logging.getLogger('trap.frame_emitter')
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class DetectionState(IntFlag):
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Tentative = 1 # state before n_init (see DeepsortTrack)
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Confirmed = 2 # after tentative
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Lost = 4 # lost when DeepsortTrack.time_since_update > 0 but not Deleted
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@classmethod
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def from_deepsort_track(cls, track: DeepsortTrack):
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if track.state == DeepsortTrackState.Tentative:
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return cls.Tentative
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if track.state == DeepsortTrackState.Confirmed:
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if track.time_since_update > 0:
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return cls.Lost
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return cls.Confirmed
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raise RuntimeError("Should not run into Deleted entries here")
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@dataclass
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class Detection:
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@ -24,13 +41,27 @@ class Detection:
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w: int # width - image space
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h: int # height - image space
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conf: float # object detector probablity
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state: DetectionState
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def get_foot_coords(self):
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return [self.l + 0.5 * self.w, self.t+self.h]
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@classmethod
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def from_deepsort(cls, dstrack: DeepsortTrack):
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return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf)
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return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf, DetectionState.from_deepsort_track(dstrack))
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def get_scaled(self, scale: float = 1):
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if scale == 1:
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return self
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return Detection(
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self.track_id,
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self.l*scale,
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self.t*scale,
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self.w*scale,
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self.h*scale,
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self.conf,
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self.state)
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def to_ltwh(self):
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return (int(self.l), int(self.t), int(self.w), int(self.h))
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@ -39,6 +70,7 @@ class Detection:
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return (int(self.l), int(self.t), int(self.l+self.w), int(self.t+self.h))
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@dataclass
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class Track:
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"""A bit of an haphazardous wrapper around the 'real' tracker to provide
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@ -63,6 +95,7 @@ class Track:
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return [{"x":c[0], "y":c[1]} for c in coords]
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@dataclass
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class Frame:
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index: int
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@ -71,6 +104,19 @@ class Frame:
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tracks: Optional[dict[str, Track]] = None
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H: Optional[np.array] = None
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def aslist(self) -> [dict]:
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return { t.track_id:
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{
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'id': t.track_id,
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'history': t.get_projected_history(self.H).tolist(),
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'det_conf': t.history[-1].conf,
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# 'det_conf': trajectory_data[node.id]['det_conf'],
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# 'bbox': trajectory_data[node.id]['bbox'],
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# 'history': history.tolist(),
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'predictions': t.predictions
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} for t in self.tracks.values()
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}
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class FrameEmitter:
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'''
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Emit frame in a separate threat so they can be throttled,
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@ -95,6 +141,7 @@ class FrameEmitter:
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def emit_video(self):
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i = 0
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for video_path in self.video_srcs:
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logger.info(f"Play from '{str(video_path)}'")
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video = cv2.VideoCapture(str(video_path))
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@ -102,8 +149,21 @@ class FrameEmitter:
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target_frame_duration = 1./fps
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logger.info(f"Emit frames at {fps} fps")
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if '-' in video_path.stem:
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path_stem = video_path.stem[:video_path.stem.rfind('-')]
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else:
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path_stem = video_path.stem
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path_stem += "-homography"
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homography_path = video_path.with_stem(path_stem).with_suffix('.txt')
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logger.info(f'check homography file {homography_path}')
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if homography_path.exists():
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logger.info(f'Found custom homography file! Using {homography_path}')
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video_H = np.loadtxt(homography_path, delimiter=',')
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else:
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video_H = None
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prev_time = time.time()
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i = 0
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while self.is_running.is_set():
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ret, img = video.read()
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@ -120,7 +180,7 @@ class FrameEmitter:
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# hack to mask out area
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cv2.rectangle(img, (0,0), (800,200), (0,0,0), -1)
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frame = Frame(index=i, img=img)
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frame = Frame(index=i, img=img, H=video_H)
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# TODO: this is very dirty, need to find another way.
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# perhaps multiprocessing Array?
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self.frame_sock.send(pickle.dumps(frame))
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@ -243,7 +243,7 @@ class PredictionServer:
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if self.config.predict_training_data:
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input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
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else:
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zmq_ev = self.trajectory_socket.poll(timeout=3)
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zmq_ev = self.trajectory_socket.poll(timeout=2000)
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if not zmq_ev:
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# on no data loop so that is_running is checked
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continue
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@ -252,7 +252,7 @@ class PredictionServer:
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frame: Frame = pickle.loads(data)
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# trajectory_data = {t.track_id: t.get_projected_history_as_dict(frame.H) for t in frame.tracks.values()}
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# trajectory_data = json.loads(data)
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logger.debug(f"Receive {frame.index}")
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# logger.debug(f"Receive {frame.index}")
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# class FakeNode:
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# def __init__(self, node_type: NodeType):
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@ -276,12 +276,12 @@ class PredictionServer:
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ax = derivative_of(vx, 0.1)
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ay = derivative_of(vy, 0.1)
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data_dict = {('position', 'x'): x[:],
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('position', 'y'): y[:],
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('velocity', 'x'): vx[:],
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('velocity', 'y'): vy[:],
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('acceleration', 'x'): ax[:],
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('acceleration', 'y'): ay[:]}
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data_dict = {('position', 'x'): x[:], # [-10:-1]
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('position', 'y'): y[:], # [-10:-1]
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('velocity', 'x'): vx[:], # [-10:-1]
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('velocity', 'y'): vy[:], # [-10:-1]
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('acceleration', 'x'): ax[:], # [-10:-1]
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('acceleration', 'y'): ay[:]} # [-10:-1]
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data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
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node_data = pd.DataFrame(data_dict, columns=data_columns)
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@ -301,7 +301,7 @@ class PredictionServer:
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# TODO: we want to send out empty result...
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# And want to update the network
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data = json.dumps({})
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# data = json.dumps({})
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self.prediction_socket.send_pyobj(frame)
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continue
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@ -325,7 +325,7 @@ class PredictionServer:
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warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
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dists, preds = trajectron.incremental_forward(input_dict,
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maps,
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prediction_horizon=25, # TODO: make variable
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prediction_horizon=125, # TODO: make variable
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num_samples=5, # TODO: make variable
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robot_present_and_future=robot_present_and_future,
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full_dist=True)
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@ -1,3 +1,4 @@
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import time
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import ffmpeg
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from argparse import Namespace
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import datetime
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@ -8,7 +9,7 @@ import numpy as np
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import zmq
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from trap.frame_emitter import Frame
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from trap.frame_emitter import DetectionState, Frame
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logger = logging.getLogger("trap.renderer")
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@ -84,7 +85,7 @@ class Renderer:
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while self.is_running.is_set():
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i+=1
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zmq_ev = self.frame_sock.poll(timeout=3)
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zmq_ev = self.frame_sock.poll(timeout=2000)
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if not zmq_ev:
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# when no data comes in, loop so that is_running is checked
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continue
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@ -95,6 +96,32 @@ class Renderer:
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except zmq.ZMQError as e:
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logger.debug(f'reuse prediction')
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if first_time is None:
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first_time = frame.time
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decorate_frame(frame, prediction_frame, first_time)
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img_path = (self.config.output_dir / f"{i:05d}.png").resolve()
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# cv2.imwrite(str(img_path), img)
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logger.debug(f"write frame {frame.time - first_time:.3f}s")
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if self.out_writer:
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self.out_writer.write(img)
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if self.streaming_process:
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self.streaming_process.stdin.write(img.tobytes())
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logger.info('Stopping')
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if i>2:
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if self.streaming_process:
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self.streaming_process.stdin.close()
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if self.out_writer:
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self.out_writer.release()
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if self.streaming_process:
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# oddly wrapped, because both close and release() take time.
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self.streaming_process.wait()
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def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.array:
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img = frame.img
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# all not working:
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@ -108,27 +135,31 @@ class Renderer:
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if not prediction_frame:
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cv2.putText(img, f"Waiting for prediction...", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
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continue
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# continue
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else:
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inv_H = np.linalg.pinv(prediction_frame.H)
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for track_id, track in prediction_frame.tracks.items():
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if not len(track.history):
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continue
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# coords = cv2.perspectiveTransform(np.array([prediction['history']]), self.inv_H)[0]
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coords = [d.get_foot_coords() for d in track.history]
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confirmations = [d.state == DetectionState.Confirmed for d in track.history]
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# logger.warning(f"{coords=}")
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for ci in range(1, len(coords)):
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start = [int(p) for p in coords[ci-1]]
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end = [int(p) for p in coords[ci]]
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cv2.line(img, start, end, (255,255,255), 2, lineType=cv2.LINE_AA)
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color = (255,255,255) if confirmations[ci] else (100,100,100)
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cv2.line(img, start, end, color, 2, lineType=cv2.LINE_AA)
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if not track.predictions or not len(track.predictions):
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continue
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for pred_i, pred in enumerate(track.predictions):
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pred_coords = cv2.perspectiveTransform(np.array([pred]), self.inv_H)[0]
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color = (0,0,255) if pred_i == 1 else (100,100,100)
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pred_coords = cv2.perspectiveTransform(np.array([pred]), inv_H)[0]
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color = (0,0,255) if pred_i else (100,100,100)
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for ci in range(1, len(pred_coords)):
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start = [int(p) for p in pred_coords[ci-1]]
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end = [int(p) for p in pred_coords[ci]]
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@ -148,36 +179,20 @@ class Renderer:
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cv2.rectangle(img, p1, p2, (255,0,0), 1)
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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)
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if first_time is None:
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first_time = frame.time
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cv2.putText(img, f"{frame.index:06d}", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
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cv2.putText(img, f"{frame.time - first_time:.3f}s", (120,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
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if prediction_frame:
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# render Δt and Δ frames
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||||
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)
|
||||
|
||||
|
||||
|
||||
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()
|
||||
|
||||
return img
|
||||
|
||||
|
||||
def run_renderer(config: Namespace, is_running: Event):
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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'],
|
||||
'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()])
|
||||
'track_id': t.track_id,
|
||||
'l': t.history[-1].l,
|
||||
't': t.history[-1].t,
|
||||
'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))
|
||||
|
|
|
|||
|
|
@ -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);
|
||||
// trajectory_socket.send(JSON.stringify(tracker))
|
||||
tracker[person_counter].addToHistory(current_pos);
|
||||
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();
|
||||
|
|
|
|||
Loading…
Reference in a new issue