File size: 6,150 Bytes
48f95a2 | 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 | from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
import os
import json
import random
import trimesh
from plyfile import PlyData, PlyElement
def project_pc_to_image(points, resolution=64):
"""project point clouds into 2D image
:param points: (n, 3) range(-1, 1)
:return: binary image
"""
img = []
for i in range(3):
canvas = np.zeros((resolution, resolution))
axis = [0, 1, 2]
axis.remove(i)
proj_points = (points[:, axis] + 1) / 2 * resolution
proj_points = proj_points.astype(np.int)
canvas[proj_points[:, 0], proj_points[:, 1]] = 1
img.append(canvas)
img = np.concatenate(img, axis=1)
return img
def write_ply(points, filename, text=False):
""" input: Nx3, write points to filename as PLY format. """
points = [(points[i,0], points[i,1], points[i,2]) for i in range(points.shape[0])]
vertex = np.array(points, dtype=[('x', 'f4'), ('y', 'f4'),('z', 'f4')])
el = PlyElement.describe(vertex, 'vertex', comments=['vertices'])
with open(filename, mode='wb') as f:
PlyData([el], text=text).write(f)
def rotate_point_cloud(points, transformation_mat):
new_points = np.dot(transformation_mat, points.T).T
return new_points
def rotate_point_cloud_by_axis_angle(points, axis, angle_deg):
""" align 3depn shapes to shapenet coordinates"""
# angle = math.radians(angle_deg)
# rot_m = pymesh.Quaternion.fromAxisAngle(axis, angle)
# rot_m = rot_m.to_matrix()
rot_m = np.array([[ 2.22044605e-16, 0.00000000e+00, 1.00000000e+00],
[ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
[-1.00000000e+00, 0.00000000e+00, 2.22044605e-16]])
new_points = rotate_point_cloud(points, rot_m)
return new_points
def downsample_point_cloud(points, n_pts):
"""downsample points by random choice
:param points: (n, 3)
:param n_pts: int
:return:
"""
p_idx = random.choices(list(range(points.shape[0])), k=n_pts)
return points[p_idx]
def upsample_point_cloud(points, n_pts):
"""upsample points by random choice
:param points: (n, 3)
:param n_pts: int, > n
:return:
"""
p_idx = random.choices(list(range(points.shape[0])), k=n_pts - points.shape[0])
dup_points = points[p_idx]
points = np.concatenate([points, dup_points], axis=0)
return points
def sample_point_cloud_by_n(points, n_pts):
"""resample point cloud to given number of points"""
if n_pts > points.shape[0]:
return upsample_point_cloud(points, n_pts)
elif n_pts < points.shape[0]:
return downsample_point_cloud(points, n_pts)
else:
return points
def collect_data_id(split_dir, classname, phase):
filename = os.path.join(split_dir, "{}.{}.json".format(classname, phase))
if not os.path.exists(filename):
raise ValueError("Invalid filepath: {}".format(filename))
all_ids = []
with open(filename, 'r') as fp:
info = json.load(fp)
for item in info:
all_ids.append(item["anno_id"])
return all_ids
class GANdatasetPartNet(Dataset):
def __init__(self, phase, data_root, category, n_pts):
super(GANdatasetPartNet, self).__init__()
if phase == "validation":
phase = "val"
self.phase = phase
self.aug = phase == "train"
self.data_root = data_root
shape_names = collect_data_id(os.path.join(self.data_root, 'partnet_labels/partnet_train_val_test_split'), category, phase)
self.shape_names = []
for name in shape_names:
path = os.path.join(self.data_root, 'partnet_labels/partnet_pc_label', name)
if os.path.exists(path):
self.shape_names.append(name)
self.n_pts = n_pts
self.raw_n_pts = self.n_pts // 2
self.rng = random.Random(1234)
@staticmethod
def load_point_cloud(path):
pc = trimesh.load(path)
pc = pc.vertices / 2.0 # scale to unit sphere
return pc
@staticmethod
def read_point_cloud_part_label(path):
with open(path, 'r') as fp:
labels = fp.readlines()
labels = np.array([int(x) for x in labels])
return labels
def random_rm_parts(self, raw_pc, part_labels):
part_ids = sorted(np.unique(part_labels).tolist())
if self.phase == "train":
random.shuffle(part_ids)
n_part_keep = random.randint(1, max(1, len(part_ids) - 1))
else:
self.rng.shuffle(part_ids)
n_part_keep = self.rng.randint(1, max(1, len(part_ids) - 1))
part_ids_keep = part_ids[:n_part_keep]
point_idx = []
for i in part_ids_keep:
point_idx.extend(np.where(part_labels == i)[0].tolist())
raw_pc = raw_pc[point_idx]
return raw_pc, n_part_keep
def __getitem__(self, index):
raw_shape_name = self.shape_names[index]
raw_ply_path = os.path.join(self.data_root, 'partnet_data', raw_shape_name, 'point_sample/ply-10000.ply')
raw_pc = self.load_point_cloud(raw_ply_path)
raw_label_path = os.path.join(self.data_root, 'partnet_labels/partnet_pc_label', raw_shape_name, 'label-merge-level1-10000.txt')
part_labels = self.read_point_cloud_part_label(raw_label_path)
raw_pc, n_part_keep = self.random_rm_parts(raw_pc, part_labels)
raw_pc = sample_point_cloud_by_n(raw_pc, self.raw_n_pts)
raw_pc = torch.tensor(raw_pc, dtype=torch.float32).transpose(1, 0)
real_shape_name = self.shape_names[index]
real_ply_path = os.path.join(self.data_root, 'partnet_data', real_shape_name, 'point_sample/ply-10000.ply')
real_pc = self.load_point_cloud(real_ply_path)
real_pc = sample_point_cloud_by_n(real_pc, self.n_pts)
real_pc = torch.tensor(real_pc, dtype=torch.float32).transpose(1, 0)
return {"raw": raw_pc, "real": real_pc, "raw_id": raw_shape_name, "real_id": real_shape_name,
'n_part_keep': n_part_keep, 'idx': index}
def __len__(self):
return len(self.shape_names) |