transition to uv, and start separting code for distinct processes

This commit is contained in:
Ruben van de Ven 2025-03-28 19:45:50 +01:00
parent 5728213e2c
commit ccddc71f83
11 changed files with 800 additions and 71 deletions

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@ -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`.

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@ -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"]

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@ -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)

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@ -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",

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@ -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()

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@ -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)

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@ -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:])

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@ -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):

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@ -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:

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@ -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"

599
uv.lock
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@ -18,6 +18,79 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/2f/7a/874c46ad2d14998bc2eedac1133c5299e12fe728d2ce91b4d64f2fcc5089/absl_py-2.2.0-py3-none-any.whl", hash = "sha256:5c432cdf7b045f89c4ddc3bba196cabb389c0c321322f8dec68eecdfa732fdad", size = 276986 },
]
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