import numpy as np
from matplotlib import pyplot as plt


def rle_encode(mask):
    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):
    return ' '.join(str(x) for x in runs)


# Used only for testing.
# This is copied from https://www.kaggle.com/paulorzp/run-length-encode-and-decode.
# Thanks to Paulo Pinto.
def rle_decode(rle_str, mask_shape, mask_dtype):
    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(mask_shape), dtype=mask_dtype)
    for lo, hi in zip(starts, ends):
        mask[lo:hi] = 1
    return mask.reshape(mask_shape[::-1]).T

def rle_decode2(mask_rle, shape):
    '''
    mask_rle: run-length as string formated (start length)
    shape: (height,width) of array to return
    Returns numpy array, 1 - mask, 0 - background

    '''
    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
    img = np.zeros(shape[0]*shape[1], dtype=np.uint8)
    for lo, hi in zip(starts, ends):
        img[lo:hi] = 1
    return img.reshape(shape)

def plt_mask(data):
    plt.imshow(data, interpolation='nearest')
    plt.show()