File size: 2,106 Bytes
8420318 | 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 59 60 61 62 63 64 65 | """Contact detection for markerless GelSight.
Provides the classical, calibration-free contact pipeline:
diff-from-reference (L2) -> threshold -> largest connected component -> mask,
plus the exact contact scalars the dataset ships (intensity/area/mixed) so
users can reproduce / recompute them.
"""
from __future__ import annotations
import numpy as np
from .reference import l2_diff
TAU = 8.0 # dataset default L2 threshold (uint8 scale)
def contact_metrics(frame, reference, tau: float = TAU):
"""Reproduce the dataset's per-frame contact scalars.
d = ||frame - ref||_2 (per pixel, over RGB)
intensity = mean(d)
area = mean(d > tau)
mixed = mean(d * (d > tau))
Matches data/<task>/meta/*.parquet tactile_*_{intensity,area,mixed}.
"""
d = l2_diff(frame, reference)
above = d > tau
return {
"intensity": float(d.mean()),
"area": float(above.mean()),
"mixed": float((d * above).mean()),
}
def contact_mask(frame, reference, tau: float = TAU, largest_only: bool = True,
min_area_px: int = 50):
"""Binary contact mask (H, W) bool.
diff-from-reference L2 > tau, then (optionally) keep only the largest
connected component and drop specks < min_area_px. No calibration needed.
"""
import cv2
d = l2_diff(frame, reference)
mask = (d > tau).astype(np.uint8)
if int(mask.sum()) < min_area_px:
return np.zeros(mask.shape, bool)
if not largest_only:
return mask.astype(bool)
n, lbl, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8)
if n <= 1:
return np.zeros(mask.shape, bool)
areas = stats[1:, cv2.CC_STAT_AREA] # skip background (label 0)
keep = int(areas.argmax()) + 1
out = lbl == keep
return out if out.sum() >= min_area_px else np.zeros(mask.shape, bool)
def contact_centroid(mask):
"""(row, col) centroid of a contact mask, or None if empty."""
ys, xs = np.nonzero(mask)
if len(ys) == 0:
return None
return float(ys.mean()), float(xs.mean())
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