def _interpolateNormalAMG(self, N0_32F, W_32F, A_8U):
h, w = N0_32F.shape[:2]
A_c, b_c = normalConstraints(W_32F, N0_32F)
A_8U = None
if self._image.shape[2] == 4:
A_8U = to8U(alpha(self._image))
A_sil, b_sil = silhouetteConstraints(A_8U)
A_L = laplacianMatrix((h, w))
A = 10.0 * A_c + A_L + A_sil
b = 10.0 * b_c + b_sil
N_32F = amg_solver.solve(A, b).reshape(h, w, 3)
N_32F = normalizeImage(N_32F)
return N_32F
def _computeDetailNormal(self, N0_32F):
h, w = N0_32F.shape[:2]
W_32F = np.zeros((h, w))
# sigma_d = 2.0 * np.max(N0_32F[:, :, 2])
# W_32F = 1.0 - np.exp( - (N0_32F[:, :, 2] ** 2) / (sigma_d ** 2))
W_32F = 1.0 - N0_32F[:, :, 2]
W_32F *= 1.0 / np.max(W_32F)
W_32F = W_32F ** 1.5
A_c, b_c = amg_constraints.normalConstraints(W_32F, N0_32F)
A_L = amg_constraints.laplacianMatrix((h, w))
lambda_d = 2.0
A = A_c + lambda_d * A_L
b = b_c
N_32F = amg_solver.solve(A, b).reshape(h, w, 3)
N_32F = computeNz(N_32F.reshape(-1, 3)).reshape(h, w, 3)
N_32F = normalizeImage(N_32F)
return N_32F
def _computeDetailNormal(self, N0_32F):
h, w = N0_32F.shape[:2]
W_32F = np.zeros((h, w))
# sigma_d = 2.0 * np.max(N0_32F[:, :, 2])
# W_32F = 1.0 - np.exp( - (N0_32F[:, :, 2] ** 2) / (sigma_d ** 2))
W_32F = 1.0 - N0_32F[:, :, 2]
W_32F *= 1.0 / np.max(W_32F)
W_32F = W_32F**1.5
A_c, b_c = amg_constraints.normalConstraints(W_32F, N0_32F)
A_L = amg_constraints.laplacianMatrix((h, w))
lambda_d = 2.0
A = A_c + lambda_d * A_L
b = b_c
N_32F = amg_solver.solve(A, b).reshape(h, w, 3)
N_32F = computeNz(N_32F.reshape(-1, 3)).reshape(h, w, 3)
N_32F = normalizeImage(N_32F)
return N_32F
