"""
Image preprocessing pipeline for crops before OCR.

Each step is independently toggleable via config.
Operates on numpy arrays (BGR or RGB), returns processed array.
"""

from __future__ import annotations

import logging

import numpy as np

logger = logging.getLogger(__name__)


def binarize(image: np.ndarray, threshold: int = 128) -> np.ndarray:
    """Convert to grayscale and apply Otsu binarization."""
    import cv2

    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    else:
        gray = image

    _, binary = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    # Convert back to 3-channel for downstream compatibility
    result = cv2.cvtColor(binary, cv2.COLOR_GRAY2RGB)
    return result


def deskew(image: np.ndarray) -> np.ndarray:
    """Correct slight rotation using minimum area rectangle."""
    import cv2

    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    else:
        gray = image

    coords = np.column_stack(np.where(gray < 128))
    if len(coords) < 10:
        return image

    rect = cv2.minAreaRect(coords)
    angle = rect[-1]

    # Normalize angle
    if angle < -45:
        angle = -(90 + angle)
    else:
        angle = -angle

    if abs(angle) < 0.5:
        return image

    h, w = image.shape[:2]
    center = (w // 2, h // 2)
    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
    result = cv2.warpAffine(
        image, rotation_matrix, (w, h),
        flags=cv2.INTER_LINEAR,
        borderMode=cv2.BORDER_REPLICATE,
    )
    return result


def enhance_contrast(image: np.ndarray) -> np.ndarray:
    """Apply CLAHE (adaptive histogram equalization) for contrast normalization."""
    import cv2

    if len(image.shape) == 3:
        lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
        l_channel = lab[:, :, 0]
    else:
        l_channel = image

    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    enhanced = clahe.apply(l_channel)

    if len(image.shape) == 3:
        lab[:, :, 0] = enhanced
        result = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
    else:
        result = enhanced

    return result


def preprocess(
    image: np.ndarray,
    do_binarize: bool = False,
    do_deskew: bool = False,
    do_contrast: bool = True,
) -> np.ndarray:
    """
    Run the preprocessing pipeline on a crop image.

    Each step is independently toggleable. Order: contrast → deskew → binarize.
    Contrast first (works best on color), binarize last (destroys color info).
    """
    result = image

    if do_contrast:
        result = enhance_contrast(result)
        logger.debug("Preprocessing: contrast enhanced")

    if do_deskew:
        result = deskew(result)
        logger.debug("Preprocessing: deskewed")

    if do_binarize:
        result = binarize(result)
        logger.debug("Preprocessing: binarized")

    return result