def _computeInitialNormal(self):
A_8U = self._A_8U
h, w = A_8U.shape
N0_32F = silhouetteNormal(A_8U)
A, b = normalConstraints(A_8U, N0_32F)
N_flat = amg_solver.solve(A, b)
N_flat = normalizeVectors(N_flat)
N_32F = N_flat.reshape(h, w, 3)
self._N0_32F = N_32F
def _computeInitialNormal(self):
A_8U = self._A_8U
h, w = A_8U.shape
N0_32F = silhouetteNormal(A_8U)
A, b = normalConstraints(A_8U, N0_32F)
N_flat = amg_solver.solve(A, b)
N_flat = normalizeVectors(N_flat)
N_32F = N_flat.reshape(h, w, 3)
self._N0_32F = N_32F
def estimateNormal(A_8U):
h, w = A_8U.shape
N0_32F = silhouetteNormal(A_8U)
A, b = normalConstraints(A_8U, N0_32F)
N_flat = amg_solver.solve(A, b)
N_flat = computeNz(N_flat)
N_flat = normalizeVectors(N_flat)
N_32F = N_flat.reshape(h, w, 3)
return 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 _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 _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 _projectBrightness(self):
I_32F = self._I_32F
h, w = I_32F.shape[:2]
I = I_32F.reshape(h * w)
L = self._L
N0_32F = self._N0_32F
N0 = N0_32F.reshape(-1, 3)
NL = np.dot(N0, L)
NL_min, NL_max = np.min(NL), np.max(NL)
I_min, I_max = np.min(I), np.max(I)
I = NL_min + (NL_max - NL_min) * (I - I_min) / (I_max - I_min)
dI = I - NL
N = np.zeros(N0.shape)
for i in range(3):
N[:, i] = N0[:, i] + L[i] * dI
N = normalizeVectors(N)
self._N_32F = N.reshape(h, w, 3)
def _projectBrightness(self):
I_32F = self._I_32F
h, w = I_32F.shape[:2]
I = I_32F.reshape(h * w)
L = self._L
N0_32F = self._N0_32F
N0 = N0_32F.reshape(-1, 3)
NL = np.dot(N0, L)
NL_min, NL_max = np.min(NL), np.max(NL)
I_min, I_max = np.min(I), np.max(I)
I = NL_min + (NL_max - NL_min) * (I - I_min) / (I_max - I_min)
dI = I - NL
N = np.zeros(N0.shape)
for i in range(3):
N[:, i] = N0[:, i] + L[i] * dI
N = normalizeVectors(N)
self._N_32F = N.reshape(h, w, 3)
def normalizeImage(N_32F, th=0.0):
N_flat = N_32F.reshape((-1, 3))
N_flat_normalized = normalizeVectors(N_flat, th=th)
N_32F_normalized = N_flat_normalized.reshape(N_32F.shape)
return N_32F_normalized
