def _optimize(self, A_I, b_I):
h, w = self._I_32F.shape[:2]
A_L = laplacianMatrix((h, w), num_elements=1)
A_L = A_L
A = A_L + A_I
b = b_I
N = amg_solver.solve(A, b).reshape(-1, 3)
computeNz(N)
#N = normalizeVectors(N)
N_32F = N.reshape(h, w, 3)
self._N_32F = N_32F
def _computeInitialNormal(self):
A_8U = self._A_8U
self._N0_32F = np.float64(silhouetteNormal(A_8U))
return
h, w = A_8U.shape
A_L = laplacianMatrix((h, w), num_elements=3)
A_sil, b_sil = silhouetteConstraints(A_8U, is_flat=True)
A = A_L + A_sil
b = b_sil
N = amg_solver.solve(A, b).reshape(-1, 3)
computeNz(N)
N = normalizeVectors(N)
N_32F = N.reshape(h, w, 3)
self._N0_32F = N_32F
def _computeInitialNormal(self):
A_8U = self._A_8U
self._N0_32F = np.float64(silhouetteNormal(A_8U))
return
h, w = A_8U.shape
A_L = laplacianMatrix((h, w), num_elements=3)
A_sil, b_sil = silhouetteConstraints(A_8U, is_flat=True)
A = A_L + A_sil
b = b_sil
N = amg_solver.solve(A, b).reshape(-1, 3)
computeNz(N)
N = normalizeVectors(N)
N_32F = N.reshape(h, w, 3)
self._N0_32F = N_32F
def _computeLumoNormal(self):
A_8U = self._A_8U
if A_8U is None:
return
h, w = A_8U.shape[:2]
A_c, b_c = amg_constraints.silhouetteConstraints(A_8U)
A_L = amg_constraints.laplacianMatrix((h, w))
A = 3.0 * A_c + A_L
b = 3.0 * 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)
self._N_lumo = np.array(N_32F)
def _computeLumoNormal(self):
A_8U = self._A_8U
if A_8U is None:
return
h, w = A_8U.shape[:2]
A_c, b_c = amg_constraints.silhouetteConstraints(A_8U)
A_L = amg_constraints.laplacianMatrix((h, w))
A = 3.0 * A_c + A_L
b = 3.0 * 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)
self._N_lumo = np.array(N_32F)
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N0_32F, A_8U = normal_data
h, w = A_8U.shape[:2]
A_c, b_c = amg_constraints.silhouetteConstraints(A_8U, is_flat=True)
A_L = amg_constraints.laplacianMatrix((h, w), num_elements=3)
A = A_c + A_L
b = b_c
N = amg_solver.solve(A, b).reshape(-1, 3)
N = computeNz(N)
N = normalizeVectors(N)
N_32F = N.reshape(h, w, 3)
file_path = self.resultFile(self._data_file_name)
saveNormal(file_path, N_32F, A_8U)
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N0_32F, A_8U = normal_data
h, w = A_8U.shape[:2]
A_c, b_c = amg_constraints.silhouetteConstraints(A_8U, is_flat=True)
A_L = amg_constraints.laplacianMatrix((h, w), num_elements=3)
A = A_c + A_L
b = b_c
N = amg_solver.solve(A, b).reshape(-1, 3)
N = computeNz(N)
N = normalizeVectors(N)
N_32F = N.reshape(h, w, 3)
file_path = self.resultFile(self._data_file_name)
saveNormal(file_path, N_32F, A_8U)
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
def func(N_32F):
h, w = N_32F.shape[:2]
N_new = computeNz(N_32F.reshape(-1, 3)).reshape(h, w, 3)
return N_new
def func(N_32F):
h, w = N_32F.shape[:2]
N_new = computeNz(N_32F.reshape(-1, 3)).reshape(h, w, 3)
return N_new
