| from __future__ import annotations |
|
|
| from dataclasses import asdict |
| from typing import Any, Dict, List |
|
|
| import numpy as np |
|
|
| from .config import Config |
| from .types import IngestResult |
| from .utils.logging import get_logger, log_params |
|
|
|
|
| logger = get_logger(__name__) |
|
|
|
|
| def stabilize_rois( |
| ingest_result: IngestResult, |
| face_metrics: Dict[str, Any], |
| roi_metrics: Dict[str, Any], |
| config: Config, |
| ) -> Dict[str, Any]: |
| """ |
| Placeholder stabilization that simply computes a dummy residual motion per window. |
| |
| A real implementation would warp ROIs into a canonical frame and compute optical |
| flow residuals; here we just synthesize a small random motion metric so that |
| downstream SQI logic has a numeric signal to work with. |
| """ |
| log_params( |
| logger, |
| "stabilization", |
| { |
| "config": asdict(config.rppg), |
| "num_windows": len(ingest_result.windows), |
| }, |
| ) |
|
|
| |
| motion_per_window: List[Dict[str, Any]] = [] |
| rng = np.random.default_rng(0) |
| for w in ingest_result.windows: |
| motion_value = float(abs(rng.normal(loc=0.02, scale=0.01))) |
| motion_per_window.append( |
| { |
| "index": w.index, |
| "start_time": w.start_time, |
| "end_time": w.end_time, |
| "residual_motion": motion_value, |
| } |
| ) |
|
|
| metrics: Dict[str, Any] = { |
| "windows": motion_per_window, |
| } |
|
|
| return {"metrics": metrics} |
|
|
|
|
