# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
from lib.dataset.mesh_util import SMPLX
from lib.common.render_utils import face_vertices
import numpy as np
import lib.smplx as smplx
import trimesh
import torch
import torch.nn.functional as F
model_init_params = dict(gender='male',
model_type='smplx',
model_path=SMPLX().model_dir,
create_global_orient=False,
create_body_pose=False,
create_betas=False,
create_left_hand_pose=False,
create_right_hand_pose=False,
create_expression=False,
create_jaw_pose=False,
create_leye_pose=False,
create_reye_pose=False,
create_transl=False,
num_pca_comps=12)
def get_smpl_model(model_type, gender):
return smplx.create(**model_init_params)
def normalization(data):
_range = np.max(data) - np.min(data)
return ((data - np.min(data)) / _range)
def sigmoid(x):
z = 1 / (1 + np.exp(-x))
return z
def load_fit_body(fitted_path,
scale,
smpl_type='smplx',
smpl_gender='neutral',
noise_dict=None):
param = np.load(fitted_path, allow_pickle=True)
for key in param.keys():
param[key] = torch.as_tensor(param[key])
smpl_model = get_smpl_model(smpl_type, smpl_gender)
model_forward_params = dict(betas=param['betas'],
global_orient=param['global_orient'],
body_pose=param['body_pose'],
left_hand_pose=param['left_hand_pose'],
right_hand_pose=param['right_hand_pose'],
jaw_pose=param['jaw_pose'],
leye_pose=param['leye_pose'],
reye_pose=param['reye_pose'],
expression=param['expression'],
return_verts=True)
if noise_dict is not None:
model_forward_params.update(noise_dict)
smpl_out = smpl_model(**model_forward_params)
smpl_verts = (
(smpl_out.vertices[0] * param['scale'] + param['translation']) *
scale).detach()
smpl_joints = (
(smpl_out.joints[0] * param['scale'] + param['translation']) *
scale).detach()
smpl_mesh = trimesh.Trimesh(smpl_verts,
smpl_model.faces,
process=False,
maintain_order=True)
return smpl_mesh, smpl_joints
def load_ori_fit_body(fitted_path, smpl_type='smplx', smpl_gender='neutral'):
param = np.load(fitted_path, allow_pickle=True)
for key in param.keys():
param[key] = torch.as_tensor(param[key])
smpl_model = get_smpl_model(smpl_type, smpl_gender)
model_forward_params = dict(betas=param['betas'],
global_orient=param['global_orient'],
body_pose=param['body_pose'],
left_hand_pose=param['left_hand_pose'],
right_hand_pose=param['right_hand_pose'],
jaw_pose=param['jaw_pose'],
leye_pose=param['leye_pose'],
reye_pose=param['reye_pose'],
expression=param['expression'],
return_verts=True)
smpl_out = smpl_model(**model_forward_params)
smpl_verts = smpl_out.vertices[0].detach()
smpl_mesh = trimesh.Trimesh(smpl_verts,
smpl_model.faces,
process=False,
maintain_order=True)
return smpl_mesh
def save_obj_mesh(mesh_path, verts, faces):
file = open(mesh_path, 'w')
for v in verts:
file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
for f in faces:
f_plus = f + 1
file.write('f %d %d %d\n' % (f_plus[0], f_plus[1], f_plus[2]))
file.close()
# https://github.com/ratcave/wavefront_reader
def read_mtlfile(fname):
materials = {}
with open(fname) as f:
lines = f.read().splitlines()
for line in lines:
if line:
split_line = line.strip().split(' ', 1)
if len(split_line) < 2:
continue
prefix, data = split_line[0], split_line[1]
if 'newmtl' in prefix:
material = {}
materials[data] = material
elif materials:
if data:
split_data = data.strip().split(' ')
# assume texture maps are in the same level
# WARNING: do not include space in your filename!!
if 'map' in prefix:
material[prefix] = split_data[-1].split('\\')[-1]
elif len(split_data) > 1:
material[prefix] = tuple(float(d) for d in split_data)
else:
try:
material[prefix] = int(data)
except ValueError:
material[prefix] = float(data)
return materials
def load_obj_mesh_mtl(mesh_file):
vertex_data = []
norm_data = []
uv_data = []
face_data = []
face_norm_data = []
face_uv_data = []
# face per material
face_data_mat = {}
face_norm_data_mat = {}
face_uv_data_mat = {}
# current material name
mtl_data = None
cur_mat = None
if isinstance(mesh_file, str):
f = open(mesh_file, "r")
else:
f = mesh_file
for line in f:
if isinstance(line, bytes):
line = line.decode("utf-8")
if line.startswith('#'):
continue
values = line.split()
if not values:
continue
if values[0] == 'v':
v = list(map(float, values[1:4]))
vertex_data.append(v)
elif values[0] == 'vn':
vn = list(map(float, values[1:4]))
norm_data.append(vn)
elif values[0] == 'vt':
vt = list(map(float, values[1:3]))
uv_data.append(vt)
elif values[0] == 'mtllib':
mtl_data = read_mtlfile(
mesh_file.replace(mesh_file.split('/')[-1], values[1]))
elif values[0] == 'usemtl':
cur_mat = values[1]
elif values[0] == 'f':
# local triangle data
l_face_data = []
l_face_uv_data = []
l_face_norm_data = []
# quad mesh
if len(values) > 4:
f = list(
map(
lambda x: int(x.split('/')[0]) if int(x.split('/')[0])
< 0 else int(x.split('/')[0]) - 1, values[1:4]))
l_face_data.append(f)
f = list(
map(
lambda x: int(x.split('/')[0])
if int(x.split('/')[0]) < 0 else int(x.split('/')[0]) -
1, [values[3], values[4], values[1]]))
l_face_data.append(f)
# tri mesh
else:
f = list(
map(
lambda x: int(x.split('/')[0]) if int(x.split('/')[0])
< 0 else int(x.split('/')[0]) - 1, values[1:4]))
l_face_data.append(f)
# deal with texture
if len(values[1].split('/')) >= 2:
# quad mesh
if len(values) > 4:
f = list(
map(
lambda x: int(x.split('/')[1])
if int(x.split('/')[1]) < 0 else int(
x.split('/')[1]) - 1, values[1:4]))
l_face_uv_data.append(f)
f = list(
map(
lambda x: int(x.split('/')[1])
if int(x.split('/')[1]) < 0 else int(
x.split('/')[1]) - 1,
[values[3], values[4], values[1]]))
l_face_uv_data.append(f)
# tri mesh
elif len(values[1].split('/')[1]) != 0:
f = list(
map(
lambda x: int(x.split('/')[1])
if int(x.split('/')[1]) < 0 else int(
x.split('/')[1]) - 1, values[1:4]))
l_face_uv_data.append(f)
# deal with normal
if len(values[1].split('/')) == 3:
# quad mesh
if len(values) > 4:
f = list(
map(
lambda x: int(x.split('/')[2])
if int(x.split('/')[2]) < 0 else int(
x.split('/')[2]) - 1, values[1:4]))
l_face_norm_data.append(f)
f = list(
map(
lambda x: int(x.split('/')[2])
if int(x.split('/')[2]) < 0 else int(
x.split('/')[2]) - 1,
[values[3], values[4], values[1]]))
l_face_norm_data.append(f)
# tri mesh
elif len(values[1].split('/')[2]) != 0:
f = list(
map(
lambda x: int(x.split('/')[2])
if int(x.split('/')[2]) < 0 else int(
x.split('/')[2]) - 1, values[1:4]))
l_face_norm_data.append(f)
face_data += l_face_data
face_uv_data += l_face_uv_data
face_norm_data += l_face_norm_data
if cur_mat is not None:
if cur_mat not in face_data_mat.keys():
face_data_mat[cur_mat] = []
if cur_mat not in face_uv_data_mat.keys():
face_uv_data_mat[cur_mat] = []
if cur_mat not in face_norm_data_mat.keys():
face_norm_data_mat[cur_mat] = []
face_data_mat[cur_mat] += l_face_data
face_uv_data_mat[cur_mat] += l_face_uv_data
face_norm_data_mat[cur_mat] += l_face_norm_data
vertices = np.array(vertex_data)
faces = np.array(face_data)
norms = np.array(norm_data)
norms = normalize_v3(norms)
face_normals = np.array(face_norm_data)
uvs = np.array(uv_data)
face_uvs = np.array(face_uv_data)
out_tuple = (vertices, faces, norms, face_normals, uvs, face_uvs)
if cur_mat is not None and mtl_data is not None:
for key in face_data_mat:
face_data_mat[key] = np.array(face_data_mat[key])
face_uv_data_mat[key] = np.array(face_uv_data_mat[key])
face_norm_data_mat[key] = np.array(face_norm_data_mat[key])
out_tuple += (face_data_mat, face_norm_data_mat, face_uv_data_mat,
mtl_data)
return out_tuple
def load_scan(mesh_file, with_normal=False, with_texture=False):
vertex_data = []
norm_data = []
uv_data = []
face_data = []
face_norm_data = []
face_uv_data = []
if isinstance(mesh_file, str):
f = open(mesh_file, "r")
else:
f = mesh_file
for line in f:
if isinstance(line, bytes):
line = line.decode("utf-8")
if line.startswith('#'):
continue
values = line.split()
if not values:
continue
if values[0] == 'v':
v = list(map(float, values[1:4]))
vertex_data.append(v)
elif values[0] == 'vn':
vn = list(map(float, values[1:4]))
norm_data.append(vn)
elif values[0] == 'vt':
vt = list(map(float, values[1:3]))
uv_data.append(vt)
elif values[0] == 'f':
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
f = list(
map(lambda x: int(x.split('/')[0]),
[values[3], values[4], values[1]]))
face_data.append(f)
# tri mesh
else:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
# deal with texture
if len(values[1].split('/')) >= 2:
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[1]), values[1:4]))
face_uv_data.append(f)
f = list(
map(lambda x: int(x.split('/')[1]),
[values[3], values[4], values[1]]))
face_uv_data.append(f)
# tri mesh
elif len(values[1].split('/')[1]) != 0:
f = list(map(lambda x: int(x.split('/')[1]), values[1:4]))
face_uv_data.append(f)
# deal with normal
if len(values[1].split('/')) == 3:
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[2]), values[1:4]))
face_norm_data.append(f)
f = list(
map(lambda x: int(x.split('/')[2]),
[values[3], values[4], values[1]]))
face_norm_data.append(f)
# tri mesh
elif len(values[1].split('/')[2]) != 0:
f = list(map(lambda x: int(x.split('/')[2]), values[1:4]))
face_norm_data.append(f)
vertices = np.array(vertex_data)
faces = np.array(face_data) - 1
if with_texture and with_normal:
uvs = np.array(uv_data)
face_uvs = np.array(face_uv_data) - 1
norms = np.array(norm_data)
if norms.shape[0] == 0:
norms = compute_normal(vertices, faces)
face_normals = faces
else:
norms = normalize_v3(norms)
face_normals = np.array(face_norm_data) - 1
return vertices, faces, norms, face_normals, uvs, face_uvs
if with_texture:
uvs = np.array(uv_data)
face_uvs = np.array(face_uv_data) - 1
return vertices, faces, uvs, face_uvs
if with_normal:
norms = np.array(norm_data)
norms = normalize_v3(norms)
face_normals = np.array(face_norm_data) - 1
return vertices, faces, norms, face_normals
return vertices, faces
def normalize_v3(arr):
''' Normalize a numpy array of 3 component vectors shape=(n,3) '''
lens = np.sqrt(arr[:, 0]**2 + arr[:, 1]**2 + arr[:, 2]**2)
eps = 0.00000001
lens[lens < eps] = eps
arr[:, 0] /= lens
arr[:, 1] /= lens
arr[:, 2] /= lens
return arr
def compute_normal(vertices, faces):
# Create a zeroed array with the same type and shape as our vertices i.e., per vertex normal
norm = np.zeros(vertices.shape, dtype=vertices.dtype)
# Create an indexed view into the vertex array using the array of three indices for triangles
tris = vertices[faces]
# Calculate the normal for all the triangles, by taking the cross product of the vectors v1-v0, and v2-v0 in each triangle
n = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])
# n is now an array of normals per triangle. The length of each normal is dependent the vertices,
# we need to normalize these, so that our next step weights each normal equally.
normalize_v3(n)
# now we have a normalized array of normals, one per triangle, i.e., per triangle normals.
# But instead of one per triangle (i.e., flat shading), we add to each vertex in that triangle,
# the triangles' normal. Multiple triangles would then contribute to every vertex, so we need to normalize again afterwards.
# The cool part, we can actually add the normals through an indexed view of our (zeroed) per vertex normal array
norm[faces[:, 0]] += n
norm[faces[:, 1]] += n
norm[faces[:, 2]] += n
normalize_v3(norm)
return norm
def compute_normal_batch(vertices, faces):
bs, nv = vertices.shape[:2]
bs, nf = faces.shape[:2]
vert_norm = torch.zeros(bs * nv, 3).type_as(vertices)
tris = face_vertices(vertices, faces)
face_norm = F.normalize(torch.cross(tris[:, :, 1] - tris[:, :, 0],
tris[:, :, 2] - tris[:, :, 0]),
dim=-1)
faces = (faces +
(torch.arange(bs).type_as(faces) * nv)[:, None, None]).view(
-1, 3)
vert_norm[faces[:, 0]] += face_norm.view(-1, 3)
vert_norm[faces[:, 1]] += face_norm.view(-1, 3)
vert_norm[faces[:, 2]] += face_norm.view(-1, 3)
vert_norm = F.normalize(vert_norm, dim=-1).view(bs, nv, 3)
return vert_norm
# compute tangent and bitangent
def compute_tangent(vertices, faces, normals, uvs, faceuvs):
# NOTE: this could be numerically unstable around [0,0,1]
# but other current solutions are pretty freaky somehow
c1 = np.cross(normals, np.array([0, 1, 0.0]))
tan = c1
normalize_v3(tan)
btan = np.cross(normals, tan)
# NOTE: traditional version is below
# pts_tris = vertices[faces]
# uv_tris = uvs[faceuvs]
# W = np.stack([pts_tris[::, 1] - pts_tris[::, 0], pts_tris[::, 2] - pts_tris[::, 0]],2)
# UV = np.stack([uv_tris[::, 1] - uv_tris[::, 0], uv_tris[::, 2] - uv_tris[::, 0]], 1)
# for i in range(W.shape[0]):
# W[i,::] = W[i,::].dot(np.linalg.inv(UV[i,::]))
# tan = np.zeros(vertices.shape, dtype=vertices.dtype)
# tan[faces[:,0]] += W[:,:,0]
# tan[faces[:,1]] += W[:,:,0]
# tan[faces[:,2]] += W[:,:,0]
# btan = np.zeros(vertices.shape, dtype=vertices.dtype)
# btan[faces[:,0]] += W[:,:,1]
# btan[faces[:,1]] += W[:,:,1]
# btan[faces[:,2]] += W[:,:,1]
# normalize_v3(tan)
# ndott = np.sum(normals*tan, 1, keepdims=True)
# tan = tan - ndott * normals
# normalize_v3(btan)
# normalize_v3(tan)
# tan[np.sum(np.cross(normals, tan) * btan, 1) < 0,:] *= -1.0
return tan, btan