def grab_new_objs(grabnet, objs_path, rot=True, n_samples=10, scale=1.):
grabnet.coarse_net.eval()
grabnet.refine_net.eval()
rh_model = mano.load(model_path=grabnet.cfg.rhm_path,
model_type='mano',
num_pca_comps=45,
batch_size=n_samples,
flat_hand_mean=True).to(grabnet.device)
grabnet.refine_net.rhm_train = rh_model
grabnet.logger(f'################# \n'
f'Grabbing the object!'
)
bps = bps_torch(custom_basis=grabnet.bps)
if not isinstance(objs_path, list):
objs_path = [objs_path]
for new_obj in objs_path:
rand_rotdeg = np.random.random([n_samples, 3]) * np.array([360, 360, 360])
rand_rotmat = euler(rand_rotdeg)
dorig = {'bps_object': [],
'verts_object': [],
'mesh_object': [],
'rotmat': []}
for samples in range(n_samples):
verts_obj, mesh_obj, rotmat = load_obj_verts(new_obj, rand_rotmat[samples], rndrotate=rot, scale=scale)
bps_object = bps.encode(verts_obj, feature_type='dists')['dists']
dorig['bps_object'].append(bps_object.to(grabnet.device))
dorig['verts_object'].append(torch.from_numpy(verts_obj.astype(np.float32)).unsqueeze(0))
dorig['mesh_object'].append(mesh_obj)
dorig['rotmat'].append(rotmat)
obj_name = os.path.basename(new_obj)
dorig['bps_object'] = torch.cat(dorig['bps_object'])
dorig['verts_object'] = torch.cat(dorig['verts_object'])
save_dir = os.path.join(grabnet.cfg.work_dir, 'grab_new_objects')
grabnet.logger(f'#################\n'
f' \n'
f'Saving results for the {obj_name.upper()}'
f' \n')
gen_meshes = get_meshes(dorig=dorig,
coarse_net=grabnet.coarse_net,
refine_net=grabnet.refine_net,
rh_model=rh_model,
save=False,
save_dir=save_dir
)
torch.save(gen_meshes, 'data/grabnet_data/meshes.pt')
def grab_new_objs(grabnet, pkl_path, rot=True, n_samples=5, scale=1.):
grabnet.coarse_net.eval()
grabnet.refine_net.eval()
rh_model = mano.load(model_path=grabnet.cfg.rhm_path,
model_type='mano',
num_pca_comps=45,
batch_size=n_samples,
flat_hand_mean=True).to(grabnet.device)
rh_model_pkl = mano.load(model_path=grabnet.cfg.rhm_path,
model_type='mano',
num_pca_comps=15,
batch_size=n_samples,
flat_hand_mean=False).to(grabnet.device)
grabnet.refine_net.rhm_train = rh_model
grabnet.logger(f'################# \n'
f'Colors Guide:'
f' \n'
f'Gray ---> GrabNet generated grasp\n')
bps = bps_torch(custom_basis=grabnet.bps)
all_samples = pickle.load(open(pkl_path, 'rb'))
if args.vis:
print('Shuffling!!!')
random.shuffle(all_samples)
all_samples = all_samples[:args.num]
all_data = []
for idx, new_obj in enumerate(tqdm(all_samples)):
print('idx', idx)
ho = new_obj['ho_aug']
obj_centroid = ho.obj_verts.mean(0)
ho.obj_verts = np.array(ho.obj_verts) - obj_centroid
ho.hand_verts = np.array(ho.hand_verts) - obj_centroid
ho.hand_mTc = np.array(ho.hand_mTc)
ho.hand_mTc[:3, 3] = ho.hand_mTc[:3, 3] - obj_centroid
rand_rotdeg = np.random.random([n_samples, 3]) * np.array([0, 0, 0])
rand_rotmat = euler(rand_rotdeg)
dorig = {
'bps_object': [],
'verts_object': [],
'mesh_object': [],
'rotmat': []
}
for samples in range(n_samples):
verts_obj, mesh_obj, rotmat = load_obj_verts(ho,
rand_rotmat[samples],
rndrotate=rot,
scale=scale)
bps_object = bps.encode(verts_obj, feature_type='dists')['dists']
dorig['bps_object'].append(bps_object.to(grabnet.device))
dorig['verts_object'].append(
torch.from_numpy(verts_obj.astype(np.float32)).unsqueeze(0))
dorig['mesh_object'].append(mesh_obj)
dorig['rotmat'].append(rotmat)
obj_name = 'test1'
dorig['bps_object'] = torch.cat(dorig['bps_object'])
dorig['verts_object'] = torch.cat(dorig['verts_object'])
save_dir = os.path.join(grabnet.cfg.work_dir, 'grab_new_objects')
# grabnet.logger(f'#################\n'
# f' \n'
# f'Showing results for the {obj_name.upper()}'
# f' \n')
verts_out, joints_out = vis_results(ho,
dorig=dorig,
coarse_net=grabnet.coarse_net,
refine_net=grabnet.refine_net,
rh_model=rh_model,
save=False,
save_dir=save_dir,
rh_model_pkl=rh_model_pkl,
vis=args.vis)
ho.obj_verts = np.array(ho.obj_verts) + obj_centroid
ho.hand_verts = np.array(ho.hand_verts) + obj_centroid
ho.hand_mTc = np.array(ho.hand_mTc)
ho.hand_mTc[:3, 3] = ho.hand_mTc[:3, 3] + obj_centroid
verts_out = np.array(
verts_out.detach().squeeze().numpy()) + obj_centroid
joints_out = np.array(
joints_out.detach().squeeze().numpy()) + obj_centroid
new_ho = hand_object.HandObject()
new_ho.load_from_verts(verts_out, new_obj['ho_gt'].obj_faces,
new_obj['ho_gt'].obj_verts)
all_data.append({
'gt_ho': new_obj['ho_gt'],
'in_ho': new_obj['ho_aug'],
'out_verts': verts_out,
'out_joints': joints_out
})
out_file = 'fitted_grabnet.pkl'
print('Saving to {}. Len {}'.format(out_file, len(all_data)))
pickle.dump(all_data, open(out_file, 'wb'))