def gen_mpis(basedir, savedir, fwh, logdir, num_planes, no_mpis, disps, psvs):
if not os.path.exists(savedir):
os.makedirs(savedir)
# load up images, poses, w/ scale factor
poses, bds, imgs = load_data(basedir, *fwh)
# load up model
ibr_runner = DeepIBR()
ibr_runner.load_graph(logdir)
patched = imgs.shape[0] * imgs.shape[1] * num_planes > 640 * 480 * 32
N = imgs.shape[-1]
close_depths = [bds.min() * .9] * N
inf_depths = [bds.max() * 2.] * N
mpi_bds = np.array([close_depths, inf_depths])
mpis = run_inference(imgs,
poses,
mpi_bds,
ibr_runner,
num_planes,
patched,
disps=disps,
psvs=psvs)
for i in range(N):
if not os.path.exists('{}/mpi{:02d}'.format(savedir, i)):
os.makedirs('{}/mpi{:02d}'.format(savedir, i))
if not no_mpis:
mpis[i].save('{}/mpi{:02d}'.format(savedir, i), False, True)
if disps:
plt.imsave(os.path.join(savedir, 'mpi{:02d}/disps.png'.format(i)),
mpis[i].disps)
if psvs:
psv = np.moveaxis(mpis[i].psv, -2, 0)
psv = (255 * np.clip(psv, 0, 1)).astype(np.uint8)
imageio.mimwrite(os.path.join(savedir,
'mpi{:02d}/psv.mp4'.format(i)),
psv,
fps=30,
quality=5)
with open(os.path.join(savedir, 'metadata.txt'), 'w') as file:
file.write('{} {} {} {}\n'.format(N, imgs.shape[1], imgs.shape[0],
num_planes))
print('Saved to', savedir)
return True
args.y_axis,
args.z_axis,
args.circle,
args.spiral,
args.cylinder_1,
args.cylinder_1_5,
args.cylinder_2,
args.grid_1,
args.grid_1_5,
args.grid_2
]
if any(comps) is False:
comps = [True] * 11
print('Path components', comps)
poses, bds = load_data(args.scenedir, load_imgs=False)
render_poses = generate_render_path(poses, bds, comps, N=30, scenedir=args.scenedir)
if args.outname.endswith('txt'):
render_poses = np.concatenate([render_poses[...,1:2],
-render_poses[...,0:1],
render_poses[...,2:]], -1)
str_out = '{}\n'.format(render_poses.shape[0])
for p in render_poses:
str_out += ' '.join(['{}'.format(x) for x in p.T.ravel()]) + '\n'
open(args.outname, 'w').write(str_out)
elif args.outname.endswith('npy'):