pose2sim/Pose2Sim/S01_Demo_SingleTrial/Config.toml

###############################################################################
## PROJECT PARAMETERS                                                        ##
###############################################################################


# Configure your project parameters here. 
# 
# IMPORTANT:
# If a parameter is not found here, Pose2Sim will look for its value in the 
# Config.toml file of the level above. This way, you can set global 
# instructions for the Session and alter them for specific Participants or Trials.
#
# If you wish to overwrite a parameter for a specific trial or participant,  
# edit its Config.toml file by uncommenting its key (e.g., [project])
# and editing its value (e.g., frame_range = [10,300]). Or else, uncomment 
# [filtering.butterworth] and set cut_off_frequency = 10, etc.



[project]
multi_person = false # If false, only the main person in scene is analyzed. 
frame_rate = 60 # fps
frame_range = [] # For example [10,300], or [] for all frames
## N.B.: If you want a time range instead, use frame_range = time_range * frame_rate
## For example if you want to analyze from 0.1 to 2 seconds with a 60 fps frame rate, 
## frame_range = [0.1, 2.0]*frame_rate = [6, 120]

exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']


[pose]
pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut' 
pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII.
						      #With mediapipe: BLAZEPOSE. 
						      #With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133. 
						      #With deeplabcut: CUSTOM. See example at the end of the file.
# What follows has not been implemented yet
overwrite_pose = false
openpose_path = '' # only checked if OpenPose is used


[synchronization]
display_sync_plots = true # true or false (lowercase)
keypoints_to_consider = 'all' # 'all' if all points should be considered (default)
                           # ['RWrist', 'RElbow'] list of keypoint names if you want to specify the keypoints to consider
approx_time_maxspeed = 'auto' # 'auto' if you want to consider the whole capture (default, slower if long sequences)
                           # [1.0, 0.2, 0.8, 1.1] list of times in seconds, one value per camera if you want to specify an approximate time where to search for synchronization.
                           # Best times: only one person is in the scene, who performs a sharp vertical movement (with keypoints_to_consider).
reset_sync = false # Revert to state before synchronization. Won't attempt to synchronize unless set to false.


# Take heart, calibration is not that complicated once you get the hang of it!
[calibration]
calibration_type = 'calculate' # 'convert' or 'calculate'

   [calibration.convert]
   convert_from = 'qualisys' # 'qualisys', 'optitrack', vicon', 'opencap', 'easymocap', 'biocv', 'anipose', or 'freemocap'
      [calibration.convert.qualisys]
      binning_factor = 1 # Usually 1, except when filming in 540p where it usually is 2
      [calibration.convert.optitrack]  # See readme for instructions
      [calibration.convert.vicon]      # No parameter needed
      [calibration.convert.opencap]    # No parameter needed
      [calibration.convert.easymocap]  # No parameter needed
      [calibration.convert.biocv]      # No parameter needed
      [calibration.convert.anipose]    # No parameter needed
      [calibration.convert.freemocap]  # No parameter needed
  

   [calibration.calculate] 
      # Camera properties, theoretically need to be calculated only once in a camera lifetime
      [calibration.calculate.intrinsics]
      overwrite_intrinsics = true # set to false if you don't want to recalculate intrinsic parameters
      show_detection_intrinsics = true # true or false (lowercase)
      intrinsics_extension = 'jpg' # any video or image extension
      extract_every_N_sec = 1 # if video, extract frames every N seconds (can be <1 )
      intrinsics_corners_nb = [4,7] 
      intrinsics_square_size = 60 # mm

      # Camera placements, need to be done before every session
      [calibration.calculate.extrinsics]
      calculate_extrinsics = true # true or false (lowercase) 
      extrinsics_method = 'scene' # 'board', 'scene', 'keypoints'
      # 'board' should be large enough to be detected when laid on the floor. Not recommended.
      # 'scene' involves manually clicking any point of know coordinates on scene. Usually more accurate if points are spread out.
      # 'keypoints' uses automatic pose estimation of a person freely walking and waving arms in the scene. Slighlty less accurate, requires synchronized cameras.
      moving_cameras = false # Not implemented yet

         [calibration.calculate.extrinsics.board]
         show_reprojection_error = true # true or false (lowercase)
         extrinsics_extension = 'png' # any video or image extension
         extrinsics_corners_nb = [4,7] # [H,W] rather than [w,h]
         extrinsics_square_size = 60 # mm # [h,w] if square is actually a rectangle

         [calibration.calculate.extrinsics.scene]
         show_reprojection_error = true # true or false (lowercase)
         extrinsics_extension = 'png' # any video or image extension
         # list of 3D coordinates to be manually labelled on images. Can also be a 2 dimensional plane. 
         # in m -> unlike for intrinsics, NOT in mm!
         object_coords_3d =   [[-2.0,  0.3,  0.0], 
                              [-2.0 , 0.0,  0.0], 
                              [-2.0, 0.0,  0.05], 
                              [-2.0, -0.3 ,  0.0], 
                              [0.0,  0.3,  0.0], 
                              [0.0, 0.0,  0.0], 
                              [0.0, 0.0,  0.05], 
                              [0.0, -0.3,  0.0]]
        
         [calibration.calculate.extrinsics.keypoints]
         # Coming soon!


[personAssociation]
   likelihood_threshold_association = 0.3

   [personAssociation.single_person]
   reproj_error_threshold_association = 20 # px
   tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py 
               # and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
   
   [personAssociation.multi_person]
   reconstruction_error_threshold = 0.1 # 0.1 = 10 cm
   min_affinity = 0.2 # affinity below which a correspondence is ignored


[triangulation]
reorder_trc = false # only checked if multi_person analysis
reproj_error_threshold_triangulation = 15 # px
likelihood_threshold_triangulation= 0.3
min_cameras_for_triangulation = 2
interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
                        # 'none' if you don't want to interpolate missing points
interp_if_gap_smaller_than = 10 # do not interpolate bigger gaps
show_interp_indices = true # true or false (lowercase). For each keypoint, return the frames that need to be interpolated
handle_LR_swap = false # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
undistort_points = false # Better if distorted image (parallel lines curvy on the edge or at least one param > 10^-2), but unnecessary (and slightly slower) if distortions are low
make_c3d = false # save triangulated data in c3d format in addition to trc # Coming soon!


[filtering]
type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
display_figures = false # true or false (lowercase)
make_c3d = false # save triangulated data in c3d format in addition to trc

   [filtering.butterworth]
   order = 4 
   cut_off_frequency = 6 # Hz
   [filtering.kalman]
   # How much more do you trust triangulation results (measurements), than previous data (process assuming constant acceleration)?
   trust_ratio = 100 # = measurement_trust/process_trust ~= process_noise/measurement_noise
   smooth = true # should be true, unless you need real-time filtering
   [filtering.butterworth_on_speed]
   order = 4 
   cut_off_frequency = 10 # Hz
   [filtering.gaussian]
   sigma_kernel = 2 #px
   [filtering.LOESS]
   nb_values_used = 30 # = fraction of data used * nb frames
   [filtering.median]
   kernel_size = 9


[markerAugmentation] 
## Only works on BODY_25 and BODY_25B models
participant_height = 1.72 # m # float if single person, list of float if multi-person (same order as the Static trials)
participant_mass = 70.0 # kg


[opensim]
static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial']
#            # If this Config.toml file is at the Trial level, set to true or false (lowercase);
#            # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial'];
#            # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial']
opensim_bin_path = 'C:\OpenSim 4.4\bin'



# CUSTOM skeleton, if you trained your own DeepLabCut model for example. 
# Make sure the node ids correspond to the column numbers of the 2D pose file, starting from zero.
# 
# If you want to perform inverse kinematics, you will also need to create an OpenSim model
# and add to its markerset the location where you expect the triangulated keypoints to be detected.
# 
# In this example, CUSTOM reproduces the BODY_25B skeleton  (default skeletons are stored in skeletons.py).
# You can create as many custom skeletons as you want, just add them further down and rename them.
# 
# Check your model hierarchy with:  for pre, _, node in RenderTree(model): 
#                                      print(f'{pre}{node.name} id={node.id}')
[pose.CUSTOM]
name = "CHip"
id = "None"
  [[pose.CUSTOM.children]]
  name = "RHip"
  id = 12
     [[pose.CUSTOM.children.children]]
     name = "RKnee"
     id = 14
        [[pose.CUSTOM.children.children.children]]
        name = "RAnkle"
        id = 16
           [[pose.CUSTOM.children.children.children.children]]
           name = "RBigToe"
           id = 22
              [[pose.CUSTOM.children.children.children.children.children]]
              name = "RSmallToe"
              id = 23
           [[pose.CUSTOM.children.children.children.children]]
           name = "RHeel"
           id = 24
  [[pose.CUSTOM.children]]
  name = "LHip"
  id = 11
     [[pose.CUSTOM.children.children]]
     name = "LKnee"
     id = 13
        [[pose.CUSTOM.children.children.children]]
        name = "LAnkle"
        id = 15
           [[pose.CUSTOM.children.children.children.children]]
           name = "LBigToe"
           id = 19
              [[pose.CUSTOM.children.children.children.children.children]]
              name = "LSmallToe"
              id = 20
           [[pose.CUSTOM.children.children.children.children]]
           name = "LHeel"
           id = 21
  [[pose.CUSTOM.children]]
  name = "Neck"
  id = 17
     [[pose.CUSTOM.children.children]]
     name = "Head"
     id = 18
        [[pose.CUSTOM.children.children.children]]
        name = "Nose"
        id = 0
     [[pose.CUSTOM.children.children]]
     name = "RShoulder"
     id = 6
        [[pose.CUSTOM.children.children.children]]
        name = "RElbow"
        id = 8
           [[pose.CUSTOM.children.children.children.children]]
           name = "RWrist"
           id = 10
     [[pose.CUSTOM.children.children]]
     name = "LShoulder"
     id = 5
        [[pose.CUSTOM.children.children.children]]
        name = "LElbow"
        id = 7
           [[pose.CUSTOM.children.children.children.children]]
           name = "LWrist"
           id = 9