from typing import List, Optional, Tuple

import cv2
import numpy as np


def resize_keep_aspect(image: np.ndarray, target_width: int) -> np.ndarray:
    if target_width <= 0:
        return image
    h, w = image.shape[:2]
    if w == target_width:
        return image
    scale = target_width / float(w)
    new_size = (target_width, int(round(h * scale)))
    return cv2.resize(image, new_size, interpolation=cv2.INTER_AREA)


def enhance_barcode_stripes(gray: np.ndarray, gaussian_ksize: int, sobel_ksize: int) -> np.ndarray:
    g = gray
    if gaussian_ksize and gaussian_ksize > 1:
        g = cv2.GaussianBlur(g, (gaussian_ksize, gaussian_ksize), 0)
    # 使用水平 Sobel 捕捉垂直边缘（条纹）
    grad_x = cv2.Sobel(g, cv2.CV_32F, 1, 0, ksize=sobel_ksize)
    grad_x = cv2.convertScaleAbs(grad_x)
    return grad_x


def binarize_image(img: np.ndarray, method: str = "otsu") -> np.ndarray:
    if method == "adaptive":
        return cv2.adaptiveThreshold(
            img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 31, 10
        )
    # 默认 OTSU
    _, th = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    return th


def morph_close(img: np.ndarray, kernel_size: int) -> np.ndarray:
    if kernel_size <= 1:
        return img
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
    closed = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
    return closed


def find_barcode_roi(binary: np.ndarray, original_shape: Tuple[int, int], min_area_ratio: float, min_wh_ratio: float) -> Optional[Tuple[np.ndarray, Tuple[int, int, int, int]]]:
    h, w = original_shape
    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    image_area = h * w
    candidates: List[Tuple[float, Tuple[int, int, int, int]]] = []
    for cnt in contours:
        x, y, cw, ch = cv2.boundingRect(cnt)
        area = cw * ch
        if area / image_area < min_area_ratio:
            continue
        wh_ratio = cw / float(ch + 1e-6)
        if wh_ratio < min_wh_ratio:
            continue
        candidates.append((area, (x, y, cw, ch)))
    if not candidates:
        return None
    candidates.sort(key=lambda t: t[0], reverse=True)
    _, bbox = candidates[0]
    x, y, cw, ch = bbox
    roi = binary[y : y + ch, x : x + cw]
    return roi, bbox


def warp_barcode_region(gray: np.ndarray, bbox: Tuple[int, int, int, int], target_height: int, crop_bottom_ratio: float = 0.0) -> np.ndarray:
    x, y, cw, ch = bbox
    crop = gray[y : y + ch, x : x + cw]
    # 去除底部数字区域干扰
    if 0 < crop_bottom_ratio < 1:
        hb = int(round(ch * (1.0 - crop_bottom_ratio)))
        hb = max(10, min(ch, hb))
        crop = crop[:hb, :]
    if target_height <= 0:
        return crop
    scale = target_height / float(ch)
    target_width = int(round(cw * scale))
    warped = cv2.resize(crop, (target_width, target_height), interpolation=cv2.INTER_CUBIC)
    return warped