File size: 1,549 Bytes
82d7501 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | 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),
},
)
# For now, approximate motion as zero with small noise per window.
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}
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