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| # Bundled black-glove processing | |
| This directory contains the code and default model used by the | |
| `black_glove_hand` workflow module. It is intentionally inside `Data | |
| Acquisition` so the worker does not import the legacy repository-level | |
| black-glove demo. | |
| Contents: | |
| - `glove_detector.py`: YOLO-World box detection, RTMPose hand keypoints and | |
| temporal stabilization. | |
| - `glove_package/`: local tracker and YOLO-World wrapper. | |
| - `d435_hands_demo.py`: local RGB-D alignment/lifting helper used when a | |
| compatible depth stream is available. | |
| - `d435_tracking.py`: local, NumPy-only depth jump gate and alpha-beta slot | |
| predictor. It is the production workflow's extracted v1.0 tracking logic; | |
| it does not import the standalone v1.0 project. The workflow adapter also | |
| keeps a slot-level depth-centre EMA, per-joint gate recovery, and label/2D | |
| geometry fallback when a detector track id is unavailable. | |
| - `hand_landmarker.task`: MediaPipe model used by the RGB-D/Hand 3D helper. | |
| - `weights/yolov8m-worldv2.pt`: default black-glove detector weight. | |
| Before publishing a public repository, verify the redistribution terms for the | |
| bundled model files. If the model terms do not allow redistribution, keep the | |
| code layout and replace the files with a documented download step. | |
| The Python dependencies are declared in the parent `processor` | |
| `requirements.txt`. RTMPose model assets may be downloaded by `rtmlib` on | |
| first use and cached by the Python environment; this is a package/runtime | |
| dependency, not a dependency on another repository directory. | |
| ## Output semantics | |
| The `hand_3d/*.parquet` artifact keeps both preview continuity and data | |
| quality explicit for each slot: | |
| - `real`: current YOLO/RTMPose + depth observation accepted; | |
| - `propagated`: bounded D435 alpha-beta prediction, for preview continuity; | |
| - `absent`: no observation and no bounded prediction. | |
| The RGB renderer hides detector rectangles, but the detector still uses its | |
| internal ROI. `propagated` frames are retained in this raw artifact for a | |
| continuous preview; they are excluded from the default training export. | |
| The raw artifact keeps propagated rows so the 3D viewer can remain continuous. | |
| LeRobot/HDF5 training export filters `state != "real"` by default, so predicted | |
| points are not silently used as labels. | |
| ## Future detector fine-tuning augmentation | |
| The current repository has no detector-training command, so augmentation is | |
| not applied to inference code. When fine-tuning a glove-specific detector or | |
| 21-point pose model, build the training set from whole video sequences and add | |
| motion blur, random frame exposure, black-object/background swaps, partial | |
| occlusion, crop/scale jitter, and hand-edge truncation. Split validation by | |
| sequence/person, not by random frames, and keep a clean unaugmented test set. | |