Compare commits

..

5 Commits

Author SHA1 Message Date
3a4100b779
Use np.clip 2024-07-04 04:18:13 +02:00
98426db9dd
Using matplotlib clipping 2024-07-04 04:11:12 +02:00
d24c883a92
Restore clipping
6f961a46cf
2024-07-04 04:08:36 +02:00
5bc0337dc8
Forcing color map 2024-07-04 04:08:10 +02:00
9d20940cb1
cmap try 2024-07-04 04:03:54 +02:00

View File

@ -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)
@ -77,13 +77,13 @@ 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], np.clip(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