digital-watermarking/test.py at master · A0-J/digital-watermarking · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
import numpy as np
import pywt
import os
from PIL import Image
from scipy.fftpack import dct
from scipy.fftpack import idct

current_path = str(os.path.dirname(__file__))

image = 'images/lena.jpg'
watermark = 'images/mandrill_256.jpg'

def convert_image(image_name, size):
    img = Image.open(image_name).resize((size, size), 1)
    img = img.convert('L')
    img.save(image_name)


    image_array = np.array(img.getdata(), dtype=np.float).reshape((size, size))
    print(image_array[0][0])
    print(image_array[10][10])

    return image_array

def process_coefficients(imArray, model, level):
    coeffs=pywt.wavedec2(data = imArray, wavelet = model, level = level)
    print(coeffs)
    # print coeffs[0].__len__()
    coeffs_H=list(coeffs)

    return coeffs_H


def embed_watermark(watermark_array, orig_image):
    watermark_array_size = len(watermark_array[0])
    watermark_flat = watermark_array.ravel()
    ind = 0

    for x in range (0, orig_image.__len__(), 8):
        for y in range (0, orig_image.__len__(), 8):
            if ind < watermark_flat.__len__():
                subdct = orig_image[x:x+8, y:y+8]
                subdct[5][5] = watermark_flat[ind]
                orig_image[x:x+8, y:y+8] = subdct
                ind += 1


    return orig_image


def apply_dct(image_array):
    size = image_array[0].__len__()
    all_subdct = np.empty((size, size))
    for i in range (0, size, 8):
        for j in range (0, size, 8):
            subpixels = image_array[i:i+8, j:j+8]
            subdct = dct(dct(subpixels.T, norm="ortho").T, norm="ortho")
            all_subdct[i:i+8, j:j+8] = subdct

    return all_subdct


def inverse_dct(all_subdct):
    size = all_subdct[0].__len__()
    all_subidct = np.empty((size, size))
    for i in range (0, size, 8):
        for j in range (0, size, 8):
            subidct = idct(idct(all_subdct[i:i+8, j:j+8].T, norm="ortho").T, norm="ortho")
            all_subidct[i:i+8, j:j+8] = subidct

    return all_subidct


def get_watermark(dct_watermarked_coeff, watermark_size):

    subwatermarks = []

    for x in range (0, dct_watermarked_coeff.__len__(), 8):
        for y in range (0, dct_watermarked_coeff.__len__(), 8):
            coeff_slice = dct_watermarked_coeff[x:x+8, y:y+8]
            subwatermarks.append(coeff_slice[5][5])

    watermark = np.array(subwatermarks).reshape(watermark_size, watermark_size)

    return watermark


def recover_watermark(image_array, model='haar', level = 1):


    coeffs_watermarked_image = process_coefficients(image_array, model, level=level)
    dct_watermarked_coeff = apply_dct(coeffs_watermarked_image[0])

    watermark_array = get_watermark(dct_watermarked_coeff, 128)

    watermark_array =  np.uint8(watermark_array)

#Save result
    img = Image.fromarray(watermark_array)
    img.save('recovered_watermark.jpg')


def print_image_from_array(image_array, name):

    image_array_copy = image_array.clip(0, 255)
    image_array_copy = image_array_copy.astype("uint8")
    img = Image.fromarray(image_array_copy)
    img.save(name)


def w2d(img):
    model = 'haar'
    level = 1
    image_array = convert_image(image, 2048)
    watermark_array = convert_image(watermark, 128)

    coeffs_image = process_coefficients(image_array, model, level=level)
    dct_array = apply_dct(coeffs_image[0])
    dct_array = embed_watermark(watermark_array, dct_array)
    coeffs_image[0] = inverse_dct(dct_array)


# reconstruction
    image_array_H=pywt.waverec2(coeffs_image, model)
    print_image_from_array(image_array_H, 'image_with_watermark.jpg')


# recover images
    recover_watermark(image_array = image_array_H, model=model, level = level)


w2d("images/lena.jpg")