| ## 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](https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker/python). | |
| ## Loading Data | |
| ### Loading `PoseLandmarkerResult` | |
| To load a `PoseLandmarkerResult` from a `.pkl` file, use the following code: | |
| ```python | |
| 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. | |
| ```python | |
| 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. |