def image_to_compressed_patches(image,mask,patchsize,subsampling): data=image.get_data() data = data.reshape(data.shape[0:3]) p = mp.array_to_patches(data,patch_shape=(patchsize,patchsize,patchsize),normalization=True) data = mask.get_data().astype(bool) data = data.reshape(data.shape[0:3]) pm = mp.array_to_patches(data,patch_shape=(patchsize,patchsize,patchsize),normalization=False) #Remove zero patches pmr = p[ ~np.all(pm==0,axis=1) ] del p,pm #Subsampling of the data np.random.shuffle(pmr) pmr = pmr[0:np.int(subsampling*pmr.shape[0]),:].astype(float) return pmr
def image_to_compressed_patches(image, mask, patchsize, subsampling):
data = image.get_data()
data = data.reshape(data.shape[0:3])
p = mp.array_to_patches(data,
patch_shape=(patchsize, patchsize, patchsize),
normalization=True)
data = mask.get_data().astype(bool)
data = data.reshape(data.shape[0:3])
pm = mp.array_to_patches(data,
patch_shape=(patchsize, patchsize, patchsize),
normalization=False)
#Remove zero patches
pmr = p[~np.all(pm == 0, axis=1)]
del p, pm
#Subsampling of the data
np.random.shuffle(pmr)
pmr = pmr[0:np.int(subsampling * pmr.shape[0]), :].astype(float)
return pmr
def scskl_reconstruction(data, mask, D):
output = np.zeros(data.shape)
fmap = np.zeros((D.shape[0]))
#fdata = np.zeros((data.shape[0],data.shape[1],data.shape[2],D.shape[0]))
px = np.int(np.around(np.power(
D.shape[1], 1 / 3))) #patch size (is assumed to be isotropic)
hpx = np.floor(px / 2).astype(int)
nblock = 2 # number of block per dimension
subsize = np.ceil(np.array(data.shape) / nblock).astype(int)
med = np.median(data)
currentblock = 1
for x in range(np.ceil(data.shape[0] / subsize[0]).astype(int)):
xmin = x * subsize[0]
xmax = np.min((data.shape[0], (x + 1) * subsize[0]))
for y in range(np.ceil(data.shape[1] / subsize[1]).astype(int)):
ymin = y * subsize[1]
ymax = np.min((data.shape[1], (y + 1) * subsize[1]))
for z in range(np.ceil(data.shape[2] / subsize[2]).astype(int)):
zmin = z * subsize[2]
zmax = np.min((data.shape[2], (z + 1) * subsize[2]))
print('Processing block : ', currentblock)
currentblock += 1
#Enlarge subimage to take into account block effect due to non-overlapping patches
xmin2 = np.max((0, xmin - hpx))
xmax2 = np.min((data.shape[0], xmax + hpx))
ymin2 = np.max((0, ymin - hpx))
ymax2 = np.min((data.shape[1], ymax + hpx))
zmin2 = np.max((0, zmin - hpx))
zmax2 = np.min((data.shape[2], zmax + hpx))
subdata = data[xmin2:xmax2, ymin2:ymax2, zmin2:zmax2]
submask = mask[xmin2:xmax2, ymin2:ymax2, zmin2:zmax2]
p = mp.array_to_patches(subdata,
patch_shape=(px, px, px),
normalization=False)
pm = mp.array_to_patches(submask,
patch_shape=(px, px, px),
normalization=False)
#remove patch we dont want to process
index = ~np.all(pm == 0, axis=1)
subp = p[index]
subp -= med
if subp.shape[0] > 0:
print('Number of patches to process: ', subp.shape[0])
#Currently, there is a bug when using n_jobs>1 (https://github.com/scikit-learn/scikit-learn/issues/5956)
coder = SparseCoder(dictionary=D,
transform_algorithm='omp')
code = coder.transform(subp).astype(np.float32)
fmap += np.sum((np.fabs(code) > 0), axis=0)
subp = np.dot(code, D)
subp += med
p[index] = subp
suboutput = mp.patches_to_array(patches=p,
patch_shape=(px, px, px),
array_shape=subdata.shape)
tmpoutput = np.empty(data.shape)
tmpoutput[xmin2:xmax2, ymin2:ymax2,
zmin2:zmax2] = suboutput
output[xmin:xmax, ymin:ymax,
zmin:zmax] = tmpoutput[xmin:xmax, ymin:ymax,
zmin:zmax]
# for a in range(D.shape[0]):
# for s in range(subp.shape[0]):
# subp[s,:] = code[s,a]
# p.fill(0)
# p[index] = subp
# fa = mp.patches_to_array(patches=p, patch_shape=(px,px,px), array_shape=subdata.shape)
# to = np.empty(data.shape)
# to[xmin2:xmax2,ymin2:ymax2,zmin2:zmax2]= fa
# fdata[xmin:xmax,ymin:ymax,zmin:zmax,a] = to[xmin:xmax,ymin:ymax,zmin:zmax]
#plt.bar(range(0,D.shape[0]), fmap)
#print('points in mask: ',np.sum(mask!=0))
#print('Number of non zero elements: ',np.sum(fmap)/np.sum(mask!=0))
#plt.show()
# return (output,fdata)
return output
def scskl_reconstruction(data,mask,D):
output = np.zeros(data.shape)
fmap = np.zeros((D.shape[0]))
#fdata = np.zeros((data.shape[0],data.shape[1],data.shape[2],D.shape[0]))
px = np.int(np.around(np.power(D.shape[1],1/3))) #patch size (is assumed to be isotropic)
hpx = np.floor(px/2).astype(int)
nblock = 2 # number of block per dimension
subsize = np.ceil(np.array(data.shape) / nblock).astype(int)
med = np.median(data)
currentblock = 1
for x in range(np.ceil(data.shape[0]/subsize[0]).astype(int)):
xmin = x*subsize[0]
xmax = np.min((data.shape[0],(x+1)*subsize[0]))
for y in range(np.ceil(data.shape[1]/subsize[1]).astype(int)):
ymin = y*subsize[1]
ymax = np.min((data.shape[1],(y+1)*subsize[1]))
for z in range(np.ceil(data.shape[2]/subsize[2]).astype(int)):
zmin = z*subsize[2]
zmax = np.min((data.shape[2],(z+1)*subsize[2]))
print('Processing block : ',currentblock)
currentblock+=1
#Enlarge subimage to take into account block effect due to non-overlapping patches
xmin2 = np.max((0,xmin-hpx))
xmax2 = np.min((data.shape[0],xmax+hpx))
ymin2 = np.max((0,ymin-hpx))
ymax2 = np.min((data.shape[1],ymax+hpx))
zmin2 = np.max((0,zmin-hpx))
zmax2 = np.min((data.shape[2],zmax+hpx))
subdata = data[xmin2:xmax2,ymin2:ymax2,zmin2:zmax2]
submask = mask[xmin2:xmax2,ymin2:ymax2,zmin2:zmax2]
p = mp.array_to_patches(subdata,patch_shape=(px,px,px),normalization=False)
pm = mp.array_to_patches(submask,patch_shape=(px,px,px),normalization=False)
#remove patch we dont want to process
index = ~np.all(pm==0,axis=1)
subp = p[index]
subp -= med
if subp.shape[0] > 0:
print('Number of patches to process: ',subp.shape[0])
#Currently, there is a bug when using n_jobs>1 (https://github.com/scikit-learn/scikit-learn/issues/5956)
coder = SparseCoder(dictionary=D, transform_algorithm='omp')
code = coder.transform(subp).astype(np.float32)
fmap += np.sum((np.fabs(code)>0),axis=0)
subp = np.dot(code, D)
subp += med
p[index] = subp
suboutput = mp.patches_to_array(patches=p, patch_shape=(px,px,px), array_shape=subdata.shape)
tmpoutput = np.empty(data.shape)
tmpoutput[xmin2:xmax2,ymin2:ymax2,zmin2:zmax2]= suboutput
output[xmin:xmax,ymin:ymax,zmin:zmax] = tmpoutput[xmin:xmax,ymin:ymax,zmin:zmax]
# for a in range(D.shape[0]):
# for s in range(subp.shape[0]):
# subp[s,:] = code[s,a]
# p.fill(0)
# p[index] = subp
# fa = mp.patches_to_array(patches=p, patch_shape=(px,px,px), array_shape=subdata.shape)
# to = np.empty(data.shape)
# to[xmin2:xmax2,ymin2:ymax2,zmin2:zmax2]= fa
# fdata[xmin:xmax,ymin:ymax,zmin:zmax,a] = to[xmin:xmax,ymin:ymax,zmin:zmax]
#plt.bar(range(0,D.shape[0]), fmap)
#print('points in mask: ',np.sum(mask!=0))
#print('Number of non zero elements: ',np.sum(fmap)/np.sum(mask!=0))
#plt.show()
# return (output,fdata)
return output
