def readBinvoxParams(binbox_f):
with open(binbox_f, 'rb') as f:
voxel = binvox_rw.read_as_3d_array(f)
voxel_d = voxel.dims[0]
voxel_t = voxel.translate
voxel_s = voxel.scale
return (voxel_d, *voxel_t, voxel_s)
def load_voxel(file):
with open(file, 'rb') as f:
voexl_array = binvoxel.read_as_3d_array(f)
print(voexl_array.dims)
print(voexl_array.scale)
voexl_array = np.array(voexl_array.data.flatten())
def load_label(self, category, model_id):
voxel_fn = get_voxel_file(category, model_id)
with open(voxel_fn, 'rb') as f:
voxel = read_as_3d_array(f)
return voxel
with open(filename, 'w') as f:
# write vertices
f.write('g\n# %d vertex\n' % len(verts))
for vert in verts:
f.write('v %f %f %f\n' % tuple(vert))
# write faces
f.write('# %d faces\n' % len(faces))
for face in faces:
f.write('f %d %d %d\n' % tuple(face))
def voxel2obj(filename, pred, surface_view=True):
verts, faces = voxel2mesh(pred, surface_view)
write_obj(filename, verts, faces)
if __name__ == '__main__':
# read binvox data and convert to ndarray
from lib.binvox_rw import read_as_3d_array
import theano
with open("model.binvox", 'rb') as f:
voxel = read_as_3d_array(f)
voxel_data = voxel.data
batch_voxel = np.zeros((1, 32, 2, 32, 32), dtype=theano.config.floatX)
batch_voxel[0, :, 0, :, :] = voxel_data < 1
batch_voxel[0, :, 1, :, :] = voxel_data
print(batch_voxel.shape)
# write obj file
voxel2obj("category1data2.obj", batch_voxel[0, :, 1, :, :])