somew1nd
12d7902a82
Fix queue cancellation, concurrency limits and worker status updates. Use direct Explorer imports, split the Diagnoser loop into helpers and remove unused tool registration and prompt-builder code. Correct model caching, endpoint selection and retry handling.
633 lines
25 KiB
Python
633 lines
25 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Perception and vision tool implementations for the Explorer agent.
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Split out of ``artemis.agents.explorer.explorer``: the ``exec_*`` tool method
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group plus its search helpers, packaged as a mixin consumed by ``Explorer``.
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Text search, the coordinate audit and the OCR inventory all answer from the
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run's in-memory :class:`~artemis.agents.explorer.screen_index.ScreenIndex`;
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no Data Engine round trip is involved, so an unsynced frame never degrades
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to "no UI data".
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"""
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import asyncio
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import glob
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import json
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from pathlib import Path
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import re
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from typing import TYPE_CHECKING, Any
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import cv2
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from artemis.agents.explorer.geometry import norm_to_pixel, pixel_to_norm
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from artemis.agents.explorer.screen_index import ScreenElement, ScreenIndex, normalize_text
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from artemis.agents.image_processor.image_processor import ImageProcessor
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from artemis.agents.object_detector.object_detector import _run_object_detection
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from artemis.config import settings
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from artemis.data_engine.trace import trace
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from artemis.utils.logger import get_logger
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from artemis.utils.visualization import draw_dots
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logger = get_logger(__name__)
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#: Text-search answer when the screen could not be indexed at all.
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NO_UI_TREE_MESSAGE = "No UI-tree data is available for this screen; rely on visual detection."
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def load_detector_templates() -> tuple[list[str], float]:
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"""Load object-detector prompt templates and the optional global timeout.
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Shared by ``exec_detect_objects`` and the flash-mode ``run`` path; the two
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call sites historically carried an identical inline copy of this block.
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"""
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detector_prompt_path = Path(__file__).parent.parent / "object_detector" / "object_detector.json"
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global_timeout = 30.0
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try:
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with open(detector_prompt_path, encoding="utf-8") as f:
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detector_config = json.load(f)
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detector_templates = detector_config.get("templates", [])
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detector_instructions = detector_config.get("instructions", "")
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raw_timeout = detector_config.get("global_timeout")
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if isinstance(raw_timeout, (int, float)) and raw_timeout > 0:
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global_timeout = float(raw_timeout)
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except Exception as e:
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logger.warning(f"Failed to load detector prompt config: {e}")
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detector_templates = ["Point to the following objects in the provided image: {labels_str}."]
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detector_instructions = ""
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templates = [f"{t}\n\n{detector_instructions}" for t in detector_templates]
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return templates, global_timeout
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class PerceptionToolsMixin:
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"""Perception/vision tool method group of :class:`Explorer`."""
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# Runtime initialization lives in ``Explorer``/``RunSetupMixin``. Keep a
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# precise declaration here so this mixin's host requirements are explicit.
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if TYPE_CHECKING:
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from artemis.context import ArtemisContext
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ctx: ArtemisContext
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global_label_idx: int
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width: int
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height: int
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image_name: str | None
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screenshot_path: str | None
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image_pool: dict[str, dict[str, Any]]
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next_img_id: int
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screen_index: ScreenIndex
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label_registry: dict[str, ScreenElement]
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# ------------------------------------------------------------------ #
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# Label registry and annotation helpers
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# ------------------------------------------------------------------ #
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def _register_label(self, label: str, element: ScreenElement) -> None:
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"""Remembers which element a label shown to the model stands for.
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Only labels backed by a real UI-tree or OCR element are registered;
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detection points (``D``) carry no bounds and are deliberately absent.
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"""
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self.label_registry[label] = element
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def _registry_element(self, entry: dict[str, Any]) -> ScreenElement:
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"""Maps a numbered-list entry back to its indexed element.
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The index is preferred over rebuilding the element from the entry
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because it carries the interaction flags the entry dropped.
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"""
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raw_bounds = entry["bounds"]
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bounds = (int(raw_bounds[0]), int(raw_bounds[1]), int(raw_bounds[2]), int(raw_bounds[3]))
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text = str(entry.get("text") or "")
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source = "ocr" if entry.get("is_ocr") else "xml"
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for element in self.screen_index.elements:
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if (
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element.bounds == bounds
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and element.source == source
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and normalize_text(element.text) == normalize_text(text)
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):
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return element
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return ScreenElement(
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text=text,
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bounds=bounds,
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source=source,
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class_name=str(entry["class"]) if entry.get("class") else None,
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resource_id=str(entry["resource_id"]) if entry.get("resource_id") else None,
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)
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def _next_output_path(self, subdir: str) -> Path:
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"""Next free ``images/<subdir>/<image>_<n>.jpg`` under the traces directory."""
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out_dir = Path(settings.TRACES_PATH) / "images" / subdir
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out_dir.mkdir(parents=True, exist_ok=True)
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stem = self.image_name or "temp_image"
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max_seq = 0
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for existing in glob.glob(str(out_dir / f"{stem}_*.jpg")):
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match = re.search(r"_(\d+)\.jpg$", existing)
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if match:
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max_seq = max(max_seq, int(match.group(1)))
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return out_dir / f"{stem}_{max_seq + 1}.jpg"
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def _draw_labeled_dots(
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self, subdir: str, points: list[list[int]], labels: list[str], color: str
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) -> str | None:
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"""Draws ``labels`` at ``points`` on the screenshot; returns the annotated path.
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Returns None when the screenshot is missing or drawing fails: the
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textual result is still delivered, so one bad annotation never sinks
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the tool call.
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"""
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if not self.screenshot_path:
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return None
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try:
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output_path = self._next_output_path(subdir)
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draw_dots(self.screenshot_path, points, labels, str(output_path), color=color)
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return str(output_path)
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except Exception as e:
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logger.warning(f"Failed to draw {subdir} dots: {e}")
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return None
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# ------------------------------------------------------------------ #
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# Screen-index searches
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# ------------------------------------------------------------------ #
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async def _search_ui_helper(self, query: str, prefix: str = "S", color: str = "red") -> dict:
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"""Fuzzy text search over the screen index, annotated with labeled dots.
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A strict pass (0.7) keeps the answer short when the label is
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unambiguous; when it yields fewer than three hits a lenient pass
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(0.4) is merged in so near-misses (abbreviations, truncated labels)
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still surface for the model to judge.
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"""
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if not len(self.screen_index):
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return {"text": NO_UI_TREE_MESSAGE, "image_path": None}
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matches = list(self.screen_index.search_text(query, 0.7))
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if len(matches) < 3:
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seen = {(m.matched_text, m.element.bounds) for m in matches}
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for m in self.screen_index.search_text(query, 0.4):
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key = (m.matched_text, m.element.bounds)
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if key not in seen:
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matches.append(m)
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seen.add(key)
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if not matches:
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return {"text": "No matches found.", "image_path": None}
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points: list[list[int]] = []
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labels: list[str] = []
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text_output: list[str] = []
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for m in matches:
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element = m.element
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m_prefix = "O" if element.source == "ocr" else prefix
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label_id = f"{m_prefix}{self.global_label_idx}"
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self.global_label_idx += 1
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self._register_label(label_id, element)
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cx, cy = element.center
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points.append([cx, cy])
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labels.append(label_id)
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nx, ny = pixel_to_norm(cx, cy, self.width, self.height)
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text_output.append(f"[{label_id}] '{m.matched_text}' at [{nx},{ny}]")
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image_path = self._draw_labeled_dots("search_ui", points, labels, color)
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return {"text": " | ".join(text_output), "image_path": image_path}
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async def _search_by_coords_helper(
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self, nx: int, ny: int, prefix: str = "X", color: str = "blue"
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) -> dict:
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"""Coordinate audit: names what actually sits under a normalized point.
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UI-tree hits come innermost first (see ``ScreenIndex.elements_at``),
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so the label is bound to the node a tap would reach. The blue dot is
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drawn even without a hit so the model can see where it probed.
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"""
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x, y = norm_to_pixel(nx, ny, self.width, self.height)
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found = self.screen_index.elements_at(x, y)
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label_id = f"{prefix}{self.global_label_idx}"
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self.global_label_idx += 1
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image_path = self._draw_labeled_dots("coords_audit", [[x, y]], [label_id], color)
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if not found:
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return {"text": f"No elements found at [{nx},{ny}].", "image_path": image_path}
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self._register_label(label_id, found[0])
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described = " | ".join(element.describe() for element in found)
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return {
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"text": f"Matched element at [{nx},{ny}]: [{label_id}] | {described}",
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"image_path": image_path,
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}
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@trace(type="tool", name="detect_objects")
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async def exec_detect_objects(
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self,
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queries: list[str],
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target_image_id: str = "img_0",
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prefix: str = "D",
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color: str = "red",
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) -> dict:
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try:
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if not self.screenshot_path:
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return {
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"text": "Error: Original screenshot is not available.",
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"image_path": None,
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}
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target_info = self.image_pool.get(target_image_id)
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if not target_info:
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return {
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"text": (f"Error: {target_image_id} not found in Image Pool."),
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"image_path": None,
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}
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target_path = target_info["path"]
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transform = target_info["transform"]
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# Load object detector templates
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templates, global_timeout = load_detector_templates()
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result = await _run_object_detection(
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self.ctx,
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target_path,
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queries,
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templates,
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global_timeout=global_timeout,
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)
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try:
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detected_items = result.get("detected", [])
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failed_queries = result.get("failed", [])
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if not detected_items:
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text_output = ["No objects detected."]
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if failed_queries:
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text_output.append(
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"\n[NOTE: Failed to detect the following queries:"
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f" {', '.join(failed_queries)}]"
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)
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return {
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"text": "".join(text_output),
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"image_path": str(target_path),
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}
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base_dir = settings.TRACES_PATH
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images_dir = base_dir / "images"
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object_detect_dir = images_dir / "object_detect"
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object_detect_dir.mkdir(parents=True, exist_ok=True)
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image_name_safe = self.image_name or "temp_image"
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existing_files = glob.glob(str(object_detect_dir / f"{image_name_safe}_*.jpg"))
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max_seq = 0
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for f in existing_files:
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match = re.search(r"_(\d+)\.jpg$", f)
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if match:
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max_seq = max(max_seq, int(match.group(1)))
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seq = max_seq + 1
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output_path = object_detect_dir / f"{image_name_safe}_{seq}.jpg"
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points = []
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d_labels = []
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text_output = []
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if target_image_id != "img_0":
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text_output.append(
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"[NOTE: Coordinates are automatically mapped back to"
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f" the original {self.width}x{self.height} screen space from"
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f" {target_image_id}]"
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)
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for item in detected_items:
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label_id = f"{prefix}{self.global_label_idx}"
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self.global_label_idx += 1
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pos = item.get("point")
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if pos and isinstance(pos, list) and len(pos) == 2:
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x_norm, y_norm = pos
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t_img = cv2.imread(target_path)
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if t_img is None:
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raise ValueError(f"Failed to read target image at {target_path}")
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t_h, t_w = t_img.shape[:2]
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# Pixels in target image
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x_target_pixel = (x_norm / 1000) * t_w
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y_target_pixel = (y_norm / 1000) * t_h
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# Pixels in original image
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x_orig_pixel = (x_target_pixel / transform["scale_x"]) + transform[
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"offset_x"
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]
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y_orig_pixel = (y_target_pixel / transform["scale_y"]) + transform[
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"offset_y"
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]
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points.append([int(x_orig_pixel), int(y_orig_pixel)])
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d_labels.append(label_id)
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# Original image norm
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x_orig_norm = int(max(0, min(1000, x_orig_pixel * 1000 / self.width)))
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y_orig_norm = int(max(0, min(1000, y_orig_pixel * 1000 / self.height)))
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text_output.append(
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f'[{label_id}] "{item.get("label")}" at [{x_orig_norm},{y_orig_norm}]'
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)
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draw_dots(
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self.screenshot_path,
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points,
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d_labels,
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str(output_path),
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color=color,
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)
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if failed_queries:
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text_output.append(
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" | [NOTE: Failed to detect the following queries:"
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f" {', '.join(failed_queries)}]"
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)
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return {
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"text": " | ".join(text_output),
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"image_path": str(output_path),
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}
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except Exception as e:
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logger.warning(f"Failed to process detection output for annotation: {e}")
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return {"text": str(result), "image_path": None}
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except Exception as e:
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return {
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"text": f"Error: Object detection failed: {e}",
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"image_path": None,
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}
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@trace(type="tool", name="ask_perception_tool")
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async def exec_ask_perception_tool(
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self, search_query: str, nx: int, ny: int, detect_queries: list[str]
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) -> dict:
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if nx is None or ny is None or not (0 <= nx <= 1000) or not (0 <= ny <= 1000):
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coords_invalid = True
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else:
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coords_invalid = False
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# Run tasks concurrently in parallel!
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tasks = [
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self._search_ui_helper(search_query, prefix="X", color="green"),
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self._search_by_coords_helper(nx, ny, prefix="X", color="blue")
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if not coords_invalid
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else asyncio.sleep(
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0,
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result={
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"text": "Error: Invalid coordinates format",
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"image_path": None,
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},
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),
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self.exec_detect_objects(
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detect_queries, target_image_id="img_0", prefix="D", color="red"
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),
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]
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results = await asyncio.gather(*tasks)
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# Consolidate text results
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text_parts = []
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xml_text = results[0].get("text", "No matches found.")
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if xml_text and xml_text != "No matches found.":
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text_parts.append(f"Text Search Results are: {xml_text}")
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coord_text = results[1].get("text", "No elements found.")
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if coord_text and not coord_text.startswith("No elements found"):
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text_parts.append(f"Coordinate Search Results are: {coord_text}")
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obj_text = results[2].get("text", "No objects detected.")
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if obj_text:
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if obj_text.startswith("No objects detected."):
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note_part = obj_text.replace("No objects detected.", "").strip()
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if note_part:
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text_parts.append(f"Object Detection Results are: {note_part}")
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else:
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text_parts.append(f"Object Detection Results are: {obj_text}")
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if not text_parts:
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unified_text = "No matches or objects found across any perception method."
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else:
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unified_text = ". ".join(text_parts) + "."
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# Collect image paths
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image_paths = []
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for r in results:
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if isinstance(r, dict) and r.get("image_path"):
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image_paths.append(r["image_path"])
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return {
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"text": unified_text,
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"image_paths": image_paths,
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}
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@trace(type="tool", name="ask_image_processor")
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async def exec_ask_image_processor(
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self, instruction: str, target_image_id: str = "img_0"
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) -> dict:
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coder = ImageProcessor(self.ctx)
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target_info = self.image_pool.get(target_image_id)
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if not target_info:
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return {
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"text": f"Error: {target_image_id} not found in Image Pool.",
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"image_path": None,
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}
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target_path = target_info["path"]
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result = await coder.run(instruction, target_path)
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if not isinstance(result, dict) or "error" in result:
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err_msg = result.get("error") if isinstance(result, dict) else "Unknown error"
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return {
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"text": f"Image Processor failed: {err_msg}",
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"image_paths": [],
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}
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outputs = result.get("outputs", [])
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summary = result.get("summary", "")
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parent_transform = target_info["transform"]
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registered_ids = []
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annotations_sections = []
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image_paths = []
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for entry in outputs:
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new_id = f"img_{self.next_img_id}"
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self.next_img_id += 1
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new_path = entry["path"]
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image_paths.append(new_path)
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transform = entry["transform"]
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final_scale_x = parent_transform["scale_x"] * transform["scale_x"]
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final_scale_y = parent_transform["scale_y"] * transform["scale_y"]
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final_offset_x = (
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transform["offset_x"] / parent_transform["scale_x"]
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) + parent_transform["offset_x"]
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final_offset_y = (
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transform["offset_y"] / parent_transform["scale_y"]
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) + parent_transform["offset_y"]
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self.image_pool[new_id] = {
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"path": new_path,
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"transform": {
|
|
"offset_x": final_offset_x,
|
|
"offset_y": final_offset_y,
|
|
"scale_x": final_scale_x,
|
|
"scale_y": final_scale_y,
|
|
},
|
|
"description": (f"Generated by coder from {target_image_id}: {summary}"),
|
|
}
|
|
registered_ids.append(new_id)
|
|
|
|
# Parse and translate annotations
|
|
annotations = entry.get("annotations", {})
|
|
if annotations:
|
|
lines = [f"{new_id}:"]
|
|
for label, coord in sorted(annotations.items(), key=lambda item: item[0]):
|
|
px, py = coord
|
|
sx = int(round((px / final_scale_x) + final_offset_x))
|
|
sy = int(round((py / final_scale_y) + final_offset_y))
|
|
lines.append(f" - [{label}]: [{sx}, {sy}]")
|
|
annotations_sections.append("\n".join(lines))
|
|
|
|
if registered_ids:
|
|
ids_str = ", ".join(registered_ids)
|
|
text = (
|
|
"Image Processor completed successfully. New image(s) saved as"
|
|
f" {ids_str} in Image Pool. Summary: {summary}"
|
|
)
|
|
if annotations_sections:
|
|
text += "\n\nAnnotations (wrt original screenshot):\n" + "\n".join(
|
|
annotations_sections
|
|
)
|
|
return {
|
|
"text": text,
|
|
"image_paths": image_paths,
|
|
}
|
|
return {
|
|
"text": "ImageProcessor error: no output images were produced.",
|
|
"image_paths": [],
|
|
}
|
|
|
|
@trace(type="tool", name="get_ocr_list")
|
|
async def exec_get_ocr_list(self, prefix: str = "O", color: str = "green") -> dict:
|
|
"""Lists every OCR-detected text on the screen with a labeled dot."""
|
|
try:
|
|
ocr_elements = self.screen_index.ocr_elements
|
|
if not ocr_elements:
|
|
return {
|
|
"text": "No text elements detected on the screen.",
|
|
"image_path": None,
|
|
}
|
|
|
|
points: list[list[int]] = []
|
|
labels: list[str] = []
|
|
text_output: list[str] = []
|
|
for element in ocr_elements:
|
|
cx, cy = element.center
|
|
if not (0 <= cx <= self.width and 0 <= cy <= self.height):
|
|
continue
|
|
label_id = f"{prefix}{self.global_label_idx}"
|
|
self.global_label_idx += 1
|
|
self._register_label(label_id, element)
|
|
points.append([cx, cy])
|
|
labels.append(label_id)
|
|
nx, ny = pixel_to_norm(cx, cy, self.width, self.height)
|
|
text_output.append(f"[{label_id}] '{element.text}' coords: [{nx},{ny}]")
|
|
|
|
if not points:
|
|
return {"text": "No valid OCR results found.", "image_path": None}
|
|
|
|
image_path = self._draw_labeled_dots("ocr", points, labels, color)
|
|
return {"text": "\n".join(text_output), "image_path": image_path}
|
|
|
|
except Exception as e:
|
|
return {
|
|
"text": f"Error retrieving or processing OCR results: {e}",
|
|
"image_path": None,
|
|
}
|
|
|
|
@trace(type="tool", name="inspect_region")
|
|
async def exec_inspect_region(
|
|
self,
|
|
x_min: int,
|
|
y_min: int,
|
|
x_max: int,
|
|
y_max: int,
|
|
zoom_factor: float = 2.0,
|
|
) -> dict:
|
|
try:
|
|
if not self.screenshot_path:
|
|
return {
|
|
"text": "Error: Original screenshot is not available.",
|
|
"image_path": None,
|
|
}
|
|
px_start = int(max(0, min(self.width, x_min * self.width / 1000.0)))
|
|
py_start = int(max(0, min(self.height, y_min * self.height / 1000.0)))
|
|
px_end = int(max(0, min(self.width, x_max * self.width / 1000.0)))
|
|
py_end = int(max(0, min(self.height, y_max * self.height / 1000.0)))
|
|
|
|
if px_end <= px_start or py_end <= py_start:
|
|
return {
|
|
"text": (
|
|
f"Error: Invalid crop coordinates. x_min={x_min},"
|
|
f" x_max={x_max}, y_min={y_min}, y_max={y_max}."
|
|
" Bounding box dimensions must be strictly positive."
|
|
),
|
|
"image_path": None,
|
|
}
|
|
|
|
img = cv2.imread(self.screenshot_path)
|
|
if img is None:
|
|
return {
|
|
"text": (
|
|
f"Error: Failed to read original screenshot at {self.screenshot_path}"
|
|
),
|
|
"image_path": None,
|
|
}
|
|
|
|
cropped = img[py_start:py_end, px_start:px_end]
|
|
|
|
if zoom_factor is None:
|
|
zoom_factor = 2.0
|
|
zoom_factor = max(1.0, min(4.0, float(zoom_factor)))
|
|
|
|
c_h, c_w = cropped.shape[:2]
|
|
if c_h > 0 and c_w > 0:
|
|
new_w = int(round(c_w * zoom_factor))
|
|
new_h = int(round(c_h * zoom_factor))
|
|
if new_w > 0 and new_h > 0:
|
|
cropped = cv2.resize(cropped, (new_w, new_h))
|
|
|
|
base_dir = settings.TRACES_PATH
|
|
images_dir = base_dir / "images"
|
|
inspect_region_dir = images_dir / "inspect_region"
|
|
inspect_region_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
image_name_safe = self.image_name or "temp_image"
|
|
output_path = inspect_region_dir / f"{image_name_safe}_{self.global_label_idx}.jpg"
|
|
cv2.imwrite(str(output_path), cropped)
|
|
|
|
return {
|
|
"text": (f"Inspected region coordinates: [{x_min}, {y_min}] to [{x_max}, {y_max}]"),
|
|
"image_path": str(output_path),
|
|
}
|
|
|
|
except Exception as e:
|
|
return {
|
|
"text": f"Error: Region inspection failed: {e}",
|
|
"image_path": None,
|
|
}
|