import World
from PIL import Image, ImageFilter
import numpy as np
from scipy import ndimage
from Skeleton import Skeleton
from typing import Union
import cv2


def get_data(world: World):
    print("[Data Analysis] Generating data...")
    heightmap, watermap, treemap = world.getData()
    heightmap.save('./data/heightmap.png')
    watermap.save('./data/watermap.png')
    treemap.save('./data/treemap.png')
    print("[Data Analysis] Data generated.")
    return heightmap, watermap, treemap


def handle_import_image(image: Union[str, Image]) -> Image:
    if isinstance(image, str):
        return Image.open(image)
    return image


def filter_negative(image: Union[str, Image]) -> Image:
    """
    Invert the colors of an image.

    Args:
        image (image): image to filter
    """
    image = handle_import_image(image)
    return Image.fromarray(np.invert(np.array(image)))


def filter_sobel(image: Union[str, Image]) -> Image:
    """
    Edge detection algorithms from an image.

    Args:
        image (image): image to filter
    """

    # Open the image
    image = handle_import_image(image).convert('RGB')

    img = np.array(image).astype(np.uint8)

    # Apply gray scale
    gray_img = np.round(
        0.299 * img[:, :, 0] + 0.587 * img[:, :, 1] + 0.114 * img[:, :, 2]
    ).astype(np.uint8)

    # Sobel Operator
    h, w = gray_img.shape
    # define filters
    horizontal = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])  # s2
    vertical = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])  # s1

    # define images with 0s
    newhorizontalImage = np.zeros((h, w))
    newverticalImage = np.zeros((h, w))
    newgradientImage = np.zeros((h, w))

    # offset by 1
    for i in range(1, h - 1):
        for j in range(1, w - 1):
            horizontalGrad = (
                    (horizontal[0, 0] * gray_img[i - 1, j - 1])
                    + (horizontal[0, 1] * gray_img[i - 1, j])
                    + (horizontal[0, 2] * gray_img[i - 1, j + 1])
                    + (horizontal[1, 0] * gray_img[i, j - 1])
                    + (horizontal[1, 1] * gray_img[i, j])
                    + (horizontal[1, 2] * gray_img[i, j + 1])
                    + (horizontal[2, 0] * gray_img[i + 1, j - 1])
                    + (horizontal[2, 1] * gray_img[i + 1, j])
                    + (horizontal[2, 2] * gray_img[i + 1, j + 1])
            )

            newhorizontalImage[i - 1, j - 1] = abs(horizontalGrad)

            verticalGrad = (
                    (vertical[0, 0] * gray_img[i - 1, j - 1])
                    + (vertical[0, 1] * gray_img[i - 1, j])
                    + (vertical[0, 2] * gray_img[i - 1, j + 1])
                    + (vertical[1, 0] * gray_img[i, j - 1])
                    + (vertical[1, 1] * gray_img[i, j])
                    + (vertical[1, 2] * gray_img[i, j + 1])
                    + (vertical[2, 0] * gray_img[i + 1, j - 1])
                    + (vertical[2, 1] * gray_img[i + 1, j])
                    + (vertical[2, 2] * gray_img[i + 1, j + 1])
            )

            newverticalImage[i - 1, j - 1] = abs(verticalGrad)

            # Edge Magnitude
            mag = np.sqrt(pow(horizontalGrad, 2.0) + pow(verticalGrad, 2.0))
            newgradientImage[i - 1, j - 1] = mag

    image = Image.fromarray(newgradientImage)
    image = image.convert("L")

    return image


def filter_smooth(image: Union[str, Image], radius: int = 3):
    """
    :param image: white and black image representing the derivative of the terrain (sobel), where black is flat and white is very steep.
    :param radius: Radius of the Gaussian blur.

    Returns:
        image: black or white image, with black as flat areas to be skeletonized
    """

    image = handle_import_image(image)

    # image = image.filter(ImageFilter.SMOOTH_MORE)
    # image = image.filter(ImageFilter.SMOOTH_MORE)
    # image = image.filter(ImageFilter.SMOOTH_MORE)
    image = image.convert('L')
    image = image.filter(ImageFilter.GaussianBlur(radius))
    array = np.array(image)

    bool_array = array > 7

    # bool_array = ndimage.binary_opening(bool_array, structure=np.ones((3,3)), iterations=1)
    # bool_array = ndimage.binary_closing(bool_array, structure=np.ones((3,3)), iterations=1)
    # bool_array = ndimage.binary_opening(bool_array, structure=np.ones((5,5)), iterations=1)
    # bool_array = ndimage.binary_closing(bool_array, structure=np.ones((5,5)), iterations=1)
    # bool_array = ndimage.binary_opening(bool_array, structure=np.ones((7,7)), iterations=1)
    # bool_array = ndimage.binary_closing(bool_array, structure=np.ones((7,7)), iterations=1)

    return Image.fromarray(bool_array)


def subtract_map(image: Union[str, Image], substractImage: Union[str, Image]) -> Image:
    image = handle_import_image(image)
    substractImage = handle_import_image(substractImage).convert('L')

    array_heightmap = np.array(image)
    array_substractImage = np.array(substractImage)

    mask = array_substractImage == 255
    array_heightmap[mask] = 0

    return Image.fromarray(array_heightmap)


def group_map(image1: Union[str, Image], image2: Union[str, Image]) -> Image:
    image1 = handle_import_image(image1).convert('L')
    image2 = handle_import_image(image2).convert('L')

    array1 = np.array(image1)
    array2 = np.array(image2)

    mask = array1 == 255
    array2[mask] = 255

    return Image.fromarray(array2)


def filter_smooth_array(array: np.ndarray, radius: int = 3) -> np.ndarray:
    image = Image.fromarray(array)
    smooth_image = filter_smooth(image, radius)
    array = np.array(smooth_image)
    return array


def filter_remove_details(image: Union[str, Image], n: int = 20) -> Image:
    image = handle_import_image(image)
    array = np.array(image)
    for _ in range(n):
        array = ndimage.binary_dilation(array, iterations=4)
        array = ndimage.binary_erosion(array, iterations=5)
        array = filter_smooth_array(array, 2)
        array = ndimage.binary_erosion(array, iterations=3)
    image = Image.fromarray(array)
    return image


def highway_map() -> Image:
    print("[Data Analysis] Generating highway map...")
    smooth_sobel = filter_smooth("./data/sobelmap.png", 1)
    negative_smooth_sobel = filter_negative(smooth_sobel)
    negative_smooth_sobel_water = subtract_map(negative_smooth_sobel, './data/watermap.png')
    array_sobel_water = np.array(negative_smooth_sobel_water)
    array_sobel_water = ndimage.binary_erosion(array_sobel_water, iterations=12)
    array_sobel_water = ndimage.binary_dilation(array_sobel_water, iterations=5)
    array_sobel_water = filter_smooth_array(array_sobel_water, 5)
    array_sobel_water = ndimage.binary_erosion(array_sobel_water, iterations=20)
    array_sobel_water = filter_smooth_array(array_sobel_water, 6)
    image = Image.fromarray(array_sobel_water)
    image_no_details = filter_remove_details(image, 15)
    image_no_details.save('./data/highwaymap.png')
    print("[Data Analysis] Highway map generated.")
    return image_no_details


def create_volume(surface: np.ndarray, heightmap: np.ndarray, make_it_flat: bool = False) -> np.ndarray:
    volume = np.full((len(surface), 255, len(surface[0])), False)
    for z in range(len(surface)):
        for x in range(len(surface[0])):
            if not make_it_flat:
                volume[x][heightmap[z][x]][z] = surface[z][x]
            else:
                volume[x][0][z] = surface[z][x]
    return volume


def convert_2D_to_3D(image: Union[str, Image], make_it_flat: bool = False) -> np.ndarray:
    image = handle_import_image(image)
    heightmap = Image.open('./data/heightmap.png').convert('L')
    heightmap = np.array(heightmap)
    surface = np.array(image)
    volume = create_volume(surface, heightmap, make_it_flat)
    return volume


def skeleton_highway_map(image: Union[str, Image] = './data/highwaymap.png') -> Skeleton:
    image_array = convert_2D_to_3D(image, True)
    skeleton = Skeleton(image_array)
    skeleton.parse_graph(True)
    heightmap_skeleton = skeleton.map()
    heightmap_skeleton.save('./data/skeleton_highway.png')
    skeleton.road_area('skeleton_highway_area.png', 10)
    return skeleton


def skeleton_mountain_map(image: Union[str, Image] = './data/mountain_map.png') -> Skeleton:
    image_array = convert_2D_to_3D(image, True)
    skeleton = Skeleton(image_array)
    skeleton.parse_graph()
    heightmap_skeleton = skeleton.map()
    heightmap_skeleton.save('./data/skeleton_mountain.png')
    skeleton.road_area('skeleton_mountain_area.png',3)
    return skeleton


def smooth_sobel_water() -> Image:
    watermap = handle_import_image("./data/watermap.png")
    watermap = filter_negative(filter_remove_details(filter_negative(watermap), 5))
    sobel = handle_import_image("./data/sobelmap.png")
    sobel = filter_remove_details(filter_smooth(sobel, 1), 2)
    group = group_map(watermap, sobel)
    group = filter_negative(group)
    group.save('./data/smooth_sobel_watermap.png')
    return group


def detect_mountain(image: Union[str, Image] = './data/sobelmap.png') -> Image:
    image = handle_import_image(image)
    sobel = np.array(image)
    pixels = sobel.reshape((-1, 1))
    pixels = np.float32(pixels)

    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
    k = 3
    _, labels, centers = cv2.kmeans(pixels, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

    centers = np.uint8(centers)
    segmented_image = centers[labels.flatten()]
    segmented_image = segmented_image.reshape(sobel.shape)
    mountain = segmented_image == segmented_image.max()

    contours, _ = cv2.findContours(mountain.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    max_contour = max(contours, key=cv2.contourArea)
    M = cv2.moments(max_contour)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])

    print(f"[Data Analysis] The center of the mountain is at ({cX}, {cY})")
    return (cX, cY)