feat: profile mode + bench suite + skip-bin-vuoti + variant pruning histogram

4 ottimizzazioni performance + visibilita':

GGG. find(profile=True) → timing per fase
- _checkpoint() registra ms tra: to_gray, spread_top, top_pruning,
  full_kernel, refine_verify_nms
- get_last_profile() ritorna dict ms per identificare bottleneck
- Costo runtime trascurabile (~5 us per call)

HHH. pm2d.bench - benchmark suite eseguibile
- 3 scenarios (rect/L/circle x scene clean/cluttered)
- 5 configs (baseline, polarity, propagate, greedy, stride)
- Auto-aggiunge gpu_umat se opencl_available()
- Tabella ms/find + profile per ogni combo
- Entry-point pm2d-bench (--quick per smoke test 2 iter)

XX. Skip dilate per bin vuoti in _spread_bitmap
- Pre-calcolo bin presenti via np.unique sui pixel valid
- Su scene a bassa varianza orientation skip 50-70% delle dilate
- Misurato benchmark: spread_top da ~0.3ms a ~0.1ms in molti casi

VV. Variant pruning preliminare via histogramma orientation
- Per ogni variante calcolo overlap (feature bins ∩ scene bins) /
  total feature bins
- Se overlap < 0.5 * min_score → skip variante (no kernel call)
- Counter n_variants_pruned_histogram nel diag
- Vantaggio: scene focalizzate (poche direzioni dominanti) skippano
  varianti template con bin assenti dalla scena

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

pyproject.toml

@@ -14,6 +14,7 @@ dependencies = [
 
 [project.scripts]
 pm2d-eval = "pm2d.eval:main"
+pm2d-bench = "pm2d.bench:main"
 
 [dependency-groups]
 dev = [