def cli(filename, segmented=False, steps=1, scale=1.):
"""Render a 3D model for an estimated body fit. Provide the image (!!) filename."""
model = {
'neutral':
_load_model(
_os.path.join(_os.path.dirname(__file__), '..', 'models', '3D',
'basicModel_neutral_lbs_10_207_0_v1.0.0.pkl'))
}
fileending = filename[-4:]
pkl_fname = filename.replace(fileending, '.pkl')
if not _path.exists(pkl_fname):
pkl_fname = filename[:-10] + '_body.pkl'
if not _path.exists(pkl_fname):
pkl_fname = filename + '_body.pkl'
_LOGGER.info("Rendering 3D model for image %s and parameters %s...",
filename, pkl_fname)
assert _os.path.exists(pkl_fname), (
'Stored body fit does not exist for {}: {}!'.format(
filename, pkl_fname))
image = cv2.imread(filename)
with open(pkl_fname) as f:
cam = pickle.load(f)
renderings = render(model['neutral'], image, cam, steps, segmented, scale)
for ridx, rim in enumerate(renderings):
if segmented:
out_fname = filename + '_body_segmented_%d.png' % (ridx)
else:
out_fname = filename + '_body_%d.png' % (ridx)
cv2.imwrite(out_fname, rim)
# 11 Left wrist 20
# 12 Neck -
# 13 Head top fake
# guess the focal length
_FLENGTH_GUESS = 5000. #2500.
# initial value for the camera translation (in meters)
_T_GUESS = _np.array([0, 0, 20.])
# number of model shape coefficients to optimize
_N_BETAS = 10
_MOD_PATH = _os.path.abspath(_os.path.dirname(__file__))
# Models:
_MODEL_NEUTRAL_FNAME = _os.path.join(
_MOD_PATH, '..', 'models', '3D',
'basicModel_neutral_lbs_10_207_0_v1.0.0.pkl')
_MODEL_NEUTRAL = _load_model(_MODEL_NEUTRAL_FNAME)
# Torso joint IDs (used for estimating camera position).
_CID_TORSO = [
landmarks_91.rhip, landmarks_91.lhip, landmarks_91.rshoulder,
landmarks_91.lshoulder
]
# Head position correction.
_HEAD_CORR_FNAME = _os.path.join(_MOD_PATH, '..', 'models', '3D',
'head_vid_heva.pkl')
with open(_HEAD_CORR_FNAME, 'r') as f:
_HEAD_REGR = _pickle.load(f)
_REGRESSORS_FNAME = _os.path.join(_MOD_PATH, '..', 'models', '3D',
'regressors_locked_normalized_hybrid.npz')
_REGRESSORS = _np.load(_REGRESSORS_FNAME)
# Project the regressors on the first 10 beta dimensions.
_REGP = dict()
# Models:
# Models:
current_dir = _os.path.dirname(_os.path.abspath(__file__))
parent_dir = _os.path.abspath(_os.path.join(current_dir, _os.pardir))
_MODEL_NEUTRAL_FNAME = _os.path.join(parent_dir, 'models', '3D',
'neutral_smpl_with_cocoplus_reg.pkl')
_MODEL_MALE_FNAME = _os.path.join(parent_dir, 'models', '3D',
'basicmodel_m_lbs_10_207_0_v1.0.0.pkl')
_MODEL_FEME_FNAME = _os.path.join(parent_dir, 'models', '3D',
'basicModel_f_lbs_10_207_0_v1.0.0.pkl')
_MODEL_N = _load_model(_MODEL_NEUTRAL_FNAME)
_MODEL_M = _load_model(_MODEL_MALE_FNAME)
_MODEL_F = _load_model(_MODEL_FEME_FNAME)
_MODEL_VT = _os.path.join(parent_dir, 'models', 'basicModel_vt.npy')
_MODEL_FT = _os.path.join(parent_dir, 'models', 'basicModel_ft.npy')
_MODEL_VT_FT_OBJ = _os.path.join(parent_dir, 'models',
'mapeamento_impaint_2.obj')
def Template_tex():
#mesh.vt,mesh.ft = Template_tex()
vt = np.load(_MODEL_VT)