Python中的快速互相关方法

Question

我最近一直试图找到一种快速有效的方法,使用Python语言在两个数组之间执行互相关检查.经过一番阅读,我发现了以下两个选项:

1. 该NumPy.correlate()方法在大型数组时速度太慢.
2. 这种cv.MatchTemplate()方法似乎要快得多.

出于显而易见的原因,我选择了第二个选项.我试图执行以下代码:

import scipy
import cv

image = cv.fromarray(scipy.float32(scipy.asarray([1,2,2,1])),allowND=True)
template = cv.fromarray(scipy.float32(scipy.asarray([2,2])),allowND=True)
result = cv.fromarray(scipy.float32(scipy.asarray([0,0,0])),allowND=True)
cv.MatchTemplate(image,template,result,cv.CV_TM_CCORR)

即使这段代码非常简单,它也会抛出下一个错误:

OpenCV Error: Bad flag (parameter or structure field) (Unrecognized or unsupported array type) in cvGetMat, file /builddir/build/BUILD/OpenCV-2.1.0/src/cxcore/cxarray.cpp, line 2476
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
cv.error: Unrecognized or unsupported array type

经过几个小时的挫败尝试,我仍然被困住了!有人有什么建议吗？

顺便说一句,这是我的Python版本输出:

Python 2.7 (r27:82500, Sep 16 2010, 18:03:06) 
[GCC 4.5.1 20100907 (Red Hat 4.5.1-3)] on linux2
Type "help", "copyright", "credits" or "license" for more information.

谢谢你们!

Answer 1

你不太可能比使用基于fft的相关方法快得多.

import numpy
from scipy import signal

data_length = 8192

a = numpy.random.randn(data_length)
b = numpy.zeros(data_length * 2)

b[data_length/2:data_length/2+data_length] = a # This works for data_length being even

# Do an array flipped convolution, which is a correlation.
c = signal.fftconvolve(b, a[::-1], mode='valid') 

# Use numpy.correlate for comparison
d = numpy.correlate(a, a, mode='same')

# c will be exactly the same as d, except for the last sample (which 
# completes the symmetry)
numpy.allclose(c[:-1], d) # Should be True

现在进行时间比较:

In [12]: timeit b[data_length/2:data_length/2+data_length] = a; c = signal.fftconvolve(b, a[::-1], mode='valid')
100 loops, best of 3: 4.67 ms per loop

In [13]: timeit d = numpy.correlate(a, a, mode='same')
10 loops, best of 3: 69.9 ms per loop

如果您可以处理循环关联,则可以删除副本.时间差将随着增加而data_length增加.