Adriano
45e3a29ff0
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>
402 lines
12 KiB
Python
402 lines
12 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 tempfile
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import time
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import uuid
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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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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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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) -> np.ndarray:
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out = scene.copy()
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H, W = scene.shape[:2]
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palette = [
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(0, 255, 0), (0, 200, 255), (255, 100, 100), (255, 200, 0),
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(200, 0, 255), (100, 255, 200), (255, 0, 0), (0, 255, 255),
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]
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for i, m in enumerate(matches):
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color = palette[i % len(palette)]
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if template_gray is not None:
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t = template_gray
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th, tw = t.shape
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edge = cv2.Canny(t, 50, 150)
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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 = cv2.warpAffine(edge, M, (W, H),
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flags=cv2.INTER_NEAREST, borderValue=0)
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mask = warped > 0
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if mask.any():
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overlay = np.zeros_like(out)
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overlay[mask] = color
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out[mask] = (0.3 * out[mask] + 0.7 * overlay[mask]).astype(np.uint8)
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poly = m.bbox_poly.astype(np.int32).reshape(-1, 1, 2)
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cv2.polylines(out, [poly], True, color, 2, cv2.LINE_AA)
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p0 = tuple(m.bbox_poly[0].astype(int))
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p1 = tuple(m.bbox_poly[1].astype(int))
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cv2.line(out, p0, p1, color, 4, cv2.LINE_AA)
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cx, cy = int(round(m.cx)), int(round(m.cy))
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cv2.drawMarker(out, (cx, cy), color, cv2.MARKER_CROSS, 22, 2, cv2.LINE_AA)
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L = int(np.linalg.norm(m.bbox_poly[1] - m.bbox_poly[0])) // 2
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a = np.deg2rad(m.angle_deg)
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cv2.arrowedLine(out, (cx, cy),
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(int(cx + L * np.cos(a)), int(cy - L * np.sin(a))),
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color, 2, cv2.LINE_AA, tipLength=0.2)
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label = f"#{i+1} {m.angle_deg:.0f}d s={m.scale:.2f} {m.score:.2f}"
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cv2.putText(out, label, (cx + 8, cy - 8),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, 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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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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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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min_score: float = 0.70
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max_matches: int = 25
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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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return {
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"num_features": nf,
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"weak_grad": tune["weak_grad"],
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"strong_grad": tune["strong_grad"],
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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 NCC più permissivo per match con variazione intensità
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# (foro su sfondo variabile, parti di oggetto ecc.)
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"verify_threshold": 0.25,
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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", response_model=UploadResp)
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async def upload(file: UploadFile = File(...)):
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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, "Immagine non valida")
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iid = _store_image(img)
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return UploadResp(id=iid, width=img.shape[1], height=img.shape[0])
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@app.get("/image/{iid}/raw")
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def image_raw(iid: str):
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img = _load_image(iid)
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if img is None:
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raise HTTPException(404, "Image not found")
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return Response(_encode_png(img), media_type="image/png")
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@app.post("/match", response_model=MatchResp)
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def match(p: MatchParams):
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model = _load_image(p.model_id)
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scene = _load_image(p.scene_id)
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if model is None or scene is None:
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raise HTTPException(404, "Immagini non trovate")
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x, y, w, h = p.roi
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x = max(0, x); y = max(0, y)
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w = max(1, min(w, model.shape[1] - x))
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h = max(1, min(h, model.shape[0] - y))
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roi_img = model[y:y + h, x:x + w]
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m = LineShapeMatcher(
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num_features=p.num_features,
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weak_grad=p.weak_grad, strong_grad=p.strong_grad,
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angle_range_deg=(p.angle_min, p.angle_max),
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angle_step_deg=p.angle_step,
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scale_range=(p.scale_min, p.scale_max),
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scale_step=p.scale_step,
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spread_radius=p.spread_radius,
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pyramid_levels=p.pyramid_levels,
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)
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t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
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nms = p.nms_radius if p.nms_radius > 0 else None
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t0 = time.time()
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matches = m.find(
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scene, min_score=p.min_score, max_matches=p.max_matches,
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nms_radius=nms, verify_threshold=p.verify_threshold,
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)
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t_find = time.time() - t0
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# Render annotated image
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tg = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
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annotated = _draw_matches(scene, matches, tg)
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ann_id = _store_image(annotated)
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return MatchResp(
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matches=[MatchResult(
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cx=m_.cx, cy=m_.cy, angle_deg=m_.angle_deg, scale=m_.scale,
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score=m_.score,
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bbox_poly=m_.bbox_poly.tolist(),
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) for m_ in matches],
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train_time=t_train, find_time=t_find,
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num_variants=n, annotated_id=ann_id,
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)
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@app.post("/match_simple", response_model=MatchResp)
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def match_simple(p: SimpleMatchParams):
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"""Match con parametri user-facing (tipo/simmetria/scala/precisione).
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Il server deriva i parametri tecnici (num_features, soglie gradiente,
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piramide, ecc.) dall'analisi automatica della ROI.
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"""
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model = _load_image(p.model_id)
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scene = _load_image(p.scene_id)
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if model is None or scene is None:
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raise HTTPException(404, "Immagini non trovate")
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x, y, w, h = p.roi
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x = max(0, x); y = max(0, y)
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w = max(1, min(w, model.shape[1] - x))
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h = max(1, min(h, model.shape[0] - y))
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roi_img = model[y:y + h, x:x + w]
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tech = _simple_to_technical(p, roi_img)
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m = LineShapeMatcher(
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num_features=tech["num_features"],
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weak_grad=tech["weak_grad"], strong_grad=tech["strong_grad"],
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angle_range_deg=(tech["angle_min"], tech["angle_max"]),
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angle_step_deg=tech["angle_step"],
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scale_range=(tech["scale_min"], tech["scale_max"]),
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scale_step=tech["scale_step"],
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spread_radius=tech["spread_radius"],
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pyramid_levels=tech["pyramid_levels"],
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)
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t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
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nms = tech["nms_radius"] if tech["nms_radius"] > 0 else None
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t0 = time.time()
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matches = m.find(
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scene, min_score=tech["min_score"], max_matches=tech["max_matches"],
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nms_radius=nms, verify_threshold=tech["verify_threshold"],
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)
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t_find = time.time() - t0
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tg = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
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annotated = _draw_matches(scene, matches, tg)
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ann_id = _store_image(annotated)
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return MatchResp(
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matches=[MatchResult(
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cx=mt.cx, cy=mt.cy, angle_deg=mt.angle_deg, scale=mt.scale,
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score=mt.score, bbox_poly=mt.bbox_poly.tolist(),
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) for mt in matches],
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train_time=t_train, find_time=t_find,
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num_variants=n, annotated_id=ann_id,
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)
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@app.post("/auto_tune")
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def tune(p: TuneParams):
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model = _load_image(p.model_id)
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if model is None:
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raise HTTPException(404, "Immagine non trovata")
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x, y, w, h = p.roi
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roi_img = model[y:y + h, x:x + w]
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t = auto_tune(roi_img)
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return {k: v for k, v in t.items() if not k.startswith("_")}
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# Mount static
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app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
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def serve(host: str = "127.0.0.1", port: int = 8080):
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import uvicorn
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uvicorn.run(app, host=host, port=port, log_level="info")
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if __name__ == "__main__":
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serve()
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