improc ass4: Source code cleanup.

improc/ass4/canny.py

@@ -7,7 +7,7 @@ from gauss import Gauss1, gD
 
 def in_image(p, F):
     """Check if given pixel coordinates p are within the bound of image F."""
-    return p[0] >= 0 and p[1] >= 0 and p[0] < F.shape[0] and p[1] < F.shape[1]
+    return 0 <= p[0] < F.shape[0] and 0 <= p[1] < F.shape[1]
 
 def canny(F, s, Tl=None, Th=None):
     """Apply the Canny Edge Detection algorithm with Gauss scale s to an
@@ -28,24 +28,24 @@ def canny(F, s, Tl=None, Th=None):
             p = (y, x)
 
             # Gradient norm and rounded angle
-            G[p] = norm(append(Gx[p], Gy[p]))
+            G[p] = norm(append(Gy[p], Gx[p]))
             A[p] = int(round(arctan2(Gy[p], Gx[p]) * 4 / pi + 1)) % 4
 
     # Non-maximum suppression
     E = zeros(F.shape)
 
-    for x in xrange(F.shape[0]):
-        for y in xrange(F.shape[1]):
-            g = G[x, y]
-            a = A[x, y]
-            compare = [((x, y - 1), (x, y + 1)), ((x - 1, y - 1), \
-                       (x + 1, y + 1)), ((x - 1, y), (x + 1, y)), \
-                       ((x + 1, y - 1), (x - 1, y + 1))]
+    for y in xrange(F.shape[0]):
+        for x in xrange(F.shape[1]):
+            g = G[y, x]
+            a = A[y, x]
+            compare = [((y, x - 1), (y, x + 1)), ((y - 1, x - 1), \
+                       (y + 1, x + 1)), ((y - 1, x), (y + 1, x)), \
+                       ((y + 1, x - 1), (y - 1, x + 1))]
             na, nb = compare[a]
 
             if (not in_image(na, G) or g > G[na]) \
                     and (not in_image(nb, G) or g > G[nb]):
-                E[x, y] = g
+                E[y, x] = g
 
     # Only execute hysteresis thresholding if the thresholds are specified
     if Tl is None or Th is None:
@@ -57,29 +57,29 @@ def canny(F, s, Tl=None, Th=None):
     T = zeros(F.shape, dtype=bool)
 
     # Clear image borders
-    for x in xrange(F.shape[0]):
-        E[x, 0] = E[x, F.shape[1] - 1] = 0
+    for y in xrange(F.shape[0]):
+        E[x, 0] = E[y, F.shape[1] - 1] = 0
 
-    for y in xrange(1, F.shape[1] - 1):
-        E[0, y] = E[F.shape[0] - 1, y] = 0
+    for x in xrange(x, F.shape[1] - 1):
+        E[0, x] = E[F.shape[0] - 1, x] = 0
 
-    def follow_nb(x, y):
+    def follow_nb(y, x):
         """Follow the neighbouring pixels of an edge pixel in E recursively."""
-        if T[x, y]:
+        if T[y, x]:
             return
 
-        T[x, y] = True
+        T[y, x] = True
 
-        for nx in xrange(-1, 2):
-            for ny in xrange(-1, 2):
-                if (nx != 0 or ny != 0) and E[nx, ny] > Tl:
-                    follow_nb(nx, ny)
+        for ny in xrange(-1, 2):
+            for nx in xrange(-1, 2):
+                if (ny or nx) and E[ny, nx] > Tl:
+                    follow_nb(ny, nx)
 
     # Follow edges that have a starting value above Th
-    for x in xrange(F.shape[0]):
-        for y in xrange(F.shape[1]):
-            if E[x, y] > Th:
-                follow_nb(x, y)
+    for y in xrange(F.shape[0]):
+        for x in xrange(F.shape[1]):
+            if E[y, x] > Th:
+                follow_nb(y, x)
 
     return E, T
 

improc/ass4/gauss.py

@@ -31,12 +31,17 @@ def Gauss(s):
     return W / W.sum()
 
 def f_gauss(x, s):
+    """Return the Gaussian function for a given x and scale."""
     return exp(-(x ** 2 / (2 * s ** 2))) / (sqrt(2 * pi) * s)
 
 def f_gauss_der_1(x, s):
+    """Return the first derivative of the Gaussian function for a given x
+    and scale."""
     return -x * exp(-(x ** 2 / (2 * s ** 2))) / (sqrt(2 * pi) * s ** 3)
 
 def f_gauss_der_2(x, s):
+    """Return the second derivative of the Gaussian function for a given x
+    and scale."""
     return (x ** 2 - s ** 2) * exp(-(x ** 2 / (2 * s ** 2))) \
             / (sqrt(2 * pi) * s ** 5)