style_model = np.load(osp.join(ROOT, 'skirt_female', 'style_model.npz'))
pca = PCA(n_components=4)
pca.components_ = style_model['pca_w']
pca.mean_ = style_model['mean']
skirt_v = pca.inverse_transform(np.zeros([1, 4])).reshape([-1, 3])
# move the skirt to the right position
with open(osp.join(ROOT, 'garment_class_info.pkl'), 'rb') as f:
garment_meta = pickle.load(f)
skirt_f = garment_meta['skirt']['f']
vert_indices = garment_meta['pant']['vert_indices']
up_bnd_inds = np.load(osp.join(ROOT, 'skirt_upper_boundary.npy'))
pant_up_bnd_inds = np.load(osp.join(ROOT, 'pant_upper_boundary.npy'))
waist_body_inds = vert_indices[pant_up_bnd_inds]
smpl = SMPLNP(gender='female')
apose = get_Apose()
body_v, _ = smpl(np.zeros([300]), apose, None, None)
trans = np.mean(body_v[waist_body_inds], 0, keepdims=True) - np.mean(
skirt_v[up_bnd_inds], 0, keepdims=True)
skirt_v = skirt_v + trans
skirt_v[:, 0] -= 0.01
p = 1
K = 100
# find closest vertices
dist = np.sqrt(np.sum(np.square(skirt_v[:, None] - body_v[None]),
2)) # n_skirt, n_body
body_ind = np.argsort(dist, 1)[:, :K]
if __name__ == '__main__':
# STAGE1_FNUM = 5
# STAGE2_FNUM = 5
# STAGE3_FNUM = 5
SM_FNUM = 6
STABLE_FNUM = 4
END_FNUM = 5
lowest = -2
apose = get_Apose()
with open(osp.join(ROOT, 'garment_class_info.pkl'), 'rb') as f:
garment_meta = pickle.load(f)
for gender in ['female']:
smpl = SMPLNP_Lres(gender=gender)
smpl_hres = SMPLNP(gender=gender)
num_betas = 10 if gender == 'neutral' else 300
betas = np.zeros([9, num_betas], dtype=np.float32)
betas[0, 0] = 2
betas[1, 0] = -2
betas[2, 1] = 2
betas[3, 1] = -2
betas[4, 0] = 1
betas[5, 0] = -1
betas[6, 1] = 1
betas[7, 1] = -1
vcanonical = smpl(np.zeros_like(betas[0]), apose)
for gc in ['skirt']:
gc_gender_dir = osp.join(ROOT, '{}_{}'.format(gc, gender))
shape_dir = osp.join(gc_gender_dir, 'shape')
if __name__ == '__main__':
garment_class = 'skirt'
RAW_DIR = osp.join(ROOT, 'skirt_orig_reg')
RAW_EXT_DIR = osp.join(ROOT, 'skirt_reg')
SAVE_DIR = osp.join(ROOT, 'raw_data')
# SAVE_DIR = '/BS/cloth-anim/static00/tailor_data/raw_data'
people_names, verts, faces = get_orig_verts(
RAW_DIR, osp.join(ROOT, 'skirt_orig_bad.txt'))
people_names_ext, verts_ext, _ = get_verts(RAW_EXT_DIR)
verts_ext /= 1.15
verts = np.concatenate((verts, verts_ext), 0)
people_names = people_names + people_names_ext
n_verts = verts[0].shape[0]
smpl = SMPLNP('female')
apose = get_Apose()
canonical_body, _ = smpl(np.zeros([300]), apose, None, None)
with open(osp.join(ROOT, 'garment_class_info.pkl'), 'rb') as f:
class_info = pickle.load(f, encoding='latin-1')
pant_ind = class_info['pant']['vert_indices']
pant_bnd = np.load(osp.join(ROOT, 'pant_upper_boundary.npy'))
skirt_bnd = np.load(osp.join(ROOT, 'skirt_upper_boundary.npy'))
pant_bnd_loc = np.mean(canonical_body[pant_ind][pant_bnd], 0)
all_v = []
for people_name, v in zip(people_names, verts):
skirt_bnd_loc = np.mean(v[skirt_bnd], 0)
trans = (pant_bnd_loc - skirt_bnd_loc)[None]
trans_v = v + trans
all_v.append(trans_v)
import torch
import numpy as np
import trimesh
from smpl_torch import SMPLNP
from utils.rotation import get_Apose
from utils.ios import read_pc2
from global_var import ROOT
if __name__ == '__main__':
garment_class = 'skirt'
gender = 'female'
lowest = -2
STABILITY_FRAMES = 2
smpl = SMPLNP(gender)
apose = torch.from_numpy(get_Apose().astype(np.float32))
data_root = osp.join(ROOT, '{}_{}'.format(garment_class, gender))
pose_dir = osp.join(ROOT, '{}_{}'.format(garment_class, gender), 'pose')
ss_dir = osp.join(data_root, 'style_shape')
shape_dir = osp.join(data_root, 'shape')
beta_strs = [k.replace('.obj', '') for k in os.listdir(shape_dir) if k.endswith('.obj')]
betas = np.load(osp.join(data_root, 'shape', 'betas.npy'))
all_ss = [k for k in os.listdir(pose_dir) if len(k) == 7]
for ss in all_ss:
beta_str, gamma_str = ss.split('_')
pose_ss_dir = osp.join(pose_dir, ss)
if garment_class in ['pant', 'skirt', 'short-pant']:
import os.path as osp
import pickle
import numpy as np
from tqdm import tqdm
from smpl_torch import SMPLNP
from utils.rotation import get_Apose
from utils.diffusion_smoothing import DiffusionSmoothing as DS
from global_var import ROOT
if __name__ == '__main__':
garment_class = 'pant'
raw_dir = osp.join(ROOT, 'raw_data')
# save_dir = osp.join(ROOT, '{}_{}/pca'.format(garment_class, gender))
smpl = SMPLNP('neutral')
apose = get_Apose()
with open(osp.join(ROOT, 'garment_class_info.pkl'), 'rb') as f:
class_info = pickle.load(f, encoding='latin-1')
vert_indices = class_info[garment_class]['vert_indices']
raw_path = osp.join(raw_dir, '{}.npy'.format(garment_class))
beta_path = osp.join(raw_dir, '{}_betas.npy'.format(garment_class))
all_disp = np.load(raw_path)
betas = np.load(beta_path)
data_num = len(all_disp)
vbody, vcloth = smpl(betas, np.tile(apose[None], [data_num, 1]),
all_disp, garment_class, batch=True)
canonical_body, _ = smpl(np.zeros([10]), apose, None, None)
trans = np.mean((canonical_body[None] - vbody)[:, vert_indices, :], 1, keepdims=True)
import os
import os.path as osp
import cv2
import numpy as np
import pickle
from renderer import Renderer
from smpl_torch import SMPLNP
from global_var import DATA_DIR
if __name__ == '__main__':
garment_class = 't-shirt'
gender = 'female'
img_size = 512
renderer = Renderer(img_size)
smpl = SMPLNP(gender=gender, cuda=False)
pose_dir = osp.join(DATA_DIR, '{}_{}'.format(garment_class, gender), 'pose')
shape_dir = osp.join(DATA_DIR, '{}_{}'.format(garment_class, gender), 'shape')
ss_dir = osp.join(DATA_DIR, '{}_{}'.format(garment_class, gender), 'style_shape')
pose_vis_dir = osp.join(DATA_DIR, '{}_{}'.format(garment_class, gender), 'pose_vis')
ss_vis_dir = osp.join(DATA_DIR, '{}_{}'.format(garment_class, gender), 'style_shape_vis')
pivots_path = osp.join(DATA_DIR, '{}_{}'.format(garment_class, gender), 'pivots.txt')
avail_path = osp.join(DATA_DIR, '{}_{}'.format(garment_class, gender), 'avail.txt')
os.makedirs(pose_vis_dir, exist_ok=True)
os.makedirs(ss_vis_dir, exist_ok=True)
with open(os.path.join(DATA_DIR, 'garment_class_info.pkl'), 'rb') as f:
class_info = pickle.load(f, encoding='latin-1')
body_f = smpl.base.faces
garment_f = class_info[garment_class]['f']