Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Use np.clip

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Benjamin Loison 2024-07-04 04:18:13 +02:00
parent 98426db9dd
commit 3a4100b779
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
1 changed files with 3 additions and 4 deletions
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7
datasets/noise_free_test_images/estimate_prnu.py
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@ -34,9 +34,8 @@ 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, vExtremes = [None, None]):
vMin, vMax = vExtremes
imShow = axis.imshow(im, vmin = vMin, vmax = vMax)
def axisImShow(axis, im):
imShow = axis.imshow(im)
plt.colorbar(imShow, label = 'Intensity', ax = axis, orientation = 'horizontal')
for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
@ -82,7 +81,7 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))
axis[3].set_title('Actual Gaussian noised PRNU\nClipped between -1 and 1')
axisImShow(axis[3], prnuNpArray, [-1, 1])
axisImShow(axis[3], np.clip(prnuNpArray, -1, 1))
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))