Feat: Actions validation avec OCR Ollama (qwen2.5-vl:7b)
- verify_element_exists: recherche visuelle OpenCV réelle - verify_text_content: OCR via Ollama (GPU) avec fallback easyocr - Paramètres ocr_mode et ollama_model dans le catalogue frontend - Support des modes de matching: exact, contains, regex, starts_with, ends_with Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Dom
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"""
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Action Validation Existence - Vérifier qu'un élément visuel existe
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Auteur : Dom, Alice, Kiro, Claude - 14 janvier 2026
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Cette action vérifie l'existence d'un élément visuel sur l'écran
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en utilisant le template matching OpenCV.
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"""
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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import time
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# Import des modules de base
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from ..base_action import BaseVWBAction, VWBActionResult, VWBActionStatus
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from ...contracts.error import VWBErrorType, create_vwb_error
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from ...contracts.evidence import VWBEvidenceType, create_screenshot_evidence
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from ...contracts.visual_anchor import VWBVisualAnchor
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class VWBVerifyElementExistsAction(BaseVWBAction):
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"""
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Action pour vérifier l'existence d'un élément visuel.
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Cette action utilise le template matching OpenCV pour rechercher
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un élément sur l'écran et valider s'il est présent ou absent.
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"""
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def __init__(
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self,
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action_id: str,
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parameters: Dict[str, Any],
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screen_capturer=None
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):
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"""
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Initialise l'action de vérification d'existence.
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Args:
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action_id: Identifiant unique de l'action
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parameters: Paramètres incluant l'ancre visuelle
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screen_capturer: Instance du ScreenCapturer (Option A thread-safe)
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"""
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super().__init__(
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action_id=action_id,
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name="Vérifier Existence Élément",
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description="Vérifie qu'un élément visuel existe ou n'existe pas sur l'écran",
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parameters=parameters,
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screen_capturer=screen_capturer
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)
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# Paramètres spécifiques à la vérification
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self.visual_anchor: Optional[VWBVisualAnchor] = parameters.get('visual_anchor')
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self.should_exist = parameters.get('should_exist', True)
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# Configuration de matching
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self.confidence_threshold = parameters.get('confidence_threshold', 0.7)
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self.search_timeout_ms = parameters.get('search_timeout_ms', 5000)
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def validate_parameters(self) -> List[str]:
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"""Valide les paramètres de l'action."""
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errors = []
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# Vérifier l'ancre visuelle
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if not self.visual_anchor:
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errors.append("Ancre visuelle requise")
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elif isinstance(self.visual_anchor, dict):
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# Si c'est un dict, vérifier qu'il a les champs nécessaires
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if not self.visual_anchor.get('screenshot') and not self.visual_anchor.get('image_base64'):
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errors.append("Ancre visuelle doit contenir une image (screenshot ou image_base64)")
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elif isinstance(self.visual_anchor, VWBVisualAnchor):
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if not self.visual_anchor.is_active:
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errors.append("Ancre visuelle inactive")
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# Vérifier le seuil de confiance
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if not (0.0 <= self.confidence_threshold <= 1.0):
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errors.append("Seuil de confiance doit être entre 0.0 et 1.0")
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return errors
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def execute_core(self, step_id: str) -> VWBActionResult:
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"""
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Exécute la vérification d'existence de l'élément.
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Args:
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step_id: Identifiant de l'étape
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Returns:
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Résultat d'exécution
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"""
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start_time = datetime.now()
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try:
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# Importer la fonction de recherche visuelle
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from ...catalog_routes import find_visual_anchor_on_screen
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# Obtenir l'image de l'ancre
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anchor_image = self._get_anchor_image()
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if not anchor_image:
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return self._create_error_result(
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step_id=step_id,
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start_time=start_time,
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error_type=VWBErrorType.PARAMETER_INVALID,
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message="Impossible d'obtenir l'image de l'ancre visuelle"
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)
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# Obtenir le bounding_box si disponible
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bounding_box = self._get_bounding_box()
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# Rechercher l'élément sur l'écran
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anchor_name = self._get_anchor_name()
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print(f"🔍 Recherche de l'élément: {anchor_name}")
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search_result = find_visual_anchor_on_screen(
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anchor_image_base64=anchor_image,
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confidence_threshold=self.confidence_threshold,
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bounding_box=bounding_box
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)
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# Analyser le résultat
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element_found = search_result is not None and search_result.get('found', False)
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confidence = search_result.get('confidence', 0.0) if search_result else 0.0
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# Vérifier si le résultat correspond à l'attente
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verification_success = (element_found == self.should_exist)
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end_time = datetime.now()
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execution_time = (end_time - start_time).total_seconds() * 1000
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# Créer le résultat
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if verification_success:
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status_msg = "présent" if element_found else "absent"
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print(f"✅ Vérification réussie: élément {status_msg} (confiance: {confidence:.2%})")
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result = VWBActionResult(
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action_id=self.action_id,
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step_id=step_id,
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status=VWBActionStatus.SUCCESS,
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start_time=start_time,
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end_time=end_time,
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execution_time_ms=execution_time,
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output_data={
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'element_found': element_found,
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'should_exist': self.should_exist,
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'verification_success': True,
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'confidence': confidence,
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'match_position': search_result if element_found else None
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},
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evidence_list=self.evidence_list.copy()
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)
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return result
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else:
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expected = "présent" if self.should_exist else "absent"
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actual = "présent" if element_found else "absent"
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error_msg = f"Élément attendu {expected} mais trouvé {actual}"
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print(f"❌ Vérification échouée: {error_msg}")
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return self._create_error_result(
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step_id=step_id,
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start_time=start_time,
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error_type=VWBErrorType.ELEMENT_NOT_FOUND if self.should_exist else VWBErrorType.VALIDATION_FAILED,
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message=error_msg,
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technical_details={
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'element_found': element_found,
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'should_exist': self.should_exist,
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'confidence': confidence,
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'match_position': search_result if element_found else None
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}
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)
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except ImportError as e:
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print(f"⚠️ Erreur import: {e}")
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# Fallback si catalog_routes n'est pas disponible
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return self._execute_fallback(step_id, start_time)
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except Exception as e:
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return self._create_error_result(
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step_id=step_id,
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start_time=start_time,
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error_type=VWBErrorType.SYSTEM_ERROR,
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message=f"Erreur lors de la vérification: {str(e)}",
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technical_details={'exception': str(e)}
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)
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def _get_anchor_image(self) -> Optional[str]:
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"""Récupère l'image base64 de l'ancre."""
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if isinstance(self.visual_anchor, dict):
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# Format dictionnaire
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return self.visual_anchor.get('screenshot') or self.visual_anchor.get('image_base64')
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elif isinstance(self.visual_anchor, VWBVisualAnchor):
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# Format objet VWBVisualAnchor
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return self.visual_anchor.screenshot_base64
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return None
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def _get_bounding_box(self) -> Optional[Dict]:
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"""Récupère le bounding_box de l'ancre si disponible."""
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if isinstance(self.visual_anchor, dict):
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return self.visual_anchor.get('bounding_box')
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elif isinstance(self.visual_anchor, VWBVisualAnchor):
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if self.visual_anchor.has_bounding_box():
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return self.visual_anchor.bounding_box
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return None
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def _get_anchor_name(self) -> str:
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"""Récupère le nom de l'ancre."""
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if isinstance(self.visual_anchor, dict):
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return self.visual_anchor.get('name', self.visual_anchor.get('anchor_id', 'Unknown'))
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elif isinstance(self.visual_anchor, VWBVisualAnchor):
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return self.visual_anchor.name
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return "Unknown"
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def _execute_fallback(self, step_id: str, start_time: datetime) -> VWBActionResult:
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"""
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Fallback si la recherche visuelle n'est pas disponible.
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Utilise pyautogui.locateOnScreen si disponible.
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"""
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try:
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import pyautogui
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from PIL import Image
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from io import BytesIO
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import base64
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anchor_image = self._get_anchor_image()
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if not anchor_image:
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return self._create_error_result(
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step_id=step_id,
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start_time=start_time,
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error_type=VWBErrorType.PARAMETER_INVALID,
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message="Image de l'ancre non disponible"
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)
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# Décoder l'image
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if ',' in anchor_image:
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anchor_image = anchor_image.split(',')[1]
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image_bytes = base64.b64decode(anchor_image)
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pil_image = Image.open(BytesIO(image_bytes))
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# Rechercher avec pyautogui
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location = pyautogui.locateOnScreen(pil_image, confidence=self.confidence_threshold)
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element_found = location is not None
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verification_success = (element_found == self.should_exist)
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end_time = datetime.now()
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execution_time = (end_time - start_time).total_seconds() * 1000
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if verification_success:
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return VWBActionResult(
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action_id=self.action_id,
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step_id=step_id,
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status=VWBActionStatus.SUCCESS,
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start_time=start_time,
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end_time=end_time,
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execution_time_ms=execution_time,
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output_data={
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'element_found': element_found,
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'should_exist': self.should_exist,
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'verification_success': True,
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'method': 'pyautogui_fallback'
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},
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evidence_list=self.evidence_list.copy()
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)
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else:
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expected = "présent" if self.should_exist else "absent"
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actual = "présent" if element_found else "absent"
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return self._create_error_result(
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step_id=step_id,
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start_time=start_time,
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error_type=VWBErrorType.VALIDATION_FAILED,
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message=f"Élément attendu {expected} mais trouvé {actual}",
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technical_details={'method': 'pyautogui_fallback'}
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)
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except Exception as e:
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return self._create_error_result(
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step_id=step_id,
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start_time=start_time,
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error_type=VWBErrorType.SYSTEM_ERROR,
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message=f"Erreur fallback: {str(e)}"
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)
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def get_action_info(self) -> Dict[str, Any]:
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"""Retourne les informations de l'action pour l'interface."""
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return {
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'action_id': self.action_id,
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'name': self.name,
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'description': self.description,
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'type': 'verify_element_exists',
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'parameters': {
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'anchor_name': self._get_anchor_name(),
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'should_exist': self.should_exist,
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'confidence_threshold': self.confidence_threshold
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},
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'status': self.current_status.value
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}
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@@ -0,0 +1,391 @@
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"""
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Action Validation Texte - Vérifier le contenu textuel d'un élément
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Auteur : Dom, Alice, Kiro, Claude - 14 janvier 2026
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Cette action vérifie qu'un texte spécifique est présent dans une zone
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de l'écran, en utilisant l'OCR pour extraire et comparer le texte.
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Modes OCR disponibles:
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- ollama: Utilise un modèle de vision local (GPU, meilleure qualité)
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- easyocr: OCR traditionnel (CPU/GPU, plus rapide)
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"""
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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import time
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import re
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import base64
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from io import BytesIO
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# Import des modules de base
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from ..base_action import BaseVWBAction, VWBActionResult, VWBActionStatus
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from ...contracts.error import VWBErrorType, create_vwb_error
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from ...contracts.evidence import VWBEvidenceType, create_screenshot_evidence
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from ...contracts.visual_anchor import VWBVisualAnchor
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class VWBVerifyTextContentAction(BaseVWBAction):
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"""
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Action pour vérifier le contenu textuel d'un élément.
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Cette action extrait le texte d'une zone de l'écran via OCR
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et vérifie qu'il correspond au texte attendu.
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Supporte deux modes OCR:
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- ollama: Modèle de vision local (meilleure qualité, utilise GPU)
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- easyocr: OCR traditionnel (plus rapide, fallback)
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"""
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# Configuration Ollama par défaut
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OLLAMA_URL = "http://localhost:11434"
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OLLAMA_MODEL = "qwen2.5-vl:7b" # Modèle de vision Qwen - excellent pour OCR
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def __init__(
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self,
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action_id: str,
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parameters: Dict[str, Any],
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screen_capturer=None
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):
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"""
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Initialise l'action de vérification de texte.
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Args:
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action_id: Identifiant unique de l'action
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parameters: Paramètres incluant l'ancre visuelle et le texte attendu
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screen_capturer: Instance du ScreenCapturer (Option A thread-safe)
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"""
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super().__init__(
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action_id=action_id,
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name="Vérifier Contenu Texte",
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description="Vérifie qu'un texte spécifique est présent dans une zone de l'écran",
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parameters=parameters,
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screen_capturer=screen_capturer
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)
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# Paramètres spécifiques à la vérification
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self.visual_anchor: Optional[VWBVisualAnchor] = parameters.get('visual_anchor')
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self.expected_text = parameters.get('expected_text', '')
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self.match_mode = parameters.get('match_mode', 'contains') # exact, contains, regex
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self.case_sensitive = parameters.get('case_sensitive', False)
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# Configuration OCR
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self.ocr_mode = parameters.get('ocr_mode', 'ollama') # ollama (GPU) ou easyocr
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self.ollama_model = parameters.get('ollama_model', self.OLLAMA_MODEL)
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self.ollama_url = parameters.get('ollama_url', self.OLLAMA_URL)
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# Configuration de matching
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self.confidence_threshold = parameters.get('confidence_threshold', 0.7)
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def validate_parameters(self) -> List[str]:
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"""Valide les paramètres de l'action."""
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errors = []
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# Vérifier le texte attendu
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if not self.expected_text:
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errors.append("Texte attendu requis")
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# Vérifier l'ancre visuelle (optionnelle pour recherche plein écran)
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if self.visual_anchor:
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if isinstance(self.visual_anchor, dict):
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if not self.visual_anchor.get('screenshot') and not self.visual_anchor.get('image_base64'):
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if not self.visual_anchor.get('bounding_box'):
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errors.append("Ancre visuelle doit contenir une image ou un bounding_box")
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elif isinstance(self.visual_anchor, VWBVisualAnchor):
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if not self.visual_anchor.is_active:
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errors.append("Ancre visuelle inactive")
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# Vérifier le mode de matching
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if self.match_mode not in ['exact', 'contains', 'regex', 'starts_with', 'ends_with']:
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errors.append(f"Mode de matching invalide: {self.match_mode}")
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# Vérifier le mode OCR
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if self.ocr_mode not in ['ollama', 'easyocr']:
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errors.append(f"Mode OCR invalide: {self.ocr_mode} (utilisez 'ollama' ou 'easyocr')")
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return errors
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def execute_core(self, step_id: str) -> VWBActionResult:
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"""
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Exécute la vérification du contenu textuel.
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Args:
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step_id: Identifiant de l'étape
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Returns:
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Résultat d'exécution
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"""
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start_time = datetime.now()
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try:
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anchor_name = self._get_anchor_name()
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print(f"🔍 Vérification texte: '{self.expected_text}' dans {anchor_name}")
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print(f" Mode OCR: {self.ocr_mode}")
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# Extraire le texte de la zone
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extracted_text = self._extract_text_from_screen()
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if extracted_text is None:
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return self._create_error_result(
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step_id=step_id,
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start_time=start_time,
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error_type=VWBErrorType.SCREEN_CAPTURE_FAILED,
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message="Impossible d'extraire le texte de l'écran"
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)
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# Comparer le texte
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text_matches = self._compare_text(extracted_text, self.expected_text)
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end_time = datetime.now()
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execution_time = (end_time - start_time).total_seconds() * 1000
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if text_matches:
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print(f"✅ Texte trouvé: '{self.expected_text}'")
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return VWBActionResult(
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action_id=self.action_id,
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step_id=step_id,
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status=VWBActionStatus.SUCCESS,
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start_time=start_time,
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end_time=end_time,
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execution_time_ms=execution_time,
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output_data={
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'text_matches': True,
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'expected_text': self.expected_text,
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||||
'extracted_text': extracted_text,
|
||||
'match_mode': self.match_mode,
|
||||
'ocr_mode': self.ocr_mode
|
||||
},
|
||||
evidence_list=self.evidence_list.copy()
|
||||
)
|
||||
else:
|
||||
error_msg = f"Texte attendu '{self.expected_text}' non trouvé"
|
||||
print(f"❌ {error_msg}")
|
||||
if extracted_text:
|
||||
print(f" Texte extrait: '{extracted_text[:100]}...'")
|
||||
|
||||
return self._create_error_result(
|
||||
step_id=step_id,
|
||||
start_time=start_time,
|
||||
error_type=VWBErrorType.VALIDATION_FAILED,
|
||||
message=error_msg,
|
||||
technical_details={
|
||||
'expected_text': self.expected_text,
|
||||
'extracted_text': extracted_text[:500] if extracted_text else '',
|
||||
'match_mode': self.match_mode,
|
||||
'ocr_mode': self.ocr_mode
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
return self._create_error_result(
|
||||
step_id=step_id,
|
||||
start_time=start_time,
|
||||
error_type=VWBErrorType.SYSTEM_ERROR,
|
||||
message=f"Erreur lors de la vérification: {str(e)}",
|
||||
technical_details={'exception': str(e)}
|
||||
)
|
||||
|
||||
def _extract_text_from_screen(self) -> Optional[str]:
|
||||
"""
|
||||
Extrait le texte de l'écran ou d'une zone spécifique.
|
||||
|
||||
Returns:
|
||||
Texte extrait ou None si erreur
|
||||
"""
|
||||
try:
|
||||
import pyautogui
|
||||
from PIL import Image
|
||||
|
||||
# Capturer l'écran
|
||||
screenshot = pyautogui.screenshot()
|
||||
|
||||
# Si on a un bounding_box, extraire la région
|
||||
if self.visual_anchor:
|
||||
bbox = self._get_bounding_box()
|
||||
if bbox:
|
||||
x = int(bbox.get('x', 0))
|
||||
y = int(bbox.get('y', 0))
|
||||
w = int(bbox.get('width', 100))
|
||||
h = int(bbox.get('height', 100))
|
||||
screenshot = screenshot.crop((x, y, x + w, y + h))
|
||||
|
||||
# Choisir le mode OCR
|
||||
if self.ocr_mode == 'ollama':
|
||||
return self._extract_with_ollama(screenshot)
|
||||
else:
|
||||
return self._extract_with_easyocr(screenshot)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Erreur extraction texte: {e}")
|
||||
return None
|
||||
|
||||
def _extract_with_ollama(self, image) -> Optional[str]:
|
||||
"""
|
||||
Extrait le texte en utilisant Ollama avec un modèle de vision.
|
||||
|
||||
Args:
|
||||
image: Image PIL à analyser
|
||||
|
||||
Returns:
|
||||
Texte extrait ou None si erreur
|
||||
"""
|
||||
try:
|
||||
import requests
|
||||
from PIL import Image
|
||||
|
||||
print(f"🤖 Extraction OCR via Ollama ({self.ollama_model})...")
|
||||
|
||||
# Convertir l'image en base64
|
||||
buffer = BytesIO()
|
||||
image.save(buffer, format='PNG')
|
||||
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
|
||||
|
||||
# Appel à l'API Ollama
|
||||
response = requests.post(
|
||||
f"{self.ollama_url}/api/generate",
|
||||
json={
|
||||
"model": self.ollama_model,
|
||||
"prompt": "Extract and return ONLY the text visible in this image. Do not add any explanation or commentary. Just return the raw text content.",
|
||||
"images": [image_base64],
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": 0.1, # Réponse plus déterministe
|
||||
"num_predict": 500 # Limite de tokens
|
||||
}
|
||||
},
|
||||
timeout=30
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
extracted_text = result.get('response', '').strip()
|
||||
print(f" ✅ Texte extrait ({len(extracted_text)} caractères)")
|
||||
return extracted_text
|
||||
else:
|
||||
print(f" ⚠️ Erreur Ollama: {response.status_code}")
|
||||
# Fallback sur easyocr
|
||||
return self._extract_with_easyocr(image)
|
||||
|
||||
except requests.exceptions.ConnectionError:
|
||||
print(f" ⚠️ Ollama non disponible, fallback sur easyocr")
|
||||
return self._extract_with_easyocr(image)
|
||||
|
||||
except Exception as e:
|
||||
print(f" ⚠️ Erreur Ollama: {e}, fallback sur easyocr")
|
||||
return self._extract_with_easyocr(image)
|
||||
|
||||
def _extract_with_easyocr(self, image) -> Optional[str]:
|
||||
"""
|
||||
Extrait le texte en utilisant EasyOCR.
|
||||
|
||||
Args:
|
||||
image: Image PIL à analyser
|
||||
|
||||
Returns:
|
||||
Texte extrait ou None si erreur
|
||||
"""
|
||||
try:
|
||||
import easyocr
|
||||
import numpy as np
|
||||
|
||||
print("📝 Extraction OCR via EasyOCR...")
|
||||
|
||||
# Convertir en array numpy
|
||||
img_array = np.array(image)
|
||||
|
||||
# EasyOCR (GPU si disponible)
|
||||
reader = easyocr.Reader(['fr', 'en'], gpu=True)
|
||||
results = reader.readtext(img_array)
|
||||
|
||||
# Combiner les résultats
|
||||
extracted_text = ' '.join([result[1] for result in results])
|
||||
print(f" ✅ Texte extrait ({len(extracted_text)} caractères)")
|
||||
return extracted_text.strip()
|
||||
|
||||
except ImportError:
|
||||
print(" ⚠️ easyocr non disponible")
|
||||
return ""
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Erreur EasyOCR: {e}")
|
||||
return None
|
||||
|
||||
def _compare_text(self, extracted: str, expected: str) -> bool:
|
||||
"""
|
||||
Compare le texte extrait avec le texte attendu.
|
||||
|
||||
Args:
|
||||
extracted: Texte extrait de l'écran
|
||||
expected: Texte attendu
|
||||
|
||||
Returns:
|
||||
True si le texte correspond selon le mode de matching
|
||||
"""
|
||||
if not extracted:
|
||||
return False
|
||||
|
||||
# Normaliser si non sensible à la casse
|
||||
if not self.case_sensitive:
|
||||
extracted = extracted.lower()
|
||||
expected = expected.lower()
|
||||
|
||||
# Appliquer le mode de matching
|
||||
if self.match_mode == 'exact':
|
||||
return extracted.strip() == expected.strip()
|
||||
|
||||
elif self.match_mode == 'contains':
|
||||
return expected in extracted
|
||||
|
||||
elif self.match_mode == 'starts_with':
|
||||
return extracted.strip().startswith(expected)
|
||||
|
||||
elif self.match_mode == 'ends_with':
|
||||
return extracted.strip().endswith(expected)
|
||||
|
||||
elif self.match_mode == 'regex':
|
||||
try:
|
||||
flags = 0 if self.case_sensitive else re.IGNORECASE
|
||||
pattern = re.compile(expected, flags)
|
||||
return bool(pattern.search(extracted))
|
||||
except re.error:
|
||||
print(f" ⚠️ Pattern regex invalide: {expected}")
|
||||
return False
|
||||
|
||||
return False
|
||||
|
||||
def _get_bounding_box(self) -> Optional[Dict]:
|
||||
"""Récupère le bounding_box de l'ancre si disponible."""
|
||||
if isinstance(self.visual_anchor, dict):
|
||||
return self.visual_anchor.get('bounding_box')
|
||||
elif isinstance(self.visual_anchor, VWBVisualAnchor):
|
||||
if self.visual_anchor.has_bounding_box():
|
||||
return self.visual_anchor.bounding_box
|
||||
return None
|
||||
|
||||
def _get_anchor_name(self) -> str:
|
||||
"""Récupère le nom de l'ancre."""
|
||||
if not self.visual_anchor:
|
||||
return "écran entier"
|
||||
if isinstance(self.visual_anchor, dict):
|
||||
return self.visual_anchor.get('name', self.visual_anchor.get('anchor_id', 'zone'))
|
||||
elif isinstance(self.visual_anchor, VWBVisualAnchor):
|
||||
return self.visual_anchor.name
|
||||
return "zone"
|
||||
|
||||
def get_action_info(self) -> Dict[str, Any]:
|
||||
"""Retourne les informations de l'action pour l'interface."""
|
||||
return {
|
||||
'action_id': self.action_id,
|
||||
'name': self.name,
|
||||
'description': self.description,
|
||||
'type': 'verify_text_content',
|
||||
'parameters': {
|
||||
'anchor_name': self._get_anchor_name(),
|
||||
'expected_text': self.expected_text,
|
||||
'match_mode': self.match_mode,
|
||||
'case_sensitive': self.case_sensitive,
|
||||
'ocr_mode': self.ocr_mode,
|
||||
'ollama_model': self.ollama_model
|
||||
},
|
||||
'status': self.current_status.value
|
||||
}
|
||||
1434
visual_workflow_builder/frontend/src/data/staticCatalog.ts
Normal file
1434
visual_workflow_builder/frontend/src/data/staticCatalog.ts
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user