feat: thumbnail picker custom per selezione modello/scena

- GET /folder_image/{filename}?w=N: PNG ridotto cache 1h - Frontend: 2 thumb-picker al posto dei select (thumb + nome + caret) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
feat: 'Filtro falsi positivi' preset user-friendly (era verify_ncc)
2026-04-24 11:10:35 +02:00 · 2026-04-24 10:52:26 +02:00 · 2026-04-24 10:50:37 +02:00 · 2026-04-24 10:12:26 +02:00 · 2026-04-24 10:02:32 +02:00 · 2026-04-24 10:00:54 +02:00
53 changed files with 1843 additions and 100 deletions
@@ -1,26 +1,10 @@
-"""Entry-point standalone Pattern Matching 2D shape-based.
+"""Entry-point PM2D — webapp HTML.

 Esegui:  uv run python main.py
+Apri:    http://127.0.0.1:8080/
 """
-from pathlib import Path
-
-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",
-    )
+    serve(host="127.0.0.1", port=8080)
@@ -261,33 +261,33 @@ 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 +297,123 @@ 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 +431,38 @@ 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:
@@ -333,9 +333,26 @@ class LineShapeMatcher:
        return _jit_score_by_shift(resp, dx, dy, bins, bin_has_data)

    @staticmethod
-    def _subpixel_peak(acc: np.ndarray, x: int, y: int) -> tuple[float, float]:
-        """Fit parabolico 2D attorno al picco per offset subpixel (±0.5 px)."""
+    def _subpixel_peak(
+        acc: np.ndarray, x: int, y: int, plateau_radius: int = 10,
+    ) -> tuple[float, float]:
+        """Posizione sub-pixel del picco.
+
+        Se c'è un plateau di valori ~massimi (spread_radius satura il peak
+        su un'area) ritorna il CENTROIDE del plateau. Altrimenti fit
+        parabolico 2D ±0.5 px.
+        """
        H, W = acc.shape
+        val = float(acc[y, x])
+        # Plateau detection: valori >= val - 0.01 entro raggio limitato
+        y0 = max(0, y - plateau_radius); y1 = min(H, y + plateau_radius + 1)
+        x0 = max(0, x - plateau_radius); x1 = min(W, x + plateau_radius + 1)
+        patch = acc[y0:y1, x0:x1]
+        plateau = patch >= val - 0.01
+        if plateau.sum() > 1:
+            ys_m, xs_m = np.where(plateau)
+            return float(x0 + xs_m.mean()), float(y0 + ys_m.mean())
+        # Fallback parabolico
        if x <= 0 or x >= W - 1 or y <= 0 or y >= H - 1:
            return float(x), float(y)
        c = acc[y, x]
@@ -359,6 +376,7 @@ class LineShapeMatcher:
        mask_full: np.ndarray,
        angle_fine_step: float = 0.5,
        search_radius: float | None = None,
+        original_score: float | None = None,
    ) -> tuple[float, float, float, float]:
        """Ricerca angolare fine (sub-step) attorno al match grezzo.

@@ -366,6 +384,11 @@ class LineShapeMatcher:
        l'angolo con score massimo (parabolic fit sulle 3 score centrali).
        Ritorna (angle_refined, score, cx_refined, cy_refined).
        """
+        # Se il match grezzo è già quasi perfetto, NON refinare: il parabolic
+        # fit su picco saturo produce spostamenti spurious di posizione e
+        # angolo (esempio: modello==scena deve dare ang=0, pos=centro ROI)
+        if original_score is not None and original_score >= 0.99:
+            return (angle_deg, original_score, cx, cy)
        if search_radius is None:
            search_radius = self.angle_step_deg / 2.0
        offsets = np.linspace(-search_radius, search_radius, 5)
@@ -652,6 +675,7 @@ class LineShapeMatcher:
                    spread0, bit_active_full, self.template_gray, cx_f, cy_f,
                    var.angle_deg, var.scale, mask_full,
                    search_radius=self.angle_step_deg / 2.0,
+                    original_score=score,
                )
            if verify_ncc:
                ncc = self._verify_ncc(gray0, cx_f, cy_f, ang_f, var.scale)
@@ -0,0 +1,486 @@
+"""FastAPI webapp standalone per PM2D.
+
+Endpoint:
+  GET  /                    → HTML UI
+  POST /upload              → upload immagine (multipart)
+  POST /match               → JSON params + ids → results
+  GET  /image/{id}/raw      → PNG originale
+  GET  /image/{id}/annotated → PNG con overlay match
+"""
+from __future__ import annotations
+
+import os
+import tempfile
+import time
+import uuid
+from pathlib import Path
+
+import cv2
+import numpy as np
+from fastapi import FastAPI, File, HTTPException, UploadFile
+from fastapi.responses import HTMLResponse, Response
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+
+
+def _load_env(root: Path) -> None:
+    """Legge .env in root e popola os.environ (no override se già set)."""
+    f = root / ".env"
+    if not f.exists():
+        return
+    for line in f.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        k, v = line.split("=", 1)
+        k = k.strip(); v = v.strip().strip('"').strip("'")
+        os.environ.setdefault(k, v)
+
+
+# Root progetto (parent di pm2d/)
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+_load_env(PROJECT_ROOT)
+
+_images_dir_raw = os.environ.get("IMAGES_DIR", "Test")
+IMAGES_DIR = Path(_images_dir_raw)
+if not IMAGES_DIR.is_absolute():
+    IMAGES_DIR = PROJECT_ROOT / IMAGES_DIR
+
+from pm2d.line_matcher import LineShapeMatcher, Match
+from pm2d.auto_tune import auto_tune
+
+
+WEB_DIR = Path(__file__).parent
+STATIC_DIR = WEB_DIR / "static"
+STATIC_DIR.mkdir(exist_ok=True)
+
+# Persistenza immagini su disco (sopravvive a restart server)
+CACHE_DIR = Path(tempfile.gettempdir()) / "pm2d_cache"
+CACHE_DIR.mkdir(exist_ok=True)
+
+# Cache in-memory (soft, ricaricata da disco se mancante)
+_IMG_CACHE: dict[str, np.ndarray] = {}
+
+
+def _store_image(img: np.ndarray) -> str:
+    iid = uuid.uuid4().hex[:12]
+    cv2.imwrite(str(CACHE_DIR / f"{iid}.png"), img)
+    _IMG_CACHE[iid] = img
+    return iid
+
+
+def _load_image(iid: str) -> np.ndarray | None:
+    cached = _IMG_CACHE.get(iid)
+    if cached is not None:
+        return cached
+    p = CACHE_DIR / f"{iid}.png"
+    if not p.exists():
+        return None
+    img = cv2.imread(str(p))
+    if img is not None:
+        _IMG_CACHE[iid] = img
+    return img
+
+app = FastAPI(title="PM2D Webapp", version="1.0.0")
+
+
+def _encode_png(img: np.ndarray) -> bytes:
+    ok, buf = cv2.imencode(".png", img)
+    if not ok:
+        raise RuntimeError("PNG encode failed")
+    return buf.tobytes()
+
+
+def _draw_matches(scene: np.ndarray, matches: list[Match],
+                   template_gray: np.ndarray | None) -> np.ndarray:
+    out = scene.copy()
+    H, W = scene.shape[:2]
+    palette = [
+        (0, 255, 0), (0, 200, 255), (255, 100, 100), (255, 200, 0),
+        (200, 0, 255), (100, 255, 200), (255, 0, 0), (0, 255, 255),
+    ]
+    for i, m in enumerate(matches):
+        color = palette[i % len(palette)]
+        if template_gray is not None:
+            t = template_gray
+            th, tw = t.shape
+            edge = cv2.Canny(t, 50, 150)
+            cx_t = (tw - 1) / 2.0; cy_t = (th - 1) / 2.0
+            M = cv2.getRotationMatrix2D((cx_t, cy_t), m.angle_deg, m.scale)
+            M[0, 2] += m.cx - cx_t
+            M[1, 2] += m.cy - cy_t
+            warped = cv2.warpAffine(edge, M, (W, H),
+                                     flags=cv2.INTER_NEAREST, borderValue=0)
+            mask = warped > 0
+            if mask.any():
+                overlay = np.zeros_like(out)
+                overlay[mask] = color
+                out[mask] = (0.3 * out[mask] + 0.7 * overlay[mask]).astype(np.uint8)
+        poly = m.bbox_poly.astype(np.int32).reshape(-1, 1, 2)
+        cv2.polylines(out, [poly], True, color, 2, cv2.LINE_AA)
+        p0 = tuple(m.bbox_poly[0].astype(int))
+        p1 = tuple(m.bbox_poly[1].astype(int))
+        cv2.line(out, p0, p1, color, 4, cv2.LINE_AA)
+        cx, cy = int(round(m.cx)), int(round(m.cy))
+        cv2.drawMarker(out, (cx, cy), color, cv2.MARKER_CROSS, 22, 2, cv2.LINE_AA)
+        L = int(np.linalg.norm(m.bbox_poly[1] - m.bbox_poly[0])) // 2
+        a = np.deg2rad(m.angle_deg)
+        cv2.arrowedLine(out, (cx, cy),
+                        (int(cx + L * np.cos(a)), int(cy - L * np.sin(a))),
+                        color, 2, cv2.LINE_AA, tipLength=0.2)
+        label = f"#{i+1} {m.angle_deg:.0f}d s={m.scale:.2f} {m.score:.2f}"
+        cv2.putText(out, label, (cx + 8, cy - 8),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, cv2.LINE_AA)
+    return out
+
+
+# ---------------- Models ----------------
+
+class UploadResp(BaseModel):
+    id: str
+    width: int
+    height: int
+
+
+class MatchParams(BaseModel):
+    model_id: str
+    scene_id: str
+    roi: list[int]  # [x, y, w, h] nell'immagine modello
+    angle_min: float = 0.0
+    angle_max: float = 360.0
+    angle_step: float = 5.0
+    scale_min: float = 1.0
+    scale_max: float = 1.0
+    scale_step: float = 0.1
+    min_score: float = 0.55
+    max_matches: int = 25
+    nms_radius: int = 0
+    num_features: int = 96
+    weak_grad: float = 30.0
+    strong_grad: float = 60.0
+    spread_radius: int = 5
+    pyramid_levels: int = 3
+    verify_threshold: float = 0.4
+
+
+class MatchResult(BaseModel):
+    cx: float
+    cy: float
+    angle_deg: float
+    scale: float
+    score: float
+    bbox_poly: list[list[float]]
+
+
+class MatchResp(BaseModel):
+    matches: list[MatchResult]
+    train_time: float
+    find_time: float
+    num_variants: int
+    annotated_id: str
+
+
+class TuneParams(BaseModel):
+    model_id: str
+    roi: list[int]
+
+
+# ---------- User-facing (simple) params ----------
+
+SYMMETRY_TO_ANGLE_MAX = {
+    "invariante": 0.0,   # oggetto simmetrico a rotazione totale (cerchi): 1 variante
+    "nessuna": 360.0,
+    "bilaterale": 180.0,
+    "rot_3": 120.0,
+    "rot_4": 90.0,
+    "rot_6": 60.0,
+    "rot_8": 45.0,
+}
+
+SCALE_PRESETS = {
+    "fissa":  (1.0, 1.0, 0.1),
+    "mini":   (0.9, 1.1, 0.05),    # ±10%
+    "medio":  (0.75, 1.25, 0.05),  # ±25%
+    "max":    (0.5, 1.5, 0.05),    # ±50%
+}
+
+PRECISION_ANGLE_STEP = {
+    "veloce": 10.0,
+    "normale": 5.0,
+    "preciso": 2.0,
+}
+
+# "Filtro falsi positivi" = mapping semantico del verify NCC threshold.
+# Un operatore sceglie il livello di rigore, non un numero astratto.
+FILTRO_FP_MAP = {
+    "off":      0.0,   # disabilitato: mantieni tutti i match shape-based
+    "leggero":  0.20,  # tollera variazioni intensità/illuminazione forti
+    "medio":    0.35,  # default bilanciato (consigliato)
+    "forte":    0.50,  # scarta match con intensità molto diversa dal template
+}
+
+
+class SimpleMatchParams(BaseModel):
+    model_id: str
+    scene_id: str
+    roi: list[int]
+    tipo: str = "intero"              # "intero" | "parziale"
+    simmetria: str = "nessuna"        # chiave SYMMETRY_TO_ANGLE_MAX
+    scala: str = "fissa"              # chiave SCALE_PRESETS
+    precisione: str = "normale"       # chiave PRECISION_ANGLE_STEP
+    filtro_fp: str = "medio"          # chiave FILTRO_FP_MAP
+    min_score: float = 0.65
+    max_matches: int = 25
+
+
+def _simple_to_technical(
+    p: SimpleMatchParams, roi_img: np.ndarray,
+) -> dict:
+    """Converti parametri user-facing → tecnici usando analisi della ROI."""
+    from pm2d.auto_tune import auto_tune as _auto
+
+    tune = _auto(roi_img)
+    h, w = roi_img.shape[:2]
+    min_side = min(h, w)
+
+    # Feature count: parziale = meno feature (area minore)
+    nf = tune["num_features"]
+    if p.tipo == "parziale":
+        nf = max(32, int(nf * 0.6))
+
+    # Piramide derivata da dimensione ROI
+    if min_side < 60:
+        pyr = 1
+    elif min_side < 150:
+        pyr = 2
+    elif min_side < 400:
+        pyr = 3
+    else:
+        pyr = 4
+
+    # Spread radius ~2-3% del lato minimo
+    spread = max(3, min(10, int(round(min_side * 0.03))))
+
+    angle_max = SYMMETRY_TO_ANGLE_MAX.get(p.simmetria, 360.0)
+    smin, smax, sstep = SCALE_PRESETS.get(p.scala, (1.0, 1.0, 0.1))
+    ang_step = PRECISION_ANGLE_STEP.get(p.precisione, 5.0)
+
+    return {
+        "num_features": nf,
+        "weak_grad": tune["weak_grad"],
+        "strong_grad": tune["strong_grad"],
+        "spread_radius": spread,
+        "pyramid_levels": pyr,
+        "angle_min": 0.0,
+        "angle_max": angle_max,
+        "angle_step": ang_step,
+        "scale_min": smin,
+        "scale_max": smax,
+        "scale_step": sstep,
+        "min_score": p.min_score,
+        "max_matches": p.max_matches,
+        "nms_radius": 0,
+        "verify_threshold": FILTRO_FP_MAP.get(p.filtro_fp, 0.35),
+    }
+
+
+# ---------------- Endpoints ----------------
+
+@app.get("/", response_class=HTMLResponse)
+def index():
+    html_path = STATIC_DIR / "index.html"
+    return HTMLResponse(html_path.read_text(encoding="utf-8"))
+
+
+@app.get("/folder_image/{filename}")
+def folder_image(filename: str, w: int = 120):
+    """Serve thumbnail PNG dell'immagine IMAGES_DIR (scalata a width w)."""
+    if "/" in filename or ".." in filename:
+        raise HTTPException(400, "nome non valido")
+    path = IMAGES_DIR / filename
+    if not path.is_file():
+        raise HTTPException(404, "non trovato")
+    img = cv2.imread(str(path), cv2.IMREAD_COLOR)
+    if img is None:
+        raise HTTPException(400, "non leggibile")
+    h0, w0 = img.shape[:2]
+    if w0 > w:
+        sc = w / w0
+        img = cv2.resize(img, (w, int(h0 * sc)), interpolation=cv2.INTER_AREA)
+    return Response(_encode_png(img), media_type="image/png",
+                    headers={"Cache-Control": "public, max-age=3600"})
+
+
+@app.get("/images")
+def list_images():
+    """Lista file immagine nella cartella configurata in IMAGES_DIR."""
+    if not IMAGES_DIR.is_dir():
+        return {"dir": str(IMAGES_DIR), "files": []}
+    exts = {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff"}
+    files = sorted(
+        p.name for p in IMAGES_DIR.iterdir()
+        if p.is_file() and p.suffix.lower() in exts
+    )
+    return {"dir": str(IMAGES_DIR), "files": files}
+
+
+class LoadFolderReq(BaseModel):
+    filename: str
+
+
+@app.post("/load_from_folder", response_model=UploadResp)
+def load_from_folder(req: LoadFolderReq):
+    """Carica immagine dalla cartella IMAGES_DIR per nome file."""
+    name = req.filename
+    if "/" in name or ".." in name:
+        raise HTTPException(400, "nome file non valido")
+    path = IMAGES_DIR / name
+    if not path.is_file():
+        raise HTTPException(404, f"file non trovato: {name}")
+    img = cv2.imread(str(path), cv2.IMREAD_COLOR)
+    if img is None:
+        raise HTTPException(400, "immagine non leggibile")
+    iid = _store_image(img)
+    return UploadResp(id=iid, width=img.shape[1], height=img.shape[0])
+
+
+@app.post("/upload", response_model=UploadResp)
+async def upload(file: UploadFile = File(...)):
+    data = await file.read()
+    arr = np.frombuffer(data, dtype=np.uint8)
+    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+    if img is None:
+        raise HTTPException(400, "Immagine non valida")
+    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)
+    if img is None:
+        raise HTTPException(404, "Image not found")
+    return Response(_encode_png(img), media_type="image/png")
+
+
+@app.post("/match", response_model=MatchResp)
+def match(p: MatchParams):
+    model = _load_image(p.model_id)
+    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 = max(0, x); y = max(0, y)
+    w = max(1, min(w, model.shape[1] - x))
+    h = max(1, min(h, model.shape[0] - y))
+    roi_img = model[y:y + h, x:x + w]
+
+    m = LineShapeMatcher(
+        num_features=p.num_features,
+        weak_grad=p.weak_grad, strong_grad=p.strong_grad,
+        angle_range_deg=(p.angle_min, p.angle_max),
+        angle_step_deg=p.angle_step,
+        scale_range=(p.scale_min, p.scale_max),
+        scale_step=p.scale_step,
+        spread_radius=p.spread_radius,
+        pyramid_levels=p.pyramid_levels,
+    )
+    t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
+    nms = p.nms_radius if p.nms_radius > 0 else None
+    t0 = time.time()
+    matches = m.find(
+        scene, min_score=p.min_score, max_matches=p.max_matches,
+        nms_radius=nms, verify_threshold=p.verify_threshold,
+    )
+    t_find = time.time() - t0
+
+    # Render annotated image
+    tg = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
+    annotated = _draw_matches(scene, matches, tg)
+    ann_id = _store_image(annotated)
+
+    return MatchResp(
+        matches=[MatchResult(
+            cx=m_.cx, cy=m_.cy, angle_deg=m_.angle_deg, scale=m_.scale,
+            score=m_.score,
+            bbox_poly=m_.bbox_poly.tolist(),
+        ) for m_ in matches],
+        train_time=t_train, find_time=t_find,
+        num_variants=n, annotated_id=ann_id,
+    )
+
+
+@app.post("/match_simple", response_model=MatchResp)
+def match_simple(p: SimpleMatchParams):
+    """Match con parametri user-facing (tipo/simmetria/scala/precisione).
+
+    Il server deriva i parametri tecnici (num_features, soglie gradiente,
+    piramide, ecc.) dall'analisi automatica della ROI.
+    """
+    model = _load_image(p.model_id)
+    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 = max(0, x); y = max(0, y)
+    w = max(1, min(w, model.shape[1] - x))
+    h = max(1, min(h, model.shape[0] - y))
+    roi_img = model[y:y + h, x:x + w]
+
+    tech = _simple_to_technical(p, roi_img)
+
+    m = LineShapeMatcher(
+        num_features=tech["num_features"],
+        weak_grad=tech["weak_grad"], strong_grad=tech["strong_grad"],
+        angle_range_deg=(tech["angle_min"], tech["angle_max"]),
+        angle_step_deg=tech["angle_step"],
+        scale_range=(tech["scale_min"], tech["scale_max"]),
+        scale_step=tech["scale_step"],
+        spread_radius=tech["spread_radius"],
+        pyramid_levels=tech["pyramid_levels"],
+    )
+    t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
+    nms = tech["nms_radius"] if tech["nms_radius"] > 0 else None
+    t0 = time.time()
+    matches = m.find(
+        scene, min_score=tech["min_score"], max_matches=tech["max_matches"],
+        nms_radius=nms, verify_threshold=tech["verify_threshold"],
+    )
+    t_find = time.time() - t0
+
+    tg = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
+    annotated = _draw_matches(scene, matches, tg)
+    ann_id = _store_image(annotated)
+
+    return MatchResp(
+        matches=[MatchResult(
+            cx=mt.cx, cy=mt.cy, angle_deg=mt.angle_deg, scale=mt.scale,
+            score=mt.score, bbox_poly=mt.bbox_poly.tolist(),
+        ) for mt in matches],
+        train_time=t_train, find_time=t_find,
+        num_variants=n, annotated_id=ann_id,
+    )
+
+
+@app.post("/auto_tune")
+def tune(p: TuneParams):
+    model = _load_image(p.model_id)
+    if model is None:
+        raise HTTPException(404, "Immagine non trovata")
+    x, y, w, h = p.roi
+    roi_img = model[y:y + h, x:x + w]
+    t = auto_tune(roi_img)
+    return {k: v for k, v in t.items() if not k.startswith("_")}
+
+
+# Mount static
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+
+def serve(host: str = "127.0.0.1", port: int = 8080):
+    import uvicorn
+    uvicorn.run(app, host=host, port=port, log_level="info")
+
+
+if __name__ == "__main__":
+    serve()
@@ -0,0 +1,368 @@
+// 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,
+};
+
+// ---------- 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-${key}`;
+    inp.type = "text";
+    inp.placeholder = "auto";
+    inp.value = def === "" ? "" : String(def);
+    inp.addEventListener("keydown", (e) => {
+      if (e.key === "Enter") doMatch();
+    });
+    form.appendChild(lbl);
+    form.appendChild(inp);
+  }
+}
+
+function readUserParams() {
+  return {
+    tipo: document.getElementById("p-tipo").value,
+    simmetria: document.getElementById("p-simmetria").value,
+    scala: document.getElementById("p-scala").value,
+    precisione: document.getElementById("p-precisione").value,
+    filtro_fp: document.getElementById("p-filtro-fp").value,
+    min_score: parseFloat(document.getElementById("p-min-score").value),
+    max_matches: parseInt(document.getElementById("p-max-matches").value, 10),
+  };
+}
+
+function readAdvancedOverrides() {
+  const out = {};
+  for (const [key, , type] of ADV_PARAMS) {
+    const v = document.getElementById(`adv-${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();
+}
+
+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;
+    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) {
+    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);
+  }
+  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;
+    const p = canvasPos(cnv, e);
+    state.drag = { x0: p.x, y0: p.y, x1: p.x, y1: p.y };
+    renderModel();
+  });
+  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();
+  });
+}
+
+// ---------- Match action ----------
+async function doMatch() {
+  if (!state.model) { setStatus("Carica modello"); return; }
+  if (!state.scene) { setStatus("Carica scena"); return; }
+  if (!state.roi) { setStatus("Seleziona ROI sul modello"); return; }
+  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};
+    const FP_MAP = {off:0, leggero:0.20, medio:0.35, forte:0.50};
+    const [smin, smax, sstep] = SCALE_MAP[user.scala];
+    body = {
+      model_id: state.model.id, scene_id: state.scene.id, roi: state.roi,
+      angle_min: 0, angle_max: SYM_MAP[user.simmetria] || 360,
+      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] ?? 0.35),
+      nms_radius: adv.nms_radius ?? 0,
+    };
+  } else {
+    body = {
+      model_id: state.model.id, scene_id: state.scene.id, roi: state.roi,
+      ...user,
+    };
+  }
+
+  const r = await fetch(url, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(body),
+  });
+  if (!r.ok) {
+    setStatus(`Errore: ${await r.text()}`); return;
+  }
+  const data = await r.json();
+  state.matches = data.matches;
+  state.annotatedImg = await loadImage(
+    `/image/${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 = `${data.find_time.toFixed(2)}s`;
+  document.getElementById("t-var").textContent = data.num_variants;
+  document.getElementById("t-match").textContent = data.matches.length;
+  setStatus(`${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;
+}
+
+// ---------- Init ----------
+window.addEventListener("DOMContentLoaded", async () => {
+  buildAdvancedForm();
+  setupROI();
+  // Popola picker immagini da IMAGES_DIR (con thumbnail)
+  const {files, dir} = await fetchImagesList();
+  buildThumbPicker("picker-model", files, onSelectModel);
+  buildThumbPicker("picker-scene", files, onSelectScene);
+  if (files.length === 0) {
+    setStatus(`Nessuna immagine in ${dir} (configura IMAGES_DIR in .env)`);
+  } else {
+    setStatus(`${files.length} immagini disponibili in ${dir}`);
+  }
+  document.getElementById("btn-match").addEventListener("click", doMatch);
+  const slider = document.getElementById("p-min-score");
+  slider.addEventListener("input", (e) => {
+    document.getElementById("v-score").textContent =
+      parseFloat(e.target.value).toFixed(2);
+  });
+  renderModel();
+  renderScene();
+});
@@ -0,0 +1,136 @@
+<!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>
+    <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>
+  </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="invariante">Invariante (cerchi — no rotazione)</option>
+        <option value="nessuna">Nessuna (0..360°)</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>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>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>
+  </section>
+</main>
+
+<footer>
+  <h2>LEGENDA</h2>
+  <div id="legend"></div>
+</footer>
+
+<script src="/static/app.js"></script>
+</body>
+</html>
@@ -0,0 +1,158 @@
+* { 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; }
@@ -3,7 +3,16 @@ 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",
+]
+
+[dependency-groups]
+dev = [
+    "httpx>=0.28.1",
 ]
@@ -2,6 +2,150 @@ version = 1
 revision = 3
 requires-python = ">=3.13"

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Author	SHA1	Message	Date
Adriano	b83e577eab	feat: thumbnail picker custom per selezione modello/scena - GET /folder_image/{filename}?w=N: PNG ridotto cache 1h - Frontend: 2 thumb-picker al posto dei select (thumb + nome + caret) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 11:10:35 +02:00
Adriano	2bca68d700	feat: 'Filtro falsi positivi' preset user-friendly (era verify_ncc) Rinomina il parametro tecnico verify_threshold in un preset semantico che operatore/cliente capisce senza leggere docs: off -> 0.00 (tutti i match shape-based passano) leggero -> 0.20 (tollera illuminazione/riflessi) medio -> 0.35 (consigliato, default) forte -> 0.50 (massima selettivita, scarta mismatch intensita) UI: dropdown etichettato 'Filtro falsi positivi (verifica intensita colori)' accanto a precisione angolare. Override tecnico (numerico) resta in sezione Avanzate. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 10:52:26 +02:00
Adriano	1671a151da	feat: selezione immagini da cartella IMAGES_DIR via .env - .env con IMAGES_DIR=Test - server: _load_env legge .env senza dip extra - GET /images lista file, POST /load_from_folder carica per nome - frontend: file picker sostituiti con 2 select popolati all avvio Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 10:50:37 +02:00
Adriano	1954bc6ffd	fix: allineamento preciso match (skip refine saturo + plateau centroid) Bug: modello == scena non sovrapponeva perfettamente. 1. refine_angle trovava angoli spurious -2.5 deg con score saturo 1.0 perche' parabolic fit su picco saturo estrapola rumore. Fix: skip refine quando original_score >= 0.99 2. Subpixel peak su plateau (spread_radius=5 satura picco su area) sceglieva pixel random via cv2.minMaxLoc. Fix: se >1 pixel a score >= max-0.01 nel raggio 10 usa CENTROIDE del plateau invece del parabolic fit. Test self-match tooth_rim foro piccolo: prima: pos=(355, 111.50) delta=(0, -3.50) ang=-2.5 deg dopo: pos=(355, 115.00) delta=(0, +0.00) ang=+0.0 deg Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 10:12:26 +02:00
Adriano	45e3a29ff0	feat: simmetria 'invariante' per oggetti circolari (1 variante angolare) Test tooth_rim foro grande: 12x piu veloce (0.14s vs 1.77s) perche angle_max=0 genera 1 sola variante angolare invece di 72. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 10:02:32 +02:00
Adriano	e1a1b956fd	fix: persistenza immagini su disco (sopravvive restart server) Bug: _IMAGES era dict in-memory, restart server → browser con id cached riceveva 404 'Immagini non trovate'. Fix: scrittura PNG in /tmp/pm2d_cache/{id}.png al upload, _load_image() prova cache memory prima di leggere disco. Rimossa funzione _store_image duplicata. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 10:00:54 +02:00
Adriano	9fba46d7f7	fix: default score 0.65 + verify 0.25 nel simple match Default precedenti scartavano match validi con variazione intensita attorno al foro (verify NCC 0.4 troppo stretto). Nuovo: 4/5 fori corona dentata trovati con default. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 09:58:15 +02:00
Adriano	d35bb574ef	ui: parametri user-friendly (tipo/simmetria/scala/precisione) Nascosti i parametri tecnici (num_features, weak/strong_grad, spread, pyramid) incomprensibili per operatori. Sostituiti da scelte semantiche: - Tipo modello: intero \| parziale - Simmetria: nessuna \| bilaterale (180) \| rotazionale 3/4/6/8x - Variazione scala: fissa \| 10% \| 25% \| 50% - Precisione: veloce 10 \| normale 5 \| preciso 2 - Score minimo: slider - Max match: input Server: nuovo endpoint POST /match_simple. Deriva tecnici via _simple_to_technical(roi) che analizza la ROI: - weak/strong_grad da percentili - num_features da densita edge x tipo - pyramid_levels da min(h,w) ROI - spread_radius proporzionale Frontend: select + slider, sezione Avanzate collassabile per override. Test rings_and_nuts preset intero/nessuna/medio/normale: 3/3 ruote in 2.14s Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 09:56:34 +02:00
Adriano	fd7585acc5	feat: interfaccia web HTML (FastAPI + canvas JS) Sostituisce GUI cv2/tkinter con webapp standalone: Server (pm2d/web/server.py): - FastAPI + uvicorn - Endpoint: GET /, POST /upload, POST /match, POST /auto_tune, GET /image/{id}/raw - In-memory image store (uuid-based) - Rendering annotated server-side via opencv (overlay bbox + edges template warpati) Frontend (pm2d/web/static/): - index.html: layout 3 colonne (MODELLO \| SCENA \| PARAMETRI) + footer legenda - style.css: tema dark, CSS grid responsive - app.js: canvas HTML5 per visualizzazione scalata fit, ROI selection con drag mouse, form parametri live, MATCH button, Auto-tune button Parametri modificabili INLINE (niente dialog separata). Enter su qualsiasi campo triggera MATCH. Legenda match in fondo con pallino colorato + dati. main.py ora lancia il server webapp. Deprecato ingresso GUI cv2 (pm2d/gui.py resta importable per backward compat). Test: /match su rings_and_nuts: 3/3 ruote in 1.14s (train 0.36s + find 0.77s). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 09:06:25 +02:00
Adriano	4ddda1ec62	ui: layout fisso 1600x900 con pannelli SX/DX, scena scalata - Finestra dimensione fissa: scena scalata fit-to-box mantenendo aspect ratio (anche immagini piccole riempiono il layout) - Pannello sinistro: MODELLO thumbnail + RISULTATI legenda numerata - Pannello destro: PARAMETRI sempre visibili (train/find time evidenziati) + HOTKEY - Rimossi parametri duplicati da pannello sinistro Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-24 08:59:06 +02:00