from __future__ import division
import nt_toolbox as nt
from nt_solutions import coding_3_natural_images as solutions
%matplotlib inline
%load_ext autoreload
%autoreload 2

n = 256
M1 = rescale(load_image('boat', n))
M2 = rescale(load_image('lena', n))

subplot(2, 2, 1)
imageplot(M1)
subplot(2, 2, 2)
[h, t] = hist(M1(: ), 60)
bar(t, h/ sum(h))
axis('square')
subplot(2, 2, 3)
imageplot(M2)
subplot(2, 2, 4)
[h, t] = hist(M2(: ), 60)
bar(t, h/ sum(h))
axis('square')

solutions.exo1()

## Insert your code here.

n = 256*2
M = rescale(load_image('lena', n))

Jmin = 4
MW = perform_wavelet_transf(M, Jmin, + 1)

MW1 = MW(1: n/ 2, n/ 2 + 1: n)

v = max(abs(MW1(: )))
k = 20
t = linspace(-v, v, 2*k + 1)
h = hist(MW1(: ), t)

subplot(1, 2, 1)
imageplot(MW1)
subplot(1, 2, 2)
bar(t, h/ sum(h)); axis('tight')
axis('square')

T = .03
MW1q = floor(abs(MW1/ T)).*sign(MW1)
nj = size(MW1, 1)

t = 2; % you can try with other values
C = reshape(MW1q([ones(1, t) 1: nj-t], : ), size(MW1))

options.normalize = 1
[H, x, xc] = compute_conditional_histogram(MW1q, C, options)

q = 8; % width for display
H = H((end + 1)/ 2-q: (end + 1)/ 2 + q, (end + 1)/ 2-q: (end + 1)/ 2 + q)

imagesc(-q: q, -q: q, max(log(H), -5)); axis image
colormap gray(256)

options.normalize = 0
[H, x, xc] = compute_conditional_histogram(MW1q, C, options)
H = H((end + 1)/ 2-q: (end + 1)/ 2 + q, (end + 1)/ 2-q: (end + 1)/ 2 + q)

contour(-q: q, -q: q, max(log(H), -5), 20, 'k'); axis image
colormap gray(256)

C = sign(reshape(MW1q([1 1: nj-1], : ), size(MW1)))

[H, x, xc] = compute_conditional_histogram(MW1q, C)

clf; plot(x, H, '.-')
axis([-10 10 0 max(H(: ))])
legend('sign = -1', 'sign = 0', 'sign = + 1')
set_graphic_sizes([], 20)

ent_total = compute_entropy(MW1q)

h0 = compute_histogram(C)
H(H = =0) = 1e-9; % avoid numerical problems
ent_partial = sum(-log2(H).*H)
ent_cond = sum(ent_partial.*h0')

disp(['Global coding: ' num2str(ent_total, 3), ' bpp.'])
disp(['Conditional coding: ' num2str(ent_cond, 3), ' bpp.'])