"""
Image quality analysis provider.

Delegates all metric computation to cores.vision.quality — no duplicated
brightness/contrast/sharpness/noise logic.
"""

from __future__ import annotations

import numpy as np

from config.settings import Settings, settings as _default_settings
from cores.vision import brightness, contrast, sharpness, noise_level, quality_score
from pipeline.feature_extraction import PipelineOutput
from providers.base import BaseProvider, ProviderCapability


class ImageQualityProvider(BaseProvider):
    name = "image_quality"
    capability = ProviderCapability.IMAGE_ANALYSIS

    def __init__(self, settings: Settings | None = None) -> None:
        super().__init__(settings=settings or _default_settings)

    def is_available(self) -> bool:
        return True

    def _run(self, pipeline_output: PipelineOutput) -> tuple[dict, dict]:
        img: np.ndarray = pipeline_output.image
        b = brightness(img)
        c = contrast(img)
        s = sharpness(img)
        n = noise_level(img)
        q = quality_score(img)

        raw = {
            "brightness": b,
            "contrast": c,
            "sharpness": s,
            "noise_level": n,
            "quality_score": q,
            "image_size": {"width": img.shape[1], "height": img.shape[0]},
        }
        normalized = {
            "quality_score": round(q, 4),
            "brightness": round(b, 4),
            "contrast": round(c, 4),
            "sharpness": round(s, 4),
            "noise_level": round(n, 4),
            "width": int(img.shape[1]),
            "height": int(img.shape[0]),
            "channels": int(img.shape[2]) if img.ndim == 3 else 1,
            "color_profile": None,
            "dominant_colors": [],
            "aspects": {
                "method": "variance_of_laplacian",
                "noise_method": "median_absolute_deviation",
            },
        }
        return raw, normalized