ImProc ass1: Finished assignment 1 code.

improc/ass1/linfilters.py

+from numpy import *
+
+def linfilter1(f, w):
+    g = empty(f.shape, dtype=f.dtype)
+    M, N = f.shape
+    K, L = (array(w.shape) - 1) / 2
+
+    def value(i, j):
+        """The function returning the value f[i,j] in case
+        (i,j) in an index 'in the image', otherwise it return 0"""
+        if i < 0 or i >= M or j < 0 or j >= N:
+            return 0
+
+        return f[i, j]
+
+    for j in xrange(N):
+        for i in xrange(M):
+            summed = 0
+            for k in xrange(-K, K + 1):
+                for l in xrange(-L, L + 1):
+                    summed += value(i + k, j + l) * w[k + K, l + L]
+            g[i, j] = summed
+
+    return g
+
+def linfilter2(f, w):
+    """Linear Correlation based on neigborhood processing without loops"""
+    g = empty(f.shape, dtype=f.dtype)
+    M, N = f.shape
+    K, L = (array(w.shape) - 1) / 2
+
+    for j in xrange(N):
+        for i in xrange(M):
+            ii = minimum(M - 1, maximum(0, arange(i - K, i + K + 1)))
+            jj = minimum(N - 1, maximum(0, arange(j - L, j + L + 1)))
+            nbh = f[ix_(ii, jj)]
+            g[i, j] = (nbh * w).sum()
+
+    return g
+
+def linfilter3(f, w):
+    """Linear Correlation using Translates of Images"""
+    M, N = f.shape
+    K, L = (array(w.shape) - 1) / 2
+
+    di, dj = meshgrid(arange(-L, L + 1), arange(-K, K + 1))
+    didjw = zip(di.flatten(), dj.flatten(), w.flatten())
+
+    def translate(di,dj):
+        ii = minimum(M - 1, maximum(0, di + arange(M)))
+        jj = minimum(N - 1, maximum(0, dj + arange(N)))
+        return f[ix_(ii, jj)]
+
+    r = 0 * f
+    for di, dj, weight in didjw:
+        r += weight * translate(di, dj)
+
+    return r
+
+def linfilter4(f, w):
+    return correlate(f, w, mode='nearest')
+

improc/ass1/timer.py

+from linfilters import *
+from scipy.ndimage.interpolation import zoom
+from pylab import *
+from time import time
+
+repeat = 1
+w = (3, 11)
+
+f = zoom(imread('cameraman.png'), .25)
+methods = [linfilter1, linfilter2, linfilter3]#, linfilter4]
+timings = [[] for i in methods]
+
+x = range(w[0], w[1] + 1, 2)
+
+for i, method in enumerate(methods):
+    for j in x:
+        weight = ones((j, j)) / (j ** 2)
+        t = 0
+
+        for k in xrange(repeat):
+            start = time()
+            method(f, weight)
+            t += time() - start
+
+        timings[i].append(t / repeat)
+
+for times in timings:
+    plot(x, times, 'o-')
+
+semilogy()
+show()
+
+#subplot(1, 3, 1)
+#imshow(a)
+#f = zoom(a, .25)
+#g = linfilter1(f, ones((5, 5)) / 25)
+#subplot(1, 3, 2)
+#imshow(f)
+#subplot(1, 3, 3)
+#imshow(g)
+#show()