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batch processing works but someth strange in filt

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This commit is contained in:
davidpagnon 2023-12-16 22:15:12 +01:00
parent 2e6700e747
commit 37354e2598
12 changed files with 471 additions and 139 deletions
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2
Pose2Sim/Demo/S00_Demo_Session/Config.toml
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@ -131,7 +131,7 @@ show_interp_indices = true # true or false (lowercase). For each keypoint, retur
[filtering]
type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
display_figures = true # true or false (lowercase)
display_figures = false # true or false (lowercase)
[filtering.butterworth]
order = 4

39
Pose2Sim/Demo/S00_Demo_Session/S00_Calibration/Calib_qualisys.toml Normal file
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@ -0,0 +1,39 @@
[cam_01]
name = "cam_01"
size = [ 1088.0, 1920.0]
matrix = [ [ 1681.244873046875, 0.0, 532.97369384375], [ 0.0, 1681.075439453125, 948.137390140625], [ 0.0, 0.0, 1.0]]
distortions = [ -0.000721609375, 0.002187234375, 9.5e-06, 1.078125e-05]
rotation = [ 1.6882754799999993, 1.0483220499999997, -0.41955852000000016]
translation = [ 0.3211048899999996, 0.9563320600000009, 2.8907130499999996]
fisheye = false
[cam_02]
name = "cam_02"
size = [ 1088.0, 1920.0]
matrix = [ [ 1673.729614265625, 0.0, 534.494567875], [ 0.0, 1673.79724121875, 963.225891109375], [ 0.0, 0.0, 1.0]]
distortions = [ -0.000747609375, 0.00213728125, 1.51875e-05, 4.546875e-06]
rotation = [ 1.34975875, 1.5963809099999993, -1.1983285799999999]
translation = [ -0.11152829000000017, 0.7766184800000001, 3.0675519599999994]
fisheye = false
[cam_03]
name = "cam_03"
size = [ 1088.0, 1920.0]
matrix = [ [ 1681.598388671875, 0.0, 513.20837403125], [ 0.0, 1681.509887703125, 955.005126953125], [ 0.0, 0.0, 1.0]]
distortions = [ -0.000729765625, 0.00215034375, -8.46875e-06, -8.078125e-06]
rotation = [ 0.8109654899999995, -2.1972129299999996, 1.3760277799999996]
translation = [ -0.7934803899999996, 0.32283594000000126, 4.353514870000001]
fisheye = false
[cam_04]
name = "cam_04"
size = [ 1088.0, 1920.0]
matrix = [ [ 1675.234985359375, 0.0, 540.106201171875], [ 0.0, 1675.204223640625, 964.0302734375], [ 0.0, 0.0, 1.0]]
distortions = [ -0.000744265625, 0.002104171875, 4.328125e-06, 3.109375e-06]
rotation = [ 1.4045571699999995, -1.3887412699999993, 0.42535743000000026]
translation = [ 0.5030217200000007, 0.04894934000000083, 4.406564460000002]
fisheye = false
[metadata]
adjusted = false
error = 0.0

39
Pose2Sim/Demo/S00_Demo_Session/S00_Calibration/Calib_scene.toml Normal file
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@ -0,0 +1,39 @@
[cam_01]
name = "int_cam1_img"
size = [ 1088.0, 1920.0]
matrix = [ [ 1684.5047197899185, 0.0, 543.5], [ 0.0, 1683.673555849299, 959.5], [ 0.0, 0.0, 1.0]]
distortions = [ -0.039138683191583944, 0.1218103935284797, 0.0032579093081951667, -0.0018536626694605751]
rotation = [ 1.6979040034605661, 1.0454503623806635, -0.3967052479550455]
translation = [ 0.2993949854075437, 0.9123682264266217, 2.9525770726222924]
fisheye = false
[cam_02]
name = "int_cam2_img"
size = [ 1088.0, 1920.0]
matrix = [ [ 1682.5869849743954, 0.0, 543.5], [ 0.0, 1682.2874421959987, 959.5], [ 0.0, 0.0, 1.0]]
distortions = [ -0.04721848950094313, 0.1486463790994366, 0.003443047027444872, -0.002371907358995211]
rotation = [ 1.3583729171384762, 1.596543959936354, -1.1775107540333758]
translation = [ -0.1432587418510512, 0.7570810073112453, 3.196791488077418]
fisheye = false
[cam_03]
name = "int_cam3_img"
size = [ 1088.0, 1920.0]
matrix = [ [ 1686.8339543928955, 0.0, 543.5], [ 0.0, 1685.6467627295945, 959.5], [ 0.0, 0.0, 1.0]]
distortions = [ -0.02766649152544831, 0.046899117070522744, 0.002969945413330381, -0.0005438362438561441]
rotation = [ 0.7975948684033358, -2.204154235951477, 1.4122848828663128]
translation = [ -0.8199180323949162, 0.3283785895343736, 4.3326000802663]
fisheye = false
[cam_04]
name = "int_cam4_img"
size = [ 1088.0, 1920.0]
matrix = [ [ 1685.1935162257228, 0.0, 543.5], [ 0.0, 1683.3046331436326, 959.5], [ 0.0, 0.0, 1.0]]
distortions = [ -0.04893927682748424, 0.1498722992646072, 0.002083782789316641, -0.0019006232728624472]
rotation = [ 1.4121158466686754, -1.3970202251874027, 0.4294398801141392]
translation = [ 0.49829663108922206, 0.04570535749730321, 4.395597511456334]
fisheye = false
[metadata]
adjusted = false
error = 0.0

17
Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T00_StaticTrial/pose-3d/S00_P00_T00_StaticTrial_0-12.trc Normal file
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@ -0,0 +1,17 @@
PathFileType 4 (X/Y/Z) S00_P00_T00_StaticTrial_0-12.trc
DataRate CameraRate NumFrames NumMarkers Units OrigDataRate OrigDataStartFrame OrigNumFrames
60 60 12 21 m 60 0 12
Frame# Time RHip RKnee RAnkle RBigToe RSmallToe RHeel LHip LKnee LAnkle LBigToe LSmallToe LHeel Neck Head Nose RShoulder RElbow RWrist LShoulder LElbow LWrist
X1 Y1 Z1 X2 Y2 Z2 X3 Y3 Z3 X4 Y4 Z4 X5 Y5 Z5 X6 Y6 Z6 X7 Y7 Z7 X8 Y8 Z8 X9 Y9 Z9 X10 Y10 Z10 X11 Y11 Z11 X12 Y12 Z12 X13 Y13 Z13 X14 Y14 Z14 X15 Y15 Z15 X16 Y16 Z16 X17 Y17 Z17 X18 Y18 Z18 X19 Y19 Z19 X20 Y20 Z20 X21 Y21 Z21
1 0.016666666666666666 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
2 0.03333333333333333 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
3 0.05 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
4 0.06666666666666667 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
5 0.08333333333333333 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
6 0.1 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
7 0.11666666666666667 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
8 0.13333333333333333 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
9 0.15 -0.08058929090227669 0.8696950332650175 0.07616397712720875 -0.09062396992529709 0.5027125987699388 0.053815664751651754 -0.08348510484278128 0.11873254569531715 0.056523881987492315 0.10932791883931053 0.033444095416829175 0.03574741457702558 0.07248983335710343 0.0330620742022174 0.07875225368890343 -0.11760879369690265 0.047451893916352274 0.046342262659457616 -0.08119480911121872 0.8696061657045612 -0.1349326287978281 -0.09131718923675154 0.5024873911329983 -0.12335261295489103 -0.08442033381266927 0.11837364921704997 -0.1374416925010638 0.10857362727170179 0.0330958675440299 -0.11707336893692397 0.07130430714843262 0.03253248587808652 -0.16054431759329563 -0.1184856407914993 0.047124908972131896 -0.12976395417015346 -0.11433658091277044 1.4866114990895252 -0.023449884506625357 -0.03715305447810577 1.7080697552316926 0.0023489874129097657 -0.007947310066847955 1.5820735921196272 -0.02165586933768429 -0.1254425869499148 1.4065244957439884 0.1775577049726504 -0.08348187441862756 1.1655200498791973 0.30777720913591766 -0.10309240416673067 0.9809612674140527 0.44940349727333995 -0.12592189367116652 1.407115065039762 -0.22679012550238412 -0.06355728043185781 1.169064538629958 -0.3607828888380723 -0.06823467675436756 0.9830199237181226 -0.5087828293513996
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Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T00_StaticTrial/pose-3d/S00_P00_T00_StaticTrial_filt_butterworth_0-12.trc Normal file
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PathFileType 4 (X/Y/Z) S00_P00_T00_StaticTrial_0-12.trc
DataRate CameraRate NumFrames NumMarkers Units OrigDataRate OrigDataStartFrame OrigNumFrames
60 60 12 21 m 60 0 12
Frame# Time RHip RKnee RAnkle RBigToe RSmallToe RHeel LHip LKnee LAnkle LBigToe LSmallToe LHeel Neck Head Nose RShoulder RElbow RWrist LShoulder LElbow LWrist
X1 Y1 Z1 X2 Y2 Z2 X3 Y3 Z3 X4 Y4 Z4 X5 Y5 Z5 X6 Y6 Z6 X7 Y7 Z7 X8 Y8 Z8 X9 Y9 Z9 X10 Y10 Z10 X11 Y11 Z11 X12 Y12 Z12 X13 Y13 Z13 X14 Y14 Z14 X15 Y15 Z15 X16 Y16 Z16 X17 Y17 Z17 X18 Y18 Z18 X19 Y19 Z19 X20 Y20 Z20 X21 Y21 Z21
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8 0.1333333333333333 -0.08058929090227662 0.8696950332650172 0.07616397712720867 -0.09062396992529695 0.5027125987699388 0.053815664751651685 -0.08348510484278117 0.11873254569531706 0.056523881987492273 0.10932791883931045 0.033444095416829085 0.03574741457702548 0.0724898333571034 0.033062074202217386 0.07875225368890336 -0.11760879369690255 0.047451893916352184 0.04634226265945759 -0.0811948091112187 0.8696061657045608 -0.134932628797828 -0.09131718923675151 0.5024873911329985 -0.12335261295489092 -0.08442033381266921 0.11837364921704989 -0.13744169250106375 0.10857362727170167 0.033095867544029885 -0.1170733689369239 0.0713043071484326 0.032532485878086485 -0.16054431759329554 -0.11848564079149927 0.047124908972131785 -0.1297639541701534 -0.11433658091277035 1.4866114990895252 -0.02344988450662529 -0.037153054478105685 1.7080697552316917 0.0023489874129097 -0.007947310066847896 1.5820735921196263 -0.021655869337684187 -0.12544258694991473 1.406524495743988 0.17755770497265028 -0.08348187441862746 1.1655200498791973 0.30777720913591755 -0.10309240416673054 0.9809612674140525 0.44940349727333967 -0.12592189367116643 1.407115065039762 -0.226790125502384 -0.06355728043185781 1.1690645386299576 -0.3607828888380721 -0.0682346767543675 0.9830199237181223 -0.5087828293513995
9 0.15 -0.08058929090227662 0.8696950332650172 0.07616397712720867 -0.09062396992529695 0.5027125987699388 0.053815664751651685 -0.08348510484278117 0.11873254569531708 0.05652388198749228 0.10932791883931045 0.033444095416829085 0.03574741457702548 0.0724898333571034 0.033062074202217386 0.07875225368890336 -0.11760879369690255 0.047451893916352184 0.04634226265945759 -0.0811948091112187 0.8696061657045608 -0.134932628797828 -0.09131718923675151 0.5024873911329985 -0.12335261295489092 -0.08442033381266921 0.11837364921704989 -0.13744169250106375 0.10857362727170167 0.033095867544029885 -0.1170733689369239 0.0713043071484326 0.032532485878086485 -0.16054431759329554 -0.11848564079149927 0.047124908972131785 -0.1297639541701534 -0.11433658091277037 1.4866114990895252 -0.02344988450662529 -0.037153054478105685 1.7080697552316917 0.0023489874129097 -0.007947310066847896 1.5820735921196263 -0.021655869337684187 -0.12544258694991473 1.406524495743988 0.17755770497265028 -0.08348187441862746 1.1655200498791973 0.30777720913591755 -0.10309240416673054 0.9809612674140525 0.44940349727333967 -0.12592189367116643 1.407115065039762 -0.22679012550238403 -0.06355728043185781 1.1690645386299576 -0.3607828888380721 -0.0682346767543675 0.9830199237181223 -0.5087828293513995
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4
Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T01_BalancingTrial/Config.toml
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@ -130,9 +130,9 @@
# show_interp_indices = true # true or false (lowercase). For each keypoint, return the frames that need to be interpolated
# [filtering]
[filtering]
# type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
# display_figures = true # true or false (lowercase)
display_figures = true # true or false (lowercase)
# [filtering.butterworth]
# order = 4

105
Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T01_BalancingTrial/pose-3d/S00_P00_T01_BalancingTrial_0-100.trc Normal file
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@ -0,0 +1,105 @@
PathFileType 4 (X/Y/Z) S00_P00_T01_BalancingTrial_0-100.trc
DataRate CameraRate NumFrames NumMarkers Units OrigDataRate OrigDataStartFrame OrigNumFrames
60 60 100 21 m 60 0 100
Frame# Time RHip RKnee RAnkle RBigToe RSmallToe RHeel LHip LKnee LAnkle LBigToe LSmallToe LHeel Neck Head Nose RShoulder RElbow RWrist LShoulder LElbow LWrist
X1 Y1 Z1 X2 Y2 Z2 X3 Y3 Z3 X4 Y4 Z4 X5 Y5 Z5 X6 Y6 Z6 X7 Y7 Z7 X8 Y8 Z8 X9 Y9 Z9 X10 Y10 Z10 X11 Y11 Z11 X12 Y12 Z12 X13 Y13 Z13 X14 Y14 Z14 X15 Y15 Z15 X16 Y16 Z16 X17 Y17 Z17 X18 Y18 Z18 X19 Y19 Z19 X20 Y20 Z20 X21 Y21 Z21
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Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T01_BalancingTrial/pose-3d/S00_P00_T01_BalancingTrial_filt_butterworth_0-100.trc Normal file
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PathFileType 4 (X/Y/Z) S00_P00_T01_BalancingTrial_0-100.trc
DataRate CameraRate NumFrames NumMarkers Units OrigDataRate OrigDataStartFrame OrigNumFrames
60 60 100 21 m 60 0 100
Frame# Time RHip RKnee RAnkle RBigToe RSmallToe RHeel LHip LKnee LAnkle LBigToe LSmallToe LHeel Neck Head Nose RShoulder RElbow RWrist LShoulder LElbow LWrist
X1 Y1 Z1 X2 Y2 Z2 X3 Y3 Z3 X4 Y4 Z4 X5 Y5 Z5 X6 Y6 Z6 X7 Y7 Z7 X8 Y8 Z8 X9 Y9 Z9 X10 Y10 Z10 X11 Y11 Z11 X12 Y12 Z12 X13 Y13 Z13 X14 Y14 Z14 X15 Y15 Z15 X16 Y16 Z16 X17 Y17 Z17 X18 Y18 Z18 X19 Y19 Z19 X20 Y20 Z20 X21 Y21 Z21
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227
Pose2Sim/Pose2Sim.py
View File

@ -172,31 +172,13 @@ def read_config_files(config):
return level, config_dicts
def base_params(config_dict, level):
'''
Retrieve sequence name and frames to be analyzed.
'''
project_dir = os.getcwd()
frame_range = config_dict.get('project').get('frame_range')
seq_name = os.path.basename(project_dir)
frames = ["all frames" if frame_range == [] else f"frames {frame_range[0]} to {frame_range[1]}"][0]
log_dir = os.path.realpath([os.getcwd() if level==3 else os.path.join(os.getcwd(), '..') if level==2 else os.path.join(os.getcwd(), '..', '..')][0])
with open(os.path.join(log_dir, 'logs.txt'), 'a+') as log_f: pass
logging.basicConfig(format='%(message)s', level=logging.INFO,
handlers = [logging.handlers.TimedRotatingFileHandler(os.path.join(log_dir, 'logs.txt'), when='D', interval=7), logging.StreamHandler()])
return project_dir, seq_name, frames
def calibration(config=None):
'''
Cameras calibration from checkerboards or from qualisys files.
config can be a dictionary,
or a trial, participant, or session directory path,
or the function can be called without an argument, in which case it is the current directory.
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
from Pose2Sim.calibration import calibrate_cams_all
@ -209,7 +191,7 @@ def calibration(config=None):
# Set up logging
setup_logging(session_dir)
# Path to the calibration directory
# Run calibration
calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
logging.info("\n\n---------------------------------------------------------------------")
logging.info("Camera calibration")
@ -227,7 +209,9 @@ def poseEstimation(config=None):
'''
Estimate pose using BlazePose, OpenPose, AlphaPose, or DeepLabCut.
config can either be a path or a dictionary (for batch processing)
config can be a dictionary,
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
@ -257,7 +241,9 @@ def synchronization(config=None):
'''
Synchronize cameras if needed.
config can either be a path or a dictionary (for batch processing)
config can be a dictionary,
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
@ -285,10 +271,12 @@ def synchronization(config=None):
def personAssociation(config=None):
'''
Tracking of the person of interest in case of multiple persons detection.
Tracking one or several persons of interest.
Needs a calibration file.
config can either be a path or a dictionary (for batch processing)
config can be a dictionary,
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
from Pose2Sim.personAssociation import track_2d_all
@ -299,11 +287,97 @@ def personAssociation(config=None):
if config_dict.get('project').get('project_dir') == None:
raise ValueError('Please specify the project directory in config_dict:\n \
config_dict.get("project").update({"project_dir":"<YOUR_PROJECT_DIRECTORY>"})')
else:
# Determine the level at which the function is called (session:3, participant:2, trial:1)
level = determine_level()
config_dicts = read_config_files(level)
level, config_dicts = read_config_files(config)
# Set up logging
session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
setup_logging(session_dir)
# Batch process all trials
start = time.time()
for config_dict in config_dicts:
project_dir = os.path.realpath(config_dict.get('project').get('project_dir'))
seq_name = os.path.basename(project_dir)
frame_range = config_dict.get('project').get('frame_range')
frames = ["all frames" if frame_range == [] else f"frames {frame_range[0]} to {frame_range[1]}"][0]
logging.info("\n\n---------------------------------------------------------------------")
logging.info(f"Associating persons for {seq_name}, for {frames}.")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nProject directory: {project_dir}")
track_2d_all(config_dict)
end = time.time()
logging.info(f'Associating persons took {end-start:.2f} s.')
def triangulation(config=None):
'''
Robust triangulation of 2D points coordinates.
config can be a dictionary,
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
from Pose2Sim.triangulation import triangulate_all
if type(config)==dict:
level = 3 # log_dir = os.getcwd()
config_dict = config
if config_dict.get('project').get('project_dir') == None:
raise ValueError('Please specify the project directory in config_dict:\n \
config_dict.get("project").update({"project_dir":"<YOUR_PROJECT_DIRECTORY>"})')
else:
# Determine the level at which the function is called (session:3, participant:2, trial:1)
level, config_dicts = read_config_files(config)
# Set up logging
session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
setup_logging(session_dir)
# Batch process all trials
start = time.time()
for config_dict in config_dicts:
project_dir = os.path.realpath(config_dict.get('project').get('project_dir'))
seq_name = os.path.basename(project_dir)
frame_range = config_dict.get('project').get('frame_range')
frames = ["all frames" if frame_range == [] else f"frames {frame_range[0]} to {frame_range[1]}"][0]
logging.info("\n\n---------------------------------------------------------------------")
logging.info(f"Triangulation of 2D points for {seq_name}, for {frames}.")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nProject directory: {project_dir}")
triangulate_all(config_dict)
end = time.time()
logging.info(f'Associating persons took {end-start:.2f} s.')
def filtering(config=None):
'''
Filter trc 3D coordinates.
config can be a dictionary,
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
from Pose2Sim.filtering import filter_all
if type(config)==dict:
level = 3 # log_dir = os.getcwd()
config_dict = config
if config_dict.get('project').get('project_dir') == None:
raise ValueError('Please specify the project directory in config_dict:\n \
config_dict.get("project").update({"project_dir":"<YOUR_PROJECT_DIRECTORY>"})')
else:
# Determine the level at which the function is called (session:3, participant:2, trial:1)
level, config_dicts = read_config_files(config)
# Set up logging
session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
@ -311,88 +385,15 @@ def personAssociation(config=None):
# Batch process all trials
for config_dict in config_dicts:
project_dir, seq_name, frames = base_params(config_dict)
project_dir = os.path.realpath(config_dict.get('project').get('project_dir'))
seq_name = os.path.basename(project_dir)
frame_range = config_dict.get('project').get('frame_range')
frames = ["all frames" if frame_range == [] else f"frames {frame_range[0]} to {frame_range[1]}"][0]
logging.info("\n\n---------------------------------------------------------------------")
logging.info(f"Tracking of the person of interest for {seq_name}, for {frames}.")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nProject directory: {project_dir}")
start = time.time()
track_2d_all(config_dict)
end = time.time()
logging.info(f'Tracking took {end-start:.2f} s.')
session_dir = os.path.realpath([os.getcwd() if level==3 else os.path.join(os.getcwd(), '..') if level==2 else os.path.join(os.getcwd(), '..', '..')][0])
config_dict.get("project").update({"project_dir":session_dir})
# Path to the calibration directory
calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
logging.info("\n\n---------------------------------------------------------------------")
logging.info("Camera calibration")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nCalibration directory: {calib_dir}")
start = time.time()
calibrate_cams_all(config_dict)
end = time.time()
logging.info(f'Calibration took {end-start:.2f} s.')
def triangulation(config=None):
'''
Robust triangulation of 2D points coordinates.
config can either be a path or a dictionary (for batch processing)
'''
from Pose2Sim.triangulation import triangulate_all
if type(config)==dict:
config_dict = config
else:
config_dict = read_config_files(config)
project_dir, seq_name, frames = base_params(config_dict)
logging.info("\n\n---------------------------------------------------------------------")
logging.info(f"Triangulation of 2D points for {seq_name}, for {frames}.")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nProject directory: {project_dir}")
start = time.time()
triangulate_all(config_dict)
end = time.time()
logging.info(f'Triangulation took {end-start:.2f} s.')
def filtering(config=None):
'''
Filter trc 3D coordinates.
config can either be a path or a dictionary (for batch processing)
'''
from Pose2Sim.filtering import filter_all
if type(config)==dict:
config_dict = config
else:
config_dict = read_config_files(config)
project_dir, seq_name, frames = base_params(config_dict)
logging.info("\n\n---------------------------------------------------------------------")
logging.info(f"Filtering 3D coordinates for {seq_name}, for {frames}.")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nProject directory: {project_dir}")
logging.info("\n\n---------------------------------------------------------------------")
logging.info(f"Filtering 3D coordinates for {seq_name}, for {frames}.")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nProject directory: {project_dir}")
filter_all(config_dict)
@ -401,7 +402,9 @@ def scalingModel(config=None):
'''
Uses OpenSim to scale a model based on a static 3D pose.
config can either be a path or a dictionary (for batch processing)
config can be a dictionary,
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
@ -431,7 +434,9 @@ def inverseKinematics(config=None):
'''
Uses OpenSim to perform inverse kinematics.
config can either be a path or a dictionary (for batch processing)
config can be a dictionary,
or a the directory path of a trial, participant, or session,
or the function can be called without an argument, in which case it the config directory is the current one.
'''
raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')

5
Pose2Sim/filtering.py
View File

@ -444,7 +444,6 @@ def filter_all(config):
# Read config
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
try:
pose_tracked_dir = os.path.join(project_dir, 'pose-associated')
os.listdir(pose_tracked_dir)
@ -452,8 +451,8 @@ def filter_all(config):
except:
pose_dir = os.path.join(project_dir, 'pose')
frame_range = config.get('project').get('frame_range')
seq_name = os.path.basename(project_dir)
pose3d_dir = os.path.join(project_dir, 'pose-3d')
seq_name = os.path.basename(os.path.realpath(project_dir))
pose3d_dir = os.path.realpath(os.path.join(project_dir, 'pose-3d'))
display_figures = config.get('filtering').get('display_figures')
filter_type = config.get('filtering').get('type')

19
Pose2Sim/personAssociation.py
View File

@ -11,7 +11,10 @@
Which is the one of interest?
This module tries all possible triangulations of a chosen anatomical
point, and chooses the person for whom the reprojection error is smallest.
point. If "single_person" mode is used, it chooses the person for whom the
reprojection error is smallest. If multi-person is used, it selects all
persons with a reprojection error smaller than a threshold, and then
associates them across time frames by minimizing the displacement speed.
INPUTS:
- a calibration file (.toml extension)
@ -193,12 +196,12 @@ def recap_tracking(config, error, nb_cams_excluded):
# Read config
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
session_dir = os.path.realpath(os.path.join(project_dir, '..', '..'))
tracked_keypoint = config.get('personAssociation').get('tracked_keypoint')
error_threshold_tracking = config.get('personAssociation').get('error_threshold_tracking')
poseTracked_dir = os.path.join(project_dir, 'pose-associated')
calib_dir = os.path.join(project_dir, 'Calibration')
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0]
calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] # lastly created calibration file
# Error
mean_error_px = np.around(np.mean(error), decimals=1)
@ -215,7 +218,7 @@ def recap_tracking(config, error, nb_cams_excluded):
# Recap
logging.info(f'\n--> Mean reprojection error for {tracked_keypoint} point on all frames is {mean_error_px} px, which roughly corresponds to {mean_error_mm} mm. ')
logging.info(f'--> In average, {mean_cam_off_count} cameras had to be excluded to reach the demanded {error_threshold_tracking} px error threshold.')
logging.info(f'\nTracked json files are stored in {poseTracked_dir}.')
logging.info(f'\nTracked json files are stored in {os.path.realpath(poseTracked_dir)}.')
def track_2d_all(config):
@ -240,13 +243,13 @@ def track_2d_all(config):
# Read config
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
session_dir = os.path.realpath(os.path.join(project_dir, '..', '..'))
pose_model = config.get('pose').get('pose_model')
tracked_keypoint = config.get('personAssociation').get('tracked_keypoint')
frame_range = config.get('project').get('frame_range')
calib_dir = os.path.join(project_dir, 'Calibration')
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0]
calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] # lastly created calibration file
pose_dir = os.path.join(project_dir, 'pose')
poseTracked_dir = os.path.join(project_dir, 'pose-associated')

31
Pose2Sim/triangulation.py
View File

@ -10,12 +10,15 @@
This module triangulates 2D json coordinates and builds a .trc file readable
by OpenSim.
The triangulation is weighted by the likelihood of each detected 2D keypoint,
strives to meet the reprojection error threshold and the likelihood threshold.
Missing values are then interpolated.
The triangulation is weighted by the likelihood of each detected 2D keypoint
(if they meet the likelihood threshold). It the reprojection error is above a
threshold, right and left sides are swapped; if it is still above, a camera
is removed for this point and this frame, until the threshold is met. If more
cameras are removed than a predefined minimum, triangulation is skipped for
the point and this frame. In the end, missing values are interpolated.
In case of multiple subjects detection, make sure you first run the track_2d
module.
In case of multiple subjects detection, make sure you first run the
personAssociation module.
INPUTS:
- a calibration file (.toml extension)
@ -142,9 +145,8 @@ def make_trc(config, Q, keypoints_names, f_range):
# Read config
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
frame_rate = config.get('project').get('frame_rate')
seq_name = os.path.basename(project_dir)
seq_name = os.path.basename(os.path.realpath(project_dir))
pose3d_dir = os.path.join(project_dir, 'pose-3d')
trc_f = f'{seq_name}_{f_range[0]}-{f_range[1]}.trc'
@ -168,7 +170,7 @@ def make_trc(config, Q, keypoints_names, f_range):
#Write file
if not os.path.exists(pose3d_dir): os.mkdir(pose3d_dir)
trc_path = os.path.join(pose3d_dir, trc_f)
trc_path = os.path.realpath(os.path.join(pose3d_dir, trc_f))
with open(trc_path, 'w') as trc_o:
[trc_o.write(line+'\n') for line in header_trc]
Q.to_csv(trc_o, sep='\t', index=True, header=None, lineterminator='\n')
@ -194,9 +196,9 @@ def recap_triangulate(config, error, nb_cams_excluded, keypoints_names, cam_excl
# Read config
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
calib_dir = os.path.join(project_dir, 'Calibration')
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0]
session_dir = os.path.realpath(os.path.join(project_dir, '..', '..'))
calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] # lastly created calibration file
calib = toml.load(calib_file)
cam_names = np.array([calib[c].get('name') for c in list(calib.keys())])
cam_names = cam_names[list(cam_excluded_count.keys())]
@ -405,16 +407,17 @@ def triangulate_all(config):
# Read config
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
session_dir = os.path.realpath(os.path.join(project_dir, '..', '..'))
pose_model = config.get('pose').get('pose_model')
frame_range = config.get('project').get('frame_range')
likelihood_threshold = config.get('triangulation').get('likelihood_threshold')
interpolation_kind = config.get('triangulation').get('interpolation')
interp_gap_smaller_than = config.get('triangulation').get('interp_if_gap_smaller_than')
show_interp_indices = config.get('triangulation').get('show_interp_indices')
calib_dir = [os.path.join(session_dir, c) for c in os.listdir(session_dir) if ('Calib' or 'calib') in c][0]
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0] # lastly created calibration file
pose_dir = os.path.join(project_dir, 'pose')
calib_dir = os.path.join(project_dir, 'Calibration')
calib_file = glob.glob(os.path.join(calib_dir, '*.toml'))[0]
poseTracked_dir = os.path.join(project_dir, 'pose-associated')
# Projection matrix from toml calibration file