def __init__(self, args):
self._args = args
# Create the generator model that will predict the SMPL paramaters
self._generator = Generator(self._args)
self._discriminator = Discriminator(self._args)
self._gen_optim = tf.optimizers.Adam(learning_rate=self._args.gen_lr)
self._disc_optim = tf.optimizers.Adam(learning_rate=self._args.disc_lr)
self._data_loader = DataLoader(args)
self._lsp_train_ds, self._lsp_test_ds = self._data_loader.load_lsp_dataset(
)
self._smpl_train_ds, self._smpl_test_ds = self._data_loader.load_smpl_dataset(
)
self._smpl_model = SMPL(self._args)
self._create_summary_writer()
# Setup saving and restoring model
self._ckpt = tf.train.Checkpoint(generator=self._generator,
discriminator=self._discriminator,
gen_optim=self._gen_optim,
disc_optim=self._disc_optim)
self._ckpt_manager = tf.train.CheckpointManager(
self._ckpt, directory=self._args.model_dir, max_to_keep=3)
if self._args.load_model:
self._ckpt.restore(self._ckpt_manager.latest_checkpoint)
self._load_train_data()
def __init__(self, img_res):
dataset_name = 'COCO'
self.data_split = 'val' # 'train' if data_split == 'train' else 'val'
self.img_path = osp.join(cfg.data_dir, dataset_name, 'images')
self.annot_path = osp.join(cfg.data_dir, dataset_name, 'annotations')
self.img_res = img_res
self.smpl = SMPL()
# COCO joint set
self.coco_joint_num = 19 # 17 + 2, manually added pelvis and neck
self.coco_joints_name = ('Nose', 'L_Eye', 'R_Eye', 'L_Ear', 'R_Ear',
'L_Shoulder', 'R_Shoulder', 'L_Elbow',
'R_Elbow', 'L_Wrist', 'R_Wrist', 'L_Hip',
'R_Hip', 'L_Knee', 'R_Knee', 'L_Ankle',
'R_Ankle', 'Pelvis', 'Neck')
self.coco_flip_pairs = ((1, 2), (3, 4), (5, 6), (7, 8), (9, 10),
(11, 12), (13, 14), (15, 16))
# self.coco_skeleton = (
# (1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), (13, 15),
# (5, 6), (11, 12))
self.coco_skeleton = (
(1, 2),
(0, 1),
(0, 2),
(2, 4),
(1, 3),
(6, 8),
(8, 10),
(5, 7),
(7, 9),
(11, 13),
(13, 15),
(17, 11),
(17, 12),
(17, 18),
(18, 5),
(18, 6),
(18, 0),
(12, 14),
(14, 16),
)
self.coco_root_joint_idx = self.coco_joints_name.index('Pelvis')
self.joint_regressor_coco = self.smpl.joint_regressor_coco
input_joint_name = 'coco'
self.joint_num, self.skeleton, self.flip_pairs = self.get_joint_setting(
input_joint_name)
self.datalist_cocoj_det = self.load_coco_joints_det(
osp.join(cfg.data_dir, dataset_name,
'hrnet_output_on_valset.json'))
self.datalist = self.load_data()[:6337]
self.graph_Adj, self.graph_L, self.graph_perm, self.graph_perm_reverse = \
build_coarse_graphs(self.smpl.face, self.joint_num, self.skeleton, self.flip_pairs, levels=9)
print("# of COCO annotation and detection data: ", len(self.datalist),
len(self.datalist_cocoj_det))
def __init__(self, data_split, args):
dataset_name = 'PW3D'
self.data_split = 'test'
self.data_path = osp.join(cfg.data_dir, dataset_name, 'data')
self.det_data_path = osp.join(self.data_path, 'darkpose_output_on_testset.json')
#'hrnet_output_on_testset.json')
self.img_path = osp.join(cfg.data_dir, dataset_name, 'imageFiles')
# SMPL joint set
self.mesh_model = SMPL()
self.smpl_root_joint_idx = self.mesh_model.root_joint_idx
self.face_kps_vertex = self.mesh_model.face_kps_vertex
self.smpl_vertex_num = 6890
self.smpl_joint_num = 24
self.smpl_flip_pairs = ((1, 2), (4, 5), (7, 8), (10, 11), (13, 14), (16, 17), (18, 19), (20, 21), (22, 23))
self.smpl_skeleton = (
(0, 1), (1, 4), (4, 7), (7, 10), (0, 2), (2, 5), (5, 8), (8, 11), (0, 3), (3, 6), (6, 9), (9, 14), (14, 17),
(17, 19), (19, 21), (21, 23), (9, 13), (13, 16), (16, 18), (18, 20), (20, 22), (9, 12), (12, 15))
self.joint_regressor_smpl = self.mesh_model.layer['neutral'].th_J_regressor
# H36M joint set
self.human36_root_joint_idx = 0
self.human36_eval_joint = (1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16)
self.human36_skeleton = (
(0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), (0, 1), (1, 2),
(2, 3), (0, 4), (4, 5), (5, 6))
self.joint_regressor_human36 = torch.Tensor(self.mesh_model.joint_regressor_h36m)
# COCO joint set
self.coco_joint_num = 19 # 17 + 2, manually added pelvis and neck
self.coco_joints_name = (
'Nose', 'L_Eye', 'R_Eye', 'L_Ear', 'R_Ear', 'L_Shoulder', 'R_Shoulder', 'L_Elbow', 'R_Elbow', 'L_Wrist',
'R_Wrist', 'L_Hip', 'R_Hip', 'L_Knee', 'R_Knee', 'L_Ankle', 'R_Ankle', 'Pelvis', 'Neck')
self.coco_flip_pairs = ((1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16))
self.coco_skeleton = (
(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13),
(13, 15), #(5, 6), (11, 12))
(17, 11), (17, 12), (17, 18), (18, 5), (18, 6), (18, 0))
self.joint_regressor_coco = torch.Tensor(self.mesh_model.joint_regressor_coco)
input_joint_name = 'coco'
self.joint_num, self.skeleton, self.flip_pairs = self.get_joint_setting(input_joint_name)
self.datalist, self.video_indices = self.load_data() # self.video_indexes: 37 video, and indices of each video
self.datadict_pose2d_det = self.load_pose2d_det(self.det_data_path)
self.graph_Adj, self.graph_L, self.graph_perm, self.graph_perm_reverse = \
build_coarse_graphs(self.mesh_model.face, self.joint_num, self.skeleton, self.flip_pairs, levels=9)
print(self.graph_perm_reverse)
print("Check lengths of annotation and detection output: ", len(self.datalist), len(self.datadict_pose2d_det))
def __init__(self, data_split, args):
dataset_name = 'SURREAL'
self.data_split = 'train' if data_split == 'train' else 'val'
self.debug = args.debug
self.data_path = osp.join(cfg.data_dir, dataset_name, 'data')
self.img_path = osp.join(cfg.data_dir, dataset_name, 'images',
self.data_split)
self.mesh_model = SMPL()
self.smpl_vertex_num = 6890
self.smpl_joint_num = 24
self.smpl_joints_name = self.mesh_model.joints_name
self.smpl_flip_pairs = ((1, 2), (4, 5), (7, 8), (10, 11), (13, 14),
(16, 17), (18, 19), (20, 21), (22, 23))
self.smpl_skeleton = ((0, 1), (1, 4), (4, 7), (7, 10), (0, 2), (2, 5),
(5, 8), (8, 11), (0, 3), (3, 6), (6, 9), (9, 14),
(14, 17), (17, 19), (19, 21), (21, 23), (9, 13),
(13, 16), (16, 18), (18, 20), (20, 22), (9, 12),
(12, 15))
self.smpl_root_joint_idx = self.mesh_model.root_joint_idx
self.joint_regressor_smpl = self.mesh_model.layer[
'neutral'].th_J_regressor
self.datalist = self.load_data()
det_file_path = osp.join(self.data_path,
f'hrnet_output_on_{self.data_split}set.json')
self.datalist_pose2d_det = self.load_pose2d_det(det_file_path)
print("Check lengths of annotation and detection output: ",
len(self.datalist), len(self.datalist_pose2d_det))
self.input_joint_name = cfg.DATASET.input_joint_set # 'coco'
self.joint_num, self.skeleton, self.flip_pairs = self.get_joint_setting(
self.input_joint_name)
self.graph_Adj, self.graph_L, self.graph_perm, self.graph_perm_reverse = \
build_coarse_graphs(self.mesh_model.face, self.joint_num, self.skeleton, self.flip_pairs, levels=9)
def __init__(self, data_split, args):
dataset_name = 'AMASS'
self.data_split = 'train'
self.debug = args.debug
self.data_path = osp.join(cfg.data_dir, dataset_name, 'data')
# SMPL joint set
self.mesh_model = SMPL()
self.smpl_root_joint_idx = self.mesh_model.root_joint_idx
self.face_kps_vertex = self.mesh_model.face_kps_vertex
self.smpl_vertex_num = 6890
self.smpl_joint_num = 24
self.smpl_flip_pairs = ((1, 2), (4, 5), (7, 8), (10, 11), (13, 14),
(16, 17), (18, 19), (20, 21), (22, 23))
self.smpl_skeleton = ((0, 1), (1, 4), (4, 7), (7, 10), (0, 2), (2, 5),
(5, 8), (8, 11), (0, 3), (3, 6), (6, 9), (9, 14),
(14, 17), (17, 19), (19, 21), (21, 23), (9, 13),
(13, 16), (16, 18), (18, 20), (20, 22), (9, 12),
(12, 15))
# h36m skeleton
self.human36_joint_num = 17
self.human36_joints_name = ('Pelvis', 'R_Hip', 'R_Knee', 'R_Ankle',
'L_Hip', 'L_Knee', 'L_Ankle', 'Torso',
'Neck', 'Nose', 'Head', 'L_Shoulder',
'L_Elbow', 'L_Wrist', 'R_Shoulder',
'R_Elbow', 'R_Wrist')
self.human36_skeleton = ((0, 7), (7, 8), (8, 9), (9, 10), (8, 11),
(11, 12), (12, 13), (8, 14), (14, 15),
(15, 16), (0, 1), (1, 2), (2, 3), (0, 4),
(4, 5), (5, 6))
self.human36_flip_pairs = ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12),
(16, 13))
self.human36_root_joint_idx = self.human36_joints_name.index('Pelvis')
self.human36_error_distribution = self.get_stat()
self.joint_regressor_h36m = self.mesh_model.joint_regressor_h36m
# COCO joint set
self.coco_joint_num = 19 # 17 + 2, manually added pelvis and neck
self.coco_joints_name = ('Nose', 'L_Eye', 'R_Eye', 'L_Ear', 'R_Ear',
'L_Shoulder', 'R_Shoulder', 'L_Elbow',
'R_Elbow', 'L_Wrist', 'R_Wrist', 'L_Hip',
'R_Hip', 'L_Knee', 'R_Knee', 'L_Ankle',
'R_Ankle', 'Pelvis', 'Neck')
self.coco_flip_pairs = ((1, 2), (3, 4), (5, 6), (7, 8), (9, 10),
(11, 12), (13, 14), (15, 16))
self.coco_skeleton = (
(1, 2),
(0, 1),
(0, 2),
(2, 4),
(1, 3),
(6, 8),
(8, 10),
(5, 7),
(7, 9),
(12, 14),
(14, 16),
(11, 13),
(13, 15), # (5, 6), #(11, 12),
(17, 11),
(17, 12),
(17, 18),
(18, 5),
(18, 6),
(18, 0))
self.coco_root_joint_idx = self.coco_joints_name.index('Pelvis')
self.joint_regressor_coco = self.mesh_model.joint_regressor_coco
self.input_joint_name = cfg.DATASET.input_joint_set
self.joint_num, self.skeleton, self.flip_pairs = self.get_joint_setting(
self.input_joint_name)
self.datalist = self.load_data()
from resize_img import *
from model import *
from smpl import SMPL
model = HMR()
smpl = SMPL(
'/Users/choehanjoon/PycharmProjects/HMR/neutral_smpl_with_cocoplus_reg.pkl'
) #SMPL('neutral_smpl_with_cocoplus_reg.pkl')
pic = 'result_7_512.jpg'
pred = model.predict(pic)[0]['theta']
print(pred)
cams = pred[:, :3][0]
poses = pred[:, 3:(3 + 72)][0]
shapes = pred[:, (3 + 72):][0]
smpl.set_params(beta=shapes, pose=poses, trans=np.array([0, 0.2, 0]))
smpl.save_to_obj('./smpl_np.obj')
def __init__(self, mode, args):
dataset_name = 'Human36M'
self.debug = args.debug
self.data_split = mode
self.img_dir = osp.join(cfg.data_dir, dataset_name, 'images')
self.annot_path = osp.join(cfg.data_dir, dataset_name, 'annotations')
self.subject_genders = {1: 'female', 5: 'female', 6: 'male', 7: 'female', 8: 'male', 9: 'male', 11: 'male'}
self.protocol = 2
self.action_name = ['Directions', 'Discussion', 'Eating', 'Greeting', 'Phoning', 'Posing', 'Purchases',
'Sitting', 'SittingDown', 'Smoking', 'Photo', 'Waiting', 'Walking', 'WalkDog',
'WalkTogether']
self.fitting_thr = 25 # milimeter
# SMPL joint set
self.mesh_model = SMPL()
self.smpl_root_joint_idx = self.mesh_model.root_joint_idx
self.smpl_face_kps_vertex = self.mesh_model.face_kps_vertex
self.smpl_vertex_num = 6890
self.smpl_joint_num = 24
self.smpl_flip_pairs = ((1, 2), (4, 5), (7, 8), (10, 11), (13, 14), (16, 17), (18, 19), (20, 21), (22, 23))
self.smpl_skeleton = (
(0, 1), (1, 4), (4, 7), (7, 10), (0, 2), (2, 5), (5, 8), (8, 11), (0, 3), (3, 6), (6, 9), (9, 14), (14, 17),
(17, 19), (19, 21), (21, 23), (9, 13), (13, 16), (16, 18), (18, 20), (20, 22), (9, 12), (12, 15))
self.joint_regressor_smpl = self.mesh_model.layer['neutral'].th_J_regressor
# H36M joint set
self.human36_joint_num = 17
self.human36_joints_name = (
'Pelvis', 'R_Hip', 'R_Knee', 'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Torso', 'Neck', 'Nose', 'Head',
'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Shoulder', 'R_Elbow', 'R_Wrist')
self.human36_flip_pairs = ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))
self.human36_skeleton = (
(0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), (0, 1), (1, 2),
(2, 3), (0, 4), (4, 5), (5, 6))
self.human36_root_joint_idx = self.human36_joints_name.index('Pelvis')
self.human36_error_distribution = self.get_stat()
self.human36_eval_joint = (1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16)
self.joint_regressor_human36 = self.mesh_model.joint_regressor_h36m
# COCO joint set
self.coco_joint_num = 19 # 17 + 2, manually added pelvis and neck
self.coco_joints_name = (
'Nose', 'L_Eye', 'R_Eye', 'L_Ear', 'R_Ear', 'L_Shoulder', 'R_Shoulder', 'L_Elbow', 'R_Elbow', 'L_Wrist',
'R_Wrist', 'L_Hip', 'R_Hip', 'L_Knee', 'R_Knee', 'L_Ankle', 'R_Ankle', 'Pelvis', 'Neck')
self.coco_flip_pairs = ((1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16))
self.coco_skeleton = (
(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13),
(13, 15), #(5, 6), #(11, 12),
(17, 11), (17, 12), (17, 18), (18, 5), (18, 6), (18, 0))
self.joint_regressor_coco = self.mesh_model.joint_regressor_coco
self.input_joint_name = cfg.DATASET.input_joint_set # 'coco'
self.joint_num, self.skeleton, self.flip_pairs = self.get_joint_setting(self.input_joint_name)
self.datalist, skip_idx, skip_img_path = self.load_data()
if self.data_split == 'test':
det_2d_data_path = osp.join(cfg.data_dir, dataset_name, 'absnet_output_on_testset.json')
self.datalist_pose2d_det = self.load_pose2d_det(det_2d_data_path, skip_img_path)
print("Check lengths of annotation and detection output: ", len(self.datalist), len(self.datalist_pose2d_det))
self.graph_Adj, self.graph_L, self.graph_perm, self.graph_perm_reverse = \
build_coarse_graphs(self.mesh_model.face, self.joint_num, self.skeleton, self.flip_pairs, levels=9)
FOCAL_LENGTH = 1500
IMG_RES = args.res
joint_set = args.joint_set
input_path = args.input
output_path = './demo/result/'
# fix config
cfg.DATASET.target_joint_set = joint_set
cfg.MODEL.posenet_pretrained = False
# get model and input
if joint_set == 'mano':
mesh_model = MANO()
else:
mesh_model = SMPL()
model, joint_regressor, joint_num, skeleton, graph_L, graph_perm_reverse = get_joint_setting(
mesh_model, joint_category=joint_set)
model = model.cuda()
project_net = models.project_net.get_model().cuda()
joint_regressor = torch.Tensor(joint_regressor).cuda()
joint_input = np.load(input_path)
# pre-process input
bbox = get_bbox(joint_input)
bbox1 = process_bbox(bbox.copy(), aspect_ratio=1.0, scale=1.25)
bbox2 = process_bbox(bbox.copy())
proj_target_joint_img, trans = j2d_processing(joint_input.copy(),
(IMG_RES, IMG_RES), bbox1, 0,
0, None)
joint_img, _ = j2d_processing(
final_principal_pt = (principal_pt + start_pt) * undo_scale
cam_for_render = np.hstack(
[np.mean(flength * undo_scale), final_principal_pt])
# This is in padded image.
# kp_original = (joints + proc_param['start_pt']) * undo_scale
# Subtract padding from joints.
margin = int(img_size / 2)
kp_original = (joints + proc_param['start_pt'] - margin) * undo_scale
return cam_for_render, vert_shifted, kp_original
from resize_img import *
from model import *
from smpl import SMPL
model = HMR()
smpl = SMPL(
'/Users/choehanjoon/PycharmProjects/HMR/neutral_smpl_with_cocoplus_reg.pkl'
)
pic = '/Users/choehanjoon/PycharmProjects/HMR/coco0.png'
pred, proc_param, img = model.predict(pic)
print(pred.keys())
joints = pred['joints']
verts = pred['verts']
cams = pred['cams']
img_path = pic
visualize(img, proc_param, joints[0], verts[0], cams[0], img_path)
def main(**args):
output_folder = args.pop('output_folder')
output_folder = osp.expandvars(output_folder)
if not osp.exists(output_folder):
os.makedirs(output_folder)
# Store the arguments for the current experiment
conf_fn = osp.join(output_folder, 'conf.yaml')
with open(conf_fn, 'w') as conf_file:
yaml.dump(args, conf_file)
result_folder = args.pop('result_folder', 'results')
result_folder = osp.join(output_folder, result_folder)
if not osp.exists(result_folder):
os.makedirs(result_folder)
mesh_folder = args.pop('mesh_folder', 'meshes')
mesh_folder = osp.join(output_folder, mesh_folder)
if not osp.exists(mesh_folder):
os.makedirs(mesh_folder)
float_dtype = args['float_dtype']
if float_dtype == 'float64':
dtype = torch.float64
elif float_dtype == 'float32':
dtype = torch.float64
else:
print('Unknown float type {}, exiting!'.format(float_dtype))
sys.exit(-1)
use_cuda = args.get('use_cuda', True)
if use_cuda and not torch.cuda.is_available():
print('CUDA is not available, exiting!')
sys.exit(-1)
dataset_obj = create_dataset(**args)
start = time.time()
input_gender = args.pop('gender', 'neutral')
gender_lbl_type = args.pop('gender_lbl_type', 'none')
max_persons = args.pop('max_persons', -1)
float_dtype = args.get('float_dtype', 'float32')
if float_dtype == 'float64':
dtype = torch.float64
elif float_dtype == 'float32':
dtype = torch.float32
else:
raise ValueError('Unknown float type {}, exiting!'.format(float_dtype))
body_model = SMPL(args.get('model_folder'), dtype=dtype, joint_regressor_extra=args.get('j_regressor_extra'))
use_hands = args.get('use_hands', True)
use_face = args.get('use_face', True)
body_pose_prior = create_prior(
prior_type=args.get('body_prior_type'),
dtype=dtype,
**args)
jaw_prior, expr_prior = None, None
if use_face:
jaw_prior = create_prior(
prior_type=args.get('jaw_prior_type'),
dtype=dtype,
**args)
expr_prior = create_prior(
prior_type=args.get('expr_prior_type', 'l2'),
dtype=dtype, **args)
left_hand_prior, right_hand_prior = None, None
if use_hands:
lhand_args = args.copy()
lhand_args['num_gaussians'] = args.get('num_pca_comps')
left_hand_prior = create_prior(
prior_type=args.get('left_hand_prior_type'),
dtype=dtype,
use_left_hand=True,
**lhand_args)
rhand_args = args.copy()
rhand_args['num_gaussians'] = args.get('num_pca_comps')
right_hand_prior = create_prior(
prior_type=args.get('right_hand_prior_type'),
dtype=dtype,
use_right_hand=True,
**rhand_args)
shape_prior = create_prior(
prior_type=args.get('shape_prior_type', 'l2'),
dtype=dtype, **args)
angle_prior = create_prior(prior_type='angle', dtype=dtype)
if use_cuda and torch.cuda.is_available():
device = torch.device('cuda')
body_model = body_model.to(device=device)
body_pose_prior = body_pose_prior.to(device=device)
angle_prior = angle_prior.to(device=device)
shape_prior = shape_prior.to(device=device)
if use_face:
expr_prior = expr_prior.to(device=device)
jaw_prior = jaw_prior.to(device=device)
if use_hands:
left_hand_prior = left_hand_prior.to(device=device)
right_hand_prior = right_hand_prior.to(device=device)
else:
device = torch.device('cpu')
# A weight for every joint of the model
joint_weights = dataset_obj.get_joint_weights().to(device=device,
dtype=dtype)
# Add a fake batch dimension for broadcasting
joint_weights.unsqueeze_(dim=0)
for idx, keypoints_3d in enumerate(dataset_obj):
curr_result_folder = os.path.join(result_folder, str(idx).zfill(7))
if not osp.exists(curr_result_folder):
os.makedirs(curr_result_folder)
curr_mesh_folder = os.path.join(mesh_folder, str(idx).zfill(7))
if not osp.exists(curr_mesh_folder):
os.makedirs(curr_mesh_folder)
for person_idx in range(len(keypoints_3d)):
print(f'Processing: Image {idx} Person {person_idx}')
curr_result_fn = osp.join(curr_result_folder, f'{person_idx:02d}.pkl')
curr_mesh_fn = osp.join(curr_mesh_folder, f'{person_idx:02d}.obj')
try:
fit_single_frame(keypoints_3d[[person_idx]],
body_model=body_model,
joint_weights=joint_weights,
dtype=dtype,
result_fn=curr_result_fn,
mesh_fn=curr_mesh_fn,
shape_prior=shape_prior,
expr_prior=expr_prior,
body_pose_prior=body_pose_prior,
left_hand_prior=left_hand_prior,
right_hand_prior=right_hand_prior,
jaw_prior=jaw_prior,
angle_prior=angle_prior,
**args)
elapsed = time.time() - start
time_msg = time.strftime('%H hours, %M minutes, %S seconds',
time.gmtime(elapsed))
print('Processing the data took: {}'.format(time_msg))
except:
print('Some error occured during fitting')
