<p>为了了解速度,使用numpy时,w与大窗口大小相同的子问题更快(也更简洁):</p>
<pre><code>a= '''import numpy as np
## Generate some test data
CurPatch = np.random.randint(20, size=(3, 3))
Data = np.random.randint(20,size=(30,30))
def best(CurPatch,Data):
# Current Location
x,y = 15,15
# Initialise Best Match
bestcost = 999.0
bestx = 0;besty=0
for Wy in xrange(-14,14):
for Wx in xrange(-14,14):
Ywj,Ywi = y+Wy,x+Wx
cost = 0.0
for py in xrange(3):
for px in xrange(3):
cost += (Data[Ywj+py-1,Ywi+px-1] - CurPatch[py,px])**2
if cost < bestcost:
bestcost = cost
besty,bestx = Ywj,Ywi
return besty,bestx,bestcost
def minimize(CurPatch,W):
max_sum=999
s= CurPatch.shape[0]
S= W.shape[0]
for i in range(0,S-s):
for j in range(0,S-s):
running= np.sum(np.square((W[i:i+3,j:j+3]-CurPatch)))
if running<max_sum:
max_sum=running
x=i+1;y=j+1
return x,y,max_sum
'''
import timeit
print min(timeit.Timer('minimize(CurPatch,Data)', a).repeat(7, 10))
print min(timeit.Timer('best(CurPatch,Data)', a).repeat(7, 10))
</code></pre>