def test_read_write_read(self):
mesh1 = openmesh.read_trimesh("TestFiles/cube-minimal.obj")
openmesh.write_mesh('OutFiles/test_read_write_read.om', mesh1)
mesh2 = openmesh.read_trimesh('OutFiles/test_read_write_read.om')
self.assertTrue(np.allclose(mesh1.points(), mesh2.points()))
self.assertTrue(
np.array_equal(mesh1.face_vertex_indices(),
mesh2.face_vertex_indices()))
def setUp(self) -> None:
MESH_BASEPATH = "./meshes"
self.meshes = {
'genus0': om.read_trimesh(f"{MESH_BASEPATH}/Genus0.obj"),
'genus1': om.read_trimesh(f"{MESH_BASEPATH}/Genus1.obj")
}
logging.basicConfig(level=logging.DEBUG)
def write_align_mesh(src, tar, filename, index = None): src_mesh=om.read_trimesh(src) tar_mesh=om.read_trimesh(tar) point_array_src = src_mesh.points() point_array_tar = tar_mesh.points() R, t = rigid_registeration(point_array_src, point_array_tar, index) register_array_src = np.dot(R, point_array_src.T).T + np.tile(t,point_array_src.shape[0]).reshape(-1,3) new_V = igl.eigen.MatrixXd(register_array_src.astype(np.float64)) V = igl.eigen.MatrixXd() F = igl.eigen.MatrixXi() igl.readOBJ(src, V,F) igl.writeOBJ(filename, new_V, F)
def compute_distance_whole(src, tar, index): ''' compute the L2 distance of average distance between 2 meshes on each vertice ''' src_mesh = om.read_trimesh(src) tar_mesh = om.read_trimesh(tar) point_array_src = src_mesh.points() point_array_tar = tar_mesh.points() R, t = rigid_registeration(point_array_src, point_array_tar, index) register_src = np.dot(R, point_array_src.T).T + np.tile(t,point_array_src.shape[0]).reshape(-1,3) # distance = np.mean(np.square(point_array_tar - register_src )) distance_mean = np.mean(np.sqrt(np.sum(np.square(point_array_tar-register_src),axis=1))) distance_max = np.max(np.sqrt(np.sum(np.square(point_array_tar-register_src),axis=1))) distance_no_rigid = np.mean(np.sqrt(np.sum(np.square(point_array_tar-point_array_src),axis=1))) return distance_mean, distance_max,distance_no_rigid
def cal_exp_disentanglement_in_file(file_format, use_registeration = True, vis = True):
index = None
loss_log = []
for j in range(0, 47):
data_array=[]
for i in range(141,151):
mesh=om.read_trimesh(file_format.format(i,j))
point_array=mesh.points()
if use_registeration:
if j > 0:
R, t = rigid_registeration(point_array, fix, index)
register_src = np.dot(R, point_array.T).T + np.tile(t,point_array.shape[0]).reshape(-1,3)
point_array = register_src
else:
fix = point_array
data_array.append(point_array.reshape(1,-1))
data_array=np.concatenate(data_array,axis=0)
# store exp std to a file
dis = np.sqrt(np.sum(np.square(np.std(data_array.reshape((10,11510,3)), axis = 0)),axis = -1))
#dis.tofile('exp_var_for_expression_{}.dat'.format(j))
loss_log.append(np.mean(dis))
#loss_log.append(compute_variance(data_array))
if vis:
print('Exp: {}, var: {}'.format(j, loss_log[-1]))
print('our exp Average variance: {}'.format(np.mean(loss_log)))
print('our exp Median variance: {}'.format(np.median(loss_log)))
print('extreme var: {}'.format(max(np.max(loss_log)-np.mean(loss_log), np.mean(loss_log)- np.min(loss_log))))
return loss_log
def main():
global mesh
mesh = om.read_trimesh('plyFile/cone.ply',vertex_color=True)
# 使用glut初始化OpenGL
glutInit()
glutInitDisplayMode(GLUT_SINGLE | GLUT_RGBA)
# 建立視窗
glutInitWindowPosition(0,0)
glutInitWindowSize(1024, 768)
glutCreateWindow(b"first")
# 初始化
glShadeModel(GL_SMOOTH)
glClearColor(0.0, 0.0, 0.0, 0.5)
glClearDepth(1.0)
glEnable(GL_DEPTH_TEST)
glDepthFunc(GL_LEQUAL)
# 視角
gluPerspective(80, 1024/768, 0.1, 1000)
gluLookAt(0, 0, 10, # eye
0, 0 , 0, # center
0, 1, 0) # Top
# 顯示函數
glutDisplayFunc(drawFunc)
glutIdleFunc(drawFunc)
glutMainLoop()
def sphere_with_feature():
fn_mesh = folder + 'sphere.obj'
mesh = om.read_trimesh(fn_mesh)
# split
field = mesh.points()[:, 0]
isocurve = mu.pyisocurve.isocurve(mesh.points(),
mesh.face_vertex_indices(), field)
pts, on_edges, ratios, isocurve_indices = isocurve.extract(0.0, 1.e-5)
mu.write_obj_lines(folder + 'curve.obj', pts, isocurve_indices)
mesh, curve_idx = mu.split_mesh(mesh.points(), mesh.face_vertex_indices(),
on_edges, ratios)
# print(curve_idx)
# om.write_mesh('../data/mesh_split.obj', mesh)
curve = [curve_idx[v] for v in isocurve_indices[0]]
feature = np.empty((len(curve) - 1, 2), 'i')
feature[:, 0] = curve[:-1]
feature[:, 1] = curve[1:]
remesher = mu.pyremesh.remesher()
remesher.init_mesh(mesh.points(), mesh.face_vertex_indices())
remesher.set_features(feature)
feat_edges = remesher.get_features()
feat_verts = remesher.get_feature_vertices()
# write_obj_lines(folder+'feature_detect_e.obj', mesh.points(), feat_edges)
# write_obj_lines(folder+'feature_detect_v.obj', mesh.points()[feat_verts], [])
remesher.remesh(0.1, 15)
verts, faces = remesher.get_mesh()
mesh = om.TriMesh(verts, faces)
om.write_mesh(folder + 'sphere_split.obj', mesh)
def __init__(self, filepath: str = None, vertices: torch.Tensor = None, faces: torch.Tensor = None):
self.vs = vertices
self.fs = faces
self.vn = None
self.fn = None
if (self.vs is not None and self.fs is not None):
if filepath is not None:
logger.warn("Using provided vertices and faces, ignore filepath")
assert(self.vs.ndim == 2 and self.fs.ndim ==2)
assert(self.vs.shape[-1] == 3)
assert(self.fs.shape[-1] == 3)
elif filepath is not None:
mesh = om.read_trimesh(filepath)
self.vs = torch.from_numpy(mesh.points()).to(dtype=torch.float32)
face_lists = []
for f in mesh.face_vertex_indices():
face_lists.append(f)
self.fs = torch.from_numpy(np.stack(face_lists, axis=0)).to(dtype=torch.int64)
mesh.request_face_normals()
if not mesh.has_vertex_normals():
mesh.request_vertex_normals()
mesh.update_normals()
v_normals = mesh.vertex_normals()
f_normals = mesh.face_normals()
self.vn = torch.from_numpy(v_normals.astype(np.float32))
self.fn = torch.from_numpy(f_normals.astype(np.float32))
else:
logger.error("[{}] Must provide a mesh".format(__class__))
# build_gemm(self, self.fs)
self.farea = compute_face_normals_and_areas(self.vs, self.fs)[1]
self.features = ['vs', 'fs', 'vn', 'fn', 'farea']
def __init__(self, pca_npz):
super(GarmentPcaDecodeLayer, self).__init__()
datas = np.load(pca_npz)
self.register_buffer(
'mean', torch.from_numpy(datas['mean'].astype(np.float32)))
self.register_buffer(
'components',
torch.from_numpy(datas['components'].astype(np.float32)))
self.register_buffer(
'std',
torch.from_numpy(datas['singular_values'].astype(np.float32)))
self.type = osp.splitext(osp.basename(pca_npz))[0]
mesh = om.read_trimesh(
osp.join(osp.dirname(pca_npz), 'garment_tmp.obj'))
self.register_buffer(
'edge_index',
torch.from_numpy(mesh.hv_indices().transpose()).to(torch.long))
self.register_buffer(
'face_index',
torch.from_numpy(mesh.face_vertex_indices()).to(torch.long))
vf_fid = torch.zeros(0, dtype=torch.long)
vf_vid = torch.zeros(0, dtype=torch.long)
for vid, fids in enumerate(mesh.vertex_face_indices()):
fids = torch.from_numpy(fids[fids >= 0]).to(torch.long)
vf_fid = torch.cat((vf_fid, fids), dim=0)
vf_vid = torch.cat((vf_vid, fids.new_ones(fids.shape) * vid),
dim=0)
self.register_buffer('vf_findex', vf_fid)
self.register_buffer('vf_vindex', vf_vid)
self.vnum = mesh.n_vertices()
self.fnum = mesh.n_faces()
def test_write_triangle(self):
self.mesh = openmesh.TriMesh()
# Generate data
v1 = self.mesh.add_vertex(np.array([1.0, 0.0, 0.0]))
v2 = self.mesh.add_vertex(np.array([0.0, 1.0, 0.0]))
v3 = self.mesh.add_vertex(np.array([0.0, 0.0, 1.0]))
self.mesh.add_face(v1, v2, v3)
# Save
filename = "OutFiles/triangle-minimal.om"
openmesh.write_mesh(filename, self.mesh)
# Load
self.mesh = openmesh.read_trimesh(filename)
# Compare
self.assertEqual(self.mesh.n_vertices(), 3)
self.assertEqual(self.mesh.n_edges(), 3)
self.assertEqual(self.mesh.n_faces(), 1)
self.assertTrue(
np.allclose(self.mesh.point(v1), np.array([1.0, 0.0, 0.0])))
self.assertTrue(
np.allclose(self.mesh.point(v2), np.array([0.0, 1.0, 0.0])))
self.assertTrue(
np.allclose(self.mesh.point(v3), np.array([0.0, 0.0, 1.0])))
# Cleanup
os.remove(filename)
def test_split_rect_1():
# two functions from https://stackoverflow.com/a/9997374
def ccw(A,B,C):
return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])
# Return true if line segments AB and CD intersect
def intersect(A,B,C,D):
return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D)
mesh = om.read_trimesh('../data/rect.obj')
mesh_pts = mesh.points()
mesh_edges = mesh.ev_indices()
x = np.linspace(-1, 1, 101)
y = T.basis(5)(x)
# export Chebyshev polynomial
pts = np.zeros((x.size, 3))
pts[:, 0] = x
pts[:, 1] = y
mu.write_obj_lines('../data/curve_Chebyshev.obj', pts, [np.arange(pts.shape[0])])
edges = [np.array([0,0], 'i')]
ratios = [0.0]
for i in range(1, x.size-2):
A = np.array([x[i], y[i]])
B = np.array([x[i+1], y[i+1]])
temp_x = []
temp_e = []
temp_u = []
for e in mesh_edges:
C = mesh_pts[e[0], :2]
D = mesh_pts[e[1], :2]
if intersect(A, B, C, D):
# A = (x1, y1), B = (x2, y2)
# C = (x3, y3), D = (x4, y4)
u = ((A[0]-C[0])*(A[1]-B[1])-(A[1]-C[1])*(A[0]-B[0]))/((A[0]-B[0])*(C[1]-D[1])-(A[1]-B[1])*(C[0]-D[0]))
temp_e.append(e)
temp_u.append(u)
temp_x.append(C[0]*(1-u)+D[0]*u)
order = np.argsort(np.array(temp_x))
edges.extend([temp_e[i] for i in order])
ratios.extend([temp_u[i] for i in order])
edges.append(np.array([2,2], 'i'))
ratios.append(0.0)
edges = np.array(edges, 'i')
#print(ratios)
ratios = np.array(ratios, 'd')
pts0 = mesh_pts[edges[:, 0], :]
pts1 = mesh_pts[edges[:, 1], :]
pts = np.multiply(pts0, 1.-ratios[:, np.newaxis]) + \
np.multiply(pts1, ratios[:, np.newaxis])
mu.write_obj_lines('../data/curve.obj', pts, [np.arange(pts.shape[0])])
# print(on_edges, ratios)
mesh, curve_idx = mu.split_mesh_complete(mesh.points(),
mesh.face_vertex_indices(),
edges, ratios)
# print(curve_idx)
om.write_mesh('../data/mesh_split.obj', mesh)
def test_load_simple_om_with_vertex_colors(self):
self.mesh = openmesh.read_trimesh(
"TestFiles/cube-minimal-vertexColors.om", vertex_color=True)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_faces(), 12)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[0], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[2], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[0], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[2], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[2], 1.0)
self.assertFalse(self.mesh.has_vertex_normals())
self.assertFalse(self.mesh.has_vertex_texcoords1D())
self.assertFalse(self.mesh.has_vertex_texcoords2D())
self.assertFalse(self.mesh.has_vertex_texcoords3D())
self.assertTrue(self.mesh.has_vertex_colors())
self.mesh.release_vertex_colors()
def read_datas(name_list, expand_corre=False):
points_list = []
meanpos_list = []
norms_list = []
vnums = []
corre_ps = []
for name in name_list:
mesh = om.read_trimesh(name)
temp = mesh.points().T
vnums.append(temp.shape[1])
points_list.append(temp)
meanpos_list.append(temp.mean(1))
mesh.request_face_normals()
mesh.request_vertex_normals()
mesh.update_normals()
norms_list.append(mesh.vertex_normals().T)
corre_ps.append(np.load(name[:-4] + '_farmarap10.npy').T)
points = np.zeros((3 * len(vnums), max(vnums)), np.float32)
meanpos = np.stack(meanpos_list).astype(np.float32)
norms = np.zeros((3 * len(vnums), max(vnums)), np.float32)
corre_ps = np.stack(corre_ps).astype(np.float32)
corre_ps = corre_ps.reshape(-1, corre_ps.shape[-1])
for i, (p, n) in enumerate(zip(points_list, norms_list)):
points[3 * i:3 * i + 3, 0:vnums[i]] = p
norms[3 * i:3 * i + 3, 0:vnums[i]] = n
return points, meanpos, norms, np.array(vnums, np.int32), corre_ps
def test_geodesic():
# run python sphere_cvt.py to generate the mesh
mesh = om.read_trimesh('../data/sphere_cvt.obj')
# find source
u, v = 0, 20
path_edge, path_ratio = pygeodesic.find_path(mesh.points(),
mesh.face_vertex_indices(),
u, v)
#source = split_along_curve(mesh, path_edge, path_ratio)
mesh, source = split_mesh(mesh.points(), mesh.face_vertex_indices(),
path_edge, path_ratio)
# compute geodesic distance field
field = pygeodesic.distance_field(mesh.points(),
mesh.face_vertex_indices(),
source, 0.05)
colormap_vertex_color('../data/field.off',
mesh.points(),
mesh.face_vertex_indices(),
field)
field = pygeodesic.distance_field(mesh.points(),
mesh.face_vertex_indices(),
source, edge_split=0.05,
max_propagation_distance=1.0)
field[field>1.e10] = 2.0
colormap_vertex_color('../data/field_1.off',
mesh.points(),
mesh.face_vertex_indices(),
field)
def process(self):
print('Processing...')
fps = self.data_files
train_data_list, test_data_list = [], []
for dx in fps:
for tt in fps[dx]:
for idx, fp in enumerate(tqdm(fps[dx][tt])):
mesh = om.read_trimesh(fp)
face = torch.from_numpy(mesh.face_vertex_indices()).T.type(
torch.long)
x = torch.tensor(mesh.points().astype('float32'))
edge_index = torch.cat([face[:2], face[1:], face[::2]],
dim=1)
edge_index = to_undirected(edge_index)
if dx == 'ad':
data = Data(x=x,
y=torch.Tensor([1, 0]),
edge_index=edge_index,
face=face)
elif dx == 'cn':
data = Data(x=x,
y=torch.Tensor([0, 1]),
edge_index=edge_index,
face=face)
if self.pre_transform is not None:
data = self.pre_transform(data)
if tt == 'test':
test_data_list.append(data)
elif tt == 'train':
train_data_list.append(data)
torch.save(self.collate(train_data_list), self.processed_paths[0])
torch.save(self.collate(test_data_list), self.processed_paths[1])
def load(self):
self.train_dataset = CoMA(self.root,
train=True,
split=self.split,
test_exp=self.test_exp,
transform=self.transform,
pre_transform=self.pre_transform)
self.test_dataset = CoMA(self.root,
train=False,
split=self.split,
test_exp=self.test_exp,
transform=self.transform,
pre_transform=self.pre_transform)
tmp_mesh = om.read_trimesh(self.template_fp)
self.template_points = tmp_mesh.points()
self.template_face = tmp_mesh.face_vertex_indices()
self.num_nodes = self.train_dataset[0].num_nodes
self.num_train_graph = len(self.train_dataset)
self.num_test_graph = len(self.test_dataset)
self.mean = self.train_dataset.data.x.view(self.num_train_graph, -1,
3).mean(dim=0)
self.std = self.train_dataset.data.x.view(self.num_train_graph, -1,
3).std(dim=0)
self.normalize()
def read_mesh(path):
mesh = om.read_trimesh(path)
face = torch.from_numpy(mesh.face_vertex_indices()).T.type(torch.long)
x = torch.tensor(mesh.points().astype('float32'))
edge_index = torch.cat([face[:2], face[1:], face[::2]], dim=1)
edge_index = to_undirected(edge_index)
return Data(x=x, edge_index=edge_index, face=face)
def test_load_simple_obj_with_vertex_colors_as_vc_lines(self):
self.mesh = openmesh.read_trimesh(
"TestFiles/cube-minimal-vertex-colors-as-vc-lines.obj",
vertex_color=True)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_faces(), 12)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[2], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[1], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[0], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[2], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[0], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[1], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[2], 1.0)
self.mesh.release_vertex_colors()
def fill_from_file(mesh, file):
mesh.filename = ntpath.split(file)[1]
mesh.fullfilename = file
vs, faces = [], []
m = om.read_trimesh(file)
vs = m.points()
faces = m.face_vertex_indices()
'''
f = open(file)
for line in f:
line = line.strip()
splitted_line = line.split()
if not splitted_line:
continue
elif splitted_line[0] == 'v':
vs.append([float(v) for v in splitted_line[1:4]])
elif splitted_line[0] == 'f':
face_vertex_ids = [int(c.split('/')[0]) for c in splitted_line[1:]]
assert len(face_vertex_ids) == 3
face_vertex_ids = [(ind - 1) if (ind >= 0) else (len(vs) + ind)
for ind in face_vertex_ids]
faces.append(face_vertex_ids)
f.close()
vs = np.asarray(vs)
faces = np.asarray(faces, dtype=int)
'''
assert np.logical_and(faces >= 0, faces < len(vs)).all()
return vs, faces
def test_load_ply_from_mesh_lab_with_vertex_colors(self):
self.mesh = openmesh.read_trimesh("TestFiles/meshlab.ply",
vertex_color=True)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_faces(), 12)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[2], 1.0)
self.assertFalse(self.mesh.has_vertex_normals())
self.assertFalse(self.mesh.has_vertex_texcoords1D())
self.assertFalse(self.mesh.has_vertex_texcoords2D())
self.assertFalse(self.mesh.has_vertex_texcoords3D())
self.assertTrue(self.mesh.has_vertex_colors())
self.mesh.release_vertex_colors()
def test_load_simple_ply_with_texcoords(self):
self.mesh = openmesh.read_trimesh(
"TestFiles/cube-minimal-texCoords.ply", vertex_tex_coord=True)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_faces(), 12)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(0))[0], 10.0)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(0))[1], 10.0)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(2))[0], 6.0)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(2))[1], 6.0)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(4))[0], 9.0)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(4))[1], 9.0)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(7))[0], 12.0)
self.assertEqual(
self.mesh.texcoord2D(self.mesh.vertex_handle(7))[1], 12.0)
self.assertFalse(self.mesh.has_vertex_normals())
self.assertTrue(self.mesh.has_vertex_texcoords1D())
self.assertTrue(self.mesh.has_vertex_texcoords2D())
self.assertTrue(self.mesh.has_vertex_texcoords3D())
self.assertFalse(self.mesh.has_vertex_colors())
self.mesh.release_vertex_texcoords2D()
def test_write_and_read_binary_float_vertex_colors_to_and_from_off_file(
self):
# Read the mesh
self.mesh = openmesh.read_trimesh("TestFiles/meshlab.ply",
vertex_color=True)
# Write the mesh
openmesh.write_mesh("OutFiles/cube_floating_binary.off",
self.mesh,
vertex_color=True,
binary=True,
color_float=True)
# Read the mesh again
self.mesh = openmesh.read_trimesh("OutFiles/cube_floating_binary.off",
vertex_color=True,
binary=True,
color_float=True)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_faces(), 12)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(0))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(3))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(4))[2], 1.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[0], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[1], 0.0)
self.assertEqual(self.mesh.color(self.mesh.vertex_handle(7))[2], 1.0)
self.assertFalse(self.mesh.has_vertex_normals())
self.assertFalse(self.mesh.has_vertex_texcoords1D())
self.assertFalse(self.mesh.has_vertex_texcoords2D())
self.assertFalse(self.mesh.has_vertex_texcoords3D())
self.assertFalse(self.mesh.has_face_colors())
self.assertTrue(self.mesh.has_vertex_colors())
self.mesh.release_vertex_colors()
def decimate(obj: pyr.Object, verbose=False):
"""
Decimates a mesh, reducing the number of faces by 2.
This is EXTREMELY inefficient, and not differentiable - use it sparingly!
Modifies the input mesh.
"""
# Let's make a temporary directory
intermediate_dir = tempfile.mkdtemp()
orig_out = os.path.join(intermediate_dir, "orig.obj")
new_out = os.path.join(intermediate_dir, "decim.obj")
if verbose:
print("Made temp dir:")
print(intermediate_dir)
# First, let's save the redner
pyr.save_obj(obj, orig_out)
# Now, let's load in openmesh
mesh = openmesh.read_trimesh(orig_out)
# Now, decimate by half
orig_nfaces = mesh.n_faces()
if verbose:
print("Original # of faces:", orig_nfaces)
decimator = openmesh.TriMeshDecimater(mesh)
algorithm = openmesh.TriMeshModQuadricHandle()
decimator.add(algorithm)
decimator.initialize()
decimator.decimate_to_faces(n_faces=round(orig_nfaces / 2))
mesh.garbage_collection()
if verbose:
print("New # of faces:", mesh.n_faces())
openmesh.write_mesh(new_out, mesh)
# Now, we have it. Load it back into redner
decim_obj = pyr.load_obj(new_out, return_objects=True)[0]
# And set the faces/indices
obj.vertices = decim_obj.vertices
obj.indices = decim_obj.indices
# Recompute normals - the face normals have been broken
recompute_normals(obj)
# Finally, clean up the dir
files_to_delete = os.listdir(intermediate_dir)
for each_file in files_to_delete:
apath = os.path.join(intermediate_dir, each_file)
if verbose:
print("Deleting", apath)
os.remove(apath)
if verbose:
print("Deleting", intermediate_dir)
os.rmdir(intermediate_dir)
def test_isocurve0():
# run python sphere_cvt.py to generate the mesh
mesh = om.read_trimesh('../data/sphere_cvt.obj')
field = mesh.points()[:, 1]
isocurve = IsoCurve(mesh.points(), mesh.face_vertex_indices(), field)
pts, _, _, isocurve_indices = isocurve.extract(0.5)
write_obj_lines('../data/curve.obj', pts, isocurve_indices)
def read_mesh(path):
mesh = om.read_trimesh(path)
#TODO: hardcoded short
face = torch.from_numpy(mesh.face_vertex_indices()).T.type(torch.short)
x = torch.tensor(mesh.points().astype('float32'))
edge_index = torch.cat([face[:2], face[1:], face[::2]], dim=1)
edge_index = to_undirected(edge_index)
#TODO: hardcoded short
return Data(x=x, edge_index=edge_index, face=face, use_short=True)
def read_ply(path):
mesh = openmesh.read_trimesh(path)
pos = torch.from_numpy(mesh.points()).to(torch.float)
face = torch.from_numpy(mesh.face_vertex_indices())
face = face.t().to(torch.long).contiguous()
data = Data(pos=pos, face=face)
return data
def read_ply(path):
if openmesh is None:
raise ImportError('`read_ply` requires the `openmesh` package.')
mesh = openmesh.read_trimesh(path)
pos = torch.from_numpy(mesh.points()).to(torch.float)
face = torch.from_numpy(mesh.face_vertex_indices())
face = face.t().to(torch.long).contiguous()
return Data(pos=pos, face=face)
def load_training_pose(idx):
ten = []
template = os.path.join(path_prefix, 'Tester_{}'.format(idx),
'TrainingPose', 'pose_aligned_{}.obj')
for i in range(n_training_pose):
file_path = template.format(i)
mesh = openmesh.read_trimesh(file_path)
ten.append(mesh.points())
ten = np.array(ten, dtype=np.float32)
return ten
def compute_distance(src, tar, index = None):#np.loadtxt('src/front_part_v.txt', dtype=int)):
'''
compute the L2 distance of average distance between 2 meshes on each vertice
'''
src_mesh = om.read_trimesh(src)
tar_mesh = om.read_trimesh(tar)
point_array_src = src_mesh.points()
point_array_tar = tar_mesh.points()
R, t = rigid_registeration(point_array_src, point_array_tar, index)
register_src = np.dot(R, point_array_src.T).T + np.tile(t,point_array_src.shape[0]).reshape(-1,3)
# distance = np.mean(np.square(point_array_tar - register_src ))\
## new way for front face distance computation
diff_array = (point_array_tar-register_src)[index]
#print(np.shape(diff_array))
distance_mean = np.mean(np.sqrt(np.sum(np.square(diff_array),axis=1)))
distance_max = np.max(np.sqrt(np.sum(np.square(diff_array),axis=1)))
distance_no_rigid = np.mean(np.sqrt(np.sum(np.square(diff_array),axis=1)))
return distance_mean, distance_max,distance_no_rigid
def cal_sted_loss_in_file(tar_file_format, src_file_format = '/raid/jzh/CVPR2019/alignpose/Tester_{}/AlignPose/pose_{}.obj',vis = False):
average_p_loss = []
for j in range(141,151):
for i in range(47):
tar_mesh = om.read_trimesh(tar_file_format.format(j,i))
src_mesh = om.read_trimesh(src_file_format.format(j,i))
src_point=src_mesh.points()
tar_point=tar_mesh.points()
p_loss, _ = sted_compute_advanced(src_point, tar_point)
#print(np.array(sted_array).astype(np.float64)[:3])
#np.savetxt((tar_file_format[:-4]+'.txt').format(j,i), sted_array)
average_p_loss.append(p_loss)
if vis:
print('people:{} exp: {}, STED {:9.6f}'.format(j, i, p_loss))
print('Average loss: {}'.format(np.mean(average_p_loss)))
print('median loss: {}'.format(np.median(average_p_loss)))
print('extreme differ: {}'.format(np.max(np.abs(np.array(average_p_loss) - np.mean(average_p_loss)))))
return average_p_loss