Dataset Overview

This dataset contains sequences of MediaPipe pose landmarks for several padel strokes.

Classes

Forehand - filename: forehand (i).pkl
Backhand - filename: backhand (i).pkl
Forehand Volley - filename: fhvolley (i).pkl
Backhand Volley - filename: bhvolley (i).pkl
Bandeja - filename: bandeja (i).pkl
Smash - filename: smash (i).pkl

File Contents

Each .pkl file contains the PoseLandmarkerResult returned by detect_for_video from MediaPipe.

`PoseLandmarkerResult` Structure

Landmarks:
- Landmark #0:
  - x: 0.638852
  - y: 0.671197
  - z: 0.129959
  - visibility: 0.9999997615814209
  - presence: 0.9999984502792358
- Landmark #1:
  - x: 0.634599
  - y: 0.536441
  - z: -0.06984
  - visibility: 0.999909
  - presence: 0.999958
- ... (33 landmarks per pose)
WorldLandmarks:
- Landmark #0:
  - x: 0.067485
  - y: 0.031084
  - z: 0.055223
  - visibility: 0.9999997615814209
  - presence: 0.9999984502792358
- Landmark #1:
  - x: 0.063209
  - y: -0.00382
  - z: 0.020920
  - visibility: 0.999976
  - presence: 0.999998
- ... (33 world landmarks per pose)

More information on its format can be found here.

Loading Data

Loading `PoseLandmarkerResult`

To load a PoseLandmarkerResult from a .pkl file, use the following code:

import joblib

detection_results = joblib.load(pkl_filepath)

Loading the Complete Dataset

To load the dataset into arrays X, Y, Z, and labels, use the sample code below. Make sure to point folderpath to the directory containing the dataset files.

import os
import joblib

def build_coordinates_dataset(folderpath):
    all_files = os.listdir(folderpath)
    pkl_filepaths = [os.path.join(folderpath, pkl_filepath) for pkl_filepath in all_files if pkl_filepath.endswith(".pkl")]
    print(f'{len(pkl_filepaths)} samples found')
    
    X, Y, Z, labels = [], [], [], []

    for pkl_filepath in pkl_filepaths:
        detection_results = joblib.load(pkl_filepath)
        try:
            loaded_pose_landmarks = detection_results
            x_frames, y_frames, z_frames = [], [], []

            for frame_detection_result in loaded_pose_landmarks:
                landmarks = frame_detection_result.pose_world_landmarks[0]

                x_landmarks, y_landmarks, z_landmarks = [], [], []

                for i in landmarks:
                    x_landmarks.append(i.x)
                    y_landmarks.append(i.y)
                    z_landmarks.append(i.z)

                x_frames.append(x_landmarks)
                y_frames.append(y_landmarks)
                z_frames.append(z_landmarks)

            X.append(x_frames)
            Y.append(y_frames)
            Z.append(z_frames)

            labels.append(os.path.basename(pkl_filepath)[:3])

        except Exception as e:
            print(e)
            print(f"Error loading {pkl_filepath}")
            
    return X, Y, Z, labels

To load the dataset into arrays X, Y, Z, and labels, use the sample code below. Make sure to point folderpath to the directory containing the dataset files.