Not correct generalized SPLIT_N_X_N due to imageWithoutPrnuNpArrayTile

datasets/noise_free_test_images/estimate_prnu.py

@@ -25,7 +25,7 @@ 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
-SPLIT_N_X_N = 1
+SPLIT_N_X_N = 4
 
 IMAGE_SIZE_SHAPE = [dimension // SPLIT_N_X_N for dimension in (704, 469)]
 
@@ -33,7 +33,6 @@ np.random.seed(0)
 
 #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
 prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE) * PRNU_FACTOR
-showImageWithMatplotlib(prnuNpArray)
 
 def isIn256Range(x):
     return 0 <= x and x <= 255
@@ -46,11 +45,14 @@ for imageName in os.listdir(datasetPath):
         imageWithoutPrnuPil = Image.open(imagePath).convert('F')
         imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
 
-        m = imageWithoutPrnuNpArray.shape[0] // SPLIT_N_X_N
-        n = imageWithoutPrnuNpArray.shape[1] // SPLIT_N_X_N
+        m = IMAGE_SIZE_SHAPE[1]
+        n = IMAGE_SIZE_SHAPE[0]
 
         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:
+            #print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
+            #if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
+            #    continue
             imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
             imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
             #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'