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>

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)
@@ -617,8 +680,14 @@ def match(p: MatchParams):
     scene = _load_image(p.scene_id)
     if model is None or scene is None:
         raise HTTPException(404, "Immagini non trovate")
-    x, y, w, h = p.roi
-    x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
+    # 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_for_cache = {
@@ -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")
-    x, y, w, h = p.roi
-    x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
+    # 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"
@@ -897,8 +980,14 @@ def save_recipe(p: SaveRecipeParams):
     model = _load_image(p.model_id)
     if model is None:
         raise HTTPException(404, "Modello non trovato")
-    x, y, w, h = p.roi
-    x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
+    # 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]
     sp = SimpleMatchParams(
         model_id=p.model_id, scene_id=p.scene_id or p.model_id, roi=p.roi,
@@ -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: