最近我一直在处理8x8像素块的图像数据。通常的做法是使用嵌套的for循环来提取这些块,例如:
for y in xrange(0,height,8):
for x in xrange(0,width,8):
d = image_data[y:y+8,x:x+8]
# further processing on the 8x8-block
我不禁想知道是否有一种方法可以向量化此操作或使用numpy/scipy的其他方法代替?有没有某种迭代器?
一个最小工作示例(MWE)1:
#!/usr/bin/env python
import sys
import numpy as np
from scipy.fftpack import dct, idct
import scipy.misc
import matplotlib.pyplot as plt
def dctdemo(coeffs=1):
unzig = np.array([
0, 1, 8, 16, 9, 2, 3, 10,
17, 24, 32, 25, 18, 11, 4, 5,
12, 19, 26, 33, 40, 48, 41, 34,
27, 20, 13, 6, 7, 14, 21, 28,
35, 42, 49, 56, 57, 50, 43, 36,
29, 22, 15, 23, 30, 37, 44, 51,
58, 59, 52, 45, 38, 31, 39, 46,
53, 60, 61, 54, 47, 55, 62, 63])
lena = scipy.misc.lena()
width, height = lena.shape
# reconstructed
rec = np.zeros(lena.shape, dtype=np.int64)
# Can this part be vectorized?
for y in xrange(0,height,8):
for x in xrange(0,width,8):
d = lena[y:y+8,x:x+8].astype(np.float)
D = dct(dct(d.T, norm='ortho').T, norm='ortho').reshape(64)
Q = np.zeros(64, dtype=np.float)
Q[unzig[:coeffs]] = D[unzig[:coeffs]]
Q = Q.reshape([8,8])
q = np.round(idct(idct(Q.T, norm='ortho').T, norm='ortho'))
rec[y:y+8,x:x+8] = q.astype(np.int64)
plt.imshow(rec, cmap='gray')
plt.show()
if __name__ == '__main__':
try:
c = int(sys.argv[1])
except ValueError:
sys.exit()
else:
if 1 <= int(sys.argv[1]) <= 64:
dctdemo(int(sys.argv[1]))
注释: