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Ruben van de Ven 2024-12-10 15:43:06 +01:00
parent 5bf60e4579
commit d6d3092e43
2 changed files with 43 additions and 40 deletions
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76
trap/frame_emitter.py
View file

@ -290,49 +290,49 @@ class Track:
t.predictions,
t.fps/step_size)
def get_binned(self, bin_size=.5, remove_overlap=True):
"""
For an experiment: what if we predict using only concrete positions, by mapping
dx,dy to a grid. Thus prediction can be for 8 moves, or rather headings
see ~/notes/attachments example svg
"""
# def get_binned(self, bin_size=.5, remove_overlap=True):
# """
# For an experiment: what if we predict using only concrete positions, by mapping
# dx,dy to a grid. Thus prediction can be for 8 moves, or rather headings
# see ~/notes/attachments example svg
# """
new_history: List[Detection] = []
for i, (det0, det1) in enumerate(zip(self.history[:-1], self.history[1:]):
if i == 0:
new_history.append(det0)
continue
if abs(det1.x - new_history[-1].x) < bin_size or abs(det1.y - new_history[-1].y) < bin_size:
continue
# new_history: List[Detection] = []
# for i, (det0, det1) in enumerate(zip(self.history[:-1], self.history[1:]):
# if i == 0:
# new_history.append(det0)
# continue
# if abs(det1.x - new_history[-1].x) < bin_size or abs(det1.y - new_history[-1].y) < bin_size:
# continue
# det1 falls outside of the box [-bin_size:+bin_size] around last detection
# # det1 falls outside of the box [-bin_size:+bin_size] around last detection
# 1. Interpolate exact point between det0 and det1 that this happens
if abs(det1.x - new_history[-1].x) >= bin_size:
if det1.x - new_history[-1].x >= bin_size:
# det1 left of last
x = new_history[-1].x + bin_size
f = inv_lerp(det0.x, det1.x, x)
elif new_history[-1].x - det1.x >= bin_size:
# det1 left of last
x = new_history[-1].x - bin_size
f = inv_lerp(det0.x, det1.x, x)
y = lerp(det0.y, det1.y, f)
if abs(det1.y - new_history[-1].y) >= bin_size:
if det1.y - new_history[-1].y >= bin_size:
# det1 left of last
y = new_history[-1].y + bin_size
f = inv_lerp(det0.y, det1.y, x)
elif new_history[-1].y - det1.y >= bin_size:
# det1 left of last
y = new_history[-1].y - bin_size
f = inv_lerp(det0.y, det1.y, x)
x = lerp(det0.x, det1.x, f)
# # 1. Interpolate exact point between det0 and det1 that this happens
# if abs(det1.x - new_history[-1].x) >= bin_size:
# if det1.x - new_history[-1].x >= bin_size:
# # det1 left of last
# x = new_history[-1].x + bin_size
# f = inv_lerp(det0.x, det1.x, x)
# elif new_history[-1].x - det1.x >= bin_size:
# # det1 left of last
# x = new_history[-1].x - bin_size
# f = inv_lerp(det0.x, det1.x, x)
# y = lerp(det0.y, det1.y, f)
# if abs(det1.y - new_history[-1].y) >= bin_size:
# if det1.y - new_history[-1].y >= bin_size:
# # det1 left of last
# y = new_history[-1].y + bin_size
# f = inv_lerp(det0.y, det1.y, x)
# elif new_history[-1].y - det1.y >= bin_size:
# # det1 left of last
# y = new_history[-1].y - bin_size
# f = inv_lerp(det0.y, det1.y, x)
# x = lerp(det0.x, det1.x, f)
# 2. Find closest point on rectangle (rectangle's four corners, or 4 midpoints)
points = [[bin_size, 0], [bin_size, bin_size], [0, bin_size], [-bin_size, bin_size], [-bin_size, 0], [-bin_size, -bin_size], [0, -bin_size], [bin_size, -bin_size]]
# todo Offsets to points:[ history for in points]
# # 2. Find closest point on rectangle (rectangle's four corners, or 4 midpoints)
# points = [[bin_size, 0], [bin_size, bin_size], [0, bin_size], [-bin_size, bin_size], [-bin_size, 0], [-bin_size, -bin_size], [0, -bin_size], [bin_size, -bin_size]]
# # todo Offsets to points:[ history for in points]
def to_trajectron_node(self, camera: Camera, env: Environment) -> Node:

7
trap/tools.py
View file

@ -11,7 +11,7 @@ import pandas as pd
import trap.tracker
from trap.config import parser
from trap.frame_emitter import Camera, Detection, DetectionState, video_src_from_config, Frame
from trap.tracker import DETECTOR_YOLOv8, Smoother, TrackReader, _yolov8_track, Track, TrainingDataWriter, Tracker, read_tracks_json
from trap.tracker import DETECTOR_YOLOv8, FinalDisplacementFilter, Smoother, TrackReader, _yolov8_track, Track, TrainingDataWriter, Tracker, read_tracks_json
from collections import defaultdict
import logging
@ -297,6 +297,9 @@ def blacklist_tracks():
path: Path = config.save_for_training
reader = TrackReader(path, config.camera.fps, exclude_whitelisted = True)
tracks = [t for t in reader]
filter = FinalDisplacementFilter(2.0)
tracks = filter.apply(tracks, config.camera)
# blacklist_file = path / "blacklist.jsonl"
# whitelist_file = path / "whitelist.jsonl" # for skipping
# tracks_file = path / "tracks.json"
@ -311,7 +314,7 @@ def blacklist_tracks():
# whitelist = []
smoother = Smoother()
try:
for track in reader:
for track in tqdm.tqdm(tracks):
if len(track.history) < 5:
continue