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gpm_improve.pyx
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# cython: profile=True
#import numpy as np
cimport numpy as np
import cython
from libc.stdlib cimport rand, RAND_MAX
#DTYPE1 = np.int
#ctypedef np.int_t DTYPE_t1
#DTYPE2 = np.float
#ctypedef np.float_t DTYPE_t2
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
def gpm_iter(float[:,:,::1] IMG, float[:,:,::1] IMG2, int[:,:,:,::1] NNF, float[:,:,::1] NND, int[:,::1] search_mask,
int max_iter, int m, int K, int k_ref, int k_range, float sim_threshold,
int n_rand_search, int i_rad, int w_loc_i):
cdef int a = IMG.shape[0]
cdef int b = IMG.shape[1]
cdef int c = IMG.shape[2]
# cdef int n_rand_search= 10
# cdef int i_rad= 32
cdef int j_rad= i_rad
cdef int k_rad= i_rad/2
cdef int i_rad_2= i_rad/2
cdef int j_rad_2= j_rad/2
cdef int k_rad_2= k_rad/2
# cdef np.ndarray[DTYPE_t1, ndim=1] new_indx= np.zeros(K, dtype= DTYPE1)
# cdef float upper_x = RAND_MAX/(a- 2.0*m-1)
# cdef float upper_y = RAND_MAX/(b- 2.0*m-1)
cdef int iter_count, i, j, k, l, i1, j1, k1, new_rand, kk, z, i0, j0, k0, ii, jj, new_indx, k_min, k_max, j11, i11
cdef int i_mn, i_mx,j_mn, j_mx,k_mn, k_mx #, i_rad_2, j_rad_2, k_rad_2
cdef float new_dist, value1 #, dist_mean
# cdef float dist_threshold= 0.0
# cdef float dist_threshold_divisor= 1.0/( (a-2.0*m)*(b-2.0*m)*K )
k_min= max(0, k_ref-k_range)
k_max= min(c, k_ref+k_range+1)
# cdef int w_loc_i= 5
cdef int w_loc_j= w_loc_i
cdef int w_loc_k= min(w_loc_i/2, k_range)
# cdef np.ndarray[DTYPE_t1, ndim=2] Del_prop= np.array([[1,0], [0,1]], dtype= DTYPE1)
# cdef np.ndarray[np.int_t, ndim=2, negative_indices=False, mode='c'] dk_randx = np.random.randint(m, a-m, (a,b))
# cdef np.ndarray[np.int_t, ndim=2, negative_indices=False, mode='c'] dk_randy = np.random.randint(m, a-m, (a,b))
for iter_count in xrange(max_iter):
# Local search
if iter_count == 0 and w_loc_i>1:
for i in xrange(m , a-m):
for j in xrange(m, b-m):
i_mn= max(m, i-w_loc_i)
i_mx= min(a-m-1, i+w_loc_i)
j_mn= max(m, j-w_loc_j)
j_mx= min(b-m-1, j+w_loc_j)
k_mn= max(0, k_ref-w_loc_k)
k_mx= min(c-1, k_ref+w_loc_k+1)
for i1 in xrange(i_mn, i_mx):
for j1 in xrange(j_mn, j_mx):
for k1 in xrange(k_mn, k_mx):
new_rand= 1
if i1==i and j1==j and k1== k_ref:
new_rand= 0
else:
for kk in xrange(K):
if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
new_rand= 0
break
if new_rand==1:
new_dist= 0
i11= i1-i
j11= j1-j
for ii in xrange(i-m,i+m+1):
for jj in xrange(j-m,j+m+1):
value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
new_dist+= value1*value1
new_dist= new_dist**0.5
new_indx=K
while NND[i,j,new_indx-1]>new_dist and new_indx>0:
new_indx+= -1
if new_indx<K:
for z in xrange(K-1,new_indx,-1):
NNF[i,j,z,0]= NNF[i,j,z-1,0]
NNF[i,j,z,1]= NNF[i,j,z-1,1]
NNF[i,j,z,2]= NNF[i,j,z-1,2]
NND[i,j,z]= NND[i,j,z-1]
NNF[i,j,new_indx,0]= i1
NNF[i,j,new_indx,1]= j1
NNF[i,j,new_indx,2]= k1
NND[i,j,new_indx]= new_dist
for i in xrange(m,a-m):
for j in xrange(m,b-m):
if NND[i,j,-1]<sim_threshold:
search_mask[i,j]= 0
# Propagation
if iter_count%2==0:
for i in xrange(m , a-m-1):
for j in xrange(m, b-m-1):
if search_mask[i,j]>0:
for k in xrange(K):
i1= NNF[i+1, j, k, 0]
j1= NNF[i+1, j, k, 1]
k1= NNF[i+1, j, k, 2]
new_rand= 1
if i1==i and j1==j and k1== k_ref:
new_rand= 0
else:
for kk in xrange(K):
if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
new_rand= 0
break
if new_rand==1:
new_dist= 0
i11= i1-i
j11= j1-j
for ii in xrange(i-m,i+m+1):
for jj in xrange(j-m,j+m+1):
value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
new_dist+= value1*value1
new_dist= new_dist**0.5
new_indx=K
while NND[i,j,new_indx-1]>new_dist and new_indx>0:
new_indx+= -1
if new_indx<K:
for z in xrange(K-1,new_indx,-1):
NNF[i,j,z,0]= NNF[i,j,z-1,0]
NNF[i,j,z,1]= NNF[i,j,z-1,1]
NNF[i,j,z,2]= NNF[i,j,z-1,2]
NND[i,j,z]= NND[i,j,z-1]
NNF[i,j,new_indx,0]= i1
NNF[i,j,new_indx,1]= j1
NNF[i,j,new_indx,2]= k1
NND[i,j,new_indx]= new_dist
i1= NNF[i, j+1, k, 0]
j1= NNF[i, j+1, k, 1]
k1= NNF[i, j+1, k, 2]
new_rand= 1
if i1==i and j1==j and k1== k_ref:
new_rand= 0
else:
for kk in xrange(K):
if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
new_rand= 0
break
if new_rand==1:
new_dist= 0
i11= i1-i
j11= j1-j
for ii in xrange(i-m,i+m+1):
for jj in xrange(j-m,j+m+1):
value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
new_dist+= value1*value1
new_dist= new_dist**0.5
new_indx=K
while NND[i,j,new_indx-1]>new_dist and new_indx>0:
new_indx+= -1
if new_indx<K:
for z in xrange(K-1,new_indx,-1):
NNF[i,j,z,0]= NNF[i,j,z-1,0]
NNF[i,j,z,1]= NNF[i,j,z-1,1]
NNF[i,j,z,2]= NNF[i,j,z-1,2]
NND[i,j,z]= NND[i,j,z-1]
NNF[i,j,new_indx,0]= i1
NNF[i,j,new_indx,1]= j1
NNF[i,j,new_indx,2]= k1
NND[i,j,new_indx]= new_dist
else:
for i in xrange(m+1 , a-m):
for j in xrange(m+1 , b-m):
if search_mask[i,j]>0:
for k in xrange(K):
i1= NNF[i-1, j, k, 0]
j1= NNF[i-1, j, k, 1]
k1= NNF[i-1, j, k, 2]
new_rand= 1
if i1==i and j1==j and k1== k_ref:
new_rand= 0
else:
for kk in xrange(K):
if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
new_rand= 0
break
if new_rand==1:
new_dist= 0
i11= i1-i
j11= j1-j
for ii in xrange(i-m,i+m+1):
for jj in xrange(j-m,j+m+1):
value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
new_dist+= value1*value1
new_dist= new_dist**0.5
new_indx=K
while NND[i,j,new_indx-1]>new_dist and new_indx>0:
new_indx+= -1
if new_indx<K:
for z in xrange(K-1,new_indx,-1):
NNF[i,j,z,0]= NNF[i,j,z-1,0]
NNF[i,j,z,1]= NNF[i,j,z-1,1]
NNF[i,j,z,2]= NNF[i,j,z-1,2]
NND[i,j,z]= NND[i,j,z-1]
NNF[i,j,new_indx,0]= i1
NNF[i,j,new_indx,1]= j1
NNF[i,j,new_indx,2]= k1
NND[i,j,new_indx]= new_dist
i1= NNF[i, j-1, k, 0]
j1= NNF[i, j-1, k, 1]
k1= NNF[i, j-1, k, 2]
new_rand= 1
if i1==i and j1==j and k1== k_ref:
new_rand= 0
else:
for kk in xrange(K):
if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
new_rand= 0
break
if new_rand==1:
new_dist= 0
i11= i1-i
j11= j1-j
for ii in xrange(i-m,i+m+1):
for jj in xrange(j-m,j+m+1):
value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
new_dist+= value1*value1
new_dist= new_dist**0.5
new_indx=K
while NND[i,j,new_indx-1]>new_dist and new_indx>0:
new_indx+= -1
if new_indx<K:
for z in xrange(K-1,new_indx,-1):
NNF[i,j,z,0]= NNF[i,j,z-1,0]
NNF[i,j,z,1]= NNF[i,j,z-1,1]
NNF[i,j,z,2]= NNF[i,j,z-1,2]
NND[i,j,z]= NND[i,j,z-1]
NNF[i,j,new_indx,0]= i1
NNF[i,j,new_indx,1]= j1
NNF[i,j,new_indx,2]= k1
NND[i,j,new_indx]= new_dist
# # Random search
#
# for i in xrange(m,a-m-1):
# for j in xrange(m,b-m-1):
#
# if search_mask[i,j]>0:
#
# for l in xrange(n_rand_search):
#
# # i1= m + int(rand()/upper_x)
# # j1= m + int(rand()/upper_y)
# i1= m + rand()% (a-2*m-1)
# j1= m + rand()% (b-2*m-1)
# k1= k_min + rand()% (k_max-k_min)
#
#
# new_rand= 1
# if i1==i and j1==j and k1== k_ref:
# new_rand= 0
# else:
# for kk in xrange(K):
# if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
# new_rand= 0
# break
#
# if new_rand==1:
#
# new_dist= 0
# i11= i1-i
# j11= j1-j
# for ii in xrange(i-m,i+m+1):
# for jj in xrange(j-m,j+m+1):
# value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
# new_dist+= value1*value1
# new_dist= new_dist**0.5
#
# new_indx=K
# while NND[i,j,new_indx-1]>new_dist and new_indx>0:
# new_indx+= -1
#
# if new_indx<K:
#
# for z in xrange(K-1,new_indx,-1):
#
# NNF[i,j,z,0]= NNF[i,j,z-1,0]
# NNF[i,j,z,1]= NNF[i,j,z-1,1]
# NNF[i,j,z,2]= NNF[i,j,z-1,2]
# NND[i,j,z]= NND[i,j,z-1]
#
# NNF[i,j,new_indx,0]= i1
# NNF[i,j,new_indx,1]= j1
# NNF[i,j,new_indx,2]= k1
# NND[i,j,new_indx]= new_dist
# Forward enrichment and random search
# if i_rad>8:
# i_rad= i_rad/2
# j_rad= j_rad/2
# k_rad= k_rad/2
# i_rad_2= i_rad/2
# j_rad_2= j_rad/2
# k_rad_2= k_rad/2
for i in xrange(m,a-m):
for j in xrange(m,b-m):
if search_mask[i,j]>0:
for k in xrange(K):
i0= NNF[i,j,k,0]
j0= NNF[i,j,k,1]
k0= NNF[i,j,k,2]
if k0==k_ref:
for l in xrange(K):
i1= NNF[i0,j0,l,0]
j1= NNF[i0,j0,l,1]
k1= NNF[i0,j0,l,2]
new_rand= 1
if i1==i and j1==j and k1== k_ref:
new_rand= 0
else:
for kk in xrange(K):
if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
new_rand= 0
break
if new_rand==1:
new_dist= 0
i11= i1-i
j11= j1-j
for ii in xrange(i-m,i+m+1):
for jj in xrange(j-m,j+m+1):
value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
new_dist+= value1*value1
new_dist= new_dist**0.5
new_indx=K
while NND[i,j,new_indx-1]>new_dist and new_indx>0:
new_indx+= -1
if new_indx<K:
for z in xrange(K-1,new_indx,-1):
NNF[i,j,z,0]= NNF[i,j,z-1,0]
NNF[i,j,z,1]= NNF[i,j,z-1,1]
NNF[i,j,z,2]= NNF[i,j,z-1,2]
NND[i,j,z]= NND[i,j,z-1]
NNF[i,j,new_indx,0]= i1
NNF[i,j,new_indx,1]= j1
NNF[i,j,new_indx,2]= k1
NND[i,j,new_indx]= new_dist
for l in xrange(n_rand_search):
i1= i0- i_rad_2+ rand()% i_rad
if i1<m:
i1= m
if i1>a-m-1:
i1= a-m-1
j1= j0- j_rad_2+ rand()% j_rad
if j1<m:
j1= m
if j1>b-m-1:
j1= b-m-1
k1= k0- k_rad_2+ rand()% k_rad
if k1<k_min:
k1= k_min
if k1>k_max-1:
k1= k_max-1
new_rand= 1
if i1==i and j1==j and k1== k_ref:
new_rand= 0
else:
for kk in xrange(K):
if NNF[i,j,kk,0]==i1 and NNF[i,j,kk,1]==j1 and NNF[i,j,kk,2]==k1:
new_rand= 0
break
if new_rand==1:
new_dist= 0
i11= i1-i
j11= j1-j
for ii in xrange(i-m,i+m+1):
for jj in xrange(j-m,j+m+1):
value1= IMG[ii,jj,k_ref]- IMG2[ii+i11, jj+j11,k1]
new_dist+= value1*value1
new_dist= new_dist**0.5
new_indx=K
while NND[i,j,new_indx-1]>new_dist and new_indx>0:
new_indx+= -1
if new_indx<K:
for z in xrange(K-1,new_indx,-1):
NNF[i,j,z,0]= NNF[i,j,z-1,0]
NNF[i,j,z,1]= NNF[i,j,z-1,1]
NNF[i,j,z,2]= NNF[i,j,z-1,2]
NND[i,j,z]= NND[i,j,z-1]
NNF[i,j,new_indx,0]= i1
NNF[i,j,new_indx,1]= j1
NNF[i,j,new_indx,2]= k1
NND[i,j,new_indx]= new_dist
# if i_rad<128 and iter_count%2==0:
# i_rad*= 2
# j_rad= i_rad
# k_rad= i_rad/2
# i_rad_2= i_rad/2
# j_rad_2= j_rad/2
# k_rad_2= k_rad/2
# if n_rand_search<64 and iter_count%2==0:
# n_rand_search*= 2
for i in xrange(m,a-m):
for j in xrange(m,b-m):
if NND[i,j,-1]<sim_threshold:
search_mask[i,j]= 0
# if iter_count==5:
#
# dist_threshold= 0.0
#
# for i in xrange(m,a-m):
# for j in xrange(m,b-m):
# for k in xrange(K):
#
# dist_threshold+= NND[i,j,k]
#
# dist_threshold= dist_threshold*dist_threshold_divisor
#
# for i in xrange(m,a-m):
# for j in xrange(m,b-m):
#
# dist_mean= 0.0
#
# for k in xrange(K):
# dist_mean+= NND[i,j,k]
# dist_mean= dist_mean/K
#
# if dist_mean<dist_threshold:
# search_mask[i,j]= 0
#
# elif iter_count>5:
#
# for i in xrange(m,a-m):
# for j in xrange(m,b-m):
#
# if search_mask[i,j]>0:
#
# dist_mean= 0.0
#
# for k in xrange(K):
# dist_mean+= NND[i,j,k]
# dist_mean= dist_mean/K
#
# if dist_mean<dist_threshold:
# search_mask[i,j]= 0
return NNF, NND, search_mask