def lrotmin(p):
if isinstance(p, np.ndarray):
p = p.ravel()[3:]
return np.concatenate([(cv2.Rodrigues(np.array(pp))[0] - np.eye(3)).ravel() for pp in p.reshape((-1, 3))]).ravel()
if p.ndim != 2 or p.shape[1] != 3:
p = p.reshape((-1, 3))
p = p[1:]
return ch.concatenate([(Rodrigues(pp) - ch.eye(3)).ravel() for pp in p]).ravel()
def lrotmin(p):
if isinstance(p, np.ndarray):
p = p.ravel()[3:]
return np.concatenate([(cv2.Rodrigues(np.array(pp))[0]-np.eye(3)).ravel() for pp in p.reshape((-1,3))]).ravel()
if p.ndim != 2 or p.shape[1] != 3:
p = p.reshape((-1,3))
p = p[1:]
return ch.concatenate([(Rodrigues(pp)-ch.eye(3)).ravel() for pp in p]).ravel()
def test_sum_mean_std_var(self):
for fn in [ch.sum, ch.mean, ch.var, ch.std]:
# Create fake input and differences in input space
data1 = ch.ones((3, 4, 7, 2))
data2 = ch.array(data1.r + .1 *
np.random.rand(data1.size).reshape(data1.shape))
diff = data2.r - data1.r
# Compute outputs
result1 = fn(data1, axis=2)
result2 = fn(data2, axis=2)
# Empirical and predicted derivatives
gt = result2.r - result1.r
pred = result1.dr_wrt(data1).dot(diff.ravel()).reshape(gt.shape)
#print(np.max(np.abs(gt - pred)))
if fn in [ch.std, ch.var]:
self.assertTrue(1e-2 > np.max(np.abs(gt - pred)))
else:
self.assertTrue(1e-14 > np.max(np.abs(gt - pred)))
# test caching
dr0 = result1.dr_wrt(data1)
data1[:] = np.random.randn(data1.size).reshape(data1.shape)
self.assertTrue(
result1.dr_wrt(data1) is
dr0) # changing values shouldn't force recompute
result1.axis = 1
self.assertTrue(result1.dr_wrt(data1) is not dr0)
self.assertEqual(
ch.mean(ch.eye(3), axis=1).ndim,
np.mean(np.eye(3), axis=1).ndim)
self.assertEqual(
ch.mean(ch.eye(3), axis=0).ndim,
np.mean(np.eye(3), axis=0).ndim)
self.assertEqual(
ch.sum(ch.eye(3), axis=1).ndim,
np.sum(np.eye(3), axis=1).ndim)
self.assertEqual(
ch.sum(ch.eye(3), axis=0).ndim,
np.sum(np.eye(3), axis=0).ndim)
dd[s] = ch.array(dd[s])
else:
print type(dd[s])
dd['v_shaped'] = dd['shapedirs'].dot(dd['betas'])+dd['v_template']
v_shaped = dd['v_shaped']
J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:,0])
J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:,1])
J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:,2])
dd['J'] = ch.vstack((J_tmpx, J_tmpy, J_tmpz)).T
if dd['pose'].ndim != 2 or p.shape[1] != 3:
p = dd['pose'].reshape((-1,3))
p = p[1:]
c= ch.concatenate([(Rodrigues(pp)-ch.eye(3)).ravel() for pp in p]).ravel()
dd['v_posed'] = v_shaped + dd['posedirs'].dot(c)
args = {
'pose': dd['pose'],
'v': dd['v_posed'],
'J': dd['J'],
'weights': dd['weights'],
'kintree_table': dd['kintree_table'],
'xp': ch,
'want_Jtr': True,
'bs_style': dd['bs_style']
}
pose=args['pose']
import chumpy as ch
import numpy as np
import chumpy as ch
from os.path import join
from smpl_webuser.serialization import load_model
from fitting.landmarks import load_embedding, landmark_error_3d
from fitting.util import load_binary_pickle, write_simple_obj, safe_mkdir, mat_save
import scipy.io as sio
from mpl_toolkits.mplot3d import Axes3D
import matplotlib
matplotlib.use("qt4agg")
import matplotlib.pyplot
import myplot.vtkplot as vp
import quaternion
x1, x2, x3, x4 = ch.eye(10), ch.array(1), ch.array(5), ch.array(10)
print "model.trans:"
print x1
y = x1 * (x2 - x3) + x4
print x1, x2, x3, x4
print y
print y.dr_wrt(x2)
def kk():
hua = 1
sddd = 2
kk()