docs: README allineato allo stato attuale

Kernel Numba JIT, precisione misurata (0.05 deg / 0.04 px), pipeline
refine (bitmap fine + LSQ pos+angolo), propagate windowed, webapp con
endpoint DXF/roi_poly/ricette, test pytest + CI, Test/ non versionate,
deploy compose build sulla VPS, parametri aggiornati ai default reali.

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

.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

@@ -10,3 +10,10 @@ __pycache__/
 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

@@ -35,4 +35,6 @@ 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"]
+# --frozen --no-dev: senza, uv run ri-sincronizza il gruppo dev
+# (pytest/ruff) a OGNI avvio del container, scaricando pacchetti a runtime
+CMD ["uv", "run", "--frozen", "--no-dev", "python", "main.py"]

README.md

@@ -1,32 +1,55 @@
 # Shape Model 2D — Standalone PM 2D
 
-Programma standalone Pattern Matching 2D shape-based.
+Pattern Matching 2D shape-based (stile Halcon `find_shape_model`), standalone:
+libreria Python + GUI desktop + webapp FastAPI.
 
 Due backend algoritmici:
 
-| Backend | Modulo | Algoritmo | Tempo clip.png (13 istanze) |
+| Backend | Modulo | Algoritmo |
-|---|---|---|---|
+|---|---|---|
-| `line` (default) | `pm2d.line_matcher.LineShapeMatcher` | Linemod-style: gradient orient quantizzata + spread + response map + feature sparse | **3.5 s, 12/13 score 1.0** |
+| `line` (default) | `pm2d.line_matcher.LineShapeMatcher` | Linemod-style: orientazione gradiente quantizzata + spread bitmap + kernel Numba JIT + refine least-squares |
-| `edge` | `pm2d.matcher.EdgeShapeMatcher` | Edge Canny + `matchTemplate` multi-rotazione | 84 s, 6/13 score ~0.3 |
+| `edge` (legacy) | `pm2d.matcher.EdgeShapeMatcher` | Edge Canny + `matchTemplate` multi-rotazione (fallback semplice, lento) |
 
-Porting algoritmico (non SIMD) di `meiqua/shape_based_matching/line2Dup`. MIPP (wrapper SIMD C++) non ha senso in Python — la vettorizzazione la fa già NumPy.
+Porting algoritmico di `meiqua/shape_based_matching/line2Dup`; gli hot-path
+sono kernel **Numba JIT** (`pm2d/_jit_kernels.py`, parallelo, ≈ velocità C)
+con fallback NumPy automatico se numba non è disponibile.
+
+## Precisione (misurata su ground-truth sintetica, 7 pose note)
+
+| Metrica | Valore |
+|---|---|
+| Errore angolare mediano | **~0.05°** (step 5°), ~0.03° (step 2°) |
+| Errore posizione mediano | **~0.04 px** |
+| Recall | 7/7 a min_score 0.5 |
+
+Pipeline di refine: golden-section sull'angolo su bitmap fine (raggio 1,
+non satura) → least-squares 3×3 congiunto (dx, dy, dθ) sui gradienti scena
+(`subpixel_lm`, ON di default). I test in `tests/` fanno da guardia di
+non-regressione su queste soglie.
 
 ## Struttura
 
 ```
-Shape_model_2d/
+shape_model_2d/
 ├── pm2d/
-│   ├── __init__.py
+│   ├── line_matcher.py   # LineShapeMatcher (default)
-│   ├── matcher.py        # EdgeShapeMatcher (fallback, semplice)
+│   ├── _jit_kernels.py   # kernel Numba JIT (score bitmap, windowed, popcount)
-│   ├── line_matcher.py   # LineShapeMatcher (default, ottimizzato)
+│   ├── matcher.py        # EdgeShapeMatcher (legacy)
-│   └── gui.py            # GUI OpenCV + tk file dialog
+│   ├── dxf.py            # rasterizzazione DXF → template (ezdxf)
-├── main.py               # entry point
+│   ├── auto_tune.py      # stima automatica parametri (simmetria, soglie)
-├── Test/                 # immagini di test
+│   ├── gui.py            # GUI OpenCV + tk file dialog
-├── pyproject.toml
+│   ├── bench.py, eval.py # CLI benchmark / valutazione
-└── README.md
+│   └── web/              # webapp FastAPI (server.py + static/)
+├── benchmarks/test_suite.py  # suite 16 scenari su immagini reali (Test/)
+├── tests/                # pytest sintetici (precisione + unit, no Test/)
+├── .gitea/workflows/ci.yml   # CI: uv sync + ruff + pytest
+├── main.py               # entry point GUI
+├── Test/                 # immagini di test LOCALI (non versionate)
+├── Dockerfile, docker-compose.yml
+└── pyproject.toml
 ```
 
-GUI e algoritmo separati: i matcher sono riusabili da qualsiasi script/backend.
+GUI/web e algoritmo separati: i matcher sono riusabili da qualsiasi script.
 
 ## Setup
 
@@ -37,12 +60,16 @@ uv sync
 ## Esecuzione
 
 ```bash
-uv run python main.py
+uv run python main.py                      # GUI desktop
+uv run python -m uvicorn pm2d.web.server:app --port 8080   # webapp
+uv run pytest tests/                       # test (sintetici, ~1 min)
+uv run python benchmarks/test_suite.py    # benchmark (richiede Test/)
 ```
 
-Flusso: file dialog modello → ROI → file dialog scena → risultati.
+Le immagini in `Test/` **non sono versionate** (vedi `.gitignore`): la suite
+benchmark le richiede in locale, i test pytest no.
 
-## API algoritmo (backend `line`, raccomandato)
+## API algoritmo (backend `line`)
 
 ```python
 import cv2
@@ -53,122 +80,129 @@ scene    = cv2.imread("scene.png")
 
 m = LineShapeMatcher(
     num_features=96,          # feature sparse per variante
-    weak_grad=30,             # soglia gradiente per spread
+    weak_grad=30,             # soglia gradiente debole (spread/hysteresis)
-    strong_grad=60,           # soglia gradiente per estrazione feature
+    strong_grad=60,           # soglia gradiente forte (estrazione feature)
     angle_range_deg=(0, 360),
-    angle_step_deg=5.0,
+    angle_step_deg=5.0,       # <=0 → step auto dal lato template
     scale_range=(0.9, 1.1),   # invarianza a scala
     scale_step=0.05,
-    spread_radius=5,          # raggio dilate per robustezza
+    spread_radius=4,          # tolleranza posizionale matching coarse
-    pyramid_levels=3,         # velocità via pruning top-level
+    pyramid_levels=3,         # clampato auto alla dimensione template
-    top_score_factor=0.5,     # soglia top = min_score * factor
 )
-m.train(template)             # ~0.2 s
+m.train(template)             # opzionale: train(template, mask=...) per ROI parziale
 matches = m.find(scene, min_score=0.55, max_matches=25)
 
 for x in matches:
-    print(x.cx, x.cy, x.angle_deg, x.scale, x.score)
+    print(x.cx, x.cy, x.angle_deg, x.scale, x.score)   # pose sub-pixel/sub-grado
 ```
 
-### Modello su regione parziale (non blob distinto)
+Opzioni `find()` utili: `search_roi=(x, y, w, h)`, `min_recall`,
+`scale_penalty`, `use_soft_score`, `debug=True` (diagnostica drop),
+`profile=True` (timing per fase via `get_last_profile()`).
 
-`train()` accetta una **maschera binaria opzionale** per limitare le feature
+Persistenza modello (Halcon write/read_shape_model):
-a una porzione della ROI (es. parte interna di un oggetto complesso, dettaglio
-distintivo, ecc.):
 
 ```python
-mask = np.zeros_like(template[:, :, 0])
+m.save_model("ricetta.npz")
-cv2.fillPoly(mask, [poligono_utente], 255)
+m2 = LineShapeMatcher.load_model("ricetta.npz")   # deploy senza re-train
-m.train(template, mask=mask)
 ```
 
-Solo i gradienti dentro la maschera contribuiscono alle feature.
-
 ## Come funziona il backend `line`
 
-### Training (costoso, ~0.2 s / 72 varianti)
+### Training
 
 Per ogni coppia (angolo, scala) del template:
-1. Rotazione + scala su canvas con padding diagonale
+1. Rotazione + scala su canvas con padding diagonale (centro di rotazione
-2. Sobel → `magnitude` + `orientation` (atan2)
+   = centro reale del template, coerente in tutta la pipeline)
-3. Quantizzazione orientazione in **8 bin modulo π** (edge simmetrici)
+2. Sobel → magnitude + orientation, quantizzata in **8 bin modulo π**
-4. Estrazione **N feature sparse**: top-magnitude sopra `strong_grad`, con spacing minimo per evitare cluster
+   (16 bin mod 2π con `use_polarity=True`)
-5. Feature salvate come `(dx, dy, bin)` relative al centro-modello
+3. Edge selection con **hysteresis** weak/strong (Halcon Contrast auto)
+4. **N feature sparse** top-magnitude con spacing minimo, salvate come
+   `(dx, dy, bin)` arrotondate rispetto al centro-modello
+5. Piramide feature per livello + dedup varianti identiche (simmetrie)
 
-### Matching (veloce)
+### Matching
 
-Scena processata **una volta per livello piramide**:
+Scena processata una volta per livello (e cachata per scene ripetute):
-- Sobel → mag → orient quantizzato → bin invalidato dove `mag < weak_grad`
+Sobel → quantizzazione → **spread bitmap** uint8/uint16 (bit b = bin b
-- **Spread**: dilate morfologica per bin (tolleranza localizzazione)
+presente nel raggio) → kernel JIT:
-- **Response map** `(8, H, W)`: response[b][y,x] = 1 dove orient b è presente
-
-Per ogni variante:
 
 ```
-score[y, x] = Σ_i  resp[bin_i][y + dy_i, x + dx_i]  /  N_features
+score[y, x] = popcount-AND feature/bitmap / N_features   (rescored vs background)
 ```
 
-Implementato con **shift+add vettorizzato NumPy** (O(N_features · H · W) invece di O(kh·kw·H·W) come `matchTemplate`).
+1. **Top-level**: valuta 1 variante ogni `cf_auto` (step angolare auto:
-
+   al livello top lo spread tollera ~atan(spread/(lato_top/2)) gradi),
-### Piramide multi-risoluzione
+   pruning per soglia + istogramma orientazioni.
-
+2. **Full-res windowed** (`pyramid_propagate`, default ON): score calcolato
-- **Top-level** (risoluzione /4 di default con `pyramid_levels=3`): score ridotto per pruning varianti. Se nessun pixel raggiunge `min_score * top_score_factor`, la variante è scartata.
+   solo in finestre attorno ai massimi locali del top-level — costo
-- **Full-res**: calcolato solo per le varianti sopravvissute → nel benchmark clip: ~5-10 varianti su 72 = 7-14× speed-up rispetto a full-res per tutte.
+   proporzionale ai candidati, non a varianti × W × H. Per template
+   elongati (>2:1) si torna automaticamente al full-scan esatto.
+3. **Refine per candidato**: subpixel 2D sul picco → golden-section
+   sull'angolo su bitmap fine → least-squares (dx, dy, dθ) sui gradienti.
+4. **Verify**: NCC su crop locale (anti falsi-positivi, mediato nello
+   score) → NMS IoU su bbox poligonali orientati.
 
 ## Parametri principali
 
 | Parametro | Default | Significato |
 |---|---|---|
 | `num_features`    | 96       | feature sparse per variante |
-| `weak_grad`       | 30       | threshold debole (per spread) |
+| `weak_grad`       | 30       | soglia debole (spread + hysteresis) |
-| `strong_grad`     | 60       | threshold forte (per estrazione feature) |
+| `strong_grad`     | 60       | soglia forte (estrazione feature) |
-| `spread_radius`   | 5        | raggio dilate spread (tolleranza posizionale) |
+| `spread_radius`   | 4        | tolleranza posizionale matching coarse |
-| `min_feature_spacing` | 3 | spacing minimo tra feature per evitare cluster |
 | `angle_range_deg` | `(0,360)` | range rotazioni |
-| `angle_step_deg`  | 5.0      | passo angolare |
+| `angle_step_deg`  | 5.0      | passo angolare (<=0 = auto) |
 | `scale_range`     | `(1,1)`  | range scale |
-| `scale_step`      | 0.1      | passo scala |
+| `pyramid_levels`  | 3        | livelli piramide (clamp auto su template piccoli) |
-| `pyramid_levels`  | 3        | livelli piramide (più = pruning più aggressivo) |
+| `min_score`       | 0.6      | soglia score finale [0..1] |
-| `top_score_factor`| 0.5      | soglia top-level = min_score * factor |
+| `max_matches`     | 20       | numero max di match |
-| `min_score`       | 0.55     | soglia score finale [0..1] |
+| `verify_threshold`| 0.4      | soglia NCC anti falso-positivo |
-| `max_matches`     | 25       | numero max di match |
+| `subpixel_lm`     | True     | least-squares finale pos+angolo |
-| `nms_radius`      | `min(w,h)/2` | raggio NMS baricentri |
+| `pyramid_propagate` | True   | full-res solo in finestre sui picchi top |
+
+## Webapp (pm2d/web)
+
+UI single-page (canvas ROI rettangolare o **poligonale**, slider parametri,
+anteprima edge, ricette) + API JSON:
+
+| Endpoint | Funzione |
+|---|---|
+| `POST /upload` | carica immagine (multipart) |
+| `POST /upload_dxf` | carica **DXF** → rasterizzato a template (`?size=128..2048`) |
+| `POST /match` | match con parametri tecnici (`roi`, opzionale `roi_poly`) |
+| `POST /match_simple` | match con profili semplificati (precisione/filtro_fp/simmetria) |
+| `POST /auto_tune` | stima automatica parametri dalla ROI |
+| `POST /recipes`, `GET /recipes`, `/match_recipe` | salva/carica/usa ricette `.npz` |
+| `GET /image/{id}/annotated` | PNG con overlay match (UCS) |
+
+Dalla UI: bottone **Esporta JSON** per scaricare i risultati completi
+(pose, score, bbox, parametri, tempi) per integrazione.
+
+## Test e CI
+
+- `tests/`: pytest **sintetici** (template/scene generati, GT nota) —
+  precisione angolo/posizione, recall, cache, save/load, mask poligonale.
+- CI Gitea Actions (`.gitea/workflows/ci.yml`): ruff + pytest su ogni push.
+- `benchmarks/test_suite.py`: 16 scenari su immagini reali per confronto
+  manuale prestazioni/recall (richiede `Test/` in locale).
 
 ## Roadmap
 
-- Subpixel refinement (interpolazione parabolic sui picchi)
+Vedi [ROADMAP.md](ROADMAP.md) — Fase 1 (speed) e Fase 2 (precisione
-- ICP locale per raffinamento pose
+rotazione + robustezza) completate; prossimo target: latency <50 ms su
-- Vincoli di orientamento: clustering delle pose per eliminare duplicati cross-variante
+1920×1080 (auto step per livello fatto; restano greediness default, GPU,
-- Numba JIT per il ciclo shift+add (eventuale 3-5× su scene grandi)
+eventuale SIMD C++).
 
 ## 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)
+- Entrypoint `websecure` (:443) e cert resolver configurato
-- Cert resolver `letsencrypt` configurato
-
-### Build e push al registry
 
 ```bash
-# Build locale
+cd /opt/docker/visionsuite/shape_model_2d
-docker build -t vps-ip:5000/pm2d:latest .
+docker compose build
-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
 ```
 
@@ -179,13 +213,8 @@ Servizio raggiungibile: **https://pm.tielogic.xyz**
 - **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,
+- **Cache matcher in-memory**: si svuota a restart container (ri-popolata
-  viene ri-popolata al primo match).
+  al primo match). Le ricette `.npz` invece persistono in `recipes/`.
 - **Healthcheck**: HTTP `GET /images` ogni 30s.
-- Se nome network Traefik diverso da `traefik`, modifica
+- Se nome network Traefik o cert resolver diversi, modifica i labels in
-  `docker-compose.yml` sezione `networks`.
+  `docker-compose.yml`.
-
-### 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`.

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/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

pm2d/line_matcher.py

@@ -38,7 +38,6 @@ _GOLDEN = (math.sqrt(5.0) - 1.0) / 2.0  # ≈ 0.618
 
 from pm2d._jit_kernels import (
     score_by_shift as _jit_score_by_shift,
-    score_bitmap as _jit_score_bitmap,
     score_bitmap_rescored as _jit_score_bitmap_rescored,
     score_bitmap_rescored_window as _jit_score_bitmap_rescored_window,
     score_bitmap_greedy as _jit_score_bitmap_greedy,
@@ -326,8 +325,6 @@ class LineShapeMatcher:
         n_vars = len(self.variants)
         n_levels = len(self.variants[0].levels)
         var_meta = np.zeros((n_vars, 6), dtype=np.float32)  # ang, scale, kh, kw, cxl, cyl
-        all_dx, all_dy, all_bin, all_offsets = [], [], [], []
-        offset = 0
         all_offsets_per_level = [[] for _ in range(n_levels)]
         all_dx_per_level = [[] for _ in range(n_levels)]
         all_dy_per_level = [[] for _ in range(n_levels)]
@@ -1483,12 +1480,9 @@ class LineShapeMatcher:
         if nms_radius is None:
             nms_radius = max(8, min(self.template_size) // 2)
         # Pruning adattivo allo step angolare: con step piccolo (<= 3 deg)
-        # ci sono molte varianti vicine, gli score top-level sono ravvicinati
+        # ci sono molte varianti vicine e gli score top-level sono
-        # e top_thresh*0.5 e' troppo aggressivo: scarta varianti valide che
+        # ravvicinati: top_thresh*0.5 e' troppo aggressivo, scarta varianti
-        # sarebbero state riprese al full-res. Stessa cosa per
+        # valide che sarebbero state riprese al full-res.
-        # coarse_angle_factor (skip 1 ogni 2): con step fine non e' utile.
-        # Risultato osservato: precisione "veloce" 10° dava risultati
-        # migliori di "preciso" 2° proprio perche evitava il pruning.
         # Il path windowed (pyramid_propagate) assume che il picco
         # top-level localizzi la posizione entro il margine finestra.
         # Su template ALLUNGATI (es. lama 40x280, o ROI parziale lungo un
@@ -1503,10 +1497,25 @@ class LineShapeMatcher:
                 pyramid_propagate = False
         eff_step = self._effective_angle_step()
         top_factor = self.top_score_factor
-        cf_eff = max(1, coarse_angle_factor)
         if eff_step <= 3.0:
             top_factor = max(top_factor, 0.7)
-            cf_eff = 1
+        # Coarse step angolare AUTO al top-level (Halcon-style): al livello
+        # top le feature distano R/2^top dal centro, quindi lo spread
+        # (raggio in px, costante per livello) tollera una rotazione
+        # ~atan(spread / (max_side_top/2)) — molto piu' ampia dello step
+        # richiesto a full-res. Si valuta al top 1 variante ogni cf_eff;
+        # le intermedie vengono riprese dall'espansione ai vicini.
+        # Es: template 160 px, 3 livelli, step 2° → tolleranza top ~11°
+        # → cf 6 → top-pruning ~6x piu' veloce a parita' di recall.
+        if self.template_size != (0, 0):
+            max_side_top = max(self.template_size) / (2 ** top)
+        else:
+            max_side_top = 64.0
+        step_top_tol = math.degrees(
+            math.atan2(float(self.spread_radius), max(8.0, max_side_top / 2.0))
+        )
+        cf_auto = int(np.clip(round(step_top_tol / max(eff_step, 1e-6)), 1, 8))
+        cf_eff = max(1, coarse_angle_factor, cf_auto)
         top_thresh = min_score * top_factor
         diag["top_thresh_used"] = float(top_thresh)
 
@@ -1562,7 +1571,6 @@ class LineShapeMatcher:
             dtype=bool,
         )
         if scene_bins.any():
-            n_scene_active = int(scene_bins.sum())
             # Soglia: variante deve avere >= 50% delle sue feature in bin
             # presenti nella scena. Sotto = score certamente < 0.5.
             pruned_idx_list = []

pm2d/web/server.py

@@ -55,6 +55,7 @@ RECIPES_DIR.mkdir(exist_ok=True)
 
 from pm2d.line_matcher import LineShapeMatcher, Match
 from pm2d.auto_tune import auto_tune
+from pm2d.dxf import dxf_to_image
 
 
 WEB_DIR = Path(__file__).parent
@@ -91,7 +92,10 @@ def _matcher_cache_key(roi: np.ndarray, tech: dict) -> str:
                 "min_feature_spacing",
                 "angle_min", "angle_max", "angle_step",
                 "scale_min", "scale_max", "scale_step",
-                "spread_radius", "pyramid_levels")
+                "spread_radius", "pyramid_levels",
+                # ROI poligonale: la mask cambia il training a parità di
+                # bbox → deve invalidare la cache (None = ROI rettangolare)
+                "roi_poly")
     for k in relevant:
         h.update(f"{k}={tech.get(k)}".encode())
     h.update(f"shape={roi.shape}".encode())
@@ -170,6 +174,39 @@ def _clamp_roi(x: int, y: int, w: int, h: int,
     return x, y, w, h
 
 
+def _poly_bbox_mask(
+    roi_poly: list[list[float]], img_w: int, img_h: int,
+) -> tuple[int, int, int, int, np.ndarray]:
+    """Valida roi_poly (vertici [x, y] in coordinate IMMAGINE) e ritorna
+    (x, y, w, h, mask): bbox del poligono clampato con _clamp_roi e mask
+    uint8 (255 dentro il poligono) nel sistema di coordinate della ROI.
+
+    Solleva 400 se il poligono ha <3 punti o area degenere.
+    """
+    pts = np.asarray(roi_poly, dtype=np.float64)
+    if pts.ndim != 2 or pts.shape[1] != 2 or pts.shape[0] < 3:
+        raise HTTPException(
+            400, "roi_poly non valido: servono almeno 3 vertici [x, y]")
+    # Area con formula shoelace: poligoni collineari/degeneri → 400
+    px_, py_ = pts[:, 0], pts[:, 1]
+    area = 0.5 * abs(np.dot(px_, np.roll(py_, 1)) - np.dot(py_, np.roll(px_, 1)))
+    if area < 16.0:
+        raise HTTPException(
+            400, f"roi_poly degenere: area {area:.1f} px² troppo piccola")
+    x0 = int(np.floor(px_.min())); y0 = int(np.floor(py_.min()))
+    bw = int(np.ceil(px_.max())) - x0
+    bh = int(np.ceil(py_.max())) - y0
+    x, y, w, h = _clamp_roi(x0, y0, bw, bh, img_w, img_h)
+    # Mask nel sistema ROI: vertici ritraslati di (-x, -y)
+    mask = np.zeros((h, w), dtype=np.uint8)
+    local = np.round(pts - [x, y]).astype(np.int32)
+    cv2.fillPoly(mask, [local], 255)
+    if not mask.any():
+        raise HTTPException(
+            400, "roi_poly fuori immagine: nessun pixel utile nella mask")
+    return x, y, w, h, mask
+
+
 def _check_trained(m: "LineShapeMatcher", n_variants: int) -> None:
     """Solleva 422 se il train non ha prodotto varianti.
 
@@ -303,6 +340,10 @@ class MatchParams(BaseModel):
     model_id: str
     scene_id: str
     roi: list[int]  # [x, y, w, h] nell'immagine modello
+    # ROI poligonale opzionale: vertici [x, y] in coordinate IMMAGINE
+    # (min 3 punti). Se presente, il bbox del poligono sostituisce `roi`
+    # e il training usa la mask del poligono.
+    roi_poly: list[list[float]] | None = None
     angle_min: float = 0.0
     angle_max: float = 360.0
     angle_step: float = 5.0
@@ -384,6 +425,8 @@ class SimpleMatchParams(BaseModel):
     model_id: str
     scene_id: str
     roi: list[int]
+    # ROI poligonale opzionale (vedi MatchParams.roi_poly)
+    roi_poly: list[list[float]] | None = None
     tipo: str = "intero"              # "intero" | "parziale"
     simmetria: str = "nessuna"        # chiave SYMMETRY_TO_ANGLE_MAX
     scala: str = "fissa"              # chiave SCALE_PRESETS
@@ -603,6 +646,26 @@ async def upload(file: UploadFile = File(...)):
     return UploadResp(id=iid, width=img.shape[1], height=img.shape[0])
 
 
+@app.post("/upload_dxf", response_model=UploadResp)
+async def upload_dxf(file: UploadFile = File(...), size: int = 512):
+    """Upload DXF: rasterizza il CAD in template grayscale e lo salva
+    nella cache immagini come un normale upload.
+
+    Query param `size` = lato del canvas (clamp 128..2048).
+    """
+    size = max(128, min(2048, int(size)))
+    data = await file.read()
+    try:
+        gray = dxf_to_image(data, target_size=size)
+    except ValueError as e:
+        raise HTTPException(400, f"DXF non valido: {e}")
+    # _store_image salva PNG e gli endpoint a valle (cvtColor BGR2GRAY su
+    # roi_img, _load_image con IMREAD_COLOR) si aspettano 3 canali → BGR.
+    img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
+    iid = _store_image(img)
+    return UploadResp(id=iid, width=img.shape[1], height=img.shape[0])
+
+
 @app.get("/image/{iid}/raw")
 def image_raw(iid: str):
     img = _load_image(iid)
@@ -617,6 +680,12 @@ def match(p: MatchParams):
     scene = _load_image(p.scene_id)
     if model is None or scene is None:
         raise HTTPException(404, "Immagini non trovate")
+    # ROI poligonale: bbox derivato dal poligono + mask per il training
+    train_mask = None
+    if p.roi_poly is not None:
+        x, y, w, h, train_mask = _poly_bbox_mask(
+            p.roi_poly, model.shape[1], model.shape[0])
+    else:
         x, y, w, h = p.roi
         x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
     roi_img = model[y:y + h, x:x + w]
@@ -630,6 +699,9 @@ def match(p: MatchParams):
         "scale_step": p.scale_step,
         "spread_radius": p.spread_radius,
         "pyramid_levels": p.pyramid_levels,
+        # Tuple per repr stabile nella cache key (None = rettangolare)
+        "roi_poly": (tuple(map(tuple, p.roi_poly))
+                     if p.roi_poly is not None else None),
     }
     key = _matcher_cache_key(roi_img, tech_for_cache)
     # Lock globale: matcher condivisi tra thread del pool FastAPI
@@ -646,7 +718,7 @@ def match(p: MatchParams):
                 spread_radius=p.spread_radius,
                 pyramid_levels=p.pyramid_levels,
             )
-            t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
+            t0 = time.time(); n = m.train(roi_img, train_mask); t_train = time.time() - t0
             _check_trained(m, n)
             _cache_put_matcher(key, m)
         else:
@@ -689,11 +761,20 @@ def match_simple(p: SimpleMatchParams):
     scene = _load_image(p.scene_id)
     if model is None or scene is None:
         raise HTTPException(404, "Immagini non trovate")
+    # ROI poligonale: bbox derivato dal poligono + mask per il training
+    train_mask = None
+    if p.roi_poly is not None:
+        x, y, w, h, train_mask = _poly_bbox_mask(
+            p.roi_poly, model.shape[1], model.shape[0])
+    else:
         x, y, w, h = p.roi
         x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
     roi_img = model[y:y + h, x:x + w]
 
     tech = _simple_to_technical(p, roi_img)
+    # Tuple per repr stabile nella cache key (None = rettangolare)
+    tech["roi_poly"] = (tuple(map(tuple, p.roi_poly))
+                        if p.roi_poly is not None else None)
 
     key = _matcher_cache_key(roi_img, tech)
     # Halcon-mode init params: incidono sul training, includere in cache key
@@ -716,7 +797,7 @@ def match_simple(p: SimpleMatchParams):
                 use_polarity=p.use_polarity,
                 use_gpu=p.use_gpu,
             )
-            t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
+            t0 = time.time(); n = m.train(roi_img, train_mask); t_train = time.time() - t0
             _check_trained(m, n)
             _cache_put_matcher(key, m)
         else:
@@ -778,6 +859,8 @@ class SaveRecipeParams(BaseModel):
     model_id: str
     scene_id: str | None = None
     roi: list[int]
+    # ROI poligonale opzionale (vedi MatchParams.roi_poly)
+    roi_poly: list[list[float]] | None = None
     # Riusa stessi param simple per training equivalente
     tipo: str = "intero"
     simmetria: str = "nessuna"
@@ -897,6 +980,12 @@ def save_recipe(p: SaveRecipeParams):
     model = _load_image(p.model_id)
     if model is None:
         raise HTTPException(404, "Modello non trovato")
+    # ROI poligonale: bbox derivato dal poligono + mask per il training
+    train_mask = None
+    if p.roi_poly is not None:
+        x, y, w, h, train_mask = _poly_bbox_mask(
+            p.roi_poly, model.shape[1], model.shape[0])
+    else:
         x, y, w, h = p.roi
         x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
     roi_img = model[y:y + h, x:x + w]
@@ -925,7 +1014,7 @@ def save_recipe(p: SaveRecipeParams):
     )
     # Lock globale: serializza il training pesante col matching in corso
     with _MATCHER_LOCK:
-        n_var = m.train(roi_img)
+        n_var = m.train(roi_img, train_mask)
     _check_trained(m, n_var)
     safe_name = "".join(c for c in p.name if c.isalnum() or c in "._-")
     if not safe_name:

pm2d/web/static/app.js

@@ -20,6 +20,10 @@ 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 ----------
@@ -148,6 +152,15 @@ async function uploadToFolder(file) {
   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);
@@ -222,6 +235,7 @@ async function onSelectModel(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();
@@ -262,12 +276,36 @@ function renderModel() {
   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) {
+  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;
@@ -301,10 +339,35 @@ 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);
@@ -331,6 +394,41 @@ function setupROI() {
   });
 }
 
+// ---------- 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; }
@@ -352,6 +450,12 @@ async function doMatchRecipe() {
   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=${Date.now()}`);
   renderScene();
@@ -371,7 +475,11 @@ async function doMatch() {
   }
   if (!state.model) { setStatus("Carica modello"); return; }
   if (!state.scene) { setStatus("Carica scena"); return; }
+  if (state.polyMode && !state.polyClosed) {
+    setStatus("Chiudi il poligono (doppio click o bottone Chiudi)"); return;
+  }
   if (!state.roi) { setStatus("Seleziona ROI sul modello"); return; }
+  const roiPoly = getRoiPoly();
   const user = readUserParams();
   const adv = readAdvancedOverrides();
   setStatus("Match in corso...");
@@ -397,6 +505,7 @@ async function doMatch() {
     const angMax = SYM_MAP[user.simmetria] ?? 360;
     body = {
       model_id: state.model.id, scene_id: state.scene.id, roi: state.roi,
+      roi_poly: roiPoly,
       angle_min: 0, angle_max: angMax,
       angle_step: PREC_MAP[user.precisione] ?? 5,
       scale_min: smin, scale_max: smax, scale_step: sstep,
@@ -412,6 +521,7 @@ async function doMatch() {
   } else {
     body = {
       model_id: state.model.id, scene_id: state.scene.id, roi: state.roi,
+      roi_poly: roiPoly,
       ...user,
     };
   }
@@ -426,6 +536,12 @@ async function doMatch() {
   }
   const data = await r.json();
   state.matches = data.matches;
+  // C: salva tutto per "Esporta JSON"
+  state.lastMatch = {
+    endpoint: url, params: body, response: data,
+    image_id: state.scene.id,
+  };
+  document.getElementById("btn-export-json").disabled = false;
   state.annotatedImg = await loadImage(
     `/image/${data.annotated_id}/raw?t=${Date.now()}`);
   renderScene();
@@ -461,6 +577,38 @@ 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;
@@ -734,6 +882,7 @@ async function saveRecipe() {
     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,
@@ -777,6 +926,24 @@ window.addEventListener("DOMContentLoaded", async () => {
   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);
@@ -789,6 +956,18 @@ window.addEventListener("DOMContentLoaded", async () => {
   });
   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",

pm2d/web/static/index.html

@@ -30,9 +30,9 @@
             title="Analizza ROI e derivata parametri ottimali (Halcon-style)">
       ⚙ Auto-tune
     </button>
-    <label class="btn" title="Carica nuovo file nella cartella immagini">
+    <label class="btn" title="Carica nuovo file nella cartella immagini (immagine o DXF)">
       ⬆ Carica file
-      <input type="file" id="file-upload" accept="image/*" hidden>
+      <input type="file" id="file-upload" accept="image/*,.dxf" hidden>
     </label>
     <span id="status">Seleziona modello, disegna ROI, seleziona scena</span>
   </div>
@@ -45,6 +45,15 @@
       <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">
@@ -248,6 +257,11 @@
     <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>

pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
     "pillow>=12.2.0",
     "python-multipart>=0.0.26",
     "uvicorn[standard]>=0.34",
+    "ezdxf>=1.3",
 ]
 
 [project.scripts]
@@ -19,4 +20,14 @@ 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

uv.lock

@@ -62,6 +62,29 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "ezdxf"
+version = "1.4.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "fonttools" },
+    { name = "numpy" },
+    { name = "pyparsing" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/d7/1b7be8db364f1c4838dfc1a40ca96577aba405deabf896a4eb3aaeb15a62/ezdxf-1.4.4.tar.gz", hash = "sha256:da5a5e0e6bdbb6656f9c017b47edc7eafceb419d61a2b5de64ffb344c168e593", size = 1866886, upload-time = "2026-05-14T09:19:19.511Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/be/ed/97aa3ba1ba923e4098169de5bca380ee1430e2b34d0dd85b73007e34df16/ezdxf-1.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:278a09a845d67a4f893aebe9ef8dcd8894ece9f255d7d8bb21719ffd902109b8", size = 3555283, upload-time = "2026-05-14T09:25:37.346Z" },
+    { url = "https://files.pythonhosted.org/packages/4e/b1/8ca9408a2b382a806837e72d35f10931fdd9f53fe66cf208a6340358fbc5/ezdxf-1.4.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b0a787f8ebcbdcb9798c6fcfe90c23afd1a0993c8d4302b6cce5d8f19ae052ae", size = 3012646, upload-time = "2026-05-14T09:25:39.01Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/aa/1e8d3130eaabb780c5e8adfdc1ead023bb04468779b18169d60f7ce5b291/ezdxf-1.4.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c451d1c20f08b30c735ca4d650fee5b44147402f4b5662f8550053ed8a3009c2", size = 2999048, upload-time = "2026-05-14T09:25:40.348Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/ca/1dd390b79df9c104c2d4b5964e80db25362edf704ac742c9db9b279658a5/ezdxf-1.4.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3d3d9a6663993fc644751647fd7843057b51742ffac27c48ce28ea9f81239613", size = 5747890, upload-time = "2026-05-14T09:27:36.028Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/1e/226a6e636ae533e0bc0afe4435d1058fac173564e4af4102862f055a46d9/ezdxf-1.4.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:82c022cf094d21ad3db68557aa83628e4152355e8a8bef0fffd4f71a9ea325df", size = 5774707, upload-time = "2026-05-14T09:27:17.625Z" },
+    { url = "https://files.pythonhosted.org/packages/24/a3/7e33c9036de944b5446982bb629d698356e48b00c4d173768f52bdeeff4b/ezdxf-1.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:3be59c2a3a93585412a2a37af29b3ad7574fa3e429e624924c3285d0573388b3", size = 5713753, upload-time = "2026-05-14T09:27:37.55Z" },
+    { url = "https://files.pythonhosted.org/packages/36/9e/ac0cdc3a8623fc38aa16670f6537aa810bc1381217a51ac3481299bbc986/ezdxf-1.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:44a9716860b59dddd49a3708c04ff4580ad1e7de92ac93bb3213d85fba5ac93f", size = 5813895, upload-time = "2026-05-14T09:27:19.514Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/22/b511a8d9ea8f23447a67fe8977213fac5e4e69df30130a3ef31043613eaf/ezdxf-1.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:207eed544417464ffaf2570880d58a792ecd2534c9c2dede45e895f3200b77e6", size = 2960937, upload-time = "2026-05-14T09:23:49.361Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/09/5ecb6f82d35a2f4a4334d0a608fcf764827fa69dccdf5987ce309c16b6c1/ezdxf-1.4.4-py3-none-any.whl", hash = "sha256:666edda631ba717270293b734f5d58dd97a1d1aba4787187f09d0cc584645865", size = 1331217, upload-time = "2026-05-14T09:19:26.601Z" },
+]
+
 [[package]]
 name = "fastapi"
 version = "0.136.1"
@@ -78,6 +101,39 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5a/ff/2e4eca3ade2c22fe1dea7043b8ee9dabe47753349eb1b56a202de8af6349/fastapi-0.136.1-py3-none-any.whl", hash = "sha256:a6e9d7eeada96c93a4d69cb03836b44fa34e2854accb7244a1ece36cd4781c3f", size = 117683, upload-time = "2026-04-23T16:49:42.437Z" },
 ]
 
+[[package]]
+name = "fonttools"
+version = "4.63.0"
+source = { registry = "https://pypi.org/simple" }
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+]
+
 [[package]]
 name = "shape-model-2d"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "ezdxf" },
     { name = "fastapi" },
     { name = "numba" },
     { name = "numpy" },
@@ -458,10 +601,13 @@ dependencies = [
 [package.dev-dependencies]
 dev = [
     { name = "httpx" },
+    { name = "pytest" },
+    { name = "ruff" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "ezdxf", specifier = ">=1.3" },
     { name = "fastapi", specifier = ">=0.115" },
     { name = "numba", specifier = ">=0.65.0" },
     { name = "numpy", specifier = ">=1.24" },
@@ -472,7 +618,11 @@ requires-dist = [
 ]
 
 [package.metadata.requires-dev]
-dev = [{ name = "httpx", specifier = ">=0.28.1" }]
+dev = [
+    { name = "httpx", specifier = ">=0.28.1" },
+    { name = "pytest", specifier = ">=8.0" },
+    { name = "ruff", specifier = ">=0.8" },
+]
 
 [[package]]
 name = "starlette"