def fit_joints()

in utilities/mano_fitting.py [0:0]

• 36 lines of code
• 8 McCabe index (conditional complexity)

  def fit_joints(both_joints, n_pose_params=15, shape_sigma=10.0,
                save_filename=None):
    """
    Fits the MANO model to hand joint 3D locations
    both_jonts: tuple of length 2, 21 joints per hand, e.g. output of ContactPose.hand_joints()
    n_pose_params: number of pose parameters (excluding 3 global rotation params)
    shape_sigma: reciprocal of shape regularization strength
    save_filename: file where the fitting output will be saved in JSON format
    """
    mano_params = []
    for hand_idx, joints in enumerate(both_joints):
      if joints is None:  # hand is not present
        mano_params.append(mano_param_dict(n_pose_params))  # dummy
        continue
      cp_joints = mutils.openpose2mano(joints)

      # MANO model
      m = mutils.load_mano_model(MANOFitter._mano_dicts[hand_idx],
                                 ncomps=n_pose_params, flat_hand_mean=False)
      m.betas[:] = np.zeros(m.betas.size)
      m.pose[:]  = np.zeros(m.pose.size)
      mano_joints = mutils.mano_joints_with_fingertips(m)
      mano_joints_np = np.array([[float(mm) for mm in m] for m in mano_joints])

      # align palm
      cp_palm = get_palm_joints(np.asarray(cp_joints))
      mano_palm = get_palm_joints(np.asarray(mano_joints_np))
      mTc = register_pcs(cp_palm, mano_palm)
      cp_joints = np.dot(mTc, np.vstack((cp_joints.T, np.ones(len(cp_joints)))))
      cp_joints = cp_joints[:3].T
      cp_joints = ch.array(cp_joints)

      # set up objective
      objective = [m-c for m,c in zip(mano_joints, cp_joints)]
      mean_betas = ch.array(np.zeros(m.betas.size))
      objective.append((m.betas - mean_betas) / shape_sigma)
      # optimize
      ch.minimize(objective, x0=(m.pose, m.betas, m.trans), method='dogleg')

      p = mano_param_dict(n_pose_params)
      p['pose']  = np.array(m.pose).tolist()
      p['betas'] = np.array(m.betas).tolist()
      p['valid'] = True
      p['mTc']['translation'] = (mTc[:3, 3] - np.array(m.trans)).tolist()
      p['mTc']['rotation'] = txq.mat2quat(mTc[:3, :3]).tolist()
      mano_params.append(p)

      # # to access hand mesh vertices and faces
      # vertices = np.array(m.r)
      # vertices = mutils.tform_points(np.linalg.inv(mTc), vertices)
      # faces = np.array(m.f)

    if save_filename is not None:
      with open(save_filename, 'w') as f:
        json.dump(mano_params, f, indent=4, separators=(',', ':'))
      print('{:s} written'.format(save_filename))
    return mano_params