import numpy as np
from scipy import ndimage


def decimate_to(shape, image):
    """Decimate an image to reduce its size if it's too big."""
    decimation = np.max(np.ceil(np.array(image.shape, dtype=np.float)[:len(shape)]/np.array(shape)))
    return image[::int(decimation), ::int(decimation), ...]


def sharpness_sum_lap2(rgb_image):
    """Return an image sharpness metric: sum(laplacian(image)**")"""
    # image_bw=np.mean(decimate_to((1000,1000), rgb_image),2)
    image_bw = np.mean(rgb_image, 2)
    image_lap = ndimage.filters.laplace(image_bw)
    return np.mean(image_lap.astype(np.float)**4)


def sharpness_edge(image):
    """Return a sharpness metric optimised for vertical lines"""
    gray = np.mean(image.astype(float), 2)
    n = 20
    edge = np.array([[-1]*n + [1]*n])
    return np.sum([np.sum(ndimage.filters.convolve(gray, W)**2) for W in [edge, edge.T]])