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7c05c060c3
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cd8a06a53b
11 changed files with 363 additions and 1550 deletions
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.gitignore
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.gitignore
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.idea/
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OUT/
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EXPERIMENTS/
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## Core latex/pdflatex auxiliary files:
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*.aux
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*.lof
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*.log
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*.lot
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*.fls
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*.out
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*.toc
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*.fmt
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*.fot
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*.cb
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*.cb2
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.*.lb
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## Intermediate documents:
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*.dvi
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*.xdv
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*-converted-to.*
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# these rules might exclude image files for figures etc.
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# *.ps
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# *.eps
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# *.pdf
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## Generated if empty string is given at "Please type another file name for output:"
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.pdf
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## Bibliography auxiliary files (bibtex/biblatex/biber):
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*.bbl
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*.bcf
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*.blg
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*-blx.aux
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*-blx.bib
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*.run.xml
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## Build tool auxiliary files:
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*.fdb_latexmk
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*.synctex
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*.synctex(busy)
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*.synctex.gz
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*.synctex.gz(busy)
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*.pdfsync
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||||||
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## Build tool directories for auxiliary files
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# latexrun
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latex.out/
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||||||
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## Auxiliary and intermediate files from other packages:
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# algorithms
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*.alg
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||||||
*.loa
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||||||
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# achemso
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acs-*.bib
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# amsthm
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*.thm
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||||||
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# beamer
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*.nav
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*.pre
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*.snm
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*.vrb
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||||||
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# changes
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*.soc
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||||||
# comment
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*.cut
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# cprotect
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*.cpt
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# elsarticle (documentclass of Elsevier journals)
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*.spl
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||||||
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# endnotes
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*.ent
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# fixme
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*.lox
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# feynmf/feynmp
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*.mf
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*.mp
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*.t[1-9]
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*.t[1-9][0-9]
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*.tfm
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||||||
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||||||
#(r)(e)ledmac/(r)(e)ledpar
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||||||
*.end
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||||||
*.?end
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||||||
*.[1-9]
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||||||
*.[1-9][0-9]
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||||||
*.[1-9][0-9][0-9]
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||||||
*.[1-9]R
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||||||
*.[1-9][0-9]R
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||||||
*.[1-9][0-9][0-9]R
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||||||
*.eledsec[1-9]
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||||||
*.eledsec[1-9]R
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||||||
*.eledsec[1-9][0-9]
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||||||
*.eledsec[1-9][0-9]R
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||||||
*.eledsec[1-9][0-9][0-9]
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*.eledsec[1-9][0-9][0-9]R
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||||||
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||||||
# glossaries
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||||||
*.acn
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||||||
*.acr
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||||||
*.glg
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||||||
*.glo
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||||||
*.gls
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||||||
*.glsdefs
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||||||
*.lzo
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||||||
*.lzs
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||||||
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||||||
# uncomment this for glossaries-extra (will ignore makeindex's style files!)
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||||||
# *.ist
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||||||
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||||||
# gnuplottex
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||||||
*-gnuplottex-*
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||||||
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||||||
# gregoriotex
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||||||
*.gaux
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||||||
*.gtex
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||||||
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||||||
# htlatex
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||||||
*.4ct
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||||||
*.4tc
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||||||
*.idv
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||||||
*.lg
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*.trc
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||||||
*.xref
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||||||
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||||||
# hyperref
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*.brf
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||||||
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||||||
# knitr
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*-concordance.tex
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# TODO Comment the next line if you want to keep your tikz graphics files
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*.tikz
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||||||
*-tikzDictionary
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||||||
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||||||
# listings
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||||||
*.lol
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||||||
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||||||
# luatexja-ruby
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*.ltjruby
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||||||
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||||||
# makeidx
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||||||
*.idx
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*.ilg
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*.ind
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||||||
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# minitoc
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*.maf
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*.mlf
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*.mlt
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*.mtc[0-9]*
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||||||
*.slf[0-9]*
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||||||
*.slt[0-9]*
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||||||
*.stc[0-9]*
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||||||
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||||||
# minted
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_minted*
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*.pyg
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||||||
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||||||
# morewrites
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*.mw
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||||||
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|
||||||
# nomencl
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||||||
*.nlg
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*.nlo
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*.nls
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||||||
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||||||
# pax
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||||||
*.pax
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||||||
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||||||
# pdfpcnotes
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|
||||||
*.pdfpc
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|
||||||
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|
||||||
# sagetex
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|
||||||
*.sagetex.sage
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|
||||||
*.sagetex.py
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||||||
*.sagetex.scmd
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|
||||||
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|
||||||
# scrwfile
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|
||||||
*.wrt
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|
||||||
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|
||||||
# sympy
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|
||||||
*.sout
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|
||||||
*.sympy
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|
||||||
sympy-plots-for-*.tex/
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|
||||||
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|
||||||
# pdfcomment
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|
||||||
*.upa
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|
||||||
*.upb
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|
||||||
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|
||||||
# pythontex
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|
||||||
*.pytxcode
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|
||||||
pythontex-files-*/
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|
||||||
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|
||||||
# tcolorbox
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|
||||||
*.listing
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|
||||||
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|
||||||
# thmtools
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|
||||||
*.loe
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||||||
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||||||
# TikZ & PGF
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||||||
*.dpth
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||||||
*.md5
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||||||
*.auxlock
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||||||
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||||||
# todonotes
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|
||||||
*.tdo
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||||||
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||||||
# vhistory
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||||||
*.hst
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||||||
*.ver
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||||||
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||||||
# easy-todo
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|
||||||
*.lod
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||||||
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|
||||||
# xcolor
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|
||||||
*.xcp
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||||||
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||||||
# xmpincl
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||||||
*.xmpi
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||||||
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|
||||||
# xindy
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|
||||||
*.xdy
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||||||
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|
||||||
# xypic precompiled matrices and outlines
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|
||||||
*.xyc
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|
||||||
*.xyd
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||||||
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|
||||||
# endfloat
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|
||||||
*.ttt
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|
||||||
*.fff
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|
||||||
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|
||||||
# Latexian
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|
||||||
TSWLatexianTemp*
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|
||||||
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|
||||||
## Editors:
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|
||||||
# WinEdt
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|
||||||
*.bak
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|
||||||
*.sav
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|
||||||
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|
||||||
# Texpad
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|
||||||
.texpadtmp
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|
||||||
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|
||||||
# LyX
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|
||||||
*.lyx~
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||||||
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|
||||||
# Kile
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|
||||||
*.backup
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|
||||||
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|
||||||
# gummi
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|
||||||
.*.swp
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||||||
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||||||
# KBibTeX
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|
||||||
*~[0-9]*
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|
||||||
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|
||||||
# TeXnicCenter
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|
||||||
*.tps
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||||||
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|
||||||
# auto folder when using emacs and auctex
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|
||||||
./auto/*
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|
||||||
*.el
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||||||
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|
||||||
# expex forward references with \gathertags
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|
||||||
*-tags.tex
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|
||||||
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|
||||||
# standalone packages
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|
||||||
*.sta
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||||||
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|
||||||
# Makeindex log files
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|
||||||
*.lpz
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|
||||||
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|
||||||
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|
||||||
logs/
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|
||||||
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|
||||||
# Byte-compiled / optimized / DLL files
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|
||||||
__pycache__/
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|
||||||
*.py[cod]
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|
||||||
*$py.class
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|
||||||
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|
||||||
# C extensions
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|
||||||
*.so
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|
||||||
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|
||||||
# Distribution / packaging
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|
||||||
.Python
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|
||||||
build/
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|
||||||
develop-eggs/
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|
||||||
dist/
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|
||||||
downloads/
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||||||
eggs/
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||||||
.eggs/
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||||||
lib/
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lib64/
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parts/
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||||||
sdist/
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||||||
var/
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||||||
wheels/
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|
||||||
pip-wheel-metadata/
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|
||||||
share/python-wheels/
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|
||||||
*.egg-info/
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|
||||||
.installed.cfg
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|
||||||
*.egg
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|
||||||
MANIFEST
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|
||||||
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|
||||||
# PyInstaller
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|
||||||
# Usually these files are written by a python script from a template
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|
||||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
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|
||||||
*.manifest
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|
||||||
*.spec
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|
||||||
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|
||||||
# Installer logs
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|
||||||
pip-log.txt
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|
||||||
pip-delete-this-directory.txt
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|
||||||
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|
||||||
# Unit test / coverage reports
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|
||||||
htmlcov/
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|
||||||
.tox/
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|
||||||
.nox/
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|
||||||
.coverage
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|
||||||
.coverage.*
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|
||||||
.cache
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|
||||||
nosetests.xml
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|
||||||
coverage.xml
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|
||||||
*.cover
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|
||||||
*.py,cover
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|
||||||
.hypothesis/
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|
||||||
.pytest_cache/
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|
||||||
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|
||||||
# Translations
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|
||||||
*.mo
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|
||||||
*.pot
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|
||||||
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|
||||||
# Django stuff:
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|
||||||
*.log
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|
||||||
local_settings.py
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|
||||||
db.sqlite3
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|
||||||
db.sqlite3-journal
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|
||||||
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|
||||||
# Flask stuff:
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|
||||||
instance/
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|
||||||
.webassets-cache
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|
||||||
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|
||||||
# Scrapy stuff:
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|
||||||
.scrapy
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|
||||||
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|
||||||
# Sphinx documentation
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|
||||||
docs/_build/
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|
||||||
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|
||||||
# PyBuilder
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|
||||||
target/
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|
||||||
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|
||||||
# Jupyter Notebook
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|
||||||
.ipynb_checkpoints
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|
||||||
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|
||||||
# IPython
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|
||||||
profile_default/
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|
||||||
ipython_config.py
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|
||||||
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|
||||||
# pyenv
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|
||||||
.python-version
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|
||||||
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|
||||||
# pipenv
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|
||||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
|
||||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
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|
||||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
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|
||||||
# install all needed dependencies.
|
|
||||||
#Pipfile.lock
|
|
||||||
|
|
||||||
# celery beat schedule file
|
|
||||||
celerybeat-schedule
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|
||||||
|
|
||||||
# SageMath parsed files
|
|
||||||
*.sage.py
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|
||||||
|
|
||||||
# Environments
|
|
||||||
.env
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|
||||||
.venv
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|
||||||
env/
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|
||||||
venv/
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|
||||||
ENV/
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|
||||||
env.bak/
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|
||||||
venv.bak/
|
|
||||||
|
|
||||||
# Spyder project settings
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|
||||||
.spyderproject
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|
||||||
.spyproject
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|
||||||
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|
||||||
# Rope project settings
|
|
||||||
.ropeproject
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|
||||||
|
|
||||||
# mkdocs documentation
|
|
||||||
/site
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|
||||||
|
|
||||||
# mypy
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|
||||||
.mypy_cache/
|
|
||||||
.dmypy.json
|
|
||||||
dmypy.json
|
|
||||||
|
|
||||||
# Pyre type checker
|
|
||||||
.pyre/
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|
||||||
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|
||||||
55
poetry.lock
generated
55
poetry.lock
generated
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|
@ -1817,9 +1817,9 @@ files = [
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||||||
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|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
numpy = [
|
numpy = [
|
||||||
|
{version = ">=1.23.5", markers = "python_version >= \"3.11\""},
|
||||||
{version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
|
{version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
|
||||||
{version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
|
{version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
|
||||||
{version = ">=1.23.5", markers = "python_version >= \"3.11\""},
|
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
|
|
@ -1939,8 +1939,8 @@ files = [
|
||||||
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|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
numpy = [
|
numpy = [
|
||||||
{version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
|
|
||||||
{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
|
{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
|
||||||
|
{version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
|
||||||
]
|
]
|
||||||
python-dateutil = ">=2.8.2"
|
python-dateutil = ">=2.8.2"
|
||||||
pytz = ">=2020.1"
|
pytz = ">=2020.1"
|
||||||
|
|
@ -1970,21 +1970,6 @@ sql-other = ["SQLAlchemy (>=1.4.36)"]
|
||||||
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
|
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
|
||||||
xml = ["lxml (>=4.8.0)"]
|
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 = "*"
|
|
||||||
files = []
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|
||||||
develop = false
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|
||||||
|
|
||||||
[package.source]
|
|
||||||
type = "git"
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|
||||||
url = "https://github.com/scls19fr/pandas-helper-calc"
|
|
||||||
reference = "HEAD"
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|
||||||
resolved_reference = "22df480f09c0fa96548833f9dee8f9128512641b"
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|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "pandocfilters"
|
name = "pandocfilters"
|
||||||
version = "1.5.0"
|
version = "1.5.0"
|
||||||
|
|
@ -2976,24 +2961,6 @@ all = ["numpy", "pytest", "pytest-cov"]
|
||||||
test = ["pytest", "pytest-cov"]
|
test = ["pytest", "pytest-cov"]
|
||||||
vectorized = ["numpy"]
|
vectorized = ["numpy"]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "simdkalman"
|
|
||||||
version = "1.0.4"
|
|
||||||
description = "Kalman filters vectorized as Single Instruction, Multiple Data"
|
|
||||||
optional = false
|
|
||||||
python-versions = "*"
|
|
||||||
files = [
|
|
||||||
{file = "simdkalman-1.0.4-py2.py3-none-any.whl", hash = "sha256:fc2c6b9e540e0a26b39d087e78623d3c1e8c6677abf5d91111f5d49e328e1668"},
|
|
||||||
]
|
|
||||||
|
|
||||||
[package.dependencies]
|
|
||||||
numpy = ">=1.9.0"
|
|
||||||
|
|
||||||
[package.extras]
|
|
||||||
dev = ["check-manifest"]
|
|
||||||
docs = ["sphinx"]
|
|
||||||
test = ["pylint"]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "six"
|
name = "six"
|
||||||
version = "1.16.0"
|
version = "1.16.0"
|
||||||
|
|
@ -3333,22 +3300,6 @@ tqdm = "^4.65.0"
|
||||||
type = "directory"
|
type = "directory"
|
||||||
url = "../Trajectron-plus-plus"
|
url = "../Trajectron-plus-plus"
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "tsmoothie"
|
|
||||||
version = "1.0.5"
|
|
||||||
description = "A python library for timeseries smoothing and outlier detection in a vectorized way."
|
|
||||||
optional = false
|
|
||||||
python-versions = ">=3"
|
|
||||||
files = [
|
|
||||||
{file = "tsmoothie-1.0.5-py3-none-any.whl", hash = "sha256:dedf8d8e011562824abe41783bf33e1b9ee1424bc572853bb82408743316a90e"},
|
|
||||||
{file = "tsmoothie-1.0.5.tar.gz", hash = "sha256:d83fa0ccae32bde7b904d9581ebf137e8eb18629cc3563d7379ca5f92461f6f5"},
|
|
||||||
]
|
|
||||||
|
|
||||||
[package.dependencies]
|
|
||||||
numpy = "*"
|
|
||||||
scipy = "*"
|
|
||||||
simdkalman = "*"
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "types-python-dateutil"
|
name = "types-python-dateutil"
|
||||||
version = "2.8.19.14"
|
version = "2.8.19.14"
|
||||||
|
|
@ -3517,4 +3468,4 @@ watchdog = ["watchdog (>=2.3)"]
|
||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "2.0"
|
lock-version = "2.0"
|
||||||
python-versions = "^3.10,<3.12,"
|
python-versions = "^3.10,<3.12,"
|
||||||
content-hash = "66f062f9db921cfa83e576288d09fd9b959780eb189d95765934ae9a6769f200"
|
content-hash = "c9d4fe6a1d054a835a689cee011753b900b696aa8a06b81aa7a10afc24a8bc70"
|
||||||
|
|
|
||||||
|
|
@ -29,8 +29,6 @@ ultralytics = "^8.0.200"
|
||||||
ffmpeg-python = "^0.2.0"
|
ffmpeg-python = "^0.2.0"
|
||||||
torchreid = "^0.2.5"
|
torchreid = "^0.2.5"
|
||||||
gdown = "^4.7.1"
|
gdown = "^4.7.1"
|
||||||
pandas-helper-calc = {git = "https://github.com/scls19fr/pandas-helper-calc"}
|
|
||||||
tsmoothie = "^1.0.5"
|
|
||||||
|
|
||||||
[build-system]
|
[build-system]
|
||||||
requires = ["poetry-core"]
|
requires = ["poetry-core"]
|
||||||
|
|
|
||||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -1,6 +1,5 @@
|
||||||
from argparse import Namespace
|
from argparse import Namespace
|
||||||
from dataclasses import dataclass, field
|
from dataclasses import dataclass, field
|
||||||
from enum import IntFlag
|
|
||||||
from itertools import cycle
|
from itertools import cycle
|
||||||
import logging
|
import logging
|
||||||
from multiprocessing import Event
|
from multiprocessing import Event
|
||||||
|
|
@ -13,25 +12,9 @@ import numpy as np
|
||||||
import cv2
|
import cv2
|
||||||
import zmq
|
import zmq
|
||||||
from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
|
from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
|
||||||
from deep_sort_realtime.deep_sort.track import TrackState as DeepsortTrackState
|
|
||||||
|
|
||||||
logger = logging.getLogger('trap.frame_emitter')
|
logger = logging.getLogger('trap.frame_emitter')
|
||||||
|
|
||||||
class DetectionState(IntFlag):
|
|
||||||
Tentative = 1 # state before n_init (see DeepsortTrack)
|
|
||||||
Confirmed = 2 # after tentative
|
|
||||||
Lost = 4 # lost when DeepsortTrack.time_since_update > 0 but not Deleted
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_deepsort_track(cls, track: DeepsortTrack):
|
|
||||||
if track.state == DeepsortTrackState.Tentative:
|
|
||||||
return cls.Tentative
|
|
||||||
if track.state == DeepsortTrackState.Confirmed:
|
|
||||||
if track.time_since_update > 0:
|
|
||||||
return cls.Lost
|
|
||||||
return cls.Confirmed
|
|
||||||
raise RuntimeError("Should not run into Deleted entries here")
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
@dataclass
|
||||||
class Detection:
|
class Detection:
|
||||||
|
|
@ -41,27 +24,13 @@ class Detection:
|
||||||
w: int # width - image space
|
w: int # width - image space
|
||||||
h: int # height - image space
|
h: int # height - image space
|
||||||
conf: float # object detector probablity
|
conf: float # object detector probablity
|
||||||
state: DetectionState
|
|
||||||
|
|
||||||
def get_foot_coords(self):
|
def get_foot_coords(self):
|
||||||
return [self.l + 0.5 * self.w, self.t+self.h]
|
return [self.l + 0.5 * self.w, self.t+self.h]
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def from_deepsort(cls, dstrack: DeepsortTrack):
|
def from_deepsort(cls, dstrack: DeepsortTrack):
|
||||||
return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf, DetectionState.from_deepsort_track(dstrack))
|
return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf)
|
||||||
|
|
||||||
def get_scaled(self, scale: float = 1):
|
|
||||||
if scale == 1:
|
|
||||||
return self
|
|
||||||
|
|
||||||
return Detection(
|
|
||||||
self.track_id,
|
|
||||||
self.l*scale,
|
|
||||||
self.t*scale,
|
|
||||||
self.w*scale,
|
|
||||||
self.h*scale,
|
|
||||||
self.conf,
|
|
||||||
self.state)
|
|
||||||
|
|
||||||
def to_ltwh(self):
|
def to_ltwh(self):
|
||||||
return (int(self.l), int(self.t), int(self.w), int(self.h))
|
return (int(self.l), int(self.t), int(self.w), int(self.h))
|
||||||
|
|
@ -69,7 +38,6 @@ class Detection:
|
||||||
def to_ltrb(self):
|
def to_ltrb(self):
|
||||||
return (int(self.l), int(self.t), int(self.l+self.w), int(self.t+self.h))
|
return (int(self.l), int(self.t), int(self.l+self.w), int(self.t+self.h))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
@dataclass
|
||||||
class Track:
|
class Track:
|
||||||
|
|
@ -93,7 +61,6 @@ class Track:
|
||||||
def get_projected_history_as_dict(self, H) -> dict:
|
def get_projected_history_as_dict(self, H) -> dict:
|
||||||
coords = self.get_projected_history(H)
|
coords = self.get_projected_history(H)
|
||||||
return [{"x":c[0], "y":c[1]} for c in coords]
|
return [{"x":c[0], "y":c[1]} for c in coords]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@dataclass
|
@dataclass
|
||||||
|
|
@ -104,19 +71,6 @@ class Frame:
|
||||||
tracks: Optional[dict[str, Track]] = None
|
tracks: Optional[dict[str, Track]] = None
|
||||||
H: Optional[np.array] = None
|
H: Optional[np.array] = None
|
||||||
|
|
||||||
def aslist(self) -> [dict]:
|
|
||||||
return { t.track_id:
|
|
||||||
{
|
|
||||||
'id': t.track_id,
|
|
||||||
'history': t.get_projected_history(self.H).tolist(),
|
|
||||||
'det_conf': t.history[-1].conf,
|
|
||||||
# 'det_conf': trajectory_data[node.id]['det_conf'],
|
|
||||||
# 'bbox': trajectory_data[node.id]['bbox'],
|
|
||||||
# 'history': history.tolist(),
|
|
||||||
'predictions': t.predictions
|
|
||||||
} for t in self.tracks.values()
|
|
||||||
}
|
|
||||||
|
|
||||||
class FrameEmitter:
|
class FrameEmitter:
|
||||||
'''
|
'''
|
||||||
Emit frame in a separate threat so they can be throttled,
|
Emit frame in a separate threat so they can be throttled,
|
||||||
|
|
@ -141,29 +95,15 @@ class FrameEmitter:
|
||||||
|
|
||||||
|
|
||||||
def emit_video(self):
|
def emit_video(self):
|
||||||
i = 0
|
|
||||||
for video_path in self.video_srcs:
|
for video_path in self.video_srcs:
|
||||||
logger.info(f"Play from '{str(video_path)}'")
|
logger.info(f"Play from '{str(video_path)}'")
|
||||||
video = cv2.VideoCapture(str(video_path))
|
video = cv2.VideoCapture(str(video_path))
|
||||||
fps = video.get(cv2.CAP_PROP_FPS)
|
fps = video.get(cv2.CAP_PROP_FPS)
|
||||||
target_frame_duration = 1./fps
|
frame_duration = 1./fps
|
||||||
logger.info(f"Emit frames at {fps} fps")
|
logger.info(f"Emit frames at {fps} fps")
|
||||||
|
|
||||||
if '-' in video_path.stem:
|
|
||||||
path_stem = video_path.stem[:video_path.stem.rfind('-')]
|
|
||||||
else:
|
|
||||||
path_stem = video_path.stem
|
|
||||||
path_stem += "-homography"
|
|
||||||
homography_path = video_path.with_stem(path_stem).with_suffix('.txt')
|
|
||||||
logger.info(f'check homography file {homography_path}')
|
|
||||||
if homography_path.exists():
|
|
||||||
logger.info(f'Found custom homography file! Using {homography_path}')
|
|
||||||
video_H = np.loadtxt(homography_path, delimiter=',')
|
|
||||||
else:
|
|
||||||
video_H = None
|
|
||||||
|
|
||||||
prev_time = time.time()
|
prev_time = time.time()
|
||||||
|
i = 0
|
||||||
while self.is_running.is_set():
|
while self.is_running.is_set():
|
||||||
ret, img = video.read()
|
ret, img = video.read()
|
||||||
|
|
||||||
|
|
@ -180,19 +120,19 @@ class FrameEmitter:
|
||||||
# hack to mask out area
|
# hack to mask out area
|
||||||
cv2.rectangle(img, (0,0), (800,200), (0,0,0), -1)
|
cv2.rectangle(img, (0,0), (800,200), (0,0,0), -1)
|
||||||
|
|
||||||
frame = Frame(index=i, img=img, H=video_H)
|
frame = Frame(index=i, img=img)
|
||||||
# TODO: this is very dirty, need to find another way.
|
# TODO: this is very dirty, need to find another way.
|
||||||
# perhaps multiprocessing Array?
|
# perhaps multiprocessing Array?
|
||||||
self.frame_sock.send(pickle.dumps(frame))
|
self.frame_sock.send(pickle.dumps(frame))
|
||||||
|
|
||||||
# defer next loop
|
# defer next loop
|
||||||
now = time.time()
|
new_frame_time = time.time()
|
||||||
time_diff = (now - prev_time)
|
time_diff = (new_frame_time - prev_time)
|
||||||
if time_diff < target_frame_duration:
|
if time_diff < frame_duration:
|
||||||
time.sleep(target_frame_duration - time_diff)
|
time.sleep(frame_duration - time_diff)
|
||||||
now += target_frame_duration - time_diff
|
new_frame_time += frame_duration - time_diff
|
||||||
|
else:
|
||||||
prev_time = now
|
prev_time = new_frame_time
|
||||||
|
|
||||||
i += 1
|
i += 1
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -243,7 +243,7 @@ class PredictionServer:
|
||||||
if self.config.predict_training_data:
|
if self.config.predict_training_data:
|
||||||
input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
|
input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
|
||||||
else:
|
else:
|
||||||
zmq_ev = self.trajectory_socket.poll(timeout=2000)
|
zmq_ev = self.trajectory_socket.poll(timeout=3)
|
||||||
if not zmq_ev:
|
if not zmq_ev:
|
||||||
# on no data loop so that is_running is checked
|
# on no data loop so that is_running is checked
|
||||||
continue
|
continue
|
||||||
|
|
@ -252,7 +252,7 @@ class PredictionServer:
|
||||||
frame: Frame = pickle.loads(data)
|
frame: Frame = pickle.loads(data)
|
||||||
# trajectory_data = {t.track_id: t.get_projected_history_as_dict(frame.H) for t in frame.tracks.values()}
|
# trajectory_data = {t.track_id: t.get_projected_history_as_dict(frame.H) for t in frame.tracks.values()}
|
||||||
# trajectory_data = json.loads(data)
|
# trajectory_data = json.loads(data)
|
||||||
# logger.debug(f"Receive {frame.index}")
|
logger.debug(f"Receive {frame.index}")
|
||||||
|
|
||||||
# class FakeNode:
|
# class FakeNode:
|
||||||
# def __init__(self, node_type: NodeType):
|
# def __init__(self, node_type: NodeType):
|
||||||
|
|
@ -276,12 +276,12 @@ class PredictionServer:
|
||||||
ax = derivative_of(vx, 0.1)
|
ax = derivative_of(vx, 0.1)
|
||||||
ay = derivative_of(vy, 0.1)
|
ay = derivative_of(vy, 0.1)
|
||||||
|
|
||||||
data_dict = {('position', 'x'): x[:], # [-10:-1]
|
data_dict = {('position', 'x'): x[:],
|
||||||
('position', 'y'): y[:], # [-10:-1]
|
('position', 'y'): y[:],
|
||||||
('velocity', 'x'): vx[:], # [-10:-1]
|
('velocity', 'x'): vx[:],
|
||||||
('velocity', 'y'): vy[:], # [-10:-1]
|
('velocity', 'y'): vy[:],
|
||||||
('acceleration', 'x'): ax[:], # [-10:-1]
|
('acceleration', 'x'): ax[:],
|
||||||
('acceleration', 'y'): ay[:]} # [-10:-1]
|
('acceleration', 'y'): ay[:]}
|
||||||
data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
|
data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
|
||||||
|
|
||||||
node_data = pd.DataFrame(data_dict, columns=data_columns)
|
node_data = pd.DataFrame(data_dict, columns=data_columns)
|
||||||
|
|
@ -301,7 +301,7 @@ class PredictionServer:
|
||||||
# TODO: we want to send out empty result...
|
# TODO: we want to send out empty result...
|
||||||
# And want to update the network
|
# And want to update the network
|
||||||
|
|
||||||
# data = json.dumps({})
|
data = json.dumps({})
|
||||||
self.prediction_socket.send_pyobj(frame)
|
self.prediction_socket.send_pyobj(frame)
|
||||||
|
|
||||||
continue
|
continue
|
||||||
|
|
@ -325,7 +325,7 @@ class PredictionServer:
|
||||||
warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
|
warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
|
||||||
dists, preds = trajectron.incremental_forward(input_dict,
|
dists, preds = trajectron.incremental_forward(input_dict,
|
||||||
maps,
|
maps,
|
||||||
prediction_horizon=125, # TODO: make variable
|
prediction_horizon=25, # TODO: make variable
|
||||||
num_samples=5, # TODO: make variable
|
num_samples=5, # TODO: make variable
|
||||||
robot_present_and_future=robot_present_and_future,
|
robot_present_and_future=robot_present_and_future,
|
||||||
full_dist=True)
|
full_dist=True)
|
||||||
|
|
|
||||||
147
trap/renderer.py
147
trap/renderer.py
|
|
@ -1,4 +1,3 @@
|
||||||
import time
|
|
||||||
import ffmpeg
|
import ffmpeg
|
||||||
from argparse import Namespace
|
from argparse import Namespace
|
||||||
import datetime
|
import datetime
|
||||||
|
|
@ -9,7 +8,7 @@ import numpy as np
|
||||||
|
|
||||||
import zmq
|
import zmq
|
||||||
|
|
||||||
from trap.frame_emitter import DetectionState, Frame
|
from trap.frame_emitter import Frame
|
||||||
|
|
||||||
|
|
||||||
logger = logging.getLogger("trap.renderer")
|
logger = logging.getLogger("trap.renderer")
|
||||||
|
|
@ -85,7 +84,7 @@ class Renderer:
|
||||||
while self.is_running.is_set():
|
while self.is_running.is_set():
|
||||||
i+=1
|
i+=1
|
||||||
|
|
||||||
zmq_ev = self.frame_sock.poll(timeout=2000)
|
zmq_ev = self.frame_sock.poll(timeout=3)
|
||||||
if not zmq_ev:
|
if not zmq_ev:
|
||||||
# when no data comes in, loop so that is_running is checked
|
# when no data comes in, loop so that is_running is checked
|
||||||
continue
|
continue
|
||||||
|
|
@ -96,16 +95,74 @@ class Renderer:
|
||||||
except zmq.ZMQError as e:
|
except zmq.ZMQError as e:
|
||||||
logger.debug(f'reuse prediction')
|
logger.debug(f'reuse prediction')
|
||||||
|
|
||||||
if first_time is None:
|
img = frame.img
|
||||||
first_time = frame.time
|
|
||||||
|
|
||||||
decorate_frame(frame, prediction_frame, first_time)
|
# all not working:
|
||||||
|
# if i == 1:
|
||||||
|
# # thanks to GpG for fixing scaling issue: https://stackoverflow.com/a/39668864
|
||||||
|
# scale_factor = 1./20 # from 10m to 1000px
|
||||||
|
# S = np.array([[scale_factor, 0,0],[0,scale_factor,0 ],[ 0,0,1 ]])
|
||||||
|
# new_H = S * self.H * np.linalg.inv(S)
|
||||||
|
# warpedFrame = cv2.warpPerspective(img, new_H, (1000,1000))
|
||||||
|
# cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
|
||||||
|
|
||||||
|
if not prediction_frame:
|
||||||
|
cv2.putText(img, f"Waiting for prediction...", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
for track_id, track in prediction_frame.tracks.items():
|
||||||
|
if not len(track.history):
|
||||||
|
continue
|
||||||
|
|
||||||
|
# coords = cv2.perspectiveTransform(np.array([prediction['history']]), self.inv_H)[0]
|
||||||
|
coords = [d.get_foot_coords() for d in track.history]
|
||||||
|
# logger.warning(f"{coords=}")
|
||||||
|
|
||||||
|
for ci in range(1, len(coords)):
|
||||||
|
start = [int(p) for p in coords[ci-1]]
|
||||||
|
end = [int(p) for p in coords[ci]]
|
||||||
|
cv2.line(img, start, end, (255,255,255), 2, lineType=cv2.LINE_AA)
|
||||||
|
|
||||||
|
if not track.predictions or not len(track.predictions):
|
||||||
|
continue
|
||||||
|
|
||||||
|
for pred_i, pred in enumerate(track.predictions):
|
||||||
|
pred_coords = cv2.perspectiveTransform(np.array([pred]), self.inv_H)[0]
|
||||||
|
color = (0,0,255) if pred_i == 1 else (100,100,100)
|
||||||
|
for ci in range(1, len(pred_coords)):
|
||||||
|
start = [int(p) for p in pred_coords[ci-1]]
|
||||||
|
end = [int(p) for p in pred_coords[ci]]
|
||||||
|
cv2.line(img, start, end, color, 1, lineType=cv2.LINE_AA)
|
||||||
|
|
||||||
|
for track_id, track in prediction_frame.tracks.items():
|
||||||
|
# draw tracker marker and track id last so it lies over the trajectories
|
||||||
|
# this goes is a second loop so it overlays over _all_ trajectories
|
||||||
|
# coords = cv2.perspectiveTransform(np.array([[track.history[-1].get_foot_coords()]]), self.inv_H)[0]
|
||||||
|
coords = track.history[-1].get_foot_coords()
|
||||||
|
|
||||||
|
center = [int(p) for p in coords]
|
||||||
|
cv2.circle(img, center, 5, (0,255,0))
|
||||||
|
(l, t, r, b) = track.history[-1].to_ltrb()
|
||||||
|
p1 = (l, t)
|
||||||
|
p2 = (r, b)
|
||||||
|
cv2.rectangle(img, p1, p2, (255,0,0), 1)
|
||||||
|
cv2.putText(img, f"{track_id} ({(track.history[-1].conf or 0):.2f})", (center[0]+8, center[1]), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.7, thickness=2, color=(0,255,0), lineType=cv2.LINE_AA)
|
||||||
|
|
||||||
|
if first_time is None:
|
||||||
|
first_time = frame.time
|
||||||
|
|
||||||
|
cv2.putText(img, f"{frame.index:06d}", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
|
||||||
|
cv2.putText(img, f"{frame.time - first_time:.3f}s", (120,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
|
||||||
|
|
||||||
|
if prediction_frame:
|
||||||
|
cv2.putText(img, f"{prediction_frame.index - frame.index}", (90,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
img_path = (self.config.output_dir / f"{i:05d}.png").resolve()
|
img_path = (self.config.output_dir / f"{i:05d}.png").resolve()
|
||||||
|
|
||||||
# cv2.imwrite(str(img_path), img)
|
# cv2.imwrite(str(img_path), img)
|
||||||
|
logger.info(f"write frame {frame.time - first_time:.3f}s")
|
||||||
logger.debug(f"write frame {frame.time - first_time:.3f}s")
|
|
||||||
if self.out_writer:
|
if self.out_writer:
|
||||||
self.out_writer.write(img)
|
self.out_writer.write(img)
|
||||||
if self.streaming_process:
|
if self.streaming_process:
|
||||||
|
|
@ -121,79 +178,7 @@ class Renderer:
|
||||||
# oddly wrapped, because both close and release() take time.
|
# oddly wrapped, because both close and release() take time.
|
||||||
self.streaming_process.wait()
|
self.streaming_process.wait()
|
||||||
|
|
||||||
def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.array:
|
|
||||||
img = frame.img
|
|
||||||
|
|
||||||
# all not working:
|
|
||||||
# if i == 1:
|
|
||||||
# # thanks to GpG for fixing scaling issue: https://stackoverflow.com/a/39668864
|
|
||||||
# scale_factor = 1./20 # from 10m to 1000px
|
|
||||||
# S = np.array([[scale_factor, 0,0],[0,scale_factor,0 ],[ 0,0,1 ]])
|
|
||||||
# new_H = S * self.H * np.linalg.inv(S)
|
|
||||||
# warpedFrame = cv2.warpPerspective(img, new_H, (1000,1000))
|
|
||||||
# cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
|
|
||||||
|
|
||||||
if not prediction_frame:
|
|
||||||
cv2.putText(img, f"Waiting for prediction...", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
|
|
||||||
# continue
|
|
||||||
else:
|
|
||||||
inv_H = np.linalg.pinv(prediction_frame.H)
|
|
||||||
for track_id, track in prediction_frame.tracks.items():
|
|
||||||
if not len(track.history):
|
|
||||||
continue
|
|
||||||
|
|
||||||
# coords = cv2.perspectiveTransform(np.array([prediction['history']]), self.inv_H)[0]
|
|
||||||
coords = [d.get_foot_coords() for d in track.history]
|
|
||||||
confirmations = [d.state == DetectionState.Confirmed for d in track.history]
|
|
||||||
|
|
||||||
# logger.warning(f"{coords=}")
|
|
||||||
|
|
||||||
for ci in range(1, len(coords)):
|
|
||||||
start = [int(p) for p in coords[ci-1]]
|
|
||||||
end = [int(p) for p in coords[ci]]
|
|
||||||
color = (255,255,255) if confirmations[ci] else (100,100,100)
|
|
||||||
cv2.line(img, start, end, color, 2, lineType=cv2.LINE_AA)
|
|
||||||
|
|
||||||
if not track.predictions or not len(track.predictions):
|
|
||||||
continue
|
|
||||||
|
|
||||||
for pred_i, pred in enumerate(track.predictions):
|
|
||||||
pred_coords = cv2.perspectiveTransform(np.array([pred]), inv_H)[0]
|
|
||||||
color = (0,0,255) if pred_i else (100,100,100)
|
|
||||||
for ci in range(1, len(pred_coords)):
|
|
||||||
start = [int(p) for p in pred_coords[ci-1]]
|
|
||||||
end = [int(p) for p in pred_coords[ci]]
|
|
||||||
cv2.line(img, start, end, color, 1, lineType=cv2.LINE_AA)
|
|
||||||
|
|
||||||
for track_id, track in prediction_frame.tracks.items():
|
|
||||||
# draw tracker marker and track id last so it lies over the trajectories
|
|
||||||
# this goes is a second loop so it overlays over _all_ trajectories
|
|
||||||
# coords = cv2.perspectiveTransform(np.array([[track.history[-1].get_foot_coords()]]), self.inv_H)[0]
|
|
||||||
coords = track.history[-1].get_foot_coords()
|
|
||||||
|
|
||||||
center = [int(p) for p in coords]
|
|
||||||
cv2.circle(img, center, 5, (0,255,0))
|
|
||||||
(l, t, r, b) = track.history[-1].to_ltrb()
|
|
||||||
p1 = (l, t)
|
|
||||||
p2 = (r, b)
|
|
||||||
cv2.rectangle(img, p1, p2, (255,0,0), 1)
|
|
||||||
cv2.putText(img, f"{track_id} ({(track.history[-1].conf or 0):.2f})", (center[0]+8, center[1]), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.7, thickness=2, color=(0,255,0), lineType=cv2.LINE_AA)
|
|
||||||
|
|
||||||
|
|
||||||
cv2.putText(img, f"{frame.index:06d}", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
|
|
||||||
cv2.putText(img, f"{frame.time - first_time:.3f}s", (120,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
|
|
||||||
|
|
||||||
if prediction_frame:
|
|
||||||
# render Δt and Δ frames
|
|
||||||
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)
|
|
||||||
|
|
||||||
return img
|
|
||||||
|
|
||||||
|
|
||||||
def run_renderer(config: Namespace, is_running: Event):
|
def run_renderer(config: Namespace, is_running: Event):
|
||||||
renderer = Renderer(config, is_running)
|
renderer = Renderer(config, is_running)
|
||||||
|
|
|
||||||
|
|
@ -1,9 +1,7 @@
|
||||||
|
|
||||||
from argparse import Namespace
|
from argparse import Namespace
|
||||||
import asyncio
|
import asyncio
|
||||||
import dataclasses
|
|
||||||
import errno
|
import errno
|
||||||
import json
|
|
||||||
import logging
|
import logging
|
||||||
from multiprocessing import Event
|
from multiprocessing import Event
|
||||||
import subprocess
|
import subprocess
|
||||||
|
|
@ -17,8 +15,6 @@ import tornado.websocket
|
||||||
import zmq
|
import zmq
|
||||||
import zmq.asyncio
|
import zmq.asyncio
|
||||||
|
|
||||||
from trap.frame_emitter import Frame
|
|
||||||
|
|
||||||
|
|
||||||
logger = logging.getLogger("trap.forwarder")
|
logger = logging.getLogger("trap.forwarder")
|
||||||
|
|
||||||
|
|
@ -28,7 +24,7 @@ class WebSocketTrajectoryHandler(tornado.websocket.WebSocketHandler):
|
||||||
self.zmq_socket = zmq_socket
|
self.zmq_socket = zmq_socket
|
||||||
|
|
||||||
async def on_message(self, message):
|
async def on_message(self, message):
|
||||||
logger.debug(f"receive msg")
|
logger.debug(f"recieve msg")
|
||||||
|
|
||||||
try:
|
try:
|
||||||
await self.zmq_socket.send_string(message)
|
await self.zmq_socket.send_string(message)
|
||||||
|
|
@ -116,14 +112,12 @@ class WsRouter:
|
||||||
|
|
||||||
context = zmq.asyncio.Context()
|
context = zmq.asyncio.Context()
|
||||||
self.trajectory_socket = context.socket(zmq.PUB)
|
self.trajectory_socket = context.socket(zmq.PUB)
|
||||||
logger.info(f'Publish trajectories on {config.zmq_trajectory_addr}')
|
self.trajectory_socket.bind(config.zmq_prediction_addr if config.bypass_prediction else config.zmq_trajectory_addr)
|
||||||
self.trajectory_socket.bind(config.zmq_trajectory_addr)
|
|
||||||
|
|
||||||
self.prediction_socket = context.socket(zmq.SUB)
|
self.prediction_socket = context.socket(zmq.SUB)
|
||||||
self.prediction_socket.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. NB. make sure this comes BEFORE connect, otherwise it's ignored!!
|
self.prediction_socket.connect(config.zmq_prediction_addr)
|
||||||
self.prediction_socket.setsockopt(zmq.SUBSCRIBE, b'')
|
self.prediction_socket.setsockopt(zmq.SUBSCRIBE, b'')
|
||||||
self.prediction_socket.connect(config.zmq_prediction_addr if not self.config.bypass_prediction else config.zmq_trajectory_addr)
|
|
||||||
|
|
||||||
self.application = tornado.web.Application(
|
self.application = tornado.web.Application(
|
||||||
[
|
[
|
||||||
(
|
(
|
||||||
|
|
@ -172,16 +166,11 @@ class WsRouter:
|
||||||
logger.info("Starting prediction forwarder")
|
logger.info("Starting prediction forwarder")
|
||||||
while self.is_running.is_set():
|
while self.is_running.is_set():
|
||||||
# timeout so that if no events occur, loop can still stop on is_running
|
# timeout so that if no events occur, loop can still stop on is_running
|
||||||
has_event = await self.prediction_socket.poll(timeout=1000)
|
has_event = await self.prediction_socket.poll(timeout=1)
|
||||||
if has_event:
|
if has_event:
|
||||||
try:
|
msg = await self.prediction_socket.recv_string()
|
||||||
frame: Frame = await self.prediction_socket.recv_pyobj()
|
logger.debug(f"Forward prediction message of {len(msg)} chars")
|
||||||
# tacks = [dataclasses.asdict(h) for t in frame.tracks.values() for t.history in t]
|
WebSocketPredictionHandler.write_to_clients(msg)
|
||||||
msg = json.dumps(frame.aslist())
|
|
||||||
logger.debug(f"Forward prediction message of {len(msg)} chars")
|
|
||||||
WebSocketPredictionHandler.write_to_clients(msg)
|
|
||||||
except Exception as e:
|
|
||||||
logger.exception(e)
|
|
||||||
|
|
||||||
# die together:
|
# die together:
|
||||||
self.evt_loop.stop()
|
self.evt_loop.stop()
|
||||||
|
|
|
||||||
|
|
@ -22,7 +22,7 @@ from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
|
||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
from ultralytics.engine.results import Results as YOLOResult
|
from ultralytics.engine.results import Results as YOLOResult
|
||||||
|
|
||||||
from trap.frame_emitter import DetectionState, Frame, Detection, Track
|
from trap.frame_emitter import Frame, Detection, Track
|
||||||
|
|
||||||
# Detection = [int, int, int, int, float, int]
|
# Detection = [int, int, int, int, float, int]
|
||||||
# Detections = [Detection]
|
# Detections = [Detection]
|
||||||
|
|
@ -66,9 +66,6 @@ class Tracker:
|
||||||
# TODO: support removal
|
# TODO: support removal
|
||||||
self.tracks = defaultdict(lambda: Track())
|
self.tracks = defaultdict(lambda: Track())
|
||||||
|
|
||||||
|
|
||||||
logger.debug(f"Load tracker: {self.config.detector}")
|
|
||||||
|
|
||||||
if self.config.detector == DETECTOR_RETINANET:
|
if self.config.detector == DETECTOR_RETINANET:
|
||||||
# weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
|
# weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
|
||||||
# self.model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.2)
|
# self.model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.2)
|
||||||
|
|
@ -79,8 +76,8 @@ class Tracker:
|
||||||
self.model.eval()
|
self.model.eval()
|
||||||
# Get the transforms for the model's weights
|
# Get the transforms for the model's weights
|
||||||
self.preprocess = weights.transforms().to(self.device)
|
self.preprocess = weights.transforms().to(self.device)
|
||||||
self.mot_tracker = DeepSort(max_iou_distance=1, max_cosine_distance=0.5, max_age=15, nms_max_overlap=0.9,
|
self.mot_tracker = DeepSort(max_iou_distance=1, max_cosine_distance=0.5, max_age=12, nms_max_overlap=0.9,
|
||||||
# embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
|
# embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
|
||||||
)
|
)
|
||||||
elif self.config.detector == DETECTOR_MASKRCNN:
|
elif self.config.detector == DETECTOR_MASKRCNN:
|
||||||
weights = MaskRCNN_ResNet50_FPN_V2_Weights.COCO_V1
|
weights = MaskRCNN_ResNet50_FPN_V2_Weights.COCO_V1
|
||||||
|
|
@ -90,8 +87,8 @@ class Tracker:
|
||||||
self.model.eval()
|
self.model.eval()
|
||||||
# Get the transforms for the model's weights
|
# Get the transforms for the model's weights
|
||||||
self.preprocess = weights.transforms().to(self.device)
|
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=15, nms_max_overlap=0.9,
|
self.mot_tracker = DeepSort(n_init=5, max_iou_distance=1, max_cosine_distance=0.5, max_age=12, nms_max_overlap=0.9,
|
||||||
# embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
|
# embedder='torchreid', embedder_wts="../MODELS/osnet_x1_0_imagenet.pth"
|
||||||
)
|
)
|
||||||
elif self.config.detector == DETECTOR_YOLOv8:
|
elif self.config.detector == DETECTOR_YOLOv8:
|
||||||
self.model = YOLO('EXPERIMENTS/yolov8x.pt')
|
self.model = YOLO('EXPERIMENTS/yolov8x.pt')
|
||||||
|
|
@ -123,7 +120,7 @@ class Tracker:
|
||||||
logger.warning(f"Path for training-data exists: {self.config.save_for_training}. Continuing assuming that's ok.")
|
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')
|
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
|
# 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', 'l', 't', 'w', 'h', 'x', 'y', 'state'], delimiter='\t', quoting=csv.QUOTE_NONE)
|
training_csv = csv.DictWriter(training_fp, fieldnames=['frame_id', 'track_id', 'x', 'y'], delimiter='\t', quoting=csv.QUOTE_NONE)
|
||||||
|
|
||||||
prev_frame_i = -1
|
prev_frame_i = -1
|
||||||
|
|
||||||
|
|
@ -136,12 +133,6 @@ class Tracker:
|
||||||
# time.sleep(max(0, prev_run_time - this_run_time + TARGET_DT))
|
# time.sleep(max(0, prev_run_time - this_run_time + TARGET_DT))
|
||||||
# prev_run_time = time.time()
|
# 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()
|
start_time = time.time()
|
||||||
frame: Frame = self.frame_sock.recv_pyobj() # frame delivery in current setup: 0.012-0.03s
|
frame: Frame = self.frame_sock.recv_pyobj() # frame delivery in current setup: 0.012-0.03s
|
||||||
|
|
||||||
|
|
@ -151,10 +142,7 @@ class Tracker:
|
||||||
|
|
||||||
prev_frame_i = frame.index
|
prev_frame_i = frame.index
|
||||||
# load homography into frame (TODO: should this be done in emitter?)
|
# load homography into frame (TODO: should this be done in emitter?)
|
||||||
if frame.H is None:
|
frame.H = self.H
|
||||||
# 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")
|
# logger.info(f"Frame delivery delay = {time.time()-frame.time}s")
|
||||||
|
|
||||||
|
|
@ -162,7 +150,7 @@ class Tracker:
|
||||||
if self.config.detector == DETECTOR_YOLOv8:
|
if self.config.detector == DETECTOR_YOLOv8:
|
||||||
detections: [Detection] = self._yolov8_track(frame.img)
|
detections: [Detection] = self._yolov8_track(frame.img)
|
||||||
else :
|
else :
|
||||||
detections: [Detection] = self._resnet_track(frame.img, scale = 1)
|
detections: [Detection] = self._resnet_track(frame.img)
|
||||||
|
|
||||||
|
|
||||||
# Store detections into tracklets
|
# Store detections into tracklets
|
||||||
|
|
@ -213,18 +201,10 @@ class Tracker:
|
||||||
if training_csv:
|
if training_csv:
|
||||||
training_csv.writerows([{
|
training_csv.writerows([{
|
||||||
'frame_id': round(frame.index * 10., 1), # not really time
|
'frame_id': round(frame.index * 10., 1), # not really time
|
||||||
'track_id': t.track_id,
|
'track_id': t['id'],
|
||||||
'l': t.history[-1].l,
|
'x': t['history'][-1]['x' if not self.config.bypass_prediction else 0],
|
||||||
't': t.history[-1].t,
|
'y': t['history'][-1]['y' if not self.config.bypass_prediction else 1],
|
||||||
'w': t.history[-1].w,
|
} for t in active_tracks.values()])
|
||||||
'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)
|
training_frames += len(active_tracks)
|
||||||
# print(time.time() - start_time)
|
# print(time.time() - start_time)
|
||||||
|
|
||||||
|
|
@ -256,13 +236,10 @@ class Tracker:
|
||||||
return []
|
return []
|
||||||
return [Detection(track_id, *bbox) for bbox, track_id in zip(results[0].boxes.xywh.cpu(), results[0].boxes.id.int().cpu().tolist())]
|
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, scale: float = 1) -> [Detection]:
|
def _resnet_track(self, img) -> [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)
|
detections = self._resnet_detect_persons(img)
|
||||||
tracks: [DeepsortTrack] = self.mot_tracker.update_tracks(detections, frame=img)
|
tracks: [DeepsortTrack] = self.mot_tracker.update_tracks(detections, frame=img)
|
||||||
return [Detection.from_deepsort(t).get_scaled(1/scale) for t in tracks]
|
return [Detection.from_deepsort(t) for t in tracks]
|
||||||
|
|
||||||
def _resnet_detect_persons(self, frame) -> [Detection]:
|
def _resnet_detect_persons(self, frame) -> [Detection]:
|
||||||
t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
||||||
|
|
|
||||||
|
|
@ -25,13 +25,12 @@
|
||||||
|
|
||||||
<script>
|
<script>
|
||||||
// minified https://github.com/joewalnes/reconnecting-websocket
|
// minified https://github.com/joewalnes/reconnecting-websocket
|
||||||
!function (a, b) { "function" == typeof define && define.amd ? define([], b) : "undefined" != typeof module && module.exports ? module.exports = b() : a.ReconnectingWebSocket = b() }(this, function () { function a(b, c, d) { function l(a, b) { var c = document.createEvent("CustomEvent"); return c.initCustomEvent(a, !1, !1, b), c } var e = { debug: !1, automaticOpen: !0, reconnectInterval: 1e3, maxReconnectInterval: 3e4, reconnectDecay: 1.5, timeoutInterval: 2e3 }; d || (d = {}); for (var f in e) this[f] = "undefined" != typeof d[f] ? d[f] : e[f]; this.url = b, this.reconnectAttempts = 0, this.readyState = WebSocket.CONNECTING, this.protocol = null; var h, g = this, i = !1, j = !1, k = document.createElement("div"); k.addEventListener("open", function (a) { g.onopen(a) }), k.addEventListener("close", function (a) { g.onclose(a) }), k.addEventListener("connecting", function (a) { g.onconnecting(a) }), k.addEventListener("message", function (a) { g.onmessage(a) }), k.addEventListener("error", function (a) { g.onerror(a) }), this.addEventListener = k.addEventListener.bind(k), this.removeEventListener = k.removeEventListener.bind(k), this.dispatchEvent = k.dispatchEvent.bind(k), this.open = function (b) { h = new WebSocket(g.url, c || []), b || k.dispatchEvent(l("connecting")), (g.debug || a.debugAll) && console.debug("ReconnectingWebSocket", "attempt-connect", g.url); var d = h, e = setTimeout(function () { (g.debug || a.debugAll) && console.debug("ReconnectingWebSocket", "connection-timeout", g.url), j = !0, d.close(), j = !1 }, g.timeoutInterval); h.onopen = function () { clearTimeout(e), (g.debug || a.debugAll) && console.debug("ReconnectingWebSocket", "onopen", g.url), g.protocol = h.protocol, g.readyState = WebSocket.OPEN, g.reconnectAttempts = 0; var d = l("open"); d.isReconnect = b, b = !1, k.dispatchEvent(d) }, h.onclose = function (c) { if (clearTimeout(e), h = null, i) g.readyState = WebSocket.CLOSED, k.dispatchEvent(l("close")); else { g.readyState = WebSocket.CONNECTING; var d = l("connecting"); d.code = c.code, d.reason = c.reason, d.wasClean = c.wasClean, k.dispatchEvent(d), b || j || ((g.debug || a.debugAll) && console.debug("ReconnectingWebSocket", "onclose", g.url), k.dispatchEvent(l("close"))); var e = g.reconnectInterval * Math.pow(g.reconnectDecay, g.reconnectAttempts); setTimeout(function () { g.reconnectAttempts++, g.open(!0) }, e > g.maxReconnectInterval ? g.maxReconnectInterval : e) } }, h.onmessage = function (b) { (g.debug || a.debugAll) && console.debug("ReconnectingWebSocket", "onmessage", g.url, b.data); var c = l("message"); c.data = b.data, k.dispatchEvent(c) }, h.onerror = function (b) { (g.debug || a.debugAll) && console.debug("ReconnectingWebSocket", "onerror", g.url, b), k.dispatchEvent(l("error")) } }, 1 == this.automaticOpen && this.open(!1), this.send = function (b) { if (h) return (g.debug || a.debugAll) && console.debug("ReconnectingWebSocket", "send", g.url, b), h.send(b); throw "INVALID_STATE_ERR : Pausing to reconnect websocket" }, this.close = function (a, b) { "undefined" == typeof a && (a = 1e3), i = !0, h && h.close(a, b) }, this.refresh = function () { h && h.close() } } return a.prototype.onopen = function () { }, a.prototype.onclose = function () { }, a.prototype.onconnecting = function () { }, a.prototype.onmessage = function () { }, a.prototype.onerror = function () { }, a.debugAll = !1, a.CONNECTING = WebSocket.CONNECTING, a.OPEN = WebSocket.OPEN, a.CLOSING = WebSocket.CLOSING, a.CLOSED = WebSocket.CLOSED, a });
|
!function(a,b){"function"==typeof define&&define.amd?define([],b):"undefined"!=typeof module&&module.exports?module.exports=b():a.ReconnectingWebSocket=b()}(this,function(){function a(b,c,d){function l(a,b){var c=document.createEvent("CustomEvent");return c.initCustomEvent(a,!1,!1,b),c}var e={debug:!1,automaticOpen:!0,reconnectInterval:1e3,maxReconnectInterval:3e4,reconnectDecay:1.5,timeoutInterval:2e3};d||(d={});for(var f in e)this[f]="undefined"!=typeof d[f]?d[f]:e[f];this.url=b,this.reconnectAttempts=0,this.readyState=WebSocket.CONNECTING,this.protocol=null;var h,g=this,i=!1,j=!1,k=document.createElement("div");k.addEventListener("open",function(a){g.onopen(a)}),k.addEventListener("close",function(a){g.onclose(a)}),k.addEventListener("connecting",function(a){g.onconnecting(a)}),k.addEventListener("message",function(a){g.onmessage(a)}),k.addEventListener("error",function(a){g.onerror(a)}),this.addEventListener=k.addEventListener.bind(k),this.removeEventListener=k.removeEventListener.bind(k),this.dispatchEvent=k.dispatchEvent.bind(k),this.open=function(b){h=new WebSocket(g.url,c||[]),b||k.dispatchEvent(l("connecting")),(g.debug||a.debugAll)&&console.debug("ReconnectingWebSocket","attempt-connect",g.url);var d=h,e=setTimeout(function(){(g.debug||a.debugAll)&&console.debug("ReconnectingWebSocket","connection-timeout",g.url),j=!0,d.close(),j=!1},g.timeoutInterval);h.onopen=function(){clearTimeout(e),(g.debug||a.debugAll)&&console.debug("ReconnectingWebSocket","onopen",g.url),g.protocol=h.protocol,g.readyState=WebSocket.OPEN,g.reconnectAttempts=0;var d=l("open");d.isReconnect=b,b=!1,k.dispatchEvent(d)},h.onclose=function(c){if(clearTimeout(e),h=null,i)g.readyState=WebSocket.CLOSED,k.dispatchEvent(l("close"));else{g.readyState=WebSocket.CONNECTING;var d=l("connecting");d.code=c.code,d.reason=c.reason,d.wasClean=c.wasClean,k.dispatchEvent(d),b||j||((g.debug||a.debugAll)&&console.debug("ReconnectingWebSocket","onclose",g.url),k.dispatchEvent(l("close")));var e=g.reconnectInterval*Math.pow(g.reconnectDecay,g.reconnectAttempts);setTimeout(function(){g.reconnectAttempts++,g.open(!0)},e>g.maxReconnectInterval?g.maxReconnectInterval:e)}},h.onmessage=function(b){(g.debug||a.debugAll)&&console.debug("ReconnectingWebSocket","onmessage",g.url,b.data);var c=l("message");c.data=b.data,k.dispatchEvent(c)},h.onerror=function(b){(g.debug||a.debugAll)&&console.debug("ReconnectingWebSocket","onerror",g.url,b),k.dispatchEvent(l("error"))}},1==this.automaticOpen&&this.open(!1),this.send=function(b){if(h)return(g.debug||a.debugAll)&&console.debug("ReconnectingWebSocket","send",g.url,b),h.send(b);throw"INVALID_STATE_ERR : Pausing to reconnect websocket"},this.close=function(a,b){"undefined"==typeof a&&(a=1e3),i=!0,h&&h.close(a,b)},this.refresh=function(){h&&h.close()}}return a.prototype.onopen=function(){},a.prototype.onclose=function(){},a.prototype.onconnecting=function(){},a.prototype.onmessage=function(){},a.prototype.onerror=function(){},a.debugAll=!1,a.CONNECTING=WebSocket.CONNECTING,a.OPEN=WebSocket.OPEN,a.CLOSING=WebSocket.CLOSING,a.CLOSED=WebSocket.CLOSED,a});
|
||||||
</script>
|
</script>
|
||||||
|
|
||||||
<script>
|
<script>
|
||||||
// map the field to coordinates of our dummy tracker
|
// map the field to coordinates of our dummy tracker
|
||||||
// see test_homography.ipynb for the logic behind these values
|
const field_range = { x: [-30, 10], y: [-10, 10] }
|
||||||
const field_range = { x: [-13.092, 15.37], y: [-4.66, 10.624] }
|
|
||||||
|
|
||||||
// Create WebSocket connection.
|
// Create WebSocket connection.
|
||||||
const trajectory_socket = new WebSocket(`ws://${window.location.hostname}:{{ ws_port }}/ws/trajectory`);
|
const trajectory_socket = new WebSocket(`ws://${window.location.hostname}:{{ ws_port }}/ws/trajectory`);
|
||||||
|
|
@ -64,7 +63,7 @@
|
||||||
function position_to_canvas_coordinate(position) {
|
function position_to_canvas_coordinate(position) {
|
||||||
const x_range = field_range.x[1] - field_range.x[0]
|
const x_range = field_range.x[1] - field_range.x[0]
|
||||||
const y_range = field_range.y[1] - field_range.y[0]
|
const y_range = field_range.y[1] - field_range.y[0]
|
||||||
|
|
||||||
const x = Array.isArray(position) ? position[0] : position.x;
|
const x = Array.isArray(position) ? position[0] : position.x;
|
||||||
const y = Array.isArray(position) ? position[1] : position.y;
|
const y = Array.isArray(position) ? position[1] : position.y;
|
||||||
return {
|
return {
|
||||||
|
|
@ -95,19 +94,19 @@
|
||||||
}
|
}
|
||||||
|
|
||||||
let current_pos = null;
|
let current_pos = null;
|
||||||
|
|
||||||
|
|
||||||
|
function appendAndSendPositions(){
|
||||||
function appendAndSendPositions() {
|
if(is_moving && current_pos!==null){
|
||||||
if (is_moving && current_pos !== null) {
|
|
||||||
// throttled update of tracker on movement
|
// throttled update of tracker on movement
|
||||||
tracker[person_counter].addToHistory(current_pos);
|
tracker[person_counter].addToHistory(current_pos);
|
||||||
}
|
}
|
||||||
|
|
||||||
for (const person_id in tracker) {
|
for(const person_id in tracker){
|
||||||
if (person_id != person_counter) { // compare int/str
|
if(person_id != person_counter){ // compare int/str
|
||||||
// fade out old tracks
|
// fade out old tracks
|
||||||
tracker[person_id].history.shift()
|
tracker[person_id].history.shift()
|
||||||
if (!tracker[person_id].history.length) {
|
if(!tracker[person_id].history.length){
|
||||||
delete tracker[person_id]
|
delete tracker[person_id]
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
@ -126,17 +125,17 @@
|
||||||
const mousePos = getMousePos(fieldEl, event);
|
const mousePos = getMousePos(fieldEl, event);
|
||||||
const position = mouse_coordinates_to_position(mousePos)
|
const position = mouse_coordinates_to_position(mousePos)
|
||||||
current_pos = position;
|
current_pos = position;
|
||||||
tracker[person_counter].addToHistory(current_pos);
|
// tracker[person_counter].addToHistory(current_pos);
|
||||||
// trajectory_socket.send(JSON.stringify(tracker))
|
// trajectory_socket.send(JSON.stringify(tracker))
|
||||||
|
|
||||||
});
|
});
|
||||||
fieldEl.addEventListener('mousemove', (event) => {
|
fieldEl.addEventListener('mousemove', (event) => {
|
||||||
if (!is_moving) return;
|
if (!is_moving) return;
|
||||||
const mousePos = getMousePos(fieldEl, event);
|
const mousePos = getMousePos(fieldEl, event);
|
||||||
const position = mouse_coordinates_to_position(mousePos)
|
const position = mouse_coordinates_to_position(mousePos)
|
||||||
current_pos = position;
|
current_pos = position;
|
||||||
tracker[person_counter].addToHistory(current_pos);
|
// tracker[person_counter].addToHistory(current_pos);
|
||||||
trajectory_socket.send(JSON.stringify(tracker))
|
// trajectory_socket.send(JSON.stringify(tracker))
|
||||||
});
|
});
|
||||||
document.addEventListener('mouseup', (e) => {
|
document.addEventListener('mouseup', (e) => {
|
||||||
person_counter++;
|
person_counter++;
|
||||||
|
|
@ -159,7 +158,7 @@
|
||||||
5, //radius
|
5, //radius
|
||||||
0, 2 * Math.PI);
|
0, 2 * Math.PI);
|
||||||
ctx.fill()
|
ctx.fill()
|
||||||
|
|
||||||
ctx.beginPath()
|
ctx.beginPath()
|
||||||
ctx.lineWidth = 3;
|
ctx.lineWidth = 3;
|
||||||
ctx.strokeStyle = "#325FA2";
|
ctx.strokeStyle = "#325FA2";
|
||||||
|
|
@ -171,14 +170,13 @@
|
||||||
ctx.stroke();
|
ctx.stroke();
|
||||||
}
|
}
|
||||||
|
|
||||||
if (person.hasOwnProperty('predictions') && person.predictions.length > 0) {
|
if(person.hasOwnProperty('predictions') && person.predictions.length > 0) {
|
||||||
// multiple predictions can be sampled
|
// multiple predictions can be sampled
|
||||||
person.predictions.forEach((prediction, i) => {
|
person.predictions.forEach((prediction, i) => {
|
||||||
ctx.beginPath()
|
ctx.beginPath()
|
||||||
ctx.lineWidth = 0.2;
|
ctx.lineWidth = i === 1 ? 3 : 0.2;
|
||||||
ctx.strokeStyle = i === 1 ? "#ff0000" : "#ccaaaa";
|
ctx.strokeStyle = i === 1 ? "#ff0000" : "#ccaaaa";
|
||||||
ctx.strokeStyle = "#ccaaaa";
|
|
||||||
|
|
||||||
// start from current position:
|
// start from current position:
|
||||||
ctx.moveTo(...coord_as_list(position_to_canvas_coordinate(person.history[person.history.length - 1])));
|
ctx.moveTo(...coord_as_list(position_to_canvas_coordinate(person.history[person.history.length - 1])));
|
||||||
for (const position of prediction) {
|
for (const position of prediction) {
|
||||||
|
|
@ -186,33 +184,6 @@
|
||||||
}
|
}
|
||||||
ctx.stroke();
|
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();
|
ctx.restore();
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue