Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Plot an exhaustive curve to make sure that results are not just by chance

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Benjamin Loison 2024-04-02 23:58:41 +02:00
parent 631ed6de34
commit aafb7ebc92
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
1 changed files with 9 additions and 11 deletions
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20
datasets/raise/split_and_compare_prnus_of_subgroups.py
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@ -14,19 +14,16 @@ sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
NUMBER_OF_SUBGROUPS = 2
fig, axes = plt.subplots(NUMBER_OF_SUBGROUPS, 1)
fig.suptitle(f'PRNU estimation and comparison for {NUMBER_OF_SUBGROUPS} subgroups with different number of flat-images')
IMAGES_FOLDER = 'flat-field/TIF'
imagesFileNames = os.listdir(IMAGES_FOLDER)
numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
numberOfImagesThresholds = [1, 5, 50]
numberOfImagesThresholds = range(1, numberOfImagesPerSubgroup + 1)
# Assume random image order to not introduce a bias.
subgroupsPrnuEstimatesNpArray = []
for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
axis = axes[subgroupIndex]
imagesPrnuEstimateNpArray = []
for imageFileName in tqdm(imagesFileNames[numberOfImagesPerSubgroup * subgroupIndex : numberOfImagesPerSubgroup * (subgroupIndex + 1)]):
imagePath = f'{IMAGES_FOLDER}/{imageFileName}'
@ -36,17 +33,18 @@ for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
subgroupPrnuEstimateNpArray = []
# Not efficient mean computation.
for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
subgroupPrnuEstimateNpArray += [np.array(imagesPrnuEstimateNpArray[:numberOfImages]).mean(axis = 0)]
subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray]
'''
axis[numberOfImagesIndex].set_title(f'PRNU estimate for subgroup {subgroupIndex} with ')
axis[numberOfImagesIndex].imshow(cameraPrnuEstimateNpArray)
'''
rmss = []
for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
rms = rmsDiffNumpy(subgroupsPrnuEstimatesNpArray[0][numberOfImagesIndex], subgroupsPrnuEstimatesNpArray[1][numberOfImagesIndex])
print(numberOfImages, round(rms, 4))
rmss += [rms]
#plt.show()
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()