gsnet

import numpy as np

__all__ = ["rle_decode", "rle_encode", "rle_to_string"]


def rle_encode(mask: np.ndarray):
    """
    Convert mask to EncodedPixels in run-length encoding
    from https://www.kaggle.com/stainsby/fast-tested-rle-and-input-routines
    """
    pixels = mask.T.flatten()
    # We need to allow for cases where there is a '1' at either end of the sequence.
    # We do this by padding with a zero at each end when needed.
    use_padding = False
    if pixels[0] or pixels[-1]:
        use_padding = True
        pixel_padded = np.zeros([len(pixels) + 2], dtype=pixels.dtype)
        pixel_padded[1:-1] = pixels
        pixels = pixel_padded
    rle = np.where(pixels[1:] != pixels[:-1])[0] + 2
    if use_padding:
        rle = rle - 1
    rle[1::2] = rle[1::2] - rle[:-1:2]
    return rle


def rle_to_string(runs) -> str:
    return " ".join(str(x) for x in runs)


def rle_decode(rle_str, shape, dtype) -> np.ndarray:
    s = rle_str.split()
    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
    starts -= 1
    ends = starts + lengths
    mask = np.zeros(np.prod(shape), dtype=dtype)
    for lo, hi in zip(starts, ends):
        mask[lo:hi] = 1
    return mask.reshape(shape[::-1]).T