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Duplicate from radames/PIFu-Clothed-Human-Digitization
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from skimage import measure
import numpy as np
import torch
from .sdf import create_grid, eval_grid_octree, eval_grid
from skimage import measure
def reconstruction(net, cuda, calib_tensor,
resolution, b_min, b_max,
use_octree=False, num_samples=10000, transform=None):
'''
Reconstruct meshes from sdf predicted by the network.
:param net: a BasePixImpNet object. call image filter beforehead.
:param cuda: cuda device
:param calib_tensor: calibration tensor
:param resolution: resolution of the grid cell
:param b_min: bounding box corner [x_min, y_min, z_min]
:param b_max: bounding box corner [x_max, y_max, z_max]
:param use_octree: whether to use octree acceleration
:param num_samples: how many points to query each gpu iteration
:return: marching cubes results.
'''
# First we create a grid by resolution
# and transforming matrix for grid coordinates to real world xyz
coords, mat = create_grid(resolution, resolution, resolution,
b_min, b_max, transform=transform)
# Then we define the lambda function for cell evaluation
def eval_func(points):
points = np.expand_dims(points, axis=0)
points = np.repeat(points, net.num_views, axis=0)
samples = torch.from_numpy(points).to(device=cuda).float()
net.query(samples, calib_tensor)
pred = net.get_preds()[0][0]
return pred.detach().cpu().numpy()
# Then we evaluate the grid
if use_octree:
sdf = eval_grid_octree(coords, eval_func, num_samples=num_samples)
else:
sdf = eval_grid(coords, eval_func, num_samples=num_samples)
# Finally we do marching cubes
try:
verts, faces, normals, values = measure.marching_cubes_lewiner(sdf, 0.5)
# transform verts into world coordinate system
verts = np.matmul(mat[:3, :3], verts.T) + mat[:3, 3:4]
verts = verts.T
return verts, faces, normals, values
except:
print('error cannot marching cubes')
return -1
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[2], f_plus[1]))
file.close()
def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
file = open(mesh_path, 'w')
for idx, v in enumerate(verts):
c = colors[idx]
file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' % (v[0], v[1], v[2], c[0], c[1], c[2]))
for f in faces:
f_plus = f + 1
file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
file.close()
def save_obj_mesh_with_uv(mesh_path, verts, faces, uvs):
file = open(mesh_path, 'w')
for idx, v in enumerate(verts):
vt = uvs[idx]
file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
file.write('vt %.4f %.4f\n' % (vt[0], vt[1]))
for f in faces:
f_plus = f + 1
file.write('f %d/%d %d/%d %d/%d\n' % (f_plus[0], f_plus[0],
f_plus[2], f_plus[2],
f_plus[1], f_plus[1]))
file.close()