Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Using matplotlib clipping

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Benjamin Loison 2024-07-04 04:11:12 +02:00
parent d24c883a92
commit 98426db9dd
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
1 changed files with 8 additions and 7 deletions
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15
datasets/noise_free_test_images/estimate_prnu.py
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@ -24,7 +24,7 @@ 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
NOISE_FACTOR = 0.2
np.random.seed(0)
@ -34,8 +34,9 @@ SPLIT_N_X_N_S = [1, 2, 4]
fig, axes = plt.subplots(2, 4)
fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')
def axisImShow(axis, im):
imShow = axis.imshow(im)
def axisImShow(axis, im, vExtremes = [None, None]):
vMin, vMax = vExtremes
imShow = axis.imshow(im, vmin = vMin, vmax = vMax)
plt.colorbar(imShow, label = 'Intensity', ax = axis, orientation = 'horizontal')
for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
@ -77,13 +78,13 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
axis[1].set_title('First image Gaussian noise')
axisImShow(axis[1], imageNoise)
axis[2].set_title('First image with Gaussian noise')
axis[2].set_title('First image with Gaussian noise\nClipped between 0 and 255')
axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))
axis[3].set_title('Actual Gaussian noised PRNU')
axisImShow(axis[3], prnuNpArray)
axis[3].set_title('Actual Gaussian noised PRNU\nClipped between -1 and 1')
axisImShow(axis[3], prnuNpArray, [-1, 1])
axes[1][0].set_title('First image with Gaussian noise and PRNU')
axes[1][0].set_title('First image with Gaussian noise and PRNU\nClipped between 0 and 255')
axisImShow(axes[1][0], np.clip(imageWithoutPrnuNpArray + prnuNpArray + imageNoise, 0, 255))
isFirstImage = False