Jules
4e267492c2
- Preview thread : poll uniquement quand ecran capture actif, keepalive 30s sinon - Sequence boot Canon : power cycle relais si absent USB au demarrage - Veille auto 30min : Canon coupe par relais apres inactivite, reveil au preview_start - Stats diagnostic /api/canon/stats : keepalive, preview, captures, connexions - Escalade reconnexion adoucie : pas d'USB reset les 2 premieres tentatives - Hard reset relais 10s (resistance decharge 990ohm sur dummy battery 8V) - Gestion fin evenement : date_fin + bouton terminer + guard 403 impression - Compteur base sur min(papier, ruban) avec cap evenement optionnel - Pre-eclairage countdown : projecteurs ON 3s avant capture pour mesure expo - Canon _configurer_init() : ISO 800, drivemode Single, viewfinder, EXIF preserve - Admin : detail evenement, compteur consommables, galerie impression Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WyN9xFp84P9VaWT3Qe2DXu
115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
"""Analyse de luminosité sur les visages détectés dans le preview DSLR.
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Produit une note d'éclairage (0-100) et une recommandation (allumer/ok/sombre).
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Échantillonne toutes les ~3 secondes pour ne pas charger le CPU.
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"""
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import logging
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import time
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from pathlib import Path
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import cv2
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import numpy as np
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log = logging.getLogger("photobooth.eclairage")
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_CASCADE_CANDIDATES = [
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"/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml",
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"/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml",
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"/usr/local/share/opencv4/haarcascades/haarcascade_frontalface_default.xml",
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]
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try:
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_CASCADE_PATH = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
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except AttributeError:
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_CASCADE_PATH = next((p for p in _CASCADE_CANDIDATES if Path(p).exists()), _CASCADE_CANDIDATES[0])
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_face_cascade = cv2.CascadeClassifier(_CASCADE_PATH)
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SEUIL_SOMBRE = 45
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SEUIL_CORRECT = 65
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_dernier_analyse: float = 0
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_INTERVALLE = 1.0
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_dernier_resultat: dict | None = None
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def analyser_frame(jpeg_data: bytes) -> dict | None:
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"""Analyse une frame JPEG du preview.
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Retourne None si l'intervalle n'est pas écoulé (throttle).
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Sinon retourne:
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{
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"score": int 0-100,
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"action": "allumer" | "attention" | "ok",
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"visages": int,
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"detail": str,
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}
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"""
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global _dernier_analyse, _dernier_resultat
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now = time.monotonic()
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if now - _dernier_analyse < _INTERVALLE:
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return None
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_dernier_analyse = now
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try:
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arr = np.frombuffer(jpeg_data, dtype=np.uint8)
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img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
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if img is None:
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return None
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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faces = _face_cascade.detectMultiScale(
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gray, scaleFactor=1.2, minNeighbors=4, minSize=(60, 60)
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)
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if len(faces) == 0:
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_dernier_resultat = {
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"score": -1,
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"action": "no_face",
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"visages": 0,
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"detail": "Aucun visage détecté",
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}
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return _dernier_resultat
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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scores = []
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for (x, y, w, h) in faces:
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pad_x = int(w * 0.1)
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pad_y = int(h * 0.1)
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x1 = max(0, x + pad_x)
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y1 = max(0, y + pad_y)
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x2 = min(img.shape[1], x + w - pad_x)
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y2 = min(img.shape[0], y + h - pad_y)
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roi_v = hsv[y1:y2, x1:x2, 2]
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score = int(np.mean(roi_v) / 2.55)
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scores.append(score)
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score_moyen = int(np.mean(scores))
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if score_moyen < SEUIL_SOMBRE:
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action = "allumer"
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detail = f"Visages sombres ({score_moyen}/100)"
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elif score_moyen < SEUIL_CORRECT:
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action = "attention"
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detail = f"Éclairage limite ({score_moyen}/100)"
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else:
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action = "ok"
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detail = f"Éclairage correct ({score_moyen}/100)"
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_dernier_resultat = {
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"score": score_moyen,
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"action": action,
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"visages": len(faces),
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"detail": detail,
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}
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return _dernier_resultat
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except Exception as e:
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log.debug(f"Erreur analyse éclairage : {e}")
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return None
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def dernier_resultat() -> dict | None:
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return _dernier_resultat
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