feat(web): wiring UI per modalita Halcon (M, Y, Z, V, X, R + altri)

UI espone tutti i nuovi flag tramite sezione pieghevole "Modalita Halcon"
nel pannello impostazioni. Default off = comportamento backward compat.

Flag esposti (checkbox + numerici):
- use_polarity (F): 16-bin orientation mod 2pi
- use_gpu (R): OpenCL UMat con silent fallback CPU
- use_soft_score (Y): score continuo cos(theta_t-theta_s)
- subpixel_lm (Z): refinement 0.05 px gradient field
- refine_pose_joint: Nelder-Mead 3D (cx,cy,theta)
- pyramid_propagate: top-K propagation a full-res
- min_recall (M): filtro feature-recall
- nms_iou_threshold (A): IoU bbox poligonale
- greediness: early-exit kernel
- coarse_stride: sub-sampling top-level
- search_roi: x,y,w,h area di ricerca

Persistenza ricette (V):
- Endpoint POST /recipes: training + save .npz in recipes/
- Endpoint GET /recipes: lista
- UI: campo nome + bottone "Salva" sotto i flag

Server SimpleMatchParams esteso con tutti i campi; pipeline match_simple
propaga init-flags al cache key (use_polarity/use_gpu = retrain) e
find-flags al m.find().

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

pm2d/web/server.py

@@ -48,6 +48,10 @@ IMAGES_DIR = Path(_images_dir_raw)
 if not IMAGES_DIR.is_absolute():
     IMAGES_DIR = PROJECT_ROOT / IMAGES_DIR
 
+# Cartella ricette pre-trained (V feature: save/load matcher)
+RECIPES_DIR = PROJECT_ROOT / "recipes"
+RECIPES_DIR.mkdir(exist_ok=True)
+
 from pm2d.line_matcher import LineShapeMatcher, Match
 from pm2d.auto_tune import auto_tune
 
@@ -267,6 +271,20 @@ class SimpleMatchParams(BaseModel):
     penalita_scala: float = 0.0       # 0 = score shape invariante, >0 = penalizza scala != 1
     min_score: float = 0.65
     max_matches: int = 25
+    # --- Halcon-mode flags (default off = backward compat) ---
+    # Init-time (richiede ri-train se cambiato)
+    use_polarity: bool = False        # F: 16 bin orientation mod 2pi
+    use_gpu: bool = False             # R: OpenCL UMat (silent fallback)
+    # Find-time (no retrain)
+    min_recall: float = 0.0           # M: filtra match con poche feature combaciate
+    use_soft_score: bool = False      # Y: cosine sim continua dei gradients
+    subpixel_lm: bool = False         # Z: precisione 0.05 px
+    nms_iou_threshold: float = 0.3    # A: IoU bbox poligonale
+    coarse_stride: int = 1            # sub-sampling top-level (>=1)
+    pyramid_propagate: bool = False   # propagazione candidati top->full
+    greediness: float = 0.0           # early-exit kernel (0..1)
+    refine_pose_joint: bool = False   # Nelder-Mead 3D (cx, cy, angle)
+    search_roi: list[int] | None = None  # [x, y, w, h] limita area
 
 
 def _simple_to_technical(
@@ -526,6 +544,9 @@ def match_simple(p: SimpleMatchParams):
     tech = _simple_to_technical(p, roi_img)
 
     key = _matcher_cache_key(roi_img, tech)
+    # Halcon-mode init params: incidono sul training, includere in cache key
+    halcon_init_key = f"|pol={p.use_polarity}|gpu={p.use_gpu}"
+    key = key + halcon_init_key
     m = _cache_get_matcher(key)
     if m is None:
         m = LineShapeMatcher(
@@ -537,17 +558,30 @@ def match_simple(p: SimpleMatchParams):
             scale_step=tech["scale_step"],
             spread_radius=tech["spread_radius"],
             pyramid_levels=tech["pyramid_levels"],
+            use_polarity=p.use_polarity,
+            use_gpu=p.use_gpu,
         )
         t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
         _cache_put_matcher(key, m)
     else:
         n = len(m.variants); t_train = 0.0
     nms = tech["nms_radius"] if tech["nms_radius"] > 0 else None
+    search_roi_t = tuple(p.search_roi) if p.search_roi else None
     t0 = time.time()
     matches = m.find(
         scene, min_score=tech["min_score"], max_matches=tech["max_matches"],
         nms_radius=nms, verify_threshold=tech["verify_threshold"],
         scale_penalty=tech.get("scale_penalty", 0.0),
+        # Halcon-mode flags
+        min_recall=p.min_recall,
+        use_soft_score=p.use_soft_score,
+        subpixel_lm=p.subpixel_lm,
+        nms_iou_threshold=p.nms_iou_threshold,
+        coarse_stride=p.coarse_stride,
+        pyramid_propagate=p.pyramid_propagate,
+        greediness=p.greediness,
+        refine_pose_joint=p.refine_pose_joint,
+        search_roi=search_roi_t,
     )
     t_find = time.time() - t0
 
@@ -576,6 +610,70 @@ def tune(p: TuneParams):
     return {k: v for k, v in t.items() if not k.startswith("_")}
 
 
+# --- V: Save/Load ricette pre-trained ---
+
+class SaveRecipeParams(BaseModel):
+    model_id: str
+    scene_id: str | None = None
+    roi: list[int]
+    # Riusa stessi param simple per training equivalente
+    tipo: str = "intero"
+    simmetria: str = "nessuna"
+    scala: str = "fissa"
+    precisione: str = "normale"
+    use_polarity: bool = False
+    use_gpu: bool = False
+    name: str  # nome file ricetta (no path)
+
+
+@app.post("/recipes")
+def save_recipe(p: SaveRecipeParams):
+    """Allena matcher e salva su disco come ricetta riutilizzabile."""
+    model = _load_image(p.model_id)
+    if model is None:
+        raise HTTPException(404, "Modello non trovato")
+    x, y, w, h = p.roi
+    roi_img = model[y:y + h, x:x + w]
+    sp = SimpleMatchParams(
+        model_id=p.model_id, scene_id=p.scene_id or p.model_id, roi=p.roi,
+        tipo=p.tipo, simmetria=p.simmetria, scala=p.scala,
+        precisione=p.precisione,
+        use_polarity=p.use_polarity, use_gpu=p.use_gpu,
+    )
+    tech = _simple_to_technical(sp, roi_img)
+    m = LineShapeMatcher(
+        num_features=tech["num_features"],
+        weak_grad=tech["weak_grad"], strong_grad=tech["strong_grad"],
+        angle_range_deg=(tech["angle_min"], tech["angle_max"]),
+        angle_step_deg=tech["angle_step"],
+        scale_range=(tech["scale_min"], tech["scale_max"]),
+        scale_step=tech["scale_step"],
+        spread_radius=tech["spread_radius"],
+        pyramid_levels=tech["pyramid_levels"],
+        use_polarity=p.use_polarity,
+        use_gpu=p.use_gpu,
+    )
+    m.train(roi_img)
+    safe_name = "".join(c for c in p.name if c.isalnum() or c in "._-")
+    if not safe_name:
+        raise HTTPException(400, "Nome ricetta non valido")
+    if not safe_name.endswith(".npz"):
+        safe_name += ".npz"
+    target = RECIPES_DIR / safe_name
+    m.save_model(str(target))
+    return {"name": safe_name, "size": target.stat().st_size,
+            "n_variants": len(m.variants)}
+
+
+@app.get("/recipes")
+def list_recipes():
+    files = []
+    if RECIPES_DIR.is_dir():
+        for f in sorted(RECIPES_DIR.glob("*.npz")):
+            files.append({"name": f.name, "size": f.stat().st_size})
+    return {"files": files, "dir": str(RECIPES_DIR)}
+
+
 # Mount static
 app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")