def main():
if len(sys.argv) != 2:
print """
Usage: python img2spline.py [path_to_img] \n"""
sys.exit(-1)
else:
path_to_img = sys.argv[1]
img = Image.open(path_to_img).convert('L') # RGB -> [0..255]
print "Image was opened and converted to grayscale."
img.show()
width, height = img.size
data = np.array(list(img.getdata()), dtype=int)
data = data.reshape((height, width))
print "Data was extracted."
# Start plotting original surface of image
fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
ax = fig.add_subplot(111)
ax.invert_yaxis()
x = range(0, width)
y = range(0, height)
# rev_y = range(height-1, -1, -1) # reverse y
X, Y = np.meshgrid(x, y)
print Y
r_stride = 1 + width / 20
c_stride = 1 + height / 20
# ax.plot_surface(X, Y, data, rstride=r_stride, cstride=c_stride)
mappable = ax.pcolor(X, Y, data)
plt.colorbar(mappable)
ax.set_title("Original grayscale image")
ax.set_xlabel('Width (px)')
ax.set_ylabel('Height (px)')
plt.draw()
# Finish plotting original surface of image
# 2D Interpolation here
spline = RectBivariateSpline(x, y, data)
print spline.get_coeffs()
plt.show()
def main():
if len(sys.argv) != 2:
print """
Usage: python img2spline.py [path_to_img] \n"""
sys.exit(-1)
else:
path_to_img = sys.argv[1]
img = Image.open(path_to_img).convert('L') # RGB -> [0..255]
print "Image was opened and converted to grayscale."
img.show()
width, height = img.size
data = np.array(list(img.getdata()), dtype=int)
data = data.reshape((height, width))
print "Data was extracted."
# Start plotting original surface of image
fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
ax = fig.add_subplot(111)
ax.invert_yaxis()
x = range(0, width)
y = range(0, height)
# rev_y = range(height-1, -1, -1) # reverse y
X, Y = np.meshgrid(x, y)
print Y
r_stride = 1 + width / 20
c_stride = 1 + height / 20
# ax.plot_surface(X, Y, data, rstride=r_stride, cstride=c_stride)
mappable = ax.pcolor(X, Y, data)
plt.colorbar(mappable)
ax.set_title("Original grayscale image")
ax.set_xlabel('Width (px)')
ax.set_ylabel('Height (px)')
plt.draw()
# Finish plotting original surface of image
# 2D Interpolation here
spline = RectBivariateSpline(x, y, data)
print spline.get_coeffs()
plt.show()
def _approx(fmapnii, s=14.):
"""
Slice-wise approximation of a smooth 2D bspline
credits: http://scipython.com/book/chapter-8-scipy/examples/two-dimensional-interpolation-\
with-scipyinterpolaterectbivariatespline/
"""
from scipy.interpolate import RectBivariateSpline
from builtins import str, bytes
if isinstance(fmapnii, (str, bytes)):
fmapnii = nb.load(fmapnii)
if not isinstance(s, (tuple, list)):
s = np.array([s] * 2)
data = fmapnii.get_data()
zooms = fmapnii.header.get_zooms()
knot_decimate = np.floor(s / np.array(zooms)[:2]).astype(np.uint8)
knot_space = np.array(zooms)[:2] * knot_decimate
xmax = 0.5 * data.shape[0] * zooms[0]
ymax = 0.5 * data.shape[1] * zooms[1]
x = np.arange(-xmax, xmax, knot_space[0])
y = np.arange(-ymax, ymax, knot_space[1])
x2 = np.arange(-xmax, xmax, zooms[0])
y2 = np.arange(-ymax, ymax, zooms[1])
coeffs = []
nslices = data.shape[-1]
for k in range(nslices):
data2d = data[..., k]
data2dsubs = data2d[::knot_decimate[0], ::knot_decimate[1]]
interp_spline = RectBivariateSpline(x, y, data2dsubs)
data[..., k] = interp_spline(x2, y2)
coeffs.append(interp_spline.get_coeffs().reshape(data2dsubs.shape))
# Save smoothed data
hdr = fmapnii.header.copy()
caff = fmapnii.affine
datanii = nb.Nifti1Image(data.astype(np.float32), caff, hdr)
# Save bspline coeffs
caff[0, 0] = knot_space[0]
caff[1, 1] = knot_space[1]
coeffnii = nb.Nifti1Image(np.stack(coeffs, axis=2), caff, hdr)
return datanii, coeffnii
def _approx(fmapnii, s=14.):
"""
Slice-wise approximation of a smooth 2D bspline
credits: http://scipython.com/book/chapter-8-scipy/examples/two-dimensional-interpolation-\
with-scipyinterpolaterectbivariatespline/
"""
from scipy.interpolate import RectBivariateSpline
from builtins import str, bytes
if isinstance(fmapnii, (str, bytes)):
fmapnii = nb.load(fmapnii)
if not isinstance(s, (tuple, list)):
s = np.array([s] * 2)
data = fmapnii.get_data()
zooms = fmapnii.header.get_zooms()
knot_decimate = np.floor(s / np.array(zooms)[:2]).astype(np.uint8)
knot_space = np.array(zooms)[:2] * knot_decimate
xmax = 0.5 * data.shape[0] * zooms[0]
ymax = 0.5 * data.shape[1] * zooms[1]
x = np.arange(-xmax, xmax, knot_space[0])
y = np.arange(-ymax, ymax, knot_space[1])
x2 = np.arange(-xmax, xmax, zooms[0])
y2 = np.arange(-ymax, ymax, zooms[1])
coeffs = []
nslices = data.shape[-1]
for k in range(nslices):
data2d = data[..., k]
data2dsubs = data2d[::knot_decimate[0], ::knot_decimate[1]]
interp_spline = RectBivariateSpline(x, y, data2dsubs)
data[..., k] = interp_spline(x2, y2)
coeffs.append(interp_spline.get_coeffs().reshape(data2dsubs.shape))
# Save smoothed data
hdr = fmapnii.header.copy()
caff = fmapnii.affine
datanii = nb.Nifti1Image(data.astype(np.float32), caff, hdr)
# Save bspline coeffs
caff[0, 0] = knot_space[0]
caff[1, 1] = knot_space[1]
coeffnii = nb.Nifti1Image(np.stack(coeffs, axis=2), caff, hdr)
return datanii, coeffnii
#now try to solve by grad descent
kacq2d=zeros(kfull.shape,complex)
kacq2d[t2_array,t1_array]=kacq
imgus=ifft2(ifftshift(kacq2d))
imgwvlt=pywt.wavedec2(abs(imgus),wavelet='db4',mode='symmetric')
Ny,Nx=imgus.shape
#fit smoothed phase, evaluate phase basis functions
ph_splfit=RectBivariateSpline(arange(imgus.shape[0]),arange(imgus.shape[1]),1.0*i_unwrap_2d(angle(imgus)),s=1.7e5)
spl_tx=ph_splfit.tck[0]
spl_ty=ph_splfit.tck[1]
spl_kx,spl_ky=ph_splfit.degrees
spl_ncoeffs=ph_splfit.get_coeffs().shape[0]
Sb=zeros([spl_ncoeffs,Nx,Ny],float)
spl_BVS=BivariateSpline()
spl_BVS.degrees=(spl_kx,spl_ky)
for j in range(spl_ncoeffs):
spl_coeffs=zeros(spl_ncoeffs,float)
spl_coeffs[j]=1.0
spl_BVS.tck=(spl_tx,spl_ty,spl_coeffs)
spl_bfunc=spl_BVS.ev(arange(Nx*Ny)%Ny,arange(Nx*Ny)/Ny)
spl_bfunc.shape=(Nx,Ny)
Sb[j,:,:]=transpose(spl_bfunc[:,:]) #unfortunately, spline funcs use transposed orientation convention
phest=ph_splfit.get_coeffs()
p0=append(compose_vec_from_wvlt(imgwvlt,imgus.shape,wavelet='db4',mode='symmetric'),phest)
l_w=[0.2]
#now try to solve by grad descent
kacq2d = zeros(kfull.shape, complex)
kacq2d[t2_array, t1_array] = kacq
imgus = ifft2(ifftshift(kacq2d))
imgwvlt = pywt.wavedec2(abs(imgus), wavelet='db4', mode='symmetric')
Ny, Nx = imgus.shape
#fit smoothed phase, evaluate phase basis functions
ph_splfit = RectBivariateSpline(arange(imgus.shape[0]),
arange(imgus.shape[1]),
1.0 * i_unwrap_2d(angle(imgus)),
s=1.7e5)
spl_tx = ph_splfit.tck[0]
spl_ty = ph_splfit.tck[1]
spl_kx, spl_ky = ph_splfit.degrees
spl_ncoeffs = ph_splfit.get_coeffs().shape[0]
Sb = zeros([spl_ncoeffs, Nx, Ny], float)
spl_BVS = BivariateSpline()
spl_BVS.degrees = (spl_kx, spl_ky)
for j in range(spl_ncoeffs):
spl_coeffs = zeros(spl_ncoeffs, float)
spl_coeffs[j] = 1.0
spl_BVS.tck = (spl_tx, spl_ty, spl_coeffs)
spl_bfunc = spl_BVS.ev(arange(Nx * Ny) % Ny, arange(Nx * Ny) / Ny)
spl_bfunc.shape = (Nx, Ny)
Sb[j, :, :] = transpose(
spl_bfunc[:, :]
) #unfortunately, spline funcs use transposed orientation convention
phest = ph_splfit.get_coeffs()
p0 = append(
