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[list(range(21)) + list(range(27, 30)) +
list(range(36, 60))], frame.smpl.trans
]
else:
x0 = [
frame.smpl.pose[list(range(21)) + list(range(27, 30)) +
list(range(36, 72))], frame.smpl.trans
]
ch.minimize(E,
x0,
method='dogleg',
options={
'e_3': .01,
},
callback=get_cb(viz_rn, frame))
def fit_consensus(frames, base_smpl, camera, frustum, model_data, nohands, icp_count, naked, display):
if nohands:
faces = faces_no_hands(base_smpl.f)
else:
faces = base_smpl.f
vis_rn_b = BoundaryRenderer(camera=camera, frustum=frustum, f=faces, num_channels=1)
vis_rn_m = ColoredRenderer(camera=camera, frustum=frustum, f=faces, vc=np.zeros_like(base_smpl), bgcolor=1,
num_channels=1)
model_template = Smpl(model_data)
model_template.betas[:] = base_smpl.betas.r
g_laplace = regularize_laplace()
g_model = regularize_model()
g_symmetry = regularize_symmetry()
face_ids = get_face_vertex_ids()
for step, (w_laplace, w_model, w_symmetry, sigma) in enumerate(zip(
np.linspace(6.5, 4.0, icp_count) if naked else np.linspace(4.0, 2.0, icp_count),
np.linspace(0.9, 0.6, icp_count) if naked else np.linspace(0.6, 0.3, icp_count),
np.linspace(3.6, 1.8, icp_count),
np.linspace(0.06, 0.003, icp_count),
)):
log.info('# Step {}'.format(step))
L = laplacian(model_template.r, base_smpl.f)
delta = L.dot(model_template.r)
w_laplace *= g_laplace.reshape(-1, 1)
w_model *= g_model.reshape(-1, 1)
w_symmetry *= g_symmetry.reshape(-1, 1)
E = {
'laplace': (sp_dot(L, base_smpl.v_shaped_personal) - delta) * w_laplace,
'model': (base_smpl.v_shaped_personal - model_template) * w_model,
'symmetry': (base_smpl.v_personal + np.array([1, -1, -1])
* base_smpl.v_personal[model_data['vert_sym_idxs']]) * w_symmetry,
}
log.info('## Matching rays with contours')
for current, f in enumerate(tqdm(frames)):
E['silh_{}'.format(current)] = ray_objective(f, sigma, base_smpl, camera, vis_rn_b, vis_rn_m)
#paper 2
E['face_{}'.format(current)] = ray_face(f, sigma, base_smpl, camera, face_ids)
log.info('## Run optimization')
ch.minimize(
E,
[base_smpl.v_personal, model_template.betas],
method='dogleg',
options={'maxiter': 15, 'e_3': 0.001},
callback=get_cb(frames[0], base_smpl, camera, frustum) if display else None
)