Update AUGMENTED

Pose2Sim/Demo/S00_Demo_Session/Config.toml

@@ -27,7 +27,7 @@ frame_range = [] # For example [10,300], or [] for all frames
 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']
 
-## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Test now!
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
 participant_height = 1.7 # m
 participant_mass = 70 # kg
 
@@ -103,11 +103,6 @@ pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII,
 overwrite_pose = false
 openpose_path = '' # only checked if OpenPose is used
 
-[BODY_25_AUGMENTED]
-## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Test now!
-ModelName = "LSTM"
-model = "v0.3" # now, 0.3 is lastest version
-offset = true # default is ture
 
 [synchronization]
 # COMING SOON! 

Pose2Sim/Demo/S00_Demo_Session/MarkerAugmenter/LSTM/v0.0/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 59], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_3_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_3", "trainable": true, "batch_input_shape": [null, null, 59], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 144, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 144, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 129, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

Pose2Sim/Demo/S00_Demo_Session/MarkerAugmenter/LSTM/v0.1_lower/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 47], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_3_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_3", "trainable": true, "batch_input_shape": [null, null, 47], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 105, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

Pose2Sim/Demo/S00_Demo_Session/MarkerAugmenter/LSTM/v0.1_upper/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 23], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_4_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "batch_input_shape": [null, null, 23], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_5", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_6", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 24, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

Pose2Sim/Demo/S00_Demo_Session/MarkerAugmenter/LSTM/v0.2_upper/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 23], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_4_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "batch_input_shape": [null, null, 23], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 64, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_5", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 64, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 24, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.8.0", "backend": "tensorflow"}

Pose2Sim/Demo/S00_Demo_Session/MarkerAugmenter/LSTM/versions.txt

@@ -1,5 +0,0 @@
-Changelog:
-
-v0.2: DEFAULT - models trained with augmented dataset (data rotated about vertical axis) -  models 20 and 21 from openpose-augmenter repository.
-v0.1: separate models for lower and upper extremities  = models used for paper - models 13 and 16 from openpose-augmenter repository.
-v0.0: single model for both lower and upper extremities - model 14 from openpose-augmenter repository.

Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/Config.toml

@@ -27,7 +27,7 @@
 # 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']
 
-## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Test now!
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
 # participant_height = 1.7 # m
 # participant_mass = 70 # kg
 
@@ -104,10 +104,6 @@
 # overwrite_pose = false
 # openpose_path = '' # only checked if OpenPose is used
 
-[BODY_25_AUGMENTED]
-ModelName = "LSTM"
-model = "v0.3" # now, 0.3 is lastest version
-offset = true # default is ture
 
 # [synchronization]
 ## COMING SOON! 

Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T00_StaticTrial/Config.toml

@@ -27,7 +27,7 @@
 # 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']
 
-## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Test now!
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
 # participant_height = 1.7 # m
 # participant_mass = 70 # kg
 
@@ -104,10 +104,6 @@
 # overwrite_pose = false
 # openpose_path = '' # only checked if OpenPose is used
 
-[BODY_25_AUGMENTED]
-ModelName = "LSTM"
-model = "v0.3" # now, 0.3 is lastest version
-offset = true # default is ture
 
 # [synchronization]
 ## COMING SOON! 

Pose2Sim/Demo/S00_Demo_Session/S00_P00_Participant/S00_P00_T01_BalancingTrial/Config.toml

@@ -27,7 +27,7 @@
 # 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']
 
-## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Test now!
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
 # participant_height = 1.7 # m
 # participant_mass = 70 # kg
 
@@ -104,10 +104,6 @@
 # overwrite_pose = false
 # openpose_path = '' # only checked if OpenPose is used
 
-[BODY_25_AUGMENTED]
-ModelName = "LSTM"
-model = "v0.3" # now, 0.3 is lastest version
-offset = true # default is ture
 
 # [synchronization]
 ## COMING SOON! 

Pose2Sim/Demo/S01_Empty_Session/Config.toml

@@ -27,7 +27,7 @@ frame_range = [] # For example [10,300], or [] for all frames
 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']
 
-## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Test this!
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
 participant_height = 1.7 # m
 participant_mass = 70 # kg
 
@@ -103,10 +103,6 @@ pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII,
 overwrite_pose = false
 openpose_path = '' # only checked if OpenPose is used
 
-[BODY_25_AUGMENTED]
-ModelName = "LSTM"
-model = "v0.3" # now, 0.3 is lastest version
-offset = true # default is ture
 
 [synchronization]
 # COMING SOON! 

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/v0.0/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 59], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_3_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_3", "trainable": true, "batch_input_shape": [null, null, 59], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 144, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 144, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 129, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/v0.1_lower/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 47], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_3_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_3", "trainable": true, "batch_input_shape": [null, null, 47], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 105, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/v0.1_upper/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 23], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_4_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "batch_input_shape": [null, null, 23], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_5", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_6", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 80, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 24, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.6.0", "backend": "tensorflow"}

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/v0.2_upper/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 23], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_4_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_4", "trainable": true, "batch_input_shape": [null, null, 23], "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 64, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_5", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 64, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_1", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 24, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.8.0", "backend": "tensorflow"}

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/v0.3_lower/metadata.json

@@ -1 +0,0 @@
-{"reference_marker": "midHip"}

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/v0.3_lower/model.json

@@ -1 +0,0 @@
-{"class_name": "Sequential", "config": {"name": "sequential_2", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 47], "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_8_input"}}, {"class_name": "LSTM", "config": {"name": "lstm_8", "trainable": true, "dtype": "float32", "batch_input_shape": [null, null, 47], "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_9", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_10", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "LSTM", "config": {"name": "lstm_11", "trainable": true, "dtype": "float32", "return_sequences": true, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 128, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "TimeDistributed", "config": {"name": "time_distributed_2", "trainable": true, "dtype": "float32", "layer": {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 105, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}}}]}, "keras_version": "2.12.0", "backend": "tensorflow"}

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/v0.3_upper/metadata.json

@@ -1 +0,0 @@
-{"reference_marker": "midHip"}

Pose2Sim/Demo/S01_Empty_Session/MarkerAugmenter/LSTM/versions.txt

@@ -1,5 +0,0 @@
-Changelog:
-
-v0.2: DEFAULT - models trained with augmented dataset (data rotated about vertical axis) -  models 20 and 21 from openpose-augmenter repository.
-v0.1: separate models for lower and upper extremities  = models used for paper - models 13 and 16 from openpose-augmenter repository.
-v0.0: single model for both lower and upper extremities - model 14 from openpose-augmenter repository.

Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/Config.toml

@@ -10,245 +10,240 @@
 # 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,  
+# 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]
-frame_rate = 60 # fps
-frame_range = [] # For example [10,300], or [] for all frames
+# [project]
+# 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'].
+# 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']
 
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
 ## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
-participant_height = 1.7 # m
-participant_mass = 70 # kg
 
 
-# Take heart, calibration is not that complicated once you get the hang of it!
-[calibration]
-calibration_type = 'convert' # 'convert' or 'calculate'
+## 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.convert]
+   # convert_from = 'qualisys' # 'qualisys', 'optitrack', vicon', 'opencap', 'easymocap', or 'biocv'
+      # [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
+   # [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
+      ## Camera placements, need to be done before every session
+      # [calibration.calculate.extrinsics]
+      # 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.
 
-         [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
+      # moving_cameras = false # Not implemented yet
+      # calculate_extrinsics = true # true or false (lowercase) 
 
-         [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.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!
+         # [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, 
-                        #               BODY_25_AUGMENTED # Coming soon!
-						      #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
-
-[BODY_25_AUGMENTED]
-ModelName = "LSTM"
-model = "v0.3" # now, 0.3 is lastest version
-offset = true # default is ture
+# [pose]
+# pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut' 
+# pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII, 
+                        #               BODY_25_AUGMENTED # Coming soon! 
+# 						      #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
+# [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]
-single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
-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 = 0.2
+# [personAssociation]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
 
 
-[triangulation]
-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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
-undistort_points = 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!
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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]
+# type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
+# display_figures = true # 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
+   # [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
 
 
-[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'
+# [opensim]
+# static_trial = ['S01_P00_Participant0/S01_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]]
-  id = 12
-  name = "RHip"
-     [[pose.CUSTOM.children.children]]
-     id = 14
-     name = "RKnee"
-        [[pose.CUSTOM.children.children.children]]
-        id = 16
-        name = "RAnkle"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 22
-           name = "RBigToe"
-              [[pose.CUSTOM.children.children.children.children.children]]
-              id = 23
-              name = "RSmallToe"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 24
-           name = "RHeel"
-  [[pose.CUSTOM.children]]
-  id = 11
-  name = "LHip"
-     [[pose.CUSTOM.children.children]]
-     id = 13
-     name = "LKnee"
-        [[pose.CUSTOM.children.children.children]]
-        id = 15
-        name = "LAnkle"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 19
-           name = "LBigToe"
-              [[pose.CUSTOM.children.children.children.children.children]]
-              id = 20
-              name = "LSmallToe"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 21
-           name = "LHeel"
-  [[pose.CUSTOM.children]]
-  id = 17
-  name = "Neck"
-     [[pose.CUSTOM.children.children]]
-     id = 18
-     name = "Head"
-        [[pose.CUSTOM.children.children.children]]
-        id = 0
-        name = "Nose"
-     [[pose.CUSTOM.children.children]]
-     id = 6
-     name = "RShoulder"
-        [[pose.CUSTOM.children.children.children]]
-        id = 8
-        name = "RElbow"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 10
-           name = "RWrist"
-     [[pose.CUSTOM.children.children]]
-     id = 5
-     name = "LShoulder"
-        [[pose.CUSTOM.children.children.children]]
-        id = 7
-        name = "LElbow"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 9
-           name = "LWrist"
+## 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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"

Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_OperSim/.gitkeep

@@ -1 +0,0 @@
-

Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T00_StaticTrial/Config.toml

@@ -0,0 +1,248 @@
+###############################################################################
+## 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]
+# 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']
+
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
+
+
+## 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', or 'biocv'
+      # [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]
+      # 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
+      # calculate_extrinsics = true # true or false (lowercase) 
+
+         # [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, 
+#                                         BODY_25_AUGMENTED # Coming soon! 
+# 						      #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]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
+
+
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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 = true # 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
+
+
+# [opensim]
+# static_trial = ['S01_P00_Participant0/S01_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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"

Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T01_MotionTrial1/Config.toml

@@ -9,246 +9,240 @@
 # 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,  
+# 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]
-frame_rate = 60 # fps
-frame_range = [] # For example [10,300], or [] for all frames
+# [project]
+# 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'].
+# 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']
 
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
 ## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
-participant_height = 1.7 # m
-participant_mass = 70 # kg
 
 
-# Take heart, calibration is not that complicated once you get the hang of it!
-[calibration]
-calibration_type = 'convert' # 'convert' or 'calculate'
+## 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.convert]
+   # convert_from = 'qualisys' # 'qualisys', 'optitrack', vicon', 'opencap', 'easymocap', or 'biocv'
+      # [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
+   # [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
+      ## Camera placements, need to be done before every session
+      # [calibration.calculate.extrinsics]
+      # 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.
 
-         [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
+      # moving_cameras = false # Not implemented yet
+      # calculate_extrinsics = true # true or false (lowercase) 
 
-         [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.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!
+         # [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, 
-                        #               BODY_25_AUGMENTED # Coming soon!
-						      #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
-
-[BODY_25_AUGMENTED]
-ModelName = "LSTM"
-model = "v0.3" # now, 0.3 is lastest version
-offset = true # default is ture
+# [pose]
+# pose_framework = 'openpose' # 'openpose', 'mediapipe', 'alphapose', 'deeplabcut' 
+# pose_model = 'BODY_25B' #With openpose: BODY_25B, BODY_25, BODY_135, COCO, MPII, 
+                        #               BODY_25_AUGMENTED # Coming soon! 
+# 						      #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
+# [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]
-single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
-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 = 0.2
+# [personAssociation]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
 
 
-[triangulation]
-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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
-undistort_points = 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!
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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)
+# type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
+display_figures = true # 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
+   # [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
 
 
-[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'
+# [opensim]
+# static_trial = ['S01_P00_Participant/0S01_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]]
-  id = 12
-  name = "RHip"
-     [[pose.CUSTOM.children.children]]
-     id = 14
-     name = "RKnee"
-        [[pose.CUSTOM.children.children.children]]
-        id = 16
-        name = "RAnkle"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 22
-           name = "RBigToe"
-              [[pose.CUSTOM.children.children.children.children.children]]
-              id = 23
-              name = "RSmallToe"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 24
-           name = "RHeel"
-  [[pose.CUSTOM.children]]
-  id = 11
-  name = "LHip"
-     [[pose.CUSTOM.children.children]]
-     id = 13
-     name = "LKnee"
-        [[pose.CUSTOM.children.children.children]]
-        id = 15
-        name = "LAnkle"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 19
-           name = "LBigToe"
-              [[pose.CUSTOM.children.children.children.children.children]]
-              id = 20
-              name = "LSmallToe"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 21
-           name = "LHeel"
-  [[pose.CUSTOM.children]]
-  id = 17
-  name = "Neck"
-     [[pose.CUSTOM.children.children]]
-     id = 18
-     name = "Head"
-        [[pose.CUSTOM.children.children.children]]
-        id = 0
-        name = "Nose"
-     [[pose.CUSTOM.children.children]]
-     id = 6
-     name = "RShoulder"
-        [[pose.CUSTOM.children.children.children]]
-        id = 8
-        name = "RElbow"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 10
-           name = "RWrist"
-     [[pose.CUSTOM.children.children]]
-     id = 5
-     name = "LShoulder"
-        [[pose.CUSTOM.children.children.children]]
-        id = 7
-        name = "LElbow"
-           [[pose.CUSTOM.children.children.children.children]]
-           id = 9
-           name = "LWrist"
+## 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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"

Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T01_MotionTrial1/pose-3d/blank

@@ -1 +0,0 @@
-

Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T01_MotionTrial1/pose/blank

@@ -1 +0,0 @@
-.

Pose2Sim/Demo/S01_Empty_Session/S01_P00_Participant0/S01_P00_T02_MotionTrial2/Config.toml

@@ -0,0 +1,248 @@
+###############################################################################
+## 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]
+# 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']
+
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
+
+
+## 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', or 'biocv'
+      # [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]
+      # 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
+      # calculate_extrinsics = true # true or false (lowercase) 
+
+         # [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, 
+                        #               BODY_25_AUGMENTED # Coming soon! 
+# 						      #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]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
+
+
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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 = true # 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
+
+
+# [opensim]
+# static_trial = ['S01_P00_Participant0/S01_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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"

Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/Config.toml

@@ -0,0 +1,249 @@
+###############################################################################
+## 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]
+# 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']
+
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
+
+
+## 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', or 'biocv'
+      # [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]
+      # 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
+      # calculate_extrinsics = true # true or false (lowercase) 
+
+         # [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, 
+                        #               BODY_25_AUGMENTED # Coming soon! 
+# 						      #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]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
+
+
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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 = true # 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
+
+
+# [opensim]
+# static_trial = ['S01_P01_Participant1/S01_P01_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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"

Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/S01_P01_T00_StaticTrial/Config.toml

@@ -0,0 +1,249 @@
+###############################################################################
+## 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]
+# 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']
+
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
+
+
+## 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', or 'biocv'
+      # [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]
+      # 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
+      # calculate_extrinsics = true # true or false (lowercase) 
+
+         # [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, 
+                        #               BODY_25_AUGMENTED # Coming soon! 
+# 						      #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]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
+
+
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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 = true # 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
+
+
+# [opensim]
+# static_trial = ['S01_P01_Participant1/S01_P01_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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"

Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/S01_P01_T01_MotionTrial1/Config.toml

@@ -0,0 +1,249 @@
+###############################################################################
+## 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]
+# 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']
+
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
+
+
+## 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', or 'biocv'
+      # [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]
+      # 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
+      # calculate_extrinsics = true # true or false (lowercase) 
+
+         # [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, 
+                        #               BODY_25_AUGMENTED # Coming soon! 
+# 						      #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]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
+
+
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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 = true # 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
+
+
+# [opensim]
+# static_trial = ['S01_P01_Participant1/S01_P01_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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"

Pose2Sim/Demo/S01_Empty_Session/S01_P01_Participant1/S01_P01_T02_MotionTrial2/Config.toml

@@ -0,0 +1,249 @@
+###############################################################################
+## 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]
+# 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']
+
+# participant_height = 1.7 # m
+# participant_mass = 70 # kg
+## Only taken into account if pose_model is 'BODY_25_AUGMENTED' # Coming soon!
+
+
+## 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', or 'biocv'
+      # [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]
+      # 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
+      # calculate_extrinsics = true # true or false (lowercase) 
+
+         # [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, 
+                        #               BODY_25_AUGMENTED # Coming soon! 
+# 						      #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]
+# single_person = true # false for multi-person analysis (not supported yet), true for only triangulating the main person in scene. 
+# 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_error_threshold_association = 0.2
+
+
+# [triangulation]
+# 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 = true # Better if few cameras (eg less than 4) with risk of limb swapping (eg camera facing sagittal plane), otherwise slightly less accurate and slower
+# undistort_points = 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 = true # 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
+
+
+# [opensim]
+# static_trial = ['S01_P01_Participant1/S01_P01_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]]
+#   id = 12
+#   name = "RHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 14
+#      name = "RKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 16
+#         name = "RAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 22
+#            name = "RBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 23
+#               name = "RSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 24
+#            name = "RHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 11
+#   name = "LHip"
+#      [[pose.CUSTOM.children.children]]
+#      id = 13
+#      name = "LKnee"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 15
+#         name = "LAnkle"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 19
+#            name = "LBigToe"
+#               [[pose.CUSTOM.children.children.children.children.children]]
+#               id = 20
+#               name = "LSmallToe"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 21
+#            name = "LHeel"
+#   [[pose.CUSTOM.children]]
+#   id = 17
+#   name = "Neck"
+#      [[pose.CUSTOM.children.children]]
+#      id = 18
+#      name = "Head"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 0
+#         name = "Nose"
+#      [[pose.CUSTOM.children.children]]
+#      id = 6
+#      name = "RShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 8
+#         name = "RElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 10
+#            name = "RWrist"
+#      [[pose.CUSTOM.children.children]]
+#      id = 5
+#      name = "LShoulder"
+#         [[pose.CUSTOM.children.children.children]]
+#         id = 7
+#         name = "LElbow"
+#            [[pose.CUSTOM.children.children.children.children]]
+#            id = 9
+#            name = "LWrist"