def optimization(ratio):
lr0 = 0.0001
lr = lr0
folder_name = os.getcwd() + '/progress-b-1-%f/' % ratio
if not os.path.isdir(folder_name):
os.mkdir(folder_name)
filename = os.getcwd() + '/setup/bunny_transient_64_0.mat'
setup = scipy.io.loadmat(filename)
gt_transient = np.array(setup['gt_transient'], dtype=np.double, order='C')
gt_mesh = MESH()
gt_mesh.v = igl.eigen.MatrixXd(np.array(setup['gt_v'], dtype=np.double))
gt_mesh.f = igl.eigen.MatrixXd(np.array(setup['gt_f'],
dtype=np.double)).castint()
opt = OPT(20000)
opt.max_distance_bin = gt_transient.shape[1]
opt.smooth_weight = 0.001
mesh = MESH()
mesh_init_location = os.getcwd() + '/init/cnlos_bunny_threshold_64.obj'
igl.readOBJ(mesh_init_location, mesh.v, mesh.f)
mesh.v = np.array(mesh.v, dtype=np.float32, order='C')
mesh.f = np.array(mesh.f, dtype=np.int32, order='C')
opt.resolution = 64
opt.smooth_ratio = ratio
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
old_v = np.array(mesh.v)
rendering.compute_mesh_affinity(mesh)
rendering.border_indicator(mesh)
weight = rendering.create_weighting_function(gt_transient, opt.gamma)
optimization_alpha = torch.Tensor([mesh.alpha])
optimization_alpha.requires_grad_()
optimizer_alpha = optim.Adam([optimization_alpha], lr=opt.alpha_lr)
dummy_loss2 = optimization_alpha**2
dummy_loss2.backward()
global_counter = 0
for t in range(3):
if mesh.f.shape[0] > 250000:
break
global_counter, l2_record = optimize_parameters.optimize_alpha(
mesh, gt_transient, weight, optimization_alpha, optimizer_alpha,
opt, 50, global_counter)
if t == 0:
l2_0 = l2_record
lr = (l2_record / l2_0) * lr0 * ((0.99)**(t / 50))
print('new lr %f' % lr)
old_v = np.array(mesh.v)
global_counter, convergence_flag = optimize_parameters.optimize_shape(
mesh, gt_transient, weight, opt, 15, lr, gt_mesh, global_counter)
if convergence_flag:
if opt.testing_flag == 1:
opt.testing_flag = 0
else:
opt.testing_flag = 1
rendering.el_topo_gradient(mesh, old_v)
rendering.el_topo_remeshing(mesh, .5 / opt.resolution)
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
rendering.compute_mesh_affinity(mesh)
rendering.removeTriangle(mesh, opt)
rendering.compute_mesh_affinity(mesh)
rendering.border_indicator(mesh)
def optimization(ratio):
lr0 = 0.0001
lr = lr0
folder_name = os.getcwd() + '/progress-b-1-%f/' % ratio
if not os.path.isdir(folder_name):
os.mkdir(folder_name)
filename = os.getcwd() + '/transient.mat'
setup = scipy.io.loadmat(filename)
gt_transient = np.array(setup['transient'], dtype=np.double, order='C')
opt = OPT(20000)
opt.max_distance_bin = gt_transient.shape[1]
opt.smooth_weight = 0.001
opt.lighting = np.array(setup['lighting'], dtype=np.float32, order='C')
opt.sensor = np.array(setup['lighting'], dtype=np.float32, order='C')
opt.gt_mesh = False
gt_mesh = MESH()
mesh = MESH()
mesh_init_location = os.getcwd() + '/cnlos_s_threshold.obj'
igl.readOBJ(mesh_init_location, mesh.v, mesh.f)
mesh.v = np.array(mesh.v, dtype=np.float32, order='C')
mesh.f = np.array(mesh.f, dtype=np.int32, order='C')
opt.resolution = 64
opt.smooth_ratio = ratio
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
old_v = np.array(mesh.v)
rendering.compute_mesh_affinity(mesh)
rendering.border_indicator(mesh)
weight = rendering.create_weighting_function(gt_transient, opt.gamma)
mesh.albedo = optimize_parameters.initial_fitting_albedo(
mesh, gt_transient, weight, opt)
global_counter = 0
l2 = np.empty(400)
albedo = np.empty(400)
resolution_cnt = 0
for t in range(400):
if mesh.f.shape[0] > 250000:
break
if t < 30 or resolution_cnt < 5:
global_counter, l2_record = optimize_parameters.optimize_albedo(
mesh, gt_transient, weight, opt, 50, global_counter,
folder_name)
if t == 0:
l2_0 = l2_record
opt.albedo_lr = (l2_reccord / l2_0) * opt_albedo_lr0 * (
(0.99)**(t / 5))
lr = (l2_record / l2_0) * lr0 * ((0.99)**(t / 5))
print('new lr %f' % lr)
old_v = np.array(mesh.v)
global_counter, convergence_flag, l2_record = optimize_parameters.optimize_shape(
mesh, gt_transient, weight, opt, 15, lr, gt_mesh, global_counter,
folder_name)
resolution_cnt += 1
if resolution_cnt == 50:
resolution_cnt = 0
opt.resolution *= 1.5
opt.sample_num *= 1.5
if convergence_flag:
if opt.testing_flag == 1:
opt.testing_flag = 0
opt.smooth_ratio = ratio / 10 + t / 100
print('shading')
else:
opt.testing_flag = 1
opt.smooth_ratio = ratio + t / 10
rendering.el_topo_gradient(mesh, old_v)
rendering.el_topo_remeshing(mesh, .5 / opt.resolution)
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
rendering.compute_mesh_affinity(mesh)
rendering.removeTriangle(mesh, opt)
rendering.compute_mesh_affinity(mesh)
rendering.border_indicator(mesh)
filename = folder_name + '%05d.mat' % (t)
scipy.io.savemat(filename,
mdict={
'v': mesh.v,
'f': mesh.f,
'albedo': mesh.albedo
})
l2[t] = l2_record
albedo[t] = mesh.albedo
filename = folder_name + 'progress.mat'
scipy.io.savemat(filename, mdict={'albedo': albedo, 'l2': l2})
def optimization(ratio):
lr0 = 0.0005
lr = lr0
folder_name = os.getcwd() + '/progress-b-1-%f/' % ratio
if not os.path.isdir(folder_name):
os.mkdir(folder_name)
filename = os.getcwd() + '/../exp_bunny/setup/bunny_transient.mat'
setup = scipy.io.loadmat(filename)
gt_transient = np.array(setup['gt_transient'], dtype=np.double, order='C')
gt_mesh = MESH()
gt_mesh.v = igl.eigen.MatrixXd(np.array(setup['gt_v'], dtype=np.double))
gt_mesh.f = igl.eigen.MatrixXd(np.array(setup['gt_f'],
dtype=np.double)).castint()
opt = OPT(20000)
opt.max_distance_bin = gt_transient.shape[1]
opt.smooth_weight = 0.001
mesh = MESH()
mesh_init_location = os.getcwd() + '/init/cnlos_bunny_threshold.obj'
igl.readOBJ(mesh_init_location, mesh.v, mesh.f)
mesh.v = np.array(mesh.v, dtype=np.float32, order='C')
mesh.f = np.array(mesh.f, dtype=np.int32, order='C')
opt.resolution = 64
opt.smooth_ratio = ratio
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
old_v = np.array(mesh.v)
rendering.compute_mesh_affinity(mesh)
rendering.border_indicator(mesh)
weight = rendering.create_weighting_function(gt_transient, opt.gamma)
global_counter = 0
old_v = np.array(mesh.v)
old_f = np.array(mesh.f)
global_counter, convergence_flag, l2_record = optimize_parameters.optimize_shape(
mesh, gt_transient, weight, opt, 15, lr, gt_mesh, global_counter,
folder_name)
grad_v = np.array(mesh.v)
rendering.el_topo_gradient(mesh, old_v)
rendering.el_topo_remeshing(mesh, .5 / opt.resolution)
eltopo_v = np.array(mesh.v)
eltopo_f = np.array(mesh.f)
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
isotropic_v = np.array(mesh.v)
isotropic_f = np.array(mesh.f)
filename = folder_name + 'data.mat'
scipy.io.savemat(filename,
mdict={
'old_v': old_v,
'old_f': old_f,
'grad_v': grad_v,
'eltopo_v': eltopo_v,
'eltopo_f': eltopo_f,
'isotropic_v': isotropic_v,
'isotropic_f': isotropic_f
})
def optimization(ratio):
lr0 = 0.0001
lr = lr0
folder_name = os.getcwd() + '/progress-b-3-%f/' % ratio
if not os.path.isdir(folder_name):
os.mkdir(folder_name)
test_num = 1
filename = os.getcwd() + '/setup/bunny_transient_64_0_%d.mat' % test_num
setup = scipy.io.loadmat(filename)
gt_transient = np.array(setup['gt_transient'], dtype=np.double, order='C')
gt_mesh = MESH()
gt_mesh.v = igl.eigen.MatrixXd(np.array(setup['gt_v'], dtype=np.double))
gt_mesh.f = igl.eigen.MatrixXd(np.array(setup['gt_f'],
dtype=np.double)).castint()
opt = OPT(20000)
opt.max_distance_bin = gt_transient.shape[1]
opt.smooth_weight = 0.001
mesh = MESH()
mesh_init_location = os.getcwd(
) + '/init/cnlos_bunny_threshold_64_0_%d.obj' % test_num
igl.readOBJ(mesh_init_location, mesh.v, mesh.f)
mesh.v = np.array(mesh.v, dtype=np.float32, order='C')
mesh.f = np.array(mesh.f, dtype=np.int32, order='C')
opt.resolution = 64
opt.smooth_ratio = ratio
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
old_v = np.array(mesh.v)
rendering.compute_mesh_affinity(mesh)
rendering.border_indicator(mesh)
weight = rendering.create_weighting_function(gt_transient, opt.gamma)
global_counter = 0
l2 = np.empty(500)
alpha = np.empty(500)
for t in range(500):
if mesh.f.shape[0] > 250000:
break
global_counter, l2_record = optimize_parameters.optimize_alpha(
mesh, gt_transient, weight, opt, 50, global_counter)
if t == 0:
l2_0 = l2_record
lr = (l2_record / l2_0) * lr0 * ((0.99)**(t / 2))
print('new lr %f' % lr)
old_v = np.array(mesh.v)
global_counter, convergence_flag, l2_record = optimize_parameters.optimize_shape(
mesh, gt_transient, weight, opt, 15, lr, gt_mesh, global_counter)
if convergence_flag:
if opt.testing_flag == 1:
opt.testing_flag = 0
opt.smooth_ratio = ratio / 10 + t / 100
print('shading')
else:
opt.testing_flag = 1
opt.smooth_ratio = ratio + t / 10
rendering.el_topo_gradient(mesh, old_v)
rendering.el_topo_remeshing(mesh, .5 / opt.resolution)
rendering.isotropic_remeshing(mesh, .5 / opt.resolution)
rendering.compute_mesh_affinity(mesh)
rendering.removeTriangle(mesh, opt)
rendering.compute_mesh_affinity(mesh)
rendering.border_indicator(mesh)
filename = folder_name + '%05d.mat' % (t)
scipy.io.savemat(filename,
mdict={
'v': mesh.v,
'f': mesh.f,
'alpha': mesh.alpha
})
l2[t] = l2_record
alpha[t] = mesh.alpha
filename = folder_name + 'progress.mat'
scipy.io.savemat(filename, mdict={'alpha': alpha, 'l2': l2})