tools/detect_elements.py

from smolagents.tools import Tool
import cv2
import numpy as np
import os

def detect_elements(screenshot_path, element_type="table"):
    """
    Detect table-like structures or text boxes in a screenshot using OpenCV.
    
    Args:
        screenshot_path (str): Path to the screenshot
        element_type (str): Type of element to detect ('table', 'textbox') (default: 'table')
    
    Returns:
        str: JSON with bounding boxes and detection details
    """
    try:
        if not os.path.exists(screenshot_path):
            return f"Screenshot not found: {screenshot_path}"

        # Read and preprocess image
        image = cv2.imread(screenshot_path)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
        edges = cv2.Canny(blurred, 50, 150)

        # Detect contours
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        detections = []

        for contour in contours:
            x, y, w, h = cv2.boundingRect(contour)
            area = w * h
            aspect_ratio = w / h if h > 0 else 0

            # Filter for tables (rectangular, large area)
            if element_type == "table" and area > 10000 and 0.5 < aspect_ratio < 2.0:
                detections.append({"type": "table", "bbox": [x, y, w, h]})
            # Filter for text boxes (narrow, horizontal)
            elif element_type == "textbox" and area > 500 and aspect_ratio > 2.0:
                detections.append({"type": "textbox", "bbox": [x, y, w, h]})

        # Draw bounding boxes on a copy of the image
        output_path = screenshot_path.replace(".png", "_detected.png")
        output_image = image.copy()
        for detection in detections:
            x, y, w, h = detection["bbox"]
            color = (0, 255, 0) if detection["type"] == "table" else (0, 0, 255)
            cv2.rectangle(output_image, (x, y), (x + w, y + h), color, 2)
        cv2.imwrite(output_path, output_image)

        return json.dumps({
            "detections": detections,
            "output_image": output_path
        }) if detections else "No elements detected"
    except Exception as e:
        return f"Failed to detect elements: {str(e)}"

# Register the tool
tool = Tool(
    name="detect_elements",
    description="Detects table-like structures or text boxes in a screenshot using OpenCV.",
    inputs={
        "screenshot_path": {"type": "str", "description": "Path to the screenshot"},
        "element_type": {"type": "str", "default": "table", "description": "Type: 'table' or 'textbox'"}
    },
    output_type="str",
    function=detect_elements
)