"""
Barcode detection + decoding provider.

Uses OpenCV's cv2.barcode.BarcodeDetector (available since OpenCV 4.5.2).
Detects and decodes common 1D/2D barcodes (EAN, UPC, Code39, Code128, etc.).

Pure OpenCV — no external deps.
"""

from __future__ import annotations

import cv2
import numpy as np

from config.settings import Settings, settings as _default_settings
from pipeline.feature_extraction import PipelineOutput
from providers.base import BaseProvider, ProviderCapability


class BarcodeProvider(BaseProvider):
    name = "barcode"
    capability = ProviderCapability.OBJECT_DETECTION

    def __init__(self, settings: Settings | None = None) -> None:
        super().__init__(settings=settings or _default_settings)
        self._detector = None
        try:
            self._detector = cv2.barcode.BarcodeDetector()
        except AttributeError:
            # OpenCV < 4.5.2 — barcode detector not available
            pass

    def is_available(self) -> bool:
        return self._detector is not None

    def _run(self, pipeline_output: PipelineOutput) -> tuple[dict, dict]:
        if self._detector is None:
            raise RuntimeError("BarcodeDetector not available (requires OpenCV >= 4.5.2)")

        img: np.ndarray = pipeline_output.image

        try:
            ok, decoded_info, decoded_types, points = self._detector.detectAndDecode(img)
        except Exception as e:
            return {"error": str(e)}, {"objects": [], "model": "opencv_barcode"}

        objects: list[dict] = []
        if ok and points is not None and len(points) > 0:
            for i, (info, btype) in enumerate(zip(decoded_info, decoded_types)):
                if i < len(points):
                    pts = points[i].reshape(-1, 2)
                    x1, y1 = int(pts[:, 0].min()), int(pts[:, 1].min())
                    x2, y2 = int(pts[:, 0].max()), int(pts[:, 1].max())
                    objects.append({
                        "label": "barcode",
                        "confidence": 1.0,
                        "box": {"x": x1, "y": y1, "w": x2 - x1, "h": y2 - y1},
                        "decoded_text": info if info else "",
                        "barcode_type": btype if btype else "",
                        "is_barcode": True,
                    })

        raw = {
            "num_objects": len(objects),
            "model": "opencv_barcode",
            "decoded_count": sum(1 for o in objects if o["decoded_text"]),
        }
        normalized = {
            "objects": objects,
            "model": "opencv_barcode",
        }
        return raw, normalized