utils_combine.py 文件源码

def calfilter(X):
  '''X is nbatch*boxheight*boxwidth image. k1 and k2 is the nbatch*(boxheight*boxwidth)*(boxheight*boxwidth)
  filters. Here we only consider 4 neigbor regeion.'''
  k1 = np.zeros((X.shape[0], X.shape[1], X.shape[2], X.shape[1], X.shape[2]))
  k2 = np.zeros((X.shape[0], X.shape[1], X.shape[2], X.shape[1], X.shape[2]))
  for i in range(X.shape[1]):
    for j in range(X.shape[2]):
      if i != 0:
        k1[:,i,j,i-1,j] = 1
        k2[:,i,j,i-1,j] = np.exp(-(X[:,i,j]-X[:,i-1,j])**2)
      if i != X.shape[1]-1:
        k1[:,i,j,i+1,j] = 1
        k2[:,i,j,i+1,j] = np.exp(-(X[:,i,j]-X[:,i+1,j])**2)
      if j != 0:
        k1[:,i,j,i,j-1] = 1
        k2[:,i,j,i,j-1] = np.exp(-(X[:,i,j]-X[:,i,j-1])**2)
      if j != X.shape[2]-1:
        k1[:,i,j,i,j+1] = 1
        k2[:,i,j,i,j+1] = np.exp(-(X[:,i,j]-X[:,i,j+1])**2)
  k1 = k1.reshape((X.shape[0], X.shape[1]*X.shape[2], X.shape[1]*X.shape[2]))
  k2 = k2.reshape((X.shape[0], X.shape[1]*X.shape[2], X.shape[1]*X.shape[2]))
  return k1, k2