from PIL import Image
import cv2
import numpy as np
import constants

def cv2_to_pil(cv_image: np.ndarray) -> Image.Image:
    """
    cv2.MatLike (BGR/RGB/Grayscale) → PIL.Image
    """
    if len(cv_image.shape) == 3 and cv_image.shape[2] == 3:
        cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
    return Image.fromarray(cv_image)

def image_to_numpy(self, image: Image.Image) -> np.ndarray:
    """Конвертирует PIL Image в numpy array для OpenCV"""
    return np.array(image)

def get_normals(H: np.ndarray, R: np.ndarray, T: np.ndarray) -> np.ndarray:
    n = cv2.decomposeHomographyMat(H, constants.K, R, T)
    return n

def estimate_transformation_matrix(src_pts: np.ndarray, dst_pts: np.ndarray) -> tuple[np.ndarray, float | None]:
    """Оценивает матрицу трансформации на основе сопоставленных точек"""
    # Используем RANSAC для оценки матрицы гомографии
    H, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 3.0, maxIters=1000)
    return H

def calc_camera_matrix(w: float, h: float):
    f = constants._K_FOCUS_DISTANCE
    return np.array([
        [f, 0, w / 2],
        [0, f, h / 2],
        [0, 0, 1]
    ])