sdf_meshing_nocolor.py

'''From the DeepSDF repository https://github.com/facebookresearch/DeepSDF
'''
#!/usr/bin/env python3

import logging
import numpy as np
import plyfile
import skimage.measure
import time
import torch
import yaml
import os

#########kitti的数据集归一化后z轴占数据很少，会有很多无效计算，增加分辨率，而z轴只用很小一部分，同样的计算资源能够支持地图更高的分辨率#######
high_resolution=False  ##用于Kitti数据集，x、y方向的尺度远大于z方向的尺度，所以
high_resolution_voxels=3000 ##3000
z_voxels=200   ##200
center_offset=1340 ###z方向尺度（-2.0～0.3）相对于x、y方向来说基本就在0附近，所以在中心1500的附近取值,1340
#################


def create_mesh(
    decoder, filename, N=256, max_batch=64 ** 3, offset=None,scale=None
):
    start = time.time()
    ply_filename = filename
    

    decoder.eval()

    # NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
    voxel_origin = [-1, -1, -1]
    
    
    if (high_resolution):
        voxel_size = 2.0 / (high_resolution_voxels - 1)
        overall_index = torch.arange(0, high_resolution_voxels*high_resolution_voxels*z_voxels, 1, out=torch.LongTensor())
        samples = torch.zeros(high_resolution_voxels*high_resolution_voxels*z_voxels, 4)
        labels=torch.zeros(high_resolution_voxels*high_resolution_voxels*z_voxels)
        samples[:, 2] = overall_index % (z_voxels)+center_offset
        samples[:, 1] = (overall_index.long() // z_voxels) % (high_resolution_voxels)
        samples[:, 0] = ((overall_index.long() // z_voxels) // (high_resolution_voxels)) % (high_resolution_voxels)
        samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]  
        samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
        samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]  
        num_samples = high_resolution_voxels*high_resolution_voxels*z_voxels  
    else:
        voxel_size = 2.0 / (N - 1)
        overall_index = torch.arange(0, N ** 3, 1, out=torch.LongTensor())
        samples = torch.zeros(N ** 3, 4)
        labels = torch.zeros(N ** 3)
        samples[:, 2] = overall_index % N
        samples[:, 1] = (overall_index.long() // N) % N
        samples[:, 0] = ((overall_index.long() // N) // N) % N
        samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]  
        samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
        samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]
        num_samples = N ** 3
     
    samples.requires_grad = False      

    head = 0

    while head < num_samples:
        print(head)
        dim=3
        sample_subset = samples[head : min(head + max_batch, num_samples), 0:dim].cuda()
        model_out= decoder(sample_subset)
        samples[head : min(head + max_batch, num_samples), dim] = (model_out['sdf_out'].squeeze().detach().cpu())      
        head += max_batch

    sdf_values = samples[:, dim]
    
    if (high_resolution):
        sdf_values = sdf_values.reshape(high_resolution_voxels, high_resolution_voxels, z_voxels)
    else:       
        sdf_values = sdf_values.reshape(N, N, N)

    end = time.time()
    print("sampling takes: %f" % (end - start))
    


    convert_sdf_samples_to_ply(
        sdf_values.data.cpu(),
        voxel_origin,
        voxel_size,
        ply_filename + ".ply",
    )


def convert_sdf_samples_to_ply(
    pytorch_3d_sdf_tensor,
    voxel_grid_origin,
    voxel_size,
    ply_filename_out,
    offset=None,
    scale=None,
):
    """
    Convert sdf samples to .ply

    :param pytorch_3d_sdf_tensor: a torch.FloatTensor of shape (n,n,n)
    :voxel_grid_origin: a list of three floats: the bottom, left, down origin of the voxel grid
    :voxel_size: float, the size of the voxels
    :ply_filename_out: string, path of the filename to save to

    This function adapted from: https://github.com/RobotLocomotion/spartan
    """

    start_time = time.time()

    numpy_3d_sdf_tensor = pytorch_3d_sdf_tensor.numpy()
    

    verts, faces, normals, values = np.zeros((0, 3)), np.zeros((0, 3)), np.zeros((0, 3)), np.zeros(0)
    try:
        verts, faces, normals, values = skimage.measure.marching_cubes_lewiner(
            numpy_3d_sdf_tensor, level=0.0, spacing=[voxel_size] * 3
        )
    except:
        pass    

    # transform from voxel coordinates to camera coordinates
    # note x and y are flipped in the output of marching_cubes
    mesh_points = np.zeros_like(verts)
    mesh_points[:, 0] = voxel_grid_origin[0] + verts[:, 0]
    mesh_points[:, 1] = voxel_grid_origin[1] + verts[:, 1]
    mesh_points[:, 2] = voxel_grid_origin[2] + verts[:, 2]

    # apply additional offset and scale
    if scale is not None:
        mesh_points = mesh_points / scale
    if offset is not None:
        mesh_points = mesh_points - offset

    # try writing to the ply file

    num_verts = verts.shape[0]
    num_faces = faces.shape[0]

    verts_tuple = np.zeros((num_verts,), dtype=[("x", "f4"), ("y", "f4"), ("z", "f4")])

    for i in range(0, num_verts):
        verts_tuple[i] = tuple(mesh_points[i, :])

    faces_building = []
    for i in range(0, num_faces):
        faces_building.append(((faces[i, :].tolist(),)))
    faces_tuple = np.array(faces_building, dtype=[("vertex_indices", "i4", (3,))])

    el_verts = plyfile.PlyElement.describe(verts_tuple, "vertex")
    el_faces = plyfile.PlyElement.describe(faces_tuple, "face")

    ply_data = plyfile.PlyData([el_verts, el_faces])
    logging.debug("saving mesh to %s" % (ply_filename_out))
    ply_data.write(ply_filename_out)

    logging.debug(
        "converting to ply format and writing to file took {} s".format(
            time.time() - start_time
        )
    )