tube aligner
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'''
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Created on 17 avr. 2026
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@author: denis
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'''
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# modules/tube_aligner.py
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import cv2
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import logging
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import numpy as np
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logger = logging.getLogger(__name__)
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class TubeAligner:
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GRBL_THRESHOLD_PX = 20
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DEAD_ZONE_PX = 5
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def __init__(
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self,
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px_per_mm : float = 10.0,
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grbl_threshold_px : int = 20,
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dead_zone_px : int = 5,
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debug : bool = False, # ← activable depuis la vue
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display = None,
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):
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self.TUBE_DIAMETER_MM = 16.0
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self.grbl_threshold_px = grbl_threshold_px
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self.dead_zone_px = dead_zone_px
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self.debug = debug
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self.display = display
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# ------------------------------------------------------------------ #
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# Détection principale
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# ------------------------------------------------------------------ #
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def detect_tube(self, frame: np.ndarray) -> dict:
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h, w = frame.shape[:2]
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cx_img = w // 2
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cy_img = h // 2
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result = {
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"detected" : False,
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"tube_cx" : None,
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"tube_cy" : None,
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"tube_radius" : None,
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"offset_x_px" : 0,
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"offset_y_px" : 0,
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"offset_x_mm" : 0.0,
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"offset_y_mm" : 0.0,
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"action" : "none",
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"frame_annotated": None,
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}
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frame_out = frame.copy()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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blurred = cv2.GaussianBlur(gray, (15, 15), 3)
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# 3 configurations légèrement différentes — vote majoritaire
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# Fonctionne sur fond sombre ET fond clair
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configs = [
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dict(param1=50, param2=30, minRadius=int(min(w,h)*0.26), maxRadius=int(min(w,h)*0.36)),
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dict(param1=60, param2=30, minRadius=int(min(w,h)*0.26), maxRadius=int(min(w,h)*0.37)),
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dict(param1=50, param2=28, minRadius=int(min(w,h)*0.25), maxRadius=int(min(w,h)*0.365)),
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]
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all_cx, all_cy, all_r = [], [], []
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for cfg in configs:
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circles = cv2.HoughCircles(
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blurred,
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cv2.HOUGH_GRADIENT,
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dp=1.2,
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minDist=min(w, h) // 2,
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**cfg
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)
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if circles is not None:
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c = np.round(circles[0]).astype(int)
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best = min(c, key=lambda c: np.sqrt((c[0]-cx_img)**2 + (c[1]-cy_img)**2))
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all_cx.append(int(best[0]))
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all_cy.append(int(best[1]))
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all_r.append(int(best[2]))
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if not all_cx:
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logger.warning("TubeAligner: aucun cercle détecté (%dx%d)", w, h)
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if self.debug:
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frame_out = self._draw_debug_no_detection(frame_out, cx_img, cy_img)
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result["frame_annotated"] = frame_out
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return result
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# Moyenne des détections convergentes
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tx = int(np.mean(all_cx))
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ty = int(np.mean(all_cy))
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tr = int(np.mean(all_r))
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if tr > 0:
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self.px_per_mm = (2 * tr) / 16.0
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offset_x_px = tx - cx_img
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offset_y_px = ty - cy_img
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#offset_x_mm = offset_x_px / self.px_per_mm
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#offset_y_mm = offset_y_px /self. px_per_mm
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offset_x_mm = offset_y_px /self. px_per_mm # (X CNC = Y image)
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offset_y_mm = -offset_x_px / self.px_per_mm # (Y CNC = -X image)
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dist_px = float(np.sqrt(offset_x_px**2 + offset_y_px**2))
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if dist_px <= self.dead_zone_px:
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action = "none"
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elif dist_px <= self.grbl_threshold_px:
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action = "crop"
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else:
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action = "grbl"
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if self.debug:
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frame_out = self._draw_debug(
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frame_out, cx_img, cy_img,
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tx, ty, tr,
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offset_x_px, offset_y_px,
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offset_x_mm, offset_y_mm,
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dist_px, action,
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votes=len(all_cx), # ← affiche le nombre de configs ayant détecté
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)
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result.update({
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"detected" : True,
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"tube_cx" : tx,
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"tube_cy" : ty,
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"tube_radius" : tr,
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"offset_x_px" : offset_x_px,
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"offset_y_px" : offset_y_px,
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"offset_x_mm" : round(offset_x_mm, 3),
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"offset_y_mm" : round(offset_y_mm, 3),
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"action" : action,
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"frame_annotated": frame_out,
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})
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return result
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def crop_to_tube(self, frame: np.ndarray, detection: dict) -> np.ndarray:
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"""
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Recadrage logiciel : recentre l'image sur le tube détecté.
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Utilisé quand action == "crop".
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"""
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if not detection["detected"]:
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return frame
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tx = detection["tube_cx"]
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ty = detection["tube_cy"]
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tr = detection["tube_radius"]
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h, w = frame.shape[:2]
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# Fenêtre carrée autour du centre du tube
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half = tr
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x1 = max(tx - half, 0)
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y1 = max(ty - half, 0)
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x2 = min(tx + half, w)
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y2 = min(ty + half, h)
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cropped = frame[y1:y2, x1:x2]
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# Redimensionne à la taille originale pour ne pas changer le pipeline
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return cv2.resize(cropped, (w, h), interpolation=cv2.INTER_LINEAR)
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def _detect_center_stable(
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self,
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capture_func, # callable() → frame bytes
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n_samples: int = 5,
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delay_s: float = 0.3,
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) -> tuple[float, float] | None:
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"""
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Capture N frames et retourne le centre moyen du tube.
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Réduit l'erreur de détection d'un facteur √N.
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:param capture_func: callable sans argument → bytes JPEG
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:param n_samples: nombre de captures à moyenner
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:param delay_s: pause entre chaque capture
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:return: (cx_mean, cy_mean) ou None si échec
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"""
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import time
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centers = []
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for i in range(n_samples):
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if i > 0:
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time.sleep(delay_s)
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frame_bytes = capture_func()
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nparr = np.frombuffer(frame_bytes, np.uint8)
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frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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if frame is None:
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continue
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detection = self.detect_tube(frame)
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if detection["detected"]:
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centers.append((detection["tube_cx"], detection["tube_cy"]))
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logger.debug(
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"_detect_center_stable [%d/%d] : cx=%d cy=%d",
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i+1, n_samples,
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detection["tube_cx"], detection["tube_cy"],
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)
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else:
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logger.warning("_detect_center_stable [%d/%d] : tube non détecté", i+1, n_samples)
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if len(centers) < 3:
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logger.error("_detect_center_stable : seulement %d détections valides", len(centers))
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return None
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# Filtre les valeurs aberrantes (écart > 2 sigma)
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cx_arr = np.array([c[0] for c in centers], dtype=float)
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cy_arr = np.array([c[1] for c in centers], dtype=float)
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cx_mean, cx_std = np.mean(cx_arr), np.std(cx_arr)
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cy_mean, cy_std = np.mean(cy_arr), np.std(cy_arr)
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mask = (
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(np.abs(cx_arr - cx_mean) <= 2 * cx_std) &
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(np.abs(cy_arr - cy_mean) <= 2 * cy_std)
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)
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filtered = [(cx_arr[i], cy_arr[i]) for i in range(len(centers)) if mask[i]]
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if not filtered:
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filtered = centers # fallback si tout est filtré
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cx_final = float(np.mean([c[0] for c in filtered]))
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cy_final = float(np.mean([c[1] for c in filtered]))
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logger.info(
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"_detect_center_stable : %d/%d valides cx=%.1f±%.1f cy=%.1f±%.1f",
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len(filtered), n_samples,
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cx_final, cx_std, cy_final, cy_std,
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)
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return cx_final, cy_final
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def calib_reset(self):
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pass
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# ------------------------------------------------------------------ #
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# Dessin debug
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# ------------------------------------------------------------------ #
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def _draw_debug(
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self, frame, cx_img, cy_img,
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tx, ty, tr,
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offset_x_px, offset_y_px,
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offset_x_mm, offset_y_mm,
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dist_px, action, votes: int = 3
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) -> np.ndarray:
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# Couleur selon action
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color = {
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"none" : (0, 255, 0), # vert — centré
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"crop" : (0, 200, 255), # orange — recadrage
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"grbl" : (0, 0, 255), # rouge — correction CNC
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}.get(action, (200, 200, 200))
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# Cercle intérieur du tube
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cv2.circle(frame, (tx, ty), tr, color, 2, cv2.LINE_AA)
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# Rayon de zone morte (dead zone) en vert clair
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cv2.circle(frame, (cx_img, cy_img), self.dead_zone_px,
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(0, 255, 100), 1, cv2.LINE_AA)
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# Rayon seuil GRBL en rouge pointillé (simulé par cercle fin)
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cv2.circle(frame, (cx_img, cy_img), self.grbl_threshold_px,
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(0, 80, 255), 1, cv2.LINE_AA)
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# Croix centre image
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cv2.drawMarker(frame, (cx_img, cy_img),
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(255, 255, 255), cv2.MARKER_CROSS, 24, 1, cv2.LINE_AA)
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# Centre tube
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cv2.circle(frame, (tx, ty), 5, color, -1, cv2.LINE_AA)
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# Vecteur offset centre image → centre tube
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if dist_px > self.dead_zone_px:
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cv2.arrowedLine(frame, (cx_img, cy_img), (tx, ty),
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color, 2, cv2.LINE_AA, tipLength=0.2)
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# Panneau info — fond semi-transparent
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overlay = frame.copy()
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cv2.rectangle(overlay, (8, 8), (400, 130), (0, 0, 0), -1)
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cv2.addWeighted(overlay, 0.45, frame, 0.55, 0, frame)
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lines = [
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(f"Tube cx={tx} cy={ty} r={tr}px", (0, 255, 180)),
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(f"Offset dx={offset_x_px:+d}px dy={offset_y_px:+d}px", color),
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(f"Offset dx={offset_x_mm:+.3f}mm dy={offset_y_mm:+.3f}mm", color),
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(f"Dist={dist_px:.1f}px action={action.upper()}", color),
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(f"px/mm={self.px_per_mm:.4f} votes={votes}/3", (180, 180, 180)), # ← votes
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]
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for i, (text, col) in enumerate(lines):
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cv2.putText(frame, text, (14, 30 + i * 20),
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cv2.FONT_HERSHEY_SIMPLEX, 0.48, col, 1, cv2.LINE_AA)
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# Légende zones
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cv2.putText(frame, "dead zone", (cx_img + self.dead_zone_px + 3, cy_img - 3),
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cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 255, 100), 1)
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cv2.putText(frame, "GRBL threshold", (cx_img + self.grbl_threshold_px + 3, cy_img + 6),
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cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 80, 255), 1)
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return frame
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def _draw_debug_no_detection(self, frame, cx_img, cy_img) -> np.ndarray:
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cv2.drawMarker(frame, (cx_img, cy_img),
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(255, 255, 255), cv2.MARKER_CROSS, 24, 1, cv2.LINE_AA)
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cv2.putText(frame, "Tube non detecte", (14, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2, cv2.LINE_AA)
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return frame
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