Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Add algorithms/image_utils/image_utils.py and move there randomGaussianImage and showImageWithMatplotlib

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Benjamin Loison 2024-03-28 16:32:38 +01:00
parent 70ccb094d5
commit 82e7026264
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
2 changed files with 16 additions and 10 deletions
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10
algorithms/image_utils/image_utils.py Normal file
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@ -0,0 +1,10 @@
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
def randomGaussianImage(scale, size):
return np.random.normal(loc = 0, scale = scale, size = size)
def showImageWithMatplotlib(npArray):
plt.imshow(npArray)
plt.show()

16
datasets/fake/generate_dataset.py
View File

@ -10,6 +10,10 @@ 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
from tqdm import tqdm
IMAGE_SIZE = 64
@ -21,13 +25,9 @@ PRNU_FACTOR = 0.1
IMAGE_SIZE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE)
# Generate PRNUs and images of phones.
# Is such `np.maximum` probabilistically correct with our theoretical method? See #19.
def randomGaussianImage(scale):
return np.random.normal(loc = 0, scale = scale, size = IMAGE_SIZE_SHAPE)
imagesWithoutPrnu = [[randomGaussianImage(scale = 1, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
imagesWithoutPrnu = [[randomGaussianImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
prnus = [randomGaussianImage(scale = PRNU_FACTOR) for _ in range(NUMBER_OF_PHONES)]
prnus = [randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_PHONES)]
imagesWithPrnu = [[imageWithoutPrnu + prnus[phoneIndex] for imageWithoutPrnu in imagesWithoutPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
@ -41,10 +41,6 @@ def showImageWithPil(npArray):
npArray = (npArray / npArray.max()) * 255
Image.fromarray(npArray).show()
def showImageWithMatplotlib(npArray):
plt.imshow(npArray)
plt.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')