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84d92e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import os
import numpy as np
from plyfile import PlyData, PlyElement
import pandas as pd
from scannet200_constants import *
def read_plymesh(filepath):
"""Read ply file and return it as numpy array. Returns None if emtpy."""
with open(filepath, 'rb') as f:
plydata = PlyData.read(f)
if plydata.elements:
vertices = pd.DataFrame(plydata['vertex'].data).values
faces = np.array([f[0] for f in plydata["face"].data])
return vertices, faces
def save_plymesh(vertices, faces, filename, verbose=True, with_label=True):
"""Save an RGB point cloud as a PLY file.
Args:
points_3d: Nx6 matrix where points_3d[:, :3] are the XYZ coordinates and points_3d[:, 4:] are
the RGB values. If Nx3 matrix, save all points with [128, 128, 128] (gray) color.
"""
assert vertices.ndim == 2
if with_label:
if vertices.shape[1] == 7:
python_types = (float, float, float, int, int, int, int)
npy_types = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'),
('blue', 'u1'), ('label', 'u4')]
if vertices.shape[1] == 8:
python_types = (float, float, float, int, int, int, int, int)
npy_types = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'),
('blue', 'u1'), ('label', 'u4'), ('instance_id', 'u4')]
else:
if vertices.shape[1] == 3:
gray_concat = np.tile(np.array([128], dtype=np.uint8), (vertices.shape[0], 3))
vertices = np.hstack((vertices, gray_concat))
elif vertices.shape[1] == 6:
python_types = (float, float, float, int, int, int)
npy_types = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'),
('blue', 'u1')]
else:
pass
vertices_array = np.empty(vertices.shape[0], dtype=npy_types)
vertices_array['x'] = vertices[:, 0].astype(np.float32, copy=False)
vertices_array['y'] = vertices[:, 1].astype(np.float32, copy=False)
vertices_array['z'] = vertices[:, 2].astype(np.float32, copy=False)
vertices_array['red'] = vertices[:, 3].astype(np.uint8, copy=False)
vertices_array['green'] = vertices[:, 4].astype(np.uint8, copy=False)
vertices_array['blue'] = vertices[:, 5].astype(np.uint8, copy=False)
if with_label:
vertices_array['label'] = vertices[:, 6].astype(np.uint32, copy=False)
if vertices.shape[1] == 8:
vertices_array['instance_id'] = vertices[:, 7].astype(np.uint32, copy=False)
elements = [PlyElement.describe(vertices_array, 'vertex')]
if faces is not None:
faces_array = np.empty(len(faces), dtype=[('vertex_indices', 'i4', (3,))])
faces_array['vertex_indices'] = faces
elements += [PlyElement.describe(faces_array, 'face')]
# Write
PlyData(elements).write(filename)
if verbose is True:
print('Saved point cloud to: %s' % filename)
# Map the raw category id to the point cloud
def point_indices_from_group(seg_indices, group, label_map, CLASS_IDs):
group_segments = np.array(group['segments'])
label = group['label']
# Map the category name to id
label_id = int(label_map.get(label, 0))
# Only store for the valid categories
if not label_id in CLASS_IDs:
label_id = 0
# get points, where segment indices (points labelled with segment ids) are in the group segment list
point_ids = np.where(np.isin(seg_indices, group_segments))[0]
return point_ids, label_id
# Uncomment out if mesh voxelization is required
# Uncomment out if mesh voxelization is required
import trimesh
from trimesh.voxel import creation
from sklearn.neighbors import KDTree
import numpy as np # 确保导入了 numpy
# VOXELIZE the scene from sampling on the mesh directly instead of vertices
def voxelize_pointcloud(points, colors, labels, instances, faces, voxel_size=0.2):
# voxelize mesh first and determine closest labels with KDTree search
trimesh_scene_mesh = trimesh.Trimesh(vertices=points, faces=faces)
voxel_grid = creation.voxelize(trimesh_scene_mesh, voxel_size)
voxel_cloud = np.asarray(voxel_grid.points)
orig_tree = KDTree(points, leaf_size=8)
_, voxel_pc_matches = orig_tree.query(voxel_cloud, k=1)
voxel_pc_matches = voxel_pc_matches.flatten()
# match colors to voxel ids
# 注意：原代码在这里已经将点除以了 voxel_size，将其缩放到了体素网格的尺度
scaled_points = points[voxel_pc_matches] / voxel_size
colors = colors[voxel_pc_matches]
labels = labels[voxel_pc_matches]
instances = instances[voxel_pc_matches]
# --- 使用纯 NumPy 替代 ME.utils.sparse_quantize ---
# 1. 将缩放后的浮点坐标向下取整，转换为离散的整数体素坐标
discrete_coords = np.floor(scaled_points).astype(np.int32)
# 2. 获取唯一的体素坐标，以及它们在原数组中第一次出现时的索引 (return_index=True)
quantized_scene, scene_inds = np.unique(discrete_coords, axis=0, return_index=True)
# --------------------------------------------------
# 根据索引获取对应的颜色、标签和实例
quantized_scene_colors = colors[scene_inds]
quantized_labels = labels[scene_inds]
quantized_instances = instances[scene_inds]
return quantized_scene, quantized_scene_colors, quantized_labels, quantized_instances |