Generalize with a partial image SPLIT_N_X_N

datasets/noise_free_test_images/estimate_prnu.py

@@ -8,7 +8,7 @@ import sys
 
 sys.path.insert(0, '../../algorithms/image_utils/')
 
-from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage
+from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage, getPrnuShownAsSuch
 
 sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
 
@@ -25,15 +25,15 @@ datasetPath = 'no_noise_images'
 # 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
-IMAGE_SIZE_SHAPE = (469, 704)
-IMAGE_SIZE_SHAPE_4x4 = [size // 4 for size in IMAGE_SIZE_SHAPE]
+SPLIT_N_X_N = 1
+
+IMAGE_SIZE_SHAPE = [dimension // SPLIT_N_X_N for dimension in (704, 469)]
 
 np.random.seed(0)
 
-splitNxN = 4
 #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
-prnuPil = Image.open(f'prnu_{"4x4_" if splitNxN == 4 else ""}noise.png').convert('F')
-prnuNpArray = np.array(prnuPil) * PRNU_FACTOR
+prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE) * PRNU_FACTOR
+showImageWithMatplotlib(prnuNpArray)
 
 def isIn256Range(x):
     return 0 <= x and x <= 255
@@ -46,8 +46,8 @@ for imageName in os.listdir(datasetPath):
         imageWithoutPrnuPil = Image.open(imagePath).convert('F')
         imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
 
-        m = imageWithoutPrnuNpArray.shape[0] // splitNxN
-        n = imageWithoutPrnuNpArray.shape[1] // splitNxN
+        m = imageWithoutPrnuNpArray.shape[0] // SPLIT_N_X_N
+        n = imageWithoutPrnuNpArray.shape[1] // SPLIT_N_X_N
 
         imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x : x + m, y : y + n] for x in range(0, imageWithoutPrnuNpArray.shape[0], m) for y in range(0, imageWithoutPrnuNpArray.shape[1], n)]
         for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles: