############################################################################### ## 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 # true for trials with multiple participants. If false, only the main person in scene is analyzed (and it run much faster). nb_persons_to_detect = 2 # checked only if multi_person is selected 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, ['', 'etc']. # e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial'] # Take heart, calibration is not that complicated once you get the hang of it! [calibration] calibration_type = 'convert' # 'convert' or 'calculate' [calibration.convert] convert_from = 'qualisys' # 'qualisys', 'optitrack', vicon', 'opencap', 'easymocap', 'biocv', 'anipose', or 'freemocap' [calibration.convert.qualisys] binning_factor = 1 # Usually 1, except when filming in 540p where it usually is 2 [calibration.convert.optitrack] # See readme for instructions [calibration.convert.vicon] # No parameter needed [calibration.convert.opencap] # No parameter needed [calibration.convert.easymocap] # No parameter needed [calibration.convert.biocv] # No parameter needed [calibration.convert.anipose] # No parameter needed [calibration.convert.freemocap] # No parameter needed [calibration.calculate] # Camera properties, theoretically need to be calculated only once in a camera lifetime [calibration.calculate.intrinsics] overwrite_intrinsics = false # overwrite (or not) if they have already been calculated? 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! [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] # COMING SOON! reset_sync = true # Recalculate synchronization even if already done frames = [2850,3490] # Frames to use for synchronization, should point to a moment with fast motion. cut_off_frequency = 10 # cut-off frequency for a 4th order low-pass Butterworth filter # Vertical speeds (on X, Y, or Z axis, or 2D speeds) speed_kind = 'y' # 'x', 'y', 'z', or '2D' vmax = 20 # px/s cam1_nb = 4 cam2_nb = 3 id_kpt = [9,10] # Pour plus tard aller chercher numéro depuis keypoint name dans skeleton.py. 'RWrist' BLAZEPOSE 16, BODY_25B 10, BODY_25 4 ; 'LWrist' BLAZEPOSE 15, BODY_25B 9, BODY_25 7 weights_kpt = [1,1] # Pris en compte uniquement si on a plusieurs keypoints [personAssociation] 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) reproj_error_threshold_association = 20 # px likelihood_threshold_association = 0.3 [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) [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