def fetch_data(self, idx):
sample = {k: self.ds[k][idx] for k in self.ds.keys()}
from human_body_prior.train.vposer_smpl import VPoser
sample['pose_matrot'] = VPoser.aa2matrot(sample['pose'].view(
[1, 1, -1, 3])).view(1, -1)
return sample
def fetch_data(self, idx):
data = {k: self.ds[k][idx] for k in self.ds.keys()}
pose = data.pop('pose')
data['pose_aa'] = pose.view(1,52,3)[:,1:22]
if 'pose_matrot' not in data.keys():
data['pose_matrot'] = VPoser.aa2matrot(data['pose_aa'][np.newaxis]).view(1,-1,9)
else:
data['pose_matrot'] = data['pose_matrot'].view(1,52,9)[:,1:22]
return data
def test_aa2matrot(self):
aa = np.random.randn(10, 3)
cv2_matrot = []
for id in range(aa.shape[0]):
cv2_matrot.append(cv2.Rodrigues(aa[id:id + 1])[0])
cv2_matrot = np.array(cv2_matrot).reshape(-1, 9)
vposer_matrot = c2c(VPoser.aa2matrot(torch.tensor(aa))).reshape(-1, 9)
self.assertAlmostEqual(
np.square((vposer_matrot - cv2_matrot)).sum(), 0.0)
def test_matrot2aa(self):
np.random.seed(100)
aa = np.random.randn(10, 3)
matrot = c2c(VPoser.aa2matrot(torch.tensor(aa))).reshape(-1, 9)
cv2_aa = []
for id in range(matrot.shape[0]):
cv2_aa.append(cv2.Rodrigues(matrot[id].reshape(3, 3))[0])
cv2_aa = np.array(cv2_aa).reshape(-1, 3)
vposer_aa = c2c(VPoser.matrot2aa(torch.tensor(matrot))).reshape(-1, 3)
self.assertAlmostEqual(np.square((vposer_aa - cv2_aa)).sum(), 0.0)
def fetch_data(self, idx):
'''
This an exampl of augmenting the data fields. Furthermore, one can add random noise to data fields here as well.
There should be a match between returning dictionary field names and the one in AMASS_ROW.
:param idx:
:return:
'''
sample = {k: self.ds[k][idx] for k in self.ds.keys()}
from human_body_prior.train.vposer_smpl import VPoser
sample['pose_matrot'] = VPoser.aa2matrot(sample['pose'].view(
[1, 1, -1, 3])).view(1, -1)
return sample