251 lines
11 KiB
TOML
251 lines
11 KiB
TOML
###############################################################################
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## PROJECT PARAMETERS ##
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###############################################################################
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# Configure your project parameters here.
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#
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# IMPORTANT:
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# If a parameter is not found here, Pose2Sim will look for its value in the
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# Config.toml file of the level above. This way, you can set global
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# instructions for the Session and alter them for specific Participants or Trials.
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#
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# If you wish to overwrite a parameter for a specific trial or participant,
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# edit its Config.toml file by uncommenting its key (e.g., [project])
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# and editing its value (e.g., frame_range = [10,300]). Or else, uncomment
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# [filtering.butterworth] and set cut_off_frequency = 10, etc.
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[project]
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frame_rate = 60 # fps
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frame_range = [] # For example [10,300], or [] for all frames
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## N.B.: If you want a time range instead, use frame_range = time_range * frame_rate
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## For example if you want to analyze from 0.1 to 2 seconds with a 60 fps frame rate,
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## frame_range = [0.1, 2.0]*frame_rate = [6, 120]
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exclude_from_batch = [] # List of trials to be excluded from batch analysis, ['<participant_dir/trial_dir>', 'etc'].
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# e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P00_Participant/S00_P00_T01_BalancingTrial']
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# Take heart, calibration is not that complicated once you get the hang of it!
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[calibration]
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calibration_type = 'convert' # 'convert' or 'calculate'
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[calibration.convert]
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convert_from = 'qualisys' # 'qualisys', 'optitrack', vicon', 'opencap', 'easymocap', 'biocv', 'anipose', or 'freemocap'
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[calibration.convert.qualisys]
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binning_factor = 1 # Usually 1, except when filming in 540p where it usually is 2
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[calibration.convert.optitrack] # See readme for instructions
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[calibration.convert.vicon] # No parameter needed
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[calibration.convert.opencap] # No parameter needed
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[calibration.convert.easymocap] # No parameter needed
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[calibration.convert.biocv] # No parameter needed
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[calibration.convert.anipose] # No parameter needed
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[calibration.convert.freemocap] # No parameter needed
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[calibration.calculate]
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# Camera properties, theoretically need to be calculated only once in a camera lifetime
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[calibration.calculate.intrinsics]
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overwrite_intrinsics = false # overwrite (or not) if they have already been calculated?
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show_detection_intrinsics = true # true or false (lowercase)
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intrinsics_extension = 'jpg' # any video or image extension
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extract_every_N_sec = 1 # if video, extract frames every N seconds (can be <1 )
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intrinsics_corners_nb = [4,7]
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intrinsics_square_size = 60 # mm
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# Camera placements, need to be done before every session
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[calibration.calculate.extrinsics]
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calculate_extrinsics = true # true or false (lowercase)
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extrinsics_method = 'scene' # 'board', 'scene', 'keypoints'
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# 'board' should be large enough to be detected when laid on the floor. Not recommended.
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# 'scene' involves manually clicking any point of know coordinates on scene. Usually more accurate if points are spread out.
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# 'keypoints' uses automatic pose estimation of a person freely walking and waving arms in the scene. Slighlty less accurate, requires synchronized cameras.
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moving_cameras = false # Not implemented yet
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[calibration.calculate.extrinsics.board]
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show_reprojection_error = true # true or false (lowercase)
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extrinsics_extension = 'png' # any video or image extension
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extrinsics_corners_nb = [4,7] # [H,W] rather than [w,h]
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extrinsics_square_size = 60 # mm # [h,w] if square is actually a rectangle
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[calibration.calculate.extrinsics.scene]
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show_reprojection_error = true # true or false (lowercase)
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extrinsics_extension = 'png' # any video or image extension
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# list of 3D coordinates to be manually labelled on images. Can also be a 2 dimensional plane.
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# in m -> unlike for intrinsics, NOT in mm!
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object_coords_3d = [[-2.0, 0.3, 0.0],
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[-2.0 , 0.0, 0.0],
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[-2.0, 0.0, 0.05],
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[-2.0, -0.3 , 0.0],
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[0.0, 0.3, 0.0],
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[0.0, 0.0, 0.0],
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[0.0, 0.0, 0.05],
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[0.0, -0.3, 0.0]]
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[calibration.calculate.extrinsics.keypoints]
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# Coming soon!
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[pose]
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pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut'
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pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII
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#With mediapipe: BLAZEPOSE.
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#With alphapose: HALPE_26, HALPE_68, HALPE_136, COCO_133.
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#With deeplabcut: CUSTOM. See example at the end of the file.
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# What follows has not been implemented yet
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overwrite_pose = false
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openpose_path = '' # only checked if OpenPose is used
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[synchronization]
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# COMING SOON!
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reset_sync = true # Recalculate synchronization even if already done
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frames = [2850,3490] # Frames to use for synchronization, should point to a moment with fast motion.
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cut_off_frequency = 10 # cut-off frequency for a 4th order low-pass Butterworth filter
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# Vertical speeds (on X, Y, or Z axis, or 2D speeds)
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speed_kind = 'y' # 'x', 'y', 'z', or '2D'
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vmax = 20 # px/s
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cam1_nb = 4
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cam2_nb = 3
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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
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weights_kpt = [1,1] # Pris en compte uniquement si on a plusieurs keypoints
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[personAssociation]
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single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene.
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tracked_keypoint = 'Neck' # If the neck is not detected by the pose_model, check skeleton.py
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# and choose a stable point for tracking the person of interest (e.g., 'right_shoulder' with BLAZEPOSE)
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reproj_error_threshold_association = 20 # px
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likelihood_threshold_association = 0.2
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[triangulation]
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reproj_error_threshold_triangulation = 15 # px
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likelihood_threshold_triangulation = 0.3
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min_cameras_for_triangulation = 2
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interpolation = 'cubic' #linear, slinear, quadratic, cubic, or none
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# 'none' if you don't want to interpolate missing points
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interp_if_gap_smaller_than = 10 # do not interpolate bigger gaps
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show_interp_indices = true # true or false (lowercase). For each keypoint, return the frames that need to be interpolated
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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
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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
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make_c3d = false # save triangulated data in c3d format in addition to trc # Coming soon!
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[filtering]
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type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
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display_figures = true # true or false (lowercase)
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[filtering.butterworth]
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order = 4
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cut_off_frequency = 6 # Hz
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[filtering.kalman]
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# How much more do you trust triangulation results (measurements), than previous data (process assuming constant acceleration)?
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trust_ratio = 100 # = measurement_trust/process_trust ~= process_noise/measurement_noise
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smooth = true # should be true, unless you need real-time filtering
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[filtering.butterworth_on_speed]
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order = 4
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cut_off_frequency = 10 # Hz
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[filtering.gaussian]
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sigma_kernel = 2 #px
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[filtering.LOESS]
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nb_values_used = 30 # = fraction of data used * nb frames
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[filtering.median]
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kernel_size = 9
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[markerAugmentation]
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## Only works on BODY_25 and BODY_25B models
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participant_height = 1.7 # m
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participant_mass = 70 # kg
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[opensim]
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static_trial = ['S00_P00_Participant/S00_P00_T00_StaticTrial']
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# # If this Config.toml file is at the Trial level, set to true or false (lowercase);
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# # At the Participant level, specify the name of the static trial folder name, e.g. ['S00_P00_T00_StaticTrial'];
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# # At the Session level, add participant subdirectory, e.g. ['S00_P00_Participant/S00_P00_T00_StaticTrial', 'S00_P01_Participant/S00_P00_T00_StaticTrial']
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opensim_bin_path = 'C:\OpenSim 4.4\bin'
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# CUSTOM skeleton, if you trained your own DeepLabCut model for example.
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# Make sure the node ids correspond to the column numbers of the 2D pose file, starting from zero.
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#
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# If you want to perform inverse kinematics, you will also need to create an OpenSim model
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# and add to its markerset the location where you expect the triangulated keypoints to be detected.
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#
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# In this example, CUSTOM reproduces the BODY_25B skeleton (default skeletons are stored in skeletons.py).
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# You can create as many custom skeletons as you want, just add them further down and rename them.
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#
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# Check your model hierarchy with: for pre, _, node in RenderTree(model):
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# print(f'{pre}{node.name} id={node.id}')
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[pose.CUSTOM]
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name = "CHip"
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id = "None"
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[[pose.CUSTOM.children]]
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id = 12
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name = "RHip"
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[[pose.CUSTOM.children.children]]
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id = 14
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name = "RKnee"
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[[pose.CUSTOM.children.children.children]]
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id = 16
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name = "RAnkle"
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[[pose.CUSTOM.children.children.children.children]]
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id = 22
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name = "RBigToe"
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[[pose.CUSTOM.children.children.children.children.children]]
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id = 23
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name = "RSmallToe"
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[[pose.CUSTOM.children.children.children.children]]
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id = 24
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name = "RHeel"
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[[pose.CUSTOM.children]]
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id = 11
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name = "LHip"
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[[pose.CUSTOM.children.children]]
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id = 13
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name = "LKnee"
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[[pose.CUSTOM.children.children.children]]
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id = 15
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name = "LAnkle"
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[[pose.CUSTOM.children.children.children.children]]
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id = 19
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name = "LBigToe"
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[[pose.CUSTOM.children.children.children.children.children]]
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id = 20
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name = "LSmallToe"
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[[pose.CUSTOM.children.children.children.children]]
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id = 21
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name = "LHeel"
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[[pose.CUSTOM.children]]
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id = 17
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name = "Neck"
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[[pose.CUSTOM.children.children]]
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id = 18
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name = "Head"
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[[pose.CUSTOM.children.children.children]]
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id = 0
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name = "Nose"
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[[pose.CUSTOM.children.children]]
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id = 6
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name = "RShoulder"
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[[pose.CUSTOM.children.children.children]]
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id = 8
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name = "RElbow"
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[[pose.CUSTOM.children.children.children.children]]
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id = 10
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name = "RWrist"
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[[pose.CUSTOM.children.children]]
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id = 5
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name = "LShoulder"
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[[pose.CUSTOM.children.children.children]]
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id = 7
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name = "LElbow"
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[[pose.CUSTOM.children.children.children.children]]
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id = 9
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name = "LWrist"
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