parser.add_argument('--image_set', type=str)
args = parser.parse_args()
image_set = args.image_set
Lref = loadtxt(args.transformed_reference)
Nc = len(Lref)
## Read the estimated orientations
data_gen = [json.loads(y) for y in open(args.input_file).readlines()]
qq = [
(a,
mtq(fixit(array(b['matrix'])).T),
b['time']['edge'] + b['time']['est'])
for a, b in enumerate(data_gen)
]
print qq
## Get orientations ordered by frame number
Lest = array([Quat(x).q for _, x, _ in sorted(qq)])
times = array([t for _, _, t in sorted(qq)])
assert len(Lref) == len(Lest)
# ## Fix and normalize reference quaternions.
if image_set == 'apast':
fix_reference_quaternions_apa(Lest)
if __name__ == '__main__':
with open('bundle.out') as fp:
aa = fp.readlines()
Nc = int(aa[1].split(' ')[0])
Lori = zeros((Nc, 3,3))
for n in range(Nc):
for j in range(3):
Lori[n,j] = array([float(x) for x in aa[3+n*5+j].split(' ')])
Lq = zeros((Nc,4))
for n in range(Nc):
ori = Quat(mtq(Lori[n]))
Lq[n] = ori.normalize().q * array([1,-1,1,1])
## Read the tardiff output
## Get orientations ordered by frame number
Lr = loadtxt('tardiff_undistorted_quaternions.dat')
## fix and normalize reference quaternions.
fix_reference_quaternions(Lr)
Le = zeros((Nc,4))
