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26225c5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | import json
import csv
import torch
import numpy as np
import os.path as osp
from plyfile import PlyData
from src.utils.color import to_float_rgb
__all__ = ['read_one_scan', 'read_one_test_scan']
########################################################################
# Votenet Utils #
# https://github.com/facebookresearch/votenet #
########################################################################
def represents_int(s):
""" if string s represents an int. """
try:
int(s)
return True
except ValueError:
return False
def read_label_mapping(filename, label_from="raw_category", label_to="nyu40id"):
assert osp.isfile(filename)
mapping = dict()
with open(filename) as csvfile:
reader = csv.DictReader(csvfile, delimiter="\t")
for row in reader:
mapping[row[label_from]] = int(row[label_to])
if represents_int(list(mapping.keys())[0]):
mapping = {int(k): v for k, v in mapping.items()}
return mapping
def read_mesh_vertices(filename, rgb=True, normal=True):
"""read XYZ RGB for each vertex.
Note: RGB values are in 0-255
"""
assert osp.isfile(filename)
with open(filename, "rb") as f:
plydata = PlyData.read(f)
num_verts = plydata["vertex"].count
vertices = np.zeros(shape=[num_verts, 9], dtype=np.float32)
vertices[:, 0] = plydata["vertex"].data["x"]
vertices[:, 1] = plydata["vertex"].data["y"]
vertices[:, 2] = plydata["vertex"].data["z"]
if rgb:
vertices[:, 3] = plydata["vertex"].data["red"]
vertices[:, 4] = plydata["vertex"].data["green"]
vertices[:, 5] = plydata["vertex"].data["blue"]
if normal:
import open3d
mesh = open3d.io.read_triangle_mesh(filename)
if not mesh.has_vertex_normals():
mesh.compute_vertex_normals()
vertices[:, 6:9] = np.asarray(mesh.vertex_normals)
return vertices
def read_aggregation(filename):
assert osp.isfile(filename)
object_id_to_segs = {}
label_to_segs = {}
with open(filename) as f:
data = json.load(f)
num_objects = len(data["segGroups"])
for i in range(num_objects):
object_id = data["segGroups"][i]["objectId"] + 1 # instance ids should be 1-indexed
label = data["segGroups"][i]["label"]
segs = data["segGroups"][i]["segments"]
object_id_to_segs[object_id] = segs
if label in label_to_segs:
label_to_segs[label].extend(segs)
else:
label_to_segs[label] = segs
return object_id_to_segs, label_to_segs
def read_axis_align_matrix(filename):
lines = open(filename).readlines()
axis_align_matrix = None
for line in lines:
if "axisAlignment" in line:
axis_align_matrix = torch.Tensor(
[float(x) for x in line.rstrip().strip("axisAlignment = ").split(" ")]).reshape((4, 4))
break
return axis_align_matrix
def read_segmentation(filename):
assert osp.isfile(filename)
seg_to_verts = {}
with open(filename) as f:
data = json.load(f)
num_verts = len(data["segIndices"])
for i in range(num_verts):
seg_id = data["segIndices"][i]
if seg_id in seg_to_verts:
seg_to_verts[seg_id].append(i)
else:
seg_to_verts[seg_id] = [i]
return seg_to_verts, num_verts
def export(mesh_file, agg_file, seg_file, meta_file, label_map_file, output_file=None):
"""points are XYZ RGB (RGB in 0-255),
semantic label as nyu40 ids,
instance label as 1-#instance,
box as (cx,cy,cz,dx,dy,dz,semantic_label)
"""
label_map = read_label_mapping(label_map_file, label_from="raw_category", label_to="nyu40id")
mesh_vertices = read_mesh_vertices(mesh_file, rgb=True, normal=True)
# Load scene axis alignment matrix
axis_align_matrix = read_axis_align_matrix(meta_file).numpy()
pts = np.ones((mesh_vertices.shape[0], 4))
pts[:, 0:3] = mesh_vertices[:, 0:3]
pts = np.dot(pts, axis_align_matrix.transpose()) # Nx4
mesh_vertices[:, 0:3] = pts[:, 0:3]
# Load semantic and instance labels
object_id_to_segs, label_to_segs = read_aggregation(agg_file)
seg_to_verts, num_verts = read_segmentation(seg_file)
label_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated
object_id_to_label_id = {}
for label, segs in label_to_segs.items():
label_id = label_map[label]
for seg in segs:
verts = seg_to_verts[seg]
label_ids[verts] = label_id
instance_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated
num_instances = len(np.unique(list(object_id_to_segs.keys())))
for object_id, segs in object_id_to_segs.items():
for seg in segs:
verts = seg_to_verts[seg]
instance_ids[verts] = object_id
if object_id not in object_id_to_label_id:
object_id_to_label_id[object_id] = label_ids[verts][0]
instance_bboxes = np.zeros((num_instances, 7))
for obj_id in object_id_to_segs:
label_id = object_id_to_label_id[obj_id]
obj_pc = mesh_vertices[instance_ids == obj_id, 0:3]
if len(obj_pc) == 0:
continue
# Compute axis aligned box
# An axis aligned bounding box is parameterized by
# (cx,cy,cz) and (dx,dy,dz) and label id
# where (cx,cy,cz) is the center point of the box,
# dx is the x-axis length of the box.
xmin = np.min(obj_pc[:, 0])
ymin = np.min(obj_pc[:, 1])
zmin = np.min(obj_pc[:, 2])
xmax = np.max(obj_pc[:, 0])
ymax = np.max(obj_pc[:, 1])
zmax = np.max(obj_pc[:, 2])
bbox = np.array(
[
(xmin + xmax) / 2.0,
(ymin + ymax) / 2.0,
(zmin + zmax) / 2.0,
xmax - xmin,
ymax - ymin,
zmax - zmin,
label_id,
]
)
# NOTE: this assumes obj_id is in 1,2,3,.,,,.NUM_INSTANCES
instance_bboxes[obj_id - 1, :] = bbox
return (
mesh_vertices.astype(np.float32),
label_ids.astype(np.int64),
instance_ids.astype(np.int64),
instance_bboxes.astype(np.float32),
object_id_to_label_id)
########################################################################
# TorchPoints3D Utils #
# https://github.com/torch-points3d/torch-points3d #
########################################################################
def read_one_scan(scannet_dir, scan_name, label_map_file):
mesh_file = osp.join(scannet_dir, scan_name, scan_name + "_vh_clean_2.ply")
agg_file = osp.join(scannet_dir, scan_name, scan_name + ".aggregation.json")
seg_file = osp.join(scannet_dir, scan_name, scan_name + "_vh_clean_2.0.010000.segs.json")
meta_file = osp.join(scannet_dir, scan_name, scan_name + ".txt")
mesh_vertices, semantic_labels, instance_labels, instance_bboxes, instance2semantic = export(
mesh_file, agg_file, seg_file, meta_file, label_map_file, None)
# Return values as tensors
pos = torch.from_numpy(mesh_vertices[:, :3])
rgb = to_float_rgb(torch.from_numpy(mesh_vertices[:, 3:6]))
normal = torch.from_numpy(mesh_vertices[:, 6:9])
y = torch.from_numpy(semantic_labels)
obj = torch.from_numpy(instance_labels)
return pos, rgb, normal, y, obj
def read_one_test_scan(scannet_dir, scan_name):
mesh_file = osp.join(scannet_dir, scan_name, scan_name + "_vh_clean_2.ply")
mesh_vertices = read_mesh_vertices(mesh_file, rgb=True, normal=True)
pos = torch.from_numpy(mesh_vertices[:, :3])
rgb = to_float_rgb(torch.from_numpy(mesh_vertices[:, 3:6]))
normal = torch.from_numpy(mesh_vertices[:, 6:9])
return pos, rgb, normal
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