Adriano
9458173ad0
- server: lock globale matcher (race nel threadpool FastAPI), LRU su _IMG_CACHE e _RECIPE_MATCHERS (leak), clamp ROI in tutti gli endpoint (400/422 invece di crash 500, check train senza varianti), filtro_fp=off disabilita davvero il verify NCC, fallback FILTRO_FP_MAP = medio, verify_threshold ricetta allineato a 0.4, _draw_matches su crop locale (era warp+Sobel full-frame per ogni match), spread_radius default 5->4 - gui: centro overlay edge (W-1)/2 -> W/2 (coerenza col train), spread_radius 5->4 - matcher legacy: _angle_list include estremo, cap candidati top-level, save/load persiste template_gray - auto_tune: ref centrato fuori dal loop angoli - test_suite: check imread con errore chiaro Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
352 lines
13 KiB
Python
352 lines
13 KiB
Python
"""Pattern Matching 2D shape-based via edge template matching multi-rotazione/scala.
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Algoritmo equivalente a Fase Alpha del documento tecnico Vision Suite:
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- Estrazione edge Canny dal template (invarianza illuminazione)
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- Generazione varianti del template edge per ogni (angolo, scala)
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- matchTemplate NCC sulla scena edge per ogni variante
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- Picchi locali con NMS spaziale per multi-istanza
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Uso: vedi `EdgeShapeMatcher.train` e `EdgeShapeMatcher.find`.
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"""
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from __future__ import annotations
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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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from pm2d.line_matcher import _oriented_bbox_polygon
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@dataclass
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class Match:
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"""Singola istanza trovata nella scena."""
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cx: float # baricentro x [px] nella scena
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cy: float # baricentro y [px] nella scena
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angle_deg: float # rotazione [0, 360)
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scale: float # fattore scala (1.0 = template originale)
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score: float # similarità NCC [0, 1]
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bbox_poly: np.ndarray # (4, 2) float32 - vertici bbox orientato
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@dataclass
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class Template:
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"""Variante precomputata del template a un dato (angolo, scala)."""
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angle_deg: float
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scale: float
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edge: np.ndarray # immagine edge ruotata+scalata (uint8 0/255)
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mask: np.ndarray # maschera supporto (uint8 0/255)
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cx_local: float # baricentro nel sistema locale variante
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cy_local: float
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class EdgeShapeMatcher:
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"""Matcher shape-based su edge Canny con rotazione + scala precomputate."""
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def __init__(
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self,
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canny_low: int = 50,
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canny_high: int = 150,
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angle_step_deg: float = 5.0,
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angle_range_deg: tuple[float, float] = (0.0, 360.0),
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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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match_method: int = cv2.TM_CCOEFF_NORMED,
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pyramid_levels: int = 3,
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top_score_factor: float = 0.6,
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) -> None:
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self.canny_low = canny_low
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self.canny_high = canny_high
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self.angle_step_deg = angle_step_deg
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self.angle_range_deg = angle_range_deg
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self.scale_range = scale_range
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self.scale_step = scale_step
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self.match_method = match_method
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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.templates: list[Template] = []
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self.template_size: tuple[int, int] = (0, 0) # w, h originale
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self.template_gray: np.ndarray | None = None
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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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def _edges(self, gray: np.ndarray) -> np.ndarray:
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return cv2.Canny(gray, self.canny_low, self.canny_high)
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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+1 valori per includere l'estremo superiore del range: con il
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# solo floor un range [0, 90] step 5 si fermava a 85° (off-by-one).
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n = int(np.floor((a1 - a0) / self.angle_step_deg)) + 1
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angles = [float(a0 + i * self.angle_step_deg) for i in range(n)]
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if a1 - a0 >= 360.0:
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# Range che copre il giro completo: a0+360° è la stessa pose di
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# a0, escludi il duplicato (variante inutile in train/find).
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eps = 1e-6
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angles = [a for a in angles if a < a0 + 360.0 - eps]
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return angles
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def train(self, template_bgr: np.ndarray) -> int:
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"""Genera varianti per tutte le combinazioni (angolo, scala)."""
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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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edge_orig = self._edges(gray)
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mask_orig = np.full((h, w), 255, dtype=np.uint8)
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self.templates.clear()
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scales = self._scale_list()
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angles = self._angle_list()
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for s in scales:
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sw = max(8, int(round(w * s)))
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sh = max(8, int(round(h * s)))
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edge_s = cv2.resize(edge_orig, (sw, sh), interpolation=cv2.INTER_LINEAR)
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mask_s = cv2.resize(mask_orig, (sw, sh), interpolation=cv2.INTER_NEAREST)
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# Re-thresh dopo resize
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_, edge_s = cv2.threshold(edge_s, 64, 255, cv2.THRESH_BINARY)
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# Padding diagonale per rotazione senza cropping
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diag = int(np.ceil(np.hypot(sh, sw))) + 4
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pad_y = (diag - sh) // 2
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pad_x = (diag - sw) // 2
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edge_p = cv2.copyMakeBorder(
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edge_s, pad_y, diag - sh - pad_y, pad_x, diag - sw - pad_x,
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cv2.BORDER_CONSTANT, value=0,
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)
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mask_p = cv2.copyMakeBorder(
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mask_s, pad_y, diag - sh - pad_y, pad_x, diag - sw - pad_x,
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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 angles:
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M = cv2.getRotationMatrix2D(center, ang, 1.0)
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edge_r = cv2.warpAffine(
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edge_p, M, (diag, diag),
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flags=cv2.INTER_LINEAR, borderValue=0,
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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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# Crop minimo bounding mask
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ys, xs = np.where(mask_r > 0)
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if len(xs) == 0:
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continue
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x0, x1 = xs.min(), xs.max() + 1
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y0, y1 = ys.min(), ys.max() + 1
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edge_c = edge_r[y0:y1, x0:x1]
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mask_c = mask_r[y0:y1, x0:x1]
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cx_local = (mask_c.shape[1] - 1) / 2.0
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cy_local = (mask_c.shape[0] - 1) / 2.0
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self.templates.append(
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Template(
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angle_deg=float(ang),
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scale=float(s),
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edge=edge_c,
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mask=mask_c,
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cx_local=cx_local,
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cy_local=cy_local,
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)
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)
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return len(self.templates)
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def _pyrdown_binary(self, img: np.ndarray) -> np.ndarray:
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"""pyrDown + re-thresh per mantenere binario 0/255."""
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d = cv2.pyrDown(img)
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_, d = cv2.threshold(d, 32, 255, cv2.THRESH_BINARY)
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return d
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def find(
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self,
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scene_bgr: np.ndarray,
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min_score: float = 0.5,
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max_matches: int = 10,
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nms_radius: int | None = None,
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) -> list[Match]:
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"""Cerca istanze del template nella scena con strategia piramidale.
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- Top-level: matching brute-force a bassa risoluzione (veloce, soglia ridotta)
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- Refinement: re-match locale a risoluzione piena per ogni candidato
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"""
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if not self.templates:
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raise RuntimeError("Matcher non addestrato: chiamare train() prima.")
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gray = self._to_gray(scene_bgr)
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scene_edge0 = self._edges(gray)
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# Piramide scena edge
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scene_pyr = [scene_edge0]
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for _ in range(self.pyramid_levels - 1):
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scene_pyr.append(self._pyrdown_binary(scene_pyr[-1]))
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top = len(scene_pyr) - 1
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sf = 2 ** top # scale factor top→0
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scene_top = scene_pyr[top]
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if nms_radius is None:
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nms_radius = max(8, min(self.template_size) // 2)
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top_thresh = min_score * self.top_score_factor
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# Top-level brute-force
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candidates: list[tuple[float, int, int, int]] = []
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for ti, tpl in enumerate(self.templates):
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edge_top = tpl.edge.copy()
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mask_top = tpl.mask.copy()
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for _ in range(top):
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edge_top = self._pyrdown_binary(edge_top)
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mask_top = self._pyrdown_binary(mask_top)
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th, tw = edge_top.shape
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if th < 6 or tw < 6:
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continue
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if scene_top.shape[0] < th or scene_top.shape[1] < tw:
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continue
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res = cv2.matchTemplate(
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scene_top, edge_top, self.match_method, mask=mask_top,
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)
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res = np.nan_to_num(res, nan=-1.0, posinf=-1.0, neginf=-1.0)
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ys, xs = np.where(res >= top_thresh)
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for y, x in zip(ys, xs):
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candidates.append((float(res[y, x]), int(x), int(y), ti))
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# Cap candidati top-level: senza limite np.where con soglia bassa
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# può generare migliaia di candidati, ognuno con un matchTemplate
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# full-res nel refinement. Tieni solo i migliori per score.
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max_candidates = max(1, max_matches * 10)
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if len(candidates) > max_candidates:
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candidates.sort(key=lambda c: -c[0])
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candidates = candidates[:max_candidates]
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# Refinement a risoluzione piena: per ogni candidato top, finestra locale
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refined: list[tuple[float, int, int, int]] = []
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margin = sf + 4
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for _, xt, yt, ti in candidates:
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tpl = self.templates[ti]
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th, tw = tpl.edge.shape
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x0 = xt * sf
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y0 = yt * sf
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sx0 = max(0, x0 - margin)
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sy0 = max(0, y0 - margin)
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sx1 = min(scene_edge0.shape[1], x0 + tw + margin)
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sy1 = min(scene_edge0.shape[0], y0 + th + margin)
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roi = scene_edge0[sy0:sy1, sx0:sx1]
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if roi.shape[0] < th or roi.shape[1] < tw:
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continue
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res = cv2.matchTemplate(
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roi, tpl.edge, self.match_method, mask=tpl.mask,
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)
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res = np.nan_to_num(res, nan=-1.0, posinf=-1.0, neginf=-1.0)
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_, max_val, _, max_loc = cv2.minMaxLoc(res)
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if max_val < min_score:
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continue
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bx = sx0 + max_loc[0]
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by = sy0 + max_loc[1]
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refined.append((float(max_val), bx, by, ti))
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refined.sort(key=lambda c: -c[0])
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# NMS spaziale baricentri
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kept: list[Match] = []
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r2 = nms_radius * nms_radius
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tw0, th0 = self.template_size
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for score, x, y, ti in refined:
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tpl = self.templates[ti]
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cx = x + tpl.cx_local
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cy = y + tpl.cy_local
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if any((k.cx - cx) ** 2 + (k.cy - cy) ** 2 < r2 for k in kept):
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continue
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poly = _oriented_bbox_polygon(
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cx, cy, tw0 * tpl.scale, th0 * tpl.scale, tpl.angle_deg,
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)
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kept.append(
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Match(
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cx=cx, cy=cy,
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angle_deg=tpl.angle_deg,
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scale=tpl.scale,
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score=score,
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bbox_poly=poly,
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)
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)
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if len(kept) >= max_matches:
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break
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return kept
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# --- Persistenza modello ---
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def save(self, path: str) -> None:
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"""Salva matcher su disco (.npz)."""
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meta = np.array(
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[(t.angle_deg, t.scale, t.cx_local, t.cy_local) for t in self.templates],
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dtype=np.float32,
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)
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params = np.array(
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[self.canny_low, self.canny_high, self.angle_step_deg,
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self.angle_range_deg[0], self.angle_range_deg[1],
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self.scale_range[0], self.scale_range[1], self.scale_step,
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self.template_size[0], self.template_size[1], self.match_method,
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self.pyramid_levels, self.top_score_factor],
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dtype=np.float32,
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)
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arrays = {f"edge_{i}": t.edge for i, t in enumerate(self.templates)}
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arrays.update({f"mask_{i}": t.mask for i, t in enumerate(self.templates)})
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# Persisti anche il grayscale originale: senza, l'overlay edge
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# spariva dopo load() (template_gray restava None).
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if self.template_gray is not None:
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arrays["template_gray"] = self.template_gray
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np.savez_compressed(path, params=params, meta=meta, **arrays)
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@classmethod
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def load(cls, path: str) -> "EdgeShapeMatcher":
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z = np.load(path)
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p = z["params"]
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m = cls(
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canny_low=int(p[0]),
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canny_high=int(p[1]),
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angle_step_deg=float(p[2]),
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angle_range_deg=(float(p[3]), float(p[4])),
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scale_range=(float(p[5]), float(p[6])),
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scale_step=float(p[7]),
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match_method=int(p[10]),
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pyramid_levels=int(p[11]) if len(p) > 11 else 3,
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top_score_factor=float(p[12]) if len(p) > 12 else 0.6,
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)
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m.template_size = (int(p[8]), int(p[9]))
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# Retrocompatibilità: modelli salvati prima non hanno template_gray
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# (resta None: overlay edge non disponibile ma find() funziona).
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if "template_gray" in z.files:
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m.template_gray = z["template_gray"]
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meta = z["meta"]
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for i in range(len(meta)):
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m.templates.append(
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Template(
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angle_deg=float(meta[i, 0]),
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scale=float(meta[i, 1]),
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edge=z[f"edge_{i}"],
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mask=z[f"mask_{i}"],
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cx_local=float(meta[i, 2]),
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cy_local=float(meta[i, 3]),
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)
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)
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return m
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