import cv2
import numpy as np
import logging

logger = logging.getLogger(__name__)

def order_points(pts):
    rect = np.zeros((4, 2), dtype="float32")
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect

def four_point_transform(image, pts):
    rect = order_points(pts)
    (tl, tr, br, bl) = rect

    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))

    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))

    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype="float32")

    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    return warped

def preprocess_image(image_bytes: bytes) -> np.ndarray:
    """
    Возвращает БИНАРНОЕ (Ч/Б) изображение для Tesseract.
    """
    nparr = np.frombuffer(image_bytes, np.uint8)
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    
    if image is None:
        raise ValueError("Could not decode image")

    # Ресайз для поиска контуров
    ratio = image.shape[0] / 500.0
    orig = image.copy()
    image_small = cv2.resize(image, (int(image.shape[1] / ratio), 500))

    gray = cv2.cvtColor(image_small, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)
    edged = cv2.Canny(gray, 75, 200)

    cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]

    screenCnt = None
    found = False

    for c in cnts:
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        if len(approx) == 4:
            screenCnt = approx
            found = True
            break

    # Изображение, с которым будем работать дальше
    target_img = None

    if found:
        logger.info("Receipt contour found (Tesseract mode).")
        target_img = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
    else:
        logger.warning("Receipt contour NOT found. Using full image.")
        target_img = orig

    # --- Подготовка для Tesseract (Бинаризация) ---
    # Переводим в Gray
    gray_final = cv2.cvtColor(target_img, cv2.COLOR_BGR2GRAY)
    
    # Адаптивный порог (превращаем в чисто черное и белое)
    # block_size=11, C=2 - классические параметры для текста
    thresh = cv2.adaptiveThreshold(
        gray_final, 255, 
        cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
        cv2.THRESH_BINARY, 11, 2
    )
    
    # Немного убираем шум
    # thresh = cv2.medianBlur(thresh, 3) 
    
    return thresh