"""
Video Quality Metrics
Copyright (c) 2014 Alex Izvorski <aizvorski@gmail.com>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""

import numpy
from scipy.ndimage import gaussian_filter

from numpy.lib.stride_tricks import as_strided as ast

"""
Hat tip: http://stackoverflow.com/a/5078155/1828289
"""
def block_view(A, block=(3, 3)):
    """Provide a 2D block view to 2D array. No error checking made.
    Therefore meaningful (as implemented) only for blocks strictly
    compatible with the shape of A."""
    # simple shape and strides computations may seem at first strange
    # unless one is able to recognize the 'tuple additions' involved ;-)
    shape = (A.shape[0]// block[0], A.shape[1]// block[1])+ block
    strides = (block[0]* A.strides[0], block[1]* A.strides[1])+ A.strides
    return ast(A, shape= shape, strides= strides)


def ssim(img1, img2, C1=0.01**2, C2=0.03**2):

    bimg1 = block_view(img1, (4,4))
    bimg2 = block_view(img2, (4,4))
    s1  = numpy.sum(bimg1, (-1, -2))
    s2  = numpy.sum(bimg2, (-1, -2))
    ss  = numpy.sum(bimg1*bimg1, (-1, -2)) + numpy.sum(bimg2*bimg2, (-1, -2))
    s12 = numpy.sum(bimg1*bimg2, (-1, -2))

    vari = ss - s1*s1 - s2*s2
    covar = s12 - s1*s2

    ssim_map =  (2*s1*s2 + C1) * (2*covar + C2) / ((s1*s1 + s2*s2 + C1) * (vari + C2))
    return numpy.mean(ssim_map)

# FIXME there seems to be a problem with this code
def ssim_exact(img1, img2, sd=1.5, C1=0.01**2, C2=0.03**2):

    mu1 = gaussian_filter(img1, sd)
    mu2 = gaussian_filter(img2, sd)
    mu1_sq = mu1 * mu1
    mu2_sq = mu2 * mu2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = gaussian_filter(img1 * img1, sd) - mu1_sq
    sigma2_sq = gaussian_filter(img2 * img2, sd) - mu2_sq
    sigma12 = gaussian_filter(img1 * img2, sd) - mu1_mu2

    ssim_num = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2))

    ssim_den = ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

    ssim_map = ssim_num / ssim_den
    return numpy.mean(ssim_map)

