import json
from typing import Sequence, Union

import cv2
import numpy as np
from PIL import Image

from cv_analysis.utils.rectangle import Rectangle

Image_t = Union[Image.Image, np.ndarray]


def contour_to_rectangle(contour):
    return box_to_rectangle(cv2.boundingRect(contour))


def box_to_rectangle(box: Sequence[int]) -> Rectangle:
    x, y, w, h = box
    return Rectangle(x, y, x + w, y + h)


def rectangle_to_box(rectangle: Rectangle) -> Sequence[int]:
    return [rectangle.x1, rectangle.y1, rectangle.width, rectangle.height]


class RectangleJSONEncoder(json.JSONEncoder):
    def __init__(self, *args, **kwargs):
        json.JSONEncoder.__init__(self, *args, **kwargs)
        self._replacement_map = {}

    def default(self, o):
        if isinstance(o, Rectangle):
            return {"x1": o.x1, "x2": o.x2, "y1": o.y1, "y2": o.y2}
        else:
            return json.JSONEncoder.default(self, o)

    def encode(self, o):
        result = json.JSONEncoder.encode(self, o)
        return result


def normalize_image_format_to_array(image: Image_t):
    return np.array(image).astype(np.uint8) if isinstance(image, Image.Image) else image


def normalize_image_format_to_pil(image: Image_t):
    return Image.fromarray(image.astype(np.uint8)) if isinstance(image, np.ndarray) else image