def depthFromGradient(I_32F, A_8U):
if A_8U is not None:
I_32F = preProcess(I_32F, A_8U)
h, w = I_32F.shape[:2]
A_g, b_g = gradientConstraints(I_32F, w_cons=1.0)
A_rg = l2Regularization(h*w, w_rg=0.000001)
A_L = laplacianMatrix((h, w))
A = A_g + A_rg
b = b_g
D_flat = amg_solver.solve(A, b)
D_32F = D_flat.reshape(h, w)
return D_32F
def depthFromGradient(I_32F, A_8U):
if A_8U is not None:
I_32F = preProcess(I_32F, A_8U)
h, w = I_32F.shape[:2]
A_g, b_g = gradientConstraints(I_32F, w_cons=1.0)
A_rg = l2Regularization(h * w, w_rg=0.000001)
A_L = laplacianMatrix((h, w))
A = A_g + A_rg
b = b_g
D_flat = amg_solver.solve(A, b)
D_32F = D_flat.reshape(h, w)
return D_32F
def smoothGradient(gx):
h, w = gx.shape[:2]
A_L = laplacianMatrix((h, w))
w_g = 1.0
gx_flat = gx.flatten()
cons_ids = np.where(gx_flat > 0.01 * np.max(0.0))
num_verts = h * w
diags = np.zeros(num_verts)
diags[cons_ids] = w_g
A = scipy.sparse.diags(diags, 0) + A_L
b = np.zeros(num_verts, dtype=np.float32)
b[cons_ids] = w_g * gx_flat[cons_ids]
gx_flat = amg_solver.solve(A, b)
gx = gx_flat.reshape(h, w)
print gx.shape
return np.float32(gx)
def smoothGradient(gx):
h, w = gx.shape[:2]
A_L = laplacianMatrix((h, w))
w_g = 1.0
gx_flat = gx.flatten()
cons_ids = np.where(gx_flat > 0.01 * np.max(0.0))
num_verts = h * w
diags = np.zeros(num_verts)
diags[cons_ids] = w_g
A = scipy.sparse.diags(diags, 0) + A_L
b = np.zeros(num_verts, dtype=np.float32)
b[cons_ids] = w_g * gx_flat[cons_ids]
gx_flat = amg_solver.solve(A, b)
gx = gx_flat.reshape(h, w)
print gx.shape
return np.float32(gx)
