Adriano
ba54b42fdc
Due ottimizzazioni chiave:
1. Spread bitmap uint8 invece di response map (N_BINS, H, W) float32
- 32x meno memoria, cache-friendly
- Nuovi kernel Numba: _jit_score_bitmap, _jit_popcount_density
- Formato: spread[y,x] bit b = bin b attivo nel raggio di spread
- _refine_angle usa slicing su bitmap con mask & bit
2. Pre-NMS prima di refine_angle/verify_ncc
- Problema: loop 'for raw in candidati' applicava refine+verify A OGNI
candidato prima del check NMS → 2000+ refine chiamati per ~25 match
- Fix: pre-NMS su (cx, cy) subpixel, limita a max_matches*3 candidati,
poi refine + verify solo su quelli
- Esempio worst case: lama_full_fast 55.9s → 1.13s (49x)
Benchmark suite 16 scenari (4 immagini x full/part x fast/preciso):
prima: totale find 94.6s
dopo: totale find 27.3s (3.5x globale)
casi peggiori <5s (prima erano >50s)
ROI parziali (solo metà oggetto) funzionano in tutti i casi.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
"""Test suite esaustivo su Test/*.png con varie configurazioni.
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Esegue matrix (immagine, ROI completa/parziale, config) e stampa tempi/match.
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"""
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from __future__ import annotations
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import time
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from pathlib import Path
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import cv2
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import numpy as np
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from pm2d import LineShapeMatcher
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from pm2d.gui import draw_matches
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TEST_DIR = Path(__file__).parent.parent / "Test"
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OUT_DIR = Path("/tmp/pm2d_suite"); OUT_DIR.mkdir(exist_ok=True)
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# Casi: (nome, immagine, (y0,y1,x0,x1) roi completa, (y0,y1,x0,x1) roi parziale)
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CASES = [
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("clip", "clip.png", ( 60, 200, 90, 290), ( 60, 135, 90, 290)),
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("ruota", "rings_and_nuts.png", ( 55, 175, 90, 215), ( 55, 115, 90, 215)),
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("dado", "rings_and_nuts.png", (255, 375, 40, 170), (255, 315, 40, 170)),
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("lama", "razors2.png", ( 90, 370, 120, 160), ( 90, 230, 120, 160)),
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]
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CONFIGS = [
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("fast", dict(angle_step_deg=10.0, scale_range=(1.0, 1.0),
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pyramid_levels=3, num_features=64)),
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("preciso", dict(angle_step_deg=5.0, scale_range=(0.5, 1.1), scale_step=0.05,
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pyramid_levels=3, num_features=96)),
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]
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def bench(case_name: str, img_path: str, roi_box: tuple, roi_kind: str,
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cfg_name: str, cfg: dict) -> dict:
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scene = cv2.imread(str(TEST_DIR / img_path))
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y0, y1, x0, x1 = roi_box
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roi = scene[y0:y1, x0:x1].copy()
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m = LineShapeMatcher(
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angle_range_deg=(0.0, 360.0),
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weak_grad=30, strong_grad=60,
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spread_radius=5, n_threads=4, **cfg,
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)
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t0 = time.time()
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n_var = m.train(roi)
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t_train = time.time() - t0
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# warmup (prima call è JIT compile)
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m.find(scene, min_score=0.55, max_matches=3, refine_angle=False)
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t0 = time.time()
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matches = m.find(
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scene, min_score=0.55, max_matches=25, nms_radius=None,
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refine_angle=True, subpixel=True, verify_threshold=0.4,
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)
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t_find = time.time() - t0
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tag = f"{case_name}_{roi_kind}_{cfg_name}"
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overlay = draw_matches(scene, matches,
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template_gray=cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY))
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cv2.imwrite(str(OUT_DIR / f"{tag}.png"), overlay)
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return {
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"case": tag,
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"roi": f"{roi.shape[1]}x{roi.shape[0]}",
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"variants": n_var,
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"train_s": t_train,
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"find_s": t_find,
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"n_match": len(matches),
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"score_range": (
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f"{min(x.score for x in matches):.2f}..{max(x.score for x in matches):.2f}"
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if matches else "-"
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),
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}
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def main():
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print(f"{'case':30s} {'roi':>9s} {'var':>4s} "
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f"{'train':>6s} {'find':>6s} {'n':>3s} score")
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print("-" * 85)
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total_find = 0.0
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for case_name, img, roi_full, roi_part in CASES:
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for roi_kind, roi_box in [("full", roi_full), ("part", roi_part)]:
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for cfg_name, cfg in CONFIGS:
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r = bench(case_name, img, roi_box, roi_kind, cfg_name, cfg)
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print(f"{r['case']:30s} {r['roi']:>9s} {r['variants']:>4d} "
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f"{r['train_s']:>5.2f}s {r['find_s']:>5.2f}s "
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f"{r['n_match']:>3d} {r['score_range']}")
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total_find += r["find_s"]
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print("-" * 85)
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print(f"totale find: {total_find:.1f}s overlay salvati in {OUT_DIR}/")
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if __name__ == "__main__":
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main()
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