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Artemis-Eapil/artemis/tools/mobile/ocr.py
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somew1nd 206a9ce921 feat: initial release of ARTEMIS Android Agent & Automation Platform
ARTEMIS is an autonomous multimodal Android agent and mobile UI automation
platform engineered for physical devices and emulators. It combines state-of-the-art
multimodal Vision-Language Models (VLMs) with hierarchical closed-loop planning,
resilient self-healing, and deep Android subsystem integration.

Key Features & Architectural Highlights:
- Dual-Engine Orchestration:
  * Pro Engine: Multi-agent closed-loop (Planner -> Operator -> Validator/Failure Analyzer -> Summarizer) powered by LangGraph.
  * Flash Engine: Ultra-fast reactive loop for deterministic, low-latency UI execution (<35 steps).
- Multimodal Perception & Grounding:
  * Hybrid visual screenshot grounding, real-time OCR extraction, and XML accessibility hierarchy parsing.
  * Dynamic-First, Coordinate-Fallback locating strategy for maximum resilience against UI drift.
- Autonomous Self-Healing & Diagnostics:
  * Failure analyzer and safety net for popup dismissal, crash recovery, and auto-retry.
  * Diagnostic sub-agents for log analysis, video recording inspection, and object detection.
- Unified CLI & Python SDK:
  * Rich CLI with commands: init, doctor, run, trace, batch, bench, server, and mcp.
  * Fluent Python SDK with Pydantic structured output models and package locking.
- Multi-Provider LLM Architecture:
  * Support for Google Gemini, OpenAI, Anthropic Claude, Vertex AI, OpenRouter, xAI Grok, and local endpoints (Ollama/vLLM).
  * Centralized configuration in config/artemis.jsonc.
- Native MCP & Observability:
  * Model Context Protocol (MCP) server endpoints for AI IDEs.
  * Full trace recording with before/after screenshots, action overlays, and execution replays.

Change-Id: I430b93ee2b76b54cc5a3c6eeccd15d2cf29830a1
2026-08-13 11:46:47 -07:00

211 lines
7.9 KiB
Python

# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
import json
from pathlib import Path
from typing import Any
from langchain_core.messages import ToolMessage
from langchain_core.tools import BaseTool
from langgraph.types import Command
from pydantic import BaseModel
from artemis.constants import VALIDATOR_MESSAGES_KEY
from artemis.context import ArtemisContext
from artemis.data_engine.trace import trace_langchain_tool
from artemis.drivers.base import BaseDeviceDriver
from artemis.graph.state import State
from artemis.tools.base import ArtemisTool, ToolCategory, ToolRegistry
from artemis.tools.tool_wrapper import ToolWrapper
from artemis.utils.logger import get_logger
from artemis.utils.ocr_api import perform_ocr
class OcrArgs(BaseModel):
"""Arguments schema for OCR recognition."""
logger = get_logger(__name__)
OCR_DOCSTRING = (
"[EXPLORER] Performs OCR on the current screen and returns the detected"
" text along with their coordinates."
)
# pylint: disable=too-many-branches,too-many-locals,too-many-statements
async def run_ocr_core(
ctx: ArtemisContext | None = None,
state: State | None = None,
driver: BaseDeviceDriver | None = None,
) -> str:
"""Universal OCR core function: reads screenshot from state or driver and returns results."""
screenshot_b64 = None
screenshot_path = getattr(state, "latest_screenshot", None) if state else None
if screenshot_path:
if not Path(screenshot_path).exists():
logger.error(f"Screenshot file does not exist: {screenshot_path}")
raise FileNotFoundError(f"Screenshot file does not exist: {screenshot_path}")
try:
with open(screenshot_path, "rb") as f:
screenshot_b64 = base64.b64encode(f.read()).decode("utf-8")
logger.info(f"OCR loaded screenshot from file: {screenshot_path}")
except Exception as e: # pylint: disable=broad-exception-caught
logger.error(f"Failed to read screenshot from {screenshot_path}: {e}")
raise e
elif driver is not None and hasattr(driver, "get_screen_data"):
try:
screen_data = await driver.get_screen_data()
if hasattr(screen_data, "screenshot_base64") and screen_data.screenshot_base64:
screenshot_b64 = screen_data.screenshot_base64
elif hasattr(screen_data, "screenshot_bytes") and screen_data.screenshot_bytes:
screenshot_b64 = base64.b64encode(screen_data.screenshot_bytes).decode("utf-8")
elif hasattr(screen_data, "base64") and screen_data.base64:
screenshot_b64 = screen_data.base64
except Exception as e: # pylint: disable=broad-exception-caught
logger.error(f"Failed to capture screenshot from driver: {e}")
raise e
else:
logger.error("No screenshot path found in state.latest_screenshot and no driver available")
raise ValueError(
"No screenshot path found in state.latest_screenshot and no driver available"
)
if not screenshot_b64:
raise ValueError("Failed to obtain screenshot for OCR.")
width = 1080
height = 2400
try:
if ctx and hasattr(ctx, "device") and ctx.device:
w = getattr(ctx.device, "device_width", 1080)
h = getattr(ctx.device, "device_height", 2400)
if isinstance(w, int):
width = w
if isinstance(h, int):
height = h
elif driver and hasattr(driver, "screen_size") and driver.screen_size:
width, height = driver.screen_size
except Exception as e: # pylint: disable=broad-exception-caught
logger.warning(f"Failed to get device resolution for OCR normalization: {e}")
results = await perform_ocr(screenshot_b64)
if results:
normalized_results = []
for item in results:
text = item.get("text")
vertices = item.get("position") or []
# Calculate center point
x_coords = [v.get("x", 0) for v in vertices]
y_coords = [v.get("y", 0) for v in vertices]
if x_coords and y_coords:
min_x, max_x = min(x_coords), max(x_coords)
min_y, max_y = min(y_coords), max(y_coords)
center_x = (min_x + max_x) / 2
center_y = (min_y + max_y) / 2
# Normalize to 0-1000
norm_x = int(max(0, min(1000, center_x * 1000 / width)))
norm_y = int(max(0, min(1000, center_y * 1000 / height)))
normalized_results.append({"text": text, "coordinates": [norm_x, norm_y]})
else:
normalized_results.append({"text": text, "coordinates": None})
return json.dumps(normalized_results, ensure_ascii=False, indent=2)
return "No text detected on the screen."
class OcrRecognitionTool(ArtemisTool):
"""Universal tool for performing OCR on the current screen."""
def __init__(self, category: ToolCategory = "explorer"):
super().__init__(
name="ocr_recognition",
description=OCR_DOCSTRING,
args_schema=OcrArgs,
category=category,
)
# pylint: disable=too-many-arguments,too-many-positional-arguments
async def execute(
self,
driver: BaseDeviceDriver | None = None,
ctx: ArtemisContext | None = None,
tool_call_id: str | None = None,
state: State | None = None,
**kwargs: Any,
) -> Any:
tcid = tool_call_id if tool_call_id is not None else kwargs.get("tool_call_id")
st = state if state is not None else kwargs.get("state")
has_failed = False
output = ""
error_message = None
try:
output = await run_ocr_core(ctx=ctx, state=st, driver=driver)
except Exception as e: # pylint: disable=broad-exception-caught
has_failed = True
error_message = f"Failed to perform OCR: {str(e)}"
if st and callable(getattr(st, "asanitize_update", None)):
tool_message = ToolMessage(
tool_call_id=tcid or "",
content=ocr_recognition_wrapper.on_failure_fn(error_message)
if has_failed
else ocr_recognition_wrapper.on_success_fn(output),
additional_kwargs={"error": error_message} if has_failed else {},
status="error" if has_failed else "success",
)
return Command(
update=await st.asanitize_update(
ctx=ctx,
update={
VALIDATOR_MESSAGES_KEY: [tool_message],
},
agent="validator",
)
)
if has_failed:
return ocr_recognition_wrapper.on_failure_fn(error_message)
return ocr_recognition_wrapper.on_success_fn(output)
# Universal tool instance & aliases
ocr_recognition = OcrRecognitionTool()
OcrRecognition = OcrRecognitionTool
OCRRecognition = OcrRecognitionTool
OcrTool = OcrRecognitionTool
ToolRegistry.register(ocr_recognition)
def get_ocr_tool(ctx: ArtemisContext) -> BaseTool:
"""Exports ocr_recognition as a LangChain BaseTool."""
return trace_langchain_tool(ocr_recognition.to_langchain_tool(ctx), ctx)
ocr_recognition_wrapper = ToolWrapper(
tool_fn_getter=get_ocr_tool,
on_success_fn=lambda output: f"OCR Recognition successful. Results:\n{output}",
on_failure_fn=lambda error: f"OCR Recognition failed: {error}",
)