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lift2DJoints.py
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lift2DJoints.py
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import numpy as np
import os
import pickle
import matplotlib.pyplot as plt
from scipy.misc import imread, imsave
import argparse, json, yaml
import cv2
from mano.webuser.verts import verts_core
import chumpy as ch
from sklearn.preprocessing import normalize
import handUtils.manoHandVis as manoVis
from eval import utilsEval
import open3d
import warnings
warnings.filterwarnings("ignore")
warnings.simplefilter("ignore", category=PendingDeprecationWarning)
from chumpy.ch_ops import UnaryElemwise as UE
delta = 50
class clipIden(UE):
_r = lambda self, x: (np.abs(x) < delta) * x + 0.0#(np.abs(x) >= delta)*(0.01*(x))#-delta) + delta)
_d = lambda self, x: (np.abs(x) < delta) * 1 + 0.0#(np.abs(x) >= delta) * 0.01
render = True
cntr = 0
jointsMap = [0,
13, 14, 15, 16,
1, 2, 3, 17,
4, 5, 6, 18,
10, 11, 12, 19,
7, 8, 9, 20]
def undo_chumpy(x):
return x if isinstance(x, np.ndarray) else x.r
class Constraints():
def __init__(self):
self.thetaLimits()
def thetaLimits(self):
MINBOUND = -5.
MAXBOUND = 5.
self.validThetaIDs = np.array([0, 1, 2, 3, 4, 5, 6, 8, 11, 13, 14, 15, 17, 20, 21, 22, 23, 25, 26, 29,
30, 31, 32, 33, 35, 38, 39, 40, 41, 42, 44, 46, 47], dtype=np.int32)
# self.invalidThetaIDs = np.array([7, 9, 10, 12, 16, 18, 19, 24,
# 25, 27, 28, 34, 36, 37, 39, 43, 45], dtype=np.int32)
invalidThetaIDsList = []
for i in range(48):
if i not in self.validThetaIDs:
invalidThetaIDsList.append(i)
self.invalidThetaIDs = np.array(invalidThetaIDsList)
self.minThetaVals = np.array([MINBOUND, MINBOUND, MINBOUND, # global rot
0, -0.15, 0.1, -0.3, MINBOUND, -0.0, MINBOUND, MINBOUND, 0, # index
MINBOUND, -0.15, 0.1, -0.5, MINBOUND, -0.0, MINBOUND, MINBOUND, 0, # middle
-1.5, -0.15, -0.1, MINBOUND, MINBOUND, -0.0, MINBOUND, MINBOUND, 0, # pinky
-0.5, -0.25, 0.1, -0.4, MINBOUND, -0.0, MINBOUND, MINBOUND, 0, # ring
MINBOUND, -0.83, -0.0, -0.15, MINBOUND, 0, MINBOUND, -0.5, -1.57, ]) # thumb
self.maxThetaVals = np.array([MAXBOUND, MAXBOUND, MAXBOUND, #global
0.45, 0.2, 1.8, 0.2, MAXBOUND, 2.0, MAXBOUND, MAXBOUND, 1.25, # index
MAXBOUND, 0.15, 2.0, -0.2, MAXBOUND, 2.0, MAXBOUND, MAXBOUND, 1.25, # middle
-0.2, 0.15, 1.6, MAXBOUND, MAXBOUND, 2.0, MAXBOUND, MAXBOUND, 1.25, # pinky
-0.4, 0.10, 1.6, -0.2, MAXBOUND, 2.0, MAXBOUND, MAXBOUND, 1.25, # ring
MAXBOUND, 0.66, 0.5, 1.6, MAXBOUND, 0.5, MAXBOUND, 0, 1.08]) # thumb
# self.minThetaVals = np.array([MINBOUND, MINBOUND, MINBOUND, # global rot
# 0, -0.15, 0.1, -0.3, MINBOUND, -0.0, MINBOUND, MINBOUND, 0, # index
# MINBOUND, -0.15, 0.1, -0.5, MINBOUND, -0.0, MINBOUND, MINBOUND, 0, # middle
# -1.0, -0.15, -0.1, MINBOUND, -0.5, -0.0, MINBOUND, MINBOUND, 0, # pinky
# -0.5, -0.25, 0.1, -0.4, MINBOUND, -0.0, MINBOUND, MINBOUND, 0, # ring
# 0.0, -0.83, -0.0, -0.15, MINBOUND, 0, MINBOUND, -0.5, -1.57, ]) # thumb
#
# self.maxThetaVals = np.array([MAXBOUND, MAXBOUND, MAXBOUND, # global
# 0.45, 0.2, 1.8, 0.2, MAXBOUND, 2.0, MAXBOUND, MAXBOUND, 1.25, # index
# MAXBOUND, 0.15, 2.0, -0.2, MAXBOUND, 2.0, MAXBOUND, MAXBOUND, 1.25, # middle
# -0.2, 0.6, 1.6, MAXBOUND, 0.6, 2.0, MAXBOUND, MAXBOUND, 1.25, # pinky
# -0.4, 0.10, 1.8, -0.2, MAXBOUND, 2.0, MAXBOUND, MAXBOUND, 1.25, # ring
# 2.0, 0.66, 0.5, 1.6, MAXBOUND, 0.5, MAXBOUND, 0, 1.08]) # thumb
self.fullThetaMat = np.zeros((48, len(self.validThetaIDs)), dtype=np.float32) #48x25
for i in range(len(self.validThetaIDs)):
self.fullThetaMat[self.validThetaIDs[i], i] = 1.0
def getHandJointConstraints(self, theta, isValidTheta=False):
'''
get constraints on the joint angles when input is theta vector itself (first 3 elems are NOT global rot)
:param theta: Nx45 tensor if isValidTheta is False and Nx25 if isValidTheta is True
:param isValidTheta:
:return:
'''
if not isValidTheta:
assert (theta.shape)[-1] == 45
validTheta = theta[self.validThetaIDs[3:] - 3]
else:
assert (theta.shape)[-1] == len(self.validThetaIDs[3:])
validTheta = theta
phyConstMax = (ch.maximum(self.minThetaVals[self.validThetaIDs[3:]] - validTheta, 0))
phyConstMin = (ch.maximum(validTheta - self.maxThetaVals[self.validThetaIDs[3:]], 0))
return phyConstMin, phyConstMax
def getHandModel():
globalJoints = ch.zeros((45,))
globalBeta = ch.zeros((10,))
chRot = ch.zeros((3,))
chTrans = ch.array([0., 0., 0.5])
fullpose = ch.concatenate([chRot, globalJoints], axis=0)
m = load_model_withInputs(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../mano/models/MANO_RIGHT.pkl'), fullpose, chTrans,
globalBeta,
ncomps=15, flat_hand_mean=True)
return m, chRot, globalJoints, chTrans, globalBeta
def getHandModelPoseCoeffs(numComp):
m = load_model_1(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../mano/models/MANO_RIGHT.pkl'), ncomps=numComp)
m.trans[:] = np.array([0., 0., 0.5])
return m, m.pose, m.betas, m.trans, m.fullpose
def lift2Dto3D(projPtsGT, camMat, filename, img, JVis=np.ones((21,), dtype=np.float32), trans=None, beta=None, wrist3D = None, withPoseCoeff=True,
weights=1.0, relDepGT=None, rel3DCoordGT = None, rel3DCoordNormGT = None, img2DGT = None, outDir = None,
poseCoeffInit=None,
transInit=None, betaInit=None):
loss = {}
if withPoseCoeff:
numComp = 30
m, poseCoeffCh, betaCh, transCh, fullposeCh = getHandModelPoseCoeffs(numComp)
if poseCoeffInit is not None:
poseCoeffCh[:] = poseCoeffInit
if transInit is not None:
transCh[:] = transInit
if betaInit is not None:
betaCh[:] = betaInit
freeVars = [poseCoeffCh]
if beta is None:
freeVars = freeVars + [betaCh]
loss['shape'] = 1e2 * betaCh
else:
betaCh[:] = beta
if trans is None:
freeVars = freeVars + [transCh]
else:
transCh[:] = trans
# loss['pose'] = 0.5e2 * poseCoeffCh[3:]/stdPCACoeff[:numComp]
thetaConstMin, thetaConstMax = Constraints().getHandJointConstraints(fullposeCh[3:])
loss['constMin'] = 5e2 * thetaConstMin
loss['constMax'] = 5e2 * thetaConstMax
loss['invalidTheta'] = 1e3 * fullposeCh[Constraints().invalidThetaIDs]
else:
m, rotCh, jointsCh, transCh, betaCh = getHandModel()
thetaConstMin, thetaConstMax = Constraints().getHandJointConstraints(jointsCh)
loss['constMin'] = 5e3 * thetaConstMin
loss['constMax'] = 5e3 * thetaConstMax
validTheta = jointsCh[Constraints().validThetaIDs[3:] - 3]
freeVars = [validTheta, rotCh]
if beta is None:
freeVars = freeVars + [betaCh]
loss['shape'] = 0.5e2 * betaCh
else:
betaCh[:] = beta
if trans is None:
freeVars = freeVars + [transCh]
else:
transCh[:] = trans
if relDepGT is not None:
relDepPred = m.J_transformed[:,2] - m.J_transformed[0,2]
loss['relDep'] = (relDepPred - relDepGT) * weights[:,0] * 5e1
if rel3DCoordGT is not None:
rel3DCoordPred = m.J_transformed - m.J_transformed[0:1,:]
loss['rel3DCoord'] = (rel3DCoordPred - rel3DCoordGT) * np.tile(weights[:,0:1], [1,3]) * 5e1
if rel3DCoordNormGT is not None:
rel3DCoordPred = m.J_transformed[jointsMap][1:,:] - m.J_transformed[jointsMap][0:1, :]
rel3DCoordPred = rel3DCoordPred/ch.expand_dims(ch.sqrt(ch.sum(ch.square(rel3DCoordPred), axis=1)), axis=1)
loss['rel3DCoordNorm'] = (1. - ch.sum(rel3DCoordPred*rel3DCoordNormGT, axis=1))*1e4
# loss['rel3DCoordNorm'] = \
# (rel3DCoordNormGT*ch.expand_dims(ch.sum(rel3DCoordPred*rel3DCoordNormGT, axis=1), axis=1) - rel3DCoordPred) * 1e2#5e2
projPts = utilsEval.chProjectPoints(m.J_transformed, camMat, False)[jointsMap]
JVis = np.tile(np.expand_dims(JVis, 1), [1, 2])
loss['joints2D'] = (projPts - projPtsGT) * JVis * weights * 1e0
loss['joints2DClip'] = clipIden(projPts - projPtsGT) * JVis * weights * 1e1
if wrist3D is not None:
dep = wrist3D[2]
if dep<0:
dep = -dep
loss['wristDep'] = (m.J_transformed[0,2] - dep)*1e2
# vis_mesh(m)
render = False
def cbPass(_):
pass
# print(loss['joints'].r)
print(filename)
warnings.simplefilter('ignore')
loss['joints2D'] = loss['joints2D'] * 1e1/ weights # dont want to use confidence now
if True:
ch.minimize({k: loss[k] for k in loss.keys() if k != 'joints2DClip'}, x0=freeVars,
callback=cbPass if render else cbPass, method='dogleg', options={'maxiter': 50})
else:
manoVis.dump3DModel2DKpsHand(img, m, filename, camMat, est2DJoints=projPtsGT, gt2DJoints=img2DGT,
outDir=outDir)
freeVars = [poseCoeffCh[:3], transCh]
ch.minimize({k: loss[k] for k in loss.keys() if k != 'joints2DClip'}, x0=freeVars,
callback=cbPass, method='dogleg', options={'maxiter': 20})
manoVis.dump3DModel2DKpsHand(img, m, filename, camMat, est2DJoints=projPtsGT, gt2DJoints=img2DGT,
outDir=outDir)
freeVars = [poseCoeffCh[3:]]
ch.minimize({k: loss[k] for k in loss.keys() if k != 'joints2DClip'}, x0=freeVars,
callback=cb if render else cbPass, method='dogleg', options={'maxiter': 20})
manoVis.dump3DModel2DKpsHand(img, m, filename, camMat, est2DJoints=projPtsGT, gt2DJoints=img2DGT,
outDir=outDir)
freeVars = [poseCoeffCh, transCh]
if beta is None:
freeVars = freeVars + [betaCh]
ch.minimize({k: loss[k] for k in loss.keys() if k != 'joints2DClip'}, x0=freeVars,
callback=cb if render else cbPass, method='dogleg', options={'maxiter': 20})
if False:
open3dVisualize(m)
else:
manoVis.dump3DModel2DKpsHand(img, m, filename, camMat, est2DJoints=projPtsGT, gt2DJoints=img2DGT, outDir=outDir)
# vis_mesh(m)
joints3D = m.J_transformed.r[jointsMap]
# print(betaCh.r)
# print((relDepPred.r - relDepGT))
return joints3D, poseCoeffCh.r.copy(), betaCh.r.copy(), transCh.r.copy(), loss['joints2D'].r.copy(), m.r.copy()
def ready_arguments(fname_or_dict, posekey4vposed='pose', shared_args=None, chTrans=None, chBetas=None):
import numpy as np
import pickle
import chumpy as ch
from chumpy.ch import MatVecMult
from mano.webuser.posemapper import posemap
if not isinstance(fname_or_dict, dict):
dd = pickle.load(open(fname_or_dict))
else:
dd = fname_or_dict
want_shapemodel = 'shapedirs' in dd
nposeparms = dd['kintree_table'].shape[1]*3
if 'trans' not in dd:
dd['trans'] = np.zeros(3)
if 'pose' not in dd:
dd['pose'] = np.zeros(nposeparms)
if 'shapedirs' in dd and 'betas' not in dd:
dd['betas'] = np.zeros(dd['shapedirs'].shape[-1])
if chTrans is not None:
dd['trans'] = chTrans
if chTrans is not None:
dd['betas'] = chBetas
for s in ['v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs', 'betas', 'J', 'fullpose', 'pose_dof']:
if (s in dd) and not hasattr(dd[s], 'dterms'):
if shared_args is not None and s in shared_args:
dd[s] = shared_args[s]
else:
dd[s] = ch.array(dd[s])
assert(posekey4vposed in dd)
if want_shapemodel:
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
dd['v_posed'] = v_shaped + dd['posedirs'].dot(posemap(dd['bs_type'])(dd[posekey4vposed]))
else:
dd['v_posed'] = dd['v_template'] + dd['posedirs'].dot(posemap(dd['bs_type'])(dd[posekey4vposed]))
return dd
def load_model_1(fname_or_dict, ncomps=6, flat_hand_mean=False, v_template=None, shared_args=None, optwrt='pose_coeff', relRot=np.eye(3), relTrans=np.array([0.0, 0.0, 0.0])):
''' This model loads the fully articulable HAND SMPL model,
and replaces the pose DOFS by ncomps from PCA'''
import numpy as np
import chumpy as ch
import pickle
import scipy.sparse as sp
np.random.seed(1)
if not isinstance(fname_or_dict, dict):
with open(fname_or_dict, 'rb') as f:
smpl_data = pickle.load(f, encoding='latin1')
else:
smpl_data = fname_or_dict
rot = 3 # for global orientation!!!
dof = 20
# smpl_data['hands_components'] = np.eye(45)
from sklearn.preprocessing import normalize
smpl_data['hands_components'] = normalize(smpl_data['hands_components'], axis=1)
hands_components = smpl_data['hands_components']
std = np.linalg.norm(hands_components, axis=1)
hands_mean = np.zeros(hands_components.shape[1]) if flat_hand_mean else smpl_data['hands_mean']
hands_coeffs = smpl_data['hands_coeffs'][:, :ncomps]
selected_components = np.vstack((hands_components[:ncomps]))
hands_mean = hands_mean.copy()
if shared_args is not None and 'pose_coeffs' in shared_args:
pose_coeffs = ch.zeros(rot + selected_components.shape[0])
pose_coeffs[:len(shared_args['pose_coeffs'])] = shared_args['pose_coeffs']
else:
pose_coeffs = ch.zeros(rot + selected_components.shape[0])
full_hand_pose = pose_coeffs[rot:(rot+ncomps)].dot(selected_components)
smpl_data['fullpose'] = ch.concatenate((pose_coeffs[:rot], hands_mean + full_hand_pose))
pose_dof = ch.zeros(rot + dof)
smpl_data['pose'] = pose_coeffs
smpl_data['pose_dof'] = pose_dof
Jreg = smpl_data['J_regressor']
if not sp.issparse(Jreg):
smpl_data['J_regressor'] = (sp.csc_matrix((Jreg.data, (Jreg.row, Jreg.col)), shape=Jreg.shape))
# slightly modify ready_arguments to make sure that it uses the fullpose
# (which will NOT be pose) for the computation of posedirs
dd = ready_arguments(smpl_data, posekey4vposed='fullpose')
# create the smpl formula with the fullpose,
# but expose the PCA coefficients as smpl.pose for compatibility
args = {
'pose': dd['fullpose'],
'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'],
}
# print(dd['J'].r)
result_previous, meta = verts_core(**args)
result_noRel = result_previous + dd['trans'].reshape((1, 3))
result = result_noRel.dot(relRot) + relTrans
result.no_translation = result_previous
if meta is not None:
for field in ['Jtr', 'A', 'A_global', 'A_weighted']:
if(hasattr(meta, field)):
setattr(result, field, getattr(meta, field))
if hasattr(result, 'Jtr'):
result.J_transformed = (result.Jtr + dd['trans'].reshape((1, 3))).dot(relRot) + relTrans
for k, v in dd.items():
setattr(result, k, v)
if v_template is not None:
result.v_template[:] = v_template
return result
def load_model_withInputs_poseCoeffs(fname_or_dict, chRot, chPoseCoeff, chTrans, chBetas,
ncomps=6, flat_hand_mean=False, v_template=None, shared_args=None,):
import numpy as np
import chumpy as ch
import pickle
import scipy.sparse as sp
np.random.seed(1)
if not isinstance(fname_or_dict, dict):
# smpl_data = pickle.load(open(fname_or_dict))
smpl_data = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1')
else:
smpl_data = fname_or_dict
rot = 3 # for global orientation!!!
dof = 20
# smpl_data['hands_components'] = np.eye(45)
from sklearn.preprocessing import normalize
smpl_data['hands_components'] = normalize(smpl_data['hands_components'], axis=1)
hands_components = smpl_data['hands_components']
hands_mean = np.zeros(hands_components.shape[1]) if flat_hand_mean else smpl_data['hands_mean']
hands_coeffs = smpl_data['hands_coeffs'][:, :ncomps]
selected_components = np.vstack((hands_components[:ncomps]))
hands_mean = hands_mean.copy()
pose_coeffs = ch.concatenate([chRot, chPoseCoeff], axis=0)
full_hand_pose = pose_coeffs[rot:(rot+ncomps)].dot(selected_components)
smpl_data['fullpose'] = ch.concatenate((pose_coeffs[:rot], hands_mean + full_hand_pose))
smpl_data['pose'] = pose_coeffs
Jreg = smpl_data['J_regressor']
if not sp.issparse(Jreg):
smpl_data['J_regressor'] = (sp.csc_matrix((Jreg.data, (Jreg.row, Jreg.col)), shape=Jreg.shape))
# slightly modify ready_arguments to make sure that it uses the fullpose
# (which will NOT be pose) for the computation of posedirs
dd = ready_arguments(smpl_data, posekey4vposed='fullpose', shared_args=shared_args, chTrans=chTrans, chBetas=chBetas)
# create the smpl formula with the fullpose,
# but expose the PCA coefficients as smpl.pose for compatibility
args = {
'pose': dd['fullpose'],
'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'],
}
# print(dd['J'].r)
result_previous, meta = verts_core(**args)
result_noRel = result_previous + dd['trans'].reshape((1, 3))
result = result_noRel
result.no_translation = result_previous
if meta is not None:
for field in ['Jtr', 'A', 'A_global', 'A_weighted']:
if(hasattr(meta, field)):
setattr(result, field, getattr(meta, field))
if hasattr(result, 'Jtr'):
result.J_transformed = (result.Jtr + dd['trans'].reshape((1, 3)))
for k, v in dd.items():
setattr(result, k, v)
if v_template is not None:
result.v_template[:] = v_template
return result
def getHandModelPoseCoeffsMultiFrame(numComp, numFrames, isOpenGLCoord):
chGlobalPoseCoeff = ch.zeros((numComp,))
chGlobalBeta = ch.zeros((10,))
chRotList = []
chTransList = []
mList = []
for i in range(numFrames):
chRot = ch.zeros((3,))
if isOpenGLCoord:
chTrans = ch.array([0., 0., -0.5])
else:
chTrans = ch.array([0., 0., 0.5])
chRotList.append(chRot)
chTransList.append(chTrans)
m = load_model_withInputs_poseCoeffs(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../mano/models/MANO_RIGHT.pkl'),
chRot=chRot,
chTrans=chTrans,
chPoseCoeff = chGlobalPoseCoeff,
chBetas=chGlobalBeta,
ncomps=numComp)
mList.append(m)
return mList, chRotList, chGlobalPoseCoeff, chTransList, chGlobalBeta
def lift2Dto3DMultiFrame(projPtsGT, camMat, filename, JVis=np.ones((21,), dtype=np.float32), trans=None, beta=None,
wrist3D = None, withPoseCoeff=True,
weights=None, relDepGT=None, rel3DCoordGT = None, isOpenGLCoord=False,
transInit = None, rotInit = None, globalPoseCoeffInit = None, betaInit = None):
'''
:param projPtsGT:
:param camMat:
:param filename:
:param JVis:
:param trans:
:param beta:
:param wrist3D:
:param withPoseCoeff:
:param weights: 21x1 array
:param relDepGT:
:param rel3DCoordGT: always in opencv
:param isOpenGLCoord:
:return: always in opencv
'''
loss = {}
numFrames = projPtsGT.shape[0]
if weights is None:
weights = [np.ones((21,1), dtype=np.float32)]*numFrames
numComp = 30
mList, chRotList, chGlobalPoseCoeff, chTransList, chGlobalBeta = getHandModelPoseCoeffsMultiFrame(numComp, numFrames, isOpenGLCoord)
freeVars = [chGlobalPoseCoeff]
if beta is None:
freeVars = freeVars + [chGlobalBeta]
loss['shape'] = 1e2 * chGlobalBeta
else:
chGlobalBeta[:] = beta
if betaInit is not None:
chGlobalBeta[:] = betaInit
if trans is None:
freeVars = freeVars + chTransList
else:
for i, t in enumerate(chTransList):
t[:] = trans[i]
if transInit is not None:
for i in range(len(chTransList)):
chTransList[i][:] = transInit[i]
if rotInit is not None:
for i in range(len(chRotList)):
chRotList[i][:] = rotInit[i]
if globalPoseCoeffInit is not None:
chGlobalPoseCoeff[:] = globalPoseCoeffInit
freeVars = freeVars + chRotList
# loss['pose'] = 0.5e2 * poseCoeffCh[3:]/stdPCACoeff[:numComp]
fullposeCh = mList[0].fullpose ##
thetaConstMin, thetaConstMax = Constraints().getHandJointConstraints(fullposeCh[3:])
loss['constMin'] = 5e2 * thetaConstMin
loss['constMax'] = 5e2 * thetaConstMax
loss['invalidTheta'] = 5e2 * fullposeCh[Constraints().invalidThetaIDs]
if relDepGT is not None:
for i in range(numFrames):
relDepPred = mList[i].J_transformed[:,2] - mList[i].J_transformed[0,2]
loss['relDep_%d'%(i)] = (relDepPred - relDepGT[i]) * weights[:,0] * 5e1
coordChangeMat = np.array([[1., 0., 0.], [0, -1., 0.], [0., 0., -1.]], dtype=np.float32)
if rel3DCoordGT is not None:
for i in range(numFrames):
rel3DCoordPred = mList[i].J_transformed - mList[i].J_transformed[0:1,:]
if isOpenGLCoord:
rel3DCoordPred = coordChangeMat.dot(coordChangeMat)
loss['rel3DCoord_%d'%(i)] = (rel3DCoordPred - rel3DCoordGT[i]) * np.tile(weights[i][:,0:1], [1,3]) * 5e1
for i in range(numFrames):
projPts = utilsEval.chProjectPoints(mList[i].J_transformed, camMat, isOpenGLCoord)[jointsMap]
# JVis = np.tile(np.expand_dims(JVis, 1), [1, 2])
loss['joints2D_%d'%(i)] = (projPts - projPtsGT[i])# * np.tile(weights[i][:,0:1], [1,2])# * 1e1
render = False
def cbPass(_):
pass
# print(loss['joints'].r)
for i in range(numFrames):
loss['joints2D_%d' % (i)] = loss['joints2D_%d' % (i)] * np.tile(weights[i][:,0:1], [1,2]) * 1e1
ch.minimize(loss, x0=freeVars, callback=cbPass, method='dogleg', options={'maxiter': 12})
# vis_mesh(mList[0])
# vis_mesh(m)
joints3DList = []
for i in range(numFrames):
joints3D = mList[i].J_transformed.r[jointsMap]
if isOpenGLCoord:
joints3D = joints3D.dot(coordChangeMat)
joints3DList.append(joints3D)
# fullpose list
fullposeList = []
betaList = []
transList = []
for m in mList:
fullposeList.append(m.fullpose.r)
betaList.append(m.betas.r)
transList.append(m.trans.r)
# print(betaCh.r)
# print((relDepPred.r - relDepGT))
return np.stack(joints3DList, axis=0), fullposeList, betaList, transList, chGlobalPoseCoeff.r.copy(), mList