您可以使用众所周知的sliding window stride trick来加速计算。它将两个“虚拟维度”添加到数组的末尾而不复制数据,然后计算它们之间的差异。
请注意,在您的代码中,im[j-w:j+w, ..]
超过索引j-w,j-w+1,...,j+w-1
,最后一个是排他性的,您可能并不是这个意思。此外,差异大于uint8范围,所以最终以整数环绕结束。
import numpy as np
import time
np.random.seed(1234)
img = (np.random.rand(200, 200)*256).astype(np.uint8)
def sliding_window(a, window, axis=-1):
shape = list(a.shape) + [window]
shape[axis] -= window - 1
if shape[axis] < 0:
raise ValueError("Array too small")
strides = a.strides + (a.strides[axis],)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
def sliding_img_var(img, window):
if window <= 0:
raise ValueError("invalid window size")
buf = sliding_window(img, 2*window, 0)
buf = sliding_window(buf, 2*window, 1)
out = np.zeros(img.shape, dtype=np.float32)
np.var(buf[:-1,:-1], axis=(-1,-2), out=out[window:-window,window:-window])
return out
def looping_img_var(im, w):
nx, ny = img.shape
varianceMatrix = np.zeros(im.shape, np.float32)
for i in range(w,nx-w):
for j in range(w,ny-w):
sampleframe = im[j-w:j+w, i-w:i+w]
variance = np.var(sampleframe)
varianceMatrix[j][i] = variance
return varianceMatrix
np.set_printoptions(linewidth=1000, edgeitems=5)
start = time.time()
print(sliding_img_var(img, 1))
time_sliding = time.time() - start
start = time.time()
print(looping_img_var(img, 1))
time_looping = time.time() - start
print("duration: sliding: {0} s, looping: {1} s".format(time_sliding, time_looping))
这里是我机器上输出的最后一行,显示了加速:'duration:sliding:0.00510311126709 s,looping:0.955919027328 s'。 – 2016-03-28 15:19:34