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Pose estimation test (#116)

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Edits from @hunminkim98's awesome work at integrating pose estimation into Pose2Sim with RTMLib. Most of the changes in syntax are not necessarily better, it is mostly for the code to be more consistent with the rest of the library. Thank you again for your fantastic work!

General:
- Automatically detects whether a valid CUDA install is available. If so, use the GPU with the ONNXRuntime backend. Otherwise, use the CPU with the OpenVINO backend
- The tensorflow version used for marker augmentation was incompatible with the cuda torch installation for pose estimation: edited code and models for it to work with the latest tf version.
- Added logging information to pose estimation
- Readme.md: provided an installation procedure for CUDA (took me a while to find something simple and robust)
- Readme.md: added information about PoseEstimation with RTMLib
- added poseEstimation to tests.py
- created videos for the multi-person case (used to only have json, no video), and reorganized Demo folders. Had to recreate calibration file as well

Json files:
- the json files only saved one person, I made it save all the detected ones
- tracking was not taken into account by rtmlib, which caused issues in synchronization: fixed, waiting for merge
- took the save_to_openpose function out from the main function
- minified the json files (they take less space when all spaces are removed)

Detection results:
- Compared the triangulated locations of RTMpose keypoints to the ones of OpenPose to potentially edit model marker locations on OpenSim. Did not seem to need it.

Others in Config.toml:
- removed the "to_openpose" option, which is not needed
- added the flag: save_video = 'to_images' # 'to_video' or 'to_images' or ['to_video', 'to_images']
- changed the way frame_range was handled (made me change synchronization in depth, as well as personAssociation and triangulation)
- added the flag: time_range_around_maxspeed in synchronization
- automatically detect framerate from video, or set to 60 fps if we work from images (or give a value)
- frame_range -> time_range
- moved height and weight to project (only read for markerAugmentation, and in the future for automatic scaling)
- removed reorder_trc from triangulation and Config -> call it for markerAugmentation instead

Others:
- Provided an installation procedure for OpenSim (for the future) and made continuous installation check its install (a bit harder since it cannot be installed via pip)
- scaling from motion instead of static pose (will have to study whether it's as good or not)
- added logging to synchronization
- Struggled quite a bit with continuous integration


* Starting point of integrating RTMPose into Pose2Sim. (#111)

* RTM_to_Open

Convert format from RTMPose to OpenPose

* rtm_intergrated

* rtm_integrated

* rtm_integrated

* rtm_integrated

* rtm

* Delete build/lib/Pose2Sim directory

* rtm

* Delete build/lib/Pose2Sim directory

* Delete onnxruntime-gpu

* device = cpu

* add pose folder

* Update tests.py

* added annotation

* fix typo

* Should work be still lots of tests to run. Detailed commit coming soon

* intermediary commit

* last checks before v0.9.0

* Update continuous-integration.yml

* Update tests.py

* replaced tabs with spaces

* unittest issue

* unittest typo

* deactivated display for CI test of pose detection

* Try to make continuous integration work

* a

* b

* c

* d

* e

* f

* g

* h

* i

* j

* k

* l

---------

Co-authored-by: HunMinKim <144449115+hunminkim98@users.noreply.github.com>
This commit is contained in:
David PAGNON 2024-07-09 16:39:33 +02:00 committed by GitHub
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commit b2fe4f7ba3
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1496 changed files with 2013 additions and 19553 deletions
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4
.github/workflows/continuous-integration.yml vendored
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@ -64,6 +64,8 @@ jobs:
conda run -n pose2sim flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- name: Test with pytest
env:
PYTHONIOENCODING: utf-8
run: |
cd Pose2Sim/S00_Demo_BatchSession
cd Pose2Sim/Utilities
conda run -n pose2sim pytest -v tests.py

3
.gitignore vendored
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@ -9,7 +9,10 @@ dist/
**/*.trc
**/*.sto
**/*.c3d
**/*.json
**/Calib_qualisys.toml
**/pose-3d/
**/pose/
**/pose-sync/
**/pose-associated/

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80
Pose2Sim/S01_Demo_SingleTrial/Config.toml → Pose2Sim/Demo_MultiPerson/Config.toml
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@ -18,9 +18,13 @@
[project]
multi_person = false # If false, only the main person in scene is analyzed.
frame_rate = 60 # fps
frame_range = [] # For example [10,300], or [] for all frames
multi_person = true # true for trials with multiple participants. If false, only the main person in scene is analyzed (and it run much faster).
participant_height = [1.72, 1.40] # m # float if single person, list of float if multi-person (same order as the Static trials) # Only used for marker augmentation
participant_mass = [70.0, 63.5] # kg # Only used for marker augmentation and scaling
frame_rate = 'auto' # fps # int or 'auto'. If 'auto', finds from video (or defaults to 60 fps if you work with images)
frame_range = [] # For example [10,300], or [] for all frames.
## If cameras are not synchronized, designates the frame range of the camera with the shortest recording time
## N.B.: If you want a time range instead, use frame_range = time_range * frame_rate
## For example if you want to analyze from 0.1 to 2 seconds with a 60 fps frame rate,
## frame_range = [0.1, 2.0]*frame_rate = [6, 120]
@ -28,26 +32,34 @@ frame_range = [] # For example [10,300], or [] for all frames
exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
[pose]
pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut'
pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII.
#With mediapipe: BLAZEPOSE.
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133.
#With deeplabcut: CUSTOM. See example at the end of the file.
# What follows has not been implemented yet
overwrite_pose = false
openpose_path = '' # only checked if OpenPose is used
vid_img_extension = 'mp4' # any video or image extension
pose_model = 'HALPE_26' #With RTMLib: HALPE_26 (default, body and feet), COCO_133 (body, feet, hands), COCO_17 (body)
# /!\ Only RTMPose is natively embeded in Pose2Sim. For all other pose estimation methods, you will have to run them yourself, and then refer to the documentation to convert the files if needed
#With MMPose: HALPE_26 (default), COCO_133, COCO_17, CUSTOM. See example at the end of the file
#With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
#With mediapipe: BLAZEPOSE
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133
#With deeplabcut: CUSTOM. See example at the end of the file
mode = 'balanced' # 'lightweight', 'balanced', 'performance'
det_frequency = 1 # Run person detection only every N frames, and inbetween track previously detected bounding boxes ( (still run keypoint detection on all frame).
#Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate.
tracking = true # Gives consistent person ID across frames. Slightly slower but might facilitate synchronization if other people are in the background
display_detection = true
save_video = 'to_video' # 'to_video' or 'to_images', 'none', or ['to_video', 'to_images']
output_format = 'openpose' # 'openpose', 'mmpose', 'deeplabcut', 'none' or a list of them # /!\ only 'openpose' is supported for now
[synchronization]
display_sync_plots = true # true or false (lowercase)
keypoints_to_consider = 'all' # 'all' if all points should be considered (default)
# ['RWrist', 'RElbow'] list of keypoint names if you want to specify the keypoints to consider
keypoints_to_consider = 'all' # 'all' if all points should be considered, for example if the participant did not perform any particicular sharp movement. In this case, the capture needs to be 5-10 seconds long at least
# ['RWrist', 'RElbow'] list of keypoint names if you want to specify the keypoints to consider.
approx_time_maxspeed = 'auto' # 'auto' if you want to consider the whole capture (default, slower if long sequences)
# [1.0, 0.2, 0.8, 1.1] list of times in seconds, one value per camera if you want to specify an approximate time where to search for synchronization.
# Best times: only one person is in the scene, who performs a sharp vertical movement (with keypoints_to_consider).
reset_sync = false # Revert to state before synchronization. Won't attempt to synchronize unless set to false.
# [10.0, 2.0, 8.0, 11.0] list of times in seconds, one value per camera if you want to specify the approximate time of a clear vertical event by one person standing alone in the scene
time_range_around_maxspeed = 2.0 # Search for best correlation in the range [approx_time_maxspeed - time_range_around_maxspeed, approx_time_maxspeed + time_range_around_maxspeed]
likelihood_threshold = 0.4 # Keypoints whose likelihood is below likelihood_threshold are filtered out
filter_cutoff = 6 # time series are smoothed to get coherent time-lagged correlation
filter_order = 4
# Take heart, calibration is not that complicated once you get the hang of it!
@ -70,7 +82,7 @@ calibration_type = 'convert' # 'convert' or 'calculate'
[calibration.calculate]
# Camera properties, theoretically need to be calculated only once in a camera lifetime
[calibration.calculate.intrinsics]
overwrite_intrinsics = true # set to false if you don't want to recalculate intrinsic parameters
overwrite_intrinsics = false # set to false if you don't want to recalculate intrinsic parameters
show_detection_intrinsics = true # true or false (lowercase)
intrinsics_extension = 'jpg' # any video or image extension
extract_every_N_sec = 1 # if video, extract frames every N seconds (can be <1 )
@ -116,7 +128,7 @@ calibration_type = 'convert' # 'convert' or 'calculate'
[personAssociation.single_person]
reproj_error_threshold_association = 20 # px
tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
# and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
# and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' or 'RShoulder')
[personAssociation.multi_person]
reconstruction_error_threshold = 0.1 # 0.1 = 10 cm
@ -134,13 +146,13 @@ interp_if_gap_smaller_than = 10 # do not interpolate bigger gaps
show_interp_indices = true # true or false (lowercase). For each keypoint, return the frames that need to be interpolated
handle_LR_swap = false # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
undistort_points = false # Better if distorted image (parallel lines curvy on the edge or at least one param > 10^-2), but unnecessary (and slightly slower) if distortions are low
make_c3d = false # save triangulated data in c3d format in addition to trc # Coming soon!
make_c3d = false # save triangulated data in c3d format in addition to trc
[filtering]
type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
display_figures = false # true or false (lowercase)
make_c3d = false # save triangulated data in c3d format in addition to trc
make_c3d = false # also save triangulated data in c3d format
[filtering.butterworth]
order = 4
@ -161,9 +173,9 @@ make_c3d = false # save triangulated data in c3d format in addition to trc
[markerAugmentation]
## Only works on BODY_25 and BODY_25B models
participant_height = 1.72 # m # float if single person, list of float if multi-person (same order as the Static trials)
participant_mass = 70.0 # kg
## Requires the following markers: ["Neck", "RShoulder", "LShoulder", "RHip", "LHip", "RKnee", "LKnee",
## "RAnkle", "LAnkle", "RHeel", "LHeel", "RSmallToe", "LSmallToe",
## "RBigToe", "LBigToe", "RElbow", "LElbow", "RWrist", "LWrist"]
make_c3d = false # save triangulated data in c3d format in addition to trc
@ -176,20 +188,20 @@ opensim_bin_path = 'C:\OpenSim 4.4\bin'
# CUSTOM skeleton, if you trained your own DeepLabCut model for example.
# CUSTOM skeleton, if you trained your own model from DeepLabCut or MMPose for example.
# Make sure the node ids correspond to the column numbers of the 2D pose file, starting from zero.
#
# If you want to perform inverse kinematics, you will also need to create an OpenSim model
# and add to its markerset the location where you expect the triangulated keypoints to be detected.
#
# In this example, CUSTOM reproduces the BODY_25B skeleton (default skeletons are stored in skeletons.py).
# In this example, CUSTOM reproduces the HALPE_26 skeleton (default skeletons are stored in skeletons.py).
# You can create as many custom skeletons as you want, just add them further down and rename them.
#
# Check your model hierarchy with: for pre, _, node in RenderTree(model):
# print(f'{pre}{node.name} id={node.id}')
[pose.CUSTOM]
name = "CHip"
id = "None"
id = "19"
[[pose.CUSTOM.children]]
name = "RHip"
id = 12
@ -201,13 +213,13 @@ id = "None"
id = 16
[[pose.CUSTOM.children.children.children.children]]
name = "RBigToe"
id = 22
id = 21
[[pose.CUSTOM.children.children.children.children.children]]
name = "RSmallToe"
id = 23
[[pose.CUSTOM.children.children.children.children]]
name = "RHeel"
id = 24
id = 25
[[pose.CUSTOM.children]]
name = "LHip"
id = 11
@ -219,19 +231,19 @@ id = "None"
id = 15
[[pose.CUSTOM.children.children.children.children]]
name = "LBigToe"
id = 19
id = 20
[[pose.CUSTOM.children.children.children.children.children]]
name = "LSmallToe"
id = 20
id = 22
[[pose.CUSTOM.children.children.children.children]]
name = "LHeel"
id = 21
id = 24
[[pose.CUSTOM.children]]
name = "Neck"
id = 17
id = 18
[[pose.CUSTOM.children.children]]
name = "Head"
id = 18
id = 17
[[pose.CUSTOM.children.children.children]]
name = "Nose"
id = 0

8
Pose2Sim/S01_Demo_SingleTrial/calibration/Calib.qca.txt → Pose2Sim/Demo_MultiPerson/calibration/Calib.qca.txt
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@ -8,7 +8,7 @@
<fov_video bottom="1919" left="0" right="1087" top="0"/>
<fov_video_max bottom="1919" left="0" right="1087" top="0"/>
<transform r11="0.5536380477336265" r12="0.8046719867383512" r13="0.2144479094331626" r21="-0.4345300743838753" r22="0.05946489349975602" r23="0.8986921279821063" r31="0.7104002579937896" r32="-0.5907342212870501" r33="0.3825762057979294" x="1460.2323709212087" y="-1909.1590482454608" z="1896.5058524092062"/>
<intrinsic centerPointU="34110.316406" centerPointV="60680.792969" focalLengthU="107599.671875" focalLengthV="107588.828125" focallength="9.314096596679686" radialDistortion1="-0.046183" radialDistortion2="0.139983" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="0.000608" tangentalDistortion2="0.00069"/>
<intrinsic centerPointU="35134.316406" centerPointV="61960.792969" focalLengthU="107599.671875" focalLengthV="107588.828125" focallength="9.314096596679686" radialDistortion1="-0.046183" radialDistortion2="0.139983" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="0.000608" tangentalDistortion2="0.00069"/>
</camera>
<camera active="1" avg-residual="0.444749" model="none" point-count="999999999" serial="cam_02" viewrotation="0">
<fov_marker bottom="1919" left="0" right="1087" top="0"/>
@ -16,7 +16,7 @@
<fov_video bottom="1919" left="0" right="1087" top="0"/>
<fov_video_max bottom="1919" left="0" right="1087" top="0"/>
<transform r11="-0.1966832217091926" r12="0.979523227309506" r13="-0.043011131806342306" r21="-0.31478107085300017" r22="-0.02153908797812718" r23="0.9489198834051846" r31="0.9285626460991909" r32="0.20017570994064315" r33="0.3125717476340885" x="2582.0136248568124" y="706.5662881637625" z="1690.9818366061595"/>
<intrinsic centerPointU="34207.652344" centerPointV="61646.457031" focalLengthU="107118.695313" focalLengthV="107123.023438" focallength="9.272462063031563" radialDistortion1="-0.047847" radialDistortion2="0.136786" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="0.000972" tangentalDistortion2="0.000291"/>
<intrinsic centerPointU="35359.652344" centerPointV="62158.457031" focalLengthU="107118.695313" focalLengthV="107123.023438" focallength="9.272462063031563" radialDistortion1="-0.047847" radialDistortion2="0.136786" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="0.000972" tangentalDistortion2="0.000291"/>
</camera>
<camera active="1" avg-residual="0.450323" model="none" point-count="999999999" serial="cam_03" viewrotation="0">
<fov_marker bottom="1919" left="0" right="1087" top="0"/>
@ -24,7 +24,7 @@
<fov_video bottom="1919" left="0" right="1087" top="0"/>
<fov_video_max bottom="1919" left="0" right="1087" top="0"/>
<transform r11="-0.740641130147863" r12="-0.6704153360235378" r13="-0.044654154988568895" r21="0.25251118212268814" r22="-0.3393170985114385" r23="0.9061467925015139" r31="-0.6226466246887545" r32="0.6598539110153292" r33="0.4205995683324882" x="-3216.86586729648" y="2231.1786368869416" z="2088.19103027083"/>
<intrinsic centerPointU="32845.335938" centerPointV="61120.328125" focalLengthU="107622.296875" focalLengthV="107616.632813" focallength="9.316055073242188" radialDistortion1="-0.046705" radialDistortion2="0.137622" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="-0.000542" tangentalDistortion2="-0.000517"/>
<intrinsic centerPointU="34893.335938" centerPointV="61440.328125" focalLengthU="107622.296875" focalLengthV="107616.632813" focallength="9.316055073242188" radialDistortion1="-0.046705" radialDistortion2="0.137622" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="-0.000542" tangentalDistortion2="-0.000517"/>
</camera>
<camera active="1" avg-residual="0.504772" model="none" point-count="999999999" serial="cam_04" viewrotation="0">
<fov_marker bottom="1919" left="0" right="1087" top="0"/>
@ -32,7 +32,7 @@
<fov_video bottom="1919" left="0" right="1087" top="0"/>
<fov_video_max bottom="1919" left="0" right="1087" top="0"/>
<transform r11="0.2586331447643361" r12="-0.875095795920923" r13="-0.40904308378315923" r21="0.49590579729377593" r22="-0.24310718522469382" r23="0.8336524076031634" r31="-0.8289670298870364" r32="-0.4184569804097585" r33="0.3710895025551282" x="-3758.720480260634" y="-1415.6654017496533" z="1881.7941041388026"/>
<intrinsic centerPointU="34566.796875" centerPointV="61697.9375" focalLengthU="107215.039063" focalLengthV="107213.070313" focallength="9.280801818890938" radialDistortion1="-0.047633" radialDistortion2="0.134667" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="0.000277" tangentalDistortion2="0.000199"/>
<intrinsic centerPointU="35014.796875" centerPointV="61697.9375" focalLengthU="107215.039063" focalLengthV="107213.070313" focallength="9.280801818890938" radialDistortion1="-0.047633" radialDistortion2="0.134667" radialDistortion3="0.000000" sensorMaxU="69568" sensorMaxV="122816" sensorMinU="0.000000" sensorMinV="0.000000" skew="0.000000" tangentalDistortion1="0.000277" tangentalDistortion2="0.000199"/>
</camera>
</cameras>
</calibration>

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Pose2Sim/S00_Demo_BatchSession/Config.toml → Pose2Sim/Demo_SinglePerson/Config.toml
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@ -19,8 +19,12 @@
[project]
multi_person = false # true for trials with multiple participants. If false, only the main person in scene is analyzed (and it run much faster).
frame_rate = 60 # fps
frame_range = [] # For example [10,300], or [] for all frames
participant_height = 1.72 # m # float if single person, list of float if multi-person (same order as the Static trials) # Only used for marker augmentation
participant_mass = 70.0 # kg # Only used for marker augmentation and scaling
frame_rate = 'auto' # fps # int or 'auto'. If 'auto', finds from video (or defaults to 60 fps if you work with images)
frame_range = [] # For example [10,300], or [] for all frames.
## If cameras are not synchronized, designates the frame range of the camera with the shortest recording time
## N.B.: If you want a time range instead, use frame_range = time_range * frame_rate
## For example if you want to analyze from 0.1 to 2 seconds with a 60 fps frame rate,
## frame_range = [0.1, 2.0]*frame_rate = [6, 120]
@ -28,26 +32,34 @@ frame_range = [] # For example [10,300], or [] for all frames
exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
[pose]
pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut'
pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII.
#With mediapipe: BLAZEPOSE.
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133.
#With deeplabcut: CUSTOM. See example at the end of the file.
# What follows has not been implemented yet
overwrite_pose = false
openpose_path = '' # only checked if OpenPose is used
vid_img_extension = 'mp4' # any video or image extension
pose_model = 'HALPE_26' #With RTMLib: HALPE_26 (default, body and feet), COCO_133 (body, feet, hands), COCO_17 (body)
# /!\ Only RTMPose is natively embeded in Pose2Sim. For all other pose estimation methods, you will have to run them yourself, and then refer to the documentation to convert the files if needed
#With MMPose: HALPE_26 (default), COCO_133, COCO_17, CUSTOM. See example at the end of the file
#With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
#With mediapipe: BLAZEPOSE
#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133
#With deeplabcut: CUSTOM. See example at the end of the file
mode = 'balanced' # 'lightweight', 'balanced', 'performance'
det_frequency = 1 # Run person detection only every N frames, and inbetween track previously detected bounding boxes ( (still run keypoint detection on all frame).
#Equal to or greater than 1, can be as high as you want in simple uncrowded cases. Much faster, but might be less accurate.
tracking = true # Gives consistent person ID across frames. Slightly slower but might facilitate synchronization if other people are in the background
display_detection = true
save_video = 'to_video' # 'to_video' or 'to_images', 'none', or ['to_video', 'to_images']
output_format = 'openpose' # 'openpose', 'mmpose', 'deeplabcut', 'none' or a list of them # /!\ only 'openpose' is supported for now
[synchronization]
display_sync_plots = true # true or false (lowercase)
keypoints_to_consider = 'all' # 'all' if all points should be considered (default)
# ['RWrist', 'RElbow'] list of keypoint names if you want to specify the keypoints to consider
keypoints_to_consider = ['RWrist'] # 'all' if all points should be considered, for example if the participant did not perform any particicular sharp movement. In this case, the capture needs to be 5-10 seconds long at least
# ['RWrist', 'RElbow'] list of keypoint names if you want to specify the keypoints to consider.
approx_time_maxspeed = 'auto' # 'auto' if you want to consider the whole capture (default, slower if long sequences)
# [1.0, 0.2, 0.8, 1.1] list of times in seconds, one value per camera if you want to specify an approximate time where to search for synchronization.
# Best times: only one person is in the scene, who performs a sharp vertical movement (with keypoints_to_consider).
reset_sync = false # Revert to state before synchronization. Won't attempt to synchronize unless set to false.
# [10.0, 2.0, 8.0, 11.0] list of times in seconds, one value per camera if you want to specify the approximate time of a clear vertical event by one person standing alone in the scene
time_range_around_maxspeed = 2.0 # Search for best correlation in the range [approx_time_maxspeed - time_range_around_maxspeed, approx_time_maxspeed + time_range_around_maxspeed]
likelihood_threshold = 0.4 # Keypoints whose likelihood is below likelihood_threshold are filtered out
filter_cutoff = 6 # time series are smoothed to get coherent time-lagged correlation
filter_order = 4
# Take heart, calibration is not that complicated once you get the hang of it!
@ -116,7 +128,7 @@ calibration_type = 'convert' # 'convert' or 'calculate'
[personAssociation.single_person]
reproj_error_threshold_association = 20 # px
tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
# and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
# and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' or 'RShoulder')
[personAssociation.multi_person]
reconstruction_error_threshold = 0.1 # 0.1 = 10 cm
@ -134,13 +146,13 @@ interp_if_gap_smaller_than = 10 # do not interpolate bigger gaps
show_interp_indices = true # true or false (lowercase). For each keypoint, return the frames that need to be interpolated
handle_LR_swap = false # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
undistort_points = false # Better if distorted image (parallel lines curvy on the edge or at least one param > 10^-2), but unnecessary (and slightly slower) if distortions are low
make_c3d = false # save triangulated data in c3d format in addition to trc
make_c3d = true # save triangulated data in c3d format in addition to trc
[filtering]
type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
display_figures = false # true or false (lowercase)
make_c3d = false # also save triangulated data in c3d format
make_c3d = true # also save triangulated data in c3d format
[filtering.butterworth]
order = 4
@ -161,9 +173,9 @@ make_c3d = false # also save triangulated data in c3d format
[markerAugmentation]
## Only works on BODY_25 and BODY_25B models
participant_height = 1.72 # m # float if single person, list of float if multi-person (same order as the Static trials)
participant_mass = 70.0 # kg
## Requires the following markers: ["Neck", "RShoulder", "LShoulder", "RHip", "LHip", "RKnee", "LKnee",
## "RAnkle", "LAnkle", "RHeel", "LHeel", "RSmallToe", "LSmallToe",
## "RBigToe", "LBigToe", "RElbow", "LElbow", "RWrist", "LWrist"]
make_c3d = false # save triangulated data in c3d format in addition to trc
@ -176,20 +188,20 @@ opensim_bin_path = 'C:\OpenSim 4.4\bin'
# CUSTOM skeleton, if you trained your own DeepLabCut model for example.
# CUSTOM skeleton, if you trained your own model from DeepLabCut or MMPose for example.
# Make sure the node ids correspond to the column numbers of the 2D pose file, starting from zero.
#
# If you want to perform inverse kinematics, you will also need to create an OpenSim model
# and add to its markerset the location where you expect the triangulated keypoints to be detected.
#
# In this example, CUSTOM reproduces the BODY_25B skeleton (default skeletons are stored in skeletons.py).
# In this example, CUSTOM reproduces the HALPE_26 skeleton (default skeletons are stored in skeletons.py).
# You can create as many custom skeletons as you want, just add them further down and rename them.
#
# Check your model hierarchy with: for pre, _, node in RenderTree(model):
# print(f'{pre}{node.name} id={node.id}')
[pose.CUSTOM]
name = "CHip"
id = "None"
id = "19"
[[pose.CUSTOM.children]]
name = "RHip"
id = 12
@ -201,13 +213,13 @@ id = "None"
id = 16
[[pose.CUSTOM.children.children.children.children]]
name = "RBigToe"
id = 22
id = 21
[[pose.CUSTOM.children.children.children.children.children]]
name = "RSmallToe"
id = 23
[[pose.CUSTOM.children.children.children.children]]
name = "RHeel"
id = 24
id = 25
[[pose.CUSTOM.children]]
name = "LHip"
id = 11
@ -219,19 +231,19 @@ id = "None"
id = 15
[[pose.CUSTOM.children.children.children.children]]
name = "LBigToe"
id = 19
id = 20
[[pose.CUSTOM.children.children.children.children.children]]
name = "LSmallToe"
id = 20
id = 22
[[pose.CUSTOM.children.children.children.children]]
name = "LHeel"
id = 21
id = 24
[[pose.CUSTOM.children]]
name = "Neck"
id = 17
id = 18
[[pose.CUSTOM.children.children]]
name = "Head"
id = 18
id = 17
[[pose.CUSTOM.children.children.children]]
name = "Nose"
id = 0

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@ -1 +1,243 @@
{"class_name": "Sequential", "config": {"name": "sequential_2", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 47], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_8_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_8", "trainable": true, "dtype": "float32", "batch_input_shape": [null, null, 47], "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_9", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_10", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_11", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_2", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 105, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.12.0", "backend": "tensorflow"}
{
"class_name": "Sequential",
"config": {
"name": "sequential_2",
"layers": [
{
"class_name": "InputLayer",
"config": {
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null,
47
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"dtype": "float32",
"sparse": false,
"ragged": false,
"name": "lstm_8_input"
}
},
{
"class_name": "LSTM",
"config": {
"name": "lstm_8",
"trainable": true,
"dtype": "float32",
"batch_input_shape": [
null,
null,
47
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"return_sequences": true,
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"bias_initializer": {
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"unit_forget_bias": true,
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},
{
"class_name": "LSTM",
"config": {
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"recurrent_initializer": {
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"bias_initializer": {
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"dropout": 0.0,
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"implementation": 2
}
},
{
"class_name": "LSTM",
"config": {
"name": "lstm_10",
"trainable": true,
"dtype": "float32",
"return_sequences": true,
"return_state": false,
"go_backwards": false,
"stateful": false,
"unroll": false,
"units": 128,
"activation": "tanh",
"recurrent_activation": "sigmoid",
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"recurrent_initializer": {
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"bias_initializer": {
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"unit_forget_bias": true,
"kernel_regularizer": null,
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"activity_regularizer": null,
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"dropout": 0.0,
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{
"class_name": "LSTM",
"config": {
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"return_sequences": true,
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"trainable": true,
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"layer": {
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"config": {
"name": "dense_2",
"trainable": true,
"dtype": "float32",
"units": 105,
"activation": "linear",
"use_bias": true,
"kernel_initializer": {
"class_name": "GlorotUniform",
"config": {
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"bias_initializer": {
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"keras_version": "2.12.0",
"backend": "tensorflow"
}

289
Pose2Sim/MarkerAugmenter/LSTM/v0.3_upper/model.json
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File diff suppressed because one or more lines are too long

4
Pose2Sim/MarkerAugmenter/utilsDataman.py
View File

@ -97,8 +97,8 @@ class TRCFile(object):
dtype = {'names': col_names,
'formats': ['int'] + ['float64'] * (3 * self.num_markers + 1)}
usecols = [i for i in range(3 * self.num_markers + 1 + 1)]
self.data = np.loadtxt(fpath, delimiter='\t', skiprows=5, dtype=dtype,
usecols=usecols)
self.data = np.genfromtxt(fpath, delimiter='\t', skip_header=5, dtype=dtype,
usecols=usecols, filling_values=np.nan)
self.time = self.data['time']
# Check the number of rows.

0
Pose2Sim/OpenSim_Setup/IK_Setup_Pose2Sim_Coco18.xml → Pose2Sim/OpenSim_Setup/IK_Setup_Pose2Sim_Coco17.xml
View File

8
Pose2Sim/OpenSim_Setup/IK_Setup_Pose2Sim_Halpe26.xml
View File

@ -1,5 +1,5 @@
<?xml version="1.0" encoding="UTF-8" ?>
<OpenSimDocument Version="40000">
<OpenSimDocument Version="40500">
<InverseKinematicsTool>
<!--Name of the directory where results are written. Be default this is the directory in which the setup file is be executed.-->
<results_directory>./</results_directory>
@ -10,9 +10,9 @@
<!--The accuracy of the solution in absolute terms, i.e. the number of significant digits to which the solution can be trusted. Default 1e-5.-->
<accuracy>1.0000000000000001e-05</accuracy>
<!--The time range for the study.-->
<time_range>0.016666666666666666 1.6666666666666667</time_range>
<time_range>0.016666666666666666 1.6333333333333333</time_range>
<!--Name of the resulting inverse kinematics motion (.mot) file.-->
<output_motion_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_OpenSim/S00_P00_T01_BalancingTrial_filt_butterworth_0-100_halpe26.mot</output_motion_file>
<output_motion_file>../Demo_SinglePerson/pose-3d/S01_Demo_SingleTrial_0-99_filt_butterworth.mot</output_motion_file>
<!--Flag (true or false) indicating whether or not to report errors from the inverse kinematics solution. Default is true.-->
<report_errors>true</report_errors>
<!--Markers and coordinates to be considered (tasks) and their weightings. The sum of weighted-squared task errors composes the cost function.-->
@ -164,7 +164,7 @@
<groups />
</IKTaskSet>
<!--TRC file (.trc) containing the time history of observations of marker positions obtained during a motion capture experiment. Markers in this file that have a corresponding task and model marker are included.-->
<marker_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_T01_BalancingTrial/pose-3d/S00_P00_T01_BalancingTrial_0-100_filt_butterworth.trc</marker_file>
<marker_file>../Demo_SinglePerson/pose-3d/S01_Demo_SingleTrial_0-99_filt_butterworth.trc</marker_file>
<!--The name of the storage (.sto or .mot) file containing the time history of coordinate observations. Coordinate values from this file are included if there is a corresponding model coordinate and task. -->
<coordinate_file>Unassigned</coordinate_file>
<!--Flag indicating whether or not to report model marker locations. Note, model marker locations are expressed in Ground.-->

0
Pose2Sim/OpenSim_Setup/Model_Pose2Sim_Coco18.osim → Pose2Sim/OpenSim_Setup/Model_Pose2Sim_Coco17.osim
View File

0
Pose2Sim/OpenSim_Setup/Scaling_Setup_Pose2Sim_Coco18.xml → Pose2Sim/OpenSim_Setup/Scaling_Setup_Pose2Sim_Coco17.xml
View File

30
Pose2Sim/OpenSim_Setup/Scaling_Setup_Pose2Sim_Halpe26.xml
View File

@ -1,5 +1,5 @@
<?xml version="1.0" encoding="UTF-8" ?>
<OpenSimDocument Version="40000">
<OpenSimDocument Version="40500">
<ScaleTool name="Pose2Sim_scaled">
<!--Mass of the subject in kg. Subject-specific model generated by scaling step will have this total mass.-->
<mass>69</mass>
@ -336,47 +336,47 @@
<ScaleSet>
<objects>
<Scale>
<scales> 0.95 0.95 0.95</scales>
<scales> 0.94999999999999995559 0.94999999999999995559 0.94999999999999995559</scales>
<segment>pelvis</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.85 0.85 0.85</scales>
<scales> 0.8499999999999999778 0.8499999999999999778 0.8499999999999999778</scales>
<segment>patella_r</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.85 0.85 0.85</scales>
<scales> 0.8499999999999999778 0.8499999999999999778 0.8499999999999999778</scales>
<segment>talus_r</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.85 0.85 0.85</scales>
<scales> 0.8499999999999999778 0.8499999999999999778 0.8499999999999999778</scales>
<segment>toes_r</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.85 0.85 0.85</scales>
<scales> 0.8499999999999999778 0.8499999999999999778 0.8499999999999999778</scales>
<segment>patella_l</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.85 0.85 0.85</scales>
<scales> 0.8499999999999999778 0.8499999999999999778 0.8499999999999999778</scales>
<segment>talus_l</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.85 0.85 0.85</scales>
<scales> 0.8499999999999999778 0.8499999999999999778 0.8499999999999999778</scales>
<segment>toes_l</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.8 0.8 0.8</scales>
<scales> 0.80000000000000004441 0.80000000000000004441 0.80000000000000004441</scales>
<segment>torso</segment>
<apply>false</apply>
</Scale>
<Scale>
<scales> 0.8 0.8 0.8</scales>
<scales> 0.80000000000000004441 0.80000000000000004441 0.80000000000000004441</scales>
<segment>head</segment>
<apply>false</apply>
</Scale>
@ -384,9 +384,9 @@
<groups />
</ScaleSet>
<!--TRC file (.trc) containing the marker positions used for measurement-based scaling. This is usually a static trial, but doesn't need to be. The marker-pair distances are computed for each time step in the TRC file and averaged across the time range.-->
<marker_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose-3d/S00_P00_T00_StaticTrial_0-12.trc</marker_file>
<marker_file>../Demo_SinglePerson/pose-3d/S01_Demo_SingleTrial_0-99_filt_butterworth.trc</marker_file>
<!--Time range over which to average marker-pair distances in the marker file (.trc) for measurement-based scaling.-->
<time_range> 0.0166667 0.1</time_range>
<time_range> 0 1.6333333333333333037</time_range>
<!--Flag (true or false) indicating whether or not to preserve relative mass between segments.-->
<preserve_mass_distribution>true</preserve_mass_distribution>
<!--Name of OpenSim model file (.osim) to write when done scaling.-->
@ -587,15 +587,15 @@
<groups />
</IKTaskSet>
<!--TRC file (.trc) containing the time history of experimental marker positions (usually a static trial).-->
<marker_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose-3d/S00_P00_T00_StaticTrial_0-12.trc</marker_file>
<marker_file>../Demo_SinglePerson/pose-3d/S01_Demo_SingleTrial_0-99_filt_butterworth.trc</marker_file>
<!--Name of file containing the joint angles used to set the initial configuration of the model for the purpose of placing the markers. These coordinate values can also be included in the optimization problem used to place the markers. Before the model markers are placed, a single frame of an inverse kinematics (IK) problem is solved. The IK problem can be solved simply by matching marker positions, but if the model markers are not in the correct locations, the IK solution will not be very good and neither will marker placement. Alternatively, coordinate values (specified in this file) can be specified and used to influence the IK solution. This is valuable particularly if you have high confidence in the coordinate values. For example, you know for the static trial the subject was standing will all joint angles close to zero. If the coordinate set (see the CoordinateSet property) contains non-zero weights for coordinates, the IK solution will try to match not only the marker positions, but also the coordinates in this file. Least-squared error is used to solve the IK problem. -->
<coordinate_file>Unassigned</coordinate_file>
<!--Time range over which the marker positions are averaged.-->
<time_range> 0.0166667 0.1</time_range>
<time_range> 0.016666699999999999571 0.10000000000000000555</time_range>
<!--Name of the motion file (.mot) written after marker relocation (optional).-->
<output_motion_file>Unassigned</output_motion_file>
<!--Output OpenSim model file (.osim) after scaling and maker placement.-->
<output_model_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_OpenSim/Model_Pose2Sim_S00_P00_Halpe26_scaled.osim</output_model_file>
<output_model_file>D:\softs\github_david\pose2sim\Pose2Sim\OpenSim_Setup\..\S00_Demo_Session\S00_P00_SingleParticipant\S00_P00_OpenSim\Model_Pose2Sim_S00_P00_Halpe26_scaled.osim</output_model_file>
<!--Output marker set containing the new marker locations after markers have been placed.-->
<output_marker_file>Unassigned</output_marker_file>
<!--Maximum amount of movement allowed in marker data when averaging frames of the static trial. A negative value means there is not limit.-->

40
Pose2Sim/OpenSim_Setup/Scaling_Setup_Pose2Sim_LSTM.xml
View File

@ -1,5 +1,5 @@
<?xml version="1.0" encoding="UTF-8" ?>
<OpenSimDocument Version="40000">
<OpenSimDocument Version="40500">
<ScaleTool name="Pose2Sim_scaled">
<!--Mass of the subject in kg. Subject-specific model generated by scaling step will have this total mass.-->
<mass>69</mass>
@ -12,7 +12,7 @@
<!--Specifies the name of the unscaled model (.osim) and the marker set.-->
<GenericModelMaker>
<!--Model file (.osim) for the unscaled model.-->
<model_file>Model_Pose2Sim_LSTM.osim</model_file>
<model_file>Unassigned</model_file>
<!--Set of model markers used to scale the model. Scaling is done based on distances between model markers compared to the same distances between the corresponding experimental markers.-->
<marker_set_file>Unassigned</marker_set_file>
</GenericModelMaker>
@ -21,7 +21,7 @@
<!--Whether or not to use the model scaler during scale-->
<apply>true</apply>
<!--Specifies the scaling method and order. Valid options are 'measurements', 'manualScale', singly or both in any sequence.-->
<scaling_order> measurements manualScale</scaling_order>
<scaling_order> measurements</scaling_order>
<!--Specifies the measurements by which body segments are to be scaled.-->
<MeasurementSet>
<objects>
@ -426,9 +426,9 @@
<groups />
</ScaleSet>
<!--TRC file (.trc) containing the marker positions used for measurement-based scaling. This is usually a static trial, but doesn't need to be. The marker-pair distances are computed for each time step in the TRC file and averaged across the time range.-->
<marker_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose-3d/S00_P00_T00_StaticTrial_0-12_filt_butterworth_LSTM.trc</marker_file>
<marker_file>../Demo_SinglePerson/pose-3d/S01_Demo_SingleTrial_0-99_filt_butterworth_LSTM.trc</marker_file>
<!--Time range over which to average marker-pair distances in the marker file (.trc) for measurement-based scaling.-->
<time_range> 0.0166663666666666666 0.4166666666666667 </time_range>
<time_range> 0 1.633333300000000099</time_range>
<!--Flag (true or false) indicating whether or not to preserve relative mass between segments.-->
<preserve_mass_distribution>true</preserve_mass_distribution>
<!--Name of OpenSim model file (.osim) to write when done scaling.-->
@ -467,18 +467,6 @@
<!--Weight given to the task when solving inverse kinematics problems, default is 0.-->
<weight>10</weight>
</IKMarkerTask>
<IKMarkerTask name="RHJC_study">
<!--Whether or not this task will be used during inverse kinematics solve, default is true.-->
<apply>true</apply>
<!--Weight given to the task when solving inverse kinematics problems, default is 0.-->
<weight>10</weight>
</IKMarkerTask>
<IKMarkerTask name="LHJC_study">
<!--Whether or not this task will be used during inverse kinematics solve, default is true.-->
<apply>true</apply>
<!--Weight given to the task when solving inverse kinematics problems, default is 0.-->
<weight>10</weight>
</IKMarkerTask>
<IKMarkerTask name="r_knee_study">
<!--Whether or not this task will be used during inverse kinematics solve, default is true.-->
<apply>true</apply>
@ -617,6 +605,18 @@
<!--Weight given to the task when solving inverse kinematics problems, default is 0.-->
<weight>5</weight>
</IKMarkerTask>
<IKMarkerTask name="RHJC_study">
<!--Whether or not this task will be used during inverse kinematics solve, default is true.-->
<apply>true</apply>
<!--Weight given to the task when solving inverse kinematics problems, default is 0.-->
<weight>10</weight>
</IKMarkerTask>
<IKMarkerTask name="LHJC_study">
<!--Whether or not this task will be used during inverse kinematics solve, default is true.-->
<apply>true</apply>
<!--Weight given to the task when solving inverse kinematics problems, default is 0.-->
<weight>10</weight>
</IKMarkerTask>
<IKCoordinateTask name="pelvis_tilt">
<!--Whether or not this task will be used during inverse kinematics solve, default is true.-->
<apply>true</apply>
@ -671,15 +671,15 @@
<groups />
</IKTaskSet>
<!--TRC file (.trc) containing the time history of experimental marker positions (usually a static trial).-->
<marker_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose-3d/S00_P00_T00_StaticTrial_0-12_filt_butterworth_LSTM.trc</marker_file>
<marker_file>../Demo_SinglePerson/pose-3d/S01_Demo_SingleTrial_0-99_filt_butterworth_LSTM.trc</marker_file>
<!--Name of file containing the joint angles used to set the initial configuration of the model for the purpose of placing the markers. These coordinate values can also be included in the optimization problem used to place the markers. Before the model markers are placed, a single frame of an inverse kinematics (IK) problem is solved. The IK problem can be solved simply by matching marker positions, but if the model markers are not in the correct locations, the IK solution will not be very good and neither will marker placement. Alternatively, coordinate values (specified in this file) can be specified and used to influence the IK solution. This is valuable particularly if you have high confidence in the coordinate values. For example, you know for the static trial the subject was standing will all joint angles close to zero. If the coordinate set (see the CoordinateSet property) contains non-zero weights for coordinates, the IK solution will try to match not only the marker positions, but also the coordinates in this file. Least-squared error is used to solve the IK problem. -->
<coordinate_file>Unassigned</coordinate_file>
<!--Time range over which the marker positions are averaged.-->
<time_range> 0.016666666666666666 0.4166666666666667 </time_range>
<time_range> 0.016666666666666666435 0.41666666666666668517</time_range>
<!--Name of the motion file (.mot) written after marker relocation (optional).-->
<output_motion_file>Unassigned</output_motion_file>
<!--Output OpenSim model file (.osim) after scaling and maker placement.-->
<output_model_file>../S00_Demo_Session/S00_P00_SingleParticipant/S00_P00_OpenSim/Model_Pose2Sim_S00_P00_LSTM_scaled.osim</output_model_file>
<output_model_file>D:\softs\github_david\pose2sim\Pose2Sim\OpenSim_Setup\..\S00_Demo_Session\S00_P00_SingleParticipant\S00_P00_OpenSim\Model_Pose2Sim_S00_P00_LSTM_scaled.osim</output_model_file>
<!--Output marker set containing the new marker locations after markers have been placed.-->
<output_marker_file>Unassigned</output_marker_file>
<!--Maximum amount of movement allowed in marker data when averaging frames of the static trial. A negative value means there is not limit.-->

80
Pose2Sim/Pose2Sim.py
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@ -11,26 +11,30 @@ This repository offers a way to perform markerless kinematics, and gives an
example workflow from an Openpose input to an OpenSim result.
It offers tools for:
- 2D pose estimation,
- Cameras calibration,
- 2D pose estimation,
- Camera synchronization,
- Tracking the person of interest,
- Robust triangulation,
- Filtration,
- Marker augmentation,
- OpenSim scaling and inverse kinematics
It has been tested on Windows, Linux and MacOS, and works for any Python version >= 3.8
It has been tested on Windows, Linux and MacOS, and works for any Python version >= 3.9
Installation:
# Open Anaconda prompt. Type:
# - conda create -n Pose2Sim python=3.8
# - conda create -n Pose2Sim python=3.9
# - conda activate Pose2Sim
# - conda install Pose2Sim
# - conda install -c opensim-org opensim -y
# - pip install Pose2Sim
Usage:
# First run Pose estimation and organize your directories (see Readme.md)
from Pose2Sim import Pose2Sim
Pose2Sim.calibration()
Pose2Sim.poseEstimation()
Pose2Sim.synchronization()
Pose2Sim.personAssociation()
Pose2Sim.triangulation()
Pose2Sim.filtering()
@ -220,49 +224,47 @@ def calibration(config=None):
def poseEstimation(config=None):
'''
Estimate pose using BlazePose, OpenPose, AlphaPose, or DeepLabCut.
Estimate pose using RTMLib
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')
from Pose2Sim.poseEstimation import rtm_estimator # The name of the function might change
level, config_dicts = read_config_files(config)
if isinstance(config, dict):
config_dict = config_dicts[0]
if config_dict.get('project').get('project_dir') is None:
raise ValueError('Please specify the project directory in config_dict:\n \
config_dict.get("project").update({"project_dir":"<YOUR_TRIAL_DIRECTORY>"})')
# 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
for config_dict in config_dicts:
start = time.time()
currentDateAndTime = datetime.now()
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 not frame_range else f"frames {frame_range[0]} to {frame_range[1]}"][0]
logging.info("\n\n---------------------------------------------------------------------")
logging.info(f"Pose estimation for {seq_name}, for {frames}.")
logging.info(f"On {currentDateAndTime.strftime('%A %d. %B %Y, %H:%M:%S')}")
logging.info("---------------------------------------------------------------------")
logging.info(f"\nProject directory: {project_dir}")
# # TODO
# # Determine the level at which the function is called (session:3, participant:2, trial:1)
# level, config_dicts = read_config_files(config)
# if type(config)==dict:
# config_dict = config_dicts[0]
# 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_TRIAL_DIRECTORY>"})')
# # 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
# for config_dict in config_dicts:
# start = time.time()
# currentDateAndTime = datetime.now()
# 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("Pose estimation")
# logging.info(f"On {currentDateAndTime.strftime('%A %d. %B %Y, %H:%M:%S')}")
# logging.info("---------------------------------------------------------------------")
# logging.info(f"\nProject directory: {project_dir}")
# pose_estimation_all(config_dict)
rtm_estimator(config_dict)
# end = time.time()
# elapsed = end-start
# logging.info(f'Pose estimation took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.')
end = time.time()
elapsed = end - start
logging.info(f'\nPose estimation took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.')
def synchronization(config=None):

51
Pose2Sim/S00_Demo_BatchSession/S00_Calibration/Calib_qualisys_multi.test
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@ -1,51 +0,0 @@
[cam_01]
name = "cam_01"
size = [ [ 1088.0, 1920.0],
[ 1088.0, 1920.0],
[ 1088.0, 1920.0] ]
matrix = [ [ [ 1681.244873046875, 0.0, 532.97369384375], [ 0.0, 1681.075439453125, 948.137390140625], [ 0.0, 0.0, 1.0]],
[ [ 1681.244873046875, 0.0, 532.97369384375], [ 0.0, 1681.075439453125, 948.137390140625], [ 0.0, 0.0, 1.0]],
[ [ 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],
[ -0.000721609375, 0.002187234375, 9.5e-06, 1.078125e-05],
[ -0.000721609375, 0.002187234375, 9.5e-06, 1.078125e-05] ]
rotation = [ [ 1.6882754799999993, 1.0483220499999997, -0.41955852000000016],
[ 1.6882754799999993, 1.0483220499999997, -0.41955852000000016],
[ 1.6882754799999993, 1.0483220499999997, -0.41955852000000016] ]
translation = [ [ 0.3211048899999996, 0.9563320600000009, 2.8907130499999996],
[ 0.3211048899999996, 0.9563320600000009, 2.8907130499999996],
[ 0.3211048899999996, 0.9563320600000009, 2.8907130499999996] ]
fisheye = false
moving = true
framerate = 60.0
[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 = [ -0.11152829000000014, 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 = [ -0.7934803899999995, 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, 0.04894934000000107, 4.406564460000002]
fisheye = false
[metadata]
adjusted = false
error = 0.0

254
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/Config.toml
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@ -1,254 +0,0 @@
###############################################################################
## PROJECT PARAMETERS ##
###############################################################################
# Configure your project parameters here.
#
# IMPORTANT:
# If a parameter is not found here, Pose2Sim will look for its value in the
# Config.toml file of the level above. This way, you can set global
# instructions for the Session and alter them for specific Participants or Trials.
#
# If you wish to overwrite a parameter for a specific trial or participant,
# edit its Config.toml file by uncommenting its key (e.g., [project])
# and editing its value (e.g., frame_range = [10,300]). Or else, uncomment
# [filtering.butterworth] and set cut_off_frequency = 10, etc.
# [project]
# multi_person = true # If false, only the main person in scene is analyzed.
# frame_rate = 60 # fps
# frame_range = [] # For example [10,300], or [] for all frames
## N.B.: If you want a time range instead, use frame_range = time_range * frame_rate
## For example if you want to analyze from 0.1 to 2 seconds with a 60 fps frame rate,
## frame_range = [0.1, 2.0]*frame_rate = [6, 120]
# exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
# [pose]
# pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut'
# pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
# #With mediapipe: BLAZEPOSE.
# #With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133.
# #With deeplabcut: CUSTOM. See example at the end of the file.
# # What follows has not been implemented yet
# overwrite_pose = false
# openpose_path = '' # only checked if OpenPose is used
# [synchronization]
# display_sync_plots = true # true or false (lowercase)
# keypoints_to_consider = 'all' # 'all' if all points should be considered (default)
# # ['RWrist', 'RElbow'] list of keypoint names if you want to specify the keypoints to consider
# approx_time_maxspeed = 'auto' # 'auto' if you want to consider the whole capture (default, slower if long sequences)
# # [1.0, 0.2, 0.8, 1.1] list of times in seconds, one value per camera if you want to specify an approximate time where to search for synchronization.
# # Best times: only one person is in the scene, who performs a sharp vertical movement (with keypoints_to_consider).
# reset_sync = false # Revert to state before synchronization. Won't attempt to synchronize unless set to false.
## Take heart, calibration is not that complicated once you get the hang of it!
# [calibration]
# calibration_type = 'convert' # 'convert' or 'calculate'
# [calibration.convert]
# convert_from = 'qualisys' # 'qualisys', 'optitrack', vicon', 'opencap', 'easymocap', 'biocv', 'anipose', or 'freemocap'
# [calibration.convert.qualisys]
# binning_factor = 1 # Usually 1, except when filming in 540p where it usually is 2
# [calibration.convert.optitrack] # See readme for instructions
# [calibration.convert.vicon] # No parameter needed
# [calibration.convert.opencap] # No parameter needed
# [calibration.convert.easymocap] # No parameter needed
# [calibration.convert.biocv] # No parameter needed
# [calibration.convert.anipose] # No parameter needed
# [calibration.convert.freemocap] # No parameter needed
# [calibration.calculate]
# # Camera properties, theoretically need to be calculated only once in a camera lifetime
# [calibration.calculate.intrinsics]
# overwrite_intrinsics = false # set to false if you don't want to recalculate intrinsic parameters
# show_detection_intrinsics = true # true or false (lowercase)
# intrinsics_extension = 'jpg' # any video or image extension
# extract_every_N_sec = 1 # if video, extract frames every N seconds (can be <1 )
# intrinsics_corners_nb = [4,7]
# intrinsics_square_size = 60 # mm
# # Camera placements, need to be done before every session
# [calibration.calculate.extrinsics]
# calculate_extrinsics = true # true or false (lowercase)
# extrinsics_method = 'scene' # 'board', 'scene', 'keypoints'
# # 'board' should be large enough to be detected when laid on the floor. Not recommended.
# # 'scene' involves manually clicking any point of know coordinates on scene. Usually more accurate if points are spread out.
# # 'keypoints' uses automatic pose estimation of a person freely walking and waving arms in the scene. Slighlty less accurate, requires synchronized cameras.
# moving_cameras = false # Not implemented yet
# [calibration.calculate.extrinsics.board]
# show_reprojection_error = true # true or false (lowercase)
# extrinsics_extension = 'png' # any video or image extension
# extrinsics_corners_nb = [4,7] # [H,W] rather than [w,h]
# extrinsics_square_size = 60 # mm # [h,w] if square is actually a rectangle
# [calibration.calculate.extrinsics.scene]
# show_reprojection_error = true # true or false (lowercase)
# extrinsics_extension = 'png' # any video or image extension
# # list of 3D coordinates to be manually labelled on images. Can also be a 2 dimensional plane.
# # in m -> unlike for intrinsics, NOT in mm!
# object_coords_3d = [[-2.0, 0.3, 0.0],
# [-2.0 , 0.0, 0.0],
# [-2.0, 0.0, 0.05],
# [-2.0, -0.3 , 0.0],
# [0.0, 0.3, 0.0],
# [0.0, 0.0, 0.0],
# [0.0, 0.0, 0.05],
# [0.0, -0.3, 0.0]]
# [calibration.calculate.extrinsics.keypoints]
# # Coming soon!
# [personAssociation]
# likelihood_threshold_association = 0.3
# [personAssociation.single_person]
# reproj_error_threshold_association = 20 # px
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
# # and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
# [personAssociation.multi_person]
# reconstruction_error_threshold = 0.1 # 0.1 = 10 cm
# min_affinity = 0.2 # affinity below which a correspondence is ignored
# [triangulation]
# reorder_trc = false # only checked if multi_person analysis
# reproj_error_threshold_triangulation = 15 # px
# likelihood_threshold_triangulation= 0.3
# min_cameras_for_triangulation = 2
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
# # 'none' if you don't want to interpolate missing points
# interp_if_gap_smaller_than = 10 # do not interpolate bigger gaps
# show_interp_indices = true # true or false (lowercase). For each keypoint, return the frames that need to be interpolated
# handle_LR_swap = false # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
# undistort_points = false # Better if distorted image (parallel lines curvy on the edge or at least one param > 10^-2), but unnecessary (and slightly slower) if distortions are low
# make_c3d = false # save triangulated data in c3d format in addition to trc
# [filtering]
# type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
# display_figures = false # true or false (lowercase)
# make_c3d = false # save triangulated data in c3d format in addition to trc
# [filtering.butterworth]
# order = 4
# cut_off_frequency = 6 # Hz
# [filtering.kalman]
# # How much more do you trust triangulation results (measurements), than previous data (process assuming constant acceleration)?
# trust_ratio = 100 # = measurement_trust/process_trust ~= process_noise/measurement_noise
# smooth = true # should be true, unless you need real-time filtering
# [filtering.butterworth_on_speed]
# order = 4
# cut_off_frequency = 10 # Hz
# [filtering.gaussian]
# sigma_kernel = 2 #px
# [filtering.LOESS]
# nb_values_used = 30 # = fraction of data used * nb frames
# [filtering.median]
# kernel_size = 9
# [markerAugmentation]
## Only works on BODY_25 and BODY_25B models
# participant_height = 1.72 # m # float if single person, list of float if multi-person (same order as the Static trials)
# participant_mass = 70.0 # kg
# make_c3d = false # save triangulated data in c3d format in addition to trc
# [opensim]
# static_trial = [# static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial']
# # If this Config.toml file is at the Trial level, set to true or false (lowercase);
# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial'];
# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial']
# opensim_bin_path = 'C:\OpenSim 4.4\bin'
## CUSTOM skeleton, if you trained your own DeepLabCut model for example.
## Make sure the node ids correspond to the column numbers of the 2D pose file, starting from zero.
##
## If you want to perform inverse kinematics, you will also need to create an OpenSim model
## and add to its markerset the location where you expect the triangulated keypoints to be detected.
##
## In this example, CUSTOM reproduces the BODY_25B skeleton (default skeletons are stored in skeletons.py).
## You can create as many custom skeletons as you want, just add them further down and rename them.
##
## Check your model hierarchy with: for pre, _, node in RenderTree(model):
## print(f'{pre}{node.name} id={node.id}')
# [pose.CUSTOM]
# name = "CHip"
# id = "None"
# [[pose.CUSTOM.children]]
# name = "RHip"
# id = 12
# [[pose.CUSTOM.children.children]]
# name = "RKnee"
# id = 14
# [[pose.CUSTOM.children.children.children]]
# name = "RAnkle"
# id = 16
# [[pose.CUSTOM.children.children.children.children]]
# name = "RBigToe"
# id = 22
# [[pose.CUSTOM.children.children.children.children.children]]
# name = "RSmallToe"
# id = 23
# [[pose.CUSTOM.children.children.children.children]]
# name = "RHeel"
# id = 24
# [[pose.CUSTOM.children]]
# name = "LHip"
# id = 11
# [[pose.CUSTOM.children.children]]
# name = "LKnee"
# id = 13
# [[pose.CUSTOM.children.children.children]]
# name = "LAnkle"
# id = 15
# [[pose.CUSTOM.children.children.children.children]]
# name = "LBigToe"
# id = 19
# [[pose.CUSTOM.children.children.children.children.children]]
# name = "LSmallToe"
# id = 20
# [[pose.CUSTOM.children.children.children.children]]
# name = "LHeel"
# id = 21
# [[pose.CUSTOM.children]]
# name = "Neck"
# id = 17
# [[pose.CUSTOM.children.children]]
# name = "Head"
# id = 18
# [[pose.CUSTOM.children.children.children]]
# name = "Nose"
# id = 0
# [[pose.CUSTOM.children.children]]
# name = "RShoulder"
# id = 6
# [[pose.CUSTOM.children.children.children]]
# name = "RElbow"
# id = 8
# [[pose.CUSTOM.children.children.children.children]]
# name = "RWrist"
# id = 10
# [[pose.CUSTOM.children.children]]
# name = "LShoulder"
# id = 5
# [[pose.CUSTOM.children.children.children]]
# name = "LElbow"
# id = 7
# [[pose.CUSTOM.children.children.children.children]]
# name = "LWrist"
# id = 9

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Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_OpenSim/S00_P00_T01_BalancingTrial_filt_butterworth_0-100_LSTM.csv
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@ -1,101 +0,0 @@
# times, pelvis_x, pelvis_y, pelvis_z, pelvis_rotx, pelvis_roty, pelvis_rotz, sacrum_x, sacrum_y, sacrum_z, sacrum_rotx, sacrum_roty, sacrum_rotz, femur_r_x, femur_r_y, femur_r_z, femur_r_rotx, femur_r_roty, femur_r_rotz, patella_r_x, patella_r_y, patella_r_z, patella_r_rotx, patella_r_roty, patella_r_rotz, tibia_r_x, tibia_r_y, tibia_r_z, tibia_r_rotx, tibia_r_roty, tibia_r_rotz, talus_r_x, talus_r_y, talus_r_z, talus_r_rotx, talus_r_roty, talus_r_rotz, calcn_r_x, calcn_r_y, calcn_r_z, calcn_r_rotx, calcn_r_roty, calcn_r_rotz, toes_r_x, toes_r_y, toes_r_z, toes_r_rotx, toes_r_roty, toes_r_rotz, femur_l_x, femur_l_y, femur_l_z, femur_l_rotx, femur_l_roty, femur_l_rotz, patella_l_x, patella_l_y, patella_l_z, patella_l_rotx, patella_l_roty, patella_l_rotz, tibia_l_x, tibia_l_y, tibia_l_z, tibia_l_rotx, tibia_l_roty, tibia_l_rotz, talus_l_x, talus_l_y, talus_l_z, talus_l_rotx, talus_l_roty, talus_l_rotz, calcn_l_x, calcn_l_y, calcn_l_z, calcn_l_rotx, calcn_l_roty, calcn_l_rotz, toes_l_x, toes_l_y, toes_l_z, toes_l_rotx, toes_l_roty, toes_l_rotz, lumbar5_x, lumbar5_y, lumbar5_z, lumbar5_rotx, lumbar5_roty, lumbar5_rotz, lumbar4_x, lumbar4_y, lumbar4_z, lumbar4_rotx, lumbar4_roty, lumbar4_rotz, lumbar3_x, lumbar3_y, lumbar3_z, lumbar3_rotx, lumbar3_roty, lumbar3_rotz, lumbar2_x, lumbar2_y, lumbar2_z, lumbar2_rotx, lumbar2_roty, lumbar2_rotz, lumbar1_x, lumbar1_y, lumbar1_z, lumbar1_rotx, lumbar1_roty, lumbar1_rotz, torso_x, torso_y, torso_z, torso_rotx, torso_roty, torso_rotz, head_x, head_y, head_z, head_rotx, head_roty, head_rotz, Abdomen_x, Abdomen_y, Abdomen_z, Abdomen_rotx, Abdomen_roty, Abdomen_rotz, scapulaPhantom_r_x, scapulaPhantom_r_y, scapulaPhantom_r_z, scapulaPhantom_r_rotx, scapulaPhantom_r_roty, scapulaPhantom_r_rotz, scapulaPhantom_l_x, scapulaPhantom_l_y, scapulaPhantom_l_z, scapulaPhantom_l_rotx, scapulaPhantom_l_roty, scapulaPhantom_l_rotz, humerus_r_x, humerus_r_y, humerus_r_z, humerus_r_rotx, humerus_r_roty, humerus_r_rotz, ulna_r_x, ulna_r_y, ulna_r_z, ulna_r_rotx, ulna_r_roty, ulna_r_rotz, radius_r_x, radius_r_y, radius_r_z, radius_r_rotx, radius_r_roty, radius_r_rotz, hand_r_x, hand_r_y, hand_r_z, hand_r_rotx, hand_r_roty, hand_r_rotz, humerus_l_x, humerus_l_y, humerus_l_z, humerus_l_rotx, humerus_l_roty, humerus_l_rotz, ulna_l_x, ulna_l_y, ulna_l_z, ulna_l_rotx, ulna_l_roty, ulna_l_rotz, radius_l_x, radius_l_y, radius_l_z, radius_l_rotx, radius_l_roty, radius_l_rotz, hand_l_x, hand_l_y, hand_l_z, hand_l_rotx, hand_l_roty, hand_l_rotz
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1 # times pelvis_x pelvis_y pelvis_z pelvis_rotx pelvis_roty pelvis_rotz sacrum_x sacrum_y sacrum_z sacrum_rotx sacrum_roty sacrum_rotz femur_r_x femur_r_y femur_r_z femur_r_rotx femur_r_roty femur_r_rotz patella_r_x patella_r_y patella_r_z patella_r_rotx patella_r_roty patella_r_rotz tibia_r_x tibia_r_y tibia_r_z tibia_r_rotx tibia_r_roty tibia_r_rotz talus_r_x talus_r_y talus_r_z talus_r_rotx talus_r_roty talus_r_rotz calcn_r_x calcn_r_y calcn_r_z calcn_r_rotx calcn_r_roty calcn_r_rotz toes_r_x toes_r_y toes_r_z toes_r_rotx toes_r_roty toes_r_rotz femur_l_x femur_l_y femur_l_z femur_l_rotx femur_l_roty femur_l_rotz patella_l_x patella_l_y patella_l_z patella_l_rotx patella_l_roty patella_l_rotz tibia_l_x tibia_l_y tibia_l_z tibia_l_rotx tibia_l_roty tibia_l_rotz talus_l_x talus_l_y talus_l_z talus_l_rotx talus_l_roty talus_l_rotz calcn_l_x calcn_l_y calcn_l_z calcn_l_rotx calcn_l_roty calcn_l_rotz toes_l_x toes_l_y toes_l_z toes_l_rotx toes_l_roty toes_l_rotz lumbar5_x lumbar5_y lumbar5_z lumbar5_rotx lumbar5_roty lumbar5_rotz lumbar4_x lumbar4_y lumbar4_z lumbar4_rotx lumbar4_roty lumbar4_rotz lumbar3_x lumbar3_y lumbar3_z lumbar3_rotx lumbar3_roty lumbar3_rotz lumbar2_x lumbar2_y lumbar2_z lumbar2_rotx lumbar2_roty lumbar2_rotz lumbar1_x lumbar1_y lumbar1_z lumbar1_rotx lumbar1_roty lumbar1_rotz torso_x torso_y torso_z torso_rotx torso_roty torso_rotz head_x head_y head_z head_rotx head_roty head_rotz Abdomen_x Abdomen_y Abdomen_z Abdomen_rotx Abdomen_roty Abdomen_rotz scapulaPhantom_r_x scapulaPhantom_r_y scapulaPhantom_r_z scapulaPhantom_r_rotx scapulaPhantom_r_roty scapulaPhantom_r_rotz scapulaPhantom_l_x scapulaPhantom_l_y scapulaPhantom_l_z scapulaPhantom_l_rotx scapulaPhantom_l_roty scapulaPhantom_l_rotz humerus_r_x humerus_r_y humerus_r_z humerus_r_rotx humerus_r_roty humerus_r_rotz ulna_r_x ulna_r_y ulna_r_z ulna_r_rotx ulna_r_roty ulna_r_rotz radius_r_x radius_r_y radius_r_z radius_r_rotx radius_r_roty radius_r_rotz hand_r_x hand_r_y hand_r_z hand_r_rotx hand_r_roty hand_r_rotz humerus_l_x humerus_l_y humerus_l_z humerus_l_rotx humerus_l_roty humerus_l_rotz ulna_l_x ulna_l_y ulna_l_z ulna_l_rotx ulna_l_roty ulna_l_rotz radius_l_x radius_l_y radius_l_z radius_l_rotx radius_l_roty radius_l_rotz hand_l_x hand_l_y hand_l_z hand_l_rotx hand_l_roty hand_l_rotz
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Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_OpenSim/S00_P00_T01_BalancingTrial_filt_butterworth_0-100_LSTM.mot
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Coordinates
version=1
nRows=100
nColumns=69
inDegrees=yes
Units are S.I. units (second, meters, Newtons, ...)
If the header above contains a line with 'inDegrees', this indicates whether rotational values are in degrees (yes) or radians (no).
endheader
time pelvis_tilt pelvis_list pelvis_rotation pelvis_tx pelvis_ty pelvis_tz hip_flexion_r hip_adduction_r hip_rotation_r knee_angle_r knee_angle_r_beta ankle_angle_r subtalar_angle_r mtp_angle_r hip_flexion_l hip_adduction_l hip_rotation_l knee_angle_l knee_angle_l_beta ankle_angle_l subtalar_angle_l mtp_angle_l L5_S1_Flex_Ext L5_S1_Lat_Bending L5_S1_axial_rotation L4_L5_Flex_Ext L4_L5_Lat_Bending L4_L5_axial_rotation L3_L4_Flex_Ext L3_L4_Lat_Bending L3_L4_axial_rotation L2_L3_Flex_Ext L2_L3_Lat_Bending L2_L3_axial_rotation L1_L2_Flex_Ext L1_L2_Lat_Bending L1_L2_axial_rotation L1_T12_Flex_Ext L1_T12_Lat_Bending L1_T12_axial_rotation Abs_r3 Abs_r2 Abs_r1 Abs_t1 Abs_t2 neck_flexion neck_bending neck_rotation sh_tx_r sh_ty_r sh_tz_r sh_plane_elev_r sh_elev_r sh_axial_rot_r elbow_flex_r pro_sup_r wrist_flex_r wrist_dev_r sh_tx_l sh_ty_l sh_tz_l sh_plane_elev_l sh_elev_l sh_axial_rot_l elbow_flex_l pro_sup_l wrist_flex_l wrist_dev_l
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101
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_OpenSim/S00_P00_T01_BalancingTrial_filt_butterworth_0-100_body25b.csv
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@ -1,101 +0,0 @@
# times, pelvis_x, pelvis_y, pelvis_z, pelvis_rotx, pelvis_roty, pelvis_rotz, sacrum_x, sacrum_y, sacrum_z, sacrum_rotx, sacrum_roty, sacrum_rotz, femur_r_x, femur_r_y, femur_r_z, femur_r_rotx, femur_r_roty, femur_r_rotz, patella_r_x, patella_r_y, patella_r_z, patella_r_rotx, patella_r_roty, patella_r_rotz, tibia_r_x, tibia_r_y, tibia_r_z, tibia_r_rotx, tibia_r_roty, tibia_r_rotz, talus_r_x, talus_r_y, talus_r_z, talus_r_rotx, talus_r_roty, talus_r_rotz, calcn_r_x, calcn_r_y, calcn_r_z, calcn_r_rotx, calcn_r_roty, calcn_r_rotz, toes_r_x, toes_r_y, toes_r_z, toes_r_rotx, toes_r_roty, toes_r_rotz, femur_l_x, femur_l_y, femur_l_z, femur_l_rotx, femur_l_roty, femur_l_rotz, patella_l_x, patella_l_y, patella_l_z, patella_l_rotx, patella_l_roty, patella_l_rotz, tibia_l_x, tibia_l_y, tibia_l_z, tibia_l_rotx, tibia_l_roty, tibia_l_rotz, talus_l_x, talus_l_y, talus_l_z, talus_l_rotx, talus_l_roty, talus_l_rotz, calcn_l_x, calcn_l_y, calcn_l_z, calcn_l_rotx, calcn_l_roty, calcn_l_rotz, toes_l_x, toes_l_y, toes_l_z, toes_l_rotx, toes_l_roty, toes_l_rotz, lumbar5_x, lumbar5_y, lumbar5_z, lumbar5_rotx, lumbar5_roty, lumbar5_rotz, lumbar4_x, lumbar4_y, lumbar4_z, lumbar4_rotx, lumbar4_roty, lumbar4_rotz, lumbar3_x, lumbar3_y, lumbar3_z, lumbar3_rotx, lumbar3_roty, lumbar3_rotz, lumbar2_x, lumbar2_y, lumbar2_z, lumbar2_rotx, lumbar2_roty, lumbar2_rotz, lumbar1_x, lumbar1_y, lumbar1_z, lumbar1_rotx, lumbar1_roty, lumbar1_rotz, torso_x, torso_y, torso_z, torso_rotx, torso_roty, torso_rotz, head_x, head_y, head_z, head_rotx, head_roty, head_rotz, Abdomen_x, Abdomen_y, Abdomen_z, Abdomen_rotx, Abdomen_roty, Abdomen_rotz, humerus_r_x, humerus_r_y, humerus_r_z, humerus_r_rotx, humerus_r_roty, humerus_r_rotz, ulna_r_x, ulna_r_y, ulna_r_z, ulna_r_rotx, ulna_r_roty, ulna_r_rotz, radius_r_x, radius_r_y, radius_r_z, radius_r_rotx, radius_r_roty, radius_r_rotz, hand_r_x, hand_r_y, hand_r_z, hand_r_rotx, hand_r_roty, hand_r_rotz, humerus_l_x, humerus_l_y, humerus_l_z, humerus_l_rotx, humerus_l_roty, humerus_l_rotz, ulna_l_x, ulna_l_y, ulna_l_z, ulna_l_rotx, ulna_l_roty, ulna_l_rotz, radius_l_x, radius_l_y, radius_l_z, radius_l_rotx, radius_l_roty, radius_l_rotz, hand_l_x, hand_l_y, hand_l_z, hand_l_rotx, hand_l_roty, hand_l_rotz
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1 # times pelvis_x pelvis_y pelvis_z pelvis_rotx pelvis_roty pelvis_rotz sacrum_x sacrum_y sacrum_z sacrum_rotx sacrum_roty sacrum_rotz femur_r_x femur_r_y femur_r_z femur_r_rotx femur_r_roty femur_r_rotz patella_r_x patella_r_y patella_r_z patella_r_rotx patella_r_roty patella_r_rotz tibia_r_x tibia_r_y tibia_r_z tibia_r_rotx tibia_r_roty tibia_r_rotz talus_r_x talus_r_y talus_r_z talus_r_rotx talus_r_roty talus_r_rotz calcn_r_x calcn_r_y calcn_r_z calcn_r_rotx calcn_r_roty calcn_r_rotz toes_r_x toes_r_y toes_r_z toes_r_rotx toes_r_roty toes_r_rotz femur_l_x femur_l_y femur_l_z femur_l_rotx femur_l_roty femur_l_rotz patella_l_x patella_l_y patella_l_z patella_l_rotx patella_l_roty patella_l_rotz tibia_l_x tibia_l_y tibia_l_z tibia_l_rotx tibia_l_roty tibia_l_rotz talus_l_x talus_l_y talus_l_z talus_l_rotx talus_l_roty talus_l_rotz calcn_l_x calcn_l_y calcn_l_z calcn_l_rotx calcn_l_roty calcn_l_rotz toes_l_x toes_l_y toes_l_z toes_l_rotx toes_l_roty toes_l_rotz lumbar5_x lumbar5_y lumbar5_z lumbar5_rotx lumbar5_roty lumbar5_rotz lumbar4_x lumbar4_y lumbar4_z lumbar4_rotx lumbar4_roty lumbar4_rotz lumbar3_x lumbar3_y lumbar3_z lumbar3_rotx lumbar3_roty lumbar3_rotz lumbar2_x lumbar2_y lumbar2_z lumbar2_rotx lumbar2_roty lumbar2_rotz lumbar1_x lumbar1_y lumbar1_z lumbar1_rotx lumbar1_roty lumbar1_rotz torso_x torso_y torso_z torso_rotx torso_roty torso_rotz head_x head_y head_z head_rotx head_roty head_rotz Abdomen_x Abdomen_y Abdomen_z Abdomen_rotx Abdomen_roty Abdomen_rotz humerus_r_x humerus_r_y humerus_r_z humerus_r_rotx humerus_r_roty humerus_r_rotz ulna_r_x ulna_r_y ulna_r_z ulna_r_rotx ulna_r_roty ulna_r_rotz radius_r_x radius_r_y radius_r_z radius_r_rotx radius_r_roty radius_r_rotz hand_r_x hand_r_y hand_r_z hand_r_rotx hand_r_roty hand_r_rotz humerus_l_x humerus_l_y humerus_l_z humerus_l_rotx humerus_l_roty humerus_l_rotz ulna_l_x ulna_l_y ulna_l_z ulna_l_rotx ulna_l_roty ulna_l_rotz radius_l_x radius_l_y radius_l_z radius_l_rotx radius_l_roty radius_l_rotz hand_l_x hand_l_y hand_l_z hand_l_rotx hand_l_roty hand_l_rotz
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Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_OpenSim/S00_P00_T01_BalancingTrial_filt_butterworth_0-100_body25b.mot
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@ -1,111 +0,0 @@
Coordinates
version=1
nRows=100
nColumns=63
inDegrees=yes
Units are S.I. units (second, meters, Newtons, ...)
If the header above contains a line with 'inDegrees', this indicates whether rotational values are in degrees (yes) or radians (no).
endheader
time pelvis_tilt pelvis_list pelvis_rotation pelvis_tx pelvis_ty pelvis_tz hip_flexion_r hip_adduction_r hip_rotation_r knee_angle_r knee_angle_r_beta ankle_angle_r subtalar_angle_r mtp_angle_r hip_flexion_l hip_adduction_l hip_rotation_l knee_angle_l knee_angle_l_beta ankle_angle_l subtalar_angle_l mtp_angle_l L5_S1_Flex_Ext L5_S1_Lat_Bending L5_S1_axial_rotation L4_L5_Flex_Ext L4_L5_Lat_Bending L4_L5_axial_rotation L3_L4_Flex_Ext L3_L4_Lat_Bending L3_L4_axial_rotation L2_L3_Flex_Ext L2_L3_Lat_Bending L2_L3_axial_rotation L1_L2_Flex_Ext L1_L2_Lat_Bending L1_L2_axial_rotation L1_T12_Flex_Ext L1_T12_Lat_Bending L1_T12_axial_rotation Abs_r3 Abs_r2 Abs_r1 Abs_t1 Abs_t2 neck_flexion neck_bending neck_rotation arm_flex_r arm_add_r arm_rot_r elbow_flex_r pro_sup_r wrist_flex_r wrist_dev_r arm_flex_l arm_add_l arm_rot_l elbow_flex_l pro_sup_l wrist_flex_l wrist_dev_l
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254
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/Config.toml
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@ -1,254 +0,0 @@
###############################################################################
## PROJECT PARAMETERS ##
###############################################################################
# Configure your project parameters here.
#
# IMPORTANT:
# If a parameter is not found here, Pose2Sim will look for its value in the
# Config.toml file of the level above. This way, you can set global
# instructions for the Session and alter them for specific Participants or Trials.
#
# If you wish to overwrite a parameter for a specific trial or participant,
# edit its Config.toml file by uncommenting its key (e.g., [project])
# and editing its value (e.g., frame_range = [10,300]). Or else, uncomment
# [filtering.butterworth] and set cut_off_frequency = 10, etc.
# [project]
# multi_person = true # If false, only the main person in scene is analyzed.
# frame_rate = 60 # fps
# frame_range = [] # For example [10,300], or [] for all frames
## N.B.: If you want a time range instead, use frame_range = time_range * frame_rate
## For example if you want to analyze from 0.1 to 2 seconds with a 60 fps frame rate,
## frame_range = [0.1, 2.0]*frame_rate = [6, 120]
# exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
## e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
# [pose]
# pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut'
# pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
# #With mediapipe: BLAZEPOSE.
# #With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133.
# #With deeplabcut: CUSTOM. See example at the end of the file.
# # What follows has not been implemented yet
# overwrite_pose = false
# openpose_path = '' # only checked if OpenPose is used
# [synchronization]
# display_sync_plots = true # true or false (lowercase)
# keypoints_to_consider = 'all' # 'all' if all points should be considered (default)
# # ['RWrist', 'RElbow'] list of keypoint names if you want to specify the keypoints to consider
# approx_time_maxspeed = 'auto' # 'auto' if you want to consider the whole capture (default, slower if long sequences)
# # [1.0, 0.2, 0.8, 1.1] list of times in seconds, one value per camera if you want to specify an approximate time where to search for synchronization.
# # Best times: only one person is in the scene, who performs a sharp vertical movement (with keypoints_to_consider).
# reset_sync = false # Revert to state before synchronization. Won't attempt to synchronize unless set to false.
## Take heart, calibration is not that complicated once you get the hang of it!
# [calibration]
# calibration_type = 'convert' # 'convert' or 'calculate'
# [calibration.convert]
# convert_from = 'qualisys' # 'qualisys', 'optitrack', vicon', 'opencap', 'easymocap', 'biocv', 'anipose', or 'freemocap'
# [calibration.convert.qualisys]
# binning_factor = 1 # Usually 1, except when filming in 540p where it usually is 2
# [calibration.convert.optitrack] # See readme for instructions
# [calibration.convert.vicon] # No parameter needed
# [calibration.convert.opencap] # No parameter needed
# [calibration.convert.easymocap] # No parameter needed
# [calibration.convert.biocv] # No parameter needed
# [calibration.convert.anipose] # No parameter needed
# [calibration.convert.freemocap] # No parameter needed
# [calibration.calculate]
# # Camera properties, theoretically need to be calculated only once in a camera lifetime
# [calibration.calculate.intrinsics]
# overwrite_intrinsics = false # # set to false if you don't want to recalculate intrinsic parameters
# show_detection_intrinsics = true # true or false (lowercase)
# intrinsics_extension = 'jpg' # any video or image extension
# extract_every_N_sec = 1 # if video, extract frames every N seconds (can be <1 )
# intrinsics_corners_nb = [4,7]
# intrinsics_square_size = 60 # mm
# # Camera placements, need to be done before every session
# [calibration.calculate.extrinsics]
# calculate_extrinsics = true # true or false (lowercase)
# extrinsics_method = 'scene' # 'board', 'scene', 'keypoints'
# # 'board' should be large enough to be detected when laid on the floor. Not recommended.
# # 'scene' involves manually clicking any point of know coordinates on scene. Usually more accurate if points are spread out.
# # 'keypoints' uses automatic pose estimation of a person freely walking and waving arms in the scene. Slighlty less accurate, requires synchronized cameras.
# moving_cameras = false # Not implemented yet
# [calibration.calculate.extrinsics.board]
# show_reprojection_error = true # true or false (lowercase)
# extrinsics_extension = 'png' # any video or image extension
# extrinsics_corners_nb = [4,7] # [H,W] rather than [w,h]
# extrinsics_square_size = 60 # mm # [h,w] if square is actually a rectangle
# [calibration.calculate.extrinsics.scene]
# show_reprojection_error = true # true or false (lowercase)
# extrinsics_extension = 'png' # any video or image extension
# # list of 3D coordinates to be manually labelled on images. Can also be a 2 dimensional plane.
# # in m -> unlike for intrinsics, NOT in mm!
# object_coords_3d = [[-2.0, 0.3, 0.0],
# [-2.0 , 0.0, 0.0],
# [-2.0, 0.0, 0.05],
# [-2.0, -0.3 , 0.0],
# [0.0, 0.3, 0.0],
# [0.0, 0.0, 0.0],
# [0.0, 0.0, 0.05],
# [0.0, -0.3, 0.0]]
# [calibration.calculate.extrinsics.keypoints]
# # Coming soon!
# [personAssociation]
# likelihood_threshold_association = 0.3
# [personAssociation.single_person]
# reproj_error_threshold_association = 20 # px
# tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
# # and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
# [personAssociation.multi_person]
# reconstruction_error_threshold = 0.1 # 0.1 = 10 cm
# min_affinity = 0.2 # affinity below which a correspondence is ignored
# [triangulation]
# reorder_trc = false # only checked if multi_person analysis
# reproj_error_threshold_triangulation = 15 # px
# likelihood_threshold_triangulation= 0.3
# min_cameras_for_triangulation = 2
# interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
# # 'none' if you don't want to interpolate missing points
# interp_if_gap_smaller_than = 10 # do not interpolate bigger gaps
# show_interp_indices = true # true or false (lowercase). For each keypoint, return the frames that need to be interpolated
# handle_LR_swap = false # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
# undistort_points = false # Better if distorted image (parallel lines curvy on the edge or at least one param > 10^-2), but unnecessary (and slightly slower) if distortions are low
# make_c3d = false # save triangulated data in c3d format in addition to trc
# [filtering]
# type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
# display_figures = false # true or false (lowercase)
# make_c3d = false # save triangulated data in c3d format in addition to trc
# [filtering.butterworth]
# order = 4
# cut_off_frequency = 6 # Hz
# [filtering.kalman]
# # How much more do you trust triangulation results (measurements), than previous data (process assuming constant acceleration)?
# trust_ratio = 100 # = measurement_trust/process_trust ~= process_noise/measurement_noise
# smooth = true # should be true, unless you need real-time filtering
# [filtering.butterworth_on_speed]
# order = 4
# cut_off_frequency = 10 # Hz
# [filtering.gaussian]
# sigma_kernel = 2 #px
# [filtering.LOESS]
# nb_values_used = 30 # = fraction of data used * nb frames
# [filtering.median]
# kernel_size = 9
# [markerAugmentation]
## Only works on BODY_25 and BODY_25B models
# participant_height = 1.72 # m # float if single person, list of float if multi-person (same order as the Static trials)
# participant_mass = 70.0 # kg
# make_c3d = false # save triangulated data in c3d format in addition to trc
# [opensim]
# static_trial = [# static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial']
# # If this Config.toml file is at the Trial level, set to true or false (lowercase);
# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial'];
# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial']
# opensim_bin_path = 'C:\OpenSim 4.4\bin'
## CUSTOM skeleton, if you trained your own DeepLabCut model for example.
## Make sure the node ids correspond to the column numbers of the 2D pose file, starting from zero.
##
## If you want to perform inverse kinematics, you will also need to create an OpenSim model
## and add to its markerset the location where you expect the triangulated keypoints to be detected.
##
## In this example, CUSTOM reproduces the BODY_25B skeleton (default skeletons are stored in skeletons.py).
## You can create as many custom skeletons as you want, just add them further down and rename them.
##
## Check your model hierarchy with: for pre, _, node in RenderTree(model):
## print(f'{pre}{node.name} id={node.id}')
# [pose.CUSTOM]
# name = "CHip"
# id = "None"
# [[pose.CUSTOM.children]]
# name = "RHip"
# id = 12
# [[pose.CUSTOM.children.children]]
# name = "RKnee"
# id = 14
# [[pose.CUSTOM.children.children.children]]
# name = "RAnkle"
# id = 16
# [[pose.CUSTOM.children.children.children.children]]
# name = "RBigToe"
# id = 22
# [[pose.CUSTOM.children.children.children.children.children]]
# name = "RSmallToe"
# id = 23
# [[pose.CUSTOM.children.children.children.children]]
# name = "RHeel"
# id = 24
# [[pose.CUSTOM.children]]
# name = "LHip"
# id = 11
# [[pose.CUSTOM.children.children]]
# name = "LKnee"
# id = 13
# [[pose.CUSTOM.children.children.children]]
# name = "LAnkle"
# id = 15
# [[pose.CUSTOM.children.children.children.children]]
# name = "LBigToe"
# id = 19
# [[pose.CUSTOM.children.children.children.children.children]]
# name = "LSmallToe"
# id = 20
# [[pose.CUSTOM.children.children.children.children]]
# name = "LHeel"
# id = 21
# [[pose.CUSTOM.children]]
# name = "Neck"
# id = 17
# [[pose.CUSTOM.children.children]]
# name = "Head"
# id = 18
# [[pose.CUSTOM.children.children.children]]
# name = "Nose"
# id = 0
# [[pose.CUSTOM.children.children]]
# name = "RShoulder"
# id = 6
# [[pose.CUSTOM.children.children.children]]
# name = "RElbow"
# id = 8
# [[pose.CUSTOM.children.children.children.children]]
# name = "RWrist"
# id = 10
# [[pose.CUSTOM.children.children]]
# name = "LShoulder"
# id = 5
# [[pose.CUSTOM.children.children.children]]
# name = "LElbow"
# id = 7
# [[pose.CUSTOM.children.children.children.children]]
# name = "LWrist"
# id = 9

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00000.json
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1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00001.json
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{"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": [1021.3256512174645, 602.3711015713403, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 825.917552286083, 673.152771671459, 1.0, 1035.5833583893284, 774.3395267483642, 1.0, 753.5349065342303, 791.4356972268777, 1.0, 1068.8016718578638, 983.779761832728, 1.0, 666.0449646448034, 861.9695584374556, 1.0, 1095.9258869156356, 1163.4832923847428, 1.0, 786.231065316051, 1024.8628135810081, 1.0, 880.1976062513037, 1091.9310659604416, 1.0, 724.5532829849652, 1227.5775894192202, 1.0, 796.2373537399884, 1290.205792638792, 1.0, 664.2223820043956, 1409.0377529832285, 1.0, 735.7633804072557, 1482.841836684363, 1.0, 948.6387949472169, 665.4060224499005, 1.0, 1032.4754623523174, 509.73432205900895, 1.0, 739.3889893581304, 1429.8440586282695, 1.0, 709.2183877925171, 1419.5531634583926, 1.0, 641.9070113137954, 1449.176693756097, 1.0, 795.3557413226708, 1485.5432595467762, 1.0, 796.7023110801829, 1507.7484577153032, 1.0, 705.599001521201, 1517.6386213203577, 1.0], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [], "pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00002.json
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@ -1 +0,0 @@
{"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": [1021.3256512174645, 602.3711015713403, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 825.917552286083, 673.152771671459, 1.0, 1035.5833583893284, 774.3395267483642, 1.0, 753.5349065342303, 791.4356972268777, 1.0, 1068.8016718578638, 983.779761832728, 1.0, 666.0449646448034, 861.9695584374556, 1.0, 1095.9258869156356, 1163.4832923847428, 1.0, 786.231065316051, 1024.8628135810081, 1.0, 880.1976062513037, 1091.9310659604416, 1.0, 724.5532829849652, 1227.5775894192202, 1.0, 796.2373537399884, 1290.205792638792, 1.0, 664.2223820043956, 1409.0377529832285, 1.0, 735.7633804072557, 1482.841836684363, 1.0, 948.6387949472169, 665.4060224499005, 1.0, 1032.4754623523174, 509.73432205900895, 1.0, 739.3889893581304, 1429.8440586282695, 1.0, 709.2183877925171, 1419.5531634583926, 1.0, 641.9070113137954, 1449.176693756097, 1.0, 795.3557413226708, 1485.5432595467762, 1.0, 796.7023110801829, 1507.7484577153032, 1.0, 705.599001521201, 1517.6386213203577, 1.0], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [], "pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00003.json
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1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00004.json
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@ -1 +0,0 @@
{"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": [1021.3256512174645, 602.3711015713403, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 825.917552286083, 673.152771671459, 1.0, 1035.5833583893284, 774.3395267483642, 1.0, 753.5349065342303, 791.4356972268777, 1.0, 1068.8016718578638, 983.779761832728, 1.0, 666.0449646448034, 861.9695584374556, 1.0, 1095.9258869156356, 1163.4832923847428, 1.0, 786.231065316051, 1024.8628135810081, 1.0, 880.1976062513037, 1091.9310659604416, 1.0, 724.5532829849652, 1227.5775894192202, 1.0, 796.2373537399884, 1290.205792638792, 1.0, 664.2223820043956, 1409.0377529832285, 1.0, 735.7633804072557, 1482.841836684363, 1.0, 948.6387949472169, 665.4060224499005, 1.0, 1032.4754623523174, 509.73432205900895, 1.0, 739.3889893581304, 1429.8440586282695, 1.0, 709.2183877925171, 1419.5531634583926, 1.0, 641.9070113137954, 1449.176693756097, 1.0, 795.3557413226708, 1485.5432595467762, 1.0, 796.7023110801829, 1507.7484577153032, 1.0, 705.599001521201, 1517.6386213203577, 1.0], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [], "pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00005.json
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@ -1 +0,0 @@
{"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": [1021.3256512174645, 602.3711015713403, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 825.917552286083, 673.152771671459, 1.0, 1035.5833583893284, 774.3395267483642, 1.0, 753.5349065342303, 791.4356972268777, 1.0, 1068.8016718578638, 983.779761832728, 1.0, 666.0449646448034, 861.9695584374556, 1.0, 1095.9258869156356, 1163.4832923847428, 1.0, 786.231065316051, 1024.8628135810081, 1.0, 880.1976062513037, 1091.9310659604416, 1.0, 724.5532829849652, 1227.5775894192202, 1.0, 796.2373537399884, 1290.205792638792, 1.0, 664.2223820043956, 1409.0377529832285, 1.0, 735.7633804072557, 1482.841836684363, 1.0, 948.6387949472169, 665.4060224499005, 1.0, 1032.4754623523174, 509.73432205900895, 1.0, 739.3889893581304, 1429.8440586282695, 1.0, 709.2183877925171, 1419.5531634583926, 1.0, 641.9070113137954, 1449.176693756097, 1.0, 795.3557413226708, 1485.5432595467762, 1.0, 796.7023110801829, 1507.7484577153032, 1.0, 705.599001521201, 1517.6386213203577, 1.0], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [], "pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00006.json
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@ -1 +0,0 @@
{"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": [1021.3256512174645, 602.3711015713403, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 825.917552286083, 673.152771671459, 1.0, 1035.5833583893284, 774.3395267483642, 1.0, 753.5349065342303, 791.4356972268777, 1.0, 1068.8016718578638, 983.779761832728, 1.0, 666.0449646448034, 861.9695584374556, 1.0, 1095.9258869156356, 1163.4832923847428, 1.0, 786.231065316051, 1024.8628135810081, 1.0, 880.1976062513037, 1091.9310659604416, 1.0, 724.5532829849652, 1227.5775894192202, 1.0, 796.2373537399884, 1290.205792638792, 1.0, 664.2223820043956, 1409.0377529832285, 1.0, 735.7633804072557, 1482.841836684363, 1.0, 948.6387949472169, 665.4060224499005, 1.0, 1032.4754623523174, 509.73432205900895, 1.0, 739.3889893581304, 1429.8440586282695, 1.0, 709.2183877925171, 1419.5531634583926, 1.0, 641.9070113137954, 1449.176693756097, 1.0, 795.3557413226708, 1485.5432595467762, 1.0, 796.7023110801829, 1507.7484577153032, 1.0, 705.599001521201, 1517.6386213203577, 1.0], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [], "pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00007.json
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@ -1 +0,0 @@
{"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": [1021.3256512174645, 602.3711015713403, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 825.917552286083, 673.152771671459, 1.0, 1035.5833583893284, 774.3395267483642, 1.0, 753.5349065342303, 791.4356972268777, 1.0, 1068.8016718578638, 983.779761832728, 1.0, 666.0449646448034, 861.9695584374556, 1.0, 1095.9258869156356, 1163.4832923847428, 1.0, 786.231065316051, 1024.8628135810081, 1.0, 880.1976062513037, 1091.9310659604416, 1.0, 724.5532829849652, 1227.5775894192202, 1.0, 796.2373537399884, 1290.205792638792, 1.0, 664.2223820043956, 1409.0377529832285, 1.0, 735.7633804072557, 1482.841836684363, 1.0, 948.6387949472169, 665.4060224499005, 1.0, 1032.4754623523174, 509.73432205900895, 1.0, 739.3889893581304, 1429.8440586282695, 1.0, 709.2183877925171, 1419.5531634583926, 1.0, 641.9070113137954, 1449.176693756097, 1.0, 795.3557413226708, 1485.5432595467762, 1.0, 796.7023110801829, 1507.7484577153032, 1.0, 705.599001521201, 1517.6386213203577, 1.0], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [], "pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00008.json
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{"version": 1.3, "people": [{"person_id": [-1], "pose_keypoints_2d": [1021.3256512174645, 602.3711015713403, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 825.917552286083, 673.152771671459, 1.0, 1035.5833583893284, 774.3395267483642, 1.0, 753.5349065342303, 791.4356972268777, 1.0, 1068.8016718578638, 983.779761832728, 1.0, 666.0449646448034, 861.9695584374556, 1.0, 1095.9258869156356, 1163.4832923847428, 1.0, 786.231065316051, 1024.8628135810081, 1.0, 880.1976062513037, 1091.9310659604416, 1.0, 724.5532829849652, 1227.5775894192202, 1.0, 796.2373537399884, 1290.205792638792, 1.0, 664.2223820043956, 1409.0377529832285, 1.0, 735.7633804072557, 1482.841836684363, 1.0, 948.6387949472169, 665.4060224499005, 1.0, 1032.4754623523174, 509.73432205900895, 1.0, 739.3889893581304, 1429.8440586282695, 1.0, 709.2183877925171, 1419.5531634583926, 1.0, 641.9070113137954, 1449.176693756097, 1.0, 795.3557413226708, 1485.5432595467762, 1.0, 796.7023110801829, 1507.7484577153032, 1.0, 705.599001521201, 1517.6386213203577, 1.0], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": [], "pose_keypoints_3d": [], "face_keypoints_3d": [], "hand_left_keypoints_3d": [], "hand_right_keypoints_3d": []}]}

1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam01_json/Static_cam_01.00009.json
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1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam02_json/Static_cam_02.00009.json
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1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam02_json/Static_cam_02.00010.json
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1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam02_json/Static_cam_02.00011.json
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1
Pose2Sim/S00_Demo_BatchSession/S00_P00_SingleParticipant/S00_P00_T00_StaticTrial/pose/cam03_json/Static_cam_03.00000.json
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