import numpy
from scipy import misc
from watermarker import Watermarker
from test import test_jpg, add_blocks, test_filter
from skimage.exposure import rescale_intensity
w = Watermarker(6, 4)
w2 = Watermarker(6, 4, mother="haar", seed=212219812811)
# src = misc.imread("../qrmark/pics/sky.png")
src = misc.imread("pics/face.jpg")
# src = misc.lena()
out = w.embed(src, "123456", k=3, tv_denoising_weight=0)
out = w2.embed(out, "789012", k=4, tv_denoising_weight=0)
misc.imsave("orig.png", out)
out2 = misc.imread("orig.png")
print w.extract(out2)
print "min jpg quality:", test_jpg(w, out2)
print w2.extract(out2)
print "min jpg quality:", test_jpg(w2, out2)
# import random
# random.seed(3286912)
# print "Max random block coverage: ", test_filter(w, out2, "blocks-%s.png",
# lambda img, k: add_blocks(img, k, 50, 50), [i/20.0 for i in range(1, 20)])
import os
import numpy
from scipy import misc
from watermarker import Watermarker
from test import test_jpg, add_blocks, add_noise, test_filter, test_recursive_filter
from scipy.ndimage.filters import gaussian_filter, gaussian_laplace, uniform_filter, median_filter
from skimage.filter import tv_denoise
w = Watermarker(6, 4)
src = misc.imread("scans/facescan.png")
print w.extract(src)
print test_jpg(w, src)
#src = misc.lena()
#markers = [Watermarker(6, 4, seed = seed) for seed in range(50)]
#text = "the quick brown fox jumped over the lazy dog and laughed a great deal"
#
#for i, w in enumerate(markers):
# src = w.embed(src, text[i:i+6])
# print "%s\t%s" % (i+1, test_jpg(markers[0], src))
#out = w2.embed(out, "789012", k = 4, tv_denoising_weight = 0)
#out2 = misc.imread("orig.png")
#out2 = misc.imread("facescan.png")
#print w.extract(out2)
#print "min jpg quality:", test_jpg(w, out2)
#print w2.extract(out2)
#print "min jpg quality:", test_jpg(w2, out2)
import os
import numpy
from scipy import misc
from watermarker import Watermarker
from test import test_jpg, add_blocks, add_noise, test_filter, test_recursive_filter
from scipy.ndimage.filters import gaussian_filter, gaussian_laplace, uniform_filter, median_filter
from skimage.filter import tv_denoise
src = misc.lena()
markers = [Watermarker(6, 4, seed = seed) for seed in range(50)]
text = "the quick brown fox jumped over the lazy dog and laughed a great deal"
for i, w in enumerate(markers):
src = w.embed(src, text[i:i+6])
print "%s\t%s" % (i+1, test_jpg(markers[0], src))
#out = w2.embed(out, "789012", k = 4, tv_denoising_weight = 0)
#out2 = misc.imread("orig.png")
#out2 = misc.imread("facescan.png")
#print w.extract(out2)
#print "min jpg quality:", test_jpg(w, out2)
#print w2.extract(out2)
#print "min jpg quality:", test_jpg(w2, out2)
#import random
#random.seed(3286912)
#print "Max random block coverage: ", test_filter(w, out2, "blocks-%s.png",
# lambda img, k: add_blocks(img, k, 50, 50), [i/20.0 for i in range(1, 20)])
