def fit_pose(frame, last_smpl, frustum, nohands, viz_rn):
if nohands:
faces = faces_no_hands(frame.smpl.f)
else:
faces = frame.smpl.f
dst_type = cv2.cv.CV_DIST_L2 if cv2.__version__[0] == '2' else cv2.DIST_L2
dist_i = cv2.distanceTransform(np.uint8(frame.mask * 255), dst_type, 5) - 1
dist_i[dist_i < 0] = 0
dist_i[dist_i > 50] = 50
dist_o = cv2.distanceTransform(255 - np.uint8(frame.mask * 255), dst_type,
5)
dist_o[dist_o > 50] = 50
rn_m = ColoredRenderer(camera=frame.camera,
v=frame.smpl,
f=faces,
vc=np.ones_like(frame.smpl),
frustum=frustum,
bgcolor=0,
num_channels=1)
E = {
'mask':
gaussian_pyramid(rn_m * dist_o * 100. + (1 - rn_m) * dist_i,
n_levels=4,
normalization='size') * 80.,
'2dpose':
GMOf(frame.pose_obj, 100),
'prior':
frame.pose_prior_obj * 4.,
'sp':
frame.collision_obj * 1e3,
}
if last_smpl is not None:
E['last_pose'] = GMOf(frame.smpl.pose - last_smpl.pose, 0.05) * 50.
E['last_trans'] = GMOf(frame.smpl.trans - last_smpl.trans, 0.05) * 50.
if nohands:
x0 = [
frame.smpl.pose[range(21) + range(27, 30) + range(36, 60)],
frame.smpl.trans
]
else:
x0 = [
frame.smpl.pose[range(21) + range(27, 30) + range(36, 72)],
frame.smpl.trans
]
ch.minimize(E,
x0,
method='dogleg',
options={
'e_3': .01,
},
callback=get_cb(viz_rn, frame))
def test_earth():
m = get_earthmesh(trans=ch.array([0, 0, 0]), rotation=ch.zeros(3))
# Create V, A, U, f: geometry, brightness, camera, renderer
V = ch.array(m.v)
A = SphericalHarmonics(vn=VertNormals(v=V, f=m.f),
components=[3., 2., 0., 0., 0., 0., 0., 0., 0.],
light_color=ch.ones(3))
# camera
U = ProjectPoints(v=V,
f=[w, w],
c=[w / 2., h / 2.],
k=ch.zeros(5),
t=ch.zeros(3),
rt=ch.zeros(3))
f = TexturedRenderer(vc=A,
camera=U,
f=m.f,
bgcolor=[0., 0., 0.],
texture_image=m.texture_image,
vt=m.vt,
ft=m.ft,
frustum={
'width': w,
'height': h,
'near': 1,
'far': 20
})
# Parameterize the vertices
translation, rotation = ch.array([0, 0, 8]), ch.zeros(3)
f.v = translation + V.dot(Rodrigues(rotation))
observed = f.r
np.random.seed(1)
# this is reactive
# in the sense that changes to values will affect function which depend on them.
translation[:] = translation.r + np.random.rand(3)
rotation[:] = rotation.r + np.random.rand(3) * .2
# Create the energy
E_raw = f - observed
E_pyr = gaussian_pyramid(E_raw, n_levels=6, normalization='size')
Image.fromarray((observed * 255).astype(np.uint8)).save(
os.path.join(save_dir, "reference.png"))
step = 0
Image.fromarray((f.r * 255).astype(np.uint8)).save(
os.path.join(save_dir, "step_{:05d}.png".format(step)))
print('OPTIMIZING TRANSLATION, ROTATION, AND LIGHT PARMS')
free_variables = [translation, rotation]
ch.minimize({'pyr': E_pyr}, x0=free_variables, callback=create_callback(f))
ch.minimize({'raw': E_raw}, x0=free_variables, callback=create_callback(f))
def final_fit(
opt,
part_mesh,
v,
v_offset,
dist_o,
dist_i,
smpl_h_ref,
rn_m,
debug_rn,
dif_mask,
v_ids_template,
faces_template,
v_ids_side,
faces_side,
max_y,
proj_cam,
ref_joint_list_coup,
):
if opt.disp_mesh_side:
mv = MeshViewer()
else:
mv = None
if opt.disp_mesh_whl:
mv2 = MeshViewer()
else:
mv2 = None
import scipy.sparse as sp
sparse_solver = lambda A, x: sp.linalg.cg(A, x, maxiter=500)[0]
tgt_pose = get_pose_prior(init_pose_path=opt.init_pose_path,
gar_type=opt.gar_type)
E = {
'mask':
gaussian_pyramid(rn_m * dist_o * opt.ref_wt_dist_o +
(1 - rn_m) * dist_i,
n_levels=4,
normalization='size') * opt.ref_wt_mask
}
x0 = [v_offset]
if opt.ref_wt_coup:
# x0 = [smpl_h_ref.trans, smpl_h_ref.betas, v_offset]
E['coupling'] = (v + v_offset -
smpl_h_ref[v_ids_template]) * opt.ref_wt_coup
if opt.ref_wt_shp:
E['beta_prior'] = ch.linalg.norm(smpl_h_ref.betas) * opt.ref_wt_shp
if opt.ref_wt_pose:
E['pose'] = (smpl_h_ref.pose - tgt_pose) * opt.ref_wt_pose
if ref_joint_list_coup != None:
range_joint = []
for item in ref_joint_list_coup:
range_joint.append(3 * int(item))
range_joint.append(3 * int(item) + 1)
range_joint.append(3 * int(item) + 2)
x0 = x0 + [smpl_h_ref.pose[range_joint]]
if opt.ref_use_betas:
x0 = x0 + [smpl_h_ref.betas]
if opt.ref_wt_proj:
error_bd = get_rings_error(proj_cam, max_y)
E['proj_bd'] = error_bd * opt.ref_wt_proj
if opt.ref_wt_bd:
gar_rings = compute_boundaries(v + v_offset, faces_template)
error = smooth_rings(gar_rings, v + v_offset)
E['boundary'] = error * opt.ref_wt_bd
if opt.ref_wt_lap:
lap_op = np.asarray(laplacian(part_mesh).todense())
lap_err = ch.dot(lap_op, v + v_offset)
E['laplacian'] = lap_err * opt.ref_wt_lap
ch.minimize(E,
x0,
method='dogleg',
options={
'e_3': .000001,
'sparse_solver': sparse_solver
},
callback=get_callback_ref(rend=debug_rn,
mask=dif_mask,
vertices=v + v_offset,
display=opt.display,
v_ids_sides=v_ids_side,
faces_template=faces_template,
faces_side=faces_side,
disp_mesh_side=opt.disp_mesh_side,
disp_mesh_whl=opt.disp_mesh_whl,
save_dir=opt.save_opt_images,
mv=mv,
mv2=mv2,
show=opt.show))
final_mask = rn_m.r
mask = dif_mask.copy()
mask[dif_mask == 0.5] = 1
final_iou = compute_iou(mask, final_mask)
return v + v_offset, final_iou
def init_fit(opt, dist_o, dist_i, dif_mask, rn_m, smpl_h, v_ids_template,
faces_template, debug_rn, v_ids_side, faces_side, joints_list):
range_joint = []
for item in joints_list:
range_joint.append(3 * int(item))
range_joint.append(3 * int(item) + 1)
range_joint.append(3 * int(item) + 2)
tgt_pose = get_pose_prior(init_pose_path=opt.init_pose_path,
gar_type=opt.gar_type)
# ============================================
# FIRST STAGE
# ============================================
from psbody.mesh import MeshViewer
if opt.disp_mesh_side:
mv = MeshViewer()
else:
mv = None
if opt.disp_mesh_whl:
mv2 = MeshViewer()
else:
mv2 = None
if opt.init_first_stage == "Trans":
x0 = [smpl_h.trans]
elif opt.init_first_stage == 'Pose':
x0 = [smpl_h.trans, smpl_h.pose[range_joint]]
# x0 = [smpl_h.trans]
elif opt.init_first_stage == 'Shape':
x0 = [smpl_h.trans, smpl_h.betas]
elif opt.init_first_stage == 'Both':
x0 = [smpl_h.trans, smpl_h.betas, smpl_h.pose[range_joint]]
E = {
'mask':
gaussian_pyramid(rn_m * dist_o * opt.init_fst_wt_dist_o +
(1 - rn_m) * dist_i,
n_levels=4,
normalization='size') * opt.init_fst_wt_mask
}
if opt.init_fst_wt_betas:
E['beta_prior'] = ch.linalg.norm(smpl_h.betas) * opt.init_fst_wt_betas
if opt.init_fst_wt_pose:
E['pose'] = (smpl_h.pose - tgt_pose) * opt.init_fst_wt_pose
ch.minimize(E,
x0,
method='dogleg',
options={
'e_3': .0001,
'disp': True
},
callback=get_callback(rend=debug_rn,
mask=dif_mask,
smpl=smpl_h,
v_ids_template=v_ids_template,
v_ids_sides=v_ids_side,
faces_template=faces_template,
faces_side=faces_side,
display=opt.display,
disp_mesh_side=opt.disp_mesh_side,
disp_mesh_whl=opt.disp_mesh_whl,
mv=mv,
mv2=mv2,
save_dir=opt.save_opt_images,
show=opt.show))
# ===============================================
# SECOND STAGE
# ===============================================
if opt.init_second_stage != "None":
if opt.init_second_stage == 'Pose':
x0 = [smpl_h.trans, smpl_h.pose[range_joint]]
elif opt.init_second_stage == 'Shape':
x0 = [smpl_h.trans, smpl_h.betas]
elif opt.init_second_stage == 'Both':
x0 = [smpl_h.trans, smpl_h.betas, smpl_h.pose[range_joint]]
E = {
'mask':
gaussian_pyramid(rn_m * dist_o * opt.init_sec_wt_dist_o +
(1 - rn_m) * dist_i,
n_levels=4,
normalization='size') * opt.init_sec_wt_mask
}
if opt.init_sec_wt_betas:
E['beta_prior'] = ch.linalg.norm(
smpl_h.betas) * opt.init_sec_wt_betas
if opt.init_sec_wt_pose:
E['pose'] = (smpl_h.pose - tgt_pose) * opt.init_sec_wt_pose
ch.minimize(E,
x0,
method='dogleg',
options={'e_3': .0001},
callback=get_callback(rend=debug_rn,
mask=dif_mask,
smpl=smpl_h,
v_ids_template=v_ids_template,
v_ids_sides=v_ids_side,
faces_template=faces_template,
faces_side=faces_side,
display=opt.display,
disp_mesh_side=opt.disp_mesh_side,
disp_mesh_whl=opt.disp_mesh_whl,
mv=mv,
mv2=mv2,
save_dir=opt.save_opt_images,
show=opt.show))
temp_params = {
'pose': smpl_h.pose.r,
'betas': smpl_h.betas.r,
'trans': smpl_h.trans.r,
'v_personal': smpl_h.v_personal.r
}
part_mesh = Mesh(smpl_h.r[v_ids_template], faces_template)
return part_mesh, temp_params
#chPositionGT[1] = 0.01
#plt.figure()
#plt.title('Init object')
_x0 = renderer.r
#plt.imshow(_x0)
#plt.show(0.1)
#sys.exit('Alles Gut!!!')
difference = renderer - rendererGT
print ('=======================>','difference', type(difference))
gpModel = gaussian_pyramid(difference).sum()
#sys.exit('Alles Gut')
#plt.title('Init object')
global iter
iter = 0
def cb(_):
pass
global method
methods = ['dogleg', 'minimize', 'BFGS', 'L-BFGS-B', 'Nelder-Mead', 'SGDMom']
method = 1
options = {'disp': True, 'maxiter': 1000, 'lr':2e-4, 'momentum' : 0.4, 'decay' :0.9, 'tol' : 1e-7}