vibe-ocr 0.1.4

A decoupled OCR helper library using PaddleOCR (via remote server) and SQLite caching.

Vibe-OCR

An intelligent, decoupled OCR helper library designed for automation tasks. It leverages a remote PaddleOCR server for text recognition and includes a robust local caching system using SQLite to optimize performance and reduce repeated network requests.

Installation

pip install vibe-ocr

Features

• Decoupled Architecture: Uses a remote OCR server (PaddleOCR) to offload heavy computation.
• Smart Caching: Local SQLite database caches OCR results for identical images/regions, significantly speeding up repeated checks.
• Declarative API: High-level GameActions API for chaining operations (filter, map, click).
• Snapshot Integration: Built-in support for taking screenshots (via airtest by default) or custom snapshot functions.
• Retry Logic: Automatic retry without cache if text is not found initially.

Usage Guide

1. Initialization

Initialize the OCRHelper.

from vibe_ocr import OCRHelper

# Basic initialization
ocr = OCRHelper(output_dir="output")

2. High-Level Declarative API (Recommended)

The GameActions class provides a powerful, fluent interface for finding and interacting with game elements. This is the preferred way to write automation scripts.

from vibe_ocr import OCRHelper, GameActions

ocr = OCRHelper(output_dir="output")
actions = GameActions(ocr)

# Find all texts, filter for "Item", and click the first one
actions.find_all() \
       .contains("Item") \
       .min_confidence(0.8) \
       .first() \
       .click()

# Find specific text with timeout (retries automatically)
actions.find("Start Game", timeout=5).click()

# Check if text exists
if actions.text_exists("Game Over"):
    print("Game ended")

# Batch operations
actions.find_all() \
       .filter(lambda e: "Coin" in e.text) \
       .click_all()

3. Low-Level API: Finding Text

You can also use the OCRHelper directly for simple tasks.

# Capture screen and find text "Login"
result = ocr.capture_and_find_text(
    "Login",
    confidence_threshold=0.7,
    occurrence=1,   # 1st occurrence
    use_cache=True  # Use cache if screen hasn't changed
)

if result and result.get("found"):
    print(f"Found 'Login' at: {result['center']}")
else:
    print("Text not found.")

4. Low-Level API: Finding and Clicking

A convenience method to find text and simulate a touch/click action (requires airtest installed).

# Find "Confirm" and click it if found
clicked = ocr.find_and_click_text(
    "Confirm",
    confidence_threshold=0.6
)

Configuration

1. PaddleX OCR Server (Required)

This library requires a running PaddleOCR server (PaddleX 3.0+). You can easily deploy it using Docker:

docker run -d --name paddlex \
  --shm-size=8g \
  --network=host \
  ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlex/paddlex:paddlex3.3.11-paddlepaddle3.2.0-cpu \
  sh -lc "paddlex --install serving && paddlex --serve --pipeline OCR"

• Port: The server listens on 8080 by default.
• Endpoint: http://localhost:8080/ocr

2. Environment Variables

• OCR_SERVER_URL: The URL of the PaddleOCR server. Defaults to http://localhost:8080/ocr.

Dependencies

• Airtest (Optional but Recommended): The click() methods and default snapshot function rely on airtest. Ensure it is installed (pip install airtest) if you plan to use these features.

Constructor Parameters

• output_dir: Directory to store cache (sqlite db) and debug images.
• snapshot_func: Callable to take screenshots. Defaults to airtest.core.api.snapshot.
• delete_temp_screenshots: Whether to delete temporary screenshot files after processing (Default: True).
• resize_image: Resize large images before sending to OCR server to improve speed (Default: True).

Caching Mechanism

vibe-ocr calculates a perceptual hash (dhash) of the screenshot. If a similar image exists in the sqlite cache, it retrieves the OCR result locally instead of calling the server. This is critical for high-frequency loops in automation scripts.