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Benjamin_Loison:evaluation
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compare: Benjamin_Loison:prnu_written_as_such
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Benjamin_Loison:master Benjamin_Loison:prnu_written_as_such
Benjamin_Loison:evaluation

1 Commits
53c3935bab ... prnu_writt

Author SHA1 Message Date
Benjamin Loison
ccb2b4bd86 #21: Write PRNU as such to make figures clearer 2024-03-26 03:15:34 +01:00
75 changed files with 36 additions and 2687 deletions
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1
.gitignore vendored
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@ -1,3 +1,2 @@
*.pdf
*.xopp
__pycache__

1
algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py
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@ -6,7 +6,6 @@ from wiener_filter import wienerFilter
# Assume greyscale PIL image passed.
# What about other color channels? See #11.
# `THRESHOLD` seems to have been designed to assume 256 range based images.
def contextAdaptiveInterpolator(I, IImage, showProgress = False):
rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
r = rImage.load()

10
algorithms/distance/rms_diff.py
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@ -14,13 +14,5 @@ def rmsDiffPil(im1, im2):
map(lambda h, i: h*(i**2), h, range(256))
) / (float(im1.size[0]) * im1.size[1]))
def normalizeImage(image):
image = image - image.min()
image = image / image.max()
return image
def rmsDiffNumpy(image0, image1, normalize = False):
if normalize:
image0 = normalizeImage(image0)
image1 = normalizeImage(image1)
def rmsDiffNumpy(image0, image1):
return np.sqrt(np.mean(np.square(image0 - image1)))

41
algorithms/image_utils/image_utils.py
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@ -1,41 +0,0 @@
from PIL import Image, ImageFont, ImageDraw
import numpy as np
from matplotlib import pyplot as plt
import os
def randomGaussianImage(scale, size):
return np.random.normal(loc = 0, scale = scale, size = size)
# `cmap` source: https://matplotlib.org/3.8.0/api/_as_gen/matplotlib.pyplot.imshow.html
def showImageWithMatplotlib(npArray, title = None, cmap = 'viridis'):
if title is not None:
plt.title(title)
plt.imshow(npArray, cmap = cmap)
plt.show()
def toPilImage(npArray):
return Image.fromarray(npArray)
def getPrnuShownAsSuch(size, gaussianNoise = 0):
# Supports `WIDTH` > `HEIGHT` and conversely.
WIDTH, HEIGHT = size
TEXT = 'PRNU'
imagePil = Image.new('L', size)
draw = ImageDraw.Draw(imagePil)
fontPath = os.path.expanduser('~/.local/share/fonts/impact.ttf')
for fontSize in range(1, HEIGHT + 1):
font = ImageFont.truetype(fontPath, fontSize)
if font.getlength(TEXT) > WIDTH:
break
# Center vertically, especially in the case `HEIGHT` > `WIDTH`.
draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
imageNpArray = np.array(imagePil)
gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
#prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
prnuShownAsSuch = imageNpArray
for y in range(HEIGHT):
for x in range(WIDTH):
if prnuShownAsSuch[y, x] != 0:
prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x]
return prnuShownAsSuch

1
articles/Additive white Gaussian noise - Wikipedia/Additive white Gaussian noise - Wikipedia.pdf.txt
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https://en.wikipedia.org/w/index.php?title=Additive_white_Gaussian_noise&oldid=1181981955

71
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articles/Bayer filter - Wikipedia/Bayer filter - Wikipedia.pdf.txt
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articles/Bernoulli distribution - Wikipedia/Bernoulli distribution - Wikipedia.pdf.txt
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1
articles/XnView - Wikipedia/XnView - Wikipedia.pdf.txt
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https://en.wikipedia.org/w/index.php?title=XnView&oldid=1214657498

33
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55
datasets/fake/generate_dataset.py
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@ -1,5 +1,3 @@
# 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
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()

100
datasets/noise_free_test_images/estimate_prnu.py
View File

@ -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
datasets/noise_free_test_images/no_noise_images.zip.txt
View File

@ -1 +0,0 @@
https://web.archive.org/web/20220121204219/https://mcolom.perso.math.cnrs.fr/download/no_noise_images/no_noise_images.zip

1
datasets/noise_free_test_images/webpage/noise_free_test_images.pdf.txt
View File

@ -1 +0,0 @@
https://web.archive.org/web/20221230200626/https://mcolom.perso.math.cnrs.fr/pages/no_noise_images/

39
datasets/noise_free_test_images/webpage/noise_free_test_images.xopp.xml
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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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65
datasets/rafael/240424/plot_dates.py
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@ -1,65 +0,0 @@
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
from PIL import Image
from PIL.ExifTags import TAGS
import os
path = 'photos'
os.chdir(path)
names = []
dates = []
for fileName in sorted(os.listdir()):
try:
image = Image.open(fileName)
except:
# Skip raw images.
continue
imageExif = image.getexif()
dateTimeKey = list(TAGS.keys())[list(TAGS.values()).index('DateTime')]
dateTime = imageExif[dateTimeKey]
names += [fileName.replace('DSC0', '').replace('.JPG', '')]
dates += [dateTime[:(-1 if fileName.endswith('.tif') else len(dateTime))]]
# Convert date strings to datetime
dates = [datetime.strptime(d, "%Y:%m:%d %H:%M:%S") for d in dates]
# Choose some nice levels
NUMBER_OF_LEVELS = 10
actualLevels = range(-NUMBER_OF_LEVELS * 2 + 1, NUMBER_OF_LEVELS * 2 , 2)
levels = np.tile(actualLevels,
int(np.ceil(len(dates)/len(actualLevels))))[:len(dates)]
# Create figure and plot a stem plot with the date
fig, ax = plt.subplots(figsize=(8.8, 4), layout="constrained")
#ax.set(title="Matplotlib release dates")
ax.vlines(dates, 0, levels, color="tab:red") # The vertical stems.
ax.plot(dates, np.zeros_like(dates), "-o",
color="k", markerfacecolor="w") # Baseline and markers on it.
# annotate lines
for d, l, r in zip(dates, levels, names):
ax.annotate(r, xy=(d, l),
xytext=(-3, np.sign(l)*3), textcoords="offset points",
horizontalalignment="right",
verticalalignment="bottom" if l > 0 else "top")
# format x-axis with 4-month intervals
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y:%m:%d %H:%M:%S"))
plt.setp(ax.get_xticklabels(), rotation=30, ha="right")
# remove y-axis and spines
ax.yaxis.set_visible(False)
ax.spines[["left", "top", "right"]].set_visible(False)
ax.margins(y=0.1)
plt.show()

35
datasets/raise/analyze_bayer_filter_mean_denoiser.py
View File

@ -1,35 +0,0 @@
import numpy as np
from utils import Color
import os
from tqdm import tqdm
import rawpy
import matplotlib.pyplot as plt
os.chdir('flat-field/NEF')
firstBayerFilterOccurrenceImages = []
for fileName in tqdm(os.listdir()):
with rawpy.imread(fileName) as raw:
colorDesc = raw.color_desc.decode('ascii')
assert colorDesc == 'RGBG'
assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
# RG
# GB
firstBayerFilterOccurrenceImage = raw.raw_image_visible.copy()[:2, :2]
firstBayerFilterOccurrenceImages += [firstBayerFilterOccurrenceImage]
firstBayerFilterOccurrenceImages = np.array(firstBayerFilterOccurrenceImages)
print(rawImageVisible)
NUMBER_OF_COLORS = 3
HEX_COLOR = '#' + '%02x' * NUMBER_OF_COLORS
COLOR_BASE = 256
def getColor(colorIndex):
return HEX_COLOR % tuple((255 if colorIndex == colorIndexTmp else 0) for colorIndexTmp in range(NUMBER_OF_COLORS))
for colorIndex, (colorY, colorX) in enumerate([(0, 0), (0, 1), (1, 1)]):
X = firstBayerFilterOccurrenceImages[:, colorY, colorX] - np.mean(firstBayerFilterOccurrenceImages, axis = 0)[colorY, colorX]
plt.hist(X, bins = len(set(X)), color = getColor(colorIndex), alpha = 0.3)
plt.show()

127
datasets/raise/attribute_source_camera.py
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#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
import sys
import os
import random
sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
DENOISER = 'wavelet'
IMAGES_CAMERAS_FOLDER = {
'RAISE': 'flat-field/nef',
'Rafael 23/04/24': 'rafael/230424',
}
TRAINING_PORTION = 0.5
setting = ','.join([escapeFilePath(imageCameraFolder) for imageCameraFolder in IMAGES_CAMERAS_FOLDER]) + f'_{DENOISER}'
imagesCamerasFileNames = {camera: os.listdir(imageCameraFolder) for camera, imageCameraFolder in IMAGES_CAMERAS_FOLDER.items()}
random.seed(0)
# To not have a bias (chronological for instance) when split to make training and testing sets.
for camera in IMAGES_CAMERAS_FOLDER:
random.shuffle(imagesCamerasFileNames[camera])
minimumNumberOfImagesCameras = 16#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
for camera in IMAGES_CAMERAS_FOLDER:
imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
print(camera, imagesCamerasFileNames[camera])
numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
camerasIterativeMean = {camera: iterativeMean() for camera in IMAGES_CAMERAS_FOLDER}
# Assume that for each camera, its images have the same resolution.
# The following consider a given color channel resolution, assuming they all have the same resolution.
minimalColorChannelCameraResolution = None
for camera in IMAGES_CAMERAS_FOLDER:
imageFileName = imagesCamerasFileNames[camera][0]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
singleColorChannelImagesShape = getColorChannel(imageFilePath, Color.RED).shape
if minimalColorChannelCameraResolution is None or singleColorChannelImagesShape < minimalColorChannelCameraResolution:
minimalColorChannelCameraResolution = singleColorChannelImagesShape
minColor = None#13#None
maxColor = None#7497#None
accuracy = []
numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
numberOfTestingImages = minimumNumberOfImagesCameras - int(minimumNumberOfImagesCameras * TRAINING_PORTION)
cameraTestingImagesNoise = {}
from utils import silentTqdm
#tqdm = silentTqdm
returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
if not computeExtremes:
print(f'{minColor=} {maxColor=}')
print('Extracting noise of testing images')
for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
# Should make a function
imageFileName = imagesCamerasFileNames[camera][numberOfTrainingImages + cameraTestingImageIndex]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
print(f'{imageFilePath=}')
# Should make a function
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]
for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
imageFileName = imagesCamerasFileNames[camera][cameraTrainingImageIndex]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
if computeExtremes:
minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
continue
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
cameraIterativeMean = camerasIterativeMean[camera]
cameraIterativeMean.add(imagePrnuEstimateNpArray)
if cameraIndex == numberOfCameras - 1:
numberOfTrainingImagesAccuracy = 0
print(f'{numberOfTestingImages=} {numberOfCameras=}')
# Loop over each camera testing image folder.
for actualCamera in IMAGES_CAMERAS_FOLDER:
for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
cameraPredicted = None
minimalDistance = None
# Loop over each camera to compute closeness between the considered testing image noise and the estimated PRNUs of the various cameras.
for camera in IMAGES_CAMERAS_FOLDER:
distance = rmsDiffNumpy(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
if minimalDistance is None or distance < minimalDistance:
minimalDistance = distance
cameraPredicted = camera
print(f'Predicted camera {cameraPredicted} {"good" if cameraPredicted == actualCamera else "bad"}')
if cameraPredicted == actualCamera:
numberOfTrainingImagesAccuracy += 1
accuracy += [numberOfTrainingImagesAccuracy / (numberOfTestingImages * numberOfCameras)]
for camera in IMAGES_CAMERAS_FOLDER:
plt.imsave(f'{setting}_estimated_prnu_camera_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
plt.title(f'Accuracy of camera source attribution thanks to a given number of images to estimate PRNUs with {DENOISER} denoiser')
plt.xlabel('Number of images to estimate PRNU')
plt.ylabel('Accuracy of camera source attribution')
plt.plot(accuracy)
internalTitle = f'{setting}_accuracy_of_camera_source_attribution'
saveNpArray(internalTitle, accuracy)
plt.savefig(f'{internalTitle}.svg')

113
datasets/raise/extract_noise.py
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#!/usr/bin/env python
import numpy as np
import os
from tqdm import tqdm
import csv
from utils import Color, denoise, iterativeMean, isARawImage, escapeFilePath, getColorChannel, saveNpArray, rescaleRawImageForDenoiser, updateExtremes
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter
imagesFolderPath = 'rafael/arw'
imagesFolderPathFileName = escapeFilePath(imagesFolderPath)
# Among:
# `denoise` possible denoisers and `mean`.
denoiser = 'mean'
raiseNotFlatFields = False
# `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color` or `[None]` for not raw images.
colors = [None]
imagesFileNames = os.listdir(imagesFolderPath + ('/png' if raiseNotFlatFields else ''))
if raiseNotFlatFields:
files = {}
with open('RAISE_all.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in tqdm(list(reader), 'CSV parsing'):
file = row['File'] + '.png'
files[file] = row
imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/png/{imageFileName}').size == (4946, 3278)]
# Among:
# - `None`
# - `'sky'`
# - `'wall'`
type_ = None
if type_ is not None:
ranges = {
'sky': range(2_699, 2_807),
'wall': range(2_807, 2_912),
}
imagesFileNames = [f'DSC0{imageIndex}.ARW' for imageIndex in ranges[type_]]
imagesFolderPathFileName += f'_{type_}'
minColor = None
maxColor = None
def getImageNpArray(imageFileName, computeExtremes, color):
global minColor, maxColor
if raiseNotFlatFields:
imageFileName = imageFileName.replace('.png', '.NEF')
imageFilePath = f'{imagesFolderPath}/nef/{imageFileName}'
else:
imageFilePath = f'{imagesFolderPath}/{imageFileName}'
imageNpArray = getColorChannel(imageFilePath, color)
if computeExtremes:
minColor, maxColor = updateExtremes(imageNpArray, minColor, maxColor)
return
if isARawImage(imageFileName) and denoiser != 'mean':
imageNpArray = rescaleRawImageForDenoiser(imageNpArray, minColor, maxColor)
# Pay attention to range of values expected by the denoiser.
# Indeed if provide the thousands valued raw image, then the denoiser only returns values between 0 and 1 and making the difference between both look pointless.
return imageNpArray
# `color` is the actual color to estimate PRNU with.
def treatImage(imageFileName, computeExtremes = False, color = None):
global estimatedPrnuIterativeMean
imageNpArray = getImageNpArray(imageFileName, computeExtremes, color)
if imageNpArray is None:
return
if denoiser != 'mean':
imageDenoisedNpArray = denoise(imageNpArray, denoiser)
else:
imageDenoisedNpArray = means[color]
imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
estimatedPrnuIterativeMean.add(imageNoiseNpArray)
if (minColor is None or maxColor is None) and denoiser != 'mean':
# Assuming same intensity scale across color channels.
for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
for color in colors:
treatImage(imageFileName, computeExtremes = True, color = color)
# To skip this step next time.
# Maybe thanks to `rawpy.RawPy` fields, possibly stating device maximal value, can avoid doing so to some extent.
print(f'{minColor=}')
print(f'{maxColor=}')
if denoiser == 'mean':
means = {}
for color in colors:
colorIterativeMean = iterativeMean()
for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
imageNpArray = getImageNpArray(imageFileName, False, color)
imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
colorIterativeMean.add(imageNpArray)
means[color] = colorIterativeMean.mean
fileName = f'mean_{imagesFolderPathFileName}_{color}'
# Then use `merge_single_color_channel_images_according_to_bayer_filter.py` to consider all color channels, instead of saving this single color channel as an image.
saveNpArray(fileName, colorIterativeMean.mean)
for color in colors:
estimatedPrnuIterativeMean = iterativeMean()
for imageFileName in tqdm(imagesFileNames, f'Denoising images for color {color}'):
treatImage(imageFileName, color = color)
npArrayFilePath = f'mean_{imagesFolderPathFileName}_{denoiser}_{color}'
saveNpArray(npArrayFilePath, estimatedPrnuIterativeMean.mean)

92
datasets/raise/flat-field_pointer.py
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import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backend_bases import MouseButton
import matplotlib.image as mpimg
import os
from tqdm import tqdm
# `Zoom to rectangle` shortcut is `o`.
os.chdir('flat-field/TIF')
fileNames = sorted(os.listdir())[41:]
fileNameIndex = 0
xys = []
progressBar = tqdm(total = len(fileNames))
def displayImage():
global fileNameIndex
if len(fileNames) > fileNameIndex:
fileName = fileNames[fileNameIndex]
image = mpimg.imread(fileName)
fig, ax = plt.subplots()
ax.imshow(image)
fileNameIndex += 1
plt.connect('button_press_event', onClick)
mng = plt.get_current_fig_manager()
mng.full_screen_toggle()
fig.canvas.toolbar.zoom()
fig.show()
def onClick(event):
global xys
button = event.button
if button is MouseButton.RIGHT:
xy = [event.xdata, event.ydata]
xys += [xy]
if button is MouseButton.MIDDLE or button is MouseButton.RIGHT:
if button is MouseButton.MIDDLE:
print(f'Skipped {fileName}')
plt.close()
displayImage()
progressBar.update(1)
displayImage()
##
xys = [[3061.83568617998, 842.2814890347822], [3048.9553647053262, 891.7951806771933], [3109.6923734795832, 878.8472318972372], [3095.1992301129835, 859.646383417094], [3044.950680354029, 830.531447782855], [3034.039239345914, 775.7097977790371], [3034.0562409262707, 753.1780473232427], [2991.7499740162116, 742.1417475753151], [3021.5541233968024, 737.7119154247914], [3022.412107608028, 703.0028101114289], [3094.4565209481857, 680.7339885543926], [3101.840188177543, 692.6886985125186], [3150.09819940581, 686.3913997808281], [3148.4955568040136, 694.3747788064451], [3165.156239230552, 703.9346483213995], [3158.7235775979357, 710.9522942666542], [3156.5302112965014, 697.5993396871294], [3106.8119050930513, 698.4561327048264], [3151.4728367372877, 694.3380683877142], [3137.283014559081, 662.9543422436452], [3008.1987322850605, 692.0180565561739], [3036.647953172549, 705.1188029786949], [2988.9166660643627, 684.0851408200217], [3001.5070402052334, 684.1944057536488], [2967.282534077184, 680.6072888791263], [2983.5140619626286, 693.469904886339], [2979.972210442175, 702.9033995969894], [2974.7535915953795, 709.6086279669086], [2972.55952140686, 700.4689248964266], [2967.1769510144627, 703.4861098128929], [2968.4714535085923, 708.5924315970815], [2978.725084963369, 725.0064286729727], [2992.7472737250696, 718.9682686788141], [2999.106406229902, 713.0463041988097], [2981.5746364633296, 692.5739107725338], [3014.22885383495, 675.1638989177214], [3004.351989366563, 635.6738446427469], [2964.9109157636794, 580.2416938434527], [2976.672441933737, 599.3922555819147], [2972.9623072548534, 578.6218471929612], [2694.5865522760287, 972.5526873692775], [2924.1211630176217, 1115.9816839025543], [2910.2861978522596, 1095.5172128924614], [2917.4530137143342, 1067.9680470058072], [2919.3469031337613, 1068.44539073963], [2959.4757277956496, 1105.6227950624348], [2936.370033263817, 1157.9142039933472], [2965.3568614486203, 1145.3186319170795], [2951.1991473505136, 1070.8989245366433], [2946.108094501549, 1045.891549058405], [2947.19213475732, 1074.4303801863052], [2943.690837961984, 1071.4536958497956], [3021.083385372544, 1116.881810271317], [3051.7205580454297, 1103.0992679894152], [3107.0873956690143, 972.8919609441568], [2861.932349690137, 1131.4847600231471], [2789.583044951935, 964.6512841164608]]
x, y = [[xy[dimensionIndex] for xy in xys] for dimensionIndex in range(2)]
plt.title('Center wall marker locations')
plt.scatter(x, y, c = range(len(x)))
for xyIndex, xy in enumerate(xys):
plt.text(xy[0], xy[1] + 10, str(xyIndex), horizontalalignment = 'center')
plt.show()
##
greatestDistances = []
#for xy in xys:
for xy, otherXy in zip(xys, xys[1:]):
greatestDistance = None
#for otherXy in xys:
if xy != otherXy:
distance = sum([(xy[dimensionIndex] - otherXy[dimensionIndex]) ** 2 for dimensionIndex in range(2)]) ** 0.5
if greatestDistance is None or distance > greatestDistance:
greatestDistance = distance
greatestDistances += [greatestDistance]
fig, ax = plt.subplots()
plt.title('Distance between a wall marker location and the next one')
ax.boxplot(greatestDistances)
ys = []
for percentile in [25, 50, 75]:
y = np.percentile(greatestDistances, percentile)
ys += [y]
ax.axhline(y = y)
ax.set_yticks(list(ax.get_yticks())[1:-1] + ys)
ax.set_xticks([], [])
fig.show()

41
datasets/raise/generate_histogram.py
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import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
os.chdir('flat-field/TIF')
NUMBER_OF_COLORS = 3
HEX_COLOR = '#' + '%02x' * NUMBER_OF_COLORS
COLOR_BASE = 256
def getColor(colorIntensity, colorIndex):
return HEX_COLOR % tuple((colorIntensity if colorIndex == colorIndexTmp else 0) for colorIndexTmp in range(NUMBER_OF_COLORS))
def getHistogram(fileName):
image = Image.open(fileName)
#image = image.crop((1, 1, 2, 2))
#print(image.size)
histogram = image.histogram()
colors = [histogram[COLOR_BASE * colorIndex:COLOR_BASE * (colorIndex + 1)] for colorIndex in range(NUMBER_OF_COLORS)]
return colors
def plotHistogram(colors):
for colorIndex, color in enumerate(colors):
for colorIntensity in range(COLOR_BASE):
plt.bar(colorIntensity, color[colorIntensity], color = getColor(colorIntensity, colorIndex), alpha = 0.3)
fileNameColors = []
for fileName in tqdm(os.listdir()):
colors = getHistogram(fileName)
fileNameColors += [colors]
meanFileNameColors = np.mean(fileNameColors, axis = 0)
plotHistogram(meanFileNameColors)
plt.show()

18
datasets/raise/merge_single_color_channel_images_according_to_bayer_filter.py
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#!/usr/bin/env python
from PIL import Image
from utils import Color, mergeSingleColorChannelImagesAccordingToBayerFilter
import matplotlib.pyplot as plt
import numpy as np
PREFIX = 'mean_rafael_arw_sky_mean_'
def getImageByColor(color):
filePath = PREFIX + f'{color}.npy'
image = np.load(filePath)
return image
singleColorChannelImages = {color: getImageByColor(color) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages, progress = True)
plt.imsave(PREFIX + 'multiple_colors.png', multipleColorsImage)

10
datasets/raise/show_mean_noise.py
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import numpy as np
import matplotlib.pyplot as plt
fileName = 'mean_flat-field_nef_wavelet_blue'
npArray = np.load(f'{fileName}.npy')
# For other than raw images:
#npArray = (npArray - npArray.min()) / (npArray.max() - npArray.min())
plt.imsave(f'{fileName}.png', npArray)
#plt.imshow(npArray)
#plt.show()

70
datasets/raise/split_and_compare_prnus_of_subgroups.py
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#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
import sys
import os
import random
sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
NUMBER_OF_SUBGROUPS = 2
DENOISER = 'wavelet'
IMAGES_FOLDER = 'flat-field/NEF'
setting = escapeFilePath(IMAGES_FOLDER) + f'_{DENOISER}'
imagesFileNames = os.listdir(IMAGES_FOLDER)
random.seed(0)
# To not have a bias (chronological for instance) when split to make subgroups.
random.shuffle(imagesFileNames)
numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
subgroupsIterativeMean = [iterativeMean() for _ in range(NUMBER_OF_SUBGROUPS)]
rmss = []
minColor = None
maxColor = None
returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
for subgroupImageIndex in tqdm(range(numberOfImagesPerSubgroup), 'Subgroup image index'):
for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
imageIndex = (subgroupIndex * NUMBER_OF_SUBGROUPS) + subgroupImageIndex
imageFileName = imagesFileNames[imageIndex]
imageFilePath = f'{IMAGES_FOLDER}/{imageFileName}'
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
if computeExtremes:
minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
continue
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
subgroupIterativeMean = subgroupsIterativeMean[subgroupIndex]
subgroupIterativeMean.add(imagePrnuEstimateNpArray)
if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:
assert NUMBER_OF_SUBGROUPS == 2
rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - subgroupIndex].mean)
rmss += [rms]
for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
plt.imsave(f'{setting}_estimated_prnu_subgroup_{subgroupIndex}.png', (subgroupsIterativeMean[subgroupIndex].mean))
plt.title(f'RMS between both subgroups estimated PRNUs with {DENOISER} denoiser 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)
saveNpArray(f'{setting}_rmss', rmss)
plt.savefig(f'{setting}_rms_between_estimated_prnu_of_2_subgroups.svg')
#plt.show()

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datasets/raise/utils.py
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from enum import Enum, auto
import skimage.restoration
import numpy as np
import rawpy
from tqdm import tqdm
from PIL import Image
from skimage import img_as_float
from datetime import datetime
import builtins as __builtin__
class Color(Enum):
RED = auto()
GREEN_RIGHT = auto()
GREEN_BOTTOM = auto()
BLUE = auto()
def __str__(self):
return self.name.lower()
# Among:
# - `wavelet`
# - `bilateral`
# - `tv_chambolle`
def denoise(imageNpArray, denoiserName):
skImageRestorationDenoise = getattr(skimage.restoration, f'denoise_{denoiserName}')
match denoiserName:
case 'wavelet':
imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, rescale_sigma=True)
case 'bilateral':
imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
case 'tv_chambolle':
imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, weight=0.2)
return imageDenoisedNpArray
class iterativeMean:
mean = None
numberOfElementsInMean = 0
def add(self, element):
if self.mean is None:
self.mean = element
else:
self.mean = ((self.mean * self.numberOfElementsInMean) + element) / (self.numberOfElementsInMean + 1)
self.numberOfElementsInMean += 1
RAW_IMAGE_FILE_EXTENSIONS = [
'arw',
'nef',
]
def getRawColorChannel(raw, color):
colorDesc = raw.color_desc.decode('ascii')
assert colorDesc == 'RGBG'
assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
# RG
# GB
rawImageVisible = raw.raw_image_visible.copy()
redRawImageVisible = rawImageVisible[::2, ::2]
greenRightRawImageVisible = rawImageVisible[::2, 1::2]
greenBottomRawImageVisible = rawImageVisible[1::2, ::2]
blueRawImageVisible = rawImageVisible[1::2, 1::2]
match color:
case Color.RED:
imageNpArray = redRawImageVisible
case Color.GREEN_RIGHT:
imageNpArray = greenRightRawImageVisible
case Color.GREEN_BOTTOM:
imageNpArray = greenBottomRawImageVisible
case Color.BLUE:
imageNpArray = blueRawImageVisible
return imageNpArray
def isARawImage(imageFilePath):
return any([imageFilePath.lower().endswith(f'.{rawImageFileExtension}') for rawImageFileExtension in RAW_IMAGE_FILE_EXTENSIONS])
def getColorChannel(imageFilePath, color):
if isARawImage(imageFilePath):
with rawpy.imread(imageFilePath) as raw:
imageNpArray = getRawColorChannel(raw, color)
else:
imagePil = Image.open(imageFilePath)
imageNpArray = img_as_float(np.array(imagePil))
return imageNpArray
def escapeFilePath(filePath):
return filePath.replace('/', '_')
def getNewIndex(index, offset):
newIndex = (index - offset) * 2 + offset
return newIndex
def silentTqdm(data, desc = None):
return data
def mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages, progress = False):
colorImage = singleColorChannelImages[list(Color)[0]]
multipleColorsImage = np.empty([dimension * 2 for dimension in colorImage.shape], dtype = np.float64)
'''
Assume Bayer Filter:
RG
GB
'''
multipleColorsImage[::2,::2] = singleColorChannelImages[Color.RED]
multipleColorsImage[1::2,::2] = singleColorChannelImages[Color.GREEN_RIGHT]
multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_BOTTOM]
multipleColorsImage[1::2,1::2] = singleColorChannelImages[Color.BLUE]
return multipleColorsImage
def saveNpArray(fileName, npArray):
with open(f'{fileName}.npy', 'wb') as f:
np.save(f, npArray)
def rescaleRawImageForDenoiser(imageNpArray, minColor, maxColor):
imageNpArray = (imageNpArray - minColor) / (maxColor - minColor)
return imageNpArray
def updateExtremes(imageNpArray, minColor, maxColor):
colorRawImageVisibleMin = imageNpArray.min()
colorRawImageVisibleMax = imageNpArray.max()
if minColor is None or colorRawImageVisibleMin < minColor:
minColor = colorRawImageVisibleMin
if maxColor is None or colorRawImageVisibleMax > maxColor:
maxColor = colorRawImageVisibleMax
return minColor, maxColor
def print(*toPrint):
__builtin__.print(datetime.now(), *toPrint)

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