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prnu_writt
| Author | SHA1 | Date | |
|---|---|---|---|
ccb2b4bd86
|
1
.gitignore
vendored
1
.gitignore
vendored
@@ -1,3 +1,2 @@
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||||
*.pdf
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*.xopp
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__pycache__
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@@ -6,7 +6,6 @@ from wiener_filter import wienerFilter
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# Assume greyscale PIL image passed.
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# What about other color channels? See #11.
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# `THRESHOLD` seems to have been designed to assume 256 range based images.
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def contextAdaptiveInterpolator(I, IImage, showProgress = False):
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rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
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r = rImage.load()
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@@ -14,13 +14,5 @@ def rmsDiffPil(im1, im2):
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map(lambda h, i: h*(i**2), h, range(256))
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) / (float(im1.size[0]) * im1.size[1]))
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def normalizeImage(image):
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image = image - image.min()
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image = image / image.max()
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return image
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def rmsDiffNumpy(image0, image1, normalize = False):
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if normalize:
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image0 = normalizeImage(image0)
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image1 = normalizeImage(image1)
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def rmsDiffNumpy(image0, image1):
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return np.sqrt(np.mean(np.square(image0 - image1)))
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@@ -1,41 +0,0 @@
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from PIL import Image, ImageFont, ImageDraw
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import numpy as np
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from matplotlib import pyplot as plt
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import os
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def randomGaussianImage(scale, size):
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return np.random.normal(loc = 0, scale = scale, size = size)
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# `cmap` source: https://matplotlib.org/3.8.0/api/_as_gen/matplotlib.pyplot.imshow.html
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def showImageWithMatplotlib(npArray, title = None, cmap = 'viridis'):
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if title is not None:
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plt.title(title)
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plt.imshow(npArray, cmap = cmap)
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plt.show()
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def toPilImage(npArray):
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return Image.fromarray(npArray)
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def getPrnuShownAsSuch(size, gaussianNoise = 0):
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# Supports `WIDTH` > `HEIGHT` and conversely.
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WIDTH, HEIGHT = size
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TEXT = 'PRNU'
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imagePil = Image.new('L', size)
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draw = ImageDraw.Draw(imagePil)
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fontPath = os.path.expanduser('~/.local/share/fonts/impact.ttf')
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for fontSize in range(1, HEIGHT + 1):
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font = ImageFont.truetype(fontPath, fontSize)
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if font.getlength(TEXT) > WIDTH:
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break
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# Center vertically, especially in the case `HEIGHT` > `WIDTH`.
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draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
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imageNpArray = np.array(imagePil)
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gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
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#prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
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prnuShownAsSuch = imageNpArray
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for y in range(HEIGHT):
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for x in range(WIDTH):
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if prnuShownAsSuch[y, x] != 0:
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prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x]
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return prnuShownAsSuch
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@@ -1 +0,0 @@
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https://en.wikipedia.org/w/index.php?title=Darktable&oldid=1204679832
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@@ -1,24 +0,0 @@
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<?xml version="1.0" standalone="no"?>
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<xournal creator="Xournal++ 1.1.3" fileversion="4">
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<title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/Darktable - Wikipedia/Darktable - Wikipedia.pdf" pageno="1"/>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" pageno="4"/>
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<layer/>
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</page>
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</xournal>
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@@ -1 +0,0 @@
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https://en.wikipedia.org/w/index.php?title=IrfanView&oldid=1214028358
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@@ -1,26 +0,0 @@
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<?xml version="1.0" standalone="no"?>
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<xournal creator="Xournal++ 1.1.3" fileversion="4">
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<title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/IrfanView - Wikipedia/IrfanView - Wikipedia.pdf" pageno="1"/>
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<background type="pdf" pageno="4"/>
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<layer/>
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</page>
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</xournal>
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@@ -1 +0,0 @@
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https://en.wikipedia.org/w/index.php?title=Law_of_large_numbers&oldid=1214684685
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@@ -1,62 +0,0 @@
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<?xml version="1.0" standalone="no"?>
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||||
<xournal creator="Xournal++ 1.1.3" fileversion="4">
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<title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/Law of large numbers - Wikipedia/Law of large numbers - Wikipedia.pdf" pageno="1"/>
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</xournal>
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@@ -1 +0,0 @@
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https://fr.wikipedia.org/w/index.php?title=Nikon_Electronic_File&oldid=209775164
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||||
@@ -1,40 +0,0 @@
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||||
<?xml version="1.0" standalone="no"?>
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<xournal creator="Xournal++ 1.1.3" fileversion="4">
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<title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/Nikon Electronic File — Wikipédia/Nikon Electronic File — Wikipédia.pdf" pageno="1"/>
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@@ -1 +0,0 @@
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https://en.wikipedia.org/w/index.php?title=Raster_graphics&oldid=1215085649
|
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@@ -1 +0,0 @@
|
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https://en.wikipedia.org/w/index.php?title=Shot_noise&oldid=1210973018
|
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@@ -1,111 +0,0 @@
|
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<?xml version="1.0" standalone="no"?>
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<xournal creator="Xournal++ 1.1.3" fileversion="4">
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<title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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<text font="Sans" size="11.00000000" x="173.37040718" y="82.43216394" color="#00c0ffff" ts="0" fn="">example of generating Poisson noise?</text>
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https://en.wikipedia.org/w/index.php?title=TIFF&oldid=1216190460
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https://en.wikipedia.org/w/index.php?title=XnView&oldid=1214657498
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<background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/XnView - Wikipedia/XnView - Wikipedia.pdf" pageno="1"/>
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@@ -1,5 +1,3 @@
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# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/21
|
||||
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
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from PIL import Image
|
||||
@@ -12,10 +10,6 @@ from rms_diff import rmsDiffPil, rmsDiffNumpy
|
||||
sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
|
||||
|
||||
from context_adaptive_interpolator import contextAdaptiveInterpolator
|
||||
|
||||
sys.path.insert(0, '../../algorithms/image_utils/')
|
||||
|
||||
from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage
|
||||
from tqdm import tqdm
|
||||
|
||||
IMAGE_SIZE = 64
|
||||
@@ -24,38 +18,30 @@ NUMBER_OF_IMAGES_PER_PHONE = 10_000
|
||||
# Compared to images being 1.
|
||||
PRNU_FACTOR = 0.1
|
||||
|
||||
IMAGE_SIZE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE)
|
||||
|
||||
np.random.seed(0)
|
||||
|
||||
# Generate PRNUs and images of phones.
|
||||
imagesWithoutPrnu = [[randomGaussianImage(scale = 1, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
|
||||
# Is such `np.maximum` probabilistically correct with our theoretical method? See #19.
|
||||
def randomImage(scale):
|
||||
return np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE))
|
||||
|
||||
prnus = [randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_PHONES)]
|
||||
imagesWithoutPrnu = [[randomImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
|
||||
|
||||
prnus = [np.array(Image.open('prnu.png').convert('F')) * PRNU_FACTOR / 255]
|
||||
|
||||
imagesWithPrnu = [[imageWithoutPrnu + prnus[phoneIndex] for imageWithoutPrnu in imagesWithoutPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
|
||||
|
||||
allImages = np.max([np.max(imagesWithoutPrnu) + np.max(prnus) + np.max(imagesWithPrnu)])
|
||||
|
||||
def toPilImage(npArray):
|
||||
return Image.fromarray(npArray)
|
||||
|
||||
def showImageWithPil(npArray):
|
||||
npArray -= npArray.min()
|
||||
npArray = (npArray / npArray.max()) * 255
|
||||
Image.fromarray(npArray).show()
|
||||
|
||||
plt.title('RMS between actual PRNU and the mean of the first $N$ images with PRNU (i.e. estimated PRNU)')
|
||||
plt.xlabel('$N$ first images with PRNU')
|
||||
plt.ylabel('RMS')
|
||||
plt.xscale('log')
|
||||
rmss = []
|
||||
mean = np.zeros(IMAGE_SIZE_SHAPE)
|
||||
for imageIndex in range(NUMBER_OF_IMAGES_PER_PHONE):
|
||||
mean = (mean * imageIndex + imagesWithPrnu[0][imageIndex]) / (imageIndex + 1)
|
||||
rms = rmsDiffNumpy(mean, prnus[0])
|
||||
rmss += [rms]
|
||||
plt.plot(rmss)
|
||||
plt.show()
|
||||
|
||||
##
|
||||
def showImageWithMatplotlib(npArray):
|
||||
plt.imshow(npArray)
|
||||
plt.show()
|
||||
|
||||
NUMBER_OF_ROWS = 5
|
||||
NUMBER_OF_COLUMNS = 3
|
||||
@@ -89,6 +75,23 @@ plt.show()
|
||||
|
||||
##
|
||||
|
||||
def toFileName(title):
|
||||
return title.lower().replace(' ', '_').replace(',', '_')
|
||||
|
||||
for title, image in zip(['Actual PRNU', 'First image without PRNU'], [prnus[0], imagesWithoutPrnu[0][0]]):
|
||||
plt.title(title)
|
||||
plt.imshow(image)
|
||||
plt.savefig(title.lower().replace(' ', '_') + '.svg')
|
||||
|
||||
for numberOfImages in [10 ** power for power in range(NUMBER_OF_ROWS)]:
|
||||
title = 'First image with PRNU' if numberOfImages == 1 else f'Mean of first {numberOfImages:,} images with PRNU'
|
||||
image = np.array(imagesWithPrnu[0][:numberOfImages]).mean(axis = 0)
|
||||
plt.title(f'{title}\ni.e. estimated PRNU\nRMS with actual PRNU = {round(rmsDiffNumpy(image, prnus[0]), 4)}')
|
||||
plt.imshow(image)
|
||||
plt.savefig(f'{toFileName(title)}.svg')
|
||||
|
||||
##
|
||||
|
||||
# Compute CAI of phone images.
|
||||
caiImages = [[contextAdaptiveInterpolator(image.load(), image) for image in imagesWithPrnuPil[phoneIndex]] for phoneIndex in tqdm(range(NUMBER_OF_PHONES))]
|
||||
#caiImages[0][0].show()
|
||||
|
||||
@@ -1,100 +0,0 @@
|
||||
# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/25
|
||||
|
||||
import os
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, '../../algorithms/image_utils/')
|
||||
|
||||
from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage, getPrnuShownAsSuch
|
||||
|
||||
sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
|
||||
|
||||
from context_adaptive_interpolator import contextAdaptiveInterpolator
|
||||
|
||||
sys.path.insert(0, '../../algorithms/distance/')
|
||||
|
||||
from rms_diff import rmsDiffNumpy
|
||||
|
||||
from skimage.restoration import denoise_tv_chambolle
|
||||
|
||||
datasetPath = 'no_noise_images'
|
||||
# Note that contrarily to `datasets/fake/`, here we do not have images being Gaussian with `scale` `1` but actual images with pixel values between 0 and 255.
|
||||
# In addition to the range difference, note that the distribution in the first set of images was a Gaussian and here is very different and specific.
|
||||
PRNU_FACTOR = 0.01
|
||||
NOISE_FACTOR = 0.1
|
||||
|
||||
np.random.seed(0)
|
||||
|
||||
SPLIT_N_X_N_S = [1, 2, 4]
|
||||
|
||||
# len(SPLIT_N_X_N_S)
|
||||
fig, axes = plt.subplots(2, 4)
|
||||
fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')
|
||||
|
||||
for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
|
||||
IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]
|
||||
|
||||
#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
|
||||
prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE, 255) * PRNU_FACTOR
|
||||
|
||||
def isIn256Range(x):
|
||||
return 0 <= x and x <= 255
|
||||
|
||||
imagesPrnuEstimateNpArray = []
|
||||
isFirstImage = True
|
||||
|
||||
for imageName in os.listdir(datasetPath):
|
||||
if imageName.endswith('.png'):
|
||||
imagePath = f'{datasetPath}/{imageName}'
|
||||
imageWithoutPrnuPil = Image.open(imagePath).convert('F')
|
||||
imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
|
||||
|
||||
m = IMAGE_SIZE_SHAPE[1]
|
||||
n = IMAGE_SIZE_SHAPE[0]
|
||||
|
||||
imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x : x + m, y : y + n] for x in range(0, imageWithoutPrnuNpArray.shape[0], m) for y in range(0, imageWithoutPrnuNpArray.shape[1], n)]
|
||||
for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
|
||||
#print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
|
||||
#if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
|
||||
# continue
|
||||
imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
|
||||
imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
|
||||
|
||||
if splitNXNIndex == 0 and isFirstImage:
|
||||
axis = axes[0]
|
||||
|
||||
axis[0].set_title('First image without noise')
|
||||
axis[0].imshow(imageWithoutPrnuNpArrayTile)
|
||||
|
||||
axis[1].set_title('Actual Gaussian noised PRNU')
|
||||
axis[1].imshow(prnuNpArray)
|
||||
|
||||
axis[2].set_title('F. i. with G. n.')
|
||||
axis[2].imshow(imageWithoutPrnuNpArray + imageNoise)
|
||||
|
||||
axis[3].set_title('F. i. with G. n. and PRNU')
|
||||
axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
|
||||
isFirstImage = False
|
||||
|
||||
#assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
|
||||
imageWithPrnuPil = toPilImage(imageWithPrnuNpArray)
|
||||
#imagePrnuEstimatePil = contextAdaptiveInterpolator(imageWithPrnuPil.load(), imageWithPrnuPil)
|
||||
#imagePrnuEstimateNpArray = np.array(imagePrnuEstimatePil)
|
||||
imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1)
|
||||
|
||||
imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
|
||||
|
||||
cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0)
|
||||
rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
|
||||
title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
|
||||
axis = axes[1]
|
||||
axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}')
|
||||
axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray)
|
||||
|
||||
axes[1][3].axis('off')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
@@ -1 +0,0 @@
|
||||
https://web.archive.org/web/20220121204219/https://mcolom.perso.math.cnrs.fr/download/no_noise_images/no_noise_images.zip
|
||||
@@ -1 +0,0 @@
|
||||
https://web.archive.org/web/20221230200626/https://mcolom.perso.math.cnrs.fr/pages/no_noise_images/
|
||||
@@ -1,39 +0,0 @@
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<?xml version="1.0" standalone="no"?>
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<xournal creator="Xournal++ 1.1.3" fileversion="4">
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<title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/datasets/noise_free_test_images/webpage/noise_free_test_images.pdf" pageno="1"/>
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<layer>
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<stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000" fill="255">44.16991935 149.86110719 276.63410082 149.86110719</stroke>
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<stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">93.80213209 345.70257013 454.50227983 345.70257013</stroke>
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<text font="Sans" size="11.00000000" x="45.82731792" y="353.90876455" color="#00c0ffff" ts="0" fn="">legends not matching actual webpage web-browser rendering</text>
|
||||
<text font="Sans" size="11.00000000" x="48.63067627" y="32.63059998" color="#00c0ffff" ts="0" fn="">Almost identical content as `no_noise_images.zip/readme.txt`.</text>
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</layer>
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</page>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" pageno="2"/>
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<layer/>
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</page>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" pageno="3"/>
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<layer/>
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</page>
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</xournal>
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@@ -1,49 +0,0 @@
|
||||
#!/usr/bin/python3
|
||||
|
||||
from skimage.restoration import denoise_tv_chambolle
|
||||
from skimage import img_as_float
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import os
|
||||
from tqdm import tqdm
|
||||
from multiprocessing import Pool
|
||||
from threading import Lock
|
||||
|
||||
imagesFolderPath = 'flat-field'
|
||||
npArrayFilePath = 'mean.npy'
|
||||
|
||||
mutex = Lock()
|
||||
mean = None#np.zeros((3264, 4928, 3))
|
||||
numberOfImagesInMean = 0
|
||||
|
||||
imagesFileNames = os.listdir(imagesFolderPath)
|
||||
|
||||
pbar = tqdm(total = len(imagesFileNames))
|
||||
|
||||
def treatImage(imageFileName):
|
||||
global mean, numberOfImagesInMean, pbar
|
||||
imageFilePath = f'{imagesFolderPath}/{imageFileName}'
|
||||
imagePil = Image.open(imageFilePath)
|
||||
imageNpArray = img_as_float(np.array(imagePil))
|
||||
print('Before mean computation')
|
||||
imageNoiseNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
|
||||
print('After mean computation')
|
||||
with mutex:
|
||||
print('Start mutex section')
|
||||
if mean is None:
|
||||
print('Inter A')
|
||||
mean = imageNoiseNpArray
|
||||
else:
|
||||
print('Inter B')
|
||||
mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
|
||||
print('Intermediary 0')
|
||||
numberOfImagesInMean += 1
|
||||
print('Intermediary 1')
|
||||
pbar.update(numberOfImagesInMean)
|
||||
print('End mutex section')
|
||||
|
||||
with Pool(5) as p:
|
||||
p.map(treatImage, imagesFileNames)
|
||||
|
||||
with open(npArrayFilePath, 'wb') as f:
|
||||
np.save(f, mean)
|
||||
@@ -1,52 +0,0 @@
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from tqdm import tqdm
|
||||
from skimage.restoration import denoise_tv_chambolle
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, '../../algorithms/image_utils/')
|
||||
|
||||
from image_utils import showImageWithMatplotlib, toPilImage
|
||||
|
||||
sys.path.insert(0, '../../algorithms/distance/')
|
||||
|
||||
from rms_diff import rmsDiffNumpy
|
||||
|
||||
NUMBER_OF_SUBGROUPS = 1
|
||||
|
||||
IMAGES_FOLDER = 'flat-field/TIF'
|
||||
imagesFileNames = os.listdir(IMAGES_FOLDER)
|
||||
numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
|
||||
|
||||
# 1
|
||||
numberOfImagesThresholds = range(numberOfImagesPerSubgroup, numberOfImagesPerSubgroup + 1)
|
||||
|
||||
# Assume random image order to not introduce a bias.
|
||||
subgroupsPrnuEstimatesNpArray = []
|
||||
for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
|
||||
imagesPrnuEstimateNpArray = []
|
||||
for imageFileName in tqdm(imagesFileNames[numberOfImagesPerSubgroup * subgroupIndex : numberOfImagesPerSubgroup * (subgroupIndex + 1)]):
|
||||
imagePath = f'{IMAGES_FOLDER}/{imageFileName}'
|
||||
imagePil = Image.open(imagePath).convert('F')
|
||||
imageNpArray = np.array(imagePil)
|
||||
imagePrnuEstimateNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
|
||||
imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
|
||||
|
||||
subgroupPrnuEstimateNpArray = []
|
||||
# Not efficient mean computation.
|
||||
for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
|
||||
subgroupPrnuEstimateNpArray += [np.array(imagesPrnuEstimateNpArray[:numberOfImages]).mean(axis = 0)]
|
||||
plt.imsave('prnu.png', subgroupPrnuEstimateNpArray[-1])
|
||||
subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray]
|
||||
|
||||
rmss = []
|
||||
for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
|
||||
rms = rmsDiffNumpy(subgroupsPrnuEstimatesNpArray[0][numberOfImagesIndex], subgroupsPrnuEstimatesNpArray[1][numberOfImagesIndex])
|
||||
rmss += [rms]
|
||||
|
||||
plt.title('RMS between both subgroups estimated PRNUs for a given number of images among them')
|
||||
plt.xlabel('Number of images of each subgroup')
|
||||
plt.ylabel('RMS between both subgroups estimated PRNUs')
|
||||
plt.plot(rmss)
|
||||
plt.show()
|
||||
@@ -1 +0,0 @@
|
||||
https://web.archive.org/web/20221206120232/http://loki.disi.unitn.it/RAISE/download.html
|
||||
@@ -1,36 +0,0 @@
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<?xml version="1.0" standalone="no"?>
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<xournal creator="Xournal++ 1.1.3" fileversion="4">
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<title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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<page width="596.00000000" height="842.00000000">
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<background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/RAISE - Dataset Download/RAISE - Dataset Download.pdf" pageno="1"/>
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<layer>
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Reference in New Issue
Block a user