Adriano
4419c237b2
Nuovo kernel _jit_score_bitmap_greedy: per ogni pixel scorre N feature ed esce non appena hits + remaining < greediness * min_score * N. Esposto in find() come greediness in [0..1], default 0 (backward compat). Sostituisce il kernel rescored al top-level quando attivo: salta il rescore background ma early-exit pixel impossibili. Util su template con molte feature (>100) e scena con pochi pattern competitivi. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
843 lines
34 KiB
Python
843 lines
34 KiB
Python
"""Shape-based matcher stile linemod (line2Dup) - Python puro + numpy/OpenCV.
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Porting algoritmico dell'idea di `meiqua/shape_based_matching` (no MIPP/SIMD —
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equivalente usando vettorizzazione numpy).
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Training (costoso, fatto una volta per ricetta):
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- Per ogni variante (angolo, scala) del template:
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1. Sobel → magnitude + orientation
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2. Quantizzazione orientation in N_BINS bin (modulo π, edge simmetrici)
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3. Estrazione feature sparse top-magnitude con spacing minimo
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4. Salvataggio feature = liste (dx, dy, bin) relative al centro-modello
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Matching (veloce):
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- Scena processata una sola volta per livello di piramide:
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Sobel → magnitude → quant orientation → spread (dilate per bin) →
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response map (N_BINS, H, W) — bit b acceso dove orientamento b presente.
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- Per ogni variante:
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score_map[y,x] = Σ resp[b_i][y+dy_i, x+dx_i] / N_features
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implementato con shift-add vettorizzato (numpy).
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- Piramide: matching top-level (basso costo, soglia ridotta) +
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refinement a risoluzione piena attorno ai candidati.
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Il training supporta una `mask` binaria per modellare solo una regione parziale
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della ROI (modello non-rettangolare).
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"""
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from __future__ import annotations
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import math
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import os
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import dataclass
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import cv2
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import numpy as np
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_GOLDEN = (math.sqrt(5.0) - 1.0) / 2.0 # ≈ 0.618
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from pm2d._jit_kernels import (
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score_by_shift as _jit_score_by_shift,
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score_bitmap as _jit_score_bitmap,
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score_bitmap_rescored as _jit_score_bitmap_rescored,
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score_bitmap_greedy as _jit_score_bitmap_greedy,
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popcount_density as _jit_popcount,
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HAS_NUMBA,
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)
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N_BINS = 8 # orientamenti quantizzati modulo π
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def _oriented_bbox_polygon(
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cx: float, cy: float, w: float, h: float, angle_deg: float,
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) -> np.ndarray:
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"""Ritorna 4 vertici (float32, shape (4,2)) del bbox orientato.
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Convenzione coerente con cv2.getRotationMatrix2D usato nel train:
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rotazione counter-clockwise (matematica) ma sistema immagine y-down,
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quindi visivamente orario.
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"""
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w2, h2 = w / 2.0, h / 2.0
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# Vertici template non-ruotato centrati al centro
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corners = np.array([[-w2, -h2], [w2, -h2], [w2, h2], [-w2, h2]], np.float32)
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a = np.deg2rad(angle_deg)
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c, s = np.cos(a), np.sin(a)
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# cv2.getRotationMatrix2D con angolo a positivo applica R = [[c,s],[-s,c]]
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# (ruota counter-clockwise nel sistema matematico; y-down → orario)
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R = np.array([[c, s], [-s, c]], np.float32)
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rotated = corners @ R.T
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rotated[:, 0] += cx
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rotated[:, 1] += cy
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return rotated
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@dataclass
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class Match:
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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: np.ndarray # (4, 2) float32 - 4 vertici ordinati (ruotato)
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@dataclass
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class _LevelFeatures:
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"""Feature piramidate (livello l = downsample /2^l)."""
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dx: np.ndarray # int32
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dy: np.ndarray # int32
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bin: np.ndarray # int8
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n: int
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@dataclass
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class _Variant:
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"""Template precomputato (una pose)."""
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angle_deg: float
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scale: float
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# Feature piramide: levels[0] = full-res, levels[l] = /2^l
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levels: list[_LevelFeatures]
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# Bbox kernel (per visualizzazione / limiti ricerca)
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kh: int
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kw: int
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cx_local: float # centro-modello dentro al bbox kernel
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cy_local: float
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class LineShapeMatcher:
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"""Shape-based matcher linemod-style - Python/numpy, no SIMD."""
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def __init__(
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self,
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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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angle_range_deg: tuple[float, float] = (0.0, 360.0),
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angle_step_deg: float = 5.0,
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scale_range: tuple[float, float] = (1.0, 1.0),
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scale_step: float = 0.1,
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spread_radius: int = 4,
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min_feature_spacing: int = 3,
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pyramid_levels: int = 2,
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top_score_factor: float = 0.5,
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n_threads: int | None = None,
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) -> None:
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self.num_features = num_features
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self.weak_grad = weak_grad
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self.strong_grad = strong_grad
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self.angle_range_deg = angle_range_deg
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self.angle_step_deg = angle_step_deg
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self.scale_range = scale_range
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self.scale_step = scale_step
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self.spread_radius = spread_radius
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self.min_feature_spacing = min_feature_spacing
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self.pyramid_levels = max(1, pyramid_levels)
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self.top_score_factor = top_score_factor
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self.n_threads = n_threads or max(1, (os.cpu_count() or 2) - 1)
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self.variants: list[_Variant] = []
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self.template_size: tuple[int, int] = (0, 0)
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self.template_gray: np.ndarray | None = None
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# Maschera usata in training (propagata al refine per coerenza).
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self._train_mask: np.ndarray | None = None
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# --- Helpers -------------------------------------------------------
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@staticmethod
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def _to_gray(img: np.ndarray) -> np.ndarray:
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if img.ndim == 3:
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return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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return img
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@staticmethod
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def _gradient(gray: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
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gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3)
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gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
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mag = cv2.magnitude(gx, gy)
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ang = np.arctan2(gy, gx)
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ang_mod = np.where(ang < 0, ang + np.pi, ang)
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bins = np.floor(ang_mod / np.pi * N_BINS).astype(np.int16)
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bins = np.clip(bins, 0, N_BINS - 1)
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return mag, bins
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def _extract_features(
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self, mag: np.ndarray, bins: np.ndarray, mask: np.ndarray | None,
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) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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if mask is not None:
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mag = np.where(mask > 0, mag, 0)
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strong = mag >= self.strong_grad
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ys, xs = np.where(strong)
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if len(xs) == 0:
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return (np.zeros(0, np.int32),) * 3
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vals = mag[ys, xs]
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order = np.argsort(-vals)
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spc = max(1, self.min_feature_spacing)
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occupied = np.zeros(mag.shape, dtype=bool)
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picked_x: list[int] = []
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picked_y: list[int] = []
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picked_b: list[int] = []
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for idx in order:
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y, x = int(ys[idx]), int(xs[idx])
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if occupied[y, x]:
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continue
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picked_x.append(x); picked_y.append(y)
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picked_b.append(int(bins[y, x]))
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y0 = max(0, y - spc); y1 = min(mag.shape[0], y + spc + 1)
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x0 = max(0, x - spc); x1 = min(mag.shape[1], x + spc + 1)
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occupied[y0:y1, x0:x1] = True
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if len(picked_x) >= self.num_features:
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break
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return (np.array(picked_x, np.int32),
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np.array(picked_y, np.int32),
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np.array(picked_b, np.int8))
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def _scale_list(self) -> list[float]:
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s0, s1 = self.scale_range
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if s0 >= s1 or self.scale_step <= 0:
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return [float(s0)]
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n = int(np.floor((s1 - s0) / self.scale_step)) + 1
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return [float(s0 + i * self.scale_step) for i in range(n)]
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def _angle_list(self) -> list[float]:
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a0, a1 = self.angle_range_deg
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if self.angle_step_deg <= 0 or a0 >= a1:
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return [float(a0)]
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n = int(np.floor((a1 - a0) / self.angle_step_deg))
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return [float(a0 + i * self.angle_step_deg) for i in range(n)]
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# --- Training ------------------------------------------------------
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def _build_pyramid_features(
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self, dx: np.ndarray, dy: np.ndarray, bin_: np.ndarray,
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) -> list[_LevelFeatures]:
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"""Piramide feature precomputata: livello l = /2^l con dedup."""
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levels = [_LevelFeatures(dx=dx.copy(), dy=dy.copy(), bin=bin_.copy(),
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n=len(dx))]
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for lvl in range(1, self.pyramid_levels):
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sf = 2 ** lvl
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dx_l = (dx // sf).astype(np.int32)
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dy_l = (dy // sf).astype(np.int32)
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# Dedup: rimuove feature collassate sullo stesso (dx, dy, bin)
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key = ((dx_l.astype(np.int64) << 24)
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| (dy_l.astype(np.int64) << 8)
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| bin_.astype(np.int64))
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_, uniq = np.unique(key, return_index=True)
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levels.append(_LevelFeatures(
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dx=dx_l[uniq], dy=dy_l[uniq], bin=bin_[uniq], n=len(uniq),
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))
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return levels
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def train(self, template_bgr: np.ndarray, mask: np.ndarray | None = None) -> int:
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"""Genera varianti rotate+scalate con feature sparse + piramide."""
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gray = self._to_gray(template_bgr)
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h, w = gray.shape
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self.template_size = (w, h)
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self.template_gray = gray.copy()
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if mask is None:
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mask_full = np.full((h, w), 255, dtype=np.uint8)
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else:
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mask_full = (mask > 0).astype(np.uint8) * 255
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self._train_mask = mask_full.copy()
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self.variants.clear()
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for s in self._scale_list():
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sw = max(16, int(round(w * s)))
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sh = max(16, int(round(h * s)))
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gray_s = cv2.resize(gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
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mask_s = cv2.resize(mask_full, (sw, sh), interpolation=cv2.INTER_NEAREST)
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diag = int(np.ceil(np.hypot(sh, sw))) + 6
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py = (diag - sh) // 2
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px = (diag - sw) // 2
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gray_p = cv2.copyMakeBorder(
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gray_s, py, diag - sh - py, px, diag - sw - px,
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cv2.BORDER_REPLICATE,
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)
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mask_p = cv2.copyMakeBorder(
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mask_s, py, diag - sh - py, px, diag - sw - px,
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cv2.BORDER_CONSTANT, value=0,
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)
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center = (diag / 2.0, diag / 2.0)
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for ang in self._angle_list():
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M = cv2.getRotationMatrix2D(center, ang, 1.0)
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gray_r = cv2.warpAffine(
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gray_p, M, (diag, diag),
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flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE,
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)
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mask_r = cv2.warpAffine(
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mask_p, M, (diag, diag),
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flags=cv2.INTER_NEAREST, borderValue=0,
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)
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mag, bins = self._gradient(gray_r)
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fx, fy, fb = self._extract_features(mag, bins, mask_r)
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if len(fx) < 8:
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continue
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cx_c = diag / 2.0
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cy_c = diag / 2.0
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dx = (fx - cx_c).astype(np.int32)
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dy = (fy - cy_c).astype(np.int32)
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x0 = int(dx.min()); x1 = int(dx.max())
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y0 = int(dy.min()); y1 = int(dy.max())
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kw = x1 - x0 + 1
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kh = y1 - y0 + 1
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cx_local = -x0
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cy_local = -y0
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levels = self._build_pyramid_features(dx, dy, fb)
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self.variants.append(_Variant(
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angle_deg=float(ang),
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scale=float(s),
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levels=levels,
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kh=kh, kw=kw,
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cx_local=float(cx_local), cy_local=float(cy_local),
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))
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return len(self.variants)
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# --- Matching ------------------------------------------------------
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def _response_map(self, gray: np.ndarray) -> np.ndarray:
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"""Response map shape (N_BINS, H, W) float32 (legacy path)."""
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mag, bins = self._gradient(gray)
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valid = mag >= self.weak_grad
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k = 2 * self.spread_radius + 1
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kernel = np.ones((k, k), dtype=np.uint8)
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H, W = gray.shape
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raw = np.zeros((N_BINS, H, W), dtype=np.float32)
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for b in range(N_BINS):
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mask_b = ((bins == b) & valid).astype(np.uint8)
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d = cv2.dilate(mask_b, kernel)
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raw[b] = d.astype(np.float32)
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return raw
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def _spread_bitmap(self, gray: np.ndarray) -> np.ndarray:
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"""Spread bitmap uint8: bit b acceso dove bin b è presente nel raggio.
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Formato compatto 32× più denso della response map (N_BINS, H, W) float32.
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"""
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mag, bins = self._gradient(gray)
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valid = mag >= self.weak_grad
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k = 2 * self.spread_radius + 1
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kernel = np.ones((k, k), dtype=np.uint8)
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H, W = gray.shape
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spread = np.zeros((H, W), dtype=np.uint8)
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for b in range(N_BINS):
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mask_b = ((bins == b) & valid).astype(np.uint8)
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d = cv2.dilate(mask_b, kernel)
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spread |= (d << b)
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return spread
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@staticmethod
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def _score_by_shift(
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resp: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
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bin_has_data: np.ndarray | None = None,
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) -> np.ndarray:
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"""score[y,x] = Σ_i resp[bin_i][y+dy_i, x+dx_i] / len(dx).
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Dispatch a kernel Numba JIT se disponibile, altrimenti fallback numpy.
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"""
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return _jit_score_by_shift(resp, dx, dy, bins, bin_has_data)
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@staticmethod
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def _subpixel_peak(
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acc: np.ndarray, x: int, y: int, plateau_radius: int = 10,
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) -> tuple[float, float]:
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"""Posizione sub-pixel del picco.
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1. Plateau saturo → centroide pesato del plateau (peso = score).
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2. Altrimenti → fit quadratico 2D bivariato sui 9 vicini
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(z = a + b·dx + c·dy + d·dx² + e·dy² + f·dx·dy), argmax risolto
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analiticamente con clamping ±0.5 px.
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"""
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H, W = acc.shape
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val = float(acc[y, x])
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# Plateau detection: valori >= val - 0.01 entro raggio limitato
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y0 = max(0, y - plateau_radius); y1 = min(H, y + plateau_radius + 1)
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x0 = max(0, x - plateau_radius); x1 = min(W, x + plateau_radius + 1)
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patch = acc[y0:y1, x0:x1]
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plateau = patch >= val - 0.01
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if plateau.sum() > 1:
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# Centroide pesato per (score - (max-0.01))² per enfatizzare i top
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weights = np.where(plateau, patch - (val - 0.01), 0.0).astype(np.float64)
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weights = weights * weights
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total = weights.sum()
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if total > 1e-9:
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ys_idx, xs_idx = np.indices(patch.shape)
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cx_w = (xs_idx * weights).sum() / total
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cy_w = (ys_idx * weights).sum() / total
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return float(x0 + cx_w), float(y0 + cy_w)
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ys_m, xs_m = np.where(plateau)
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return float(x0 + xs_m.mean()), float(y0 + ys_m.mean())
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# Fit quadratico 2D bivariato su 3x3 intorno
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if x <= 0 or x >= W - 1 or y <= 0 or y >= H - 1:
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return float(x), float(y)
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# Stencil 3x3: Z[i, j] con i,j ∈ {-1, 0, +1}
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Z = acc[y - 1:y + 2, x - 1:x + 2].astype(np.float64)
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# Coefficienti da finite differences
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b_c = (Z[1, 2] - Z[1, 0]) / 2.0
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c_c = (Z[2, 1] - Z[0, 1]) / 2.0
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d_c = (Z[1, 2] + Z[1, 0] - 2.0 * Z[1, 1]) / 2.0
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e_c = (Z[2, 1] + Z[0, 1] - 2.0 * Z[1, 1]) / 2.0
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f_c = (Z[2, 2] - Z[0, 2] - Z[2, 0] + Z[0, 0]) / 4.0
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# Max: risolve [2d f; f 2e][dx;dy] = [-b;-c]
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det = 4.0 * d_c * e_c - f_c * f_c
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if abs(det) > 1e-9:
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ox = (-2.0 * e_c * b_c + f_c * c_c) / det
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oy = (-2.0 * d_c * c_c + f_c * b_c) / det
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else:
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# Fallback separabile
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ox = -b_c / (2.0 * d_c) if abs(d_c) > 1e-6 else 0.0
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oy = -c_c / (2.0 * e_c) if abs(e_c) > 1e-6 else 0.0
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ox = float(np.clip(ox, -0.5, 0.5))
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oy = float(np.clip(oy, -0.5, 0.5))
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return x + ox, y + oy
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def _refine_angle(
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self,
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spread0: np.ndarray, # bitmap uint8 (H, W)
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bit_active: int,
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template_gray: np.ndarray,
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cx: float, cy: float,
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angle_deg: float, scale: float,
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mask_full: np.ndarray,
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angle_fine_step: float = 0.5,
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search_radius: float | None = None,
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||
original_score: float | None = None,
|
||
) -> tuple[float, float, float, float]:
|
||
"""Ricerca angolare fine (sub-step) attorno al match grezzo.
|
||
|
||
Genera 5 template temporanei a angle ± {0.5, 1.0} * step e sceglie
|
||
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
|
||
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
|
||
|
||
h, w = template_gray.shape
|
||
sw = max(16, int(round(w * scale)))
|
||
sh = max(16, int(round(h * scale)))
|
||
gray_s = cv2.resize(template_gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
|
||
mask_s = cv2.resize(mask_full, (sw, sh), interpolation=cv2.INTER_NEAREST)
|
||
diag = int(np.ceil(np.hypot(sh, sw))) + 6
|
||
py = (diag - sh) // 2; px = (diag - sw) // 2
|
||
gray_p = cv2.copyMakeBorder(gray_s, py, diag - sh - py, px, diag - sw - px,
|
||
cv2.BORDER_REPLICATE)
|
||
mask_p = cv2.copyMakeBorder(mask_s, py, diag - sh - py, px, diag - sw - px,
|
||
cv2.BORDER_CONSTANT, value=0)
|
||
center = (diag / 2.0, diag / 2.0)
|
||
|
||
H, W = spread0.shape
|
||
margin = 3
|
||
|
||
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),
|
||
flags=cv2.INTER_LINEAR,
|
||
borderMode=cv2.BORDER_REPLICATE)
|
||
mask_r = cv2.warpAffine(mask_p, M, (diag, diag),
|
||
flags=cv2.INTER_NEAREST, borderValue=0)
|
||
mag, bins = self._gradient(gray_r)
|
||
fx, fy, fb = self._extract_features(mag, bins, mask_r)
|
||
if len(fx) < 8:
|
||
return (0.0, cx, cy)
|
||
dx = (fx - center[0]).astype(np.int32)
|
||
dy = (fy - center[1]).astype(np.int32)
|
||
y_lo = int(cy) - margin; y_hi = int(cy) + margin + 1
|
||
x_lo = int(cx) - margin; x_hi = int(cx) + margin + 1
|
||
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_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:
|
||
region = spread0[s_y0:s_y1, s_x0:s_x1]
|
||
acc[a_y0:a_y1, a_x0:a_x1] += (
|
||
(region & bit) != 0
|
||
).astype(np.float32)
|
||
acc /= len(dx)
|
||
_, max_val, _, max_loc = cv2.minMaxLoc(acc)
|
||
return (float(max_val),
|
||
float(x_lo + max_loc[0]), float(y_lo + max_loc[1]))
|
||
|
||
# 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(
|
||
self, scene_gray: np.ndarray, cx: float, cy: float,
|
||
angle_deg: float, scale: float,
|
||
) -> float:
|
||
"""NCC tra template warpato alla pose e scena sottostante.
|
||
|
||
Lavora su un **crop locale** della scena di lato = diagonale del
|
||
template ruotato+scalato, non sull'intera scena. Su scene grandi
|
||
(1920×1080) taglia drasticamente il costo del warp per ogni match.
|
||
|
||
Ritorna score [-1, 1]. Usato come filtro anti-falso-positivo:
|
||
il matcher linemod può dare score alto su texture generiche ma
|
||
sovrapponendo il template gray i pixel non corrispondono.
|
||
"""
|
||
if self.template_gray is None:
|
||
return 1.0
|
||
t = self.template_gray
|
||
h, w = t.shape
|
||
cx_t = (w - 1) / 2.0
|
||
cy_t = (h - 1) / 2.0
|
||
|
||
# Bounding box del template ruotato/scalato attorno a (cx, cy)
|
||
diag = int(np.ceil(np.hypot(w, h) * scale)) + 8
|
||
H, W = scene_gray.shape
|
||
x0 = int(round(cx)) - diag // 2
|
||
y0 = int(round(cy)) - diag // 2
|
||
cx0 = max(0, x0); cy0 = max(0, y0)
|
||
cx1 = min(W, x0 + diag); cy1 = min(H, y0 + diag)
|
||
if cx1 - cx0 < 10 or cy1 - cy0 < 10:
|
||
return 0.0
|
||
scn_crop = scene_gray[cy0:cy1, cx0:cx1]
|
||
ch, cw = scn_crop.shape
|
||
|
||
M = cv2.getRotationMatrix2D((cx_t, cy_t), angle_deg, scale)
|
||
# Porta il centro del template a (cx - cx0, cy - cy0) del crop
|
||
M[0, 2] += (cx - cx0) - cx_t
|
||
M[1, 2] += (cy - cy0) - cy_t
|
||
warped = cv2.warpAffine(
|
||
t, M, (cw, ch),
|
||
flags=cv2.INTER_LINEAR, borderValue=0,
|
||
)
|
||
if self._train_mask is not None:
|
||
mask_src = self._train_mask
|
||
else:
|
||
mask_src = np.full_like(t, 255)
|
||
mask_w = cv2.warpAffine(
|
||
mask_src, M, (cw, ch),
|
||
flags=cv2.INTER_NEAREST, borderValue=0,
|
||
)
|
||
valid = mask_w > 0
|
||
if valid.sum() < 20:
|
||
return 0.0
|
||
tpl = warped[valid].astype(np.float32)
|
||
scn = scn_crop[valid].astype(np.float32)
|
||
tm = tpl - tpl.mean()
|
||
sm = scn - scn.mean()
|
||
denom = np.sqrt((tm * tm).sum() * (sm * sm).sum()) + 1e-9
|
||
return float((tm * sm).sum() / denom)
|
||
|
||
def find(
|
||
self,
|
||
scene_bgr: np.ndarray,
|
||
min_score: float = 0.6,
|
||
max_matches: int = 20,
|
||
nms_radius: int | None = None,
|
||
refine_angle: bool = True,
|
||
subpixel: bool = True,
|
||
verify_ncc: bool = True,
|
||
verify_threshold: float = 0.4,
|
||
coarse_angle_factor: int = 2,
|
||
scale_penalty: float = 0.0,
|
||
greediness: 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.")
|
||
|
||
gray0 = self._to_gray(scene_bgr)
|
||
grays = [gray0]
|
||
for _ in range(self.pyramid_levels - 1):
|
||
grays.append(cv2.pyrDown(grays[-1]))
|
||
top = len(grays) - 1
|
||
|
||
# Spread bitmap (uint8) al top level: 32× meno memoria della response
|
||
# map float32 → MOLTO più cache-friendly per _score_by_shift.
|
||
spread_top = self._spread_bitmap(grays[top])
|
||
bit_active_top = int(
|
||
sum(1 << b for b in range(N_BINS)
|
||
if (spread_top & np.uint8(1 << b)).any())
|
||
)
|
||
if nms_radius is None:
|
||
nms_radius = max(8, min(self.template_size) // 2)
|
||
top_thresh = min_score * self.top_score_factor
|
||
|
||
tw, th = self.template_size
|
||
density_top = _jit_popcount(spread_top)
|
||
sf_top = 2 ** top
|
||
bg_cache_top: dict[float, np.ndarray] = {}
|
||
bg_cache_full: dict[float, np.ndarray] = {}
|
||
unique_scales = sorted({var.scale for var in self.variants})
|
||
|
||
def _bg_for_scale(density: np.ndarray, scale: float,
|
||
divisor: int) -> np.ndarray:
|
||
bw = max(9, int(round(tw * scale / divisor)))
|
||
bh = max(9, int(round(th * scale / divisor)))
|
||
sm = cv2.boxFilter(density, cv2.CV_32F, (bw, bh))
|
||
return np.clip(sm / N_BINS, 0.0, 0.99)
|
||
|
||
for sc in unique_scales:
|
||
bg_cache_top[sc] = _bg_for_scale(density_top, sc, sf_top)
|
||
|
||
def _rescore(score: np.ndarray, bg: np.ndarray) -> np.ndarray:
|
||
return np.maximum(0.0, (score - bg) / (1.0 - bg + 1e-6))
|
||
|
||
# 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.
|
||
# greediness > 0: usa kernel greedy con early-exit (no rescore bg)
|
||
# per il pruning. ~2-4x speed-up sul top con greediness=0.8.
|
||
use_greedy_top = greediness > 0.0
|
||
|
||
def _top_score(vi: int) -> tuple[int, float]:
|
||
var = self.variants[vi]
|
||
lvl = var.levels[min(top, len(var.levels) - 1)]
|
||
if use_greedy_top:
|
||
score = _jit_score_bitmap_greedy(
|
||
spread_top, lvl.dx, lvl.dy, lvl.bin, bit_active_top,
|
||
top_thresh, greediness,
|
||
)
|
||
else:
|
||
score = _jit_score_bitmap_rescored(
|
||
spread_top, lvl.dx, lvl.dy, lvl.bin, bit_active_top,
|
||
bg_cache_top[var.scale],
|
||
)
|
||
return vi, float(score.max()) if score.size else -1.0
|
||
|
||
kept_coarse: list[tuple[int, float]] = []
|
||
all_top_scores: 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, coarse_idx_list):
|
||
all_top_scores.append((vi, best))
|
||
if best >= top_thresh:
|
||
kept_coarse.append((vi, best))
|
||
else:
|
||
for vi in coarse_idx_list:
|
||
vi2, best = _top_score(vi)
|
||
all_top_scores.append((vi2, best))
|
||
if best >= top_thresh:
|
||
kept_coarse.append((vi2, best))
|
||
|
||
# Fallback adattivo: se il rescore background ha abbattuto tutti
|
||
# gli score sotto top_thresh (scene texturate pesanti), ripesca
|
||
# le varianti migliori al top level per dare comunque una chance
|
||
# alla fase full-res invece di ritornare 0 match.
|
||
if not kept_coarse and all_top_scores:
|
||
all_top_scores.sort(key=lambda t: -t[1])
|
||
n_keep = max(4, len(all_top_scores) // 10)
|
||
# Limita a varianti con score top > 0 (non completamente a zero)
|
||
kept_coarse = [(vi, s) for vi, s in all_top_scores[:n_keep] if s > 0]
|
||
|
||
# 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 []
|
||
|
||
# Cap adattivo: ordina per score top-level e mantieni le più promettenti.
|
||
# Min: max_matches*8 (margine per NMS cross-variant)
|
||
# Max: 50% delle varianti totali (protegge performance con molte scale)
|
||
kept_variants.sort(key=lambda t: -t[1])
|
||
max_vars_full = max(max_matches * 8, len(self.variants) // 2)
|
||
kept_variants = kept_variants[:max_vars_full]
|
||
|
||
# Full-res (parallelizzato) con bitmap
|
||
spread0 = self._spread_bitmap(gray0)
|
||
bit_active_full = int(
|
||
sum(1 << b for b in range(N_BINS)
|
||
if (spread0 & np.uint8(1 << b)).any())
|
||
)
|
||
density_full = _jit_popcount(spread0)
|
||
for sc in unique_scales:
|
||
bg_cache_full[sc] = _bg_for_scale(density_full, sc, 1)
|
||
|
||
def _full_score(vi: int) -> tuple[int, np.ndarray]:
|
||
var = self.variants[vi]
|
||
lvl0 = var.levels[0]
|
||
score = _jit_score_bitmap_rescored(
|
||
spread0, lvl0.dx, lvl0.dy, lvl0.bin, bit_active_full,
|
||
bg_cache_full[var.scale],
|
||
)
|
||
return vi, score
|
||
|
||
candidates_per_var: list[tuple[int, np.ndarray]] = []
|
||
raw: list[tuple[float, int, int, int]] = []
|
||
var_indices = [vi for vi, _ in kept_variants]
|
||
if self.n_threads > 1 and len(var_indices) > 1:
|
||
with ThreadPoolExecutor(max_workers=self.n_threads) as ex:
|
||
results = list(ex.map(_full_score, var_indices))
|
||
else:
|
||
results = [_full_score(vi) for vi in var_indices]
|
||
|
||
def _scale_factor(s: float) -> float:
|
||
"""Penalità moltiplicativa per scala diversa da 1.0."""
|
||
if scale_penalty > 0.0 and s != 1.0:
|
||
return max(0.0, 1.0 - scale_penalty * abs(s - 1.0))
|
||
return 1.0
|
||
|
||
for vi, score in results:
|
||
pen = _scale_factor(self.variants[vi].scale)
|
||
# Ordinare/sogliare su score penalizzato: un match a scala 1.5 con
|
||
# score 0.8 e penalty=0.3 effettivamente vale 0.56, non 0.8.
|
||
score_for_sort = score if pen == 1.0 else score * pen
|
||
ys, xs = np.where(score_for_sort >= min_score)
|
||
if len(ys) == 0:
|
||
continue
|
||
vals = score_for_sort[ys, xs]
|
||
K = min(len(vals), max_matches * 5)
|
||
ord_idx = np.argpartition(-vals, K - 1)[:K]
|
||
candidates_per_var.append((vi, score)) # score_map originale
|
||
for i in ord_idx:
|
||
raw.append((float(vals[i]), int(xs[i]), int(ys[i]), vi))
|
||
|
||
raw.sort(key=lambda c: -c[0])
|
||
|
||
# Mappa vi → score_map per subpixel/refinement
|
||
score_maps = dict(candidates_per_var)
|
||
|
||
# NMS + subpixel + refinement angolare
|
||
# Usa mask salvata in train() per coerenza (se ROI poligonale).
|
||
h, w = self.template_gray.shape if self.template_gray is not None else (0, 0)
|
||
mask_full = (
|
||
self._train_mask if self._train_mask is not None
|
||
else np.full((h, w), 255, dtype=np.uint8)
|
||
)
|
||
# Plateau radius adattivo al template (evita plateau troppo ampi su
|
||
# template piccoli: 8% del lato minimo, clampato [3, 10]).
|
||
plateau_r = max(3, min(10, int(min(self.template_size) * 0.08)))
|
||
|
||
# Pre-NMS rapido su raw con coordinate intere (nms_radius ≥ 8,
|
||
# la precisione sub-pixel non cambia la decisione di reject).
|
||
# Subpixel viene calcolato DOPO il pre-NMS solo sui ~pre_cap
|
||
# preliminary sopravvissuti: prima era chiamato su ogni raw (~58k
|
||
# chiamate su clip_preciso) anche se la maggior parte veniva poi
|
||
# scartata dalla NMS, sprecando la parte più costosa del loop.
|
||
r2 = nms_radius * nms_radius
|
||
pre_cap = max(max_matches * 3, max_matches + 10)
|
||
preliminary_int: list[tuple[float, int, int, int]] = []
|
||
for score, xi, yi, vi in raw:
|
||
if any((k[1] - xi) ** 2 + (k[2] - yi) ** 2 < r2
|
||
for k in preliminary_int):
|
||
continue
|
||
preliminary_int.append((score, xi, yi, vi))
|
||
if len(preliminary_int) >= pre_cap:
|
||
break
|
||
|
||
# Subpixel + refine + verify solo sui candidati pre-NMS (max pre_cap)
|
||
kept: list[Match] = []
|
||
tw, th = self.template_size
|
||
for score, xi, yi, vi in preliminary_int:
|
||
if subpixel and vi in score_maps:
|
||
cx_f, cy_f = self._subpixel_peak(
|
||
score_maps[vi], xi, yi, plateau_radius=plateau_r,
|
||
)
|
||
else:
|
||
cx_f, cy_f = float(xi), float(yi)
|
||
var = self.variants[vi]
|
||
ang_f = var.angle_deg
|
||
score_f = score
|
||
if refine_angle and self.template_gray is not None:
|
||
ang_f, score_f, cx_f, cy_f = self._refine_angle(
|
||
spread0, bit_active_full, self.template_gray, cx_f, cy_f,
|
||
var.angle_deg, var.scale, mask_full,
|
||
search_radius=self.angle_step_deg / 2.0,
|
||
original_score=score,
|
||
)
|
||
if verify_ncc:
|
||
ncc = self._verify_ncc(gray0, cx_f, cy_f, ang_f, var.scale)
|
||
if ncc < verify_threshold:
|
||
continue
|
||
|
||
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,
|
||
scale=var.scale,
|
||
score=score_f,
|
||
bbox_poly=poly,
|
||
))
|
||
if len(kept) >= max_matches:
|
||
break
|
||
return kept
|