Adriano
d37833076e
L'UCS del match precedentemente proiettava il baricentro feature template alla pose, ma: - Il baricentro veniva calcolato da una variante a 0° (v0) i cui dx/dy sono offsets relativi al centro PADDED (non al centro template puro) - _extract_features dipende dai parametri matcher che possono differire da quelli del preview se la ricetta e' caricata - Risultato: UCS appariva con offset costante errato rispetto al centro visibile del pezzo Fix: UCS sul centro POSE del match (m.cx, m.cy) = posizione del centro template originale nella scena (questo e' esattamente cio' che _subpixel_peak ritorna). Coerente, prevedibile, "fissato" sul centro del pezzo. Per coerenza visiva, anche preview_edges sposta UCS dal baricentro al CENTRO ROI (rh/2, rw/2). Cosi' il modello mostra UCS nello stesso identico punto relativo dove apparira' nel match dopo traslazione+rotazione della pose. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
949 lines
34 KiB
Python
949 lines
34 KiB
Python
"""FastAPI webapp standalone per PM2D.
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Endpoint:
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GET / → HTML UI
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POST /upload → upload immagine (multipart)
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POST /match → JSON params + ids → results
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GET /image/{id}/raw → PNG originale
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GET /image/{id}/annotated → PNG con overlay match
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"""
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from __future__ import annotations
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import hashlib
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import os
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import tempfile
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import time
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import uuid
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from collections import OrderedDict
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from pathlib import Path
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import cv2
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import numpy as np
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from fastapi import FastAPI, File, HTTPException, UploadFile
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from fastapi.responses import HTMLResponse, Response
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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def _load_env(root: Path) -> None:
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"""Legge .env in root e popola os.environ (no override se già set)."""
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f = root / ".env"
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if not f.exists():
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return
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for line in f.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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k, v = line.split("=", 1)
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k = k.strip(); v = v.strip().strip('"').strip("'")
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os.environ.setdefault(k, v)
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# Root progetto (parent di pm2d/)
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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_load_env(PROJECT_ROOT)
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_images_dir_raw = os.environ.get("IMAGES_DIR", "Test")
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IMAGES_DIR = Path(_images_dir_raw)
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if not IMAGES_DIR.is_absolute():
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IMAGES_DIR = PROJECT_ROOT / IMAGES_DIR
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# Cartella ricette pre-trained (V feature: save/load matcher)
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RECIPES_DIR = PROJECT_ROOT / "recipes"
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RECIPES_DIR.mkdir(exist_ok=True)
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from pm2d.line_matcher import LineShapeMatcher, Match
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from pm2d.auto_tune import auto_tune
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WEB_DIR = Path(__file__).parent
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STATIC_DIR = WEB_DIR / "static"
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STATIC_DIR.mkdir(exist_ok=True)
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# Persistenza immagini su disco (sopravvive a restart server)
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CACHE_DIR = Path(tempfile.gettempdir()) / "pm2d_cache"
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CACHE_DIR.mkdir(exist_ok=True)
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# Cache in-memory (soft, ricaricata da disco se mancante)
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_IMG_CACHE: dict[str, np.ndarray] = {}
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# Cache matcher addestrati: (roi_hash, params_hash) -> LineShapeMatcher
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# LRU con capacità limitata
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_MATCHER_CACHE: OrderedDict = OrderedDict()
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_MATCHER_CACHE_SIZE = 8
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def _matcher_cache_key(roi: np.ndarray, tech: dict) -> str:
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h = hashlib.md5()
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h.update(roi.tobytes())
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# Solo parametri che influenzano il training
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relevant = ("num_features", "weak_grad", "strong_grad",
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"min_feature_spacing",
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"angle_min", "angle_max", "angle_step",
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"scale_min", "scale_max", "scale_step",
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"spread_radius", "pyramid_levels")
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for k in relevant:
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h.update(f"{k}={tech.get(k)}".encode())
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h.update(f"shape={roi.shape}".encode())
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return h.hexdigest()
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def _cache_get_matcher(key: str):
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m = _MATCHER_CACHE.get(key)
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if m is not None:
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_MATCHER_CACHE.move_to_end(key) # LRU touch
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return m
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def _cache_put_matcher(key: str, matcher) -> None:
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_MATCHER_CACHE[key] = matcher
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_MATCHER_CACHE.move_to_end(key)
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while len(_MATCHER_CACHE) > _MATCHER_CACHE_SIZE:
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_MATCHER_CACHE.popitem(last=False)
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def _store_image(img: np.ndarray) -> str:
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iid = uuid.uuid4().hex[:12]
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cv2.imwrite(str(CACHE_DIR / f"{iid}.png"), img)
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_IMG_CACHE[iid] = img
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return iid
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def _load_image(iid: str) -> np.ndarray | None:
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cached = _IMG_CACHE.get(iid)
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if cached is not None:
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return cached
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p = CACHE_DIR / f"{iid}.png"
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if not p.exists():
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return None
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img = cv2.imread(str(p))
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if img is not None:
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_IMG_CACHE[iid] = img
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return img
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app = FastAPI(title="PM2D Webapp", version="1.0.0")
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def _encode_png(img: np.ndarray) -> bytes:
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ok, buf = cv2.imencode(".png", img)
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if not ok:
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raise RuntimeError("PNG encode failed")
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return buf.tobytes()
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def _draw_matches(scene: np.ndarray, matches: list[Match],
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template_gray: np.ndarray | None,
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matcher: "LineShapeMatcher | None" = None) -> np.ndarray:
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"""Disegna SOLO UCS (richiesta utente) per ogni match trovato.
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UCS = sistema di coordinate (X rosso, Y verde) posizionato sul
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baricentro feature del modello, ruotato secondo l'angolo del match.
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Niente edge, niente cerchietti feature, niente bbox: i match sulla
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scena reale devono essere puliti, gli edge filtrati si vedono solo
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nell'anteprima modello.
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"""
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out = scene.copy()
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# Lunghezza assi UCS: stessa formula dell'anteprima modello
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# (0.15 * max lato template) scalata per m.scale → coerenza dimensionale.
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if matcher is not None and matcher.template_size != (0, 0):
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L_base = int(0.15 * max(matcher.template_size))
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else:
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L_base = 30
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H_scene, W_scene = scene.shape[:2]
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for i, m in enumerate(matches):
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# UCS posizionato esattamente sul CENTRO POSE del match (m.cx, m.cy):
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# equivale al centro template traslato alla scena, ruotato con
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# m.angle_deg. Coerente con UCS dell'anteprima modello che ora
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# e' anche sul centro ROI (vedi preview_edges).
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ax = np.deg2rad(m.angle_deg)
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ca, sa = np.cos(ax), np.sin(ax)
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cx, cy = int(round(m.cx)), int(round(m.cy))
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# Overlay edge del modello orientato (richiesta utente):
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# warpa template alla pose, applica hysteresis identica al matcher,
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# disegna pixel edge come overlay verde tenue.
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if template_gray is not None and matcher is not None:
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t = template_gray
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th, tw = t.shape
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cx_t = (tw - 1) / 2.0; cy_t = (th - 1) / 2.0
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M = cv2.getRotationMatrix2D((cx_t, cy_t), m.angle_deg, m.scale)
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M[0, 2] += m.cx - cx_t
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M[1, 2] += m.cy - cy_t
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warped_gray = cv2.warpAffine(
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t, M, (W_scene, H_scene),
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flags=cv2.INTER_LINEAR, borderValue=0)
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mag, _ = matcher._gradient(warped_gray)
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if matcher.weak_grad < matcher.strong_grad:
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edge_mask = matcher._hysteresis_mask(mag)
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else:
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edge_mask = mag >= matcher.strong_grad
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if edge_mask.any():
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edge_overlay = np.zeros_like(out)
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edge_overlay[edge_mask] = (0, 220, 0) # verde brillante
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out = cv2.addWeighted(out, 1.0, edge_overlay, 0.6, 0)
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L = max(20, int(L_base * m.scale))
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# X axis = rotazione di (1, 0) con cv2 matrix → (cos, -sin)
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x_end = (int(cx + L * ca), int(cy - L * sa))
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# Y axis = rotazione di (0, 1) con cv2 matrix → (sin, cos)
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# A m.angle_deg=0 deve puntare GIU' (image y-down convenzione modello)
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y_end = (int(cx + L * sa), int(cy + L * ca))
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cv2.arrowedLine(out, (cx, cy), x_end,
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(0, 0, 255), 2, cv2.LINE_AA, tipLength=0.2)
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cv2.putText(out, "X", (x_end[0] + 4, x_end[1] + 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
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cv2.arrowedLine(out, (cx, cy), y_end,
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(0, 255, 0), 2, cv2.LINE_AA, tipLength=0.2)
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cv2.putText(out, "Y", (y_end[0] + 4, y_end[1] + 12),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
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# Origine UCS: cerchio bianco con bordo nero
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cv2.circle(out, (cx, cy), 4, (0, 0, 0), -1, cv2.LINE_AA)
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cv2.circle(out, (cx, cy), 3, (255, 255, 255), -1, cv2.LINE_AA)
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return out
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# ---------------- Models ----------------
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class UploadResp(BaseModel):
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id: str
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width: int
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height: int
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class MatchParams(BaseModel):
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model_id: str
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scene_id: str
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roi: list[int] # [x, y, w, h] nell'immagine modello
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angle_min: float = 0.0
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angle_max: float = 360.0
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angle_step: float = 5.0
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scale_min: float = 1.0
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scale_max: float = 1.0
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scale_step: float = 0.1
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min_score: float = 0.55
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max_matches: int = 25
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nms_radius: int = 0
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num_features: int = 96
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weak_grad: float = 30.0
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strong_grad: float = 60.0
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spread_radius: int = 5
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pyramid_levels: int = 3
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verify_threshold: float = 0.4
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class MatchResult(BaseModel):
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cx: float
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cy: float
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angle_deg: float
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scale: float
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score: float
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bbox_poly: list[list[float]]
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class MatchResp(BaseModel):
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matches: list[MatchResult]
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train_time: float
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find_time: float
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num_variants: int
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annotated_id: str
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diag: dict | None = None # CC: diagnostica pipeline (drop reasons)
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class TuneParams(BaseModel):
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model_id: str
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roi: list[int]
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# ---------- User-facing (simple) params ----------
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SYMMETRY_TO_ANGLE_MAX = {
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"invariante": 0.0, # oggetto simmetrico a rotazione totale (cerchi): 1 variante
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"nessuna": 360.0,
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"bilaterale": 180.0,
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"rot_3": 120.0,
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"rot_4": 90.0,
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"rot_6": 60.0,
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"rot_8": 45.0,
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}
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SCALE_PRESETS = {
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"fissa": (1.0, 1.0, 0.1),
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"mini": (0.9, 1.1, 0.05), # ±10%
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"medio": (0.75, 1.25, 0.05), # ±25%
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"max": (0.5, 1.5, 0.05), # ±50%
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}
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PRECISION_ANGLE_STEP = {
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"veloce": 10.0,
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"normale": 5.0,
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"preciso": 2.0,
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}
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# "Filtro falsi positivi" = mapping semantico del verify NCC threshold.
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# Un operatore sceglie il livello di rigore, non un numero astratto.
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FILTRO_FP_MAP = {
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"off": 0.0, # disabilitato: mantieni tutti i match shape-based
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"leggero": 0.30, # tollera variazioni intensità/illuminazione forti
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"medio": 0.50, # default bilanciato (consigliato)
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"forte": 0.70, # scarta match con intensità molto diversa dal template
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}
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class SimpleMatchParams(BaseModel):
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model_id: str
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scene_id: str
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roi: list[int]
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tipo: str = "intero" # "intero" | "parziale"
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simmetria: str = "nessuna" # chiave SYMMETRY_TO_ANGLE_MAX
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scala: str = "fissa" # chiave SCALE_PRESETS
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precisione: str = "normale" # chiave PRECISION_ANGLE_STEP
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filtro_fp: str = "medio" # chiave FILTRO_FP_MAP
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penalita_scala: float = 0.0 # 0 = score shape invariante, >0 = penalizza scala != 1
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min_score: float = 0.65
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max_matches: int = 25
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# --- Override edge da pannello "Anteprima edge" (None = auto_tune) ---
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# Quando settati, sovrascrivono i valori derivati da auto_tune e
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# vengono usati identici sia nel training del matcher sia nel find.
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# Salvati nella ricetta cosi' la stessa pulizia rumore e' replicata
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# quando la ricetta viene caricata.
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edge_weak_grad: float | None = None
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edge_strong_grad: float | None = None
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edge_num_features: int | None = None
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edge_min_feature_spacing: int | None = None
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# --- Halcon-mode flags (default off = backward compat) ---
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# Init-time (richiede ri-train se cambiato)
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use_polarity: bool = False # F: 16 bin orientation mod 2pi
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use_gpu: bool = False # R: OpenCL UMat (silent fallback)
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# Find-time (no retrain)
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min_recall: float = 0.0 # M: filtra match con poche feature combaciate
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use_soft_score: bool = False # Y: cosine sim continua dei gradients
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subpixel_lm: bool = False # Z: precisione 0.05 px
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nms_iou_threshold: float = 0.3 # A: IoU bbox poligonale
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coarse_stride: int = 1 # sub-sampling top-level (>=1)
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pyramid_propagate: bool = False # propagazione candidati top->full
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greediness: float = 0.0 # early-exit kernel (0..1)
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refine_pose_joint: bool = False # Nelder-Mead 3D (cx, cy, angle)
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search_roi: list[int] | None = None # [x, y, w, h] limita area
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def _simple_to_technical(
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p: SimpleMatchParams, roi_img: np.ndarray,
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) -> dict:
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"""Converti parametri user-facing → tecnici usando analisi della ROI."""
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from pm2d.auto_tune import auto_tune as _auto
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tune = _auto(roi_img)
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h, w = roi_img.shape[:2]
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min_side = min(h, w)
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# Feature count: parziale = meno feature (area minore)
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nf = tune["num_features"]
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if p.tipo == "parziale":
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nf = max(32, int(nf * 0.6))
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# Piramide derivata da dimensione ROI
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if min_side < 60:
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pyr = 1
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elif min_side < 150:
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pyr = 2
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elif min_side < 400:
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pyr = 3
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else:
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pyr = 4
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# Spread radius ~2-3% del lato minimo
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spread = max(3, min(10, int(round(min_side * 0.03))))
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angle_max = SYMMETRY_TO_ANGLE_MAX.get(p.simmetria, 360.0)
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smin, smax, sstep = SCALE_PRESETS.get(p.scala, (1.0, 1.0, 0.1))
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ang_step = PRECISION_ANGLE_STEP.get(p.precisione, 5.0)
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# Override edge dal pannello "Anteprima edge" se utente li ha settati.
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# Questi sostituiscono i valori auto_tune nel training del matcher,
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# garantendo che la selezione edge identica a quella del preview
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# venga usata sia in training sia in find.
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weak_g = (p.edge_weak_grad if p.edge_weak_grad is not None
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else tune["weak_grad"])
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strong_g = (p.edge_strong_grad if p.edge_strong_grad is not None
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else tune["strong_grad"])
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n_feat = (p.edge_num_features if p.edge_num_features is not None
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else nf)
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min_sp = (p.edge_min_feature_spacing if p.edge_min_feature_spacing is not None
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else 3)
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return {
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"num_features": n_feat,
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"weak_grad": weak_g,
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"strong_grad": strong_g,
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"min_feature_spacing": min_sp,
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"spread_radius": spread,
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"pyramid_levels": pyr,
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"angle_min": 0.0,
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"angle_max": angle_max,
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"angle_step": ang_step,
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"scale_min": smin,
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"scale_max": smax,
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"scale_step": sstep,
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"min_score": p.min_score,
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"max_matches": p.max_matches,
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"nms_radius": 0,
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"verify_threshold": FILTRO_FP_MAP.get(p.filtro_fp, 0.35),
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"scale_penalty": p.penalita_scala,
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}
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# ---------------- Endpoints ----------------
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@app.get("/", response_class=HTMLResponse)
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def index():
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html_path = STATIC_DIR / "index.html"
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return HTMLResponse(html_path.read_text(encoding="utf-8"))
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@app.post("/upload_to_folder")
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async def upload_to_folder(file: UploadFile = File(...)):
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"""Salva file caricato nella cartella IMAGES_DIR. Ritorna lista aggiornata."""
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if not IMAGES_DIR.is_dir():
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raise HTTPException(500, f"IMAGES_DIR non esiste: {IMAGES_DIR}")
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# Sanitizza nome file (no traversal)
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name = Path(file.filename or "upload.png").name
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if not name:
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raise HTTPException(400, "nome file vuoto")
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ext = Path(name).suffix.lower()
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allowed = {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff"}
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if ext not in allowed:
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raise HTTPException(400, f"estensione non supportata: {ext}")
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# Leggi contenuto e valida come immagine
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data = await file.read()
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arr = np.frombuffer(data, dtype=np.uint8)
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img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
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if img is None:
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raise HTTPException(400, "file non è un'immagine valida")
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# Evita overwrite: se esiste, aggiungi suffisso numerico
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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)."""
|
|
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]
|
|
|
|
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(
|
|
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, matcher=m)
|
|
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,
|
|
diag=m.get_last_diag() if hasattr(m, "get_last_diag") else None,
|
|
)
|
|
|
|
|
|
@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)
|
|
|
|
key = _matcher_cache_key(roi_img, tech)
|
|
# Halcon-mode init params: incidono sul training, includere in cache key
|
|
halcon_init_key = f"|pol={p.use_polarity}|gpu={p.use_gpu}"
|
|
key = key + halcon_init_key
|
|
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"],
|
|
min_feature_spacing=tech.get("min_feature_spacing", 3),
|
|
pyramid_levels=tech["pyramid_levels"],
|
|
use_polarity=p.use_polarity,
|
|
use_gpu=p.use_gpu,
|
|
)
|
|
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
|
|
search_roi_t = tuple(p.search_roi) if p.search_roi 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),
|
|
# Halcon-mode flags
|
|
min_recall=p.min_recall,
|
|
use_soft_score=p.use_soft_score,
|
|
subpixel_lm=p.subpixel_lm,
|
|
nms_iou_threshold=p.nms_iou_threshold,
|
|
coarse_stride=p.coarse_stride,
|
|
pyramid_propagate=p.pyramid_propagate,
|
|
greediness=p.greediness,
|
|
refine_pose_joint=p.refine_pose_joint,
|
|
search_roi=search_roi_t,
|
|
)
|
|
t_find = time.time() - t0
|
|
|
|
tg = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
|
|
annotated = _draw_matches(scene, matches, tg, matcher=m)
|
|
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,
|
|
diag=m.get_last_diag() if hasattr(m, "get_last_diag") else None,
|
|
)
|
|
|
|
|
|
@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)
|
|
# Esponi parametri tecnici + meta diagnostica (_self_score, _validation,
|
|
# _symmetry_order, _orient_entropy) per feedback UI.
|
|
return t
|
|
|
|
|
|
# --- V: Save/Load ricette pre-trained ---
|
|
|
|
class SaveRecipeParams(BaseModel):
|
|
model_id: str
|
|
scene_id: str | None = None
|
|
roi: list[int]
|
|
# Riusa stessi param simple per training equivalente
|
|
tipo: str = "intero"
|
|
simmetria: str = "nessuna"
|
|
scala: str = "fissa"
|
|
precisione: str = "normale"
|
|
use_polarity: bool = False
|
|
use_gpu: bool = False
|
|
# Override edge dal pannello "Anteprima edge" (None = auto_tune)
|
|
edge_weak_grad: float | None = None
|
|
edge_strong_grad: float | None = None
|
|
edge_num_features: int | None = None
|
|
edge_min_feature_spacing: int | None = None
|
|
name: str # nome file ricetta (no path)
|
|
|
|
|
|
class EdgePreviewParams(BaseModel):
|
|
model_id: str
|
|
roi: list[int]
|
|
weak_grad: float = 30.0
|
|
strong_grad: float = 60.0
|
|
num_features: int = 96
|
|
min_feature_spacing: int = 3
|
|
use_polarity: bool = False
|
|
|
|
|
|
@app.post("/preview_edges")
|
|
def preview_edges(p: EdgePreviewParams):
|
|
"""Estrae edge feature dalla ROI con i parametri dati e ritorna
|
|
immagine annotata con i pixel selezionati come overlay.
|
|
|
|
Permette tuning interattivo delle soglie weak/strong_grad e
|
|
num_features per "togliere le sporcizie" (rumore di sfondo,
|
|
edge spuri) prima di trainare il matcher vero.
|
|
"""
|
|
model = _load_image(p.model_id)
|
|
if model is None:
|
|
raise HTTPException(404, "Modello non trovato")
|
|
x, y, w, h = p.roi
|
|
H_m, W_m = model.shape[:2]
|
|
x = max(0, min(int(x), W_m - 1)); y = max(0, min(int(y), H_m - 1))
|
|
w = max(1, min(int(w), W_m - x)); h = max(1, min(int(h), H_m - y))
|
|
roi_img = model[y:y + h, x:x + w]
|
|
# Matcher temporaneo solo per estrazione feature (no train completo)
|
|
m = LineShapeMatcher(
|
|
weak_grad=p.weak_grad,
|
|
strong_grad=p.strong_grad,
|
|
num_features=p.num_features,
|
|
min_feature_spacing=p.min_feature_spacing,
|
|
use_polarity=p.use_polarity,
|
|
)
|
|
gray = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY) if roi_img.ndim == 3 else roi_img
|
|
mag, bins = m._gradient(gray)
|
|
fx, fy, fb = m._extract_features(mag, bins, None)
|
|
# Mostra anche i pixel "weak/strong" come heatmap di sfondo
|
|
out = roi_img.copy() if roi_img.ndim == 3 else cv2.cvtColor(roi_img, cv2.COLOR_GRAY2BGR)
|
|
# Overlay magnitude leggera
|
|
mag_norm = np.clip(mag / max(1.0, mag.max()) * 255, 0, 255).astype(np.uint8)
|
|
mag_color = cv2.applyColorMap(mag_norm, cv2.COLORMAP_BONE)
|
|
out = cv2.addWeighted(out, 0.6, mag_color, 0.4, 0)
|
|
# Pixel "strong" con hysteresis: contorno verde scuro tenue
|
|
if m.weak_grad < m.strong_grad:
|
|
edge_mask = m._hysteresis_mask(mag).astype(np.uint8) * 255
|
|
else:
|
|
edge_mask = (mag >= m.strong_grad).astype(np.uint8) * 255
|
|
edge_overlay = np.zeros_like(out)
|
|
edge_overlay[edge_mask > 0] = (0, 80, 0) # verde scuro
|
|
out = cv2.addWeighted(out, 1.0, edge_overlay, 0.5, 0)
|
|
# Feature scelte: cerchietti colorati per bin
|
|
bin_colors = [
|
|
(255, 0, 0), (255, 128, 0), (255, 255, 0), (0, 255, 0),
|
|
(0, 255, 255), (0, 128, 255), (0, 0, 255), (255, 0, 255),
|
|
(255, 100, 100), (255, 180, 100), (255, 230, 100), (180, 255, 100),
|
|
(100, 255, 200), (100, 180, 255), (180, 100, 255), (255, 100, 200),
|
|
]
|
|
for i in range(len(fx)):
|
|
b = int(fb[i])
|
|
col = bin_colors[b % len(bin_colors)]
|
|
cv2.circle(out, (int(fx[i]), int(fy[i])), 2, col, -1, cv2.LINE_AA)
|
|
# UCS sul CENTRO ROI (coerente con _draw_matches che usa centro pose).
|
|
# In questo modo l'UCS visualizzato nel modello = UCS del match (modulo
|
|
# rotazione/traslazione data dalla pose del pezzo trovato).
|
|
rh, rw = roi_img.shape[:2]
|
|
bx, by = (rw - 1) // 2, (rh - 1) // 2
|
|
axis_len = max(20, int(0.15 * max(rw, rh)))
|
|
cv2.arrowedLine(out, (bx, by), (bx + axis_len, by),
|
|
(0, 0, 255), 2, cv2.LINE_AA, tipLength=0.2)
|
|
cv2.putText(out, "X", (bx + axis_len + 4, by + 5),
|
|
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
|
|
cv2.arrowedLine(out, (bx, by), (bx, by + axis_len),
|
|
(0, 255, 0), 2, cv2.LINE_AA, tipLength=0.2)
|
|
cv2.putText(out, "Y", (bx + 4, by + axis_len + 12),
|
|
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
|
|
cv2.circle(out, (bx, by), 4, (0, 0, 0), -1, cv2.LINE_AA)
|
|
cv2.circle(out, (bx, by), 3, (255, 255, 255), -1, cv2.LINE_AA)
|
|
bary_cx, bary_cy = float(bx), float(by)
|
|
img_id = _store_image(out)
|
|
n_edge_strong = int((mag >= m.strong_grad).sum())
|
|
n_edge_total = int(edge_mask.sum() / 255)
|
|
return {
|
|
"preview_id": img_id,
|
|
"n_features": len(fx),
|
|
"n_edge_strong": n_edge_strong,
|
|
"n_edge_after_hysteresis": n_edge_total,
|
|
"mag_max": float(mag.max()),
|
|
"mag_p50": float(np.percentile(mag, 50)),
|
|
"mag_p85": float(np.percentile(mag, 85)),
|
|
"ucs_baricentro": (
|
|
{"cx": round(bary_cx, 2), "cy": round(bary_cy, 2)}
|
|
if bary_cx is not None else None
|
|
),
|
|
}
|
|
|
|
|
|
@app.post("/recipes")
|
|
def save_recipe(p: SaveRecipeParams):
|
|
"""Allena matcher e salva su disco come ricetta riutilizzabile."""
|
|
model = _load_image(p.model_id)
|
|
if model is None:
|
|
raise HTTPException(404, "Modello non trovato")
|
|
x, y, w, h = p.roi
|
|
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,
|
|
tipo=p.tipo, simmetria=p.simmetria, scala=p.scala,
|
|
precisione=p.precisione,
|
|
use_polarity=p.use_polarity, use_gpu=p.use_gpu,
|
|
edge_weak_grad=p.edge_weak_grad,
|
|
edge_strong_grad=p.edge_strong_grad,
|
|
edge_num_features=p.edge_num_features,
|
|
edge_min_feature_spacing=p.edge_min_feature_spacing,
|
|
)
|
|
tech = _simple_to_technical(sp, 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"],
|
|
use_polarity=p.use_polarity,
|
|
use_gpu=p.use_gpu,
|
|
)
|
|
m.train(roi_img)
|
|
safe_name = "".join(c for c in p.name if c.isalnum() or c in "._-")
|
|
if not safe_name:
|
|
raise HTTPException(400, "Nome ricetta non valido")
|
|
if not safe_name.endswith(".npz"):
|
|
safe_name += ".npz"
|
|
target = RECIPES_DIR / safe_name
|
|
m.save_model(str(target))
|
|
return {"name": safe_name, "size": target.stat().st_size,
|
|
"n_variants": len(m.variants)}
|
|
|
|
|
|
@app.get("/recipes")
|
|
def list_recipes():
|
|
files = []
|
|
if RECIPES_DIR.is_dir():
|
|
for f in sorted(RECIPES_DIR.glob("*.npz")):
|
|
files.append({"name": f.name, "size": f.stat().st_size})
|
|
return {"files": files, "dir": str(RECIPES_DIR)}
|
|
|
|
|
|
# Cache di matcher caricati da .npz (V feature). Key: nome ricetta.
|
|
_RECIPE_MATCHERS: OrderedDict = OrderedDict()
|
|
_RECIPE_MATCHERS_SIZE = 4
|
|
|
|
|
|
@app.post("/recipes/{name}/load")
|
|
def load_recipe(name: str):
|
|
"""Carica ricetta .npz e popola cache matcher in memoria.
|
|
|
|
Una volta caricata, /match_recipe la usa direttamente senza
|
|
re-train. Halcon-equivalent read_shape_model + handle.
|
|
"""
|
|
safe_name = "".join(c for c in name if c.isalnum() or c in "._-")
|
|
if not safe_name.endswith(".npz"):
|
|
safe_name += ".npz"
|
|
path = RECIPES_DIR / safe_name
|
|
if not path.is_file():
|
|
raise HTTPException(404, f"Ricetta non trovata: {safe_name}")
|
|
m = LineShapeMatcher.load_model(str(path))
|
|
_RECIPE_MATCHERS[safe_name] = m
|
|
_RECIPE_MATCHERS.move_to_end(safe_name)
|
|
while len(_RECIPE_MATCHERS) > _RECIPE_MATCHERS_SIZE:
|
|
_RECIPE_MATCHERS.popitem(last=False)
|
|
return {
|
|
"name": safe_name,
|
|
"n_variants": len(m.variants),
|
|
"template_size": list(m.template_size),
|
|
"use_polarity": m.use_polarity,
|
|
}
|
|
|
|
|
|
class RecipeMatchParams(BaseModel):
|
|
recipe: str
|
|
scene_id: str
|
|
# Solo find-time params (training gia' fatto offline)
|
|
min_score: float = 0.65
|
|
max_matches: int = 25
|
|
min_recall: float = 0.0
|
|
use_soft_score: bool = False
|
|
subpixel_lm: bool = False
|
|
nms_iou_threshold: float = 0.3
|
|
coarse_stride: int = 1
|
|
pyramid_propagate: bool = False
|
|
greediness: float = 0.0
|
|
refine_pose_joint: bool = False
|
|
search_roi: list[int] | None = None
|
|
verify_threshold: float = 0.5
|
|
scale_penalty: float = 0.0
|
|
|
|
|
|
@app.post("/match_recipe", response_model=MatchResp)
|
|
def match_recipe(p: RecipeMatchParams):
|
|
"""Match con ricetta pre-trained: zero training, solo find."""
|
|
safe_name = p.recipe if p.recipe.endswith(".npz") else f"{p.recipe}.npz"
|
|
m = _RECIPE_MATCHERS.get(safe_name)
|
|
if m is None:
|
|
# Auto-load on demand
|
|
path = RECIPES_DIR / safe_name
|
|
if not path.is_file():
|
|
raise HTTPException(404, f"Ricetta non trovata: {safe_name}")
|
|
m = LineShapeMatcher.load_model(str(path))
|
|
_RECIPE_MATCHERS[safe_name] = m
|
|
scene = _load_image(p.scene_id)
|
|
if scene is None:
|
|
raise HTTPException(404, "Scena non trovata")
|
|
search_roi_t = tuple(p.search_roi) if p.search_roi else None
|
|
t0 = time.time()
|
|
matches = m.find(
|
|
scene,
|
|
min_score=p.min_score, max_matches=p.max_matches,
|
|
verify_threshold=p.verify_threshold,
|
|
scale_penalty=p.scale_penalty,
|
|
min_recall=p.min_recall,
|
|
use_soft_score=p.use_soft_score,
|
|
subpixel_lm=p.subpixel_lm,
|
|
nms_iou_threshold=p.nms_iou_threshold,
|
|
coarse_stride=p.coarse_stride,
|
|
pyramid_propagate=p.pyramid_propagate,
|
|
greediness=p.greediness,
|
|
refine_pose_joint=p.refine_pose_joint,
|
|
search_roi=search_roi_t,
|
|
)
|
|
t_find = time.time() - t0
|
|
tg = m.template_gray if m.template_gray is not None else np.zeros((1, 1), np.uint8)
|
|
annotated = _draw_matches(scene, matches, tg, matcher=m)
|
|
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=0.0, find_time=t_find,
|
|
num_variants=len(m.variants), annotated_id=ann_id,
|
|
diag=m.get_last_diag() if hasattr(m, "get_last_diag") else None,
|
|
)
|
|
|
|
|
|
# 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()
|