Dom/rpa_vision_v3
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: stratégie hybride OCR→grounding VLM / icônes→template matching

Browse Source 
Résolution 4/4 (100%) validée localement :
- Texte OCR (by_text_source="ocr") → grounding Qwen2.5-VL (dist < 0.04)
- Icônes sans texte (by_text_source="") → template matching crop 80x80 (dist = 0.000)

Le VLM identify element est supprimé pour les icônes (descriptions
non-déterministes qui faisaient échouer le grounding). Le template
matching est instantané et parfait quand le crop est net (80x80).

Ajout de by_text_source dans target_spec pour distinguer OCR vs VLM.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Dom
2026-03-31 23:21:06 +02:00
parent d99b17394a
commit 6724f43950
2 changed files with 20 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
agent_v0/server_v1/api_stream.py
View File

@@ -3970,12 +3970,14 @@ def _resolve_target_sync(
vlm_description = _build_target_description(target_spec)
# ---------------------------------------------------------------
# Étape 0 : Grounding VLM Direct (Qwen2.5-VL)
# Le VLM reçoit le screenshot + description textuelle et retourne
# directement les coordonnées. Plus fiable que SomEngine + numérotation.
# Étape 0 : Choisir la stratégie selon le type d'élément
# - Texte OCR fiable → grounding VLM (description textuelle)
# - Icône sans texte → template matching (crop 80x80)
# ---------------------------------------------------------------
grounding_desc = by_text_strict or vlm_description
if grounding_desc:
by_text_source = target_spec.get("by_text_source", "")
if by_text_strict and by_text_source == "ocr":
# Texte OCR fiable → grounding VLM direct
grounding_result = _resolve_by_grounding(
screenshot_path=screenshot_path,
target_spec=target_spec,
@@ -3987,14 +3989,11 @@ def _resolve_target_sync(
"Strict resolve GROUNDING : OK (%.4f, %.4f) pour '%s'",
grounding_result.get("x_pct", 0),
grounding_result.get("y_pct", 0),
grounding_desc[:50],
by_text_strict[:50],
)
return grounding_result
# ---------------------------------------------------------------
# Étape 0.5 : Template matching pour icônes sans texte (crop 80x80)
# ---------------------------------------------------------------
if not by_text_strict:
if not by_text_strict or by_text_source != "ocr":
result = _resolve_by_template_matching(
screenshot_path=screenshot_path,
anchor_image_b64=anchor_image_b64,

19
agent_v0/server_v1/stream_processor.py
View File

@@ -1141,25 +1141,28 @@ def build_replay_from_raw_events(
)
# Déterminer le texte de l'élément cliqué (by_text)
# Priorité : vision_info.text > som_element.label > VLM identification
# Priorité : vision_info.text > som_element.label
# Source "ocr" = fiable (texte réel), "vlm" = bavardage non-fiable
element_text = ""
element_type = ""
text_source = "" # "ocr" ou "vlm"
if isinstance(vision_info, dict):
element_text = vision_info.get("text", "")
element_type = vision_info.get("type", "")
if element_text:
text_source = "ocr"
if not element_text and som_elem and som_elem.get("label"):
element_text = som_elem["label"]
text_source = "ocr"
# Si pas de texte (icône sans label), demander au VLM
# d'identifier CE QUE c'est à partir du crop
if not element_text and anchor_b64:
element_text = _vlm_identify_element(
anchor_b64, window_title,
)
# Icônes sans texte OCR → NE PAS utiliser le VLM pour nommer
# (descriptions non-déterministes qui font échouer le grounding)
# Le template matching du crop 80x80 sera utilisé à la place
action["target_spec"] = {
"anchor_image_base64": anchor_b64,
"by_text": element_text, # CE QUE l'élément EST
"by_text": element_text,
"by_text_source": text_source, # "ocr" = fiable, "" = icône
"by_role": element_type or (som_elem.get("source", "") if som_elem else ""),
"vlm_description": vlm_description,
"window_title": window_title,