ae65be2555
Suppression du .git embarqué dans agent_v0/ — le code est maintenant tracké normalement dans le repo principal. Inclut : agent_v1 (client), server_v1 (streaming), lea_ui (chat client) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
# agent_v1/vision/capturer.py
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"""
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Gestionnaire de vision avancé pour Agent V1.
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Optimisé pour le streaming fibre avec détection de changement.
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"""
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import os
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import time
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import logging
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import hashlib
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from PIL import Image, ImageFilter, ImageStat
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import mss
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from ..config import TARGETED_CROP_SIZE, SCREENSHOT_QUALITY
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logger = logging.getLogger(__name__)
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class VisionCapturer:
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def __init__(self, session_dir: str):
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self.session_dir = session_dir
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self.shots_dir = os.path.join(session_dir, "shots")
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os.makedirs(self.shots_dir, exist_ok=True)
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# On ne crée plus self.sct ici car mss n'est pas thread-safe sous Windows
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self.last_img_hash = None
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def capture_full_context(self, name_suffix: str, force=False) -> str:
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"""
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Capture l'écran complet.
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Si force=False, vérifie d'abord si l'écran a changé.
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"""
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try:
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with mss.mss() as sct:
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monitor = sct.monitors[1]
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sct_img = sct.grab(monitor)
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img = Image.frombytes("RGB", sct_img.size, sct_img.bgra, "raw", "BGRX")
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# Détection de changement (pour Heartbeat)
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if not force:
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current_hash = self._compute_quick_hash(img)
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if current_hash == self.last_img_hash:
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return "" # Pas de changement, on économise la fibre
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self.last_img_hash = current_hash
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path = os.path.join(self.shots_dir, f"context_{int(time.time())}_{name_suffix}.png")
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img.save(path, "PNG", quality=SCREENSHOT_QUALITY)
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return path
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except Exception as e:
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logger.error(f"Erreur Context Capture: {e}")
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return ""
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def capture_dual(self, x: int, y: int, screenshot_id: str, anonymize=False) -> dict:
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"""Capture duale (Full + Crop) systématique (forcée car liée à une action)."""
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try:
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with mss.mss() as sct:
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full_path = os.path.join(self.shots_dir, f"{screenshot_id}_full.png")
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monitor = sct.monitors[1]
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sct_img = sct.grab(monitor)
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img = Image.frombytes("RGB", sct_img.size, sct_img.bgra, "raw", "BGRX")
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# Capture du Crop (Cœur de l'apprentissage qwen3-vl)
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crop_path = os.path.join(self.shots_dir, f"{screenshot_id}_crop.png")
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w, h = TARGETED_CROP_SIZE
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left = max(0, x - w // 2)
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top = max(0, y - h // 2)
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crop_img = img.crop((left, top, left + w, top + h))
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if anonymize:
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crop_img = crop_img.filter(ImageFilter.GaussianBlur(radius=4))
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img.save(full_path, "PNG", quality=SCREENSHOT_QUALITY)
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crop_img.save(crop_path, "PNG", quality=SCREENSHOT_QUALITY)
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# Mise à jour du hash pour le prochain heartbeat
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self.last_img_hash = self._compute_quick_hash(img)
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return {"full": full_path, "crop": crop_path}
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except Exception as e:
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logger.error(f"Erreur Dual Capture: {e}")
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return {}
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def _compute_quick_hash(self, img: Image) -> str:
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"""Calcule un hash rapide basé sur une vignette réduite pour détecter les changements."""
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# On réduit l'image à 64x64 pour comparer les masses de couleurs (très rapide)
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small_img = img.resize((64, 64), Image.NEAREST).convert("L")
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return hashlib.md5(small_img.tobytes()).hexdigest()
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