在MATLAB中有blkproc (blockproc)对数据处理, 在python下没找到对...

shaoheshaohe

在MATLAB中有blkproc (blockproc)对数据处理, 在python下没找到对应的Function,

这里利用numpy 的split(hsplit和vsplit) 对数据分块处理成8x8的小块, 然后在利用OpenCV的dct函数做变换, 同时利用idct 验证数据变换是否正确.

import numpy as np
import cv2

a = np.arange(256).reshape((16,16))
print("ori data: \n{}".format(a))
m,n = a.shape
print(m,n)

# Y = np.zeros(256).reshape((16,16))

hdata = np.vsplit(a,n/8) # 垂直分成高度度为8 的块
for i in range(0, n//8):
     blockdata = np.hsplit(hdata,m/8)
     #垂直分成高度为8的块后,在水平切成长度是8的块, 也就是8x8 的块
     for j in range(0, m//8):
         block = blockdata[j]
         print("block[{},{}] data \n{}".format(i,j,blockdata[j]))
         Yb = cv2.dct(block.astype(np.float))
         print("dct data\n{}".format(Yb))
         iblock = cv2.idct(Yb)
         print("idct data\n{}".format(iblock))

以下是最后个8x8块的数据:

block[1,1] data
[[136 137 138 139 140 141 142 143]
  [152 153 154 155 156 157 158 159]
  [168 169 170 171 172 173 174 175]
  [184 185 186 187 188 189 190 191]
  [200 201 202 203 204 205 206 207]
  [216 217 218 219 220 221 222 223]
  [232 233 234 235 236 237 238 239]
  [248 249 250 251 252 253 254 255]]
dct data
[[ 1.56400000e+03 -1.82216412e+01  0.00000000e+00 -1.90481783e+00
    0.00000000e+00 -5.68239222e-01  0.00000000e+00 -1.43407825e-01]
  [-2.91546259e+02  0.00000000e+00  0.00000000e+00  0.00000000e+00
    0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
    0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [-3.04770852e+01  0.00000000e+00  0.00000000e+00  0.00000000e+00
    0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
    0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [-9.09182756e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
    0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
    0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
  [-2.29452520e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
    0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]]
idct data
[[136. 137. 138. 139. 140. 141. 142. 143.]
  [152. 153. 154. 155. 156. 157. 158. 159.]
  [168. 169. 170. 171. 172. 173. 174. 175.]
  [184. 185. 186. 187. 188. 189. 190. 191.]
  [200. 201. 202. 203. 204. 205. 206. 207.]
  [216. 217. 218. 219. 220. 221. 222. 223.]
  [232. 233. 234. 235. 236. 237. 238. 239.]
  [248. 249. 250. 251. 252. 253. 254. 255.]]

数据与原数据值大小一致.

cnblogs Tags: python, opencv, DCT

shaoheshaohe

https://www.cnblogs.com/gxgl314/p/9287628.html