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import torch
import math
from pathlib import Path
import time
def euler_rotation(rx=0, ry=0, rz=0, backwards=False):
Ms = []
if rz != 0:
cosz = math.cos(rz)
sinz = math.sin(rz)
Ms.append(torch.tensor(
[[cosz, -sinz, 0],
[sinz, cosz, 0],
[0, 0, 1]]))
if ry != 0:
cosy = math.cos(ry)
siny = math.sin(ry)
Ms.append(torch.tensor(
[[cosy, 0, siny],
[0, 1, 0],
[-siny, 0, cosy]]))
if rx != 0:
cosx = math.cos(rx)
sinx = math.sin(rx)
Ms.append(torch.tensor(
[[1, 0, 0],
[0, cosx, -sinx],
[0, sinx, cosx]]))
rotation = torch.eye(3)
if backwards and len(Ms) > 0:
Ms = Ms[::-1]
for mat in Ms[::-1]:
rotation = torch.matmul(rotation, mat)
return rotation
def export(file, vs, faces, vn=None, color=None):
with open(file, 'w+') as f:
for vi, v in enumerate(vs):
if color is None:
f.write("v %f %f %f\n" % (v[0], v[1], v[2]))
else:
f.write("v %f %f %f %f %f %f\n" % (v[0], v[1], v[2], color[vi][0], color[vi][1], color[vi][2]))
if vn is not None:
f.write("vn %f %f %f\n" % (vn[vi, 0], vn[vi, 1], vn[vi, 2]))
for face in faces:
f.write("f %d %d %d\n" % (face[0] + 1, face[1] + 1, face[2] + 1))
def xyz2tensor(txt, append_normals=False):
pts = []
for line in txt.split('\n'):
line = line.strip()
line = line.lstrip('v ')
spt = line.split(' ')
if 'nan' in line:
continue
if len(spt) == 6:
pts.append(torch.tensor([float(x) for x in spt]))
if len(spt) == 3:
t = [float(x) for x in spt]
if append_normals:
t += [0.0 for _ in range(3)]
pts.append(torch.tensor(t))
rtn = torch.stack(pts, dim=0)
return rtn
def read_xyz_file(path: Path):
with open(path, 'r') as file:
return xyz2tensor(file.read(), append_normals=True)
def embed_color(img: torch.Tensor, color, box_size=70):
shp = img.shape
D2 = [shp[2] - box_size, shp[2]]
D3 = [shp[3] - box_size, shp[3]]
img = img.clone()
img[:, :3, D2[0]:D2[1], D3[0]:D3[1]] = color[:, :, None, None]
if img.shape[1] == 4:
img[:, -1, D2[0]:D2[1], D3[0]:D3[1]] = 1
return img
def get_n_params(model):
pp=0
for p in list(model.parameters()):
nn=1
for s in list(p.size()):
nn = nn*s
pp += nn
return pp
def xyz2tensor(txt, append_normals=False):
pts = []
for line in txt.split('\n'):
line = line.strip()
line = line.lstrip('v ')
spt = line.split(' ')
if 'nan' in line:
continue
if len(spt) == 6:
pts.append(torch.tensor([float(x) for x in spt]))
if len(spt) == 3:
t = [float(x) for x in spt]
if append_normals:
t += [0.0 for _ in range(3)]
pts.append(torch.tensor(t))
rtn = torch.stack(pts, dim=0)
return rtn
def read_xyz_file(path: Path):
with open(path, 'r') as file:
return xyz2tensor(file.read(), append_normals=True)
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