我需要找到较小的2d数组array1匹配另一个2d数组array2中最接近的数组的位置。
array1的大小为grid_size 46x46至96x96。
array2将更大(184x184)。
我只能访问numpy。
我目前正在尝试使用Tversky formula,但并不局限于此。
效率是最重要的部分,因为它将多次运行。我下面显示的当前解决方案非常慢。
for i in range(array2.shape[0] - grid_size):
for j in range(array2.shape[1] - grid_size):
r[i, j] = np.sum(array2[i:i+grid_size, j:j+grid_size] == array1 ) / (np.sum(array2[i:i+grid_size, j:j+grid_size] != array1 ) + np.sum(Si[i:i+grid_size, j:j+grid_size] == array1 ))
编辑:目的是找到较小图像与另一图像匹配的位置。
[这是基于FFT /卷积的方法,可将欧几里德距离减至最小:
import numpy as np
from numpy import fft
N = 184
n = 46
pad = 192
def best_offs(A,a):
A,a = A.astype(float),a.astype(float)
Ap,ap = (np.zeros((pad,pad)) for _ in "Aa")
Ap[:N,:N] = A
ap[:n,:n] = a
sim = fft.irfft2(fft.rfft2(ap).conj()*fft.rfft2(Ap))[:N-n+1,:N-n+1]
Ap[:N,:N] = A*A
ap[:n,:n] = 1
ref = fft.irfft2(fft.rfft2(ap).conj()*fft.rfft2(Ap))[:N-n+1,:N-n+1]
return np.unravel_index((ref-2*sim).argmin(),sim.shape)
# example
# random picture
A = np.random.randint(0,256,(N,N),dtype=np.uint8)
# random offset
offy,offx = np.random.randint(0,N-n+1,2)
# sub pic at random offset
# randomly flip half of the least significant 75% of all bits
a = A[offy:offy+n,offx:offx+n] ^ np.random.randint(0,64,(n,n))
# reconstruct offset
oyrec,oxrec = best_offs(A,a)
assert offy==oyrec and offx==oxrec
# speed?
from timeit import timeit
print(timeit(lambda:best_offs(A,a),number=100)*10,"ms")
# example with zero a
a[...] = 0
# make A smaller in a matching subsquare
A[offy:offy+n,offx:offx+n]>>=1
# reconstruct offset
oyrec,oxrec = best_offs(A,a)
assert offy==oyrec and offx==oxrec
样品运行:
3.458537160186097 ms