class SphericalHarmonic:
def __init__(self, input_, copy_=True, max_l=None, norm=4):
"""
Projects `input_` to its spherical harmonics basis up to degree `max_l`.
norm = 4 means orthonormal harmonics.
For more details, please see https://shtools.oca.eu/shtools/pyshexpanddh.html
"""
if copy_:
self.spatial = input_.copy()
else:
self.spatial = input_
if not isinstance(self.spatial, EnvironmentMap):
self.spatial = EnvironmentMap(self.spatial, 'LatLong')
if self.spatial.format_ != "latlong":
self.spatial = self.spatial.convertTo("latlong")
self.norm = norm
self.coeffs = []
for i in range(self.spatial.data.shape[2]):
self.coeffs.append(
SHExpandDH(self.spatial.data[:, :, i],
norm=norm,
sampling=2,
lmax_calc=max_l))
def reconstruct(self, height=None, max_l=None, clamp_negative=True):
"""
:height: height of the reconstructed image
:clamp_negative: Remove reconstructed values under 0
"""
retval = []
for i in range(len(self.coeffs)):
retval.append(
MakeGridDH(self.coeffs[i],
norm=self.norm,
sampling=2,
lmax=height,
lmax_calc=max_l))
retval = np.asarray(retval).transpose((1, 2, 0))
if clamp_negative:
retval = np.maximum(retval, 0)
return retval
class SphericalHarmonic:
def __init__(self, input_, copy_=True, max_l=None, norm=4):
"""
Projects `input_` to its spherical harmonics basis up to degree `max_l`.
norm = 4 means orthonormal harmonics.
For more details, please see https://shtools.oca.eu/shtools/pyshexpanddh.html
"""
if copy_:
self.spatial = input_.copy()
else:
self.spatial = input_
if not isinstance(self.spatial, EnvironmentMap):
self.spatial = EnvironmentMap(self.spatial, 'LatLong')
if self.spatial.format_ != "latlong":
self.spatial = self.spatial.convertTo("latlong")
self.norm = norm
#from cffi import FFI
#ffi = FFI()
#ffi.cdef("""
# void generateAssociatedLegendreFactors(const float N, float *data_out, const float * nodes, const unsigned int num_nodes);
#""")
#if os.name == 'nt':
# C = ffi.dlopen(os.path.join(os.path.dirname(os.path.realpath(__file__)), "spharm_tools.dll"))
#else:
# C = ffi.dlopen(os.path.join(os.path.dirname(os.path.realpath(__file__)), "spharm_tools.so"))
self.coeffs = []
for i in range(self.spatial.data.shape[2]):
self.coeffs.append(
SHExpandDH(self.spatial.data[:, :, i],
norm=norm,
sampling=2,
lmax_calc=max_l))
def reconstruct(self, height=None, max_l=None, clamp_negative=True):
"""
:height: height of the reconstructed image
:clamp_negative: Remove reconstructed values under 0
"""
retval = []
for i in range(len(self.coeffs)):
retval.append(
MakeGridDH(self.coeffs[i],
norm=self.norm,
sampling=2,
lmax=height,
lmax_calc=max_l))
retval = np.asarray(retval).transpose((1, 2, 0))
if clamp_negative:
retval = np.maximum(retval, 0)
#original fork return here !
#return retval
# nodes = []
# weights = []
# for d in range(1, degrees + 2):
# x, y = np.polynomial.legendre.leggauss(d)
# nodes.extend(x)
# weights.extend(y)
retval = np.zeros((int((2 * (degrees + 1) + 1)**2 / 8), ch),
dtype=np.complex128)
P, nodes = _getP(envmap, degrees)
print(degrees, P.shape)
# Gauss-Legendre / Gauss-Chebyshev quadrature to speed up?
# Perform in C
# for j in range(envmap.data.shape[1]):
# for k in range(ch):
# #fmi = np.interp(nodes, np.linspace(-np.pi/2, np.pi/2, fm.shape[0]), np.squeeze(fm[:,j,k]))
# i = 0
# for l in range(degrees + 1):
# for m in range(0, l + 1):
# retval[l,m,k] += fm * P[l,m]
# i += 1
import operator
i = 0
for l in tqdm(range(degrees + 1)):
for m in range(0, l + 1):
#coef = np.sqrt(( (2.*l+1.) / (4.*np.pi) ) * ( 1. / (functools.reduce(operator.mul, range(l-m+1, l+m+1), 1)) ) )
for c in range(ch):
#retval[i,c] = np.nansum(coef*P[:,i]*np.squeeze(fm[:,m,c]))
retval[i, c] = np.nansum(P[:, i] * np.squeeze(fm[:, m, c]))
#import pdb; pdb.set_trace()
#retval[i,c] = np.nansum(coef*P[:,i]*np.exp(1j*m*theta.ravel())*envmap.data.reshape([-1,ch])[:,c])
#retval[i,c] = np.nansum(coef*ref[:,i]*np.exp(-1j*m*(theta).ravel())*f[:,c])
#print("2: ", coef*P[:,i]*np.exp(1j*m*theta.ravel())*f[:,c])
i += 1
#import pdb; pdb.set_trace()
from matplotlib import pyplot as plt
#plt.scatter(nodes_cos, ref); plt.show()
return retval
elif reduction_type == 'right':
retval[_triangleRightSide(degrees)] = coeffs
for i in set(range(
(degrees + 1)**2)) - set(_triangleRightSide(degrees)):
l = np.sqrt(i).astype('int')
m = abs((l) - (i - l**2))
retval[i] = (-1)**m * np.conj(retval[l**2 + l + m])
return retval
if __name__ == '__main__':
from matplotlib import pyplot as plt
e = EnvironmentMap('envmap.exr', 'angular')
e.resize((64, 64))
e.convertTo('latlong')
se = SphericalHarmonic(e)
err = []
from tqdm import tqdm
for i in tqdm(range(32)):
recons = se.reconstruct(max_l=i)
err.append(np.sum((recons - e.data)**2))
plt.plot(err)
plt.figure()
plt.imshow(recons)
plt.show()