First PRNU distance test

algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py

@@ -6,14 +6,15 @@ from wiener_filter import wienerFilter
 
 # Assume greyscale PIL image passed.
 # What about other color channels? See #11.
-def contextAdaptiveInterpolator(I, IImage):
+def contextAdaptiveInterpolator(I, IImage, showProgress = False):
     rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
     r = rImage.load()
 
     # This threshold is debatable. See #13.
     THRESHOLD = 20
 
-    print('before for loops')
+    if showProgress:
+        print('before for loops')
     # Equation (10)
     # Accelerate computation. See #15.
     for m in range(1, IImage.size[0] - 1):
@@ -40,7 +41,8 @@ def contextAdaptiveInterpolator(I, IImage):
                 else:
                     newPixel = I[m, n] - median(A)
                 r[m - 1, n - 1] = round(newPixel)
-    print('after for loops')
+    if showProgress:
+        print('after for loops')
 
     # Why need to rotate the image? See #14.
     #rImage.rotate(-90).show()
@@ -49,5 +51,6 @@ def contextAdaptiveInterpolator(I, IImage):
     # $\sigma_0^2$ is the noise variance.
     sigma_0 = 9 ** 0.5
 
-    print('before wiener filter')
-    return wienerFilter(r, rImage, Q, sigma_0)
+    if showProgress:
+        print('before wiener filter')
+    return wienerFilter(r, rImage, Q, sigma_0, showProgress)

algorithms/context_adaptive_interpolator/wiener_filter.py

@@ -1,7 +1,7 @@
 from PIL import Image
 from tqdm import tqdm
 
-def wienerFilter(r, rImage, Q, sigma_0):
+def wienerFilter(r, rImage, Q, sigma_0, showProgress):
     h_wImage = Image.new('L', (rImage.size[0], rImage.size[1]))
     h_wImagePixels = h_wImage.load()
 
@@ -29,10 +29,13 @@ def wienerFilter(r, rImage, Q, sigma_0):
         B_q = [(x, z) for x in getPixelIndexesAround(i, numberOfPixelsInEachDirection) for z in getPixelIndexesAround(j, numberOfPixelsInEachDirection)]
         return max(0, (1 / q ** 2) * sum([h[getPixelWithinImage(x, hImage.size[0]), getPixelWithinImage(z, hImage.size[1])] ** 2 - sigma_0 ** 2 for (x, z) in B_q]))
 
-    print('wiener filter start for loops')
-    for i in tqdm(range(rImage.size[0])):
+    if showProgress:
+        print('wiener filter start for loops')
+    rImageSize0Range = range(rImage.size[0])
+    for i in tqdm(rImageSize0Range) if showProgress else rImageSize0Range:
         for j in range(rImage.size[1]):
             h_wImagePixels[i, j] = round(h_w(rImage, r, i, j))
-    print('wiener filter end for loops')
+    if showProgress:
+        print('wiener filter end for loops')
 
     return h_wImage.rotate(-90)