Fix #12: Use a new image (#15)

algorithms/context-adaptive_interpolator.py

@@ -1,19 +1,24 @@
-# Based on https://web.archive.org/web/20231116015653/http://nrl.northumbria.ac.uk/id/eprint/29339/1/Paper_accepted.pdf
+# 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
 
-# What about other color channels?
-image = Image.open('9f04e2005fddb9d5512e2f42a3b826b019755717.jpg').convert('L')
-r = image.load()
-I = image.copy().load()
-DEFAULT_COLOR = 255
-# This threshold is debatable.
+# 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
-# How to manage the border of the image?
+
 # Equation (10)
-for m in range(1, image.size[0] - 1):
-    for n in range(1, image.size[1] - 1):
+# 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]
@@ -35,7 +40,7 @@ for m in range(1, image.size[0] - 1):
                 newPixel = I[m, n] - (sw + ne) / 2
             else:
                 newPixel = I[m, n] - median(A)
-            r[m, n] = round(newPixel)
+            r[m - 1, n - 1] = round(newPixel)
 
 Q = 3
 
@@ -48,6 +53,9 @@ def sigma(i, j):
     return sigma_q(i, j, Q)
 
 def sigma_q(i, j, q):
-    # Equation
+    # Equation (8)
+    # TODO:
+    pass
 
-image.rotate(-90).show()
+# Why need to rotate the image? See #14.
+rImage.rotate(-90).show()