assess_sharpness() scores a 112x112 crop on variance-of-Laplacian, a
contrast-normalised variant, Tenengrad, a spectral high-frequency ratio
and dir_min_tenengrad, over a fixed 64x64 window on the face interior.
The window excludes the corners because studio headshots are routinely
shot at a wide aperture, and background bokeh measured over the whole
crop would drag the score down on the sharpest images in the set.
Five rather than one because AR-029's threshold has to be located, not
chosen: VR-012 ranks them by how well each predicts real identity loss.
The T1 ladders drove two corrections during development. The spectral
ratio applied its Hann window before removing the mean, so the DC term
smeared into the low-frequency bins and the "ratio" tracked absolute
brightness (a 20/255 brightening moved it 23%). And no measure taken
from the literature survived directional blur: normalising by total
energy divides out the loss being measured, so both ratio measures are
U-shaped in motion-blur length and score a 21 px smear about as sharp as
a 3 px one. dir_min_tenengrad exists to fix that -- a low-frequency
contrast denominator that blur leaves alone, and the worse of the two
Sobel axes rather than their sum.
The tests pin the disqualifying behaviours as well as the desirable
ones, so a change that makes var_laplacian contrast-free is a deliberate
act rather than an accident. They also record that every candidate falls
under downscale-upscale as well as under blur: the aligned crop is
scale-normalised geometrically, not informationally.
Exposed through sae_embed alongside the AR-030 alignment residual, so a
study scores through shipped code rather than a numpy copy -- the same
argument that already applies to the calibration.
TRACES: AR-028, AR-029 | SR-002
