feat: VLM grounding direct (Qwen2.5-VL) — nouvelle stratégie de résolution
Nouvelle approche basée sur les recherches état de l'art : - _resolve_by_grounding() : le VLM retourne directement les coordonnées (pas de SomEngine + numérotation intermédiaire) - Utilise Qwen2.5-VL (entraîné pour le GUI grounding) au lieu de qwen3-vl - Parse les formats natifs : bbox_2d, JSON x/y, arrays bruts - Fallback multi-image : screenshot + crop → grounding sans description - Identification des icônes via Qwen2.5-VL (meilleur que qwen3-vl) Résultats sur session réelle (validation locale) : - Éléments avec texte (Word, Document, Fichier) : 100% corrects - Icônes sans texte (Windows logo, disquette) : en cours d'amélioration Cascade strict mode : 0. Grounding VLM direct (Qwen2.5-VL) — NOUVEAU 0.5. Template matching pour icônes 1. VLM Quick Find (fallback) 1.5. SoM + VLM 2. Template matching strict Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Dom
@@ -3366,6 +3366,206 @@ def _vlm_quick_find(
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return None
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# ---------------------------------------------------------------------------
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# Résolution par VLM Grounding Direct (Qwen2.5-VL)
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# ---------------------------------------------------------------------------
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def _resolve_by_grounding(
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screenshot_path: str,
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target_spec: Dict[str, Any],
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screen_width: int,
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screen_height: int,
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) -> Optional[Dict[str, Any]]:
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"""Résoudre une cible via grounding VLM direct (Qwen2.5-VL).
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Le VLM reçoit le screenshot + une description textuelle et retourne
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directement les coordonnées (bbox_2d) de l'élément. Pas de SomEngine,
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pas de numérotation — le VLM est entraîné pour le grounding UI.
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Approche plus fiable que SomEngine+VLM pour les icônes et éléments
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visuels sans texte (logo Windows, disquette, bouton fermer).
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"""
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import base64
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import io
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import re
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t0 = time.time()
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# Construire la description de la cible
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by_text = target_spec.get("by_text", "").strip()
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vlm_desc = target_spec.get("vlm_description", "").strip()
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window_title = target_spec.get("window_title", "").strip()
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if by_text:
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description = by_text
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elif vlm_desc:
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description = vlm_desc
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else:
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return None
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# Redimensionner le screenshot (800px de large pour le VLM)
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try:
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from PIL import Image as PILImage
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img = PILImage.open(screenshot_path)
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orig_w, orig_h = img.size
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target_w = 800
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ratio = target_w / orig_w
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img_small = img.resize((target_w, int(orig_h * ratio)))
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small_w, small_h = img_small.size
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buf = io.BytesIO()
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img_small.save(buf, format="JPEG", quality=75)
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shot_b64 = base64.b64encode(buf.getvalue()).decode()
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except Exception as e:
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logger.warning("Grounding : erreur redimensionnement — %s", e)
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return None
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# Construire le prompt — Qwen2.5-VL retourne naturellement des bbox_2d
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prompt = (
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f"Look at this screenshot. Find: {description}\n"
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"Where is it? Give the center position as percentage of the image.\n"
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'Answer ONLY with JSON: {"x": 0.XX, "y": 0.YY}'
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)
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# Appel VLM (Qwen2.5-VL pour le grounding)
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try:
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import requests as _requests
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resp = _requests.post("http://localhost:11434/api/chat", json={
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"model": "qwen2.5vl:7b",
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"messages": [
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{"role": "system", "content": "You locate UI elements on screenshots. Return coordinates."},
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{"role": "user", "content": prompt, "images": [shot_b64]},
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],
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"stream": False,
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"options": {"temperature": 0.1, "num_predict": 80},
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}, timeout=60)
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content = resp.json().get("message", {}).get("content", "")
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except Exception as e:
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logger.info("Grounding VLM timeout/erreur : %s", e)
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return None
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elapsed = time.time() - t0
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# Parser la réponse — Qwen2.5-VL retourne soit bbox_2d en pixels, soit JSON %
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x_pct, y_pct = None, None
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# Format 1 : bbox_2d en pixels [x, y] ou [x1, y1, x2, y2]
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bbox_match = re.search(r'"bbox_2d"\s*:\s*\[([^\]]+)\]', content)
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if bbox_match:
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coords = [float(v.strip()) for v in bbox_match.group(1).split(",")]
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if len(coords) == 2:
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x_pct = coords[0] / small_w
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y_pct = coords[1] / small_h
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elif len(coords) >= 4:
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x_pct = (coords[0] + coords[2]) / 2 / small_w
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y_pct = (coords[1] + coords[3]) / 2 / small_h
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# Format 2 : JSON {"x": 0.XX, "y": 0.YY}
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if x_pct is None:
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json_match = re.search(r'"x"\s*:\s*([\d.]+).*?"y"\s*:\s*([\d.]+)', content)
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if json_match:
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x_val, y_val = float(json_match.group(1)), float(json_match.group(2))
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# Si > 1, c'est en pixels
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if x_val > 1:
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x_pct = x_val / small_w
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y_pct = y_val / small_h
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else:
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x_pct = x_val
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y_pct = y_val
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# Format 3 : {"x_pct": 0.XX, "y_pct": 0.YY}
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if x_pct is None:
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pct_match = re.search(r'"x_pct"\s*:\s*([\d.]+).*?"y_pct"\s*:\s*([\d.]+)', content)
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if pct_match:
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x_pct = float(pct_match.group(1))
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y_pct = float(pct_match.group(2))
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# Format 4 : array brut [x1, y1, x2, y2] ou [x, y]
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if x_pct is None:
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arr_match = re.search(r'\[[\s]*([\d.]+)\s*,\s*([\d.]+)(?:\s*,\s*([\d.]+)\s*,\s*([\d.]+))?\s*\]', content)
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if arr_match:
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vals = [float(v) for v in arr_match.groups() if v is not None]
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if len(vals) >= 4:
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x_pct = (vals[0] + vals[2]) / 2 / small_w
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y_pct = (vals[1] + vals[3]) / 2 / small_h
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elif len(vals) == 2:
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x_pct = vals[0] / small_w
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y_pct = vals[1] / small_h
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if x_pct is None or y_pct is None:
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# Fallback multi-image : screenshot + crop → grounding sans description
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anchor_b64 = target_spec.get("anchor_image_base64", "")
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if anchor_b64:
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try:
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prompt_mi = (
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"Image 1 is a screenshot. Image 2 shows a UI element.\n"
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"Find where Image 2 appears on Image 1.\n"
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'Return position: {"x": NNN, "y": NNN} in pixels of Image 1.'
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)
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resp2 = _requests.post("http://localhost:11434/api/chat", json={
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"model": "qwen2.5vl:7b",
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"messages": [
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{"role": "user", "content": prompt_mi, "images": [shot_b64, anchor_b64]},
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],
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"stream": False,
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"options": {"temperature": 0.1, "num_predict": 50},
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}, timeout=60)
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content2 = resp2.json().get("message", {}).get("content", "")
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elapsed = time.time() - t0
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# Parser tous les formats
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arr2 = re.search(r'\[[\s]*([\d.]+)\s*,\s*([\d.]+)(?:\s*,\s*([\d.]+)\s*,\s*([\d.]+))?\s*\]', content2)
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if arr2:
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vals = [float(v) for v in arr2.groups() if v is not None]
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if len(vals) >= 4:
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x_pct = (vals[0] + vals[2]) / 2 / small_w
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y_pct = (vals[1] + vals[3]) / 2 / small_h
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elif len(vals) == 2:
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x_pct = vals[0] / small_w
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y_pct = vals[1] / small_h
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if x_pct is None:
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json2 = re.search(r'"x"\s*:\s*([\d.]+).*?"y"\s*:\s*([\d.]+)', content2)
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if json2:
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x_pct = float(json2.group(1)) / small_w
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y_pct = float(json2.group(2)) / small_h
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if x_pct is not None:
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logger.info("Grounding multi-image OK (%.1fs)", elapsed)
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except Exception as e:
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logger.debug("Grounding multi-image erreur: %s", e)
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if x_pct is None or y_pct is None:
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logger.info(
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"Grounding : réponse non parsable (%.1fs) — %s",
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elapsed, content[:120],
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)
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return None
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# Valider les bornes
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if not (0.0 <= x_pct <= 1.0 and 0.0 <= y_pct <= 1.0):
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logger.info("Grounding : coordonnées hors bornes (%.3f, %.3f)", x_pct, y_pct)
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return None
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logger.info(
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"Grounding OK [qwen2.5vl] : '%s' → (%.4f, %.4f) en %.1fs",
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description[:50], x_pct, y_pct, elapsed,
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)
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return {
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"resolved": True,
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"method": "grounding_vlm",
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"x_pct": round(x_pct, 6),
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"y_pct": round(y_pct, 6),
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"matched_element": {
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"label": description[:60],
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"type": "grounding",
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"role": "grounding_vlm",
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"confidence": 0.85,
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},
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"score": 0.85,
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}
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# ---------------------------------------------------------------------------
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# Résolution Set-of-Mark : SomEngine (détection) + VLM (identification)
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# ---------------------------------------------------------------------------
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@@ -3770,9 +3970,29 @@ def _resolve_target_sync(
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vlm_description = _build_target_description(target_spec)
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# ---------------------------------------------------------------
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# Étape 0 : Template matching PRIORITAIRE pour les icônes sans texte
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# Les crops 80x80 sont très discriminants pour les icônes (logo Windows,
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# disquette, croix). Le VLM se trompe souvent sur ces éléments.
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# Étape 0 : Grounding VLM Direct (Qwen2.5-VL)
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# Le VLM reçoit le screenshot + description textuelle et retourne
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# directement les coordonnées. Plus fiable que SomEngine + numérotation.
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# ---------------------------------------------------------------
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grounding_desc = by_text_strict or vlm_description
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if grounding_desc:
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grounding_result = _resolve_by_grounding(
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screenshot_path=screenshot_path,
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target_spec=target_spec,
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screen_width=screen_width,
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screen_height=screen_height,
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)
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if grounding_result and grounding_result.get("resolved"):
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logger.info(
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"Strict resolve GROUNDING : OK (%.4f, %.4f) pour '%s'",
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grounding_result.get("x_pct", 0),
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grounding_result.get("y_pct", 0),
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grounding_desc[:50],
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)
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return grounding_result
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# ---------------------------------------------------------------
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# Étape 0.5 : Template matching pour icônes sans texte (crop 80x80)
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# ---------------------------------------------------------------
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if not by_text_strict:
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result = _resolve_by_template_matching(
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@@ -3784,13 +4004,13 @@ def _resolve_target_sync(
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)
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if result and result.get("score", 0) >= 0.70:
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logger.info(
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"Strict resolve icon : template matching OK (score=%.3f) pour icône sans texte",
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"Strict resolve TEMPLATE : icon match (score=%.3f)",
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result.get("score", 0),
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)
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return result
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# ---------------------------------------------------------------
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# Étape 1 : VLM Quick Find (compréhension sémantique)
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# Étape 1 : VLM Quick Find (fallback, multi-image)
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# ---------------------------------------------------------------
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if vlm_description or anchor_image_b64:
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vlm_result = _vlm_quick_find(
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@@ -458,26 +458,24 @@ def _vlm_identify_element(anchor_b64: str, window_title: str = "") -> str:
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img.save(tmp, format="PNG")
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tmp_path = tmp.name
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from core.detection.ollama_client import OllamaClient
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client = OllamaClient(
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endpoint="http://localhost:11434",
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model="qwen3-vl:8b",
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timeout=15,
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)
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context = f" in the window '{window_title}'" if window_title else ""
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result = client.generate(
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prompt=(
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f"This is a cropped UI element{context}. "
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"What is it? Answer with a short label (2-5 words max). "
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"Examples: 'search bar icon', 'Word application icon', 'close button', "
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"'file menu', 'save button'.\n"
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"Answer ONLY the label, nothing else."
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),
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image_path=tmp_path,
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system_prompt="You identify UI elements. Answer with a short label only.",
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temperature=0.1,
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max_tokens=20,
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)
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import requests as _requests
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context = f" from the window '{window_title}'" if window_title else ""
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# Utiliser Qwen2.5-VL (meilleur pour l'identification UI que qwen3-vl)
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crop_b64 = base64.b64encode(open(tmp_path, "rb").read()).decode()
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resp = _requests.post("http://localhost:11434/api/chat", json={
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"model": "qwen2.5vl:7b",
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"messages": [
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{"role": "system", "content": "You name UI elements in 2-5 words. No explanation."},
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{"role": "user", "content": (
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f"This is a UI element{context}. "
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"Name it in 2-5 words. Examples: 'save icon in title bar', "
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"'Windows search icon', 'close button', 'file menu'."
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), "images": [crop_b64]},
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],
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"stream": False,
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"options": {"temperature": 0.1, "num_predict": 20},
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}, timeout=30)
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result = {"success": resp.ok, "response": resp.json().get("message", {}).get("content", "")}
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import os
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os.unlink(tmp_path)
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