train_action_recogn_pipeline.yaml

classes: ['stand', 'walk', 'run', 'jump', 'sit', 'squat', 'kick', 'punch', 'wave'] # change with your custom classes

img_format: "{:05d}.jpg" # dataset image extensions
window_size: 5 # Number of adjacent frames for extracting features.
data_root: &data_root ../datasets/realtime_action_recognition # set your dataset path
extract_path: &extract_path extracted_data/raw_skeletons_512/ # main dataset path/ extracted path

s1_extract_trtpose_skeletons.py:
    input:
        valid_imgs:
            - *data_root # main dataset path
            - source_images3/valid_images.txt # training path
        imgs_folder:
            - *data_root
            - source_images3/
    output:
        skeletons_folder: &skels_folder
            - *data_root # main dataset path
            - *extract_path
            - skeleton_res/
        imgs_folder:
            - *data_root # main dataset path
            - *extract_path
            - image_res/
        imgs_info_txt:
            - *data_root # main dataset path
            - *extract_path
            - images_info.txt

s2_combine_skeletons_txt.py:
    input:
        skeletons_folder: *skels_folder
    output:
        skeletons_txt: &skels_txt
            - *data_root # main dataset path
            - *extract_path
            - all_skeletons.txt

s3_gen_features.py:
    input:
        skeletons_txt: *skels_txt
    output:
        features_x: &features_x
            - *data_root # main dataset path
            - *extract_path
            - features_X.csv
        features_y: &features_y
            - *data_root # main dataset path
            - *extract_path
            - features_Y.csv

s4_train_classifier.py:
    input:
        features_x: *features_x
        features_y: *features_y
    output:
        model_path: ../weights/classifier/dnn/action_classifier2.pkl # classifier output path