Add PRNU showing such

datasets/noise_free_test_images/estimate_prnu.py

@@ -12,20 +12,20 @@ from image_utils import showImageWithMatplotlib, randomGaussianImage
 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 = 255 * 0.1
+PRNU_FACTOR = 0.1
 IMAGE_SIZE_SHAPE = (469, 704)
 
 np.random.seed(0)
 
-# Should first try with an image showing `PRNU`.
-prnu = randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
+#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
+prnuPil = Image.open('prnu.png').convert('F')
+prnuNpArray = np.array(prnuPil) * PRNU_FACTOR
 
 for imageName in os.listdir(datasetPath):
     if imageName.endswith('.png'):
-        #print(imageName)
         imagePath = f'{datasetPath}/{imageName}'
         imagePil = Image.open(imagePath).convert('F')
         imageNpArray = np.array(imagePil)
-        showImageWithMatplotlib(imageNpArray)
-        showImageWithMatplotlib(imageNpArray + prnu)
+        #showImageWithMatplotlib(imageNpArray)
+        showImageWithMatplotlib(imageNpArray + prnuNpArray)
         break