Fix #10: Implement Wiener filter

algorithms/context-adaptive_interpolator.py

@@ -2,6 +2,7 @@
 
 from PIL import Image
 from statistics import mean, median
+from tqdm import tqdm
 
 # What about other color channels? See #11.
 MODE = 'L'
@@ -42,29 +43,38 @@ for m in range(1, IImage.size[0] - 1):
                 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 = sqrt(9)
+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(i, j):
+def h_w(h, i, j):
     # Equation (7)
-    return h[i, j] * sigma(i, j) / (sigma(i, j) + sigma_0 ** 2)
+    return h[i, j] * sigma(h, i, j) / (sigma(h, i, j) + sigma_0 ** 2)
 
 # Minimum of the considered variances.
-def sigma(i, j):
+def sigma(h, i, j):
     # Equation (9)
-    return sigma_q(i, j, Q)
+    return sigma_q(h, i, j, Q)
 
 def getPixelIndexesAround(i, numberOfPixelsInEachDirection):
     return range(i - numberOfPixelsInEachDirection, i + numberOfPixelsInEachDirection)
 
 # Local variance obtained by Maximum A Posteriori (MAP).
-def sigma_q(i, j, q):
+def sigma_q(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[x, z] ** 2 - sigma_0 ** 2 for (x, z) in B_q]))
 
-# Why need to rotate the image? See #14.
-rImage.rotate(-90).show()
+for i in tqdm(range(rImage.size[0])):
+    for j in range(rImage.size[1]):
+        h_wImagePixels[i, j] = round(h_w(r, i, j))
+
+h_wImage.rotate(-90).show()