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import numpy as np
import os
import sys
module_path = os.path.abspath(os.path.join('PyMO'))
if sys.path[0] != module_path:
sys.path.insert(0,module_path)
from analysis.pymo.parsers import BVHParser
from analysis.pymo.preprocessing import *
from analysis.pymo.viz_tools import *
from analysis.pymo.writers import *
import pickle
#%% Default skeleton
p = BVHParser()
default_skel = p.parse("analysis/mixamo.bvh")
#%%
# scale=1.0
# # Scale the default sceleton
# for joint in default_skel.traverse():
# offs = np.array(default_skel.skeleton[joint]['offsets'])*scale
# default_skel.skeleton[joint]['offsets'] = offs.tolist()
bvh2expmap = MocapParameterizer('expmap')
default_exp_map_data = bvh2expmap.fit_transform([default_skel])
unity_joints_vals = default_exp_map_data[0].values.columns
npfier = Numpyfier()
npfier.fit_transform(default_exp_map_data)
def angles2smplbvh(out_data, dest_dir, filename, framerate):
smpl_bvh = bvh2expmap.inverse_transform(npfier.inverse_transform(out_data))
smpl_bvh[0].framerate=float(1.0/framerate)
writer = BVHWriter()
with open(f'{dest_dir}/{filename}.bvh','w') as f:
writer.write(smpl_bvh[0], f)
def convert_smpl(filepath, dest_dir, result_filename, framerate):
data = pickle.load(open(filepath, "rb"))
smpl_angs = data["smpl_poses"].reshape(-1,24*3)
smpl_trans = data["smpl_trans"]
# print(data)
smpl_scale = data["smpl_scaling"]
smpl_joints = ["Pelvis", "L_Hip", "R_Hip", "Spine1", "L_Knee", "R_Knee", "Spine2", "L_Ankle", "R_Ankle",
"Spine3", "L_Foot", "R_Foot", "Neck", "L_Collar", "R_Collar", "Head", "L_Shoulder", "R_Shoulder",
"L_Elbow", "R_Elbow", "L_Wrist", "R_Wrist", "L_Hand", "R_Hand"]
unity_joints = [j for j in default_skel.traverse() if j[-3:]!="Nub"]
smpl_joints_to_unity_joints = {
"Pelvis": "Hips",
"L_Hip": "LeftUpLeg",
"R_Hip": "RightUpLeg",
"Spine1": "Spine",
"L_Knee": "LeftLeg",
"R_Knee": "RightLeg",
"L_Ankle": "LeftFoot",
"R_Ankle": "RightFoot",
"Spine2": "Spine1",
"Spine3": "Spine2",
"L_Foot": "LeftToeBase",
"R_Foot": "RightToeBase",
"Neck": "Neck",
"Head": "Head",
"L_Collar": "LeftShoulder",
"R_Collar": "RightShoulder",
"L_Shoulder": "LeftArm",
"R_Shoulder": "RightArm",
"L_Elbow": "LeftForeArm",
"R_Elbow": "RightForeArm",
"L_Wrist": "LeftHand",
"R_Wrist": "RightHand",
# "L_Hand": "LeftHand",
# "R_Hand": "RightHand",
}
out_data = np.zeros((1, smpl_angs.shape[0], len(unity_joints)*3 + 3))
poss = np.zeros((smpl_angs.shape[0], 3))
for smpl_joint, unity_joint in smpl_joints_to_unity_joints.items():
out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_alpha")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3]
out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_beta")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3+1]
out_data[0,:,unity_joints_vals.get_loc(unity_joint+"_gamma")] = smpl_angs[:,smpl_joints.index(smpl_joint)*3+2]
# relative_scale=188.7
# relative_scale=100
smpl_scale = 100
relative_scale=smpl_scale
vertical_offset=-0.6*(smpl_scale/100)
out_data[0,:,unity_joints_vals.get_loc("Hips_Xposition")] = smpl_trans[:,0]/relative_scale
out_data[0,:,unity_joints_vals.get_loc("Hips_Yposition")] = smpl_trans[:,1]/relative_scale+vertical_offset
out_data[0,:,unity_joints_vals.get_loc("Hips_Zposition")] = smpl_trans[:,2]/relative_scale
angles2smplbvh(out_data, dest_dir, result_filename, framerate)
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