Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Forcing color map

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Benjamin Loison 2024-07-04 04:08:10 +02:00
parent 9d20940cb1
commit 5bc0337dc8
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 = 0.2
NOISE_FACTOR = 0.1
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, cmap = None):
imShow = axis.imshow(im, cmap = cmap)
def axisImShow(axis, im, cMap = (None, None)):
vMin, vMax = cMap
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):
@ -74,23 +75,23 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
images = [imageWithoutPrnuNpArrayTile, imageWithoutPrnuNpArray + imageNoise, imageWithoutPrnuNpArray + prnuNpArray + imageNoise]
vMin = np.min(images)#min([np.min(image) for image in [imageWithoutPrnuNpArrayTile, imageWithoutPrnuNpArray + imageNoise, imageWithoutPrnuNpArray + prnuNpArray + imageNoise]])
vMax = np.max(images)
cmap = (vMin, vMax)
cMap = (vMin, vMax)
#print(f'{vMin=}')
axis[0].set_title('First image without noise')
axisImShow(axis[0], imageWithoutPrnuNpArrayTile, cmap = cmap)
axisImShow(axis[0], imageWithoutPrnuNpArrayTile, cMap = cMap)
axis[1].set_title('First image Gaussian noise')
axisImShow(axis[1], imageNoise)
axis[2].set_title('First image with Gaussian noise')
axisImShow(axis[2], imageWithoutPrnuNpArray + imageNoise, cmap = cmap)
axisImShow(axis[2], imageWithoutPrnuNpArray + imageNoise, cMap = cMap)
axis[3].set_title('Actual Gaussian noised PRNU')
axisImShow(axis[3], prnuNpArray)
axes[1][0].set_title('First image with Gaussian noise and PRNU')
axisImShow(axes[1][0], imageWithoutPrnuNpArray + prnuNpArray + imageNoise, cmap = cmap)
axisImShow(axes[1][0], imageWithoutPrnuNpArray + prnuNpArray + imageNoise, cMap = cMap)
isFirstImage = False
#assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'