def volumeProcessing():
knit_path = r't' # uint16, [256, 1014, 992]
ds_path = r't'
qs_path = r't'
J_path = r't'
orientation_path = r't'
density_path = r''
orientation_before_eJ_path = r''
s = 1
t = 2
h = 16
ed = 0.15
eJ = -40
nx = 32
ny = 32
nz = 6
print 'load volume ..'
v = load(knit_path).astype('float32')
v = v.swapaxes(0, 1).swapaxes(1, 2)
v = v[:, :, 0:215]
print 'volume shape: ', v.shape
# denoise
print 'denoising using ed ..'
v[v < ed * 65535.0] = 0.0
# compute orientation
print 'binarize ..'
vo = v.copy()
fiber = vo > 0.0
background = np.logical_not(fiber)
vo[fiber] = 0.0
vo[background] = 1.0
if not os.path.exists(ds_path):
print 'compute ds and qs: ..'
ds = generateDirections(nx, ny, nz)
qs = computeFilters(h, s, t, ds)
print 'dump ds and qs ..'
dump(ds_path, ds)
dump(qs_path, qs)
else:
print 'load ds and qs ..'
ds = load(ds_path)
qs = load(qs_path)
print 'compute orientation ..'
start = time_current()
J_max, d_max = computeFiberOrientationFFT(vo, ds, qs)
print 'time used: ', time_current() - start
del vo, ds, qs
background2 = J_max < eJ
background = np.logical_or(background, background2)
del background2
print 'denoising using eJ ..'
v[background] = 0.0
d_max_before_denoise = d_max.copy()
d_max[background] = 0.0
del fiber, background
print 'dump density, J, orientation ..'
dump(density_path, v)
dump(J_path, J_max)
dump(orientation_path, d_max)
dump(orientation_before_eJ_path, d_max_before_denoise)
def volumeProcessing():
silk_path = r'D:\Dataset\round2\silk\silk.dat'
ds_path = r'D:\Dataset\round2\silk\ds.dat'
qs_path = r'D:\Dataset\round2\silk\qs.dat'
J_path = r'D:\Dataset\round2\silk\J.dat'
orientation_path = r'D:\Dataset\round2\silk\silk_orientation.dat'
density_path = r'D:\Dataset\round2\silk\silk_density.dat'
s = 3
t = 4
h = 12
ed = 0.4
eJ = -6
nx = 32
ny = 32
nz = 6
print 'load volume ...'
v = load(silk_path)
v = v[:, :, 25:85]
print 'volume shape: ', v.shape
# denoise
print 'denoising using ed ...'
v[v < ed * 65535.0] = 0.0
# fit
print 'fit volume ...'
vf = v.copy()
fiber = vf > 0.0
background = np.logical_not(fiber)
vf[fiber] = 1.0
vf[background] = 0.0
m = poly2ndfitVolume(vf)
X, Y, Z = generatePoly2ndSurfaceSimple(m, vf.shape[0], vf.shape[1])
del vf
# resample
print 'resample volume ...'
v = resampleVolume(v, Z)
print 'volume shape after resampling: ', v.shape
del X, Y, Z
# compute orientation
v = v[:, :, 22:62]
print 'volume shape for computing orientation: ', v.shape
# binarize volume for filtering
# 0: fiber, 1: background
print 'binarize ...'
vo = v.copy()
fiber = vo > 0.0
background = np.logical_not(fiber)
vo[fiber] = 0.0
vo[background] = 1.0
if not os.path.exists(ds_path):
print 'compute ds and qs ...'
ds = generateDirections(nx, ny, nz)
qs = computeFilters(h, s, t, ds)
print 'dump ds and qs ...'
dump(ds_path, ds)
dump(qs_path, qs)
else:
print 'load ds and qs ...'
ds = load(ds_path)
qs = load(qs_path)
print 'compute orientation ...'
start = time_current()
J_max, d_max = computeFiberOrientationFFT(vo, ds, qs)
print 'time used: ', time_current() - start
del vo
background2 = J_max < eJ
background = np.logical_or(background, background2)
print 'denoising using eJ ...'
v[background] = 0.0
d_max_before_denoise = d_max.copy()
d_max[background] = 0.0
print 'dump density, J, orientation ...'
dump(density_path, v)
dump(J_path, J_max)
dump(orientation_path, d_max)
dump(orientation_path + '_before_denoise', d_max_before_denoise)
def volumeProcessing():
silk_path = r'D:\Dataset\round2\silk\silk.dat'
ds_path = r'D:\Dataset\round2\silk\ds.dat'
qs_path = r'D:\Dataset\round2\silk\qs.dat'
J_path = r'D:\Dataset\round2\silk\J.dat'
orientation_path = r'D:\Dataset\round2\silk\silk_orientation.dat'
density_path = r'D:\Dataset\round2\silk\silk_density.dat'
s = 3
t = 4
h = 12
ed = 0.4
eJ = -6
nx = 32
ny = 32
nz = 6
print 'load volume ...'
v = load(silk_path)
v = v[:,:,25:85]
print 'volume shape: ', v.shape
# denoise
print 'denoising using ed ...'
v[v < ed * 65535.0] = 0.0
# fit
print 'fit volume ...'
vf = v.copy()
fiber = vf > 0.0
background = np.logical_not(fiber)
vf[fiber] = 1.0
vf[background] = 0.0
m = poly2ndfitVolume(vf)
X, Y, Z = generatePoly2ndSurfaceSimple(m, vf.shape[0], vf.shape[1])
del vf
# resample
print 'resample volume ...'
v = resampleVolume(v, Z)
print 'volume shape after resampling: ', v.shape
del X, Y, Z
# compute orientation
v = v[:,:,22:62]
print 'volume shape for computing orientation: ', v.shape
# binarize volume for filtering
# 0: fiber, 1: background
print 'binarize ...'
vo = v.copy()
fiber = vo > 0.0
background = np.logical_not(fiber)
vo[fiber] = 0.0
vo[background] = 1.0
if not os.path.exists(ds_path):
print 'compute ds and qs ...'
ds = generateDirections(nx, ny, nz)
qs = computeFilters(h, s, t, ds)
print 'dump ds and qs ...'
dump(ds_path, ds)
dump(qs_path, qs)
else:
print 'load ds and qs ...'
ds = load(ds_path)
qs = load(qs_path)
print 'compute orientation ...'
start = time_current()
J_max, d_max = computeFiberOrientationFFT(vo, ds, qs)
print 'time used: ', time_current() - start
del vo
background2 = J_max < eJ
background = np.logical_or(background, background2)
print 'denoising using eJ ...'
v[background] = 0.0
d_max_before_denoise = d_max.copy()
d_max[background] = 0.0
print 'dump density, J, orientation ...'
dump(density_path, v)
dump(J_path, J_max)
dump(orientation_path, d_max)
dump(orientation_path + '_before_denoise', d_max_before_denoise)
def volumeProcessing():
felt_path = r'' # uint16, [508, 1013, 992]
ds_path = r''
qs_path = r''
J_path = r''
orientation_path = r''
density_path = r''
s = 1
t = 2
h = 16
ed = 0.4
eJ = -30
nx = 32
ny = 32
nz = 6
print 'load volume ..'
v = load(felt_path).astype('float32')
v = v.swapaxes(0, 1).swapaxes(1, 2)
v = v[:,:,5:490]
print 'volume shape: ', v.shape
# denoise
print 'denoising using ed ..'
v[v < ed * 65535.0] = 0.0
# compute orientation
print 'binarize ..'
vo = v.copy()
fiber = vo > 0.0
background = np.logical_not(fiber)
vo[fiber] = 0.0
vo[background] = 1.0
if not os.path.exists(ds_path):
print 'compute ds and qs: ..'
ds = generateDirections(nx, ny, nz)
qs = computeFilters(h, s, t, ds)
print 'dump ds and qs ..'
dump(ds_path, ds)
dump(qs_path, qs)
else:
print 'load ds and qs ..'
ds = load(ds_path)
qs = load(qs_path)
print 'compute orientation ..'
start = time_current()
J_max, d_max = computeFiberOrientationFFT(vo, ds, qs)
print 'time used: ', time_current() - start
del vo, ds, qs
background2 = J_max < eJ
background = np.logical_or(background, background2)
del background2
print 'denoising using eJ ..'
v[background] = 0.0
#d_max_before_denoise = d_max.copy()
d_max[background] = 0.0
del fiber, background
print 'dump density, J, orientation ..'
dump(density_path, v)
dump(J_path, J_max)
dumpLarge(orientation_path, d_max)