Gym-MiniGrid/minigrid/envs/wfc/wfclogic/tiles.py

"""Breaks an image into consituant tiles. Implementation based on https://github.com/ikarth/wfc_2019f"""

from __future__ import annotations

import numpy as np
from numpy.typing import NDArray

from minigrid.envs.wfc.wfclogic.utilities import hash_downto


def image_to_tiles(img: NDArray[np.integer], tile_size: int) -> NDArray[np.integer]:
    """
    Takes an images, divides it into tiles, return an array of tiles.
    """
    padding_argument = [(0, 0), (0, 0), (0, 0)]
    for input_dim in [0, 1]:
        padding_argument[input_dim] = (
            0,
            (tile_size - img.shape[input_dim]) % tile_size,
        )
    img = np.pad(img, padding_argument, mode="constant")
    tiles = img.reshape(
        (
            img.shape[0] // tile_size,
            tile_size,
            img.shape[1] // tile_size,
            tile_size,
            img.shape[2],
        )
    ).swapaxes(1, 2)
    return tiles


def make_tile_catalog(image_data: NDArray[np.integer], tile_size: int) -> tuple[
    dict[int, NDArray[np.integer]],
    NDArray[np.int64],
    NDArray[np.int64],
    tuple[NDArray[np.int64], NDArray[np.int64]],
]:
    """
    Takes an image and tile size and returns the following:
    tile_catalog is a dictionary tiles, with the hashed ID as the key
    tile_grid is the original image, expressed in terms of hashed tile IDs
    code_list is the original image, expressed in terms of hashed tile IDs and reduced to one dimension
    unique_tiles is the set of tiles, plus the frequency of their occurrence
    """
    channels = image_data.shape[2]  # Number of color channels in the image
    tiles = image_to_tiles(image_data, tile_size)
    tile_list: NDArray[np.integer] = tiles.reshape(
        (tiles.shape[0] * tiles.shape[1], tile_size, tile_size, channels)
    )
    code_list: NDArray[np.int64] = hash_downto(tiles, 2).reshape(
        tiles.shape[0] * tiles.shape[1]
    )
    tile_grid: NDArray[np.int64] = hash_downto(tiles, 2)
    unique_tiles: tuple[NDArray[np.int64], NDArray[np.int64]] = np.unique(
        tile_grid, return_counts=True
    )

    tile_catalog: dict[int, NDArray[np.integer]] = {}
    for i, j in enumerate(code_list):
        tile_catalog[j] = tile_list[i]
    return tile_catalog, tile_grid, code_list, unique_tiles