def __init__(self, JOINT_REGRESSOR_H36M='data/J_regressor_h36m.npy', **kwargs):
super().__init__(**kwargs)
self.J_regressor = torch.from_numpy(np.load(JOINT_REGRESSOR_H36M)).float()
self.p1_meter = AverageMeter('P1', ':.2f')
self.p2_meter = AverageMeter('P2', ':.2f')
self.p3_meter = AverageMeter('P3', ':.2f')
self.stats = list()
self.mismatch_cnt = 0
# Initialize SMPL model
openpose_joints = [24, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, 8, 1, 4,
7, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]
extra_joints = [8, 5, 45, 46, 4, 7, 21, 19, 17, 16, 18, 20, 47, 48, 49, 50, 51, 52, 53, 24, 26, 25, 28, 27]
joints = torch.tensor(openpose_joints + extra_joints, dtype=torch.int32)
joint_mapper = JointMapper(joints)
smpl_params = dict(model_folder='data/smpl',
joint_mapper=joint_mapper,
create_glb_pose=True,
body_pose_param='identity',
create_body_pose=True,
create_betas=True,
# create_trans=True,
dtype=torch.float32,
vposer_ckpt=None,
gender='neutral')
self.smpl = SMPL(**smpl_params)
self.J_regressor = torch.from_numpy(np.load(JOINT_REGRESSOR_H36M)).float()
self.result_list = list()
self.result_list_2d = list()
self.h36m_to_MPI = [10, 8, 14, 15, 16, 11, 12, 13, 4, 5, 6, 1, 2, 3, 0, 7, 9]
self.collision_meter = AverageMeter('collision', ':.2f')
self.collision_volume = CollisionVolume(self.smpl.faces, grid_size=64).cuda()
self.coll_cnt = 0
def __init__(self,
JOINT_REGRESSOR_H36M='data/J_regressor_h36m.npy',
**kwargs):
super().__init__(**kwargs)
self.J_regressor = torch.from_numpy(
np.load(JOINT_REGRESSOR_H36M)).float()
self.p1_meter = AverageMeter('P1', ':.2f')
self.p2_meter = AverageMeter('P2', ':.2f')
self.p3_meter = AverageMeter('P3', ':.2f')
self.stats = list()
self.mismatch_cnt = 0
# Initialize SMPL model
openpose_joints = [
24, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, 8, 1, 4, 7, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34
]
extra_joints = [
8, 5, 45, 46, 4, 7, 21, 19, 17, 16, 18, 20, 47, 48, 49, 50, 51, 52,
53, 24, 26, 25, 28, 27
]
joints = torch.tensor(openpose_joints + extra_joints,
dtype=torch.int32)
self.smpl = SMPL('data/smpl')
self.J_regressor = torch.from_numpy(
np.load(JOINT_REGRESSOR_H36M)).float()
self.result_list = list()
self.result_list_2d = list()
self.h36m_to_MPI = [
10, 8, 14, 15, 16, 11, 12, 13, 4, 5, 6, 1, 2, 3, 0, 7, 9
]
self.collision_meter = AverageMeter('collision', ':.2f')
self.collision_volume = CollisionVolume(self.smpl.faces,
grid_size=64).cuda()
self.coll_cnt = 0
def __init__(self,
JOINT_REGRESSOR_H36M='data/J_regressor_h36m.npy',
**kwargs):
super().__init__(**kwargs)
self.J_regressor = torch.from_numpy(
np.load(JOINT_REGRESSOR_H36M)).float()
self.p1_meter = AverageMeter('P1', ':.2f')
self.stats = list()
self.mismatch_cnt = 0
# Initialize SMPL model
openpose_joints = [
24, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, 8, 1, 4, 7, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34
]
extra_joints = [
8, 5, 45, 46, 4, 7, 21, 19, 17, 16, 18, 20, 47, 48, 49, 50, 51, 52,
53, 24, 26, 25, 28, 27
]
joints = torch.tensor(openpose_joints + extra_joints,
dtype=torch.int32)
self.smpl = SMPL('data/smpl')
self.J_regressor = torch.from_numpy(
np.load(JOINT_REGRESSOR_H36M)).float()
self.collision_meter = AverageMeter('P3', ':.2f')
self.collision_volume = CollisionVolume(self.smpl.faces,
grid_size=64).cuda()
self.coll_cnt = 0
self.threshold_list = [0.1, 0.15, 0.2]
self.total_ordinal_cnt = {i: 0 for i in self.threshold_list}
self.correct_ordinal_cnt = {i: 0 for i in self.threshold_list}
def __init__(self,
JOINT_REGRESSOR_H36M='data/J_regressor_h36m.npy',
pattern='.60457274_',
**kwargs):
super().__init__(**kwargs)
self.J_regressor = torch.from_numpy(
np.load(JOINT_REGRESSOR_H36M)).float()
self.pattern = pattern
self.p1_meter = AverageMeter('P1', ':.2f')
self.p2_meter = AverageMeter('P2', ':.2f')