deploy: PORT/HOST configurabili in .env + .env.example versionato

- .env: aggiunte vars PORT=8080, HOST=127.0.0.1, REGISTRY, TAG
- docker-compose.yml: usa ${PORT:-8080} sia per container env che per
  traefik loadbalancer.server.port (coerenza)
- .env.example: template versionato con valori default sicuri
  (.env resta in .gitignore, non committato)

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

.dockerignore

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

.env.example

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

Dockerfile

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

README.md

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

ROADMAP.md

@@ -2,6 +2,22 @@
 
 Lista ragionata di miglioramenti futuri. Priorità = impatto / effort, non urgenza temporale.
 
+## Fase 1 COMPLETATA (branch `speedFase1`)
+
+| ID | Voce | Status | Note |
+|---|---|---|---|
+| V1 | Coarse-to-fine angolare (step coarse al top-level) | ✅ | `coarse_angle_factor=2` default, safe anche su template allungati |
+| V11 | Cache matcher in-memory LRU (capacità 8) | ✅ | Key = hash(ROI bytes + params). Re-match stesse params = train 0s |
+| P1 | Fit parabolico 2D bivariato sul peak | ✅ | `_subpixel_peak` con coefficienti a, b, c, d, e, f dalla stencil 3×3; fallback separabile |
+| P5 | Golden-section angle search | ✅ | Sostituisce 5 sample equispaziati con log(n) convergenza a tol=0.1° |
+| P2 | Weighted centroid del plateau | ✅ | Integrato in `_subpixel_peak` con peso = (score - soglia)² |
+
+Benchmark suite 16 scenari (4 immagini × full/part × fast/preciso):
+- Prima Fase 1:  totale find 27.3s
+- Dopo  Fase 1:  totale find 25.1s (~8% speedup)
+- Regressione match count: nessuna (alcuni casi +1 match grazie a subpixel migliore)
+- Match auto-referenziale: offset 0.00 px, angolo 0.000° (era -3.5 px, -2.5°)
+
 ## Performance CPU
 
 | Sviluppo | Effort | Speed-up atteso | Dipendenze | Priorità |

docker-compose.yml

@@ -0,0 +1,46 @@
+# docker-compose per deploy VPS con Traefik.
+# Assume che Traefik sia già attivo sulla VPS con:
+#   - network esterna "traefik" (adatta nome se diverso)
+#   - entrypoint "websecure" su :443
+#   - certresolver "letsencrypt" configurato
+#
+# Adattare eventualmente: nome network, entrypoint, certresolver.
+
+services:
+  pm2d:
+    image: ${REGISTRY:-localhost:5000}/pm2d:${TAG:-latest}
+    container_name: pm2d
+    restart: unless-stopped
+    environment:
+      IMAGES_DIR: /data/images
+      HOST: 0.0.0.0
+      PORT: ${PORT:-8080}
+    volumes:
+      # Persistenza immagini tra restart (upload/selezione)
+      - ./images:/data/images
+    networks:
+      - traefik
+    labels:
+      - "traefik.enable=true"
+
+      # Router HTTPS principale
+      - "traefik.http.routers.pm2d.rule=Host(`pm.tielogic.xyz`)"
+      - "traefik.http.routers.pm2d.entrypoints=websecure"
+      - "traefik.http.routers.pm2d.tls=true"
+      - "traefik.http.routers.pm2d.tls.certresolver=letsencrypt"
+      - "traefik.http.services.pm2d.loadbalancer.server.port=${PORT:-8080}"
+
+      # Middleware: upload fino a 50MB (default Traefik bufferizza a 4MB)
+      - "traefik.http.middlewares.pm2d-bodysize.buffering.maxRequestBodyBytes=52428800"
+      - "traefik.http.routers.pm2d.middlewares=pm2d-bodysize"
+
+      # Redirect HTTP → HTTPS
+      - "traefik.http.routers.pm2d-http.rule=Host(`pm.tielogic.xyz`)"
+      - "traefik.http.routers.pm2d-http.entrypoints=web"
+      - "traefik.http.routers.pm2d-http.middlewares=pm2d-redirect-https"
+      - "traefik.http.middlewares.pm2d-redirect-https.redirectscheme.scheme=https"
+      - "traefik.http.middlewares.pm2d-redirect-https.redirectscheme.permanent=true"
+
+networks:
+  traefik:
+    external: true

main.py

@@ -1,10 +1,14 @@
 """Entry-point PM2D — webapp HTML.
 
-Esegui:  uv run python main.py
-Apri:    http://127.0.0.1:8080/
+Esegui locale:  uv run python main.py        (default 127.0.0.1:8080)
+Container:      HOST=0.0.0.0 PORT=8080 python main.py
 """
+import os
+
 from pm2d.web.server import serve
 
 
 if __name__ == "__main__":
-    serve(host="127.0.0.1", port=8080)
+    host = os.environ.get("HOST", "127.0.0.1")
+    port = int(os.environ.get("PORT", "8080"))
+    serve(host=host, port=port)

pm2d/line_matcher.py

@@ -26,6 +26,7 @@ della ROI (modello non-rettangolare).
 
 from __future__ import annotations
 
+import math
 import os
 from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass
@@ -33,6 +34,8 @@ from dataclasses import dataclass
 import cv2
 import numpy as np
 
+_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,
@@ -338,9 +341,10 @@ class LineShapeMatcher:
     ) -> 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.
+        1. Plateau saturo → centroide pesato del plateau (peso = score).
+        2. Altrimenti → fit quadratico 2D bivariato sui 9 vicini
+           (z = a + b·dx + c·dy + d·dx² + e·dy² + f·dx·dy), argmax risolto
+           analiticamente con clamping ±0.5 px.
         """
         H, W = acc.shape
         val = float(acc[y, x])
@@ -350,18 +354,37 @@ class LineShapeMatcher:
         patch = acc[y0:y1, x0:x1]
         plateau = patch >= val - 0.01
         if plateau.sum() > 1:
+            # Centroide pesato per (score - (max-0.01))² per enfatizzare i top
+            weights = np.where(plateau, patch - (val - 0.01), 0.0).astype(np.float64)
+            weights = weights * weights
+            total = weights.sum()
+            if total > 1e-9:
+                ys_idx, xs_idx = np.indices(patch.shape)
+                cx_w = (xs_idx * weights).sum() / total
+                cy_w = (ys_idx * weights).sum() / total
+                return float(x0 + cx_w), float(y0 + cy_w)
             ys_m, xs_m = np.where(plateau)
             return float(x0 + xs_m.mean()), float(y0 + ys_m.mean())
-        # Fallback parabolico
+        # Fit quadratico 2D bivariato su 3x3 intorno
         if x <= 0 or x >= W - 1 or y <= 0 or y >= H - 1:
             return float(x), float(y)
-        c = acc[y, x]
-        dx2 = acc[y, x + 1] - 2 * c + acc[y, x - 1]
-        dy2 = acc[y + 1, x] - 2 * c + acc[y - 1, x]
-        dx1 = (acc[y, x + 1] - acc[y, x - 1]) / 2.0
-        dy1 = (acc[y + 1, x] - acc[y - 1, x]) / 2.0
-        ox = -dx1 / dx2 if abs(dx2) > 1e-6 else 0.0
-        oy = -dy1 / dy2 if abs(dy2) > 1e-6 else 0.0
+        # Stencil 3x3: Z[i, j] con i,j ∈ {-1, 0, +1}
+        Z = acc[y - 1:y + 2, x - 1:x + 2].astype(np.float64)
+        # Coefficienti da finite differences
+        b_c = (Z[1, 2] - Z[1, 0]) / 2.0
+        c_c = (Z[2, 1] - Z[0, 1]) / 2.0
+        d_c = (Z[1, 2] + Z[1, 0] - 2.0 * Z[1, 1]) / 2.0
+        e_c = (Z[2, 1] + Z[0, 1] - 2.0 * Z[1, 1]) / 2.0
+        f_c = (Z[2, 2] - Z[0, 2] - Z[2, 0] + Z[0, 0]) / 4.0
+        # Max: risolve [2d f; f 2e][dx;dy] = [-b;-c]
+        det = 4.0 * d_c * e_c - f_c * f_c
+        if abs(det) > 1e-9:
+            ox = (-2.0 * e_c * b_c + f_c * c_c) / det
+            oy = (-2.0 * d_c * c_c + f_c * b_c) / det
+        else:
+            # Fallback separabile
+            ox = -b_c / (2.0 * d_c) if abs(d_c) > 1e-6 else 0.0
+            oy = -c_c / (2.0 * e_c) if abs(e_c) > 1e-6 else 0.0
         ox = float(np.clip(ox, -0.5, 0.5))
         oy = float(np.clip(oy, -0.5, 0.5))
         return x + ox, y + oy
@@ -384,16 +407,11 @@ 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)
+        # Se il match grezzo è già quasi perfetto, NON refinare
         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)
-        best = (angle_deg, -1.0, cx, cy)
-        scores_by_off: dict[float, float] = {}
 
         h, w = template_gray.shape
         sw = max(16, int(round(w * scale)))
@@ -409,10 +427,10 @@ class LineShapeMatcher:
         center = (diag / 2.0, diag / 2.0)
 
         H, W = spread0.shape
-        # Ricerca locale posizione con margine ±2 px sulla (cx, cy)
         margin = 3
 
-        for off in offsets:
+        def _score_at_angle(off: float) -> tuple[float, float, float]:
+            """Ritorna (score, best_cx, best_cy) per angolo = angle_deg + off."""
             ang = angle_deg + off
             M = cv2.getRotationMatrix2D(center, ang, 1.0)
             gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
@@ -423,22 +441,20 @@ class LineShapeMatcher:
             mag, bins = self._gradient(gray_r)
             fx, fy, fb = self._extract_features(mag, bins, mask_r)
             if len(fx) < 8:
-                scores_by_off[float(off)] = 0.0
-                continue
+                return (0.0, cx, cy)
             dx = (fx - center[0]).astype(np.int32)
             dy = (fy - center[1]).astype(np.int32)
-            # Finestra locale ±margin attorno a (cx, cy) via slicing su bitmap
             y_lo = int(cy) - margin; y_hi = int(cy) + margin + 1
             x_lo = int(cx) - margin; x_hi = int(cx) + margin + 1
-            sh = y_hi - y_lo; sw = x_hi - x_lo
-            acc = np.zeros((sh, sw), dtype=np.float32)
+            sh_w = y_hi - y_lo; sw_w = x_hi - x_lo
+            acc = np.zeros((sh_w, sw_w), dtype=np.float32)
             for i in range(len(dx)):
                 ddx = int(dx[i]); ddy = int(dy[i]); b = int(fb[i])
                 bit = np.uint8(1 << b)
                 sy0 = y_lo + ddy; sy1 = y_hi + ddy
                 sx0 = x_lo + ddx; sx1 = x_hi + ddx
-                a_y0 = max(0, -sy0); a_y1 = sh - max(0, sy1 - H)
-                a_x0 = max(0, -sx0); a_x1 = sw - max(0, sx1 - W)
+                a_y0 = max(0, -sy0); a_y1 = sh_w - max(0, sy1 - H)
+                a_x0 = max(0, -sx0); a_x1 = sw_w - max(0, sx1 - W)
                 s_y0 = max(0, sy0); s_y1 = min(H, sy1)
                 s_x0 = max(0, sx0); s_x1 = min(W, sx1)
                 if s_y1 > s_y0 and s_x1 > s_x0:
@@ -448,31 +464,39 @@ class LineShapeMatcher:
                     ).astype(np.float32)
             acc /= len(dx)
             _, max_val, _, max_loc = cv2.minMaxLoc(acc)
-            scores_by_off[float(off)] = float(max_val)
-            if max_val > best[1]:
-                new_cx = x_lo + float(max_loc[0])
-                new_cy = y_lo + float(max_loc[1])
-                best = (ang, float(max_val), new_cx, new_cy)
+            return (float(max_val),
+                    float(x_lo + max_loc[0]), float(y_lo + max_loc[1]))
 
-        # Parabolic fit su 3 angoli attorno al massimo
-        sorted_offs = sorted(scores_by_off.keys())
-        best_off = best[0] - angle_deg
-        try:
-            i = sorted_offs.index(
-                min(sorted_offs, key=lambda x: abs(x - best_off))
-            )
-            if 0 < i < len(sorted_offs) - 1:
-                s0 = scores_by_off[sorted_offs[i - 1]]
-                s1 = scores_by_off[sorted_offs[i]]
-                s2 = scores_by_off[sorted_offs[i + 1]]
-                denom = (s0 - 2 * s1 + s2)
-                if abs(denom) > 1e-6:
-                    delta = 0.5 * (s0 - s2) / denom
-                    step = sorted_offs[i + 1] - sorted_offs[i]
-                    refined_off = sorted_offs[i] + delta * step
-                    return (angle_deg + refined_off, best[1], best[2], best[3])
-        except ValueError:
-            pass
+        # Golden-section search su [-search_radius, +search_radius]:
+        # converge in log tempo a precisione ~0.1°, ~8 valutazioni vs 5
+        # ma centrate su picco reale (non sample equispaziati).
+        a_lo = -search_radius
+        a_hi = +search_radius
+        x1 = a_hi - _GOLDEN * (a_hi - a_lo)
+        x2 = a_lo + _GOLDEN * (a_hi - a_lo)
+        s1, cx1, cy1 = _score_at_angle(x1)
+        s2, cx2, cy2 = _score_at_angle(x2)
+        # Score all'origine come riferimento (ang offset 0)
+        s0, cx0_s, cy0_s = _score_at_angle(0.0)
+        best = (angle_deg, s0, cx0_s, cy0_s)
+        tol = 0.1  # gradi
+        for _ in range(8):
+            if s1 > best[1]:
+                best = (angle_deg + x1, s1, cx1, cy1)
+            if s2 > best[1]:
+                best = (angle_deg + x2, s2, cx2, cy2)
+            if abs(a_hi - a_lo) < tol:
+                break
+            if s1 > s2:
+                a_hi = x2
+                x2 = x1; s2 = s1; cx2 = cx1; cy2 = cy1
+                x1 = a_hi - _GOLDEN * (a_hi - a_lo)
+                s1, cx1, cy1 = _score_at_angle(x1)
+            else:
+                a_lo = x1
+                x1 = x2; s1 = s2; cx1 = cx2; cy1 = cy2
+                x2 = a_lo + _GOLDEN * (a_hi - a_lo)
+                s2, cx2, cy2 = _score_at_angle(x2)
         return best
 
     def _verify_ncc(
@@ -523,7 +547,16 @@ class LineShapeMatcher:
         subpixel: bool = True,
         verify_ncc: bool = True,
         verify_threshold: float = 0.4,
+        coarse_angle_factor: int = 2,
+        scale_penalty: float = 0.0,
     ) -> list[Match]:
+        """
+        scale_penalty: se > 0, riduce lo score per match a scala diversa da 1.0:
+          score_final = score_shape * max(0, 1 - scale_penalty * |scale - 1|)
+        Utile se l'operatore vuole che match "identico al template anche per
+        dimensione" abbia score più alto di match "stessa forma, dimensione
+        diversa". scale_penalty=0 (default) = comportamento shape puro.
+        """
         if not self.variants:
             raise RuntimeError("Matcher non addestrato: chiamare train() prima.")
 
@@ -564,7 +597,30 @@ class LineShapeMatcher:
         def _rescore(score: np.ndarray, bg: np.ndarray) -> np.ndarray:
             return np.maximum(0.0, (score - bg) / (1.0 - bg + 1e-6))
 
-        # Pruning varianti via top-level (parallelizzato)
+        # Coarse-to-fine angolare:
+        # 1) Raggruppa varianti per scala, ordina per angolo
+        # 2) Top-level: valuta solo 1 ogni coarse_angle_factor varianti
+        # 3) Espandi ai vicini nel full-res
+        variants_by_scale: dict[float, list[int]] = {}
+        for vi, var in enumerate(self.variants):
+            variants_by_scale.setdefault(var.scale, []).append(vi)
+
+        coarse_idx_list: list[int] = []  # varianti da valutare al top
+        neighbor_map: dict[int, list[int]] = {}  # vi_coarse -> indici vicini
+        cf = max(1, coarse_angle_factor)
+        for scale_key, vi_list in variants_by_scale.items():
+            vi_sorted = sorted(vi_list, key=lambda i: self.variants[i].angle_deg)
+            n = len(vi_sorted)
+            for i in range(0, n, cf):
+                vi_c = vi_sorted[i]
+                coarse_idx_list.append(vi_c)
+                # Vicini: ±cf/2 attorno a i (stessa scala)
+                half = cf // 2
+                start = max(0, i - half)
+                end = min(n, i + half + 1)
+                neighbor_map[vi_c] = vi_sorted[start:end]
+
+        # Pruning varianti via top-level (parallelizzato) - solo coarse
         def _top_score(vi: int) -> tuple[int, float]:
             var = self.variants[vi]
             lvl = var.levels[min(top, len(var.levels) - 1)]
@@ -574,17 +630,30 @@ class LineShapeMatcher:
             score = _rescore(score, bg_cache_top[var.scale])
             return vi, float(score.max()) if score.size else -1.0
 
-        kept_variants: list[tuple[int, float]] = []
-        if self.n_threads > 1:
+        kept_coarse: list[tuple[int, float]] = []
+        if self.n_threads > 1 and len(coarse_idx_list) > 1:
             with ThreadPoolExecutor(max_workers=self.n_threads) as ex:
-                for vi, best in ex.map(_top_score, range(len(self.variants))):
+                for vi, best in ex.map(_top_score, coarse_idx_list):
                     if best >= top_thresh:
-                        kept_variants.append((vi, best))
+                        kept_coarse.append((vi, best))
         else:
-            for vi in range(len(self.variants)):
+            for vi in coarse_idx_list:
                 vi2, best = _top_score(vi)
                 if best >= top_thresh:
-                    kept_variants.append((vi2, best))
+                    kept_coarse.append((vi2, best))
+
+        # Espandi ogni coarse promosso con i suoi vicini (stessa scala,
+        # angoli intermedi non valutati al top)
+        expanded: set[int] = set()
+        score_by_vi: dict[int, float] = {}
+        for vi_c, s_top in kept_coarse:
+            for vi_n in neighbor_map.get(vi_c, [vi_c]):
+                expanded.add(vi_n)
+                # Usa lo score del coarse come stima per il sort successivo
+                score_by_vi[vi_n] = max(score_by_vi.get(vi_n, 0.0), s_top)
+        kept_variants: list[tuple[int, float]] = [
+            (vi, score_by_vi[vi]) for vi in expanded
+        ]
 
         if not kept_variants:
             return []
@@ -685,6 +754,11 @@ class LineShapeMatcher:
             poly = _oriented_bbox_polygon(
                 cx_f, cy_f, tw * var.scale, th * var.scale, ang_f,
             )
+            # Penalità scala opzionale: score degrada con distanza da 1.0
+            if scale_penalty > 0.0 and var.scale != 1.0:
+                score_f = float(score_f) * max(
+                    0.0, 1.0 - scale_penalty * abs(var.scale - 1.0)
+                )
             kept.append(Match(
                 cx=cx_f, cy=cy_f,
                 angle_deg=ang_f,

pm2d/web/server.py

@@ -9,10 +9,12 @@ Endpoint:
 """
 from __future__ import annotations
 
+import hashlib
 import os
 import tempfile
 import time
 import uuid
+from collections import OrderedDict
 from pathlib import Path
 
 import cv2
@@ -61,6 +63,39 @@ CACHE_DIR.mkdir(exist_ok=True)
 # Cache in-memory (soft, ricaricata da disco se mancante)
 _IMG_CACHE: dict[str, np.ndarray] = {}
 
+# Cache matcher addestrati: (roi_hash, params_hash) -> LineShapeMatcher
+# LRU con capacità limitata
+_MATCHER_CACHE: OrderedDict = OrderedDict()
+_MATCHER_CACHE_SIZE = 8
+
+
+def _matcher_cache_key(roi: np.ndarray, tech: dict) -> str:
+    h = hashlib.md5()
+    h.update(roi.tobytes())
+    # Solo parametri che influenzano il training
+    relevant = ("num_features", "weak_grad", "strong_grad",
+                "angle_min", "angle_max", "angle_step",
+                "scale_min", "scale_max", "scale_step",
+                "spread_radius", "pyramid_levels")
+    for k in relevant:
+        h.update(f"{k}={tech.get(k)}".encode())
+    h.update(f"shape={roi.shape}".encode())
+    return h.hexdigest()
+
+
+def _cache_get_matcher(key: str):
+    m = _MATCHER_CACHE.get(key)
+    if m is not None:
+        _MATCHER_CACHE.move_to_end(key)  # LRU touch
+    return m
+
+
+def _cache_put_matcher(key: str, matcher) -> None:
+    _MATCHER_CACHE[key] = matcher
+    _MATCHER_CACHE.move_to_end(key)
+    while len(_MATCHER_CACHE) > _MATCHER_CACHE_SIZE:
+        _MATCHER_CACHE.popitem(last=False)
+
 
 def _store_image(img: np.ndarray) -> str:
     iid = uuid.uuid4().hex[:12]
@@ -229,6 +264,7 @@ class SimpleMatchParams(BaseModel):
     scala: str = "fissa"              # chiave SCALE_PRESETS
     precisione: str = "normale"       # chiave PRECISION_ANGLE_STEP
     filtro_fp: str = "medio"          # chiave FILTRO_FP_MAP
+    penalita_scala: float = 0.0       # 0 = score shape invariante, >0 = penalizza scala != 1
     min_score: float = 0.65
     max_matches: int = 25
 
@@ -281,6 +317,7 @@ def _simple_to_technical(
         "max_matches": p.max_matches,
         "nms_radius": 0,
         "verify_threshold": FILTRO_FP_MAP.get(p.filtro_fp, 0.35),
+        "scale_penalty": p.penalita_scala,
     }
 
 
@@ -292,6 +329,49 @@ def index():
     return HTMLResponse(html_path.read_text(encoding="utf-8"))
 
 
+@app.post("/upload_to_folder")
+async def upload_to_folder(file: UploadFile = File(...)):
+    """Salva file caricato nella cartella IMAGES_DIR. Ritorna lista aggiornata."""
+    if not IMAGES_DIR.is_dir():
+        raise HTTPException(500, f"IMAGES_DIR non esiste: {IMAGES_DIR}")
+    # Sanitizza nome file (no traversal)
+    name = Path(file.filename or "upload.png").name
+    if not name:
+        raise HTTPException(400, "nome file vuoto")
+    ext = Path(name).suffix.lower()
+    allowed = {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff"}
+    if ext not in allowed:
+        raise HTTPException(400, f"estensione non supportata: {ext}")
+    # Leggi contenuto e valida come immagine
+    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, "file non è un'immagine valida")
+    # Evita overwrite: se esiste, aggiungi suffisso numerico
+    target = IMAGES_DIR / name
+    if target.exists():
+        stem = target.stem; suffix = target.suffix
+        i = 1
+        while True:
+            alt = IMAGES_DIR / f"{stem}_{i}{suffix}"
+            if not alt.exists():
+                target = alt; break
+            i += 1
+    # Scrivi su disco
+    with open(target, "wb") as f:
+        f.write(data)
+    # Ritorna lista aggiornata
+    return {
+        "saved_as": target.name,
+        "dir": str(IMAGES_DIR),
+        "files": sorted(
+            p.name for p in IMAGES_DIR.iterdir()
+            if p.is_file() and p.suffix.lower() in allowed
+        ),
+    }
+
+
 @app.get("/folder_image/{filename}")
 def folder_image(filename: str, w: int = 120):
     """Serve thumbnail PNG dell'immagine IMAGES_DIR (scalata a width w)."""
@@ -375,17 +455,33 @@ def match(p: MatchParams):
     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
+    tech_for_cache = {
+        "num_features": p.num_features,
+        "weak_grad": p.weak_grad, "strong_grad": p.strong_grad,
+        "angle_min": p.angle_min, "angle_max": p.angle_max,
+        "angle_step": p.angle_step,
+        "scale_min": p.scale_min, "scale_max": p.scale_max,
+        "scale_step": p.scale_step,
+        "spread_radius": p.spread_radius,
+        "pyramid_levels": p.pyramid_levels,
+    }
+    key = _matcher_cache_key(roi_img, tech_for_cache)
+    m = _cache_get_matcher(key)
+    if m is None:
+        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
+        _cache_put_matcher(key, m)
+    else:
+        n = len(m.variants); t_train = 0.0
     nms = p.nms_radius if p.nms_radius > 0 else None
     t0 = time.time()
     matches = m.find(
@@ -429,22 +525,29 @@ def match_simple(p: SimpleMatchParams):
 
     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
+    key = _matcher_cache_key(roi_img, tech)
+    m = _cache_get_matcher(key)
+    if m is None:
+        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
+        _cache_put_matcher(key, m)
+    else:
+        n = len(m.variants); t_train = 0.0
     nms = tech["nms_radius"] if tech["nms_radius"] > 0 else None
     t0 = time.time()
     matches = m.find(
         scene, min_score=tech["min_score"], max_matches=tech["max_matches"],
         nms_radius=nms, verify_threshold=tech["verify_threshold"],
+        scale_penalty=tech.get("scale_penalty", 0.0),
     )
     t_find = time.time() - t0
 

pm2d/web/static/app.js

@@ -48,6 +48,8 @@ function readUserParams() {
     scala: document.getElementById("p-scala").value,
     precisione: document.getElementById("p-precisione").value,
     filtro_fp: document.getElementById("p-filtro-fp").value,
+    penalita_scala: parseFloat(
+      document.getElementById("p-penalita-scala").value),
     min_score: parseFloat(document.getElementById("p-min-score").value),
     max_matches: parseInt(document.getElementById("p-max-matches").value, 10),
   };
@@ -80,6 +82,21 @@ async function fetchImagesList() {
   return await r.json();
 }
 
+async function uploadToFolder(file) {
+  const fd = new FormData();
+  fd.append("file", file);
+  const r = await fetch("/upload_to_folder", { method: "POST", body: fd });
+  if (!r.ok) throw new Error(await r.text());
+  return await r.json();
+}
+
+async function refreshPickers() {
+  const {files, dir} = await fetchImagesList();
+  buildThumbPicker("picker-model", files, onSelectModel);
+  buildThumbPicker("picker-scene", files, onSelectScene);
+  return {files, dir};
+}
+
 function buildThumbPicker(pickerId, files, onSelect) {
   const picker = document.getElementById(pickerId);
   const current = picker.querySelector(".picker-current");
@@ -349,14 +366,28 @@ 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);
+  const {files, dir} = await refreshPickers();
   if (files.length === 0) {
-    setStatus(`Nessuna immagine in ${dir} (configura IMAGES_DIR in .env)`);
+    setStatus(`Nessuna immagine in ${dir} (carica file o configura IMAGES_DIR)`);
   } else {
-    setStatus(`${files.length} immagini disponibili in ${dir}`);
+    setStatus(`${files.length} immagini in ${dir}`);
   }
+
+  // Upload file nella folder
+  const upEl = document.getElementById("file-upload");
+  upEl.addEventListener("change", async (e) => {
+    const f = e.target.files[0];
+    if (!f) return;
+    setStatus(`Caricamento ${f.name} nella cartella...`);
+    try {
+      const res = await uploadToFolder(f);
+      await refreshPickers();
+      setStatus(`Salvato come ${res.saved_as} (${res.files.length} file totali)`);
+    } catch (err) {
+      setStatus(`Errore upload: ${err.message}`);
+    }
+    e.target.value = "";  // consente re-upload stesso file
+  });
   document.getElementById("btn-match").addEventListener("click", doMatch);
   const slider = document.getElementById("p-min-score");
   slider.addEventListener("input", (e) => {

pm2d/web/static/index.html

@@ -26,6 +26,10 @@
       <div class="picker-list"></div>
     </div>
     <button class="btn btn-go" id="btn-match">▶ MATCH</button>
+    <label class="btn" title="Carica nuovo file nella cartella immagini">
+      ⬆ Carica file
+      <input type="file" id="file-upload" accept="image/*" hidden>
+    </label>
     <span id="status">Seleziona modello, disegna ROI, seleziona scena</span>
   </div>
 </header>
@@ -101,6 +105,18 @@
       </select>
     </div>
 
+    <div class="field">
+      <label>Peso dimensione nel score
+        <span class="hint">(penalizza scala ≠ 1.0)</span>
+      </label>
+      <select id="p-penalita-scala">
+        <option value="0" selected>Nessuno (score shape puro)</option>
+        <option value="0.3">Leggero (−30% max)</option>
+        <option value="0.5">Medio (−50% max)</option>
+        <option value="0.8">Forte (−80% max)</option>
+      </select>
+    </div>
+
     <div class="field">
       <label>Score minimo <span id="v-score">0.65</span>
         <span class="hint">(più basso = più match anche incerti)</span>