"""
OCR Image Enhancement — Improve scan quality before OCR.

Applies CLAHE contrast enhancement + bilateral filter denoising
to degraded scans. Only runs when scan_quality.is_degraded is True.

Pattern adapted from handwriting_htr_api.py (lines 50-68) and
cv_layout.py (lines 229-241).

All operations use OpenCV (Apache-2.0).
"""

import logging

import cv2
import numpy as np

logger = logging.getLogger(__name__)


def enhance_for_ocr(
    img_bgr: np.ndarray,
    is_degraded: bool = False,
    clip_limit: float = 3.0,
    tile_size: int = 8,
    denoise_d: int = 9,
    denoise_sigma_color: float = 75,
    denoise_sigma_space: float = 75,
    sharpen: bool = True,
) -> np.ndarray:
    """
    Enhance image quality for OCR processing.

    Only applies aggressive enhancement when is_degraded is True.
    For good scans, applies minimal enhancement (light CLAHE only).

    Args:
        img_bgr: Input BGR image
        is_degraded: Whether the scan is degraded (from ScanQualityReport)
        clip_limit: CLAHE clip limit (higher = more contrast)
        tile_size: CLAHE tile grid size
        denoise_d: Bilateral filter diameter
        denoise_sigma_color: Bilateral filter sigma for color
        denoise_sigma_space: Bilateral filter sigma for space
        sharpen: Apply unsharp mask for blurry scans

    Returns:
        Enhanced BGR image
    """
    if not is_degraded:
        # For good scans: light CLAHE only (preserves quality)
        lab = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LAB)
        l_channel, a_channel, b_channel = cv2.split(lab)
        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
        l_enhanced = clahe.apply(l_channel)
        lab_enhanced = cv2.merge([l_enhanced, a_channel, b_channel])
        result = cv2.cvtColor(lab_enhanced, cv2.COLOR_LAB2BGR)
        logger.info("enhance_for_ocr: light CLAHE applied (good scan)")
        return result

    # Degraded scan: full enhancement pipeline
    logger.info(
        f"enhance_for_ocr: full enhancement "
        f"(CLAHE clip={clip_limit}, denoise d={denoise_d}, sharpen={sharpen})"
    )

    # 1. CLAHE on L-channel of LAB colorspace (preserves color for RapidOCR)
    lab = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LAB)
    l_channel, a_channel, b_channel = cv2.split(lab)
    clahe = cv2.createCLAHE(
        clipLimit=clip_limit,
        tileGridSize=(tile_size, tile_size),
    )
    l_enhanced = clahe.apply(l_channel)
    lab_enhanced = cv2.merge([l_enhanced, a_channel, b_channel])
    enhanced = cv2.cvtColor(lab_enhanced, cv2.COLOR_LAB2BGR)

    # 2. Bilateral filter: denoises while preserving edges
    enhanced = cv2.bilateralFilter(
        enhanced,
        d=denoise_d,
        sigmaColor=denoise_sigma_color,
        sigmaSpace=denoise_sigma_space,
    )

    # 3. Unsharp mask for sharpening blurry text
    if sharpen:
        gaussian = cv2.GaussianBlur(enhanced, (0, 0), 3)
        enhanced = cv2.addWeighted(enhanced, 1.5, gaussian, -0.5, 0)

    logger.info("enhance_for_ocr: full enhancement pipeline complete")
    return enhanced