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import numpy as np

import math

from scipy.special import gamma

import scipy

import scipy.ndimage

def paired_product(new_im):

shift1 = np.roll(new_im.copy(), 1, axis=1)

shift2 = np.roll(new_im.copy(), 1, axis=0)

shift3 = np.roll(np.roll(new_im.copy(), 1, axis=0), 1, axis=1)

shift4 = np.roll(np.roll(new_im.copy(), 1, axis=0), -1, axis=1)

H_img = shift1 * new_im

V_img = shift2 * new_im

D1_img = shift3 * new_im

D2_img = shift4 * new_im

return (H_img, V_img, D1_img, D2_img)

def gen_gauss_window(lw, sigma):

sd = np.float32(sigma)

lw = int(lw)

weights = [0.0] * (2 * lw + 1)

weights[lw] = 1.0

sum = 1.0

sd *= sd

for ii in range(1, lw + 1):

tmp = np.exp(-0.5 * np.float32(ii * ii) / sd)

weights[lw + ii] = tmp

weights[lw - ii] = tmp

sum += 2.0 * tmp

for ii in range(2 * lw + 1):

weights[ii] /= sum

return weights

def estimateggdparam(vec):

gam = np.asarray([x / 1000.0 for x in range(200, 10000, 1)])

r_gam = (gamma(1.0/gam)*gamma(3.0/gam))/((gamma(2.0/gam))**2)

# print(np.mean(vec))

sigma_sq = np.mean(vec**2) #-(np.mean(vec))**2

sigma = np.sqrt(sigma_sq)

E = np.mean(np.abs(vec))

rho = sigma_sq / (E**2 + 1e-6)

array_position = (np.abs(rho - r_gam)).argmin()

alphaparam = gam[array_position]

return alphaparam, sigma

def compute_image_mscn_transform(image, C=1, avg_window=None, extend_mode='constant'):

if avg_window is None:

avg_window = gen_gauss_window(3, 7.0/6.0)

assert len(np.shape(image)) == 2

h, w = np.shape(image)

mu_image = np.zeros((h, w), dtype=np.float32)

var_image = np.zeros((h, w), dtype=np.float32)

image = np.array(image).astype('float32')

scipy.ndimage.correlate1d(image, avg_window, 0, mu_image, mode=extend_mode)

scipy.ndimage.correlate1d(mu_image, avg_window, 1, mu_image, mode=extend_mode)

scipy.ndimage.correlate1d(image**2, avg_window, 0, var_image, mode=extend_mode)

scipy.ndimage.correlate1d(var_image, avg_window, 1, var_image, mode=extend_mode)

var_image = np.sqrt(np.abs(var_image - mu_image**2))

return (image - mu_image)/(var_image + C)

def extract_subband_feats(mscncoefs):

# alpha_m, = extract_ggd_features(mscncoefs)

alpha_m, sigma = estimateggdparam(mscncoefs)

pps1, pps2, pps3, pps4 = paired_product(mscncoefs)

alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)

alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)

alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)

alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)

return np.array([

alpha_m, sigma,

alpha1, N1, lsq1**2, rsq1**2, # (V)

alpha2, N2, lsq2**2, rsq2**2, # (H)

alpha3, N3, lsq3**2, rsq3**2, # (D1)

alpha4, N4, lsq4**2, rsq4**2, # (D2)

])

def aggd_features(imdata):

# Flatten imdata

imdata.shape = (len(imdata.flat),)

imdata2 = imdata*imdata

left_data = imdata2[imdata < 0]

right_data = imdata2[imdata >= 0]

left_mean_sqrt = 0

right_mean_sqrt = 0

if len(left_data) > 0:

left_mean_sqrt = np.sqrt(np.average(left_data))

if len(right_data) > 0:

right_mean_sqrt = np.sqrt(np.average(right_data))

if right_mean_sqrt != 0:

gamma_hat = left_mean_sqrt/right_mean_sqrt

else:

gamma_hat = np.inf

# Solve r-hat norm

imdata2_mean = np.mean(imdata2)

if imdata2_mean != 0:

r_hat = (np.average(np.abs(imdata))**2) / (np.average(imdata2))

else:

r_hat = np.inf

rhat_norm = r_hat * (((math.pow(gamma_hat, 3) + 1)*(gamma_hat + 1)) / math.pow(math.pow(gamma_hat, 2) + 1, 2))

# win = np.array(gen_gauss_window(3, 7.0/6.0))

gamma_range = np.arange(0.2, 10, 0.001)

a = scipy.special.gamma(2.0/gamma_range)

a *= a

b = scipy.special.gamma(1.0/gamma_range)

c = scipy.special.gamma(3.0/gamma_range)

prec_gammas = a/(b*c)

# solve alpha by guessing values that minimize ro

pos = np.argmin((prec_gammas - rhat_norm)**2)

alpha = gamma_range[pos]

gam1 = scipy.special.gamma(1.0/alpha)

gam2 = scipy.special.gamma(2.0/alpha)

gam3 = scipy.special.gamma(3.0/alpha)

aggdratio = np.sqrt(gam1) / np.sqrt(gam3)

bl = aggdratio * left_mean_sqrt

br = aggdratio * right_mean_sqrt

# Mean parameter

N = (br - bl)*(gam2 / gam1) #*aggdratio

return (alpha, N, bl, br, left_mean_sqrt, right_mean_sqrt)

def integral_image(x):

M, N = x.shape

int_x = np.zeros((M+1, N+1))

int_x[1:, 1:] = np.cumsum(np.cumsum(x, 0), 1)

return int_x

def moments(x, y, k, stride, padding=None):

kh = kw = k

k_norm = k**2

if padding is None:

x_pad = x

y_pad = y

else:

x_pad = np.pad(x, int((kh - stride)/2), mode=padding)

y_pad = np.pad(y, int((kh - stride)/2), mode=padding)

int_1_x = integral_image(x_pad)

int_1_y = integral_image(y_pad)

mu_x = (int_1_x[:-kh:stride, :-kw:stride] - int_1_x[:-kh:stride, kw::stride] - int_1_x[kh::stride, :-kw:stride] + int_1_x[kh::stride, kw::stride]) / k_norm

mu_y = (int_1_y[:-kh:stride, :-kw:stride] - int_1_y[:-kh:stride, kw::stride] - int_1_y[kh::stride, :-kw:stride] + int_1_y[kh::stride, kw::stride]) / k_norm

int_2_x = integral_image(x_pad**2)

int_2_y = integral_image(y_pad**2)

int_xy = integral_image(x_pad*y_pad)

var_x = (int_2_x[:-kh:stride, :-kw:stride] - int_2_x[:-kh:stride, kw::stride] - int_2_x[kh::stride, :-kw:stride] + int_2_x[kh::stride, kw::stride]) / k_norm - mu_x**2

var_y = (int_2_y[:-kh:stride, :-kw:stride] - int_2_y[:-kh:stride, kw::stride] - int_2_y[kh::stride, :-kw:stride] + int_2_y[kh::stride, kw::stride]) / k_norm - mu_y**2

cov_xy = (int_xy[:-kh:stride, :-kw:stride] - int_xy[:-kh:stride, kw::stride] - int_xy[kh::stride, :-kw:stride] + int_xy[kh::stride, kw::stride]) / k_norm - mu_x*mu_y

# Correcting negative values of variance.

mask_x = (var_x < 0)

mask_y = (var_y < 0)

var_x[mask_x] = 0

var_y[mask_y] = 0

# If either variance was negative, it has been set to zero.

# So, the correponding covariance should also be zero.

cov_xy[mask_x | mask_y] = 0

return (mu_x, mu_y, var_x, var_y, cov_xy)

def im2col(img, k, stride=1):

# Parameters

m, n = img.shape

s0, s1 = img.strides

nrows = m - k + 1

ncols = n - k + 1

shape = (k, k, nrows, ncols)

arr_stride = (s0, s1, s0, s1)

ret = np.lib.stride_tricks.as_strided(img, shape=shape, strides=arr_stride)

return ret[:, :, ::stride, ::stride].reshape(k*k, -1)

def extract_on_patches(img, blocksizerow, blocksizecol):

h, w = img.shape

blocksizerow = np.int(blocksizerow)

blocksizecol = np.int(blocksizecol)

patches = []

for j in range(0, np.int(h-blocksizerow+1), np.int(blocksizerow)):

for i in range(0, np.int(w-blocksizecol+1), np.int(blocksizecol)):

patch = img[j:j+blocksizerow, i:i+blocksizecol]

patches.append(patch)

patches = np.array(patches)

patch_features = []

for p in patches:

p_brisque_features = extract_subband_feats(p)

patch_features.append(p_brisque_features)

patch_features = np.array(patch_features)

return patch_features