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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import chumpy as ch
import numpy as np
import cPickle as pkl
import scipy.sparse as sp
from chumpy.ch import Ch
from vendor.smpl.posemapper import posemap, Rodrigues
from vendor.smpl.serialization import backwards_compatibility_replacements
VERT_NOSE = 331
VERT_EAR_L = 3485
VERT_EAR_R = 6880
VERT_EYE_L = 2802
VERT_EYE_R = 6262
class Smpl(Ch):
"""
Class to store SMPL object with slightly improved code and access to more matrices
"""
terms = 'model',
dterms = 'trans', 'betas', 'pose', 'v_personal'
def __init__(self, *args, **kwargs):
self.on_changed(self._dirty_vars)
def on_changed(self, which):
if not hasattr(self, 'trans'):
self.trans = ch.zeros(3)
if not hasattr(self, 'betas'):
self.betas = ch.zeros(10)
if not hasattr(self, 'pose'):
self.pose = ch.zeros(72)
if 'model' in which:
if not isinstance(self.model, dict):
dd = pkl.load(open(self.model))
else:
dd = self.model
backwards_compatibility_replacements(dd)
for s in ['posedirs', 'shapedirs']:
if (s in dd) and not hasattr(dd[s], 'dterms'):
dd[s] = ch.array(dd[s])
self.f = dd['f']
self.v_template = dd['v_template']
if not hasattr(self, 'v_personal'):
self.v_personal = ch.zeros_like(self.v_template)
self.shapedirs = dd['shapedirs']
self.J_regressor = dd['J_regressor']
if 'J_regressor_prior' in dd:
self.J_regressor_prior = dd['J_regressor_prior']
if sp.issparse(self.J_regressor):
self.J_regressor = self.J_regressor.toarray()
self.bs_type = dd['bs_type']
self.weights = dd['weights']
if 'vert_sym_idxs' in dd:
self.vert_sym_idxs = dd['vert_sym_idxs']
if 'weights_prior' in dd:
self.weights_prior = dd['weights_prior']
self.kintree_table = dd['kintree_table']
self.posedirs = dd['posedirs']
self._set_up()
def _set_up(self):
self.v_shaped = self.shapedirs.dot(self.betas) + self.v_template
self.v_shaped_personal = self.v_shaped + self.v_personal
self.J = ch.sum(self.J_regressor.T.reshape(-1, 1, 24) * self.v_shaped.reshape(-1, 3, 1), axis=0).T
self.v_posevariation = self.posedirs.dot(posemap(self.bs_type)(self.pose))
self.v_poseshaped = self.v_shaped_personal + self.v_posevariation
self.A, A_global = self._global_rigid_transformation()
self.Jtr = ch.vstack([g[:3, 3] for g in A_global])
self.J_transformed = self.Jtr + self.trans.reshape((1, 3))
self.V = self.A.dot(self.weights.T)
rest_shape_h = ch.hstack((self.v_poseshaped, ch.ones((self.v_poseshaped.shape[0], 1))))
self.v_posed = ch.sum(self.V.T * rest_shape_h.reshape(-1, 4, 1), axis=1)[:, :3]
self.v = self.v_posed + self.trans
def _global_rigid_transformation(self):
results = {}
pose = self.pose.reshape((-1, 3))
parent = {i: self.kintree_table[0, i] for i in range(1, self.kintree_table.shape[1])}
with_zeros = lambda x: ch.vstack((x, ch.array([[0.0, 0.0, 0.0, 1.0]])))
pack = lambda x: ch.hstack([ch.zeros((4, 3)), x.reshape((4, 1))])
results[0] = with_zeros(ch.hstack((Rodrigues(pose[0, :]), self.J[0, :].reshape((3, 1)))))
for i in range(1, self.kintree_table.shape[1]):
results[i] = results[parent[i]].dot(with_zeros(ch.hstack((
Rodrigues(pose[i, :]), # rotation around bone endpoint
(self.J[i, :] - self.J[parent[i], :]).reshape((3, 1)) # bone
))))
results = [results[i] for i in sorted(results.keys())]
results_global = results
# subtract rotated J position
results2 = [results[i] - (pack(
results[i].dot(ch.concatenate((self.J[i, :], [0]))))
) for i in range(len(results))]
result = ch.dstack(results2)
return result, results_global
def compute_r(self):
return self.v.r
def compute_dr_wrt(self, wrt):
if wrt is not self.trans and wrt is not self.betas and wrt is not self.pose and wrt is not self.v_personal:
return None
return self.v.dr_wrt(wrt)
def copy_smpl(smpl, model):
new = Smpl(model, betas=smpl.betas)
new.pose[:] = smpl.pose.r
new.trans[:] = smpl.trans.r
return new
def joints_coco(smpl):
J = smpl.J_transformed
nose = smpl[VERT_NOSE]
ear_l = smpl[VERT_EAR_L]
ear_r = smpl[VERT_EAR_R]
eye_l = smpl[VERT_EYE_L]
eye_r = smpl[VERT_EYE_R]
shoulders_m = ch.sum(J[[14, 13]], axis=0) / 2.
neck = J[12] - 0.55 * (J[12] - shoulders_m)
return ch.vstack((
nose,
neck,
2.1 * (J[14] - shoulders_m) + neck,
J[[19, 21]],
2.1 * (J[13] - shoulders_m) + neck,
J[[18, 20]],
J[2] + 0.38 * (J[2] - J[1]),
J[[5, 8]],
J[1] + 0.38 * (J[1] - J[2]),
J[[4, 7]],
eye_r,
eye_l,
ear_r,
ear_l,
))
def model_params_in_camera_coords(trans, pose, J0, camera_t, camera_rt):
root = Rodrigues(np.matmul(Rodrigues(camera_rt).r, Rodrigues(pose[:3]).r)).r.reshape(-1)
pose[:3] = root
trans = (Rodrigues(camera_rt).dot(J0 + trans) - J0 + camera_t).r
return trans, pose
if __name__ == '__main__':
smpl = Smpl(model='../vendor/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl')
smpl.pose[:] = np.random.randn(72) * .2
smpl.pose[0] = np.pi
# smpl.v_personal[:] = np.random.randn(*smpl.shape) / 500.
# render test
from opendr.renderer import ColoredRenderer
from opendr.camera import ProjectPoints
from opendr.lighting import LambertianPointLight
rn = ColoredRenderer()
# Assign attributes to renderer
w, h = (640, 480)
rn.camera = ProjectPoints(v=smpl, rt=np.zeros(3), t=np.array([0, 0, 3.]), f=np.array([w, w]),
c=np.array([w, h]) / 2., k=np.zeros(5))
rn.frustum = {'near': 1., 'far': 10., 'width': w, 'height': h}
rn.set(v=smpl, f=smpl.f, bgcolor=np.zeros(3))
# Construct point light source
rn.vc = LambertianPointLight(
f=smpl.f,
v=rn.v,
num_verts=len(smpl),
light_pos=np.array([-1000, -1000, -2000]),
vc=np.ones_like(smpl) * .9,
light_color=np.array([1., 1., 1.]))
# Show it using OpenCV
import cv2
cv2.imshow('render_SMPL', rn.r)
print ('..Print any key while on the display window')
cv2.waitKey(0)
cv2.destroyAllWindows()
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