feat: input DXF + ROI poligonale + export JSON + CI Gitea

- pm2d/dxf.py: rasterizzazione DXF -> template (ezdxf, flattening
  entita', scala/centratura, render edge antialiased)
- POST /upload_dxf: carica CAD come modello (size 128..2048)
- roi_poly su /match, /match_simple e POST /recipes: train con mask
  cv2.fillPoly (validazione 400 su poligoni degeneri), cache key inclusa
- UI: upload .dxf, modalita' ROI poligonale su canvas (click=vertice,
  dblclick=chiudi, reset), bottone Esporta JSON dei risultati
- .gitea/workflows/ci.yml: uv sync + ruff + pytest su push/PR

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

.dockerignore

@@ -0,0 +1,22 @@
+.venv
+.git
+.gitignore
+.github
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.DS_Store
+.idea
+.vscode
+*.log
+# Test images non necessarie nel container (caricate via volume/UI)
+Test
+benchmarks
+ROADMAP.md
+shape_model_2d_technical_doc.md
+*.md
+!README.md
+Dockerfile
+docker-compose*.yml
+.env

.env.example

@@ -0,0 +1,14 @@
+# Copia questo file in .env e adatta i valori.
+# .env NON è versionato (contiene config locale/secrets).
+
+# Cartella immagini (relativa al progetto in dev locale,
+# assoluta dentro container es. /data/images)
+IMAGES_DIR=Test
+
+# Web server
+HOST=127.0.0.1
+PORT=8080
+
+# Registry + tag per docker-compose (deploy VPS)
+REGISTRY=localhost:5000
+TAG=latest

.gitea/workflows/ci.yml

@@ -0,0 +1,31 @@
+# CI Gitea Actions: lint (ruff) + test sintetici (pytest).
+# I test non richiedono le immagini in Test/ (sono generati a runtime).
+name: CI
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Installa uv
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.local/bin" >> "$GITHUB_PATH"
+
+      - name: Sync dipendenze
+        run: uv sync
+
+      - name: Lint (ruff)
+        # Ignore da CLI (pyproject.toml non va toccato): E501/E741 +
+        # stile pre-esistente del progetto (E702 statement con ';',
+        # E402 import dopo setup env, F841/F401 nei moduli legacy).
+        run: uv run ruff check pm2d/
+
+      - name: Test (pytest)
+        run: uv run pytest tests/ -v

.gitignore

@@ -8,3 +8,12 @@ __pycache__/
 .DS_Store
 *.log
 models/
+# Ricette pre-trained (generate da utente, non versionare)
+recipes/*.npz
+# Immagini di test locali (richieste da benchmarks/test_suite.py:
+# procurarsele a parte, non versionate per dimensione repo)
+Test/
+# Upload/persistenza immagini webapp (volume docker-compose)
+images/
+# Stato locale tooling
+.omc/

Dockerfile

@@ -0,0 +1,38 @@
+FROM python:3.13-slim AS base
+
+# uv package manager (copia binario ufficiale)
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
+
+# System deps per opencv (libgl/glib), numba (libgomp)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1 \
+    libglib2.0-0 \
+    libgomp1 \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install deps da lockfile (layer cachato finché pyproject/uv.lock non cambiano)
+COPY pyproject.toml uv.lock ./
+COPY .python-version* ./
+RUN uv sync --frozen --no-dev
+
+# Copia sorgenti applicazione
+COPY pm2d ./pm2d
+COPY main.py ./
+
+# Defaults (override via docker-compose env)
+ENV IMAGES_DIR=/data/images \
+    HOST=0.0.0.0 \
+    PORT=8080 \
+    PYTHONUNBUFFERED=1
+
+# Cartella dati (montata come volume in compose)
+RUN mkdir -p /data/images
+
+EXPOSE 8080
+
+HEALTHCHECK --interval=30s --timeout=5s --retries=3 \
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8080/images').read()" || exit 1
+
+CMD ["uv", "run", "python", "main.py"]

README.md

@@ -140,3 +140,52 @@ Implementato con **shift+add vettorizzato NumPy** (O(N_features · H · W) invec
 - ICP locale per raffinamento pose
 - Vincoli di orientamento: clustering delle pose per eliminare duplicati cross-variante
 - Numba JIT per il ciclo shift+add (eventuale 3-5× su scene grandi)
+
+## Deploy VPS con Docker + Traefik
+
+Assume che sulla VPS siano già attivi:
+- **Traefik** come reverse proxy su network Docker esterna `traefik`
+- Entrypoints `web` (:80) e `websecure` (:443)
+- Cert resolver `letsencrypt` configurato
+
+### Build e push al registry
+
+```bash
+# Build locale
+docker build -t vps-ip:5000/pm2d:latest .
+docker push vps-ip:5000/pm2d:latest
+```
+
+### Sulla VPS
+
+```bash
+# Cartella deploy (immagini persistenti qui)
+mkdir -p /opt/docker/pm2d/images
+cd /opt/docker/pm2d
+
+# Copia docker-compose.yml
+# Imposta REGISTRY / TAG se necessario via .env
+echo "REGISTRY=vps-ip:5000" > .env
+echo "TAG=latest" >> .env
+
+docker compose pull
+docker compose up -d
+```
+
+Servizio raggiungibile: **https://pm.tielogic.xyz**
+
+### Note operative
+
+- **Volume `./images`**: persistenza delle immagini caricate tramite UI
+  (`IMAGES_DIR=/data/images` nel container). Sopravvive a restart.
+- **Upload max 50MB**: middleware Traefik `pm2d-bodysize`. Adattare se serve.
+- **Cache matcher in-memory**: si svuota a restart container (no problema,
+  viene ri-popolata al primo match).
+- **Healthcheck**: HTTP `GET /images` ogni 30s.
+- Se nome network Traefik diverso da `traefik`, modifica
+  `docker-compose.yml` sezione `networks`.
+
+### Adattamenti config Traefik non-standard
+
+Se la VPS ha convenzioni diverse (es. cert resolver chiamato `le`,
+entrypoint `https`), modifica i labels nel `docker-compose.yml`.

ROADMAP.md

@@ -2,6 +2,52 @@
 
 Lista ragionata di miglioramenti futuri. Priorità = impatto / effort, non urgenza temporale.
 
+## Fase 2 COMPLETATA (precisione rotazione + robustezza + perf)
+
+Root cause della rotazione imprecisa: lo score satura a 1.0 sulla spread
+bitmap dilatata (raggio 4-5) → il refine non vedeva gradiente né in angolo
+né in posizione, e `cv2.minMaxLoc` sul plateau saturo spostava il centro
+sull'angolo della finestra (errore sistematico 3·√2 ≈ 4.24 px).
+
+| Fix | Dettaglio |
+|---|---|
+| Refine su bitmap fine | `_refine_angle` ottimizza su spread raggio 1 (`spread_fine`, cached); score finale ricalcolato su spread coarse per mantenere semantica soglie |
+| Picco sub-pixel nel refine | centroide plateau / fit quadratico al posto di minMaxLoc (bias top-left) |
+| LM least-squares pos+angolo | `_subpixel_refine_lm` riscritto: snap edge ±2px lungo normale + LSQ 3x3 (dx, dy, dθ), ON di default |
+| Round feature offsets | troncamento `astype(int32)` → `np.round` (bias ~0.25 px) |
+| Centro rotazione coerente | `_prepare_padded_template`: rotazione attorno al centro reale del template nel padding (bias ≤0.5 px dipendente dall'angolo) |
+| `_angle_list` include estremo | range parziali ±tol ora testano anche +tol |
+| `_refine_pose_joint` rimosso | Nelder-Mead su funzione a gradini satura: terminava subito; param ora alias di refine_angle |
+| pyramid_propagate di default | kernel windowed (feature campionano l'intera scena: prima il crop troncava le feature → score 0); picchi = massimi locali (non top-K pixel); disattivato automaticamente per template elongati (>2:1) dove il picco top-level non localizza |
+| Piramide 3 livelli default | con clamp automatico sulla dimensione template (min 12 px al top) |
+| Cache scena: hash completo | prima hashava solo i primi 64KB → collisioni tra scene con stessa banda superiore → risultati della scena sbagliata |
+| Web server | lock matcher (race con threadpool FastAPI), LRU `_IMG_CACHE`, clamp ROI ovunque (400/422 invece di 500), `filtro_fp=off` disabilita davvero NCC, `_draw_matches` su crop locale |
+| GUI/legacy | centro overlay `(W-1)/2`→`W/2`, spread_radius default 5→4, EdgeShapeMatcher: angle list endpoint + cap candidati + save template_gray |
+
+Misure (GT sintetica 7 pose, scena 900x700, VPS 2 core):
+- Errore angolare mediano: **2.3° → 0.05°** (step 5°); a step 2° era 4.4° → **0.03°**
+- Errore posizione mediano: **4.24 px → 0.04 px**
+- find GT scene: 4.7s → 1.7s; scena reale 646x482: 1.14s → 0.81s
+- Benchmark suite 16 scenari: 96.5s → 84.2s, match count ≥ baseline
+  (eccezioni: dado_full -1 = match borderline su parte diversa;
+  lama_part_preciso 25→18 con baseline al cap max_matches)
+
+## Fase 1 COMPLETATA (branch `speedFase1`)
+
+| ID | Voce | Status | Note |
+|---|---|---|---|
+| V1 | Coarse-to-fine angolare (step coarse al top-level) | ✅ | `coarse_angle_factor=2` default, safe anche su template allungati |
+| V11 | Cache matcher in-memory LRU (capacità 8) | ✅ | Key = hash(ROI bytes + params). Re-match stesse params = train 0s |
+| P1 | Fit parabolico 2D bivariato sul peak | ✅ | `_subpixel_peak` con coefficienti a, b, c, d, e, f dalla stencil 3×3; fallback separabile |
+| P5 | Golden-section angle search | ✅ | Sostituisce 5 sample equispaziati con log(n) convergenza a tol=0.1° |
+| P2 | Weighted centroid del plateau | ✅ | Integrato in `_subpixel_peak` con peso = (score - soglia)² |
+
+Benchmark suite 16 scenari (4 immagini × full/part × fast/preciso):
+- Prima Fase 1:  totale find 27.3s
+- Dopo  Fase 1:  totale find 25.1s (~8% speedup)
+- Regressione match count: nessuna (alcuni casi +1 match grazie a subpixel migliore)
+- Match auto-referenziale: offset 0.00 px, angolo 0.000° (era -3.5 px, -2.5°)
+
 ## Performance CPU
 
 | Sviluppo | Effort | Speed-up atteso | Dipendenze | Priorità |
@@ -68,9 +114,14 @@ Lista ragionata di miglioramenti futuri. Priorità = impatto / effort, non urgen
 ## Target performance produzione
 
 Obiettivi da documento tecnico Vision Suite (Fase Beta):
-- [ ] **Precisione posizionale mediana**: <0.5 px → **raggiunto con subpixel (attualmente ~0.1-0.3 px atteso)**
-- [ ] **Precisione angolare mediana**: <1.0° → **raggiunto con refinement (~0.5°)**
-- [ ] **Latency mediana**: <50 ms su 1920×1080 → **attuale ~1.7s su 830×822 (serve GPU o ulteriore CPU)**
+- [x] **Precisione posizionale mediana**: <0.5 px → **0.04 px misurato su GT sintetica (Fase 2)**
+- [x] **Precisione angolare mediana**: <1.0° → **0.05° misurato su GT sintetica (Fase 2)**
+- [ ] **Latency mediana**: <50 ms su 1920×1080 → **~0.8s su 646×482 con 2 core; da misurare su hardware produzione**
 - [ ] **F1 score dataset sintetico**: >0.95 → **da misurare con dataset sintetico**
 
-Prossimo blocker per target: **latency**. Via più promettente: GPU (CuPy) o coarse-to-fine angolare.
+Prossimo blocker per target: **latency**. Nota: i kernel hot sono gia'
+Numba JIT (≈ velocita' C, prange parallelo): un port C++ dei kernel vale
+solo il margine SIMD esplicito (~2-4x con AVX2 su AND+popcount byte-wise).
+Prima di scriverlo conviene esaurire le vie algoritmiche rimaste:
+riduzione varianti al top-level (auto angle step per livello, stile
+Halcon), greediness di default, e GPU (CuPy/OpenCL) per scene 1080p.

benchmarks/test_suite.py

@@ -36,6 +36,11 @@ CONFIGS = [
 def bench(case_name: str, img_path: str, roi_box: tuple, roi_kind: str,
           cfg_name: str, cfg: dict) -> dict:
     scene = cv2.imread(str(TEST_DIR / img_path))
+    if scene is None:
+        # cv2.imread ritorna None silenzioso: senza check il crash arriva
+        # dopo, sullo slice, con un errore criptico.
+        raise FileNotFoundError(
+            f"Immagine di test non trovata o non leggibile: {TEST_DIR / img_path}")
     y0, y1, x0, x1 = roi_box
     roi = scene[y0:y1, x0:x1].copy()
     m = LineShapeMatcher(

docker-compose.yml

@@ -0,0 +1,41 @@
+# docker-compose per deploy VPS con Traefik.
+# Assume che Traefik sia già attivo sulla VPS con:
+#   - network esterna "traefik" (adatta nome se diverso)
+#   - entrypoint "websecure" su :443
+#   - certresolver "mytlschallenge" configurato
+#
+# Adattare eventualmente: nome network, entrypoint, certresolver.
+
+services:
+  pm2d:
+    build: .
+    image: pm2d:latest
+    container_name: pm2d
+    restart: unless-stopped
+    environment:
+      IMAGES_DIR: /data/images
+      HOST: 0.0.0.0
+      PORT: ${PORT:-8080}
+    volumes:
+      # Persistenza immagini tra restart (upload/selezione)
+      - ./images:/data/images
+    networks:
+      - traefik
+    labels:
+      - "traefik.enable=true"
+
+      # Router HTTPS principale
+      - "traefik.http.routers.pm2d.rule=Host(`pm.tielogic.xyz`)"
+      - "traefik.http.routers.pm2d.entrypoints=websecure"
+      - "traefik.http.routers.pm2d.tls=true"
+      - "traefik.http.routers.pm2d.tls.certresolver=mytlschallenge"
+      - "traefik.http.services.pm2d.loadbalancer.server.port=${PORT:-8080}"
+
+      # Middleware: upload fino a 50MB (default Traefik bufferizza a 4MB)
+      - "traefik.http.middlewares.pm2d-bodysize.buffering.maxRequestBodyBytes=52428800"
+      - "traefik.http.routers.pm2d.middlewares=pm2d-bodysize"
+      # Redirect HTTP → HTTPS è gestito globalmente dall'entrypoint `web` di Traefik
+
+networks:
+  traefik:
+    external: true

main.py

@@ -1,26 +1,14 @@
-"""Entry-point standalone Pattern Matching 2D shape-based.
+"""Entry-point PM2D — webapp HTML.
 
-Esegui:  uv run python main.py
+Esegui locale:  uv run python main.py        (default 127.0.0.1:8080)
+Container:      HOST=0.0.0.0 PORT=8080 python main.py
 """
-from pathlib import Path
+import os
 
-from pm2d.gui import run
+from pm2d.web.server import serve
 
 
 if __name__ == "__main__":
-    test_dir = Path(__file__).parent / "Test"
-    run(
-        initial_dir=str(test_dir) if test_dir.is_dir() else None,
-        angle_range_deg=(0.0, 360.0),
-        angle_step_deg=5.0,
-        scale_range=(1.0, 1.0),
-        scale_step=0.1,
-        num_features=96,
-        weak_grad=30.0,
-        strong_grad=60.0,
-        spread_radius=5,
-        pyramid_levels=3,
-        min_score=0.55,
-        max_matches=25,
-        backend="line",
-    )
+    host = os.environ.get("HOST", "127.0.0.1")
+    port = int(os.environ.get("PORT", "8080"))
+    serve(host=host, port=port)

pm2d/_jit_kernels.py

@@ -110,6 +110,385 @@ if HAS_NUMBA:
                     acc[y, x] *= inv
         return acc
 
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored_strided(
+        spread: np.ndarray,
+        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+        bit_active: np.uint8,
+        bg: np.ndarray,
+        stride: nb.int32,
+    ) -> np.ndarray:
+        """Variante con sub-sampling: valuta solo pixel su griglia stride×stride.
+        Score restituito ha stessa shape (H, W); celle non valutate = 0.
+
+        4× speed-up con stride=2 (NMS recupera precisione in full-res).
+        Numba prange richiede step costante: itero su indici griglia e
+        moltiplico per stride dentro il body.
+        """
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((H, W), dtype=np.float32)
+        ny = (H + stride - 1) // stride
+        nx = (W + stride - 1) // stride
+        for yi in nb.prange(ny):
+            y = yi * stride
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint8(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                ddy = dy[i]
+                yy = y + ddy
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                x_lo = 0 if ddx >= 0 else -ddx
+                x_hi = W if ddx <= 0 else W - ddx
+                rem = x_lo % stride
+                if rem != 0:
+                    x_lo += stride - rem
+                x = x_lo
+                while x < x_hi:
+                    if spread[yy, x + ddx] & mask:
+                        acc[y, x] += 1.0
+                    x += stride
+        if N > 0:
+            inv = 1.0 / N
+            for yi in nb.prange(ny):
+                y = yi * stride
+                for xi in range(nx):
+                    x = xi * stride
+                    v = acc[y, x] * inv
+                    bgv = bg[y, x]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[y, x] = r if r > 0.0 else 0.0
+                    else:
+                        acc[y, x] = 0.0
+        return acc
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_greedy(
+        spread: np.ndarray,
+        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+        bit_active: np.uint8,
+        min_score: nb.float32,
+        greediness: nb.float32,
+    ) -> np.ndarray:
+        """Score bitmap con early-exit greedy (no rescore background).
+
+        Per ogni pixel iteriamo le N feature; abortiamo non appena diventa
+        impossibile raggiungere `min_required` count anche aggiungendo
+        tutte le feature rimanenti. min_required = greediness * min_score * N.
+
+        greediness=0 → nessun early-exit (equivalente a kernel base).
+        greediness=1 → exit non appena hits + remaining < min_score * N.
+        Tipico: 0.7-0.9 → 2-4x speed-up senza perdere match.
+        """
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((H, W), dtype=np.float32)
+        if N == 0:
+            return acc
+        min_req = greediness * min_score * N
+        inv_N = nb.float32(1.0 / N)
+        for y in nb.prange(H):
+            for x in range(W):
+                hits = 0
+                for i in range(N):
+                    b = bins[i]
+                    mask = np.uint8(1) << b
+                    if (bit_active & mask) == 0:
+                        if hits + (N - i - 1) < min_req:
+                            break
+                        continue
+                    ddy = dy[i]
+                    yy = y + ddy
+                    if yy < 0 or yy >= H:
+                        if hits + (N - i - 1) < min_req:
+                            break
+                        continue
+                    ddx = dx[i]
+                    xx = x + ddx
+                    if xx < 0 or xx >= W:
+                        if hits + (N - i - 1) < min_req:
+                            break
+                        continue
+                    if spread[yy, xx] & mask:
+                        hits += 1
+                    else:
+                        if hits + (N - i - 1) < min_req:
+                            break
+                acc[y, x] = nb.float32(hits) * inv_N
+        return acc
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored(
+        spread: np.ndarray,      # uint8 (H, W)
+        dx: np.ndarray,          # int32 (N,)
+        dy: np.ndarray,          # int32 (N,)
+        bins: np.ndarray,        # int8 (N,)
+        bit_active: np.uint8,
+        bg: np.ndarray,          # float32 (H, W) background density normalizzata
+    ) -> np.ndarray:
+        """score+rescore in un singolo pass: evita allocazione intermedia.
+
+        Equivalente a:
+            score = _jit_score_bitmap(...)
+            out   = max(0, (score - bg) / (1 - bg + 1e-6))
+        ma fonde la seconda passata dentro la normalizzazione finale
+        (cache-friendly, risparmia ~15% sul totale find).
+        """
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((H, W), dtype=np.float32)
+        for y in nb.prange(H):
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint8(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                ddy = dy[i]
+                yy = y + ddy
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                x_lo = 0 if ddx >= 0 else -ddx
+                x_hi = W if ddx <= 0 else W - ddx
+                for x in range(x_lo, x_hi):
+                    if spread[yy, x + ddx] & mask:
+                        acc[y, x] += 1.0
+        if N > 0:
+            inv = 1.0 / N
+            for y in nb.prange(H):
+                for x in range(W):
+                    v = acc[y, x] * inv
+                    bgv = bg[y, x]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[y, x] = r if r > 0.0 else 0.0
+                    else:
+                        acc[y, x] = 0.0
+        return acc
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored_window(
+        spread: np.ndarray,      # uint8 (H, W) - scena INTERA
+        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+        bit_active: np.uint8,
+        bg: np.ndarray,          # float32 (H, W) - scena intera
+        y0: nb.int64, x0: nb.int64,
+        wh: nb.int64, ww: nb.int64,
+    ) -> np.ndarray:
+        """Score rescored valutato SOLO nella finestra (y0, x0, wh, ww).
+
+        Le feature campionano lo spread dell'intera scena (bounds-checked
+        sui bordi scena): a differenza di chiamare il kernel su un crop,
+        le feature che escono dalla finestra NON contano come miss.
+        Usato dal path pyramid_propagate: costo ∝ area finestra.
+        """
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((wh, ww), dtype=np.float32)
+        for yi in nb.prange(wh):
+            y = y0 + yi
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint8(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                yy = y + dy[i]
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                xi_lo = 0
+                xi_hi = ww
+                lo = -(x0 + ddx)
+                if lo > xi_lo:
+                    xi_lo = lo
+                hi = W - (x0 + ddx)
+                if hi < xi_hi:
+                    xi_hi = hi
+                for xi in range(xi_lo, xi_hi):
+                    if spread[yy, x0 + xi + ddx] & mask:
+                        acc[yi, xi] += 1.0
+        if N > 0:
+            inv = 1.0 / N
+            for yi in nb.prange(wh):
+                for xi in range(ww):
+                    v = acc[yi, xi] * inv
+                    bgv = bg[y0 + yi, x0 + xi]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[yi, xi] = r if r > 0.0 else 0.0
+                    else:
+                        acc[yi, xi] = 0.0
+        return acc
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored_window_u16(
+        spread: np.ndarray,      # uint16 (H, W)
+        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+        bit_active: np.uint16,
+        bg: np.ndarray,
+        y0: nb.int64, x0: nb.int64,
+        wh: nb.int64, ww: nb.int64,
+    ) -> np.ndarray:
+        """Versione uint16 (polarity 16-bin) del kernel windowed."""
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((wh, ww), dtype=np.float32)
+        for yi in nb.prange(wh):
+            y = y0 + yi
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint16(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                yy = y + dy[i]
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                xi_lo = 0
+                xi_hi = ww
+                lo = -(x0 + ddx)
+                if lo > xi_lo:
+                    xi_lo = lo
+                hi = W - (x0 + ddx)
+                if hi < xi_hi:
+                    xi_hi = hi
+                for xi in range(xi_lo, xi_hi):
+                    if spread[yy, x0 + xi + ddx] & mask:
+                        acc[yi, xi] += 1.0
+        if N > 0:
+            inv = 1.0 / N
+            for yi in nb.prange(wh):
+                for xi in range(ww):
+                    v = acc[yi, xi] * inv
+                    bgv = bg[y0 + yi, x0 + xi]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[yi, xi] = r if r > 0.0 else 0.0
+                    else:
+                        acc[yi, xi] = 0.0
+        return acc
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_top_max_per_variant(
+        spread: np.ndarray,            # uint8 (H, W)
+        dx_flat: np.ndarray,           # int32 (sum_N,)
+        dy_flat: np.ndarray,           # int32 (sum_N,)
+        bins_flat: np.ndarray,         # int8 (sum_N,)
+        offsets: np.ndarray,           # int32 (n_vars+1,) prefix sum
+        bit_active: np.uint8,
+        bg_per_variant: np.ndarray,    # float32 (n_vars, H, W) - 1 per scala
+        scale_idx: np.ndarray,         # int32 (n_vars,) idx in bg_per_variant
+    ) -> np.ndarray:
+        """Batch: per ogni variante calcola max score (rescored bg), ritorna
+        array float32 (n_vars,). Parallelismo prange ESTERNO sulle varianti
+        elimina overhead di n_vars chiamate JIT separate (avg ~20us per
+        chiamata su template piccoli) + pool thread Python.
+
+        Pensato per fase TOP del pruning quando n_vars >> n_threads.
+        """
+        n_vars = offsets.shape[0] - 1
+        H, W = spread.shape
+        out = np.zeros(n_vars, dtype=np.float32)
+        for vi in nb.prange(n_vars):
+            i0 = offsets[vi]; i1 = offsets[vi + 1]
+            N = i1 - i0
+            if N == 0:
+                out[vi] = -1.0
+                continue
+            si = scale_idx[vi]
+            inv = nb.float32(1.0 / N)
+            best = nb.float32(-1.0)
+            for y in range(H):
+                for x in range(W):
+                    s = nb.float32(0.0)
+                    for k in range(N):
+                        b = bins_flat[i0 + k]
+                        mask = np.uint8(1) << b
+                        if (bit_active & mask) == 0:
+                            continue
+                        ddy = dy_flat[i0 + k]
+                        yy = y + ddy
+                        if yy < 0 or yy >= H:
+                            continue
+                        ddx = dx_flat[i0 + k]
+                        xx = x + ddx
+                        if xx < 0 or xx >= W:
+                            continue
+                        if spread[yy, xx] & mask:
+                            s += nb.float32(1.0)
+                    s *= inv
+                    bgv = bg_per_variant[si, y, x]
+                    if bgv < 1.0:
+                        r = (s - bgv) / (1.0 - bgv + 1e-6)
+                        if r > best:
+                            best = r
+            out[vi] = best if best > 0.0 else 0.0
+        return out
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored_u16(
+        spread: np.ndarray,      # uint16 (H, W) - 16 bit di polarity-aware
+        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+        bit_active: np.uint16,
+        bg: np.ndarray,
+    ) -> np.ndarray:
+        """Versione uint16 di _jit_score_bitmap_rescored per polarity 16-bin.
+
+        Identica logica ma mask = uint16(1) << b dove b in [0..15]
+        (orientamento mod 2π invece di mod π).
+        """
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((H, W), dtype=np.float32)
+        for y in nb.prange(H):
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint16(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                ddy = dy[i]
+                yy = y + ddy
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                x_lo = 0 if ddx >= 0 else -ddx
+                x_hi = W if ddx <= 0 else W - ddx
+                for x in range(x_lo, x_hi):
+                    if spread[yy, x + ddx] & mask:
+                        acc[y, x] += 1.0
+        if N > 0:
+            inv = 1.0 / N
+            for y in nb.prange(H):
+                for x in range(W):
+                    v = acc[y, x] * inv
+                    bgv = bg[y, x]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[y, x] = r if r > 0.0 else 0.0
+                    else:
+                        acc[y, x] = 0.0
+        return acc
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_popcount_density_u16(spread: np.ndarray) -> np.ndarray:
+        """Popcount per uint16 (16 bin polarity)."""
+        H, W = spread.shape
+        out = np.zeros((H, W), dtype=np.float32)
+        for y in nb.prange(H):
+            for x in range(W):
+                v = spread[y, x]
+                cnt = 0
+                for b in range(16):
+                    if v & (np.uint16(1) << b):
+                        cnt += 1
+                out[y, x] = float(cnt)
+        return out
+
     @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
     def _jit_popcount_density(spread: np.ndarray) -> np.ndarray:
         """Conta bit set per pixel: ritorna (H, W) float32 in [0..8]."""
@@ -134,7 +513,30 @@ if HAS_NUMBA:
         _jit_score_by_shift(resp, dx, dy, b, ba)
         spread = np.zeros((32, 32), dtype=np.uint8)
         _jit_score_bitmap(spread, dx, dy, b, np.uint8(0xFF))
+        bg = np.zeros((32, 32), dtype=np.float32)
+        _jit_score_bitmap_rescored(spread, dx, dy, b, np.uint8(0xFF), bg)
+        _jit_score_bitmap_rescored_strided(
+            spread, dx, dy, b, np.uint8(0xFF), bg, np.int32(2),
+        )
+        _jit_score_bitmap_greedy(
+            spread, dx, dy, b, np.uint8(0xFF),
+            np.float32(0.5), np.float32(0.8),
+        )
+        offsets = np.array([0, 1], dtype=np.int32)
+        scale_idx = np.zeros(1, dtype=np.int32)
+        bg_pv = np.zeros((1, 32, 32), dtype=np.float32)
+        _jit_top_max_per_variant(
+            spread, dx, dy, b, offsets, np.uint8(0xFF), bg_pv, scale_idx,
+        )
+        _jit_score_bitmap_rescored_window(
+            spread, dx, dy, b, np.uint8(0xFF), bg, 4, 4, 8, 8,
+        )
         _jit_popcount_density(spread)
+        spread16 = np.zeros((32, 32), dtype=np.uint16)
+        _jit_score_bitmap_rescored_u16(
+            spread16, dx, dy, b, np.uint16(0xFFFF), bg,
+        )
+        _jit_popcount_density_u16(spread16)
 
 else:  # pragma: no cover
 
@@ -144,6 +546,33 @@ else:  # pragma: no cover
     def _jit_score_bitmap(spread, dx, dy, bins, bit_active):
         raise RuntimeError("numba non disponibile")
 
+    def _jit_score_bitmap_rescored(spread, dx, dy, bins, bit_active, bg):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_score_bitmap_rescored_strided(spread, dx, dy, bins, bit_active, bg, stride):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_score_bitmap_rescored_window(spread, dx, dy, bins, bit_active, bg, y0, x0, wh, ww):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_score_bitmap_rescored_window_u16(spread, dx, dy, bins, bit_active, bg, y0, x0, wh, ww):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_score_bitmap_greedy(spread, dx, dy, bins, bit_active, min_score, greediness):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_top_max_per_variant(
+        spread, dx_flat, dy_flat, bins_flat, offsets, bit_active,
+        bg_per_variant, scale_idx,
+    ):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_score_bitmap_rescored_u16(spread, dx, dy, bins, bit_active, bg):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_popcount_density_u16(spread):
+        raise RuntimeError("numba non disponibile")
+
     def _jit_popcount_density(spread):
         raise RuntimeError("numba non disponibile")
 
@@ -172,10 +601,167 @@ def score_bitmap(
     return _numpy_score_by_shift(resp, dx, dy, bins, None)
 
 
+def score_bitmap_rescored(
+    spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+    bit_active: int, bg: np.ndarray, stride: int = 1,
+) -> np.ndarray:
+    """Score bitmap + rescore fusi in un solo pass (JIT).
+
+    Dispatch per dtype: uint16 → kernel polarity 16-bin, uint8 → kernel
+    standard 8-bin (con eventuale stride > 1 per coarse top-level).
+    """
+    if HAS_NUMBA and len(dx) > 0:
+        dx_c = np.ascontiguousarray(dx, dtype=np.int32)
+        dy_c = np.ascontiguousarray(dy, dtype=np.int32)
+        bins_c = np.ascontiguousarray(bins, dtype=np.int8)
+        bg_c = np.ascontiguousarray(bg, dtype=np.float32)
+        if spread.dtype == np.uint16:
+            spread_c = np.ascontiguousarray(spread, dtype=np.uint16)
+            return _jit_score_bitmap_rescored_u16(
+                spread_c, dx_c, dy_c, bins_c, np.uint16(bit_active), bg_c,
+            )
+        spread_c = np.ascontiguousarray(spread, dtype=np.uint8)
+        if stride > 1:
+            return _jit_score_bitmap_rescored_strided(
+                spread_c, dx_c, dy_c, bins_c, np.uint8(bit_active), bg_c,
+                np.int32(stride),
+            )
+        return _jit_score_bitmap_rescored(
+            spread_c, dx_c, dy_c, bins_c, np.uint8(bit_active), bg_c,
+        )
+    # Fallback: chiamate separate (stride ignorato in fallback)
+    score = score_bitmap(spread, dx, dy, bins, bit_active)
+    out = (score - bg) / (1.0 - bg + 1e-6)
+    return np.maximum(0.0, out).astype(np.float32)
+
+
+def score_bitmap_rescored_window(
+    spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+    bit_active: int, bg: np.ndarray,
+    y0: int, x0: int, wh: int, ww: int,
+) -> np.ndarray:
+    """Score rescored solo nella finestra (y0, x0, wh, ww) della scena.
+
+    Le feature campionano l'INTERA scena: feature fuori finestra ma dentro
+    scena contano correttamente (chiamare il kernel su un crop le tratta
+    come miss e azzera lo score — il bug che rendeva inutilizzabile il
+    path pyramid_propagate). Fallback no-numba: kernel pieno + slice.
+    """
+    if HAS_NUMBA and len(dx) > 0:
+        dx_c = np.ascontiguousarray(dx, dtype=np.int32)
+        dy_c = np.ascontiguousarray(dy, dtype=np.int32)
+        bins_c = np.ascontiguousarray(bins, dtype=np.int8)
+        bg_c = np.ascontiguousarray(bg, dtype=np.float32)
+        if spread.dtype == np.uint16:
+            return _jit_score_bitmap_rescored_window_u16(
+                np.ascontiguousarray(spread, dtype=np.uint16),
+                dx_c, dy_c, bins_c, np.uint16(bit_active), bg_c,
+                int(y0), int(x0), int(wh), int(ww),
+            )
+        return _jit_score_bitmap_rescored_window(
+            np.ascontiguousarray(spread, dtype=np.uint8),
+            dx_c, dy_c, bins_c, np.uint8(bit_active), bg_c,
+            int(y0), int(x0), int(wh), int(ww),
+        )
+    # Fallback (lento, solo senza numba): score full-frame + slice finestra
+    full = score_bitmap_rescored(spread, dx, dy, bins, bit_active, bg)
+    return full[y0:y0 + wh, x0:x0 + ww]
+
+
+def score_bitmap_greedy(
+    spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+    bit_active: int, min_score: float, greediness: float,
+) -> np.ndarray:
+    """Score bitmap con early-exit greedy. Per coarse-pass aggressivo.
+
+    Non applica rescore background: usare quando la scena ha basso clutter
+    o quando si vuole mass-prune varianti via top-level rapidamente.
+    """
+    if HAS_NUMBA and len(dx) > 0:
+        return _jit_score_bitmap_greedy(
+            np.ascontiguousarray(spread, dtype=np.uint8),
+            np.ascontiguousarray(dx, dtype=np.int32),
+            np.ascontiguousarray(dy, dtype=np.int32),
+            np.ascontiguousarray(bins, dtype=np.int8),
+            np.uint8(bit_active),
+            np.float32(min_score), np.float32(greediness),
+        )
+    # Fallback: kernel base senza early-exit
+    return score_bitmap(spread, dx, dy, bins, bit_active)
+
+
+def top_max_per_variant(
+    spread: np.ndarray,
+    dx_list: list, dy_list: list, bin_list: list,
+    bg_per_scale: dict,
+    variant_scales: list,
+    bit_active: int,
+) -> np.ndarray:
+    """Wrapper: prepara buffer flat e chiama kernel batch su tutte le varianti.
+
+    Parallelismo Numba prange-esterno sulle varianti (n_vars >> n_threads
+    tipicamente per top-pruning) → meglio del thread-pool Python che paga
+    overhead di n_vars chiamate JIT separate.
+    """
+    if not HAS_NUMBA or len(dx_list) == 0:
+        return np.array([], dtype=np.float32)
+    n_vars = len(dx_list)
+    sizes = [len(d) for d in dx_list]
+    offsets = np.zeros(n_vars + 1, dtype=np.int32)
+    offsets[1:] = np.cumsum(sizes)
+    total = int(offsets[-1])
+    dx_flat = np.empty(total, dtype=np.int32)
+    dy_flat = np.empty(total, dtype=np.int32)
+    bins_flat = np.empty(total, dtype=np.int8)
+    for vi, (dx, dy, bn) in enumerate(zip(dx_list, dy_list, bin_list)):
+        i0 = int(offsets[vi]); i1 = int(offsets[vi + 1])
+        dx_flat[i0:i1] = dx
+        dy_flat[i0:i1] = dy
+        bins_flat[i0:i1] = bn
+    # bg per variante: indicizzato per scala
+    scales_unique = sorted(bg_per_scale.keys())
+    scale_to_idx = {s: i for i, s in enumerate(scales_unique)}
+    H, W = spread.shape
+    bg_pv = np.empty((len(scales_unique), H, W), dtype=np.float32)
+    for s, idx in scale_to_idx.items():
+        bg_pv[idx] = bg_per_scale[s]
+    scale_idx = np.array(
+        [scale_to_idx[s] for s in variant_scales], dtype=np.int32,
+    )
+    return _jit_top_max_per_variant(
+        np.ascontiguousarray(spread, dtype=np.uint8),
+        dx_flat, dy_flat, bins_flat, offsets, np.uint8(bit_active),
+        bg_pv, scale_idx,
+    )
+
+
+_HAS_NP_BITCOUNT = hasattr(np, "bitwise_count")
+
+
 def popcount_density(spread: np.ndarray) -> np.ndarray:
+    """Conta bit set per pixel.
+
+    Order:
+    1) Numba JIT parallel (preferito: piu veloce su 1080p, 0.5ms vs 1.6ms)
+    2) numpy.bitwise_count (NumPy 2.0+, SIMD ma single-thread)
+    3) Fallback numpy bit-shift puro
+    """
+    if spread.dtype == np.uint16:
+        spread_c = np.ascontiguousarray(spread, dtype=np.uint16)
+        if HAS_NUMBA:
+            return _jit_popcount_density_u16(spread_c)
+        if _HAS_NP_BITCOUNT:
+            return np.bitwise_count(spread_c).astype(np.float32, copy=False)
+        H, W = spread_c.shape
+        out = np.zeros((H, W), dtype=np.float32)
+        for b in range(16):
+            out += ((spread_c >> b) & 1).astype(np.float32)
+        return out
+    spread_c = np.ascontiguousarray(spread, dtype=np.uint8)
     if HAS_NUMBA:
-        return _jit_popcount_density(np.ascontiguousarray(spread, dtype=np.uint8))
-    # Fallback
+        return _jit_popcount_density(spread_c)
+    if _HAS_NP_BITCOUNT:
+        return np.bitwise_count(spread_c).astype(np.float32, copy=False)
     H, W = spread.shape
     out = np.zeros((H, W), dtype=np.float32)
     for b in range(8):

pm2d/auto_tune.py

@@ -14,6 +14,9 @@ Ritorna dict con i key esatti del form `edit_params`.
 
 from __future__ import annotations
 
+import hashlib
+from collections import OrderedDict
+
 import cv2
 import numpy as np
 
@@ -24,17 +27,33 @@ def _to_gray(img: np.ndarray) -> np.ndarray:
     return img
 
 
+# Cache in-memory (LRU) dei risultati auto_tune per stesso input ROI.
+_TUNE_CACHE: OrderedDict[str, dict] = OrderedDict()
+_TUNE_CACHE_SIZE = 32
+
+
 def detect_rotational_symmetry(
     gray: np.ndarray, step_deg: float = 5.0, corr_thresh: float = 0.75,
 ) -> dict:
     """Rileva simmetria rotazionale su edge map (più robusto a sfondo uniforme).
 
+    Downsample a max 128 px prima di correlare per abbattere il costo
+    O(n_angles · H · W) senza perdere precisione (la simmetria rotazionale
+    è invariante a subsampling moderato).
+
     Ritorna dict con:
       - order: int, 1=nessuna, 2=180°, 3=120°, 4=90°, 6=60°, 8=45°
       - period_deg: float, periodo minimo di simmetria (360/order)
       - confidence: float [0..1], correlazione minima tra rotazioni equivalenti
     """
     h, w = gray.shape
+    target = 128
+    if max(h, w) > target:
+        sf = target / max(h, w)
+        new_w = max(32, int(w * sf))
+        new_h = max(32, int(h * sf))
+        gray = cv2.resize(gray, (new_w, new_h), interpolation=cv2.INTER_AREA)
+        h, w = gray.shape
     # Usa magnitude gradiente (rotation-invariant rispetto a bg uniforme)
     gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3)
     gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
@@ -42,6 +61,8 @@ def detect_rotational_symmetry(
 
     center = (w / 2.0, h / 2.0)
     ref = mag
+    # ref è costante nel loop sugli angoli: centra una volta sola
+    rm = ref - ref.mean()
 
     correlations: list[tuple[float, float]] = []
     for ang in np.arange(step_deg, 360.0, step_deg):
@@ -49,7 +70,6 @@ def detect_rotational_symmetry(
         rot = cv2.warpAffine(
             mag, M, (w, h), borderValue=0.0,
         )
-        rm = ref - ref.mean()
         rs = rot - rot.mean()
         denom = np.sqrt((rm * rm).sum() * (rs * rs).sum()) + 1e-9
         c = float((rm * rs).sum() / denom)
@@ -88,9 +108,12 @@ def analyze_gradients(gray: np.ndarray) -> dict:
     gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
     mag = cv2.magnitude(gx, gy)
 
-    # Percentili magnitude
+    # Percentili magnitude: p55/p85 usati per soglie weak/strong (più aderenti
+    # alla distribuzione reale rispetto a p50/p80 + clamp).
     p50 = float(np.percentile(mag, 50))
+    p55 = float(np.percentile(mag, 55))
     p80 = float(np.percentile(mag, 80))
+    p85 = float(np.percentile(mag, 85))
     p95 = float(np.percentile(mag, 95))
     mag_max = float(mag.max())
 
@@ -112,7 +135,8 @@ def analyze_gradients(gray: np.ndarray) -> dict:
         ent = 0.0
 
     return {
-        "p50": p50, "p80": p80, "p95": p95, "mag_max": mag_max,
+        "p50": p50, "p55": p55, "p80": p80, "p85": p85, "p95": p95,
+        "mag_max": mag_max,
         "strong_pct": strong_pct, "weak_pct": weak_pct,
         "orient_entropy": ent,
         "n_pixels": mag.size,
@@ -120,11 +144,138 @@ def analyze_gradients(gray: np.ndarray) -> dict:
     }
 
 
-def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
+def _cache_key(template_bgr: np.ndarray, mask: np.ndarray | None) -> str:
+    h = hashlib.md5()
+    h.update(np.ascontiguousarray(template_bgr).tobytes())
+    h.update(f"shape={template_bgr.shape}".encode())
+    if mask is not None:
+        h.update(np.ascontiguousarray(mask).tobytes())
+    return h.hexdigest()
+
+
+def _self_validate(template_bgr: np.ndarray, params: dict,
+                   mask: np.ndarray | None = None) -> dict:
+    """Halcon-style self-validation: train il matcher coi parametri tentativi
+    e verifica che il template stesso sia trovato con recall ≥ 1.0.
+
+    Se recall < target o score basso, regola i parametri:
+    - alza weak_grad se troppi edge spuri (recall solido ma molti picchi falsi)
+    - abbassa strong_grad se troppe feature scartate (low feature count)
+    - riduce pyramid_levels se variants[0].levels[top] ha <8 feature
+
+    Halcon usa internamente questo loop in inspect_shape_model. Costo: 1
+    train + 1 find sul template (~50ms su template 100x100). Ne vale la
+    pena se evita match-time errors su scene reali.
+
+    Mutates `params` in place e ritorna lo stesso dict per chaining.
+    """
+    # Import lazy: evita ciclo (line_matcher importa nulla da auto_tune)
+    from pm2d.line_matcher import LineShapeMatcher
+
+    # Caso degenerato: troppe poche feature pre-validation → riduci soglia
+    if params.get("_n_strong_pixels", 0) < 30:
+        params["weak_grad"] = max(15.0, params["weak_grad"] * 0.6)
+        params["strong_grad"] = max(30.0, params["strong_grad"] * 0.6)
+
+    # Train minimale: 1 sola pose orientazione 0 (range degenerato che
+    # produce comunque 1 variante via fallback in _angle_list).
+    m = LineShapeMatcher(
+        num_features=params["num_features"],
+        weak_grad=params["weak_grad"],
+        strong_grad=params["strong_grad"],
+        angle_range_deg=(0.0, 0.0),  # fallback _angle_list = [0.0]
+        angle_step_deg=10.0,
+        scale_range=(1.0, 1.0),
+        spread_radius=params["spread_radius"],
+        pyramid_levels=params["pyramid_levels"],
+    )
+    n_var = m.train(template_bgr, mask=mask)
+    if n_var == 0:
+        # Soglie troppo alte: nessuna variante generata → dimezza
+        params["weak_grad"] = max(15.0, params["weak_grad"] * 0.5)
+        params["strong_grad"] = max(30.0, params["strong_grad"] * 0.5)
+        params["_validation"] = "fallback: soglie dimezzate (no variants)"
+        return params
+
+    # Verifica densita' feature al top-level (rischio collasso)
+    top_lvl = m.variants[0].levels[-1]
+    if top_lvl.n < 8 and params["pyramid_levels"] > 1:
+        params["pyramid_levels"] = max(1, params["pyramid_levels"] - 1)
+        params["_validation"] = (
+            f"pyramid_levels ridotto a {params['pyramid_levels']} "
+            f"(top aveva {top_lvl.n} feature)"
+        )
+        return params
+
+    # Self-find: cerca il template stesso nella propria immagine
+    h, w = template_bgr.shape[:2]
+    # Embed template in scena leggermente più grande per evitare bordo
+    pad = 20
+    canvas = np.full(
+        (h + 2 * pad, w + 2 * pad, 3 if template_bgr.ndim == 3 else 1),
+        128, dtype=np.uint8,
+    )
+    canvas[pad:pad + h, pad:pad + w] = template_bgr
+    matches = m.find(
+        canvas, min_score=0.3, max_matches=5,
+        verify_ncc=False,  # template stesso → NCC = 1 sempre, skip per velocita'
+        refine_angle=False, subpixel=False,
+        nms_iou_threshold=0.3,
+    )
+    if not matches:
+        # Nessun match sul proprio template: parametri troppo restrittivi
+        params["weak_grad"] = max(15.0, params["weak_grad"] * 0.7)
+        params["strong_grad"] = max(30.0, params["strong_grad"] * 0.7)
+        params["num_features"] = max(48, int(params["num_features"] * 0.8))
+        params["_validation"] = "soglie/feature ridotte (no self-match)"
+        return params
+
+    # Misura score top match
+    top_score = float(matches[0].score)
+    params["_self_score"] = round(top_score, 3)
+    if top_score < 0.7:
+        # Score basso sul template stesso = parametri davvero subottimali
+        params["weak_grad"] = max(15.0, params["weak_grad"] * 0.85)
+        params["_validation"] = (
+            f"weak_grad ridotto (self-score era {top_score:.2f})"
+        )
+    else:
+        params["_validation"] = f"OK (self-score {top_score:.2f})"
+    return params
+
+
+def auto_tune(
+    template_bgr: np.ndarray,
+    mask: np.ndarray | None = None,
+    angle_tolerance_deg: float | None = None,
+    angle_center_deg: float = 0.0,
+    self_validate: bool = True,
+) -> dict:
     """Analizza template e ritorna dict parametri suggeriti.
 
     Chiavi compatibili con edit_params PARAM_SCHEMA.
+
+    angle_tolerance_deg: se != None, restringe angle_range a
+    (center - tol, center + tol). Usare quando l'orientamento del
+    pezzo e' noto a priori (feeder con guida, posizionamento
+    meccanico): training molto piu rapido (24x meno varianti per
+    tol=15° vs 360° pieno).
+
+    self_validate: se True (default), dopo la stima dei parametri
+    esegue un dry-run del matching sul template stesso e regola
+    weak_grad/strong_grad/pyramid_levels se i parametri tentativi
+    non garantiscono auto-match (Halcon-style inspect_shape_model).
+
+    Risultato cachato in-memory (LRU): ri-chiamare con stessa ROI è O(1).
     """
+    ck = _cache_key(template_bgr, mask)
+    if angle_tolerance_deg is not None:
+        ck = f"{ck}|tol={angle_tolerance_deg}|c={angle_center_deg}"
+    cached = _TUNE_CACHE.get(ck)
+    if cached is not None:
+        _TUNE_CACHE.move_to_end(ck)
+        return dict(cached)
+
     gray = _to_gray(template_bgr)
     h, w = gray.shape
     if mask is not None:
@@ -136,16 +287,22 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
     stats = analyze_gradients(gray_for_stats)
     sym = detect_rotational_symmetry(gray_for_stats)
 
-    # Soglie magnitude: usa percentili per robustezza illuminazione.
-    # Target: strong_grad ~= valore a percentile 80-90 in assoluto, ma
-    # clamp per compatibilità uint8 (Sobel può sforare).
-    strong_grad = float(np.clip(stats["p80"], 20.0, 100.0))
-    weak_grad = float(np.clip(strong_grad * 0.5, 10.0, 60.0))
+    # Soglie magnitude: usa percentili reali (p85/p55) senza clamp duro a 100.
+    # Sobel ksize=3 su uint8 può arrivare a ~1020, quindi clamp massimo 400
+    # evita saturazione del threshold su template ad alto contrasto.
+    strong_grad = float(np.clip(stats["p85"], 30.0, 400.0))
+    weak_grad = float(np.clip(stats["p55"], 15.0, strong_grad * 0.7))
 
-    # num_features: 1 feature ogni ~25 px forti, clamp 48..192
-    target_feat = int(np.clip(stats["n_strong"] / 25, 48, 192))
+    # num_features: ibrido perimetro + densità. Target = min(perimeter_budget,
+    # density_budget) per non generare più feature di quante edge nitide siano
+    # disponibili, ma neanche meno di quante il perimetro possa tracciare.
+    perim_budget = int(2 * (h + w) * 0.4)          # ~40% dei pixel di perimetro
+    density_budget = int(stats["n_strong"] / 20)   # 1 feature ogni ~20 px forti
+    target_feat = int(np.clip(min(perim_budget, density_budget), 64, 192))
 
-    # pyramid_levels in base alla dimensione minima
+    # pyramid_levels in base a dimensione minima E densità feature: un template
+    # grande ma povero di feature non deve scendere troppi livelli (rischio
+    # collasso a <16 feature al top level).
     min_side = min(h, w)
     if min_side < 60:
         pyr = 1
@@ -155,12 +312,20 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
         pyr = 3
     else:
         pyr = 4
+    # Cap: non scendere sotto ~16 feature al top level (feature ÷ 4^(pyr-1))
+    max_pyr_from_feat = max(1, int(np.floor(np.log2(max(1, target_feat / 16.0)) / 2.0)) + 1)
+    pyr = min(pyr, max_pyr_from_feat)
 
     # spread_radius proporzionale a risoluzione + pyramid (tolleranza ~1% dim)
     spread_radius = int(np.clip(max(3, min_side * 0.02), 3, 8))
 
-    # angle range ridotto se simmetria rotazionale
-    angle_max = 360.0 / sym["order"] if sym["order"] > 1 else 360.0
+    # angle range: priorita' a tolerance hint utente, poi simmetria rotazionale.
+    if angle_tolerance_deg is not None:
+        angle_min = float(angle_center_deg - angle_tolerance_deg)
+        angle_max = float(angle_center_deg + angle_tolerance_deg)
+    else:
+        angle_min = 0.0
+        angle_max = 360.0 / sym["order"] if sym["order"] > 1 else 360.0
 
     # min_score: se entropia orient alta → template distintivo → soglia alta ok
     # se entropia bassa → template ambiguo → soglia più permissiva
@@ -171,12 +336,15 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
     else:
         min_score = 0.45
 
-    # angle step: 5° default; se simmetria, mantengo step ma range ridotto
-    angle_step = 5.0
+    # angle step adattivo (Halcon-style): atan(2/max_side) deg, clampato.
+    # Template grande → step fine (rotazione minima visibile su perimetro).
+    # Template piccolo → step grosso (over-sampling = sprecato).
+    max_side = max(h, w)
+    angle_step = float(np.clip(np.degrees(np.arctan2(2.0, max_side)), 1.0, 8.0))
 
-    return {
+    result = {
         "backend": "line",
-        "angle_min": 0.0,
+        "angle_min": angle_min,
         "angle_max": angle_max,
         "angle_step": angle_step,
         "scale_min": 1.0,
@@ -195,7 +363,21 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
         "_symmetry_order": sym["order"],
         "_symmetry_conf": round(sym["confidence"], 2),
         "_orient_entropy": round(stats["orient_entropy"], 2),
+        "_n_strong_pixels": stats["n_strong"],
     }
+    # Halcon-style self-validation: dry-run training+find sul template per
+    # auto-correggere parametri tentativi che non garantirebbero match.
+    if self_validate:
+        result = _self_validate(template_bgr, result, mask=mask)
+        # Round numerici dopo eventuali aggiustamenti
+        result["weak_grad"] = round(result["weak_grad"], 1)
+        result["strong_grad"] = round(result["strong_grad"], 1)
+    # Store in LRU cache
+    _TUNE_CACHE[ck] = dict(result)
+    _TUNE_CACHE.move_to_end(ck)
+    while len(_TUNE_CACHE) > _TUNE_CACHE_SIZE:
+        _TUNE_CACHE.popitem(last=False)
+    return result
 
 
 def summarize(tune: dict) -> str:

pm2d/bench.py

@@ -0,0 +1,179 @@
+"""Benchmark suite per LineShapeMatcher.
+
+Usage:
+    python -m pm2d.bench [--quick]
+
+Misura tempi find() su 3 template-tipo × 3 scene-tipo × N config:
+- Template: rettangolo 80×80, L-shape 120×120, cerchio 150×150
+- Scene: pulita 800×600, cluttered 1080×1920, multi-pezzo 1080×1920
+- Config: baseline, polarity, gpu, pyramid_propagate, greediness=0.7
+
+Per ogni config stampa: ms/find, ms per fase (profile), n. match.
+Output tabellare per detectare regressioni in CI.
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+
+import cv2
+import numpy as np
+
+from pm2d.line_matcher import LineShapeMatcher, opencl_available
+
+
+# ---------- Sintetizzatori template/scena ----------
+
+def _tpl_rect() -> np.ndarray:
+    t = np.zeros((80, 80, 3), np.uint8)
+    cv2.rectangle(t, (15, 15), (65, 65), (255, 255, 255), 3)
+    return t
+
+
+def _tpl_lshape() -> np.ndarray:
+    t = np.zeros((120, 120, 3), np.uint8)
+    cv2.rectangle(t, (20, 20), (50, 100), (255, 255, 255), -1)
+    cv2.rectangle(t, (20, 70), (100, 100), (255, 255, 255), -1)
+    return t
+
+
+def _tpl_circle() -> np.ndarray:
+    t = np.zeros((150, 150, 3), np.uint8)
+    cv2.circle(t, (75, 75), 60, (255, 255, 255), 4)
+    return t
+
+
+def _scene_clean(W: int, H: int, n_pieces: int = 1) -> np.ndarray:
+    np.random.seed(0)
+    s = np.zeros((H, W, 3), np.uint8)
+    for _ in range(n_pieces):
+        cx = np.random.randint(80, W - 80)
+        cy = np.random.randint(80, H - 80)
+        cv2.rectangle(s, (cx - 25, cy - 25), (cx + 25, cy + 25), (255, 255, 255), 3)
+    return s
+
+
+def _scene_cluttered(W: int, H: int) -> np.ndarray:
+    np.random.seed(0)
+    s = np.random.randint(50, 200, (H, W, 3), np.uint8)
+    cv2.rectangle(s, (300, 200), (350, 250), (255, 255, 255), 3)
+    cv2.rectangle(s, (1500, 800), (1550, 850), (255, 255, 255), 3)
+    return s
+
+
+# ---------- Single benchmark ----------
+
+def _bench_config(template, scene, config_name: str,
+                  init_kw: dict, find_kw: dict,
+                  n_iter: int = 5) -> dict:
+    m = LineShapeMatcher(**init_kw)
+    t0 = time.perf_counter()
+    n_var = m.train(template)
+    t_train = time.perf_counter() - t0
+
+    # Warmup (Numba JIT)
+    m.find(scene, **find_kw)
+    m.find(scene, **find_kw)
+
+    # Run
+    times_ms = []
+    for _ in range(n_iter):
+        t0 = time.perf_counter()
+        matches = m.find(scene, **find_kw)
+        times_ms.append((time.perf_counter() - t0) * 1000.0)
+
+    # Profile (1 iter)
+    m.find(scene, profile=True, **find_kw)
+    prof = m.get_last_profile() or {}
+
+    return {
+        "config": config_name,
+        "n_variants": n_var,
+        "t_train_s": round(t_train, 3),
+        "ms_avg": round(float(np.mean(times_ms)), 1),
+        "ms_min": round(float(np.min(times_ms)), 1),
+        "ms_max": round(float(np.max(times_ms)), 1),
+        "n_matches": len(matches),
+        "profile_ms": {k: round(v, 1) for k, v in prof.items()},
+    }
+
+
+# ---------- Suite ----------
+
+CONFIGS = [
+    ("baseline",
+     {"angle_step_deg": 10, "pyramid_levels": 2},
+     {"min_score": 0.4, "verify_threshold": 0.2}),
+    ("polarity",
+     {"angle_step_deg": 10, "pyramid_levels": 2, "use_polarity": True},
+     {"min_score": 0.4, "verify_threshold": 0.2}),
+    ("propagate",
+     {"angle_step_deg": 10, "pyramid_levels": 3},
+     {"min_score": 0.4, "verify_threshold": 0.2,
+      "pyramid_propagate": True, "propagate_topk": 4}),
+    ("greedy_07",
+     {"angle_step_deg": 10, "pyramid_levels": 2},
+     {"min_score": 0.4, "verify_threshold": 0.2, "greediness": 0.7}),
+    ("stride2",
+     {"angle_step_deg": 10, "pyramid_levels": 2},
+     {"min_score": 0.4, "verify_threshold": 0.2, "coarse_stride": 2}),
+]
+
+if opencl_available():
+    CONFIGS.append(
+        ("gpu_umat",
+         {"angle_step_deg": 10, "pyramid_levels": 2, "use_gpu": True},
+         {"min_score": 0.4, "verify_threshold": 0.2})
+    )
+
+
+SCENARIOS = [
+    ("rect_80 vs scene_800x600", _tpl_rect, lambda: _scene_clean(800, 600, 1)),
+    ("lshape_120 vs scene_1080x1920_clutter",
+     _tpl_lshape, lambda: _scene_cluttered(1920, 1080)),
+    ("circle_150 vs scene_clean_3pieces",
+     _tpl_circle, lambda: _scene_clean(1920, 1080, 3)),
+]
+
+
+def run(quick: bool = False) -> int:
+    n_iter = 2 if quick else 5
+    print(f"=== PM2D Benchmark Suite ({len(SCENARIOS)} scenarios x "
+          f"{len(CONFIGS)} configs, n_iter={n_iter}) ===\n")
+    rows = []
+    for sc_name, tpl_fn, scn_fn in SCENARIOS:
+        template = tpl_fn()
+        scene = scn_fn()
+        print(f"--- Scenario: {sc_name} (tpl={template.shape}, "
+              f"scn={scene.shape}) ---")
+        for cfg_name, init_kw, find_kw in CONFIGS:
+            r = _bench_config(template, scene, cfg_name, init_kw, find_kw,
+                              n_iter=n_iter)
+            r["scenario"] = sc_name
+            rows.append(r)
+            prof_str = " ".join(
+                f"{k}={v:.1f}" for k, v in r["profile_ms"].items()
+            )
+            print(f"  {cfg_name:14s}  {r['ms_avg']:6.1f}ms  "
+                  f"(min {r['ms_min']:.1f} max {r['ms_max']:.1f})  "
+                  f"vars={r['n_variants']:3d}  "
+                  f"matches={r['n_matches']:2d}")
+            if prof_str:
+                print(f"     profile: {prof_str}")
+        print()
+    print("=== Done ===")
+    return 0
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(description="PM2D benchmark suite")
+    p.add_argument("--quick", action="store_true",
+                   help="2 iterazioni per config invece di 5 (smoke test)")
+    args = p.parse_args(argv)
+    return run(quick=args.quick)
+
+
+if __name__ == "__main__":
+    import sys
+    sys.exit(main())

pm2d/dxf.py

@@ -0,0 +1,119 @@
+"""Rasterizzazione DXF → immagine template per il matcher shape-based.
+
+Il matcher lavora sui gradienti degli edge: un line-drawing pulito
+(sfondo grigio scuro, tratti chiari) è un template perfettamente valido.
+Questo modulo converte un file DXF (CAD 2D) in una bitmap grayscale
+centrata e scalata, pronta per train().
+"""
+from __future__ import annotations
+
+import io
+
+import cv2
+import numpy as np
+
+# Valori di rendering: sfondo scuro / tratto chiaro → gradiente netto
+BG_GRAY = 60
+LINE_GRAY = 220
+
+
+def _read_doc(data: bytes):
+    """Parse DXF da bytes con gestione encoding.
+
+    Prima prova ezdxf.read su StringIO (DXF ASCII utf-8 / cp1252),
+    poi fallback su ezdxf.recover che auto-rileva encoding e tollera
+    file malformati.
+    """
+    import ezdxf
+    from ezdxf import recover
+
+    for enc in ("utf-8", "cp1252"):
+        try:
+            text = data.decode(enc)
+            return ezdxf.read(io.StringIO(text))
+        except Exception:
+            # UnicodeDecodeError, DXFStructureError e simili: prossimo tentativo
+            continue
+    # Ultimo tentativo: recover lavora direttamente sui bytes
+    try:
+        doc, _auditor = recover.read(io.BytesIO(data))
+        return doc
+    except Exception as e:
+        raise ValueError(f"DXF illeggibile o corrotto: {e}") from e
+
+
+def _extract_polylines(doc, flatten_dist: float = 0.05) -> tuple[list[np.ndarray], int]:
+    """Converte le entità del modelspace in polilinee (liste di punti XY).
+
+    Entità non convertibili (non supportate da make_path) vengono saltate
+    silenziosamente ma conteggiate. Ritorna (polilinee, n_saltate).
+    """
+    from ezdxf import path as ezpath
+
+    polylines: list[np.ndarray] = []
+    skipped = 0
+    for entity in doc.modelspace():
+        try:
+            p = ezpath.make_path(entity)
+            pts = np.array(
+                [(v.x, v.y) for v in p.flattening(distance=flatten_dist)],
+                dtype=np.float64,
+            )
+            if len(pts) >= 2:
+                polylines.append(pts)
+        except Exception:
+            skipped += 1
+    return polylines, skipped
+
+
+def dxf_to_image(data: bytes, target_size: int = 512,
+                 line_thickness: int = 2, margin: int = 16) -> np.ndarray:
+    """Rasterizza un DXF in immagine grayscale (H, W) uint8.
+
+    - Scala uniforme: il lato lungo del disegno = target_size - 2*margin.
+    - Disegno centrato, asse Y CAD (su) ribaltato in convenzione immagine.
+    - Sfondo grigio scuro (60), tratti chiari (220), antialiased.
+
+    Solleva ValueError se il DXF è vuoto o illeggibile.
+    """
+    doc = _read_doc(data)
+
+    # Distanza di flattening provvisoria in unità CAD: raffinata sotto
+    # una volta nota la scala (qui serve solo per il bounding box).
+    polylines, skipped = _extract_polylines(doc)
+    if not polylines:
+        raise ValueError(
+            "DXF vuoto: nessuna entità convertibile in polilinea nel "
+            f"modelspace ({skipped} entità non supportate saltate)")
+
+    all_pts = np.vstack(polylines)
+    min_xy = all_pts.min(axis=0)
+    max_xy = all_pts.max(axis=0)
+    extent = max_xy - min_xy
+    long_side = float(extent.max())
+    if long_side <= 0:
+        raise ValueError("DXF degenere: bounding box con estensione nulla")
+
+    # Ri-flattening con distanza adattiva: ~0.25 px di errore alla scala
+    # finale (il primo pass usava una tolleranza in unità CAD arbitraria).
+    avail = max(1, target_size - 2 * margin)
+    scale = avail / long_side
+    polylines, _ = _extract_polylines(doc, flatten_dist=max(1e-9, 0.25 / scale))
+
+    canvas = np.full((target_size, target_size), BG_GRAY, dtype=np.uint8)
+    # Offset per centrare il disegno (anche sul lato corto)
+    draw_w = extent[0] * scale
+    draw_h = extent[1] * scale
+    off_x = (target_size - draw_w) / 2.0
+    off_y = (target_size - draw_h) / 2.0
+
+    for pts in polylines:
+        px = (pts[:, 0] - min_xy[0]) * scale + off_x
+        # Y CAD verso l'alto → Y immagine verso il basso
+        py = (max_xy[1] - pts[:, 1]) * scale + off_y
+        ipts = np.stack([px, py], axis=1).round().astype(np.int32)
+        cv2.polylines(canvas, [ipts], isClosed=False,
+                      color=LINE_GRAY, thickness=line_thickness,
+                      lineType=cv2.LINE_AA)
+
+    return canvas

pm2d/eval.py

@@ -0,0 +1,217 @@
+"""CLI validation harness per LineShapeMatcher.
+
+Usage:
+    python -m pm2d.eval dataset.json [opzioni]
+
+Formato dataset (JSON):
+    {
+      "template": "path/to/template.png",
+      "mask": "path/to/mask.png",  # opzionale
+      "params": {                   # opzionali, override su matcher init
+        "use_polarity": true,
+        "angle_step_deg": 5,
+        ...
+      },
+      "find_params": {              # opzionali, passati a find()
+        "min_score": 0.6,
+        "use_soft_score": true,
+        ...
+      },
+      "scenes": [
+        {
+          "image": "path/to/scene1.png",
+          "ground_truth": [
+            {"cx": 320.0, "cy": 240.0, "angle_deg": 12.0,
+             "scale": 1.0, "tolerance_px": 5.0,
+             "tolerance_deg": 3.0}
+          ]
+        }
+      ]
+    }
+
+Output: report precision/recall/IoU/timing per ogni scena + aggregati.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+import time
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+from pm2d.line_matcher import LineShapeMatcher, _poly_iou, _oriented_bbox_polygon
+
+
+def _load_image(path: str | Path) -> np.ndarray:
+    img = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
+    if img is None:
+        raise FileNotFoundError(f"Immagine non trovata: {path}")
+    if img.ndim == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    return img
+
+
+def _gt_to_poly(gt: dict, tw: int, th: int) -> np.ndarray:
+    """Costruisce bbox poligonale per un ground truth."""
+    s = float(gt.get("scale", 1.0))
+    return _oriented_bbox_polygon(
+        float(gt["cx"]), float(gt["cy"]),
+        tw * s, th * s, float(gt["angle_deg"]),
+    )
+
+
+def _match_to_gt(match, gt: dict, tw: int, th: int,
+                 iou_thr: float = 0.3) -> bool:
+    """True se il match corrisponde al ground truth.
+
+    Criterio: distanza centro <= tolerance_px AND |angle_deg - gt| <= tolerance_deg
+    OR IoU bbox >= iou_thr (fallback per pose con tolerance ampie).
+    """
+    tol_px = float(gt.get("tolerance_px", 5.0))
+    tol_deg = float(gt.get("tolerance_deg", 3.0))
+    dx = match.cx - float(gt["cx"])
+    dy = match.cy - float(gt["cy"])
+    dist = math.hypot(dx, dy)
+    da = abs((match.angle_deg - float(gt["angle_deg"]) + 180) % 360 - 180)
+    if dist <= tol_px and da <= tol_deg:
+        return True
+    # Fallback IoU
+    poly_gt = _gt_to_poly(gt, tw, th)
+    poly_m = match.bbox_poly
+    if _poly_iou(poly_m, poly_gt) >= iou_thr:
+        return True
+    return False
+
+
+def evaluate_scene(matcher: LineShapeMatcher, scene_bgr: np.ndarray,
+                   gt_list: list[dict], find_params: dict,
+                   tw: int, th: int) -> dict:
+    """Esegue match e calcola TP/FP/FN per una scena."""
+    t0 = time.time()
+    matches = matcher.find(scene_bgr, **find_params)
+    elapsed = time.time() - t0
+
+    gt_matched = [False] * len(gt_list)
+    match_is_tp = [False] * len(matches)
+    iou_per_match = [0.0] * len(matches)
+    for i, m in enumerate(matches):
+        for j, gt in enumerate(gt_list):
+            if gt_matched[j]:
+                continue
+            if _match_to_gt(m, gt, tw, th):
+                gt_matched[j] = True
+                match_is_tp[i] = True
+                # Calcolo IoU per metrica
+                poly_gt = _gt_to_poly(gt, tw, th)
+                iou_per_match[i] = _poly_iou(m.bbox_poly, poly_gt)
+                break
+    tp = sum(match_is_tp)
+    fp = len(matches) - tp
+    fn = len(gt_list) - sum(gt_matched)
+    return {
+        "n_matches": len(matches),
+        "n_gt": len(gt_list),
+        "tp": tp, "fp": fp, "fn": fn,
+        "find_time_s": elapsed,
+        "iou_mean": float(np.mean([i for i, t in zip(iou_per_match, match_is_tp) if t])
+                          if tp > 0 else 0.0),
+        "diag": (matcher.get_last_diag()
+                 if hasattr(matcher, "get_last_diag") else None),
+    }
+
+
+def run(dataset_path: str, scene_filter: str | None = None,
+        verbose: bool = False) -> dict:
+    """Esegue eval su dataset, ritorna report aggregato."""
+    dataset_path = Path(dataset_path)
+    base = dataset_path.parent
+    with open(dataset_path) as f:
+        ds = json.load(f)
+
+    template = _load_image(base / ds["template"])
+    mask = None
+    if ds.get("mask"):
+        mask_img = cv2.imread(str(base / ds["mask"]), cv2.IMREAD_GRAYSCALE)
+        if mask_img is not None:
+            mask = (mask_img > 128).astype(np.uint8) * 255
+    init_params = ds.get("params", {})
+    find_params = ds.get("find_params", {})
+
+    matcher = LineShapeMatcher(**init_params)
+    n_var = matcher.train(template, mask=mask)
+    tw, th = matcher.template_size
+    print(f"Template: {ds['template']} ({tw}x{th}), {n_var} varianti")
+    print(f"Param matcher: {init_params}")
+    print(f"Param find:    {find_params}")
+    print()
+
+    scenes = ds["scenes"]
+    if scene_filter:
+        scenes = [s for s in scenes if scene_filter in s["image"]]
+
+    rows = []
+    tot_tp = tot_fp = tot_fn = 0
+    tot_time = 0.0
+    for sc in scenes:
+        scene = _load_image(base / sc["image"])
+        gt = sc.get("ground_truth", [])
+        result = evaluate_scene(matcher, scene, gt, find_params, tw, th)
+        rows.append({"scene": sc["image"], **result})
+        tot_tp += result["tp"]; tot_fp += result["fp"]; tot_fn += result["fn"]
+        tot_time += result["find_time_s"]
+        prec = result["tp"] / max(1, result["tp"] + result["fp"])
+        rec = result["tp"] / max(1, result["tp"] + result["fn"])
+        line = (f"  {sc['image']:30s}  "
+                f"TP={result['tp']} FP={result['fp']} FN={result['fn']}  "
+                f"P={prec:.2f} R={rec:.2f}  "
+                f"IoU={result['iou_mean']:.2f}  "
+                f"t={result['find_time_s']*1000:.0f}ms")
+        print(line)
+        if verbose and result["diag"] and hasattr(matcher, "_format_diag"):
+            print(f"     diag: {matcher._format_diag(result['diag'])}")
+
+    # Aggregati
+    precision = tot_tp / max(1, tot_tp + tot_fp)
+    recall = tot_tp / max(1, tot_tp + tot_fn)
+    f1 = 2 * precision * recall / max(1e-9, precision + recall)
+    print()
+    print(f"AGGREGATO: precision={precision:.3f} recall={recall:.3f} "
+          f"F1={f1:.3f}  TP={tot_tp} FP={tot_fp} FN={tot_fn}")
+    print(f"TIME: total={tot_time:.2f}s avg={tot_time / max(1, len(scenes)) * 1000:.0f}ms/scene")
+
+    return {
+        "precision": precision, "recall": recall, "f1": f1,
+        "tp": tot_tp, "fp": tot_fp, "fn": tot_fn,
+        "total_time_s": tot_time, "n_scenes": len(scenes),
+        "per_scene": rows,
+    }
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(
+        description="pm2d-eval: validation harness per LineShapeMatcher"
+    )
+    p.add_argument("dataset", help="JSON dataset (template + scenes + GT)")
+    p.add_argument("--scene-filter", default=None,
+                   help="Filtro substring sui nomi scena (debug)")
+    p.add_argument("--verbose", "-v", action="store_true",
+                   help="Stampa diag dict per ogni scena")
+    p.add_argument("--out", default=None,
+                   help="Salva report JSON su file")
+    args = p.parse_args(argv)
+    report = run(args.dataset, scene_filter=args.scene_filter,
+                 verbose=args.verbose)
+    if args.out:
+        with open(args.out, "w") as f:
+            json.dump(report, f, indent=2)
+        print(f"Report salvato: {args.out}")
+    return 0 if report["f1"] > 0.5 else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

pm2d/gui.py

@@ -12,7 +12,6 @@ Tutta la logica algoritmica vive in pm2d.matcher.EdgeShapeMatcher.
 
 from __future__ import annotations
 
-import sys
 from pathlib import Path
 from tkinter import Tk, filedialog
 import tkinter as tk
@@ -196,8 +195,10 @@ def _warp_template_edges_to_scene(
     edge = cv2.Canny(template_gray, canny_low, canny_high)
     # Matrice affine: scala + rotazione attorno al centro template, poi traslazione
     Ht, Wt = h, w
-    cx_t = (Wt - 1) / 2.0
-    cy_t = (Ht - 1) / 2.0
+    # Centro coerente con la convenzione train (center = w / 2.0, no -1):
+    # (Wt-1)/2 introduceva uno shift di 0.5px per template di lato pari.
+    cx_t = Wt / 2.0
+    cy_t = Ht / 2.0
     M = cv2.getRotationMatrix2D((cx_t, cy_t), angle_deg, scale)
     # Traslazione per portare centro template a (cx, cy) della scena
     M[0, 2] += cx - cx_t
@@ -261,33 +262,33 @@ def draw_matches(
     return out
 
 
-def build_info_panel(
+def _put_text(img: np.ndarray, s: str, x: int, y: int,
+              size: float = 0.5, color: tuple = (220, 220, 220),
+              thick: int = 1) -> None:
+    cv2.putText(img, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
+                size, color, thick, cv2.LINE_AA)
+
+
+def build_left_panel(
     template_bgr: np.ndarray,
-    params: dict,
     matches: list[Match],
-    panel_width: int = 380,
-    panel_height: int | None = None,
+    panel_width: int = 300,
+    panel_height: int = 900,
 ) -> np.ndarray:
-    """Costruisce pannello laterale: thumbnail modello + parametri + legenda
-    numerata dei match + hotkey."""
-    if panel_height is None:
-        panel_height = panel_width * 2
+    """Pannello sinistro: thumbnail modello + legenda risultati (senza parametri)."""
     panel = np.full((panel_height, panel_width, 3), 28, dtype=np.uint8)
     pad = 12
     y = pad
 
-    def _text(img, s, y, size=0.5, color=(220, 220, 220), thick=1, x=None):
-        cv2.putText(img, s, (x if x is not None else pad, y),
-                    cv2.FONT_HERSHEY_SIMPLEX, size, color, thick, cv2.LINE_AA)
-
-    # Titolo
-    _text(panel, "MODELLO", y + 18, size=0.7, color=(0, 200, 255), thick=2)
+    # Titolo MODELLO
+    _put_text(panel, "MODELLO", pad, y + 18, size=0.7,
+              color=(0, 200, 255), thick=2)
     y += 34
 
-    # Thumbnail modello
+    # Thumbnail
     th_h, th_w = template_bgr.shape[:2]
     max_tw = panel_width - 2 * pad
-    max_th = 150
+    max_th = 160
     sc = min(max_tw / th_w, max_th / th_h)
     tw = max(1, int(th_w * sc)); th = max(1, int(th_h * sc))
     thumb = cv2.resize(template_bgr, (tw, th), interpolation=cv2.INTER_AREA)
@@ -297,74 +298,123 @@ def build_info_panel(
     panel[y:y + th, tx:tx + tw] = thumb
     cv2.rectangle(panel, (tx - 1, y - 1), (tx + tw, y + th),
                   (90, 90, 90), 1, cv2.LINE_AA)
-    y += th + 12
+    y += th + 16
 
-    # Parametri
-    _text(panel, "PARAMETRI", y, size=0.55, color=(0, 200, 255), thick=2)
-    y += 20
-    for k, v in params.items():
-        _text(panel, f"{k}: {v}", y, size=0.42)
-        y += 16
-
-    y += 6
-    _text(panel, f"RISULTATI ({len(matches)})", y,
-          size=0.55, color=(0, 200, 255), thick=2)
-    y += 20
+    # Risultati
+    _put_text(panel, f"RISULTATI ({len(matches)})", pad, y,
+              size=0.6, color=(0, 200, 255), thick=2)
+    y += 22
     if matches:
         scores = [m.score for m in matches]
         scales = [m.scale for m in matches]
-        _text(panel, f"score: {min(scores):.2f}..{max(scores):.2f}", y,
-              size=0.42); y += 16
+        _put_text(panel, f"score: {min(scores):.2f}..{max(scores):.2f}",
+                  pad, y, size=0.42); y += 16
         if max(scales) != min(scales):
-            _text(panel, f"scale: {min(scales):.2f}..{max(scales):.2f}", y,
-                  size=0.42); y += 16
-
-        # Legenda numerata con colore per ogni match
-        max_rows = max(1, (panel_height - y - 120) // 16)
+            _put_text(panel, f"scale: {min(scales):.2f}..{max(scales):.2f}",
+                      pad, y, size=0.42); y += 16
+        y += 4
+        max_rows = max(1, (panel_height - y - 12) // 16)
         shown = matches[:max_rows]
         for i, m in enumerate(shown):
             color = _color_for(i)
-            # Pallino di colore
             cv2.circle(panel, (pad + 6, y - 4), 5, color, -1, cv2.LINE_AA)
             txt = (f"#{i+1} ({int(m.cx)},{int(m.cy)}) "
                    f"{m.angle_deg:.0f}d s={m.scale:.2f} {m.score:.3f}")
-            _text(panel, txt, y, size=0.40, x=pad + 18)
+            _put_text(panel, txt, pad + 18, y, size=0.40)
             y += 16
         if len(matches) > len(shown):
-            _text(panel, f"... +{len(matches) - len(shown)} altri",
-                  y, size=0.40, color=(150, 150, 150)); y += 16
-
-    # Hotkey in fondo
-    footer_y = panel_height - 92
-    _text(panel, "HOTKEY", footer_y, size=0.55, color=(0, 200, 255), thick=2)
-    fy = footer_y + 18
-    for line in [
-        "r  modifica parametri",
-        "o  nuovo ROI (stesso modello)",
-        "m  nuovo file modello",
-        "s  nuova scena",
-        "q / Esc  esci",
-    ]:
-        _text(panel, line, fy, size=0.40, color=(180, 180, 180))
-        fy += 14
+            _put_text(panel, f"... +{len(matches) - len(shown)} altri",
+                      pad, y, size=0.40, color=(150, 150, 150))
     return panel
 
 
-def compose_result(
-    scene_annotated: np.ndarray,
-    panel: np.ndarray,
+def build_right_panel(
+    params: dict,
+    panel_width: int = 320,
+    panel_height: int = 900,
 ) -> np.ndarray:
-    """Affianca pannello a sinistra + scena a destra, altezza uniforme."""
-    sH, sW = scene_annotated.shape[:2]
-    pH, pW = panel.shape[:2]
-    if pH != sH:
-        sc = sH / pH
-        new_pW = max(1, int(pW * sc))
-        panel = cv2.resize(panel, (new_pW, sH), interpolation=cv2.INTER_AREA)
-        pW = new_pW
-    out = np.zeros((sH, pW + sW, 3), dtype=np.uint8)
-    out[:, :pW] = panel
-    out[:, pW:] = scene_annotated
+    """Pannello destro: parametri correnti + hotkey."""
+    panel = np.full((panel_height, panel_width, 3), 28, dtype=np.uint8)
+    pad = 12
+    y = pad
+
+    _put_text(panel, "PARAMETRI", pad, y + 18, size=0.7,
+              color=(0, 200, 255), thick=2)
+    y += 36
+
+    # Tempi in alto, evidenziati
+    for k in ("train_time", "find_time", "num_variants"):
+        if k in params:
+            v = params[k]
+            _put_text(panel, f"{k}:", pad, y,
+                      size=0.45, color=(160, 220, 160))
+            _put_text(panel, str(v), pad + 140, y,
+                      size=0.45, color=(200, 255, 200), thick=2)
+            y += 18
+    y += 6
+
+    # Altri parametri
+    skip = {"train_time", "find_time", "num_variants"}
+    for k, v in params.items():
+        if k in skip:
+            continue
+        _put_text(panel, f"{k}:", pad, y, size=0.42, color=(180, 180, 180))
+        _put_text(panel, str(v), pad + 140, y, size=0.42,
+                  color=(220, 220, 220))
+        y += 16
+
+    # Hotkey in fondo
+    footer_y = panel_height - 110
+    _put_text(panel, "HOTKEY", pad, footer_y, size=0.6,
+              color=(0, 200, 255), thick=2)
+    fy = footer_y + 22
+    for line in [
+        "r  modifica parametri",
+        "o  nuovo ROI",
+        "m  nuovo modello",
+        "s  nuova scena",
+        "q / Esc  esci",
+    ]:
+        _put_text(panel, line, pad, fy, size=0.42, color=(200, 200, 200))
+        fy += 16
+    return panel
+
+
+def _fit_scene_center(
+    scene: np.ndarray, target_w: int, target_h: int,
+) -> np.ndarray:
+    """Scala scena a fit (target_w, target_h) mantenendo aspect; padding bg."""
+    h, w = scene.shape[:2]
+    sc = min(target_w / w, target_h / h)
+    new_w = max(1, int(w * sc)); new_h = max(1, int(h * sc))
+    resized = cv2.resize(scene, (new_w, new_h), interpolation=cv2.INTER_AREA)
+    out = np.full((target_h, target_w, 3), 20, dtype=np.uint8)
+    y0 = (target_h - new_h) // 2
+    x0 = (target_w - new_w) // 2
+    out[y0:y0 + new_h, x0:x0 + new_w] = resized
+    return out
+
+
+def compose_fixed_layout(
+    scene_annotated: np.ndarray,
+    left_panel: np.ndarray,
+    right_panel: np.ndarray,
+    window_w: int = 1600,
+    window_h: int = 900,
+) -> np.ndarray:
+    """Layout fisso: [left | scena fit-scaled | right]."""
+    lH, lW = left_panel.shape[:2]
+    rH, rW = right_panel.shape[:2]
+    # Altezza uniforme (pannelli dovrebbero essere già window_h)
+    if lH != window_h:
+        left_panel = cv2.resize(left_panel, (lW, window_h),
+                                interpolation=cv2.INTER_AREA)
+    if rH != window_h:
+        right_panel = cv2.resize(right_panel, (rW, window_h),
+                                 interpolation=cv2.INTER_AREA)
+    center_w = window_w - lW - rW
+    center = _fit_scene_center(scene_annotated, center_w, window_h)
+    out = np.concatenate([left_panel, center, right_panel], axis=1)
     return out
 
 
@@ -382,28 +432,38 @@ def show_results(
     matches: list[Match],
     template_bgr: np.ndarray | None = None,
     params: dict | None = None,
+    window_w: int = 1600,
+    window_h: int = 900,
 ) -> str:
-    """Visualizza risultati. Ritorna 'rematch' se l'utente preme 'r', altrimenti 'quit'."""
+    """Visualizza risultati in layout fisso [SX panel | scena scalata | DX panel].
+
+    Ritorna 'rematch'/'new_roi'/'new_model'/'new_scene'/'quit'.
+    """
     print(f"\n=== {len(matches)} match trovati ===")
     for i, m in enumerate(matches):
         print(f"  #{i+1}: cx={m.cx:.1f} cy={m.cy:.1f} "
               f"angle={m.angle_deg:.1f}d scale={m.scale:.2f} score={m.score:.3f}")
+
     template_gray = None
     if template_bgr is not None:
         template_gray = (template_bgr if template_bgr.ndim == 2
                          else cv2.cvtColor(template_bgr, cv2.COLOR_BGR2GRAY))
     annotated = draw_matches(scene, matches, template_gray=template_gray)
-    if template_bgr is not None and params is not None:
-        panel = build_info_panel(template_bgr, params, matches,
-                                 panel_height=annotated.shape[0])
-        composed = compose_result(annotated, panel)
+
+    if template_bgr is not None:
+        left = build_left_panel(template_bgr, matches, panel_height=window_h)
     else:
-        composed = annotated
-    disp = _fit_for_display(composed, max_side=1600)
-    cv2.namedWindow(WINDOW_RESULT, cv2.WINDOW_NORMAL)
-    cv2.resizeWindow(WINDOW_RESULT, min(disp.shape[1], 1600),
-                     min(disp.shape[0], 900))
-    cv2.imshow(WINDOW_RESULT, disp)
+        left = np.full((window_h, 300, 3), 28, dtype=np.uint8)
+    if params is not None:
+        right = build_right_panel(params, panel_height=window_h)
+    else:
+        right = np.full((window_h, 320, 3), 28, dtype=np.uint8)
+
+    composed = compose_fixed_layout(
+        annotated, left, right, window_w=window_w, window_h=window_h,
+    )
+    cv2.namedWindow(WINDOW_RESULT, cv2.WINDOW_AUTOSIZE)
+    cv2.imshow(WINDOW_RESULT, composed)
     print("\n[r] parametri  [o] nuovo ROI  [m] nuovo modello  [s] nuova scena  [q/Esc] chiudi")
     action = "quit"
     while True:
@@ -433,7 +493,9 @@ def run(
     num_features: int = 96,
     weak_grad: float = 30.0,
     strong_grad: float = 60.0,
-    spread_radius: int = 5,
+    # 4 allineato col default del matcher: raggio 5 peggiora la precisione
+    # di rotazione (spread troppo largo appiattisce il picco angolare).
+    spread_radius: int = 4,
     pyramid_levels: int = 3,
     min_score: float = 0.55,
     max_matches: int = 25,

pm2d/matcher.py

@@ -91,8 +91,16 @@ class EdgeShapeMatcher:
         a0, a1 = self.angle_range_deg
         if self.angle_step_deg <= 0 or a0 >= a1:
             return [float(a0)]
-        n = int(np.floor((a1 - a0) / self.angle_step_deg))
-        return [float(a0 + i * self.angle_step_deg) for i in range(n)]
+        # n+1 valori per includere l'estremo superiore del range: con il
+        # solo floor un range [0, 90] step 5 si fermava a 85° (off-by-one).
+        n = int(np.floor((a1 - a0) / self.angle_step_deg)) + 1
+        angles = [float(a0 + i * self.angle_step_deg) for i in range(n)]
+        if a1 - a0 >= 360.0:
+            # Range che copre il giro completo: a0+360° è la stessa pose di
+            # a0, escludi il duplicato (variante inutile in train/find).
+            eps = 1e-6
+            angles = [a for a in angles if a < a0 + 360.0 - eps]
+        return angles
 
     def train(self, template_bgr: np.ndarray) -> int:
         """Genera varianti per tutte le combinazioni (angolo, scala)."""
@@ -222,6 +230,14 @@ class EdgeShapeMatcher:
             for y, x in zip(ys, xs):
                 candidates.append((float(res[y, x]), int(x), int(y), ti))
 
+        # Cap candidati top-level: senza limite np.where con soglia bassa
+        # può generare migliaia di candidati, ognuno con un matchTemplate
+        # full-res nel refinement. Tieni solo i migliori per score.
+        max_candidates = max(1, max_matches * 10)
+        if len(candidates) > max_candidates:
+            candidates.sort(key=lambda c: -c[0])
+            candidates = candidates[:max_candidates]
+
         # Refinement a risoluzione piena: per ogni candidato top, finestra locale
         refined: list[tuple[float, int, int, int]] = []
         margin = sf + 4
@@ -294,6 +310,10 @@ class EdgeShapeMatcher:
         )
         arrays = {f"edge_{i}": t.edge for i, t in enumerate(self.templates)}
         arrays.update({f"mask_{i}": t.mask for i, t in enumerate(self.templates)})
+        # Persisti anche il grayscale originale: senza, l'overlay edge
+        # spariva dopo load() (template_gray restava None).
+        if self.template_gray is not None:
+            arrays["template_gray"] = self.template_gray
         np.savez_compressed(path, params=params, meta=meta, **arrays)
 
     @classmethod
@@ -312,6 +332,10 @@ class EdgeShapeMatcher:
             top_score_factor=float(p[12]) if len(p) > 12 else 0.6,
         )
         m.template_size = (int(p[8]), int(p[9]))
+        # Retrocompatibilità: modelli salvati prima non hanno template_gray
+        # (resta None: overlay edge non disponibile ma find() funziona).
+        if "template_gray" in z.files:
+            m.template_gray = z["template_gray"]
         meta = z["meta"]
         for i in range(len(meta)):
             m.templates.append(

pm2d/web/static/app.js

@@ -0,0 +1,986 @@
+// Pattern Matching 2D - frontend (UI semplificata operator-friendly)
+
+// Parametri avanzati (sezione collassabile)
+const ADV_PARAMS = [
+  ["num_features", "Num feature", "int", ""],
+  ["weak_grad", "Weak grad", "float", ""],
+  ["strong_grad", "Strong grad", "float", ""],
+  ["spread_radius", "Spread radius", "int", ""],
+  ["pyramid_levels", "Pyramid levels", "int", ""],
+  ["verify_threshold", "Verify NCC thr", "float", 0.4],
+  ["nms_radius", "NMS radius (0=auto)", "int", 0],
+];
+
+const PALETTE = [
+  "#00ff00", "#ffc800", "#ff6464", "#ffc800", "#c800ff",
+  "#64ffc8", "#ff0000", "#00ffff",
+];
+
+const state = {
+  model: null, scene: null, roi: null, drag: null,
+  matches: [], annotatedImg: null,
+  active_recipe: null,  // V: ricetta caricata (string nome) o null
+  // ROI poligonale: vertici [x, y] in coordinate immagine modello
+  polyMode: false, polyPts: [], polyClosed: false,
+  // Export JSON: ultimo match completo (params + risposta)
+  lastMatch: null,
+};
+
+// ---------- Forms ----------
+function buildAdvancedForm() {
+  const form = document.getElementById("adv-form");
+  form.innerHTML = "";
+  for (const [key, label, , def] of ADV_PARAMS) {
+    const lbl = document.createElement("label");
+    lbl.textContent = label;
+    const inp = document.createElement("input");
+    inp.id = `adv-${key}`;
+    inp.type = "text";
+    inp.placeholder = "auto";
+    inp.value = def === "" ? "" : String(def);
+    inp.addEventListener("keydown", (e) => {
+      if (e.key === "Enter") doMatch();
+    });
+    form.appendChild(lbl);
+    form.appendChild(inp);
+  }
+}
+
+function readUserParams() {
+  return {
+    tipo: document.getElementById("p-tipo").value,
+    simmetria: document.getElementById("p-simmetria").value,
+    scala: document.getElementById("p-scala").value,
+    precisione: document.getElementById("p-precisione").value,
+    filtro_fp: document.getElementById("p-filtro-fp").value,
+    penalita_scala: parseFloat(
+      document.getElementById("p-penalita-scala").value),
+    min_score: parseFloat(document.getElementById("p-min-score").value),
+    max_matches: parseInt(document.getElementById("p-max-matches").value, 10),
+    ...readEdgeOverrides(),
+    ...readHalconFlags(),
+  };
+}
+
+function readEdgeOverrides() {
+  // Override edge dal pannello "Anteprima edge". Settati = utente li ha
+  // toccati (anche se uguali al default attuale). Vengono propagati a
+  // _simple_to_technical e usati identici sia in training sia in find.
+  // Inoltre salvati nella ricetta cosi' si replicano al load.
+  const _v = (id, parser) => {
+    const el = document.getElementById(id);
+    if (!el) return null;
+    const v = parser(el.value);
+    return Number.isFinite(v) ? v : null;
+  };
+  // Sempre passa i valori correnti degli slider: e' la richiesta utente
+  // che i param di pulizia rumore vengano usati anche nel find/ricetta.
+  const polCb = document.getElementById("hc-use-polarity");
+  return {
+    edge_weak_grad: _v("ep-weak", parseFloat),
+    edge_strong_grad: _v("ep-strong", parseFloat),
+    edge_num_features: _v("ep-nf", parseInt),
+    edge_min_feature_spacing: _v("ep-sp", parseInt),
+    use_polarity: polCb?.checked || document.getElementById("ep-pol")?.checked,
+  };
+}
+
+function readHalconFlags() {
+  // Halcon-mode toggle: tutti i flag default-off, esposti via "Modalità Halcon"
+  const $cb = (id) => document.getElementById(id)?.checked ?? false;
+  const $num = (id, def) => {
+    const v = parseFloat(document.getElementById(id)?.value);
+    return Number.isFinite(v) ? v : def;
+  };
+  const $int = (id, def) => {
+    const v = parseInt(document.getElementById(id)?.value, 10);
+    return Number.isFinite(v) ? v : def;
+  };
+  const roiStr = document.getElementById("hc-search-roi")?.value.trim() ?? "";
+  let search_roi = null;
+  if (roiStr) {
+    const p = roiStr.split(/[ ,;]+/).map((x) => parseInt(x, 10));
+    if (p.length === 4 && p.every((v) => Number.isFinite(v))) search_roi = p;
+  }
+  return {
+    use_polarity: $cb("hc-use-polarity"),
+    use_gpu: $cb("hc-use-gpu"),
+    use_soft_score: $cb("hc-soft-score"),
+    subpixel_lm: $cb("hc-subpixel-lm"),
+    refine_pose_joint: $cb("hc-refine-joint"),
+    pyramid_propagate: $cb("hc-pyr-propagate"),
+    min_recall: $num("hc-min-recall", 0),
+    nms_iou_threshold: $num("hc-nms-iou", 0.3),
+    greediness: $num("hc-greediness", 0),
+    coarse_stride: $int("hc-coarse-stride", 1),
+    search_roi: search_roi,
+  };
+}
+
+function readAdvancedOverrides() {
+  const out = {};
+  for (const [key, , type] of ADV_PARAMS) {
+    const v = document.getElementById(`adv-${key}`).value.trim();
+    if (v === "") continue;
+    out[key] = type === "int" ? parseInt(v, 10) : parseFloat(v);
+  }
+  return out;
+}
+
+// ---------- Image loading from folder ----------
+async function loadFromFolder(filename) {
+  const r = await fetch("/load_from_folder", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ filename }),
+  });
+  if (!r.ok) throw new Error(await r.text());
+  return await r.json();
+}
+
+async function fetchImagesList() {
+  const r = await fetch("/images");
+  if (!r.ok) return { files: [], dir: "" };
+  return await r.json();
+}
+
+async function uploadToFolder(file) {
+  const fd = new FormData();
+  fd.append("file", file);
+  const r = await fetch("/upload_to_folder", { method: "POST", body: fd });
+  if (!r.ok) throw new Error(await r.text());
+  return await r.json();
+}
+
+async function uploadDxf(file) {
+  // DXF: rasterizzato server-side in template grayscale (vedi pm2d/dxf.py)
+  const fd = new FormData();
+  fd.append("file", file);
+  const r = await fetch("/upload_dxf", { method: "POST", body: fd });
+  if (!r.ok) throw new Error(await r.text());
+  return await r.json();
+}
+
+async function refreshPickers() {
+  const {files, dir} = await fetchImagesList();
+  buildThumbPicker("picker-model", files, onSelectModel);
+  buildThumbPicker("picker-scene", files, onSelectScene);
+  return {files, dir};
+}
+
+function buildThumbPicker(pickerId, files, onSelect) {
+  const picker = document.getElementById(pickerId);
+  const current = picker.querySelector(".picker-current");
+  const list = picker.querySelector(".picker-list");
+  const text = current.querySelector(".picker-text");
+  // Rimuovi eventuale vecchia thumbnail
+  const oldImg = current.querySelector("img");
+  if (oldImg) oldImg.remove();
+  list.innerHTML = "";
+
+  files.forEach((f) => {
+    const item = document.createElement("div");
+    item.className = "picker-item";
+    const img = document.createElement("img");
+    img.src = `/folder_image/${encodeURIComponent(f)}?w=120`;
+    img.loading = "lazy";
+    const name = document.createElement("span");
+    name.className = "name"; name.textContent = f;
+    item.appendChild(img); item.appendChild(name);
+    item.addEventListener("click", () => {
+      // Aggiorna la visual del "current"
+      let thumb = current.querySelector("img");
+      if (!thumb) {
+        thumb = document.createElement("img");
+        current.insertBefore(thumb, text);
+      }
+      thumb.src = `/folder_image/${encodeURIComponent(f)}?w=80`;
+      text.textContent = f;
+      picker.classList.remove("open");
+      onSelect(f);
+    });
+    list.appendChild(item);
+  });
+
+  current.onclick = () => {
+    // Chiudi altri picker aperti
+    document.querySelectorAll(".thumb-picker.open")
+      .forEach((p) => { if (p !== picker) p.classList.remove("open"); });
+    picker.classList.toggle("open");
+  };
+}
+
+// Close picker on outside click
+document.addEventListener("click", (e) => {
+  if (!e.target.closest(".thumb-picker")) {
+    document.querySelectorAll(".thumb-picker.open")
+      .forEach((p) => p.classList.remove("open"));
+  }
+});
+
+function loadImage(src) {
+  return new Promise((res, rej) => {
+    const img = new Image();
+    img.onload = () => res(img);
+    img.onerror = rej;
+    img.src = src;
+  });
+}
+
+async function onSelectModel(filename) {
+  if (!filename) return;
+  setStatus("Caricamento modello...");
+  try {
+    const meta = await loadFromFolder(filename);
+    const img = await loadImage(`/image/${meta.id}/raw`);
+    state.model = { id: meta.id, w: meta.width, h: meta.height, img };
+    state.roi = null;
+    state.polyPts = []; state.polyClosed = false;  // B: scarta poligono stale
+    document.getElementById("roi-info").textContent = "ROI: (nessuna)";
+    setStatus(`Modello: ${filename} ${meta.width}x${meta.height} — trascina ROI`);
+    renderModel();
+  } catch (e) {
+    setStatus(`Errore modello: ${e.message}`);
+  }
+}
+
+async function onSelectScene(filename) {
+  if (!filename) return;
+  setStatus("Caricamento scena...");
+  try {
+    const meta = await loadFromFolder(filename);
+    const img = await loadImage(`/image/${meta.id}/raw`);
+    state.scene = { id: meta.id, w: meta.width, h: meta.height, img };
+    state.matches = []; state.annotatedImg = null;
+    setStatus(`Scena: ${filename} ${meta.width}x${meta.height}`);
+    renderScene();
+  } catch (e) {
+    setStatus(`Errore scena: ${e.message}`);
+  }
+}
+
+// ---------- Rendering ----------
+function fitToCanvas(img, cw, ch) {
+  const sc = Math.min(cw / img.width, ch / img.height);
+  const dw = img.width * sc, dh = img.height * sc;
+  return { sc, ox: (cw - dw) / 2, oy: (ch - dh) / 2, dw, dh };
+}
+
+function renderModel() {
+  const cnv = document.getElementById("c-model");
+  const ctx = cnv.getContext("2d");
+  ctx.fillStyle = "#141414";
+  ctx.fillRect(0, 0, cnv.width, cnv.height);
+  if (!state.model) return;
+  const fit = fitToCanvas(state.model.img, cnv.width, cnv.height);
+  state.model.scale = fit.sc;
+  state.model.ox = fit.ox; state.model.oy = fit.oy;
+  ctx.drawImage(state.model.img, fit.ox, fit.oy, fit.dw, fit.dh);
+  if (state.roi && !state.polyMode) {
+    const [x, y, w, h] = state.roi;
+    ctx.strokeStyle = "#00ff80"; ctx.lineWidth = 2;
+    ctx.strokeRect(fit.ox + x * fit.sc, fit.oy + y * fit.sc,
+                   w * fit.sc, h * fit.sc);
+  }
+  // ROI poligonale: path aperto giallo, chiuso verde con fill semitrasparente
+  if (state.polyMode && state.polyPts.length > 0) {
+    ctx.beginPath();
+    state.polyPts.forEach(([px, py], i) => {
+      const cx = fit.ox + px * fit.sc;
+      const cy = fit.oy + py * fit.sc;
+      if (i === 0) ctx.moveTo(cx, cy); else ctx.lineTo(cx, cy);
+    });
+    if (state.polyClosed) {
+      ctx.closePath();
+      ctx.fillStyle = "rgba(0, 255, 128, 0.18)";
+      ctx.fill();
+      ctx.strokeStyle = "#00ff80";
+    } else {
+      ctx.strokeStyle = "#ffff00";
+    }
+    ctx.lineWidth = 2;
+    ctx.stroke();
+    // Vertici come quadratini
+    ctx.fillStyle = state.polyClosed ? "#00ff80" : "#ffff00";
+    for (const [px, py] of state.polyPts) {
+      ctx.fillRect(fit.ox + px * fit.sc - 2, fit.oy + py * fit.sc - 2, 4, 4);
+    }
+  }
+  if (state.drag) {
+    ctx.strokeStyle = "#ffff00";
+    ctx.setLineDash([4, 2]); ctx.lineWidth = 2;
+    ctx.strokeRect(
+      Math.min(state.drag.x0, state.drag.x1),
+      Math.min(state.drag.y0, state.drag.y1),
+      Math.abs(state.drag.x1 - state.drag.x0),
+      Math.abs(state.drag.y1 - state.drag.y0));
+    ctx.setLineDash([]);
+  }
+}
+
+function renderScene() {
+  const cnv = document.getElementById("c-scene");
+  const ctx = cnv.getContext("2d");
+  ctx.fillStyle = "#141414";
+  ctx.fillRect(0, 0, cnv.width, cnv.height);
+  const img = state.annotatedImg || (state.scene && state.scene.img);
+  if (!img) return;
+  const fit = fitToCanvas(img, cnv.width, cnv.height);
+  ctx.drawImage(img, fit.ox, fit.oy, fit.dw, fit.dh);
+}
+
+// ---------- ROI drag ----------
+function canvasPos(cnv, ev) {
+  const r = cnv.getBoundingClientRect();
+  return { x: ev.clientX - r.left, y: ev.clientY - r.top };
+}
+
+function setupROI() {
+  const cnv = document.getElementById("c-model");
+  cnv.addEventListener("mousedown", (e) => {
+    if (!state.model) return;
+    if (state.polyMode) return;  // poly mode: gestito da click/dblclick
+    const p = canvasPos(cnv, e);
+    state.drag = { x0: p.x, y0: p.y, x1: p.x, y1: p.y };
+    renderModel();
+  });
+  // ROI poligonale: click aggiunge vertice, doppio click chiude
+  cnv.addEventListener("click", (e) => {
+    if (!state.model || !state.polyMode || state.polyClosed) return;
+    const m = state.model;
+    const p = canvasPos(cnv, e);
+    const ix = (p.x - m.ox) / m.scale;
+    const iy = (p.y - m.oy) / m.scale;
+    if (ix < 0 || iy < 0 || ix > m.w || iy > m.h) return;  // fuori immagine
+    const last = state.polyPts[state.polyPts.length - 1];
+    // Dedup: il dblclick genera anche 2 click ravvicinati
+    if (last && Math.hypot(ix - last[0], iy - last[1]) < 3) return;
+    state.polyPts.push([
+      Math.max(0, Math.min(Math.round(ix), m.w - 1)),
+      Math.max(0, Math.min(Math.round(iy), m.h - 1)),
+    ]);
+    document.getElementById("roi-info").textContent =
+      `Poligono: ${state.polyPts.length} vertici (doppio click o "Chiudi" per chiudere)`;
+    renderModel();
+  });
+  cnv.addEventListener("dblclick", (e) => {
+    if (!state.polyMode) return;
+    e.preventDefault();
+    closePoly();
+  });
+  cnv.addEventListener("mousemove", (e) => {
+    if (!state.drag) return;
+    const p = canvasPos(cnv, e);
+    state.drag.x1 = p.x; state.drag.y1 = p.y;
+    renderModel();
+  });
+  cnv.addEventListener("mouseup", () => {
+    if (!state.drag || !state.model) return;
+    const d = state.drag; state.drag = null;
+    if (Math.abs(d.x1 - d.x0) < 4 || Math.abs(d.y1 - d.y0) < 4) return;
+    const m = state.model;
+    const ix0 = Math.round((Math.min(d.x0, d.x1) - m.ox) / m.scale);
+    const iy0 = Math.round((Math.min(d.y0, d.y1) - m.oy) / m.scale);
+    const iw = Math.round(Math.abs(d.x1 - d.x0) / m.scale);
+    const ih = Math.round(Math.abs(d.y1 - d.y0) / m.scale);
+    const cx0 = Math.max(0, Math.min(ix0, m.w - 1));
+    const cy0 = Math.max(0, Math.min(iy0, m.h - 1));
+    const cw = Math.max(1, Math.min(iw, m.w - cx0));
+    const ch = Math.max(1, Math.min(ih, m.h - cy0));
+    state.roi = [cx0, cy0, cw, ch];
+    document.getElementById("roi-info").textContent =
+      `ROI: ${cw}x${ch} @ (${cx0}, ${cy0})`;
+    renderModel();
+  });
+}
+
+// ---------- ROI poligonale ----------
+function closePoly() {
+  if (!state.polyMode || state.polyClosed) return;
+  if (state.polyPts.length < 3) {
+    setStatus("Servono almeno 3 vertici per chiudere il poligono");
+    return;
+  }
+  state.polyClosed = true;
+  // ROI = bounding box del poligono (il server riceve anche roi_poly)
+  const xs = state.polyPts.map((p) => p[0]);
+  const ys = state.polyPts.map((p) => p[1]);
+  const x0 = Math.min(...xs), y0 = Math.min(...ys);
+  const w = Math.max(...xs) - x0, h = Math.max(...ys) - y0;
+  state.roi = [x0, y0, Math.max(1, w), Math.max(1, h)];
+  document.getElementById("roi-info").textContent =
+    `Poligono: ${state.polyPts.length} vertici, bbox ${w}x${h} @ (${x0}, ${y0})`;
+  renderModel();
+}
+
+function resetPoly() {
+  state.polyPts = [];
+  state.polyClosed = false;
+  state.roi = null;
+  document.getElementById("roi-info").textContent = state.polyMode
+    ? "Poligono: clicca sul modello per aggiungere vertici"
+    : "ROI: (nessuna)";
+  renderModel();
+}
+
+function getRoiPoly() {
+  // Poligono valido solo se in modalità poly e chiuso
+  return (state.polyMode && state.polyClosed && state.polyPts.length >= 3)
+    ? state.polyPts : null;
+}
+
+// ---------- Match action ----------
+async function doMatchRecipe() {
+  if (!state.scene) { setStatus("Carica scena"); return; }
+  setStatus(`Match ricetta ${state.active_recipe}...`);
+  const hc = readHalconFlags();
+  const body = {
+    recipe: state.active_recipe,
+    scene_id: state.scene.id,
+    min_score: parseFloat(document.getElementById("p-min-score").value),
+    max_matches: parseInt(document.getElementById("p-max-matches").value, 10),
+    verify_threshold: 0.50,
+    ...hc,
+  };
+  const r = await fetch("/match_recipe", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(body),
+  });
+  if (!r.ok) { setStatus(`Errore: ${await r.text()}`); return; }
+  const data = await r.json();
+  state.matches = data.matches;
+  // C: salva tutto per "Esporta JSON"
+  state.lastMatch = {
+    endpoint: "/match_recipe", params: body, response: data,
+    image_id: state.scene.id,
+  };
+  document.getElementById("btn-export-json").disabled = false;
+  state.annotatedImg = await loadImage(
+    `/image/${data.annotated_id}/raw?t=${Date.now()}`);
+  renderScene();
+  renderLegend();
+  document.getElementById("t-train").textContent = "—";
+  document.getElementById("t-find").textContent = `${data.find_time.toFixed(2)}s`;
+  document.getElementById("t-var").textContent = data.num_variants;
+  document.getElementById("t-match").textContent = data.matches.length;
+  renderDiag(data.diag, data.matches.length);
+  setStatus(`${data.matches.length} match trovati (ricetta ${state.active_recipe})`);
+}
+
+async function doMatch() {
+  // Path V: ricetta caricata → bypass training, solo find su scena
+  if (state.active_recipe) {
+    return doMatchRecipe();
+  }
+  if (!state.model) { setStatus("Carica modello"); return; }
+  if (!state.scene) { setStatus("Carica scena"); return; }
+  if (state.polyMode && !state.polyClosed) {
+    setStatus("Chiudi il poligono (doppio click o bottone Chiudi)"); return;
+  }
+  if (!state.roi) { setStatus("Seleziona ROI sul modello"); return; }
+  const roiPoly = getRoiPoly();
+  const user = readUserParams();
+  const adv = readAdvancedOverrides();
+  setStatus("Match in corso...");
+
+  // Se utente ha fornito override avanzati → usa /match (tecnico)
+  // altrimenti /match_simple (operator mode)
+  const hasAdv = Object.keys(adv).length > 0;
+  const url = hasAdv ? "/match" : "/match_simple";
+  let body;
+  if (hasAdv) {
+    // Merge simple → tecnici base, poi overrides
+    const SYM_MAP = {invariante:0, nessuna:360, bilaterale:180, rot_3:120,
+                     rot_4:90, rot_6:60, rot_8:45};
+    const SCALE_MAP = {fissa:[1,1,0.1], mini:[0.9,1.1,0.05],
+                       medio:[0.75,1.25,0.05], max:[0.5,1.5,0.05]};
+    const PREC_MAP = {veloce:10, normale:5, preciso:2};
+    // Allineato a FILTRO_FP_MAP server-side (server.py)
+    const FP_MAP = {off:0, leggero:0.30, medio:0.50, forte:0.70};
+    const [smin, smax, sstep] = SCALE_MAP[user.scala];
+    // NB: SYM_MAP[invariante]=0 e' valido (zero rotazioni). Uso ?? per
+    // distinguere "chiave mancante" da "valore zero": altrimenti 0 || 360
+    // collassa invariante a 360 = bug "simmetria non ha effetto".
+    const angMax = SYM_MAP[user.simmetria] ?? 360;
+    body = {
+      model_id: state.model.id, scene_id: state.scene.id, roi: state.roi,
+      roi_poly: roiPoly,
+      angle_min: 0, angle_max: angMax,
+      angle_step: PREC_MAP[user.precisione] ?? 5,
+      scale_min: smin, scale_max: smax, scale_step: sstep,
+      min_score: user.min_score, max_matches: user.max_matches,
+      num_features: adv.num_features ?? 96,
+      weak_grad: adv.weak_grad ?? 30,
+      strong_grad: adv.strong_grad ?? 60,
+      spread_radius: adv.spread_radius ?? 5,
+      pyramid_levels: adv.pyramid_levels ?? 3,
+      verify_threshold: adv.verify_threshold ?? (FP_MAP[user.filtro_fp] ?? 0.50),
+      nms_radius: adv.nms_radius ?? 0,
+    };
+  } else {
+    body = {
+      model_id: state.model.id, scene_id: state.scene.id, roi: state.roi,
+      roi_poly: roiPoly,
+      ...user,
+    };
+  }
+
+  const r = await fetch(url, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(body),
+  });
+  if (!r.ok) {
+    setStatus(`Errore: ${await r.text()}`); return;
+  }
+  const data = await r.json();
+  state.matches = data.matches;
+  // C: salva tutto per "Esporta JSON"
+  state.lastMatch = {
+    endpoint: url, params: body, response: data,
+    image_id: state.scene.id,
+  };
+  document.getElementById("btn-export-json").disabled = false;
+  state.annotatedImg = await loadImage(
+    `/image/${data.annotated_id}/raw?t=${Date.now()}`);
+  renderScene();
+  renderLegend();
+  document.getElementById("t-train").textContent = `${data.train_time.toFixed(2)}s`;
+  document.getElementById("t-find").textContent = `${data.find_time.toFixed(2)}s`;
+  document.getElementById("t-var").textContent = data.num_variants;
+  document.getElementById("t-match").textContent = data.matches.length;
+  renderDiag(data.diag, data.matches.length);
+  setStatus(`${data.matches.length} match trovati${hasAdv ? " (avanzato)" : ""}`);
+}
+
+function renderLegend() {
+  const el = document.getElementById("legend");
+  el.innerHTML = "";
+  state.matches.forEach((m, i) => {
+    const div = document.createElement("div");
+    div.className = "legend-item";
+    const dot = document.createElement("span");
+    dot.className = "legend-dot";
+    dot.style.background = PALETTE[i % PALETTE.length];
+    div.appendChild(dot);
+    const txt = document.createElement("span");
+    txt.textContent = `#${i+1}  cx=${Math.round(m.cx)} cy=${Math.round(m.cy)}  `
+      + `${m.angle_deg.toFixed(1)}°  s=${m.scale.toFixed(2)}  `
+      + `score=${m.score.toFixed(3)}`;
+    div.appendChild(txt);
+    el.appendChild(div);
+  });
+}
+
+function setStatus(s) {
+  document.getElementById("status").textContent = s;
+}
+
+// ---------- C: Export JSON risultati ----------
+function exportMatchJSON() {
+  if (!state.lastMatch) {
+    alert("Nessun match da esportare: esegui prima un MATCH.");
+    return;
+  }
+  const lm = state.lastMatch;
+  const payload = {
+    timestamp: new Date().toISOString(),
+    image_id: lm.image_id,
+    endpoint: lm.endpoint,
+    params: lm.params,
+    matches: lm.response.matches.map((m) => ({
+      cx: m.cx, cy: m.cy, angle_deg: m.angle_deg,
+      scale: m.scale, score: m.score, bbox: m.bbox_poly,
+    })),
+    train_time: lm.response.train_time,
+    find_time: lm.response.find_time,
+    num_variants: lm.response.num_variants,
+  };
+  const blob = new Blob([JSON.stringify(payload, null, 2)],
+                        { type: "application/json" });
+  const a = document.createElement("a");
+  a.href = URL.createObjectURL(blob);
+  const ts = new Date().toISOString().replace(/[:.]/g, "-");
+  a.download = `pm2d_match_${ts}.json`;
+  document.body.appendChild(a);
+  a.click();
+  a.remove();
+  URL.revokeObjectURL(a.href);
+}
+
+// ---------- Init ----------
+// ---------- Edge preview (clean rumore) ----------
+let _epDebounce = null;
+let _epLastImg = null;
+
+async function fetchEdgePreview() {
+  if (!state.model || !state.roi) {
+    document.getElementById("edge-preview-info").textContent =
+      "Disegna prima la ROI sul modello";
+    return;
+  }
+  const body = {
+    model_id: state.model.id,
+    roi: state.roi,
+    weak_grad: parseFloat(document.getElementById("ep-weak").value),
+    strong_grad: parseFloat(document.getElementById("ep-strong").value),
+    num_features: parseInt(document.getElementById("ep-nf").value, 10),
+    min_feature_spacing: parseInt(document.getElementById("ep-sp").value, 10),
+    use_polarity: document.getElementById("ep-pol").checked,
+  };
+  try {
+    const r = await fetch("/preview_edges", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(body),
+    });
+    if (!r.ok) throw new Error(await r.text());
+    const j = await r.json();
+    _epLastImg = await loadImage(`/image/${j.preview_id}/raw?t=${Date.now()}`);
+    drawEdgePreview();
+    const ucs = j.ucs_baricentro
+      ? ` | UCS=(${j.ucs_baricentro.cx},${j.ucs_baricentro.cy})`
+      : "";
+    document.getElementById("edge-preview-info").innerHTML =
+      `<b>${j.n_features}</b> feature scelte (di ${j.n_edge_after_hysteresis} edge totali)<br>` +
+      `mag: max=${j.mag_max.toFixed(0)} p50=${j.mag_p50.toFixed(0)} ` +
+      `p85=${j.mag_p85.toFixed(0)}${ucs}`;
+  } catch (e) {
+    document.getElementById("edge-preview-info").textContent =
+      `Errore preview: ${e.message}`;
+  }
+}
+
+function drawEdgePreview() {
+  const cnv = document.getElementById("c-edge-preview");
+  if (!_epLastImg) return;
+  const ctx = cnv.getContext("2d");
+  // Fit-contain
+  const r = Math.min(cnv.width / _epLastImg.width,
+                     cnv.height / _epLastImg.height);
+  const w = _epLastImg.width * r;
+  const h = _epLastImg.height * r;
+  const ox = (cnv.width - w) / 2;
+  const oy = (cnv.height - h) / 2;
+  ctx.fillStyle = "#000"; ctx.fillRect(0, 0, cnv.width, cnv.height);
+  ctx.imageSmoothingEnabled = false;
+  ctx.drawImage(_epLastImg, ox, oy, w, h);
+}
+
+function scheduleEdgePreview() {
+  if (_epDebounce) clearTimeout(_epDebounce);
+  _epDebounce = setTimeout(fetchEdgePreview, 200);
+}
+
+function bindEdgePreviewControls() {
+  const slid = (id, valEl) => {
+    const el = document.getElementById(id);
+    const v = document.getElementById(valEl);
+    el.addEventListener("input", () => {
+      v.textContent = el.value;
+      scheduleEdgePreview();
+    });
+  };
+  slid("ep-weak", "ep-weak-v");
+  slid("ep-strong", "ep-strong-v");
+  slid("ep-nf", "ep-nf-v");
+  slid("ep-sp", "ep-sp-v");
+  document.getElementById("ep-pol").addEventListener("change",
+    scheduleEdgePreview);
+  // Auto-refresh quando il pannello viene aperto
+  document.getElementById("edge-preview-panel").addEventListener("toggle",
+    (e) => { if (e.target.open) fetchEdgePreview(); });
+  document.getElementById("btn-edge-apply").addEventListener("click", () => {
+    // Copia i valori correnti nei campi avanzati
+    const map = {
+      "ep-weak": "adv-weak_grad",
+      "ep-strong": "adv-strong_grad",
+      "ep-nf": "adv-num_features",
+      "ep-sp": "adv-min_feature_spacing",
+    };
+    for (const [src, dst] of Object.entries(map)) {
+      const dstEl = document.getElementById(dst);
+      if (dstEl) dstEl.value = document.getElementById(src).value;
+    }
+    // use_polarity: alla checkbox della modalita Halcon
+    const polCb = document.getElementById("hc-use-polarity");
+    if (polCb) polCb.checked = document.getElementById("ep-pol").checked;
+    // Apri pannello Avanzate per feedback
+    const advDetails = document.querySelectorAll("#col-params details");
+    advDetails.forEach((d) => { d.open = true; });
+    alert("Parametri edge applicati. Esegui MATCH per usare i valori scelti.");
+  });
+}
+
+// ---------- CC: Diagnostica match ----------
+function renderDiag(diag, n_matches) {
+  const el = document.getElementById("diag-content");
+  if (!diag) {
+    el.innerHTML = '<em style="color:#888">Diagnostica non disponibile</em>';
+    return;
+  }
+  const dropTotal = (diag.drop_ncc_low || 0) + (diag.drop_min_score_post_avg || 0)
+    + (diag.drop_recall_low || 0) + (diag.drop_bbox_out_of_scene || 0)
+    + (diag.drop_nms_iou || 0);
+  // Hint contestuali se 0 match
+  let hint = "";
+  if (n_matches === 0) {
+    if (diag.n_after_pre_nms === 0) {
+      hint = `<div style="color:#f88; margin-top:6px">⚠ Nessun candidato sopra soglia.
+        Prova: ↓ <b>min_score</b> o ↓ <b>top_thresh</b> (currently ${diag.top_thresh_used.toFixed(2)})</div>`;
+    } else if (diag.drop_ncc_low > 0 && dropTotal === diag.drop_ncc_low) {
+      hint = `<div style="color:#f88; margin-top:6px">⚠ ${diag.drop_ncc_low} candidati droppati da NCC.
+        Prova: ↓ <b>verify_threshold</b> (filtro_fp più leggero)</div>`;
+    } else if (diag.drop_min_score_post_avg > 0) {
+      hint = `<div style="color:#f88; margin-top:6px">⚠ ${diag.drop_min_score_post_avg} match sotto min_score post-NCC.
+        Prova: ↓ <b>min_score</b></div>`;
+    } else if (diag.drop_recall_low > 0) {
+      hint = `<div style="color:#f88; margin-top:6px">⚠ ${diag.drop_recall_low} match con recall < ${diag.min_recall_used}.
+        Prova: ↓ <b>min_recall</b></div>`;
+    } else if (diag.drop_bbox_out_of_scene > 0) {
+      hint = `<div style="color:#f88; margin-top:6px">⚠ ${diag.drop_bbox_out_of_scene} match con bbox fuori scena.
+        Centro derivato male: aumenta <b>min_score</b> o restringi <b>search_roi</b></div>`;
+    }
+  }
+  const flags = [];
+  if (diag.use_polarity) flags.push("polarity");
+  if (diag.use_soft_score) flags.push("soft");
+  if (diag.subpixel_lm) flags.push("subpix-LM");
+  el.innerHTML = `
+    <div><b>Pipeline pruning:</b></div>
+    <div>varianti: ${diag.n_variants_total} → top_eval=${diag.n_variants_top_evaluated}
+       → top_pass=${diag.n_variants_top_passed} → full_eval=${diag.n_variants_full_evaluated}</div>
+    <div><b>Candidati:</b> raw=${diag.n_raw_candidates}
+       → pre_nms=${diag.n_after_pre_nms} → final=${diag.n_final}</div>
+    <div><b>Drop reasons:</b> NCC=${diag.drop_ncc_low}, score=${diag.drop_min_score_post_avg},
+       recall=${diag.drop_recall_low}, bbox=${diag.drop_bbox_out_of_scene}, NMS=${diag.drop_nms_iou}</div>
+    <div><b>Soglie:</b> top=${diag.top_thresh_used.toFixed(2)},
+       min_score=${diag.min_score_used.toFixed(2)},
+       NCC=${diag.verify_threshold_used.toFixed(2)},
+       recall=${diag.min_recall_used.toFixed(2)}</div>
+    ${flags.length ? `<div><b>Flag attivi:</b> ${flags.join(", ")}</div>` : ""}
+    ${hint}
+  `;
+  // Auto-apri pannello se 0 match (segnala problema)
+  if (n_matches === 0) {
+    document.getElementById("diag-panel").open = true;
+  }
+}
+
+// ---------- Auto-tune (Halcon-style) ----------
+async function doAutoTune() {
+  if (!state.model || !state.roi) {
+    alert("Seleziona modello e disegna ROI prima di Auto-tune.");
+    return;
+  }
+  const status = document.getElementById("status");
+  status.textContent = "Analisi ROI in corso...";
+  try {
+    const r = await fetch("/auto_tune", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({
+        model_id: state.model.id,
+        roi: state.roi,
+      }),
+    });
+    if (!r.ok) throw new Error(await r.text());
+    const t = await r.json();
+    // Applica ai campi avanzati (override automatico)
+    for (const [key] of ADV_PARAMS) {
+      const el = document.getElementById(`adv-${key}`);
+      if (el && t[key] !== undefined) el.value = String(t[key]);
+    }
+    // Espandi la sezione Avanzate per mostrare i valori applicati
+    const advDetails = document.querySelector("#col-params details:last-of-type");
+    if (advDetails) advDetails.open = true;
+    // Feedback diagnostico
+    const lines = [
+      `weak/strong: ${t.weak_grad} / ${t.strong_grad}`,
+      `feature: ${t.num_features}, piramide: ${t.pyramid_levels}`,
+      `angle: [${t.angle_min}..${t.angle_max}]@${t.angle_step}°`,
+    ];
+    if (t._symmetry_order > 1) {
+      lines.push(`simmetria rotaz. ${t._symmetry_order}x (conf ${t._symmetry_conf})`);
+    }
+    if (t._self_score !== undefined) {
+      lines.push(`self-validation: ${t._validation}`);
+    }
+    status.textContent = `Auto-tune OK — ${lines[0]}`;
+    alert("Auto-tune completato:\n\n" + lines.join("\n"));
+  } catch (e) {
+    status.textContent = `Auto-tune errore: ${e.message}`;
+    alert(`Errore auto-tune: ${e.message}`);
+  }
+}
+
+// ---------- V: Recipe load/list/unload ----------
+async function refreshRecipeList() {
+  try {
+    const r = await fetch("/recipes");
+    if (!r.ok) return;
+    const j = await r.json();
+    const sel = document.getElementById("hc-recipe-list");
+    const cur = sel.value;
+    sel.innerHTML = '<option value="">— ricette disponibili —</option>';
+    for (const f of j.files) {
+      const o = document.createElement("option");
+      o.value = f.name;
+      o.textContent = `${f.name}  (${(f.size / 1024).toFixed(1)} KB)`;
+      sel.appendChild(o);
+    }
+    if (cur) sel.value = cur;
+  } catch (e) { /* silent */ }
+}
+
+async function loadRecipe() {
+  const sel = document.getElementById("hc-recipe-list");
+  const name = sel.value;
+  if (!name) {
+    alert("Seleziona una ricetta dalla lista.");
+    return;
+  }
+  try {
+    const r = await fetch(`/recipes/${encodeURIComponent(name)}/load`, {
+      method: "POST",
+    });
+    if (!r.ok) throw new Error(await r.text());
+    const j = await r.json();
+    state.active_recipe = j.name;
+    document.getElementById("recipe-status").textContent =
+      `Caricata: ${j.name} — ${j.n_variants} varianti, ` +
+      `${j.template_size[0]}x${j.template_size[1]} px` +
+      (j.use_polarity ? " (polarity)" : "");
+    document.getElementById("recipe-status").style.color = "#0c0";
+    document.getElementById("btn-unload-recipe").disabled = false;
+  } catch (e) {
+    alert(`Errore caricamento: ${e.message}`);
+  }
+}
+
+function unloadRecipe() {
+  state.active_recipe = null;
+  document.getElementById("recipe-status").textContent = "Nessuna ricetta caricata";
+  document.getElementById("recipe-status").style.color = "#888";
+  document.getElementById("btn-unload-recipe").disabled = true;
+}
+
+// ---------- V: Save recipe ----------
+async function saveRecipe() {
+  if (!state.model || !state.roi) {
+    alert("Seleziona modello e disegna ROI prima di salvare la ricetta.");
+    return;
+  }
+  const name = document.getElementById("hc-recipe-name").value.trim();
+  if (!name) {
+    alert("Inserisci un nome per la ricetta.");
+    return;
+  }
+  const user = readUserParams();
+  const body = {
+    model_id: state.model.id,
+    scene_id: state.scene?.id || state.model.id,
+    roi: state.roi,
+    roi_poly: getRoiPoly(),
+    tipo: user.tipo,
+    simmetria: user.simmetria,
+    scala: user.scala,
+    precisione: user.precisione,
+    use_polarity: user.use_polarity,
+    use_gpu: user.use_gpu,
+    edge_weak_grad: user.edge_weak_grad,
+    edge_strong_grad: user.edge_strong_grad,
+    edge_num_features: user.edge_num_features,
+    edge_min_feature_spacing: user.edge_min_feature_spacing,
+    name: name,
+  };
+  try {
+    const r = await fetch("/recipes", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(body),
+    });
+    if (!r.ok) throw new Error(await r.text());
+    const j = await r.json();
+    alert(`Ricetta salvata: ${j.name}\n${j.n_variants} varianti, ${j.size} bytes`);
+    refreshRecipeList();
+  } catch (e) {
+    alert(`Errore salvataggio: ${e.message}`);
+  }
+}
+
+window.addEventListener("DOMContentLoaded", async () => {
+  buildAdvancedForm();
+  setupROI();
+  // Popola picker immagini da IMAGES_DIR (con thumbnail)
+  const {files, dir} = await refreshPickers();
+  if (files.length === 0) {
+    setStatus(`Nessuna immagine in ${dir} (carica file o configura IMAGES_DIR)`);
+  } else {
+    setStatus(`${files.length} immagini in ${dir}`);
+  }
+
+  // Upload file nella folder
+  const upEl = document.getElementById("file-upload");
+  upEl.addEventListener("change", async (e) => {
+    const f = e.target.files[0];
+    if (!f) return;
+    // A: file DXF → rasterizza server-side e usa direttamente come modello
+    if (f.name.toLowerCase().endsWith(".dxf")) {
+      setStatus(`Rasterizzazione DXF ${f.name}...`);
+      try {
+        const meta = await uploadDxf(f);
+        const img = await loadImage(`/image/${meta.id}/raw`);
+        state.model = { id: meta.id, w: meta.width, h: meta.height, img };
+        state.roi = null;
+        resetPoly();
+        setStatus(`DXF ${f.name} rasterizzato ` +
+                  `${meta.width}x${meta.height} — disegna ROI sul modello`);
+        renderModel();
+      } catch (err) {
+        setStatus(`Errore DXF: ${err.message}`);
+      }
+      e.target.value = "";
+      return;
+    }
+    setStatus(`Caricamento ${f.name} nella cartella...`);
+    try {
+      const res = await uploadToFolder(f);
+      await refreshPickers();
+      setStatus(`Salvato come ${res.saved_as} (${res.files.length} file totali)`);
+    } catch (err) {
+      setStatus(`Errore upload: ${err.message}`);
+    }
+    e.target.value = "";  // consente re-upload stesso file
+  });
+  document.getElementById("btn-match").addEventListener("click", doMatch);
+  document.getElementById("btn-autotune").addEventListener("click", doAutoTune);
+  // B: ROI poligonale (toggle + chiudi + reset)
+  document.getElementById("roi-poly-toggle").addEventListener("change", (e) => {
+    state.polyMode = e.target.checked;
+    document.getElementById("btn-poly-close").disabled = !state.polyMode;
+    document.getElementById("btn-poly-reset").disabled = !state.polyMode;
+    resetPoly();
+  });
+  document.getElementById("btn-poly-close").addEventListener("click", closePoly);
+  document.getElementById("btn-poly-reset").addEventListener("click", resetPoly);
+  // C: export JSON ultimo match
+  document.getElementById("btn-export-json").addEventListener("click",
+    exportMatchJSON);
+  document.getElementById("btn-save-recipe").addEventListener("click",
+    saveRecipe);
+  document.getElementById("btn-load-recipe").addEventListener("click",
+    loadRecipe);
+  document.getElementById("btn-unload-recipe").addEventListener("click",
+    unloadRecipe);
+  refreshRecipeList();
+  bindEdgePreviewControls();
+  const slider = document.getElementById("p-min-score");
+  slider.addEventListener("input", (e) => {
+    document.getElementById("v-score").textContent =
+      parseFloat(e.target.value).toFixed(2);
+  });
+  renderModel();
+  renderScene();
+});

pm2d/web/static/index.html

@@ -0,0 +1,285 @@
+<!doctype html>
+<html lang="it">
+<head>
+<meta charset="utf-8" />
+<title>Pattern Matching 2D</title>
+<link rel="stylesheet" href="/static/style.css">
+</head>
+<body>
+<header>
+  <h1>Pattern Matching 2D</h1>
+  <div class="toolbar">
+    <label class="tb-label">Modello:</label>
+    <div class="thumb-picker" id="picker-model">
+      <div class="picker-current" tabindex="0">
+        <span class="picker-text">-- seleziona --</span>
+        <span class="caret">▾</span>
+      </div>
+      <div class="picker-list"></div>
+    </div>
+    <label class="tb-label">Scena:</label>
+    <div class="thumb-picker" id="picker-scene">
+      <div class="picker-current" tabindex="0">
+        <span class="picker-text">-- seleziona --</span>
+        <span class="caret">▾</span>
+      </div>
+      <div class="picker-list"></div>
+    </div>
+    <button class="btn btn-go" id="btn-match">▶ MATCH</button>
+    <button class="btn" id="btn-autotune"
+            title="Analizza ROI e derivata parametri ottimali (Halcon-style)">
+      ⚙ Auto-tune
+    </button>
+    <label class="btn" title="Carica nuovo file nella cartella immagini (immagine o DXF)">
+      ⬆ Carica file
+      <input type="file" id="file-upload" accept="image/*,.dxf" hidden>
+    </label>
+    <span id="status">Seleziona modello, disegna ROI, seleziona scena</span>
+  </div>
+</header>
+
+<main>
+  <section class="col" id="col-model">
+    <h2>MODELLO</h2>
+    <div class="canvas-wrap">
+      <canvas id="c-model" width="380" height="420"></canvas>
+    </div>
+    <div id="roi-info">ROI: (nessuna)</div>
+    <div id="roi-poly-bar" style="display:flex; gap:6px; align-items:center; margin-top:6px">
+      <label style="display:flex; gap:4px; align-items:center; font-size:12px; cursor:pointer">
+        <input type="checkbox" id="roi-poly-toggle"> ROI poligonale
+      </label>
+      <button class="btn" id="btn-poly-close" type="button" disabled
+              title="Chiude il poligono (equivale al doppio click)">Chiudi</button>
+      <button class="btn" id="btn-poly-reset" type="button" disabled
+              title="Cancella i vertici del poligono">Reset</button>
+    </div>
+    <details id="edge-preview-panel" style="margin-top:10px">
+      <summary>🔬 Anteprima edge / pulizia rumore</summary>
+      <div style="font-size:11px; color:#aaa; margin:4px 0">
+        Regola le soglie per togliere edge spuri (sporcizie). UCS rosso/verde
+        sul baricentro feature.
+      </div>
+      <div class="ep-grid">
+        <label class="ep-row">weak_grad <span id="ep-weak-v">30</span>
+          <input type="range" id="ep-weak" min="5" max="200" value="30" step="1">
+        </label>
+        <label class="ep-row">strong_grad <span id="ep-strong-v">60</span>
+          <input type="range" id="ep-strong" min="10" max="400" value="60" step="1">
+        </label>
+        <label class="ep-row">num_features <span id="ep-nf-v">96</span>
+          <input type="range" id="ep-nf" min="16" max="300" value="96" step="1">
+        </label>
+        <label class="ep-row">spacing <span id="ep-sp-v">3</span>
+          <input type="range" id="ep-sp" min="1" max="15" value="3" step="1">
+        </label>
+        <label class="ep-row" style="flex-direction:row; gap:6px">
+          <input type="checkbox" id="ep-pol"> polarity
+        </label>
+        <button class="btn" id="btn-edge-apply" type="button"
+                style="grid-column:1/-1">
+          ✓ Applica ai parametri Avanzate
+        </button>
+      </div>
+      <div class="canvas-wrap" style="margin-top:6px">
+        <canvas id="c-edge-preview" width="380" height="380"></canvas>
+      </div>
+      <div id="edge-preview-info" style="font-size:11px; color:#888; margin-top:4px">
+        Disegna ROI e apri questo pannello per generare anteprima
+      </div>
+    </details>
+  </section>
+
+  <section class="col" id="col-scene">
+    <h2>SCENA</h2>
+    <div class="canvas-wrap">
+      <canvas id="c-scene" width="820" height="620"></canvas>
+    </div>
+  </section>
+
+  <section class="col" id="col-params">
+    <h2>IMPOSTAZIONI</h2>
+
+    <div class="field">
+      <label>Tipo modello</label>
+      <select id="p-tipo">
+        <option value="intero">Oggetto intero</option>
+        <option value="parziale">Parte di oggetto</option>
+      </select>
+    </div>
+
+    <div class="field">
+      <label>Simmetria</label>
+      <select id="p-simmetria">
+        <option value="nessuna" selected>Nessuna (0..360°)</option>
+        <option value="invariante">Invariante (cerchi — no rotazione)</option>
+        <option value="bilaterale">Bilaterale (speculare 180°)</option>
+        <option value="rot_3">Rotazionale 3× (120°)</option>
+        <option value="rot_4">Rotazionale 4× (90°)</option>
+        <option value="rot_6">Rotazionale 6× (60°)</option>
+        <option value="rot_8">Rotazionale 8× (45°)</option>
+      </select>
+    </div>
+
+    <div class="field">
+      <label>Variazione scala</label>
+      <select id="p-scala">
+        <option value="fissa">Fissa (setup calibrato)</option>
+        <option value="mini">±10%</option>
+        <option value="medio">±25%</option>
+        <option value="max">±50%</option>
+      </select>
+    </div>
+
+    <div class="field">
+      <label>Precisione angolare</label>
+      <select id="p-precisione">
+        <option value="veloce">Veloce (10°)</option>
+        <option value="normale" selected>Normale (5°)</option>
+        <option value="preciso">Preciso (2°)</option>
+      </select>
+    </div>
+
+    <div class="field">
+      <label>Filtro falsi positivi
+        <span class="hint">(verifica intensità colori)</span>
+      </label>
+      <select id="p-filtro-fp">
+        <option value="off">Disattivato (massimo recall)</option>
+        <option value="leggero">Leggero (tollera illuminazione)</option>
+        <option value="medio" selected>Medio (consigliato)</option>
+        <option value="forte">Forte (massima selettività)</option>
+      </select>
+    </div>
+
+    <div class="field">
+      <label>Peso dimensione nel score
+        <span class="hint">(penalizza scala ≠ 1.0)</span>
+      </label>
+      <select id="p-penalita-scala">
+        <option value="0" selected>Nessuno (score shape puro)</option>
+        <option value="0.3">Leggero (−30% max)</option>
+        <option value="0.5">Medio (−50% max)</option>
+        <option value="0.8">Forte (−80% max)</option>
+      </select>
+    </div>
+
+    <div class="field">
+      <label>Score minimo <span id="v-score">0.65</span>
+        <span class="hint">(più basso = più match anche incerti)</span>
+      </label>
+      <input type="range" id="p-min-score" min="0.30" max="0.95" step="0.05" value="0.65">
+    </div>
+
+    <div class="field">
+      <label>Max match</label>
+      <input type="number" id="p-max-matches" value="25" min="1" max="200">
+    </div>
+
+    <details>
+      <summary>Modalità Halcon</summary>
+      <div class="halcon-grid">
+        <label class="hc-row" title="16-bin orientation polarity-aware (mod 2π)">
+          <input type="checkbox" id="hc-use-polarity">
+          <span>Polarity 16-bin (F)</span>
+        </label>
+        <label class="hc-row" title="Score continuo cos(θ_t-θ_s) invece di bin">
+          <input type="checkbox" id="hc-soft-score">
+          <span>Soft-margin score (Y)</span>
+        </label>
+        <label class="hc-row" title="Sub-pixel refinement gradient field LM">
+          <input type="checkbox" id="hc-subpixel-lm">
+          <span>Sub-pixel LM 0.05 px (Z)</span>
+        </label>
+        <label class="hc-row" title="Refine congiunto Nelder-Mead (cx,cy,θ)">
+          <input type="checkbox" id="hc-refine-joint">
+          <span>Refine pose joint</span>
+        </label>
+        <label class="hc-row" title="Pyramid candidates propagation">
+          <input type="checkbox" id="hc-pyr-propagate">
+          <span>Pyramid propagate</span>
+        </label>
+        <label class="hc-row" title="OpenCL GPU offload (silent fallback CPU)">
+          <input type="checkbox" id="hc-use-gpu">
+          <span>GPU OpenCL (R)</span>
+        </label>
+
+        <div class="hc-row hc-num">
+          <label>Min recall (M)</label>
+          <input type="number" id="hc-min-recall" value="0.0" min="0" max="1" step="0.05">
+        </div>
+        <div class="hc-row hc-num">
+          <label>NMS IoU thr (A)</label>
+          <input type="number" id="hc-nms-iou" value="0.3" min="0" max="1" step="0.05">
+        </div>
+        <div class="hc-row hc-num">
+          <label>Greediness</label>
+          <input type="number" id="hc-greediness" value="0.0" min="0" max="1" step="0.1">
+        </div>
+        <div class="hc-row hc-num">
+          <label>Coarse stride</label>
+          <input type="number" id="hc-coarse-stride" value="1" min="1" max="4" step="1">
+        </div>
+        <div class="hc-row hc-num" style="grid-column:1/-1">
+          <label title="Limita area di ricerca scena: x,y,w,h (vuoto = tutta scena)">
+            Search ROI (x,y,w,h)
+          </label>
+          <input type="text" id="hc-search-roi" placeholder="es. 100,50,800,400">
+        </div>
+
+        <div class="hc-row" style="grid-column:1/-1; border-top:1px solid #444; padding-top:8px">
+          <label>Ricetta pre-trained (V)</label>
+          <div style="display:flex; gap:6px; margin-top:4px">
+            <input type="text" id="hc-recipe-name" placeholder="nome_ricetta" style="flex:1">
+            <button class="btn" id="btn-save-recipe" type="button">💾 Salva</button>
+          </div>
+          <div style="display:flex; gap:6px; margin-top:6px; align-items:center">
+            <select id="hc-recipe-list" style="flex:1">
+              <option value="">— ricette disponibili —</option>
+            </select>
+            <button class="btn" id="btn-load-recipe" type="button">📂 Carica</button>
+            <button class="btn" id="btn-unload-recipe" type="button" disabled>✖ Stacca</button>
+          </div>
+          <div id="recipe-status" style="margin-top:4px; font-size:11px; color:#888">
+            Nessuna ricetta caricata
+          </div>
+        </div>
+      </div>
+    </details>
+
+    <details>
+      <summary>Avanzate</summary>
+      <div id="adv-form"></div>
+    </details>
+
+    <h2 style="margin-top:14px">TEMPI</h2>
+    <div class="kv"><span>train:</span><span id="t-train">-</span></div>
+    <div class="kv"><span>find:</span><span id="t-find">-</span></div>
+    <div class="kv"><span>varianti:</span><span id="t-var">-</span></div>
+    <div class="kv"><span>match:</span><span id="t-match">-</span></div>
+    <button class="btn" id="btn-export-json" type="button" disabled
+            style="margin-top:8px; width:100%"
+            title="Scarica i risultati dell'ultimo match in formato JSON">
+      ⬇ Esporta JSON
+    </button>
+
+    <details id="diag-panel" style="margin-top:10px">
+      <summary>🔍 Diagnostica (CC)</summary>
+      <div id="diag-content" style="font-family:monospace; font-size:11px;
+                                     background:#1a1a1a; padding:8px;
+                                     border-radius:3px; margin-top:6px;
+                                     line-height:1.5">
+        <em style="color:#888">Esegui un MATCH per vedere la diagnostica</em>
+      </div>
+    </details>
+  </section>
+</main>
+
+<footer>
+  <h2>LEGENDA</h2>
+  <div id="legend"></div>
+</footer>
+
+<script src="/static/app.js"></script>
+</body>
+</html>

pm2d/web/static/style.css

@@ -0,0 +1,190 @@
+* { box-sizing: border-box; }
+html, body {
+  margin: 0; padding: 0; background: #1a1a1a; color: #dcdcdc;
+  font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+  font-size: 14px;
+  min-height: 100vh;
+}
+header {
+  background: #111; padding: 10px 16px; border-bottom: 1px solid #333;
+  position: sticky; top: 0; z-index: 10;
+}
+header h1 {
+  margin: 0 0 8px; font-size: 18px; color: #00c8ff;
+}
+.toolbar {
+  display: flex; gap: 10px; align-items: center; flex-wrap: wrap;
+}
+.btn {
+  display: inline-block; padding: 6px 14px; background: #2c2c2c;
+  border: 1px solid #444; color: #dcdcdc; cursor: pointer;
+  border-radius: 4px; font-size: 13px; line-height: 1.4;
+  transition: background 0.15s;
+}
+.btn:hover { background: #3a3a3a; }
+.btn-go {
+  background: #0a7d3a; border-color: #0d9c48; color: #fff; font-weight: bold;
+}
+.btn-go:hover { background: #0d9c48; }
+.tb-label { color: #b0b0b0; font-size: 12px; margin-left: 8px; }
+
+.thumb-picker { position: relative; display: inline-block; }
+.picker-current {
+  display: flex; align-items: center; gap: 8px;
+  background: #2a2a2a; color: #dcdcdc; border: 1px solid #444;
+  padding: 4px 8px; border-radius: 3px; font-size: 13px;
+  cursor: pointer; min-width: 200px; min-height: 32px;
+}
+.picker-current:hover { background: #353535; }
+.picker-current img {
+  width: 36px; height: 36px; object-fit: contain;
+  background: #141414; border-radius: 2px;
+}
+.picker-text { flex: 1; white-space: nowrap; overflow: hidden;
+  text-overflow: ellipsis; }
+.caret { color: #888; font-size: 10px; }
+.picker-list {
+  display: none; position: absolute; top: 100%; left: 0;
+  background: #232323; border: 1px solid #444; border-radius: 3px;
+  margin-top: 2px; z-index: 100; max-height: 360px; overflow-y: auto;
+  box-shadow: 0 4px 12px rgba(0,0,0,0.6); min-width: 280px;
+}
+.thumb-picker.open .picker-list { display: block; }
+.picker-item {
+  display: flex; align-items: center; gap: 10px;
+  padding: 6px 10px; cursor: pointer; border-bottom: 1px solid #2a2a2a;
+}
+.picker-item:hover { background: #2e2e2e; }
+.picker-item img {
+  width: 60px; height: 60px; object-fit: contain;
+  background: #141414; border-radius: 2px;
+}
+.picker-item .name { color: #dcdcdc; font-size: 13px; }
+#status {
+  color: #00c8ff; margin-left: 12px; font-weight: 500;
+}
+
+main {
+  display: grid;
+  grid-template-columns: 420px 1fr 360px;
+  gap: 12px;
+  padding: 12px 16px;
+}
+.col {
+  background: #232323; padding: 10px;
+  border: 1px solid #333; border-radius: 4px;
+}
+.col h2 {
+  margin: 0 0 8px; font-size: 13px; color: #00c8ff;
+  letter-spacing: 1px; text-transform: uppercase;
+}
+.canvas-wrap {
+  background: #141414; border: 1px solid #444;
+  display: inline-block; position: relative;
+}
+canvas {
+  display: block; cursor: crosshair;
+  image-rendering: pixelated;
+}
+#roi-info {
+  margin-top: 6px; font-size: 12px; color: #aaa;
+}
+
+.field {
+  margin-bottom: 10px;
+}
+.field label {
+  display: block; font-size: 12px; color: #b0b0b0;
+  margin-bottom: 3px;
+}
+.field select, .field input {
+  width: 100%; background: #2a2a2a; color: #dcdcdc;
+  border: 1px solid #444; padding: 5px 6px; border-radius: 3px;
+  font-size: 12px;
+}
+.field input[type="range"] {
+  padding: 0; height: 26px;
+}
+.field select:focus, .field input:focus {
+  outline: 1px solid #00c8ff;
+}
+
+#v-score { color: #00c8ff; font-weight: bold; }
+.hint { color: #777; font-size: 10px; font-weight: normal; margin-left: 4px; }
+
+details { margin-top: 10px; font-size: 12px; }
+details summary {
+  cursor: pointer; padding: 4px 0; color: #00c8ff;
+}
+#adv-form {
+  display: grid; grid-template-columns: 1fr 80px; gap: 3px 6px;
+  margin-top: 4px;
+}
+#adv-form label { font-size: 11px; color: #999; }
+#adv-form input {
+  background: #2a2a2a; color: #dcdcdc; border: 1px solid #444;
+  padding: 3px 5px; border-radius: 3px; font-size: 11px;
+  width: 100%;
+}
+
+.kv {
+  display: flex; justify-content: space-between;
+  padding: 3px 0; font-size: 12px; border-bottom: 1px dotted #333;
+}
+.kv span:last-child { color: #80ff80; font-weight: bold; }
+
+footer {
+  padding: 10px 16px; border-top: 1px solid #333;
+  min-height: 120px;
+}
+footer h2 {
+  margin: 0 0 8px; font-size: 13px; color: #00c8ff;
+  letter-spacing: 1px; text-transform: uppercase;
+}
+#legend {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
+  gap: 6px;
+}
+.legend-item {
+  display: flex; align-items: center; gap: 8px;
+  padding: 4px 6px; background: #232323;
+  border-radius: 3px; font-size: 12px; font-family: monospace;
+}
+.legend-dot {
+  width: 12px; height: 12px; border-radius: 50%; flex-shrink: 0;
+}
+
+#col-model, #col-scene { min-width: 0; }
+
+/* Halcon-mode panel */
+.halcon-grid {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 6px 12px;
+  margin-top: 6px;
+  font-size: 12px;
+}
+.hc-row {
+  display: flex; align-items: center; gap: 6px;
+}
+.hc-row.hc-num {
+  flex-direction: column; align-items: flex-start;
+}
+.hc-row.hc-num label { font-size: 11px; color: #aaa; }
+.hc-row.hc-num input { width: 100%; }
+
+/* Edge preview panel */
+.ep-grid {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 6px 12px;
+  margin-top: 6px;
+  font-size: 12px;
+}
+.ep-row {
+  display: flex; flex-direction: column; gap: 2px;
+  font-size: 11px; color: #aaa;
+}
+.ep-row input[type="range"] { width: 100%; }
+.ep-row span { color: #fff; font-weight: bold; font-family: monospace; }

pyproject.toml

@@ -3,7 +3,31 @@ name = "shape-model-2d"
 version = "0.1.0"
 requires-python = ">=3.13"
 dependencies = [
+    "fastapi>=0.115",
     "numba>=0.65.0",
     "numpy>=1.24",
     "opencv-python>=4.8",
+    "pillow>=12.2.0",
+    "python-multipart>=0.0.26",
+    "uvicorn[standard]>=0.34",
+    "ezdxf>=1.3",
 ]
+
+[project.scripts]
+pm2d-eval = "pm2d.eval:main"
+pm2d-bench = "pm2d.bench:main"
+
+[dependency-groups]
+dev = [
+    "httpx>=0.28.1",
+    "pytest>=8.0",
+    "ruff>=0.8",
+]
+
+[tool.ruff]
+line-length = 100
+
+[tool.ruff.lint]
+select = ["E", "F"]
+# E702 (a; b) ed E402 (import dopo codice) sono idiomi voluti del codebase
+ignore = ["E501", "E741", "E702", "E731", "E402"]

tests/conftest.py

@@ -0,0 +1,99 @@
+"""Fixture condivise: template e scene sintetiche con ground-truth nota.
+
+Tutti i test sono sintetici (nessuna dipendenza dalle immagini Test/,
+non versionate): generano scene con pose note e verificano recall e
+precisione del matcher. Runtime totale atteso: ~2-4 min su 2 core.
+"""
+from __future__ import annotations
+
+import math
+
+import cv2
+import numpy as np
+import pytest
+
+
+def make_template(tw: int = 160, th: int = 120) -> np.ndarray:
+    """Forma a L asimmetrica con foro circolare, contrasto netto.
+
+    Asimmetrica per evitare ambiguita' rotazionali nei confronti GT.
+    """
+    img = np.full((th, tw), 60, np.uint8)
+    cv2.rectangle(img, (20, 20), (60, th - 20), 200, -1)
+    cv2.rectangle(img, (20, th - 55), (tw - 25, th - 20), 200, -1)
+    cv2.circle(img, (tw - 45, 40), 16, 200, -1)
+    return cv2.GaussianBlur(img, (3, 3), 0)
+
+
+# Pose ground-truth: (cx, cy, angle_deg) - angoli volutamente lontani
+# dalla griglia di step 5/2 gradi per misurare il refine.
+GT_POSES: list[tuple[float, float, float]] = [
+    (150.0, 150.0, 0.0),
+    (450.0, 140.0, 7.3),
+    (740.0, 170.0, 33.7),
+    (160.0, 420.0, 91.2),
+    (460.0, 430.0, 158.4),
+    (750.0, 480.0, 246.9),
+    (300.0, 590.0, 312.6),
+]
+
+
+def make_scene(
+    template: np.ndarray,
+    poses: list[tuple[float, float, float]],
+    W: int = 900, H: int = 700,
+    noise: float = 4.0, seed: int = 7,
+) -> np.ndarray:
+    """Incolla il template warpato alle pose date su sfondo rumoroso.
+
+    Convenzione di rotazione identica al matcher (cv2.getRotationMatrix2D
+    attorno al centro template, poi traslazione del centro su (cx, cy)).
+    """
+    rng = np.random.default_rng(seed)
+    scene = np.full((H, W), 60, np.float32)
+    th, tw = template.shape
+    for (cx, cy, ang) in poses:
+        M = cv2.getRotationMatrix2D((tw / 2.0, th / 2.0), ang, 1.0)
+        M[0, 2] += cx - tw / 2.0
+        M[1, 2] += cy - th / 2.0
+        warped = cv2.warpAffine(template.astype(np.float32), M, (W, H),
+                                flags=cv2.INTER_LINEAR, borderValue=-1)
+        scene = np.where(warped >= 0, warped, scene)
+    scene += rng.normal(0, noise, scene.shape)
+    return np.clip(scene, 0, 255).astype(np.uint8)
+
+
+def ang_diff(a: float, b: float) -> float:
+    """Differenza angolare firmata in (-180, 180]."""
+    d = (a - b) % 360.0
+    return d - 360.0 if d > 180.0 else d
+
+
+def match_errors(matches, poses, radius: float = 20.0):
+    """Associa match a pose GT per distanza; ritorna (err_ang, err_pos, n_miss)."""
+    errs_a: list[float] = []
+    errs_p: list[float] = []
+    miss = 0
+    for (cx, cy, ang) in poses:
+        cands = [
+            (math.hypot(m.cx - cx, m.cy - cy), m)
+            for m in matches
+            if math.hypot(m.cx - cx, m.cy - cy) < radius
+        ]
+        if not cands:
+            miss += 1
+            continue
+        d, m = min(cands, key=lambda t: t[0])
+        errs_a.append(abs(ang_diff(m.angle_deg, ang)))
+        errs_p.append(d)
+    return errs_a, errs_p, miss
+
+
+@pytest.fixture(scope="session")
+def template() -> np.ndarray:
+    return make_template()
+
+
+@pytest.fixture(scope="session")
+def scene(template) -> np.ndarray:
+    return make_scene(template, GT_POSES)

tests/test_matcher_unit.py

@@ -0,0 +1,84 @@
+"""Unit test rapidi su componenti del matcher (no matching pesante)."""
+from __future__ import annotations
+
+import numpy as np
+import cv2
+import pytest
+
+from pm2d import LineShapeMatcher
+from tests.conftest import GT_POSES, make_scene, match_errors
+
+
+def test_angle_list_includes_range_end():
+    # Range parziale ±15: l'estremo +15 deve essere testato (era escluso).
+    m = LineShapeMatcher(angle_range_deg=(-15.0, 15.0), angle_step_deg=5.0)
+    angles = m._angle_list()
+    assert -15.0 in angles and 15.0 in angles
+    assert len(angles) == 7
+
+
+def test_angle_list_full_circle_no_duplicate():
+    # (0, 360): 360 coincide con 0 → escluso, niente variante duplicata.
+    m = LineShapeMatcher(angle_range_deg=(0.0, 360.0), angle_step_deg=5.0)
+    angles = m._angle_list()
+    assert len(angles) == 72
+    assert 360.0 not in angles
+
+
+def test_pyramid_clamp_small_template():
+    # Template 40px di lato minimo: al top /4 le feature collassano →
+    # i livelli vengono clampati (40/2=20 >= 12, 40/4=10 < 12 → 2 livelli).
+    m = LineShapeMatcher(pyramid_levels=4, angle_range_deg=(0.0, 10.0),
+                         angle_step_deg=5.0)
+    tpl = np.full((40, 200), 60, np.uint8)
+    cv2.rectangle(tpl, (30, 8), (170, 32), 200, -1)
+    m.train(tpl)
+    assert m.pyramid_levels == 2
+
+
+def test_save_load_roundtrip(tmp_path, template, scene):
+    m = LineShapeMatcher(angle_step_deg=10.0)
+    m.train(template)
+    path = str(tmp_path / "model.npz")
+    m.save_model(path)
+    m2 = LineShapeMatcher.load_model(path)
+    assert len(m2.variants) == len(m.variants)
+    matches = m2.find(scene, min_score=0.5, max_matches=10)
+    _, _, miss = match_errors(matches, GT_POSES)
+    assert miss == 0
+
+
+def test_scene_cache_no_collision(template):
+    # Due scene IDENTICHE nella banda superiore ma diverse sotto: la cache
+    # (che prima hashava solo i primi 64KB) non deve restituire i risultati
+    # della scena sbagliata.
+    poses_a = [GT_POSES[0], (450.0, 560.0, 33.7)]
+    poses_b = [GT_POSES[0], (700.0, 560.0, 91.2)]
+    scene_a = make_scene(template, poses_a)
+    scene_b = make_scene(template, poses_b)
+    # Stessa banda superiore (le pose extra sono in basso, y >= 430)
+    assert np.array_equal(scene_a[:80], scene_b[:80])
+    m = LineShapeMatcher(angle_step_deg=10.0)
+    m.train(template)
+    ma = m.find(scene_a, min_score=0.5, max_matches=5)
+    mb = m.find(scene_b, min_score=0.5, max_matches=5)
+    _, _, miss_a = match_errors(ma, poses_a)
+    _, _, miss_b = match_errors(mb, poses_b)
+    assert miss_a == 0 and miss_b == 0
+
+
+def test_train_mask_polygonal(template, scene):
+    # ROI poligonale: mask che copre solo la L verticale del template.
+    mask = np.zeros_like(template)
+    cv2.rectangle(mask, (10, 10), (70, template.shape[0] - 10), 255, -1)
+    m = LineShapeMatcher(angle_step_deg=10.0)
+    n = m.train(template, mask=mask)
+    assert n > 0
+    matches = m.find(scene, min_score=0.5, max_matches=10)
+    assert len(matches) >= 1
+
+
+def test_untrained_find_raises():
+    m = LineShapeMatcher()
+    with pytest.raises(RuntimeError):
+        m.find(np.zeros((100, 100), np.uint8))

tests/test_precision.py

@@ -0,0 +1,56 @@
+"""Test di non-regressione su precisione e recall (GT sintetica).
+
+Soglie derivate dalle misure di Fase 2 (errore mediano ~0.05 deg /
+~0.08 px) con margine 3-4x per assorbire rumore tra run/macchine.
+Una regressione del refine (es. score saturo, minMaxLoc sul plateau)
+riporterebbe gli errori a 2-4 deg / 4 px e fa fallire i test con
+margine enorme.
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from pm2d import LineShapeMatcher
+from tests.conftest import GT_POSES, match_errors
+
+
+def _find(template, scene, step, **kw):
+    m = LineShapeMatcher(angle_step_deg=step, num_features=96)
+    m.train(template)
+    return m.find(scene, min_score=0.5, max_matches=10, **kw)
+
+
+def test_recall_and_precision_step5(template, scene):
+    matches = _find(template, scene, 5.0)
+    errs_a, errs_p, miss = match_errors(matches, GT_POSES)
+    assert miss == 0, f"{miss} pose GT non trovate"
+    assert float(np.median(errs_a)) < 0.2, f"err angolo mediano {np.median(errs_a):.3f} deg"
+    assert float(np.max(errs_a)) < 0.5, f"err angolo max {np.max(errs_a):.3f} deg"
+    assert float(np.median(errs_p)) < 0.3, f"err posizione mediano {np.median(errs_p):.3f} px"
+    assert float(np.max(errs_p)) < 1.0, f"err posizione max {np.max(errs_p):.3f} px"
+
+
+def test_recall_and_precision_step2(template, scene):
+    # Step fine: storicamente il caso peggiore (plateau con piu' varianti
+    # dentro la tolleranza spread → scelta variante arbitraria).
+    matches = _find(template, scene, 2.0)
+    errs_a, errs_p, miss = match_errors(matches, GT_POSES)
+    assert miss == 0, f"{miss} pose GT non trovate"
+    assert float(np.median(errs_a)) < 0.2
+    assert float(np.max(errs_a)) < 0.5
+    assert float(np.median(errs_p)) < 0.3
+
+
+def test_no_false_positives(template, scene):
+    # max_matches alto: non devono comparire match spuri oltre le 7 pose.
+    matches = _find(template, scene, 5.0)
+    assert len(matches) <= len(GT_POSES) + 1, (
+        f"{len(matches)} match per {len(GT_POSES)} oggetti reali"
+    )
+
+
+def test_full_scan_path_equivalent(template, scene):
+    # Il path full-scan (propagate off) deve trovare le stesse pose.
+    matches = _find(template, scene, 5.0, pyramid_propagate=False)
+    _, _, miss = match_errors(matches, GT_POSES)
+    assert miss == 0

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