[25, 129],
[76, 25],
[76, 77],
[76, 129],
])
_ipoints = np.unique(np.random.randint(0, np.prod(im.shape), 200))
rpoints = np.c_[np.asarray(np.indices(im.shape))[0, :, :].flat[_ipoints],
np.asarray(np.indices(im.shape))[1, :, :].flat[_ipoints], ]
# pyplot.figure()
# pyplot.imshow(imw.T, cmap='gray')
# pyplot.scatter(rpoints[:,0], rpoints[:,1])
# pyplot.show()
ipoints = np.argmin(distance_matrix.euclidian(points, rpoints), axis=1)
npoints = len(ipoints)
# geodesic distances
rE = distance_matrix.euclidian(rpoints)
rC = delaunay_2d.connectivity(rpoints)
rG, rparents = fastm.fast_marching_3d_aniso(
# im*np.mean(rE[rC==1]),
imw,
rpoints,
heap_size=1e6,
offset=1e-5,
connectivity26=True,
output_arguments=('DM', 'parents'),
)
pathes, connections = fastm.shortestpath(rparents)
gt_ = np.where((ordering - np.c_[gt]) == 0)[1]
print '\ncompute unary cost'
nlabels = 1 #nnodes2
ucost = 100
print 'unary cost = %d for %d distinct labels' % (ucost, nlabels)
labels = np.arange(nnodes2) % nlabels
unary = (ordering % nlabels != np.tile(np.c_[gt % nlabels],
(1, nnodes2))) * ucost
use_proximity = True
if use_proximity:
thresh = step_size
print 'use proximity with thresh = %f' % thresh
DM_ = distm.euclidian(pt1, pt2) # - thresh
# DM_[DM_<0] = 0
unary += DM_ / thresh + np.power(DM_ / thresh, 3.)
print '\ncompute binary costs'
if 'connectivity' not in dir():
connectivity = 'full' #'delaunay', 'full'
if connectivity == 'delaunay':
print '(delaunay connected graph)'
pairs = delaunay.delaunay(pt1[:, 0], pt1[:, 1])[1]
elif connectivity == 'smallworld':
prop = 1
print '(small world connected graph with %d pc random edges added)' % prop
pairs = delaunay.delaunay(pt1[:, 0], pt1[:, 1])[1]
import numpy as np
from rmn.algebra import spectralg, distance_matrix as distm
pt1 = [
[0,0],
[0,1],
[1,0],
]
pt2 = [
[0.1,0.2],
[-0.2,0.05],
[0.0,0.1],
[-0.1,0.97],
[0.2,1.1],
[1.2,0.0],
]
DM1 = distm.euclidian(pt1)
DM2 = distm.euclidian(pt2)
A1 = 1./(1e-10 + DM1)
A2 = 1./(1e-10 + DM2)
w1,V1 = spectralg.laplacian_eigen_maps(A1,symmetric=True)
w2,V2 = spectralg.laplacian_eigen_maps(A2,symmetric=True)
[76, 129],
])
_ipoints = np.unique(np.random.randint(0, np.prod(im.shape), 200))
rpoints = np.c_[
np.asarray(np.indices(im.shape))[0,:,:].flat[_ipoints],
np.asarray(np.indices(im.shape))[1,:,:].flat[_ipoints],
]
# pyplot.figure()
# pyplot.imshow(imw.T, cmap='gray')
# pyplot.scatter(rpoints[:,0], rpoints[:,1])
# pyplot.show()
ipoints = np.argmin(distance_matrix.euclidian(points, rpoints), axis=1)
npoints= len(ipoints)
# geodesic distances
rE = distance_matrix.euclidian(rpoints)
rC = delaunay_2d.connectivity(rpoints)
rG,rparents = fastm.fast_marching_3d_aniso(
# im*np.mean(rE[rC==1]),
imw,
rpoints,
heap_size=1e6,
offset=1e-5,
connectivity26=True,
output_arguments=('DM','parents'),
import numpy as np
from rmn.algebra import spectralg, distance_matrix as distm
pt1 = [
[0, 0],
[0, 1],
[1, 0],
]
pt2 = [
[0.1, 0.2],
[-0.2, 0.05],
[0.0, 0.1],
[-0.1, 0.97],
[0.2, 1.1],
[1.2, 0.0],
]
DM1 = distm.euclidian(pt1)
DM2 = distm.euclidian(pt2)
A1 = 1. / (1e-10 + DM1)
A2 = 1. / (1e-10 + DM2)
w1, V1 = spectralg.laplacian_eigen_maps(A1, symmetric=True)
w2, V2 = spectralg.laplacian_eigen_maps(A2, symmetric=True)
ordering = np.tile(range(nnodes2),(nnodes1,1))
gt_ = np.where((ordering-np.c_[gt])==0)[1]
print '\ncompute unary cost'
nlabels = 1 #nnodes2
ucost = 100
print 'unary cost = %d for %d distinct labels' %(ucost,nlabels)
labels = np.arange(nnodes2)%nlabels
unary = (ordering%nlabels != np.tile(np.c_[gt%nlabels],(1,nnodes2)))*ucost
use_proximity = True
if use_proximity:
thresh = step_size
print 'use proximity with thresh = %f' %thresh
DM_ = distm.euclidian(pt1,pt2)# - thresh
# DM_[DM_<0] = 0
unary += DM_/thresh + np.power(DM_/thresh,3.)
print '\ncompute binary costs'
if 'connectivity' not in dir():
connectivity = 'full' #'delaunay', 'full'
if connectivity=='delaunay':
print '(delaunay connected graph)'
pairs = delaunay.delaunay(pt1[:,0],pt1[:,1])[1]
elif connectivity=='smallworld':
prop = 1
print '(small world connected graph with %d pc random edges added)' %prop
pairs = delaunay.delaunay(pt1[:,0],pt1[:,1])[1]
