def smpl_losses(self, pred_rotmat, pred_betas, gt_pose, gt_betas):
pred_rotmat_valid = batch_rodrigues(pred_rotmat.reshape(-1,3)).reshape(-1, 24, 3, 3)
gt_rotmat_valid = batch_rodrigues(gt_pose.reshape(-1,3)).reshape(-1, 24, 3, 3)
pred_betas_valid = pred_betas
gt_betas_valid = gt_betas
if len(pred_rotmat_valid) > 0:
loss_regr_pose = self.criterion_regr(pred_rotmat_valid, gt_rotmat_valid)
loss_regr_betas = self.criterion_regr(pred_betas_valid, gt_betas_valid)
else:
loss_regr_pose = torch.FloatTensor(1).fill_(0.).to(self.device)
loss_regr_betas = torch.FloatTensor(1).fill_(0.).to(self.device)
return loss_regr_pose, loss_regr_betas
def smpl_losses(self, pred_rotmat, gt_pose):
print('pred_pose inside smpl loss',pred_rotmat.shape)
pred_rotmat_valid = batch_rodrigues(pred_rotmat.reshape(-1,3)).reshape(-1, 24, 3, 3)
gt_rotmat_valid = batch_rodrigues(gt_pose.reshape(-1,3)).reshape(-1, 24, 3, 3)
#print('smpl loss success',pred_rotmat_valid.shape,gt_rotmat_valid.shape)
# pred_betas_valid = pred_betas
# gt_betas_valid = gt_betas
if len(pred_rotmat_valid) > 0:
loss_regr_pose = self.criterion_regr(pred_rotmat_valid, gt_rotmat_valid)
# loss_regr_betas = self.criterion_regr(pred_betas_valid, gt_betas_valid)
else:
loss_regr_pose = torch.FloatTensor(1).fill_(0.).to(self.device)
# loss_regr_betas = torch.FloatTensor(1).fill_(0.).to(self.device)
return loss_regr_pose #, loss_regr_betas
def forward(self, thetas):
# inputs is N x 85(3 + 72 + 10)
batch_size = thetas.shape[0]
cams, poses, shapes = thetas[:, :3], thetas[:, 3:75], thetas[:, 75:]
shape_disc_value = self.shape_discriminator(shapes)
rotate_matrixs = batch_rodrigues(poses.contiguous().view(-1, 3)).view(
-1, 24, 9)[:, 1:, :]
pose_disc_value, pose_inter_disc_value = self.pose_discriminator(
rotate_matrixs)
full_pose_disc_value = self.full_pose_discriminator(
pose_inter_disc_value.contiguous().view(batch_size, -1))
return torch.cat(
(pose_disc_value, full_pose_disc_value, shape_disc_value), 1)
def read_data(folder, set, debug=False):
dataset = {
'vid_name': [],
'frame_id': [],
'joints3D': [],
'joints2D': [],
'shape': [],
'pose': [],
'bbox': [],
'img_name': [],
'features': [],
'valid': [],
}
model = spin.get_pretrained_hmr()
if set == 'val': set = 'test'
sequences = [
x.split('.')[0]
for x in os.listdir(osp.join(folder, 'sequenceFiles', set))
]
J_regressor = None
smpl = SMPL(SMPL_MODEL_DIR, batch_size=1, create_transl=False)
if set == 'test':
J_regressor = torch.from_numpy(
np.load(osp.join(VIBE_DATA_DIR, 'J_regressor_h36m.npy'))).float()
for i, seq in tqdm(enumerate(sequences)):
data_file = osp.join(folder, 'sequenceFiles', set, seq + '.pkl')
data = pkl.load(open(data_file, 'rb'), encoding='latin1')
img_dir = osp.join(folder, 'imageFiles', seq)
num_people = len(data['poses'])
num_frames = len(data['img_frame_ids'])
assert (data['poses2d'][0].shape[0] == num_frames)
for p_id in range(num_people):
pose = torch.from_numpy(data['poses'][p_id]).float()
shape = torch.from_numpy(data['betas'][p_id][:10]).float().repeat(
pose.size(0), 1)
trans = torch.from_numpy(data['trans'][p_id]).float()
j2d = data['poses2d'][p_id].transpose(0, 2, 1)
cam_pose = data['cam_poses']
campose_valid = data['campose_valid'][p_id]
# ======== Align the mesh params ======== #
rot = pose[:, :3]
rot_mat = batch_rodrigues(rot)
Rc = torch.from_numpy(cam_pose[:, :3, :3]).float()
Rs = torch.bmm(Rc, rot_mat.reshape(-1, 3, 3))
rot = rotation_matrix_to_angle_axis(Rs)
pose[:, :3] = rot
# ======== Align the mesh params ======== #
output = smpl(betas=shape,
body_pose=pose[:, 3:],
global_orient=pose[:, :3],
transl=trans)
# verts = output.vertices
j3d = output.joints
if J_regressor is not None:
vertices = output.vertices
J_regressor_batch = J_regressor[None, :].expand(
vertices.shape[0], -1, -1).to(vertices.device)
j3d = torch.matmul(J_regressor_batch, vertices)
j3d = j3d[:, H36M_TO_J14, :]
img_paths = []
for i_frame in range(num_frames):
img_path = os.path.join(img_dir +
'/image_{:05d}.jpg'.format(i_frame))
img_paths.append(img_path)
bbox_params, time_pt1, time_pt2 = get_smooth_bbox_params(
j2d, vis_thresh=VIS_THRESH, sigma=8)
# process bbox_params
c_x = bbox_params[:, 0]
c_y = bbox_params[:, 1]
scale = bbox_params[:, 2]
w = h = 150. / scale
w = h = h * 1.1
bbox = np.vstack([c_x, c_y, w, h]).T
# process keypoints
j2d[:, :, 2] = j2d[:, :, 2] > 0.3 # set the visibility flags
# Convert to common 2d keypoint format
perm_idxs = get_perm_idxs('3dpw', 'common')
perm_idxs += [0, 0] # no neck, top head
j2d = j2d[:, perm_idxs]
j2d[:, 12:, 2] = 0.0
# print('j2d', j2d[time_pt1:time_pt2].shape)
# print('campose', campose_valid[time_pt1:time_pt2].shape)
img_paths_array = np.array(img_paths)[time_pt1:time_pt2]
dataset['vid_name'].append(
np.array([f'{seq}_{p_id}'] * num_frames)[time_pt1:time_pt2])
dataset['frame_id'].append(
np.arange(0, num_frames)[time_pt1:time_pt2])
dataset['img_name'].append(img_paths_array)
dataset['joints3D'].append(j3d.numpy()[time_pt1:time_pt2])
dataset['joints2D'].append(j2d[time_pt1:time_pt2])
dataset['shape'].append(shape.numpy()[time_pt1:time_pt2])
dataset['pose'].append(pose.numpy()[time_pt1:time_pt2])
dataset['bbox'].append(bbox)
dataset['valid'].append(campose_valid[time_pt1:time_pt2])
features = extract_features(model,
img_paths_array,
bbox,
kp_2d=j2d[time_pt1:time_pt2],
debug=debug,
dataset='3dpw',
scale=1.2)
dataset['features'].append(features)
for k in dataset.keys():
dataset[k] = np.concatenate(dataset[k])
print(k, dataset[k].shape)
# Filter out keypoints
indices_to_use = np.where(
(dataset['joints2D'][:, :, 2] > VIS_THRESH).sum(-1) > MIN_KP)[0]
for k in dataset.keys():
dataset[k] = dataset[k][indices_to_use]
return dataset
