fulyvv/pose2sim
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

beta test opensim scaling and kinematics with edits

Browse Source
This commit is contained in:
davidpagnon 2024-09-20 02:23:13 +02:00
parent e321228a75
commit 65e675ab64
13 changed files with 554 additions and 471 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
Pose2Sim/Demo_Batch/Config.toml
View File

@ -136,7 +136,6 @@ calibration_type = 'convert' # 'convert' or 'calculate'
[triangulation] [triangulation]
reorder_trc = false # only checked if multi_person analysis
reproj_error_threshold_triangulation = 15 # px reproj_error_threshold_triangulation = 15 # px
likelihood_threshold_triangulation= 0.3 likelihood_threshold_triangulation= 0.3
min_cameras_for_triangulation = 2 min_cameras_for_triangulation = 2
@ -181,14 +180,10 @@ make_c3d = true # save triangulated data in c3d format in addition to trc
[opensim] [opensim]
static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial'] use_augmentation = true # true or false (lowercase) # Set to true if you want to use the model with augmented markers
# # If this Config.toml file is at the Trial level, set to true or false (lowercase); right_left_symmetry = true # true or false (lowercase) # Set to false only if you have good reasons to think the participant is not symmetrical (e.g. prosthetic limb)
# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial']; remove_individual_scaling_setup = true # true or false (lowercase) # If true, the individual scaling setup files are removed to avoid cluttering
# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial'] remove_individual_IK_setup = true # true or false (lowercase) # If true, the individual IK setup files are removed to avoid cluttering
opensim_bin_path = 'C:\OpenSim 4.4\bin'
use_augmentation = false # If using augmented measurements then set it true
load_trc_name = 'filtered' # 'default' or 'filtered', if use_augmentation = true, this line will be ignored instead using __LSTM.trc
IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
@ -204,7 +199,7 @@ IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
# Check your model hierarchy with: for pre, _, node in RenderTree(model): # Check your model hierarchy with: for pre, _, node in RenderTree(model):
# print(f'{pre}{node.name} id={node.id}') # print(f'{pre}{node.name} id={node.id}')
[pose.CUSTOM] [pose.CUSTOM]
name = "CHip" name = "Hip"
id = "19" id = "19"
[[pose.CUSTOM.children]] [[pose.CUSTOM.children]]
name = "RHip" name = "RHip"

15
Pose2Sim/Demo_Batch/Trial_1/Config.toml
View File

@ -136,7 +136,6 @@
# [triangulation] # [triangulation]
# reorder_trc = false # only checked if multi_person analysis
# reproj_error_threshold_triangulation = 15 # px # reproj_error_threshold_triangulation = 15 # px
# likelihood_threshold_triangulation= 0.3 # likelihood_threshold_triangulation= 0.3
# min_cameras_for_triangulation = 2 # min_cameras_for_triangulation = 2
@ -181,14 +180,10 @@
# [opensim] # [opensim]
#static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial'] # use_augmentation = true # true or false (lowercase) # Set to true if you want to use the model with augmented markers
# # If this Config.toml file is at the Trial level, set to true or false (lowercase); # right_left_symmetry = true # true or false (lowercase) # Set to false only if you have good reasons to think the participant is not symmetrical (e.g. prosthetic limb)
# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial']; # remove_individual_scaling_setup = true # true or false (lowercase) # If true, the individual scaling setup files are removed to avoid cluttering
# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial'] # remove_individual_IK_setup = true # true or false (lowercase) # If true, the individual IK setup files are removed to avoid cluttering
#opensim_bin_path = 'C:\OpenSim 4.4\bin'
#use_augmentation = false # If using augmented measurements then set it true
#load_trc_name = 'filtered' # 'default' or 'filtered', if use_augmentation = true, this line will be ignored instead using __LSTM.trc
#IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
@ -204,7 +199,7 @@
# # Check your model hierarchy with: for pre, _, node in RenderTree(model): # # Check your model hierarchy with: for pre, _, node in RenderTree(model):
# # print(f'{pre}{node.name} id={node.id}') # # print(f'{pre}{node.name} id={node.id}')
# [pose.CUSTOM] # [pose.CUSTOM]
# name = "CHip" # name = "Hip"
# id = "19" # id = "19"
# [[pose.CUSTOM.children]] # [[pose.CUSTOM.children]]
# name = "RHip" # name = "RHip"

19
Pose2Sim/Demo_Batch/Trial_2/Config.toml
View File

@ -19,8 +19,8 @@
[project] [project]
multi_person = true # true for trials with multiple participants. If false, only the main person in scene is analyzed (and it run much faster). 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, 1.86] # m # float if single person, list of float if multi-person (same order as the Static trials) # Only used for marker augmentation 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, 88.0] # kg # Only used for marker augmentation and scaling 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_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. # frame_range = [] # For example [10,300], or [] for all frames.
@ -136,7 +136,6 @@ keypoints_to_consider = 'all' # 'all' if all points should be considered, for ex
# [triangulation] # [triangulation]
# reorder_trc = false # only checked if multi_person analysis
# reproj_error_threshold_triangulation = 15 # px # reproj_error_threshold_triangulation = 15 # px
# likelihood_threshold_triangulation= 0.3 # likelihood_threshold_triangulation= 0.3
# min_cameras_for_triangulation = 2 # min_cameras_for_triangulation = 2
@ -181,14 +180,10 @@ keypoints_to_consider = 'all' # 'all' if all points should be considered, for ex
# [opensim] # [opensim]
#static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial'] # use_augmentation = true # true or false (lowercase) # Set to true if you want to use the model with augmented markers
# # If this Config.toml file is at the Trial level, set to true or false (lowercase); # right_left_symmetry = true # true or false (lowercase) # Set to false only if you have good reasons to think the participant is not symmetrical (e.g. prosthetic limb)
# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial']; # remove_individual_scaling_setup = true # true or false (lowercase) # If true, the individual scaling setup files are removed to avoid cluttering
# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial'] # remove_individual_IK_setup = true # true or false (lowercase) # If true, the individual IK setup files are removed to avoid cluttering
#opensim_bin_path = 'C:\OpenSim 4.4\bin'
#use_augmentation = false # If using augmented measurements then set it true
#load_trc_name = 'filtered' # 'default' or 'filtered', if use_augmentation = true, this line will be ignored instead using __LSTM.trc
#IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
@ -204,7 +199,7 @@ keypoints_to_consider = 'all' # 'all' if all points should be considered, for ex
# # Check your model hierarchy with: for pre, _, node in RenderTree(model): # # Check your model hierarchy with: for pre, _, node in RenderTree(model):
# # print(f'{pre}{node.name} id={node.id}') # # print(f'{pre}{node.name} id={node.id}')
# [pose.CUSTOM] # [pose.CUSTOM]
# name = "CHip" # name = "Hip"
# id = "19" # id = "19"
# [[pose.CUSTOM.children]] # [[pose.CUSTOM.children]]
# name = "RHip" # name = "RHip"

19
Pose2Sim/Demo_MultiPerson/Config.toml
View File

@ -19,8 +19,8 @@
[project] [project]
multi_person = true # true for trials with multiple participants. If false, only the main person in scene is analyzed (and it run much faster). 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, 1.90] # m # float if single person, list of float if multi-person (same order as the Static trials) # Only used for marker augmentation 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, 90.0] # kg # Only used for marker augmentation and scaling 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_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. frame_range = [] # For example [10,300], or [] for all frames.
@ -136,7 +136,6 @@ calibration_type = 'convert' # 'convert' or 'calculate'
[triangulation] [triangulation]
reorder_trc = false # only checked if multi_person analysis
reproj_error_threshold_triangulation = 15 # px reproj_error_threshold_triangulation = 15 # px
likelihood_threshold_triangulation= 0.3 likelihood_threshold_triangulation= 0.3
min_cameras_for_triangulation = 2 min_cameras_for_triangulation = 2
@ -181,14 +180,10 @@ make_c3d = true # save triangulated data in c3d format in addition to trc
[opensim] [opensim]
static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial'] use_augmentation = true # true or false (lowercase) # Set to true if you want to use the model with augmented markers
# # If this Config.toml file is at the Trial level, set to true or false (lowercase); right_left_symmetry = true # true or false (lowercase) # Set to false only if you have good reasons to think the participant is not symmetrical (e.g. prosthetic limb)
# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial']; remove_individual_scaling_setup = true # true or false (lowercase) # If true, the individual scaling setup files are removed to avoid cluttering
# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial'] remove_individual_IK_setup = true # true or false (lowercase) # If true, the individual IK setup files are removed to avoid cluttering
opensim_bin_path = 'C:\OpenSim 4.4\bin'
use_augmentation = false # If using augmented measurements then set it true
load_trc_name = 'filtered' # 'default' or 'filtered', if use_augmentation = true, this line will be ignored instead using __LSTM.trc
IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
@ -204,7 +199,7 @@ IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
# Check your model hierarchy with: for pre, _, node in RenderTree(model): # Check your model hierarchy with: for pre, _, node in RenderTree(model):
# print(f'{pre}{node.name} id={node.id}') # print(f'{pre}{node.name} id={node.id}')
[pose.CUSTOM] [pose.CUSTOM]
name = "CHip" name = "Hip"
id = "19" id = "19"
[[pose.CUSTOM.children]] [[pose.CUSTOM.children]]
name = "RHip" name = "RHip"

23
Pose2Sim/Demo_SinglePerson/Config.toml
View File

@ -53,9 +53,9 @@ output_format = 'openpose' # 'openpose', 'mmpose', 'deeplabcut', 'none' or a lis
[synchronization] [synchronization]
display_sync_plots = true # true or false (lowercase) display_sync_plots = true # true or false (lowercase)
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 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. # ['RWrist', 'RElbow'] list of keypoint names if you want to specify keypoints with a sharp vertical motion.
approx_time_maxspeed = 'auto' # 'auto' if you want to consider the whole capture (default, slower if long sequences) approx_time_maxspeed = 'auto' # 'auto' if you want to consider the whole capture (default, slower if long sequences)
# [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 # [10.0, 2.0, 8.0, 11.0] list of times (seconds) if you want to specify the approximate time of a clear vertical event for each camera
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] 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 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_cutoff = 6 # time series are smoothed to get coherent time-lagged correlation
@ -136,7 +136,6 @@ calibration_type = 'convert' # 'convert' or 'calculate'
[triangulation] [triangulation]
reorder_trc = false # only checked if multi_person analysis
reproj_error_threshold_triangulation = 15 # px reproj_error_threshold_triangulation = 15 # px
likelihood_threshold_triangulation= 0.3 likelihood_threshold_triangulation= 0.3
min_cameras_for_triangulation = 2 min_cameras_for_triangulation = 2
@ -180,17 +179,11 @@ make_c3d = true # also save triangulated data in c3d format
make_c3d = true # save triangulated data in c3d format in addition to trc make_c3d = true # save triangulated data in c3d format in addition to trc
[opensim] [kinematics]
static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial'] use_augmentation = true # true or false (lowercase) # Set to true if you want to use the model with augmented markers
# # If this Config.toml file is at the Trial level, set to true or false (lowercase); right_left_symmetry = true # true or false (lowercase) # Set to false only if you have good reasons to think the participant is not symmetrical (e.g. prosthetic limb)
# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial']; remove_individual_scaling_setup = true # true or false (lowercase) # If true, the individual scaling setup files are removed to avoid cluttering
# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial'] remove_individual_IK_setup = true # true or false (lowercase) # If true, the individual IK setup files are removed to avoid cluttering
opensim_bin_path = 'C:\OpenSim 4.4\bin'
use_augmentation = false # If using augmented measurements then set it true
load_trc_name = 'filtered' # 'default' or 'filtered', if use_augmentation = true, this line will be ignored instead using __LSTM.trc
IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
@ -206,7 +199,7 @@ IK_timeRange = [] #left empty to IK full range or eg.[0.5,1.0]
# Check your model hierarchy with: for pre, _, node in RenderTree(model): # Check your model hierarchy with: for pre, _, node in RenderTree(model):
# print(f'{pre}{node.name} id={node.id}') # print(f'{pre}{node.name} id={node.id}')
[pose.CUSTOM] [pose.CUSTOM]
name = "CHip" name = "Hip"
id = "19" id = "19"
[[pose.CUSTOM.children]] [[pose.CUSTOM.children]]
name = "RHip" name = "RHip"

2
Pose2Sim/OpenSim_Setup/Model_Pose2Sim_Coco133.osim
View File

@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" ?> <?xml version="1.0" encoding="UTF-8" ?>
<OpenSimDocument Version="40000"> <OpenSimDocument Version="40000">
<Model name="Pose2Sim_AlphaPose"> <Model name="Pose2Sim_RTMPose">
<!--The model's ground reference frame.--> <!--The model's ground reference frame.-->
<Ground name="ground"> <Ground name="ground">
<!--The geometry used to display the axes of this Frame.--> <!--The geometry used to display the axes of this Frame.-->

4
Pose2Sim/OpenSim_Setup/Model_Pose2Sim_Halpe26.osim
View File

@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" ?> <?xml version="1.0" encoding="UTF-8" ?>
<OpenSimDocument Version="40000"> <OpenSimDocument Version="40000">
<Model name="Pose2Sim_AlphaPose"> <Model name="Pose2Sim_RTMPose">
<!--The model's ground reference frame.--> <!--The model's ground reference frame.-->
<Ground name="ground"> <Ground name="ground">
<!--The geometry used to display the axes of this Frame.--> <!--The geometry used to display the axes of this Frame.-->
@ -6808,7 +6808,7 @@
<!--Path to a Component that satisfies the Socket 'parent_frame' of type PhysicalFrame (description: The frame to which this station is fixed.).--> <!--Path to a Component that satisfies the Socket 'parent_frame' of type PhysicalFrame (description: The frame to which this station is fixed.).-->
<socket_parent_frame>/bodyset/pelvis</socket_parent_frame> <socket_parent_frame>/bodyset/pelvis</socket_parent_frame>
<!--The fixed location of the station expressed in its parent frame.--> <!--The fixed location of the station expressed in its parent frame.-->
<location>-0.063927399999999995 0 0</location> <location>-0.063927399999999995 -0.081343112945556642 0</location>
</Marker> </Marker>
<Marker name="LHip"> <Marker name="LHip">
<!--Path to a Component that satisfies the Socket 'parent_frame' of type PhysicalFrame (description: The frame to which this station is fixed.).--> <!--Path to a Component that satisfies the Socket 'parent_frame' of type PhysicalFrame (description: The frame to which this station is fixed.).-->

2
Pose2Sim/OpenSim_Setup/Model_Pose2Sim_Halpe68_136.osim
View File

@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" ?> <?xml version="1.0" encoding="UTF-8" ?>
<OpenSimDocument Version="40000"> <OpenSimDocument Version="40000">
<Model name="Pose2Sim_AlphaPose"> <Model name="Pose2Sim_RTMPose">
<!--The model's ground reference frame.--> <!--The model's ground reference frame.-->
<Ground name="ground"> <Ground name="ground">
<!--The geometry used to display the axes of this Frame.--> <!--The geometry used to display the axes of this Frame.-->

2
Pose2Sim/Pose2Sim.py
View File

@ -539,7 +539,7 @@ def kinematics(config=None):
end = time.time() end = time.time()
elapsed = end - start elapsed = end - start
logging.info(f'\nOpenSim scaling and inverse kinematics took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.\n') logging.info(f'OpenSim scaling and inverse kinematics took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.\n')
def runAll(config=None, do_calibration=True, do_poseEstimation=True, do_synchronization=True, do_personAssociation=True, do_triangulation=True, do_filtering=True, do_markerAugmentation=True, do_kinematics=True): def runAll(config=None, do_calibration=True, do_poseEstimation=True, do_synchronization=True, do_personAssociation=True, do_triangulation=True, do_filtering=True, do_markerAugmentation=True, do_kinematics=True):

32
Pose2Sim/common.py
View File

@ -219,11 +219,11 @@ def euclidean_distance(q1, q2):
INPUTS: INPUTS:
- q1: list of N_dimensional coordinates of point - q1: list of N_dimensional coordinates of point
or list of N points of N_dimensional coordinates
- q2: idem - q2: idem
OUTPUTS: OUTPUTS:
- euc_dist: float. Euclidian distance between q1 and q2 - euc_dist: float. Euclidian distance between q1 and q2
''' '''
q1 = np.array(q1) q1 = np.array(q1)
@ -233,11 +233,40 @@ def euclidean_distance(q1, q2):
dist = np.empty_like(dist) dist = np.empty_like(dist)
dist[...] = np.inf dist[...] = np.inf
if len(dist.shape)==1:
euc_dist = np.sqrt(np.nansum( [d**2 for d in dist])) euc_dist = np.sqrt(np.nansum( [d**2 for d in dist]))
else:
euc_dist = np.sqrt(np.nansum( [d**2 for d in dist], axis=1))
return euc_dist return euc_dist
def trimmed_mean(arr, trimmed_percent=0.5):
'''
Trimmed mean calculation for an array.
INPUTS:
- arr (np.array): The input array.
- trimmed_percent (float): The percentage of values to be trimmed from both ends.
OUTPUTS:
- float: The trimmed mean of the array.
'''
# Sort the array
sorted_arr = np.sort(arr)
# Determine the indices for the 25th and 75th percentiles (if trimmed_percent = 0.5)
lower_idx = int(len(sorted_arr) * (trimmed_percent/2))
upper_idx = int(len(sorted_arr) * (1 - trimmed_percent/2))
# Slice the array to exclude the 25% lowest and highest values
trimmed_arr = sorted_arr[lower_idx:upper_idx]
# Return the mean of the remaining values
return np.mean(trimmed_arr)
def world_to_camera_persp(r, t): def world_to_camera_persp(r, t):
''' '''
Converts rotation R and translation T Converts rotation R and translation T
@ -364,6 +393,7 @@ def natural_sort_key(s):
Sorts list of strings with numbers in natural order (alphabetical and numerical) Sorts list of strings with numbers in natural order (alphabetical and numerical)
Example: ['item_1', 'item_2', 'item_10', 'stuff_1'] Example: ['item_1', 'item_2', 'item_10', 'stuff_1']
''' '''
s=str(s)
return [int(c) if c.isdigit() else c.lower() for c in re.split(r'(\d+)', s)] return [int(c) if c.isdigit() else c.lower() for c in re.split(r'(\d+)', s)]

808
Pose2Sim/kinematics.py
View File

@ -24,352 +24,213 @@ OUTPUT:
- scaled OpenSim model files (.osim) - scaled OpenSim model files (.osim)
- joint angle data files (.mot) - joint angle data files (.mot)
''' '''
## INIT
import os import os
import sys import sys
from collections import defaultdict
from pathlib import Path from pathlib import Path
import numpy as np import numpy as np
import pandas as pd import pandas as pd
from lxml import etree from lxml import etree
import logging import logging
from anytree import PreOrderIter
import opensim import opensim
from Pose2Sim.common import natural_sort_key, euclidean_distance, trimmed_mean
from Pose2Sim.skeletons import *
## AUTHORSHIP INFORMATION
__author__ = "Ivan Sun, David Pagnon"
__copyright__ = "Copyright 2021, Pose2Sim"
__credits__ = ["Ivan Sun", "David Pagnon"]
__license__ = "BSD 3-Clause License"
__version__ = "0.10.0"
__maintainer__ = "David Pagnon"
__email__ = "contact@david-pagnon.com"
__status__ = "Development"
## FUNCTIONS ## FUNCTIONS
def find_config_and_pose3d(project_dir):
"""
Find configuration files and associated pose-3d directories in the project directory.
Args:
project_dir (str): The root directory of the project.
Returns:
list: A list of tuples containing the config path and the corresponding pose-3d directory.
"""
config_paths = []
for root, dirs, files in os.walk(project_dir):
if 'Config.toml' in files:
config_path = Path(root) / 'Config.toml'
possible_pose3d_dir = Path(root) / 'pose-3d'
if not possible_pose3d_dir.exists():
possible_pose3d_dir = Path(root).parent / 'pose-3d'
if possible_pose3d_dir.exists():
config_paths.append((config_path, possible_pose3d_dir))
else:
logging.warning(f"No pose-3d directory found for config: {config_path}")
return config_paths
def get_grouped_files(directory, pattern='*.trc'):
"""
Group TRC files by person ID or treat them as single-person if no ID is found.
Args:
directory (str): The directory containing TRC files.
pattern (str): The file pattern to search for.
Returns:
dict: A dictionary grouping TRC files by person ID.
"""
files = list(Path(directory).glob(pattern))
grouped_files = defaultdict(list)
for file in files:
parts = file.stem.split('_')
if len(parts) > 2 and 'P' in parts[2]: # Multi-person file naming convention
person_id = parts[2]
else:
person_id = "SinglePerson"
grouped_files[person_id].append(file)
return grouped_files
def process_all_groups(config_dict):
"""
Process all groups (single or multi-person) based on the configuration.
Args:
config_dict (dict): The configuration dictionary containing project details.
"""
logging.info("Processing all groups in the project.")
project_dir = config_dict.get('project', {}).get('project_dir')
config_and_pose3d_paths = find_config_and_pose3d(project_dir)
for config_path, pose3d_dir in config_and_pose3d_paths:
logging.info(f"Processing setup with config: {config_path}")
trc_groups = get_grouped_files(pose3d_dir)
trial_name = Path(pose3d_dir).parent.name # Use the parent directory name as the trial name
for person_id, trc_files in trc_groups.items():
filtered_trc_files = load_trc(config_dict, trc_files)
# Ensure output directory includes the trial name
trial_output_dir = get_output_dir(Path(config_dict['project']['project_dir']).parent / trial_name, person_id)
perform_scaling(config_dict, person_id, filtered_trc_files, trial_output_dir)
perform_inverse_kinematics(config_dict, person_id, filtered_trc_files, trial_output_dir)
def load_trc(config_dict, trc_files):
"""
Load and filter TRC files according to the configuration.
Args:
config_dict (dict): The configuration dictionary.
trc_files (list): A list of TRC file paths.
Returns:
list: A list of filtered TRC files based on the criteria specified in the configuration.
"""
opensim_config = config_dict.get('opensim', {})
use_lstm = opensim_config.get('use_augmentation', False)
load_trc_name = opensim_config.get('load_trc_name', 'default')
# Filter out any scaled TRC files
unscaled_trc_files = [file for file in trc_files if '_scaling' not in str(file)]
logging.info(f"Starting TRC file filtering with criteria: use_lstm = {use_lstm}, load_trc_name = {load_trc_name}")
logging.info(f"Initial list of TRC files: {unscaled_trc_files}")
# Initialize the list to store filtered TRC files
trc_files = []
# Check for LSTM files if LSTM is being used
if use_lstm:
lstm_files = [file for file in unscaled_trc_files if '_LSTM.trc' in str(file)]
if not lstm_files:
raise FileNotFoundError("No LSTM TRC file found in the provided list.")
trc_files.extend(lstm_files)
# Check for default or filtered TRC files
if load_trc_name == 'default':
default_files = [file for file in unscaled_trc_files if '_LSTM' not in str(file) and '_filt_butterworth' not in str(file)]
trc_files.extend(default_files)
elif load_trc_name == 'filtered':
filtered_files = [file for file in unscaled_trc_files if '_filt_butterworth' in str(file) and '_LSTM' not in str(file)]
trc_files.extend(filtered_files)
# If no TRC files are found after filtering, raise an error
if not trc_files:
logging.error(f"No suitable TRC files found with the specified criteria: use_lstm = {use_lstm}, load_trc_name = {load_trc_name}")
raise FileNotFoundError(f"No suitable TRC files found in the provided list with the specified criteria: use_lstm = {use_lstm}, load_trc_name = {load_trc_name}")
logging.info(f"Filtered TRC files: {trc_files}")
return trc_files
def read_trc(trc_path): def read_trc(trc_path):
""" '''
Read a TRC file and extract its contents. Read a TRC file and extract its contents.
Args: INPUTS:
trc_path (str): The path to the TRC file. - trc_path (str): The path to the TRC file.
OUTPUTS:
- tuple: A tuple containing the Q coordinates, frames column, time column, and header.
'''
Returns:
tuple: A tuple containing the Q coordinates, frames column, time column, and header.
"""
try: try:
logging.info(f"Attempting to read TRC file: {trc_path}")
with open(trc_path, 'r') as trc_file: with open(trc_path, 'r') as trc_file:
header = [next(trc_file) for _ in range(5)] header = [next(trc_file) for _ in range(5)]
markers = header[3].split('\t')[2::3][:-1]
trc_df = pd.read_csv(trc_path, sep="\t", skiprows=4, encoding='utf-8') trc_df = pd.read_csv(trc_path, sep="\t", skiprows=4, encoding='utf-8')
frames_col, time_col = trc_df.iloc[:, 0], trc_df.iloc[:, 1] frames_col, time_col = trc_df.iloc[:, 0], trc_df.iloc[:, 1]
Q_coords = trc_df.drop(trc_df.columns[[0, 1]], axis=1) Q_coords = trc_df.drop(trc_df.columns[[0, 1, -1]], axis=1)
return Q_coords, frames_col, time_col, header
return Q_coords, frames_col, time_col, markers, header
except Exception as e: except Exception as e:
logging.error(f"Error reading TRC file at {trc_path}: {e}") logging.error(f"Error reading TRC file at {trc_path}: {e}")
raise raise
def make_trc_with_Q(Q, header, trc_path): def get_opensim_setup_dir():
""" '''
Write the processed Q coordinates back to a TRC file.
Args:
Q (pd.DataFrame): The Q coordinates data.
header (list): The header of the original TRC file.
trc_path (str): Path to save the new TRC file.
"""
header_2_split = header[2].split('\t')
header_2_split[2] = str(len(Q))
header_2_split[-1] = str(len(Q))
header[2] = '\t'.join(header_2_split) + '\n'
time = pd.Series(np.arange(len(Q)) / float(header_2_split[0]), name='t')
Q.insert(0, 't', time)
with open(trc_path, 'w') as trc_o:
[trc_o.write(line) for line in header]
Q.to_csv(trc_o, sep='\t', index=True, header=None, lineterminator='\n')
def get_key(config_dict):
"""
Determine the key for the OpenSim model and setup files based on the configuration.
Args:
config_dict (dict): The configuration dictionary.
Returns:
str: The key used to select the model and setup files.
"""
use_augmentation = config_dict.get('opensim', {}).get('use_augmentation', False)
if use_augmentation:
return 'LSTM'
pose_model = config_dict.get('pose', {}).get('pose_model', '').upper()
if not pose_model:
raise ValueError(f"Invalid or missing 'pose_model' in config: {pose_model}")
return pose_model
def get_OpenSim_Setup():
"""
Locate the OpenSim setup directory within the Pose2Sim package. Locate the OpenSim setup directory within the Pose2Sim package.
Returns: OUTPUTS:
Path: The path to the OpenSim setup directory. - Path: The path to the OpenSim setup directory.
""" '''
pose2sim_path = Path(sys.modules['Pose2Sim'].__file__).resolve().parent pose2sim_path = Path(sys.modules['Pose2Sim'].__file__).resolve().parent
setup_dir = pose2sim_path / 'OpenSim_Setup' setup_dir = pose2sim_path / 'OpenSim_Setup'
return setup_dir return setup_dir
def get_Model(config_dict): def get_model_path(model_name, osim_setup_dir):
""" '''
Retrieve the OpenSim model file path based on the configuration. Retrieve the path of the OpenSim model file.
Args: INPUTS:
config_dict (dict): The configuration dictionary. - model_name (str): Name of the model
- osim_setup_dir (Path): Path to the OpenSim setup directory.
Returns: OUTPUTS:
str: The path to the OpenSim model file. - pose_model_path: (Path) Path to the OpenSim model file.
""" '''
setup_key = get_key(config_dict)
setup_dir = get_OpenSim_Setup()
if setup_key == 'LSTM': if model_name == 'BODY_25B':
pose_model_file = 'Model_Pose2Sim_LSTM.osim'
elif setup_key == 'BLAZEPOSE':
pose_model_file = 'Model_Pose2Sim_Blazepose.osim'
elif setup_key == 'BODY_25':
pose_model_file = 'Model_Pose2Sim_Body25.osim'
elif setup_key == 'BODY_25B':
pose_model_file = 'Model_Setup_Pose2Sim_Body25b.osim' pose_model_file = 'Model_Setup_Pose2Sim_Body25b.osim'
elif setup_key == 'BODY_135': elif model_name == 'BODY_25':
pose_model_file = 'Model_Pose2Sim_Body25.osim'
elif model_name == 'BODY_135':
pose_model_file = 'Model_Pose2Sim_Body135.osim' pose_model_file = 'Model_Pose2Sim_Body135.osim'
elif setup_key == 'COCO_17': elif model_name == 'BLAZEPOSE':
pose_model_file = 'Model_Pose2Sim_Coco17.osim' pose_model_file = 'Model_Pose2Sim_Blazepose.osim'
elif setup_key == 'COCO_133': elif model_name == 'HALPE_26':
pose_model_file = 'Model_Pose2Sim_Coco133.osim'
elif setup_key == 'HALPE_26':
pose_model_file = 'Model_Pose2Sim_Halpe26.osim' pose_model_file = 'Model_Pose2Sim_Halpe26.osim'
elif setup_key == 'HALPE_68': elif model_name == 'HALPE_68' or model_name == 'HALPE_136':
pose_model_file = 'Model_Pose2Sim_Halpe68_136.osim' pose_model_file = 'Model_Pose2Sim_Halpe68_136.osim'
elif model_name == 'COCO_133':
pose_model_file = 'Model_Pose2Sim_Coco133.osim'
# elif model_name == 'COCO' or model_name == 'MPII':
# pose_model_file = 'Model_Pose2Sim_Coco.osim'
elif model_name == 'COCO_17':
pose_model_file = 'Model_Pose2Sim_Coco17.osim'
elif model_name == 'LSTM':
pose_model_file = 'Model_Pose2Sim_LSTM.osim'
else: else:
raise ValueError(f"pose_model '{setup_key}' not found.") raise ValueError(f"Pose model '{model_name}' not found.")
pose_model_path = os.path.join(setup_dir, pose_model_file) unscaled_model_path = osim_setup_dir / pose_model_file
return pose_model_path return unscaled_model_path
def get_Scale_Setup(config_dict): def get_scaling_setup(model_name, osim_setup_dir):
""" '''
Retrieve the OpenSim scaling setup file path based on the configuration. Retrieve the OpenSim scaling setup file path.
Args: INPUTS:
config_dict (dict): The configuration dictionary. - model_name (str): Name of the model
- osim_setup_dir (Path): Path to the OpenSim setup directory.
Returns: OUTPUTS:
str: The path to the OpenSim scaling setup file. - scaling_setup_path: (Path) Path to the OpenSim scaling setup file.
""" '''
setup_key = get_key(config_dict)
setup_dir = get_OpenSim_Setup()
if setup_key == 'LSTM': if model_name == 'BODY_25B':
scale_setup_file = 'Scaling_Setup_Pose2Sim_LSTM.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Body25b.xml'
elif setup_key == 'BLAZEPOSE': elif model_name == 'BODY_25':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Blazepose.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Body25.xml'
elif setup_key == 'BODY_25': elif model_name == 'BODY_135':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Body25.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Body135.xml'
elif setup_key == 'BODY_25B': elif model_name == 'BLAZEPOSE':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Body25b.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Blazepose.xml'
elif setup_key == 'BODY_135': elif model_name == 'HALPE_26':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Body135.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Halpe26.xml'
elif setup_key == 'COCO_17': elif model_name == 'HALPE_68' or model_name == 'HALPE_136':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Coco17.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Halpe68_136.xml'
elif setup_key == 'COCO_133': elif model_name == 'COCO_133':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Coco133.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Coco133.xml'
elif setup_key == 'HALPE_26': # elif model_name == 'COCO' or model_name == 'MPII':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Halpe26.xml' # scaling_setup_file = 'Scaling_Setup_Pose2Sim_Coco.xml'
elif setup_key == 'HALPE_68': elif model_name == 'COCO_17':
scale_setup_file = 'Scaling_Setup_Pose2Sim_Halpe68_136.xml' scaling_setup_file = 'Scaling_Setup_Pose2Sim_Coco17.xml'
elif model_name == 'LSTM':
scaling_setup_file = 'Scaling_Setup_Pose2Sim_LSTM.xml'
else: else:
raise ValueError(f"pose_model '{setup_key}' not found.") raise ValueError(f"Pose model '{model_name}' not found.")
scale_setup_path = os.path.join(setup_dir, scale_setup_file) scaling_setup_path = osim_setup_dir / scaling_setup_file
return scale_setup_path return scaling_setup_path
def get_IK_Setup(config_dict): def get_IK_Setup(model_name, osim_setup_dir):
""" '''
Retrieve the OpenSim inverse kinematics setup file path based on the configuration. Retrieve the OpenSim inverse kinematics setup file path.
Args: INPUTS:
config_dict (dict): The configuration dictionary. - model_name (str): Name of the model
- osim_setup_dir (Path): Path to the OpenSim setup directory.
Returns: OUTPUTS:
str: The path to the OpenSim inverse kinematics setup file. - ik_setup_path: (Path) Path to the OpenSim IK setup file.
""" '''
setup_key = get_key(config_dict)
setup_dir = get_OpenSim_Setup()
if setup_key == 'LSTM': if model_name == 'BODY_25B':
ik_setup_file = 'IK_Setup_Pose2Sim_LSTM.xml'
elif setup_key == 'BLAZEPOSE':
ik_setup_file = 'IK_Setup_Pose2Sim_Blazepose.xml'
elif setup_key == 'BODY_25':
ik_setup_file = 'IK_Setup_Pose2Sim_Body25.xml'
elif setup_key == 'BODY_25B':
ik_setup_file = 'IK_Setup_Pose2Sim_Body25b.xml' ik_setup_file = 'IK_Setup_Pose2Sim_Body25b.xml'
elif setup_key == 'BODY_135': elif model_name == 'BODY_25':
ik_setup_file = 'IK_Setup_Pose2Sim_Body25.xml'
elif model_name == 'BODY_135':
ik_setup_file = 'IK_Setup_Pose2Sim_Body135.xml' ik_setup_file = 'IK_Setup_Pose2Sim_Body135.xml'
elif setup_key == 'COCO_17': elif model_name == 'BLAZEPOSE':
ik_setup_file = 'IK_Setup_Pose2Sim_Coco17.xml' ik_setup_file = 'IK_Setup_Pose2Sim_Blazepose.xml'
elif setup_key == 'COCO_133': elif model_name == 'HALPE_26':
ik_setup_file = 'IK_Setup_Pose2Sim_Coco133.xml'
elif setup_key == 'HALPE_26':
ik_setup_file = 'IK_Setup_Pose2Sim_Halpe26.xml' ik_setup_file = 'IK_Setup_Pose2Sim_Halpe26.xml'
elif setup_key == 'HALPE_68': elif model_name == 'HALPE_68' or model_name == 'HALPE_136':
ik_setup_file = 'IK_Setup_Pose2Sim_Halpe68_136.xml' ik_setup_file = 'IK_Setup_Pose2Sim_Halpe68_136.xml'
elif model_name == 'COCO_133':
ik_setup_file = 'IK_Setup_Pose2Sim_Coco133.xml'
# elif model_name == 'COCO' or model_name == 'MPII':
# ik_setup_file = 'IK_Setup_Pose2Sim_Coco.xml'
elif model_name == 'COCO_17':
ik_setup_file = 'IK_Setup_Pose2Sim_Coco17.xml'
elif model_name == 'LSTM':
ik_setup_file = 'IK_Setup_Pose2Sim_LSTM.xml'
else: else:
raise ValueError(f"pose_model '{setup_key}' not found.") raise ValueError(f"Pose model '{model_name}' not found.")
ik_setup_path = os.path.join(setup_dir, ik_setup_file) ik_setup_path = osim_setup_dir / ik_setup_file
return ik_setup_path return ik_setup_path
def get_output_dir(config_dir, person_id): # def get_output_dir(config_dir, person_id):
""" '''
Determines the correct output directory based on the configuration and the person identifier. Determines the correct output directory based on the configuration and the person identifier.
Args: INPUTS:
config_dir (Path): The root directory where the configuration file is located. - config_dir (Path): The root directory where the configuration file is located.
person_id (str): Identifier for the person (e.g., 'SinglePerson', 'P1'). - person_id (str): Identifier for the person (e.g., 'SinglePerson', 'P1').
Returns: OUTPUTS:
Path: The path where the output files should be stored. - Path: The path where the output files should be stored.
""" '''
output_dir = config_dir / 'opensim' # Assuming 'opensim' as the default output subdirectory
output_dir = config_dir / 'kinematics' # Assuming 'opensim' as the default output subdirectory
# Append the person_id to the output directory if it's a multi-person setup # Append the person_id to the output directory if it's a multi-person setup
if person_id != "SinglePerson": if person_id != "SinglePerson":
output_dir = output_dir / person_id output_dir = output_dir / person_id
logging.debug(f"Output directory determined as: {output_dir}") logging.info(f"Output directory determined as: {output_dir}")
# Create the directory if it does not exist # Create the directory if it does not exist
if not output_dir.exists(): if not output_dir.exists():
@ -378,134 +239,329 @@ def get_output_dir(config_dir, person_id):
return output_dir return output_dir
def perform_scaling(config_dict, person_id, trc_files, output_dir): def get_kpt_pairs_from_tree(root_node):
""" '''
Perform scaling on the TRC files according to the OpenSim configuration. Get name pairs for all parent-child relationships in the tree.
# Excludes the root node.
Args: INPUTS:
config_dict (dict): The configuration dictionary. - root_node (Node): The root node of the tree.
person_id (str): The person identifier (e.g., 'SinglePerson', 'P1').
trc_files (list): List of TRC files to be processed. OUTPUTS:
output_dir (Path): The directory where the output files should be saved. - list: A list of name pairs for all parent-child relationships in the tree.
""" '''
geometry_path = Path(get_OpenSim_Setup()) / 'Geometry'
geometry_path_str = str(geometry_path) pairs = []
opensim.ModelVisualizer.addDirToGeometrySearchPaths(geometry_path_str) for node in PreOrderIter(root_node):
# if node.is_root:
# continue
for child in node.children:
pairs.append([node.name, child.name])
return pairs
def get_kpt_pairs_from_scaling(scaling_root):
'''
Get name pairs for all marker pairs in the scaling setup file.
'''
pairs = [pair.find('markers').text.strip().split(' ') for pair in scaling_root[0].findall(".//MarkerPair")]
return pairs
def dict_segment_marker_pairs(scaling_root, right_left_symmetry=True):
'''
'''
measurement_dict = {}
for measurement in scaling_root.findall(".//Measurement"):
# Collect all marker pairs for this measurement
marker_pairs = [pair.find('markers').text.strip().split() for pair in measurement.findall(".//MarkerPair")]
# Collect all body scales for this measurement
for body_scale in measurement.findall(".//BodyScale"):
body_name = body_scale.get('name')
if right_left_symmetry:
measurement_dict[body_name] = marker_pairs
else:
if body_name.endswith('_r'):
marker_pairs_r = [pair for pair in marker_pairs if any([pair[0].startswith('R'), pair[1].startswith('R')])]
measurement_dict[body_name] = marker_pairs_r
elif body_name.endswith('_l'):
marker_pairs_l = [pair for pair in marker_pairs if any([pair[0].startswith('L'), pair[1].startswith('L')])]
measurement_dict[body_name] = marker_pairs_l
return measurement_dict
def dict_segment_ratio(scaling_root, unscaled_model, Q_coords_scaling, markers, right_left_symmetry=True):
'''
'''
# segment_pairs = get_kpt_pairs_from_tree(eval(model_name))
segment_pairs = get_kpt_pairs_from_scaling(scaling_root)
# Get model segment lengths
model_markers_locs = [unscaled_model.getMarkerSet().get(marker).getLocationInGround(unscaled_model.getWorkingState()).to_numpy() for marker in markers]
model_segment_lengths = np.array([euclidean_distance(model_markers_locs[markers.index(pt1)],
model_markers_locs[markers.index(pt2)])
for (pt1,pt2) in segment_pairs])
# Get median segment lengths from Q_coords_scaling. Trimmed mean works better than mean or median
trc_segment_lengths = np.array([euclidean_distance(Q_coords_scaling.iloc[:,markers.index(pt1)*3:markers.index(pt1)*3+3],
Q_coords_scaling.iloc[:,markers.index(pt2)*3:markers.index(pt2)*3+3])
for (pt1,pt2) in segment_pairs])
# trc_segment_lengths = np.median(trc_segment_lengths, axis=1)
# trc_segment_lengths = np.mean(trc_segment_lengths, axis=1)
trc_segment_lengths = np.array([trimmed_mean(arr, trimmed_percent=0.5) for arr in trc_segment_lengths])
# Calculate ratio for each segment
segment_ratios = trc_segment_lengths / model_segment_lengths
segment_markers_dict = dict_segment_marker_pairs(scaling_root, right_left_symmetry=right_left_symmetry)
segment_ratio_dict = segment_markers_dict.copy()
segment_ratio_dict.update({key: np.mean([segment_ratios[segment_pairs.index(k)]
for k in segment_markers_dict[key]])
for key in segment_markers_dict.keys()})
return segment_ratio_dict
def deactivate_measurements(scaling_root):
'''
'''
measurement_set = scaling_root.find(".//MeasurementSet/objects")
for measurement in measurement_set.findall('Measurement'):
apply_elem = measurement.find('apply')
apply_elem.text = 'false'
def update_scale_values(scaling_root, segment_ratio_dict):
'''
'''
# Get the ScaleSet/objects element
scale_set = scaling_root.find(".//ScaleSet/objects")
# Remove all existing Scale elements
for scale in scale_set.findall('Scale'):
scale_set.remove(scale)
# Add new Scale elements based on scale_dict
for segment, scale in segment_ratio_dict.items():
new_scale = etree.Element('Scale')
# scales
scales_elem = etree.SubElement(new_scale, 'scales')
scales_elem.text = ' '.join([str(scale)]*3)
# segment name
segment_elem = etree.SubElement(new_scale, 'segment')
segment_elem.text = segment
# apply True
apply_elem = etree.SubElement(new_scale, 'apply')
apply_elem.text = 'true'
scale_set.append(new_scale)
def perform_scaling(trc_file, kinematics_dir, osim_setup_dir, model_name, right_left_symmetry=True, subject_height=1.75, subject_mass=70, remove_scaling_setup=True):
'''
Perform model scaling based on the (not necessarily static) TRC file:
- Retrieve the 80% slowest frames, excluding frames where the person is out of frame.
- From these frames, measure median segment lengths.
- Calculate ratio between model and measured segment lengths -> OpenSim manual scaling.
INPUTS:
- config_dict (dict): The configuration dictionary.
- person_id (str): The person identifier (e.g., 'SinglePerson', 'P1').
- trc_files (list): List of TRC files to be processed.
- output_dir (Path): The directory where the output files should be saved.
'''
try: try:
athlete_config = config_dict.get('project', {}) # Load model
athlete_height = athlete_config.get('participant_height', -1) opensim.ModelVisualizer.addDirToGeometrySearchPaths(str(osim_setup_dir / 'Geometry'))
athlete_weight = athlete_config.get('participant_mass', -1) unscaled_model_path = get_model_path(model_name, osim_setup_dir)
if not unscaled_model_path:
raise ValueError(f"Unscaled OpenSim model not found at: {unscaled_model_path}")
unscaled_model = opensim.Model(str(unscaled_model_path))
unscaled_model.initSystem()
scaled_model_path = (kinematics_dir / (trc_file.stem + '.osim')).resolve()
if person_id == "SinglePerson": # Load scaling setup
if not isinstance(athlete_height, float) or not isinstance(athlete_weight, float): scaling_path = get_scaling_setup(model_name, osim_setup_dir)
raise ValueError("For a single person configuration, 'participant_height' and 'participant_mass' must be floats.") scaling_tree = etree.parse(scaling_path)
else: scaling_root = scaling_tree.getroot()
if person_id.startswith("P"): scaling_path_temp = str(kinematics_dir / Path(scaling_path).name)
try:
person_idx = int(person_id.replace('P', '')) - 1
athlete_height = athlete_height[person_idx]
athlete_weight = athlete_weight[person_idx]
except (ValueError, IndexError) as e:
raise ValueError(f"Error processing multi-person data for '{person_id}': {e}")
else:
raise ValueError(f"Unexpected person_id format: '{person_id}'")
logging.debug(f"Performing scaling. Output directory: {output_dir}") # Read trc file
Q_coords, _, _, markers, _ = read_trc(trc_file)
pose_model = get_Model(config_dict)
if not pose_model:
raise ValueError(f"Model path not found for pose_model: {pose_model}")
for trc_file in trc_files:
trc_file = Path(trc_file)
scaling_path = get_Scale_Setup(config_dict)
Q_coords, _, _, header = read_trc(trc_file)
# Using 80% slowest frames for scaling, removing frames when person is out of frame
Q_diff = Q_coords.diff(axis=0).sum(axis=1) Q_diff = Q_coords.diff(axis=0).sum(axis=1)
Q_diff = Q_diff[Q_diff != 0] Q_diff = Q_diff[Q_diff != 0] # remove when speed is 0 (person out of frame)
min_speed_indices = Q_diff.abs().nsmallest(int(len(Q_diff) * 0.1)).index min_speed_indices = Q_diff.abs().nsmallest(int(len(Q_diff) * 0.8)).index
Q_coords_scaling = Q_coords.iloc[min_speed_indices].reset_index(drop=True) Q_coords_scaling = Q_coords.iloc[min_speed_indices].reset_index(drop=True)
trc_scaling_path = trc_file.parent / (trc_file.stem + '_scaling.trc') # Get manual scale values (scale on trimmed mean of measured segments rather than on raw keypoints)
make_trc_with_Q(Q_coords_scaling, header, str(trc_scaling_path)) segment_ratio_dict = dict_segment_ratio(scaling_root, unscaled_model, Q_coords_scaling, markers, right_left_symmetry=right_left_symmetry)
scaling_file_path = str(trc_file.parent / (trc_file.stem + '_' + Path(scaling_path).name)) # Update scaling setup file
scaled_model_path = (output_dir / (trc_file.stem + '_scaled.osim')).resolve() scaling_root[0].find('mass').text = str(subject_mass)
scaling_tree = etree.parse(str(scaling_path)) scaling_root[0].find('height').text = str(subject_height)
scaling_root = scaling_tree.getroot() scaling_root[0].find('GenericModelMaker').find('model_file').text = str(unscaled_model_path)
scaling_root[0].find(".//scaling_order").text = ' manualScale measurements'
scaling_root[0].find('mass').text = str(athlete_weight) deactivate_measurements(scaling_root)
scaling_root[0].find('height').text = str(athlete_height) update_scale_values(scaling_root, segment_ratio_dict)
scaling_root[0].find('GenericModelMaker').find('model_file').text = str(pose_model) for mk_f in scaling_root[0].findall(".//marker_file"):
scaling_root[0].find('ModelScaler').find('marker_file').text = trc_scaling_path.name mk_f.text = "Unassigned"
scaling_root[0].find('ModelScaler').find('time_range').text = '0 ' + str(Q_coords_scaling['t'].iloc[-1])
scaling_root[0].find('ModelScaler').find('output_model_file').text = str(scaled_model_path) scaling_root[0].find('ModelScaler').find('output_model_file').text = str(scaled_model_path)
scaling_root[0].find('MarkerPlacer').find('marker_file').text = trc_scaling_path.name
scaling_root[0].find('MarkerPlacer').find('time_range').text = '0 ' + str(Q_coords_scaling['t'].iloc[-1])
scaling_root[0].find('MarkerPlacer').find('output_model_file').text = str(scaled_model_path)
scaling_tree.write(scaling_file_path)
logging.debug(f"Running ScaleTool with scaling file: {scaling_file_path}") etree.indent(scaling_tree, space='\t', level=0)
opensim.ScaleTool(scaling_file_path).run() scaling_tree.write(scaling_path_temp, pretty_print=True, xml_declaration=True, encoding='utf-8')
# Run scaling
opensim.ScaleTool(scaling_path_temp).run()
# Remove scaling setup
if remove_scaling_setup:
Path(scaling_path_temp).unlink()
except Exception as e: except Exception as e:
logging.error(f"Error during scaling for {person_id}: {e}") logging.error(f"Error during scaling for {trc_file}: {e}")
raise raise
def perform_inverse_kinematics(config_dict, person_id, trc_files, output_dir):
""" def perform_IK(trc_file, kinematics_dir, osim_setup_dir, model_name, remove_IK_setup=True):
'''
Perform inverse kinematics on the TRC files according to the OpenSim configuration. Perform inverse kinematics on the TRC files according to the OpenSim configuration.
Args: INPUTS:
config_dict (dict): The configuration dictionary. - config_dict (dict): The configuration dictionary.
person_id (str): The person identifier (e.g., 'SinglePerson', 'P1'). - person_id (str): The person identifier (e.g., 'SinglePerson', 'P1').
trc_files (list): List of TRC files to be processed. - trc_files (list): List of TRC files to be processed.
output_dir (Path): The directory where the output files should be saved. - output_dir (Path): The directory where the output files should be saved.
""" '''
try: try:
logging.debug(f"Performing inverse kinematics. Output directory: {output_dir}") # Retrieve data
ik_path = get_IK_Setup(model_name, osim_setup_dir)
ik_path_temp = str(kinematics_dir / Path(ik_path).name)
scaled_model_path = (kinematics_dir / (trc_file.stem + '.osim')).resolve()
output_motion_file = Path(kinematics_dir, trc_file.stem + '.mot').resolve()
if not trc_file.exists():
raise FileNotFoundError(f"TRC file does not exist: {trc_file}")
_, _, time_col, _, _ = read_trc(trc_file)
start_time, end_time = time_col.iloc[0], time_col.iloc[-1]
for trc_file in trc_files: # Update IK setup file
trc_file_path = Path(trc_file).resolve() ik_tree = etree.parse(ik_path)
scaled_model_path = Path(output_dir) / (trc_file_path.stem + '_scaled.osim')
ik_setup_path = get_IK_Setup(config_dict)
Q_coords, frames_col, time_col, header = read_trc(trc_file_path)
ik_time_range = config_dict.get('opensim', {}).get('IK_timeRange', [])
if not ik_time_range:
start_time = time_col.iloc[0]
end_time = time_col.iloc[-1]
else:
start_time, end_time = ik_time_range[0], ik_time_range[1]
ik_file_path = Path(trc_file_path.parent / (trc_file_path.stem + '_' + Path(ik_setup_path).name)).resolve()
scaled_model_path = scaled_model_path.resolve()
output_motion_file = Path(output_dir, trc_file_path.stem + '.mot').resolve()
ik_tree = etree.parse(ik_setup_path)
ik_root = ik_tree.getroot() ik_root = ik_tree.getroot()
ik_root.find('.//model_file').text = str(scaled_model_path) ik_root.find('.//model_file').text = str(scaled_model_path)
ik_root.find('.//time_range').text = f'{start_time} {end_time}' ik_root.find('.//time_range').text = f'{start_time} {end_time}'
ik_root.find('.//output_motion_file').text = str(output_motion_file) ik_root.find('.//output_motion_file').text = str(output_motion_file)
ik_root.find('.//marker_file').text = str(trc_file_path) ik_root.find('.//marker_file').text = str(trc_file.resolve())
ik_tree.write(ik_file_path) ik_tree.write(ik_path_temp)
logging.info(f"Running InverseKinematicsTool with TRC file: {trc_file_path}") # Run IK
if not trc_file_path.exists(): opensim.InverseKinematicsTool(str(ik_path_temp)).run()
raise FileNotFoundError(f"TRC file does not exist: {trc_file_path}")
logging.debug(f"Running InverseKinematicsTool with IK setup file: {ik_file_path}") # Remove IK setup
opensim.InverseKinematicsTool(str(ik_file_path)).run() if remove_IK_setup:
Path(ik_path_temp).unlink()
except Exception as e: except Exception as e:
logging.error(f"Error during IK for {person_id}: {e}") logging.error(f"Error during IK for {trc_file}: {e}")
raise raise
def opensimProcessing(config_dict): def kinematics(config_dict):
logging.info("Starting OpenSim processing...") '''
process_all_groups(config_dict) Runs OpenSim scaling and inverse kinematics on the trc files of triangulated coordinates.
logging.info("OpenSim processing completed successfully.")
INPUTS:
- config_dict (dict): Generated from a .toml calibration file
OUTPUTS:
- A scaled .osim model for each person.
- Joint angle data files (.mot) for each person.
'''
try:
# Read config_dict
project_dir = config_dict.get('project').get('project_dir')
# if batch
session_dir = Path(project_dir) / '..'
# if single trial
session_dir = session_dir if 'Config.toml' in os.listdir(session_dir) else os.getcwd()
use_augmentation = config_dict.get('kinematics').get('use_augmentation')
if use_augmentation: model_name = 'LSTM'
else: model_name = config_dict.get('pose').get('pose_model').upper()
right_left_symmetry = config_dict.get('kinematics').get('right_left_symmetry')
remove_scaling_setup = config_dict.get('kinematics').get('remove_individual_scaling_setup')
remove_IK_setup = config_dict.get('kinematics').get('remove_individual_IK_setup')
subject_height = config_dict.get('project').get('participant_height')
subject_mass = config_dict.get('project').get('participant_mass')
pose3d_dir = Path(project_dir) / 'pose-3d'
kinematics_dir = Path(project_dir) / 'kinematics'
kinematics_dir.mkdir(parents=True, exist_ok=True)
osim_setup_dir = get_opensim_setup_dir()
# OpenSim logs saved to a different file
opensim_logs_file = kinematics_dir / 'opensim_logs.txt'
opensim.Logger.setLevelString('Info')
opensim.Logger.removeFileSink()
opensim.Logger.addFileSink(str(opensim_logs_file))
# Find all trc files
trc_files = []
if use_augmentation:
trc_files = [f for f in pose3d_dir.glob('*.trc') if '_LSTM' in f.name]
if len(trc_files) == 0:
model_name = config_dict.get('pose').get('pose_model').upper()
logging.warning("No LSTM trc files found. Using non augmented trc files instead.")
if len(trc_files) == 0: # filtered files by default
trc_files = [f for f in pose3d_dir.glob('*.trc') if '_LSTM' not in f.name and '_filt' in f.name and '_scaling' not in f.name]
if len(trc_files) == 0:
trc_files = [f for f in pose3d_dir.glob('*.trc') if '_LSTM' not in f.name and '_scaling' not in f.name]
if len(trc_files) == 0:
raise ValueError(f'No trc files found in {pose3d_dir}.')
sorted(trc_files, key=natural_sort_key)
# Get subject heights and masses
if subject_height is None or subject_height == 0:
subject_height = [1.75] * len(trc_files)
logging.warning("No subject height found in Config.toml. Using default height of 1.75m.")
elif not type(subject_height) == list: # int or float
subject_height = [subject_height]
elif len(subject_height) < len(trc_files):
logging.warning("Number of subject heights does not match number of TRC files. Missing heights are set to 1.75m.")
subject_height += [1.75] * (len(trc_files) - len(subject_height))
if subject_mass is None or subject_mass == 0:
subject_mass = [70] * len(trc_files)
logging.warning("No subject mass found in Config.toml. Using default mass of 70kg.")
elif not type(subject_mass) == list:
subject_mass = [subject_mass]
elif len(subject_mass) < len(trc_files):
logging.warning("Number of subject masses does not match number of TRC files. Missing masses are set to 70kg.")
subject_mass += [70] * (len(trc_files) - len(subject_mass))
# Perform scaling and IK for each trc file
for p, trc_file in enumerate(trc_files):
logging.info(f"Processing TRC file: {trc_file.resolve()}")
logging.info("Scaling...")
perform_scaling(trc_file, kinematics_dir, osim_setup_dir, model_name, right_left_symmetry=right_left_symmetry, subject_height=subject_height[p], subject_mass=subject_mass[p], remove_scaling_setup=remove_scaling_setup)
logging.info(f"\tDone. OpenSim logs saved to {opensim_logs_file.resolve()}.")
logging.info(f"\tScaled model saved to {(kinematics_dir / (trc_file.stem + '_scaled.osim')).resolve()}")
logging.info("\nInverse Kinematics...")
perform_IK(trc_file, kinematics_dir, osim_setup_dir, model_name, remove_IK_setup=remove_IK_setup)
logging.info(f"\tDone. OpenSim logs saved to {opensim_logs_file.resolve()}.")
logging.info(f"\tJoint angle data saved to {(kinematics_dir / (trc_file.stem + '.mot')).resolve()}\n")
except RuntimeError as e:
logging.error(f"Error occurred: {e}")

35
Pose2Sim/markerAugmentation.py
View File

@ -49,7 +49,7 @@ __status__ = "Development"
def check_midhip_data(trc_file): def check_midhip_data(trc_file):
try: try:
# Find MidHip data # Find MidHip data
midhip_data = trc_file.marker("CHip") midhip_data = trc_file.marker("Hip")
if midhip_data is None or len(midhip_data) == 0: if midhip_data is None or len(midhip_data) == 0:
raise ValueError("MidHip data is empty") raise ValueError("MidHip data is empty")
except (KeyError, ValueError): except (KeyError, ValueError):
@ -57,7 +57,7 @@ def check_midhip_data(trc_file):
rhip_data = trc_file.marker("RHip") rhip_data = trc_file.marker("RHip")
lhip_data = trc_file.marker("LHip") lhip_data = trc_file.marker("LHip")
midhip_data = (rhip_data + lhip_data) / 2 midhip_data = (rhip_data + lhip_data) / 2
trc_file.add_marker('CHip', *midhip_data.T) trc_file.add_marker('Hip', *midhip_data.T)
return trc_file return trc_file
@ -83,14 +83,10 @@ def augmentTRC(config_dict):
project_dir = config_dict.get('project').get('project_dir') project_dir = config_dict.get('project').get('project_dir')
pathInputTRCFile = os.path.realpath(os.path.join(project_dir, 'pose-3d')) pathInputTRCFile = os.path.realpath(os.path.join(project_dir, 'pose-3d'))
pathOutputTRCFile = os.path.realpath(os.path.join(project_dir, 'pose-3d')) pathOutputTRCFile = os.path.realpath(os.path.join(project_dir, 'pose-3d'))
subject_height = config_dict.get('project').get('participant_height')
if subject_height is None or subject_height == 0 or subject_height==0:
raise ValueError("Subject height is not set or is invalid.")
subject_mass = config_dict.get('project').get('participant_mass')
if not type(subject_height) == list:
subject_height = [subject_height]
subject_mass = [subject_mass]
make_c3d = config_dict.get('markerAugmentation').get('make_c3d') make_c3d = config_dict.get('markerAugmentation').get('make_c3d')
subject_height = config_dict.get('project').get('participant_height')
subject_mass = config_dict.get('project').get('participant_mass')
augmenterDir = os.path.dirname(utilsDataman.__file__) augmenterDir = os.path.dirname(utilsDataman.__file__)
augmenterModelName = 'LSTM' augmenterModelName = 'LSTM'
augmenter_model = 'v0.3' augmenter_model = 'v0.3'
@ -110,6 +106,25 @@ def augmentTRC(config_dict):
trc_files = trc_no_filtering trc_files = trc_no_filtering
sorted(trc_files, key=natural_sort_key) sorted(trc_files, key=natural_sort_key)
# Get subject heights and masses
if subject_height is None or subject_height == 0:
subject_height = [1.75] * len(trc_files)
logging.warning("No subject height found in Config.toml. Using default height of 1.75m.")
elif not type(subject_height) == list: # int or float
subject_height = [subject_height]
elif len(subject_height) < len(trc_files):
logging.warning("Number of subject heights does not match number of TRC files. Missing heights are set to 1.75m.")
subject_height += [1.75] * (len(trc_files) - len(subject_height))
if subject_mass is None or subject_mass == 0:
subject_mass = [70] * len(trc_files)
logging.warning("No subject mass found in Config.toml. Using default mass of 70kg.")
elif not type(subject_mass) == list:
subject_mass = [subject_mass]
elif len(subject_mass) < len(trc_files):
logging.warning("Number of subject masses does not match number of TRC files. Missing masses are set to 70kg.")
subject_mass += [70] * (len(trc_files) - len(subject_mass))
for p in range(len(subject_mass)): for p in range(len(subject_mass)):
pathInputTRCFile = trc_files[p] pathInputTRCFile = trc_files[p]
pathOutputTRCFile = os.path.splitext(pathInputTRCFile)[0] + '_LSTM.trc' pathOutputTRCFile = os.path.splitext(pathInputTRCFile)[0] + '_LSTM.trc'
@ -172,7 +187,7 @@ def augmentTRC(config_dict):
trc_data_data = trc_data[:,1:] trc_data_data = trc_data[:,1:]
# Step 2: Normalize with reference marker position. # Step 2: Normalize with reference marker position.
referenceMarker_data = trc_file.marker("CHip") # instead of trc_file.marker(referenceMarker) # change by HunMin referenceMarker_data = trc_file.marker("Hip") # instead of trc_file.marker(referenceMarker) # change by HunMin
norm_trc_data_data = np.zeros((trc_data_data.shape[0], norm_trc_data_data = np.zeros((trc_data_data.shape[0],
trc_data_data.shape[1])) trc_data_data.shape[1]))
for i in range(0,trc_data_data.shape[1],3): for i in range(0,trc_data_data.shape[1],3):

33
README.md
View File

@ -16,9 +16,9 @@
##### N.B:. Please set undistort_points and handle_LR_swap to false for now since it currently leads to inaccuracies. I'll try to fix it soon. ##### N.B:. Please set undistort_points and handle_LR_swap to false for now since it currently leads to inaccuracies. I'll try to fix it soon.
> **_News_: Version 0.9:**\ > **_News_: Version 0.109:**\
> **Pose estimation with RTMPose is now included in Pose2Sim!**\ > **OpenSim scaling and inverse kinematics are now integrated in Pose2Sim!** No static trial needed.\
> **Other recently added features**: Automatic camera synchronization, multi-person analysis, Blender visualization, Marker augmentation, Batch processing. > **Other recently added features**: Pose estimation, Automatic camera synchronization, Multi-person analysis, Blender visualization, Marker augmentation, Batch processing.
<!-- Incidentally, right/left limb swapping is now handled, which is useful if few cameras are used;\ <!-- Incidentally, right/left limb swapping is now handled, which is useful if few cameras are used;\
and lens distortions are better taken into account.\ --> and lens distortions are better taken into account.\ -->
> To upgrade, type `pip install pose2sim --upgrade` (note that you need Python 3.9 or higher). > To upgrade, type `pip install pose2sim --upgrade` (note that you need Python 3.9 or higher).
@ -27,6 +27,14 @@ and lens distortions are better taken into account.\ -->
`Pose2Sim` provides a workflow for 3D markerless kinematics, as an alternative to marker-based motion capture methods. It aims to provide a free tool to obtain research-grade results from consumer-grade equipment. Any combination of phone, webcam, GoPro, etc. can be used. `Pose2Sim` provides a workflow for 3D markerless kinematics, as an alternative to marker-based motion capture methods. It aims to provide a free tool to obtain research-grade results from consumer-grade equipment. Any combination of phone, webcam, GoPro, etc. can be used.
<!--powerfull, flexible, intuitive -->
**Pose2Sim** stands for "OpenPose to OpenSim", as it originally used *OpenPose* inputs (2D keypoints coordinates) from multiple videos and lead to an [OpenSim](https://opensim.stanford.edu/) result (full-body 3D joint angles). Pose estimation is now performed with more recent models from [RTMPose](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose). OpenPose and other models are kept as legacy options. **Pose2Sim** stands for "OpenPose to OpenSim", as it originally used *OpenPose* inputs (2D keypoints coordinates) from multiple videos and lead to an [OpenSim](https://opensim.stanford.edu/) result (full-body 3D joint angles). Pose estimation is now performed with more recent models from [RTMPose](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose). OpenPose and other models are kept as legacy options.
For real-time analysis with a single camera, please consider **[Sports2D](https://github.com/davidpagnon/Sports2D)** (note that the motion must lie in the sagittal or frontal plane). For real-time analysis with a single camera, please consider **[Sports2D](https://github.com/davidpagnon/Sports2D)** (note that the motion must lie in the sagittal or frontal plane).
@ -48,11 +56,12 @@ For real-time analysis with a single camera, please consider **[Sports2D](https:
- [x] **v0.6** *(02/2024)*: Marker augmentation, Blender visualizer - [x] **v0.6** *(02/2024)*: Marker augmentation, Blender visualizer
- [x] **v0.7** *(03/2024)*: Multi-person analysis - [x] **v0.7** *(03/2024)*: Multi-person analysis
- [x] **v0.8** *(04/2024)*: New synchronization tool - [x] **v0.8** *(04/2024)*: New synchronization tool
- [x] **v0.9: *(07/2024)*: Integration of pose estimation in the pipeline** - [x] **v0.9** *(07/2024)*: Integration of pose estimation in the pipeline
- [ ] v0.10: Integration of OpenSim in the pipeline - [x] **v0.10 *(09/2024)*: Integration of OpenSim in the pipeline**
- [ ] v0.11: Calibration based on keypoint detection, Handling left/right swaps, Correcting lens distortions - [ ] v0.11: Migrated documentation to new github.io website
- [ ] v0.12: Graphical User Interface - [ ] v0.12: Calibration based on keypoint detection, Handling left/right swaps, Correcting lens distortions
- [ ] v1.0: First accomplished release - [ ] v0.13: Graphical User Interface
- [ ] v1.0: First full release
</br> </br>
@ -175,7 +184,7 @@ Pose2Sim.personAssociation()
Pose2Sim.triangulation() Pose2Sim.triangulation()
Pose2Sim.filtering() Pose2Sim.filtering()
Pose2Sim.markerAugmentation() Pose2Sim.markerAugmentation()
Pose2Sim.opensimProcessing() Pose2Sim.kinematics()
``` ```
3D results are stored as .trc files in each trial folder in the `pose-3d` directory. 3D results are stored as .trc files in each trial folder in the `pose-3d` directory.
@ -189,7 +198,8 @@ OpenSim results are stored as scaled model .osim and .mot in each trial folder i
- You can run all stages at once: - You can run all stages at once:
``` python ``` python
from Pose2Sim import Pose2Sim from Pose2Sim import Pose2Sim
Pose2Sim.runAll(do_calibration=True, do_poseEstimation=True, do_synchronization=True, do_personAssociation=True, do_triangulation=True, do_filtering=True, do_markerAugmentation=True, do_opensimProcessing=True) Pose2Sim.runAll(do_calibration=True, do_poseEstimation=True, do_synchronization=True, do_personAssociation=True, do_triangulation=True, do_filtering=True, do_markerAugmentation=True, do_kinematics=True)
# or simply: Pose2Sim.runAll()
``` ```
- Try the calibration tool by changing `calibration_type` to `calculate` instead of `convert` in [Config.toml](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Demo_SinglePerson/Config.toml) (more info [there](#calculate-from-scratch)). - Try the calibration tool by changing `calibration_type` to `calculate` instead of `convert` in [Config.toml](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Demo_SinglePerson/Config.toml) (more info [there](#calculate-from-scratch)).
- If the results are not convincing, refer to Section [OpenSim-kinematics](#OpenSim-kinematics) in the document. - If the results are not convincing, refer to Section [OpenSim-kinematics](#OpenSim-kinematics) in the document.
@ -260,8 +270,7 @@ You can then run OpenSim scaling and inverse kinematics for each resulting .trc
You can also visualize your results with Blender as in [Demonstration Part-3](#demonstration-part-3-optional-visualize-your-results-with-blender). You can also visualize your results with Blender as in [Demonstration Part-3](#demonstration-part-3-optional-visualize-your-results-with-blender).
*N.B.:* Set *[project]* `multi_person = true` for each trial that contains multiple persons.\ *N.B.:* Set *[project]* `multi_person = true` for each trial that contains multiple persons.\
Set *[triangulation]* `reorder_trc = true` if you need to run OpenSim and to match the generated .trc files with the static trials.\ Make sure that the order of *[markerAugmentation]* `participant_height` and `participant_mass` matches the person's IDs.
Make sure that the order of *[markerAugmentation]* `participant_height` and `participant_mass` matches the order of the static trials.
<br/> <br/>