196 lines
6.9 KiB
Python
196 lines
6.9 KiB
Python
"""
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@Author: Yue Wang
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@Contact: yuewangx@mit.edu
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@File: data.py
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@Time: 2018/10/13 6:21 PM
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Modified by
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@Author: Tiange Xiang
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@Contact: txia7609@uni.sydney.edu.au
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@Time: 2021/1/21 3:10 PM
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"""
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import os
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import sys
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import glob
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import h5py
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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# change this to your data root
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DATA_DIR = 'data/'
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os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
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def download_modelnet40():
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if not os.path.exists(DATA_DIR):
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os.mkdir(DATA_DIR)
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if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')):
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os.mkdir(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048'))
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www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip'
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zipfile = os.path.basename(www)
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os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
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os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
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os.system('rm %s' % (zipfile))
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def download_shapenetpart():
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if not os.path.exists(DATA_DIR):
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os.mkdir(DATA_DIR)
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if not os.path.exists(os.path.join(DATA_DIR)):
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os.mkdir(os.path.join(DATA_DIR))
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www = 'https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip'
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zipfile = os.path.basename(www)
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os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
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os.system('mv %s %s' % (zipfile[:-4], os.path.join(DATA_DIR)))
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os.system('rm %s' % (zipfile))
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def load_data_normal(partition):
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f = h5py.File(os.path.join(DATA_DIR, 'modelnet40_normal', 'normal_%s.h5'%partition), 'r+')
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data = f['xyz'][:].astype('float32')
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label = f['normal'][:].astype('float32')
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f.close()
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return data, label
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def load_data_cls(partition):
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download_modelnet40()
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all_data = []
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all_label = []
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for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40*hdf5_2048', '*%s*.h5'%partition)):
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f = h5py.File(h5_name, 'r+')
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data = f['data'][:].astype('float32')
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label = f['label'][:].astype('int64')
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f.close()
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all_data.append(data)
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all_label.append(label)
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all_data = np.concatenate(all_data, axis=0)
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all_label = np.concatenate(all_label, axis=0)
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return all_data, all_label
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def load_data_partseg(partition):
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download_shapenetpart()
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all_data = []
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all_label = []
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all_seg = []
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if partition == 'trainval':
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file = glob.glob(os.path.join(DATA_DIR, 'part_segmentation_data', '*train*.h5')) \
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+ glob.glob(os.path.join(DATA_DIR, 'part_segmentation_data', '*val*.h5'))
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else:
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file = glob.glob(os.path.join(DATA_DIR, 'part_segmentation_data', '*%s*.h5'%partition))
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for h5_name in file:
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f = h5py.File(h5_name, 'r+')
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data = f['data'][:].astype('float32')
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label = f['label'][:].astype('int64')
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seg = f['pid'][:].astype('int64')
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f.close()
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all_data.append(data)
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all_label.append(label)
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all_seg.append(seg)
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all_data = np.concatenate(all_data, axis=0)
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all_label = np.concatenate(all_label, axis=0)
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all_seg = np.concatenate(all_seg, axis=0)
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return all_data, all_label, all_seg
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def translate_pointcloud(pointcloud):
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xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3])
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xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
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translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32')
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return translated_pointcloud
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def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02):
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N, C = pointcloud.shape
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pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip)
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return pointcloud
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def rotate_pointcloud(pointcloud):
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theta = np.pi*2 * np.random.uniform()
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rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])
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pointcloud[:,[0,2]] = pointcloud[:,[0,2]].dot(rotation_matrix) # random rotation (x,z)
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return pointcloud
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class ModelNet40(Dataset):
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def __init__(self, num_points, partition='train'):
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self.data, self.label = load_data_cls(partition)
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self.num_points = num_points
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self.partition = partition
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def __getitem__(self, item):
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pointcloud = self.data[item][:self.num_points]
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label = self.label[item]
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if self.partition == 'train':
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pointcloud = translate_pointcloud(pointcloud)
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#pointcloud = rotate_pointcloud(pointcloud)
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np.random.shuffle(pointcloud)
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return pointcloud, label
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def __len__(self):
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return self.data.shape[0]
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class ModelNetNormal(Dataset):
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def __init__(self, num_points, partition='train'):
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self.data, self.label = load_data_normal(partition)
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self.num_points = num_points
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self.partition = partition
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def __getitem__(self, item):
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pointcloud = self.data[item][:self.num_points]
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label = self.label[item][:self.num_points]
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if self.partition == 'train':
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#pointcloud = translate_pointcloud(pointcloud)
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idx = np.arange(0, pointcloud.shape[0], dtype=np.int64)
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np.random.shuffle(idx)
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pointcloud = self.data[item][idx]
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label = self.label[item][idx]
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return pointcloud, label
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def __len__(self):
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return self.data.shape[0]
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class ShapeNetPart(Dataset):
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def __init__(self, num_points=2048, partition='train', class_choice=None):
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self.data, self.label, self.seg = load_data_partseg(partition)
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self.cat2id = {'airplane': 0, 'bag': 1, 'cap': 2, 'car': 3, 'chair': 4,
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'earphone': 5, 'guitar': 6, 'knife': 7, 'lamp': 8, 'laptop': 9,
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'motor': 10, 'mug': 11, 'pistol': 12, 'rocket': 13, 'skateboard': 14, 'table': 15}
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self.seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
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self.index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47]
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self.num_points = num_points
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self.partition = partition
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self.class_choice = class_choice
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if self.class_choice != None:
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id_choice = self.cat2id[self.class_choice]
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indices = (self.label == id_choice).squeeze()
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self.data = self.data[indices]
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self.label = self.label[indices]
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self.seg = self.seg[indices]
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self.seg_num_all = self.seg_num[id_choice]
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self.seg_start_index = self.index_start[id_choice]
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else:
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self.seg_num_all = 50
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self.seg_start_index = 0
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def __getitem__(self, item):
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pointcloud = self.data[item][:self.num_points]
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label = self.label[item]
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seg = self.seg[item][:self.num_points]
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if self.partition == 'trainval':
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pointcloud = translate_pointcloud(pointcloud)
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indices = list(range(pointcloud.shape[0]))
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np.random.shuffle(indices)
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pointcloud = pointcloud[indices]
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seg = seg[indices]
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return pointcloud, label, seg
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def __len__(self):
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return self.data.shape[0]
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