# and make sure to back up your version if you did parameter changes! vame.update_config(config) # Step 1.2: # Align your behavior videos egocentric and create training dataset: # pose_ref_index: list of reference coordinate indices for alignment # Example: 0: snout, 1: forehand_left, 2: forehand_right, 3: hindleft, 4: hindright, 5: tail vame.egocentric_alignment(config, pose_ref_index=[0, 1, 2, 3, 4, 5, 6, 7, 8]) # If your experiment is by design egocentrical (e.g. head-fixed experiment on treadmill etc) # you can use the following to convert your .csv to a .npy array, ready to train vame on it #vame.csv_to_numpy(config, datapath='C:\\Research\\VAME\\vame_alpha_release-Mar16-2021\\videos\\pose_estimation\\') # Step 1.3: # create the training set for the VAME model vame.create_trainset(config) # Step 2: # Train VAME: vame.train_model(config) # Step 3: # Evaluate model vame.evaluate_model(config) # Step 4: # Segment motifs/pose vame.pose_segmentation(config) #------------------------------------------------------------------------------ #------------------------------------------------------------------------------
# and make sure to back up your version if you did parameter changes! vame.update_config(config) # Step 1.2: # Align your behavior videos egocentric and create training dataset: # pose_ref_index: list of reference coordinate indices for alignment # Example: 0: snout, 1: forehand_left, 2: forehand_right, 3: hindleft, 4: hindright, 5: tail vame.egocentric_alignment(config, pose_ref_index=[0, 5]) # If your experiment is by design egocentrical (e.g. head-fixed experiment on treadmill etc) # you can use the following to convert your .csv to a .npy array, ready to train vame on it vame.csv_to_numpy(config) # Step 1.3: # create the training set for the VAME model vame.create_trainset(config, check_parameter=False) # Step 2: # Train VAME: vame.train_model(config) # Step 3: # Evaluate model vame.evaluate_model(config) # Step 4: # Segment motifs/pose vame.pose_segmentation(config) #------------------------------------------------------------------------------ #------------------------------------------------------------------------------
