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undistort to false by default

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davidpagnon 2024-01-08 13:01:39 +01:00
parent 4b3f41fbcd
commit 57eef5f0c4
17 changed files with 203 additions and 110 deletions
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9
Pose2Sim/Demo/S00_Demo_Session/Config.toml
View File

@ -135,8 +135,8 @@ interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
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 = true # 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 = true # 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
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!
[filtering]
@ -172,10 +172,15 @@ 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"

9
Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
# [filtering]
@ -172,10 +172,15 @@
## 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"

9
Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T00_StaticTrial/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
# [filtering]
@ -172,10 +172,15 @@
## 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"

9
Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T01_BalancingTrial/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
[filtering]
@ -172,10 +172,15 @@ display_figures = true # true or false (lowercase)
## 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"

21
Pose2Sim/Demo/S01_Empty_Session/Config.toml
View File

@ -135,8 +135,8 @@ interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
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 = true # 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 = true # 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
# 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!
[filtering]
@ -170,12 +170,17 @@ 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.
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
# [filtering]
@ -172,10 +172,15 @@
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T00_StaticTrial/Config.toml
View File

@ -135,8 +135,8 @@
# 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 = true # 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 = true # 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
# 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!
# [filtering]
@ -171,10 +171,15 @@
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T01_MotionTrial1/Config.toml
View File

@ -135,8 +135,8 @@
# 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 = true # 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 = true # 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
# 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!
[filtering]
@ -171,10 +171,15 @@ display_figures = true # true or false (lowercase)
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T02_MotionTrial2/Config.toml
View File

@ -135,8 +135,8 @@
# 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 = true # 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 = true # 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
# 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!
[filtering]
@ -171,10 +171,15 @@ display_figures = true # true or false (lowercase)
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
# [filtering]
@ -172,10 +172,15 @@
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/S01_P01_T00_StaticTrial/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
# [filtering]
@ -172,10 +172,15 @@
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/S01_P01_T01_MotionTrial1/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
[filtering]
@ -172,10 +172,15 @@ display_figures = true # true or false (lowercase)
## 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"

9
Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/S01_P01_T02_MotionTrial2/Config.toml
View File

@ -136,8 +136,8 @@
# 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 = true # 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 = true # 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
# 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!
[filtering]
@ -172,10 +172,15 @@ display_figures = true # true or false (lowercase)
## 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"

2
Pose2Sim/Utilities/json_display_with_img.py
View File

@ -91,7 +91,7 @@ def json_display_with_img_func(**args):
# Display
if display == True or display == 'True' or display =='1':
cv2.imshow('', img)
cv2.waitKey(10)
cv2.waitKey(0)
# Save
if save == True or save == 'True' or save == '1':

175
Pose2Sim/Utilities/reproj_from_trc_calib.py
View File

@ -19,7 +19,7 @@
Usage:
from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(r'<input_trc_file>', r'<input_calib_file>', '<output_format>', r'<output_file_root>')
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -f 'openpose'
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -f 'openpose' -u
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -f 'deeplabcut' -o output_file_root
'''
@ -51,44 +51,64 @@ __status__ = "Development"
'''BODY_25B (full-body without hands, experimental, from OpenPose)
https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/blob/master/experimental_models/README.md
Adjust it if your want to reproject in OpenPose format with a different model'''
nb_joints = 25
BODY_25B = Node("CHip", id=None, children=[
Node("RHip", id=12, children=[
Node("RKnee", id=14, children=[
Node("RAnkle", id=16, children=[
Node("RBigToe", id=22, children=[
Node("RSmallToe", id=23),
]),
Node("RHeel", id=24),
]),
]),
]),
Node("LHip", id=11, children=[
Node("LKnee", id=13, children=[
Node("LAnkle", id=15, children=[
Node("LBigToe", id=19, children=[
Node("LSmallToe", id=20),
]),
Node("LHeel", id=21),
]),
]),
]),
Node("Neck", id=17, children=[
Node("Head", id=18, children=[
Node("Nose", id=0),
]),
Node("RShoulder", id=6, children=[
Node("RElbow", id=8, children=[
Node("RWrist", id=10),
]),
]),
Node("LShoulder", id=5, children=[
Node("LElbow", id=7, children=[
Node("LWrist", id=9),
]),
]),
]),
])
# nb_joints = 25
# BODY_25B = Node("CHip", id=None, children=[
# Node("RHip", id=12, children=[
# Node("RKnee", id=14, children=[
# Node("RAnkle", id=16, children=[
# Node("RBigToe", id=22, children=[
# Node("RSmallToe", id=23),
# ]),
# Node("RHeel", id=24),
# ]),
# ]),
# ]),
# Node("LHip", id=11, children=[
# Node("LKnee", id=13, children=[
# Node("LAnkle", id=15, children=[
# Node("LBigToe", id=19, children=[
# Node("LSmallToe", id=20),
# ]),
# Node("LHeel", id=21),
# ]),
# ]),
# ]),
# Node("Neck", id=17, children=[
# Node("Head", id=18, children=[
# Node("Nose", id=0),
# ]),
# Node("RShoulder", id=6, children=[
# Node("RElbow", id=8, children=[
# Node("RWrist", id=10),
# ]),
# ]),
# Node("LShoulder", id=5, children=[
# Node("LElbow", id=7, children=[
# Node("LWrist", id=9),
# ]),
# ]),
# ]),
# ])
nb_joints = 17
BODY_25B = Node("None", id=None, children=[
Node("Origin", id=0),
Node("Board1", id=1),
Node("Board2", id=2),
Node("Board3", id=3),
Node("Board4", id=4),
Node("Furniture5", id=5),
Node("Furniture6", id=6),
Node("Furniture7", id=7),
Node("Screen8", id=8),
Node("Screen9", id=9),
Node("Furniture10", id=10),
Node("Furniture11", id=11),
Node("Furniture12", id=12),
Node("Furniture13", id=13),
Node("Furniture14", id=14),
Node("Furniture15", id=15),
Node("Table16", id=16)])
## FUNCTIONS
@ -221,7 +241,7 @@ def yup2zup(Q):
return Q
def reproj_from_trc_calib_func(*args):
def reproj_from_trc_calib_func(**args):
'''
Reproject 3D points from a trc file to the camera planes determined by a
toml calibration file.
@ -233,34 +253,20 @@ def reproj_from_trc_calib_func(*args):
format with a different model.
Usage:
from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(r'<input_trc_file>', r'<input_calib_file>', '<output_format>', r'<output_file_root>')
from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(input_trc_file = r'<input_trc_file>', input_calib_file = r'<input_calib_file>', openpose_output=True, deeplabcut_output=True, undistort_points=True, output_file_root = r'<output_file_root>')
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -f 'openpose'
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -f 'deeplabcut' -o output_file_root
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file --openpose_output --deeplabcut_output --undistort_points --output_file_root output_file_root
python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -o -O output_file_root
'''
try:
input_trc_file = args[0]['input_trc_file'] # invoked with argparse
input_calib_file = args[0]['input_calib_file']
if args[0]['output_format'] == None:
output_format = 'deeplabcut'
else:
output_format = args[0]['output_format']
if args[0]['output_file_root'] == None:
output_file_root = input_trc_file.replace('.trc', '_reproj')
else:
output_file_root = args[0]['output_file_root']
except:
input_trc_file = args[0] # invoked as a function
input_calib_file = args[1]
try:
output_format = args[2]
except:
output_format = 'deeplabcut'
try:
output_file_root = args[3]
except:
output_file_root = input_trc_file.replace('.trc', '_reproj')
input_trc_file = os.path.realpath(args.get('input_trc_file')) # invoked with argparse
input_calib_file = os.path.realpath(args.get('input_calib_file'))
openpose_output = args.get('openpose_output')
deeplabcut_output = args.get('deeplabcut_output')
undistort_points = args.get('undistort_points')
output_file_root = args.get('output_file_root')
if output_file_root == None:
output_file_root = input_trc_file.replace('.trc', '_reproj')
# Extract data from trc file
header_trc, data_trc = df_from_trc(input_trc_file)
@ -270,7 +276,13 @@ def reproj_from_trc_calib_func(*args):
filename = os.path.splitext(os.path.basename(input_trc_file))[0]
# Extract data from calibration file
P_all = computeP(input_calib_file, undistort_points=True)
P_all = computeP(input_calib_file, undistort=undistort_points)
if undistort_points:
calib_params = retrieve_calib_params(input_calib_file)
calib_params_R_filt = [calib_params['R'][i] for i in range(len(P_all))]
calib_params_T_filt = [calib_params['T'][i] for i in range(len(P_all))]
calib_params_K_filt = [calib_params['K'][i] for i in range(len(P_all))]
calib_params_dist_filt = [calib_params['dist'][i] for i in range(len(P_all))]
# Create camera folders
reproj_dir = os.path.realpath(output_file_root)
@ -295,13 +307,18 @@ def reproj_from_trc_calib_func(*args):
coords = [[] for cam in range(len(P_all))]
for keypoint in range(num_bodyparts):
q = np.append(Q.iloc[row,3*keypoint:3*keypoint+3], 1)
x_all, y_all = reprojection(P_all, q)
if undistort_points:
coords_2D_all = [cv2.projectPoints(np.array(q[:-1]), calib_params_R_filt[i], calib_params_T_filt[i], calib_params_K_filt[i], calib_params_dist_filt[i])[0] for i in range(len(P_all))]
x_all = [coords_2D_all[i][0,0,0] for i in range(len(P_all))]
y_all = [coords_2D_all[i][0,0,1] for i in range(len(P_all))]
else:
x_all, y_all = reprojection(P_all, q)
[coords[cam].extend([x_all[cam], y_all[cam]]) for cam in range(len(P_all))]
for cam in range(len(P_all)):
data_proj[cam].iloc[row,:] = coords[cam]
# Save as h5 and csv if DeepLabCut format
if output_format == 'deeplabcut':
if deeplabcut_output:
# to h5
h5_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}.h5') for i,cam_dir in enumerate(cam_dirs)]
[data_proj[i].to_hdf(h5_files[i], index=True, key='reprojected_points') for i in range(len(P_all))]
@ -311,9 +328,13 @@ def reproj_from_trc_calib_func(*args):
[data_proj[i].to_csv(csv_files[i], sep=',', index=True, lineterminator='\n') for i in range(len(P_all))]
# Save as json if OpenPose format
elif output_format == 'openpose':
if openpose_output:
# read body_25b tree
bodyparts_ids = [[node.id for _, _, node in RenderTree(BODY_25B) if node.name==b][0] for b in bodyparts]
model = BODY_25B
print('Keypoint hierarchy:')
for pre, _, node in RenderTree(model):
print(f'{pre}{node.name} id={node.id}')
bodyparts_ids = [[node.id for _, _, node in RenderTree(model) if node.name==b][0] for b in bodyparts]
#prepare json files
json_dict = {'version':1.3, 'people':[]}
json_dict['people'] = [{'person_id':[-1],
@ -327,11 +348,13 @@ def reproj_from_trc_calib_func(*args):
'hand_right_keypoints_3d':[]}]
# write one json file per camera and per frame
for cam, cam_dir in enumerate(cam_dirs):
print('\n', cam)
for frame in range(len(Q)):
json_dict_copy = deepcopy(json_dict)
data_proj_frame = data_proj[cam].iloc[row]['DavidPagnon']['person0']
# store 2D keypoints and respect body_25b keypoint order
for (i,b) in zip(bodyparts_ids, bodyparts):
print(repr(data_proj_frame[b].values))
json_dict_copy['people'][0]['pose_keypoints_2d'][[i*3,i*3+1,i*3+2]] = np.append(data_proj_frame[b].values, 1)
json_dict_copy['people'][0]['pose_keypoints_2d'] = json_dict_copy['people'][0]['pose_keypoints_2d'].tolist()
# write json file
@ -346,10 +369,12 @@ def reproj_from_trc_calib_func(*args):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--input_trc_file', required = True, help='trc 3D coordinates input file')
parser.add_argument('-c', '--input_calib_file', required = True, help='toml calibration input file')
parser.add_argument('-f', '--output_format', required=False, help='deeplabcut or openpose output format')
parser.add_argument('-o', '--output_file_root', required=False, help='output file root, without extension')
parser.add_argument('-t', '--input_trc_file', required = True, help='trc 3D coordinates input file path')
parser.add_argument('-c', '--input_calib_file', required = True, help='toml calibration input file path')
parser.add_argument('-o', '--openpose_output', required=False, action='store_true', help='output format in the openpose json format')
parser.add_argument('-d', '--deeplabcut_output', required=False, action='store_true', help='output format in the deeplabcut csv and json formats')
parser.add_argument('-u', '--undistort_points', required=False, action='store_true', help='takes distortion into account if True')
parser.add_argument('-O', '--output_file_root', required=False, help='output file root path, without extension')
args = vars(parser.parse_args())
reproj_from_trc_calib_func(args)
reproj_from_trc_calib_func(**args)

5
README.md
View File

@ -14,7 +14,7 @@
# Pose2Sim
## Please set undistort_points to false for now since reprojection error is currently inaccurate. Should be fixed this weekend (6-7 Jan 2024)
## Please set undistort_points to false for now since reprojection error is currently inaccurate. I'll try to fix it soon.
> **_News_: Version 0.5 released:** \
> **Deep change in the folder structure to allow for automatic batch processing!**\
@ -555,6 +555,9 @@ Build a trc file from a .mot motion file and a .osim model file.
[bodykin_from_mot_osim.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/bodykin_from_mot_osim.py)
Converts a mot file to a .csv file with rotation and orientation of all segments.
[reproj_from_trc_calib.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/reproj_from_trc_calib.py)
Reprojects 3D coordinates of a trc file to the image planes defined by a calibration file. Output in OpenPose or DeepLabCut format.
</pre>
</details>

2
setup.cfg
View File

@ -1,6 +1,6 @@
[metadata]
name = pose2sim
version = 0.5.4
version = 0.5.5
author = David Pagnon
author_email = contact@david-pagnon.com
description = Perform a markerless kinematic analysis from multiple calibrated views as a unified workflow from an OpenPose input to an OpenSim result.