First iteration of fake dataset generation

algorithms/context-adaptive_interpolator.py

@@ -1,84 +0,0 @@
-# Based on https://web.archive.org/web/20231116015653/http://nrl.northumbria.ac.uk/id/eprint/29339/1/Paper_accepted.pdf IV. B..
-
-from PIL import Image
-from statistics import mean, median
-from tqdm import tqdm
-
-# What about other color channels? See #11.
-MODE = 'L'
-
-IImage = Image.open('9f04e2005fddb9d5512e2f42a3b826b019755717.jpg').convert(MODE)
-I = IImage.load()
-
-rImage = Image.new(MODE, (IImage.size[0] - 2, IImage.size[1] - 2))
-r = rImage.load()
-
-# This threshold is debatable. See #13.
-THRESHOLD = 20
-
-# Equation (10)
-# Accelerate computation. See #15.
-for m in range(1, IImage.size[0] - 1):
-    for n in range(1, IImage.size[1] - 1):
-            e = I[m, n + 1]
-            se = I[m + 1, n + 1]
-            s = I[m + 1, n]
-            sw = I[m + 1, n - 1]
-            w = I[m, n - 1]
-            nw = I[m - 1, n - 1]
-            no = I[m - 1, n]
-            ne = I[m - 1, n + 1]
-            A = [e, se, s, sw, w, nw, no, ne]
-            if max(A) - min(A) <= THRESHOLD:
-                newPixel = I[m, n] - mean(A)
-            elif abs(e - w) - abs(no - s) > THRESHOLD:
-                newPixel = I[m, n] - (s + no) / 2
-            elif abs(s - no) - abs(e - w) > THRESHOLD:
-                newPixel = I[m, n] - (e + w) / 2
-            elif abs(sw - ne) - abs(se - nw) > THRESHOLD:
-                newPixel = I[m, n] - (se + nw) / 2
-            elif abs(se - nw) - abs(sw - ne) > THRESHOLD:
-                newPixel = I[m, n] - (sw + ne) / 2
-            else:
-                newPixel = I[m, n] - median(A)
-            r[m - 1, n - 1] = round(newPixel)
-
-# Why need to rotate the image? See #14.
-rImage.rotate(-90).show()
-
-Q = 3
-# $\sigma_0^2$ is the noise variance.
-sigma_0 = 9 ** 0.5
-
-h_wImage = Image.new(MODE, (rImage.size[0], rImage.size[1]))
-h_wImagePixels = h_wImage.load()
-
-# Wiener filter.
-def h_w(hImage, h, i, j):
-    # Equation (7)
-    return h[i, j] * sigma(hImage, h, i, j) / (sigma(hImage, h, i, j) + sigma_0 ** 2)
-
-# Minimum of the considered variances.
-def sigma(hImage, h, i, j):
-    # Equation (9)
-    return sigma_q(hImage, h, i, j, Q)
-
-def getPixelIndexesAround(i, numberOfPixelsInEachDirection):
-    return range(i - numberOfPixelsInEachDirection, i + numberOfPixelsInEachDirection + 1)
-
-# Expand image with border pixels.
-def getPixelWithinImage(z, upperBound):
-    return max(min(z, upperBound - 1), 0)
-
-# Local variance obtained by Maximum A Posteriori (MAP).
-def sigma_q(hImage, h, i, j, q):
-    # Equation (8)
-    numberOfPixelsInEachDirection = (q - 1) // 2
-    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]))
-
-for i in tqdm(range(rImage.size[0])):
-    for j in range(rImage.size[1]):
-        h_wImagePixels[i, j] = round(h_w(rImage, r, i, j))
-
-h_wImage.rotate(-90).show()

datasets/fake/generate_dataset.py

@@ -1,13 +1,24 @@
-from PIL import Image
-import numpy
+import numpy as np
+from matplotlib import pyplot as plt
+import sys
+
+sys.path.insert(0, '../../algorithms/distance/')
+
+from rmsdiff import rmsdiff
 
 IMAGE_SIZE = 64
-MODE = 'L'
 
-IImage = Image.new(MODE, (IMAGE_SIZE, IMAGE_SIZE))
-I = IImage.load()
-for y in range(IMAGE_SIZE):
-    for x in range(IMAGE_SIZE):
-        I[y, x] = numpy.random.normal(loc = 0, scale = 1, size = 1)
+def randomImage(scale):
+    # Is `np.clip` necessary?
+    return np.round(np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE)))
 
-I.show()
+prnu = randomImage(scale = 1)
+
+plt.imshow(prnu)
+plt.show()
+
+images = [randomImage(scale = 10) + prnu for _ in range(10)]
+
+for image in images:
+    print(rmsdiff(image, prnu))
+    print(rmsdiff(contextAdaptiveInterpolator(image), prnu))