import numpy as np

def rle_encode(x, fg_val=1):
    """
    Args:
        x:  numpy array of shape (height, width), 1 - mask, 0 - background
    Returns: run length encoding as list
    """

    dots = np.where(
        x.T.flatten() == fg_val)[0]  # .T sets Fortran order down-then-right
    run_lengths = []
    prev = -2
    for b in dots:
        if b > prev + 1:
            run_lengths.extend((b + 1, 0))
        run_lengths[-1] += 1
        prev = b
    return run_lengths

def list_to_string(x):
    """
    Converts list to a string representation
    Empty list returns '-'
    """
    if x: # non-empty list
        s = str(x).replace("[", "").replace("]", "").replace(",", "")
    else:
        s = '-'
    return s


def rle_decode(mask_rle, shape=(256, 256)):
    '''
    mask_rle: run-length as string formatted (start length)
              empty predictions need to be encoded with '-'
    shape: (height, width) of array to return 
    Returns numpy array, 1 - mask, 0 - background
    '''

    img = np.zeros(shape[0]*shape[1], dtype=np.uint8)
    if mask_rle != '-': 
        s = mask_rle.split()
        starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
        starts -= 1
        ends = starts + lengths
        for lo, hi in zip(starts, ends):
            img[lo:hi] = 1
    return img.reshape(shape, order='F')