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data.py
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data.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import glob
import h5py
import numpy as np
from scipy.spatial.transform import Rotation
from torch.utils.data import Dataset
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import minkowski
# Part of the code is referred from: https://github.com/charlesq34/pointnet
def download():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
if not os.path.exists(DATA_DIR):
os.mkdir(DATA_DIR)
if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')):
www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip'
zipfile = os.path.basename(www)
os.system('wget %s; unzip %s' % (www, zipfile))
os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
os.system('rm %s' % (zipfile))
def load_data(partition):
# download()
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, '../../datasets')
all_data = []
all_label = []
for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5' % partition)):
f = h5py.File(h5_name)
data = f['data'][:].astype('float32')
label = f['label'][:].astype('int64')
f.close()
all_data.append(data)
all_label.append(label)
all_data = np.concatenate(all_data, axis=0)
all_label = np.concatenate(all_label, axis=0)
return all_data, all_label
def translate_pointcloud(pointcloud):
xyz1 = np.random.uniform(low=2. / 3., high=3. / 2., size=[3])
xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32')
return translated_pointcloud
def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.05):
N, C = pointcloud.shape
pointcloud += np.clip(sigma * np.random.randn(N, C), -1 * clip, clip)
return pointcloud
def farthest_subsample_points(pointcloud1, pointcloud2, num_subsampled_points=768):
pointcloud1 = pointcloud1.T
pointcloud2 = pointcloud2.T
num_points = pointcloud1.shape[0]
nbrs1 = NearestNeighbors(n_neighbors=num_subsampled_points, algorithm='auto',
metric=lambda x, y: minkowski(x, y), n_jobs=1).fit(pointcloud1)
random_p1 = np.random.random(size=(1, 3)) + np.array([[500, 500, 500]]) * np.random.choice([1, -1, 1, -1])
idx1 = nbrs1.kneighbors(random_p1, return_distance=False).reshape((num_subsampled_points,))
nbrs2 = NearestNeighbors(n_neighbors=num_subsampled_points, algorithm='auto',
metric=lambda x, y: minkowski(x, y), n_jobs=1).fit(pointcloud2)
random_p2 = random_p1 #np.random.random(size=(1, 3)) + np.array([[500, 500, 500]]) * np.random.choice([1, -1, 2, -2])
idx2 = nbrs2.kneighbors(random_p2, return_distance=False).reshape((num_subsampled_points,))
return pointcloud1[idx1, :].T, pointcloud2[idx2, :].T
class ModelNet40(Dataset):
def __init__(self, num_points, num_subsampled_points = 768, partition='train', gaussian_noise=False, unseen=False, factor=4):
self.data, self.label = load_data(partition)
self.num_points = num_points
self.partition = partition
self.gaussian_noise = gaussian_noise
self.unseen = unseen
self.label = self.label.squeeze()
self.factor = factor
self.num_subsampled_points = num_subsampled_points
if num_points != num_subsampled_points:
self.subsampled = True
else:
self.subsampled = False
if self.unseen:
######## simulate testing on first 20 categories while training on last 20 categories
if self.partition == 'test':
self.data = self.data[self.label>=20]
self.label = self.label[self.label>=20]
elif self.partition == 'train':
self.data = self.data[self.label<20]
self.label = self.label[self.label<20]
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
# if self.gaussian_noise:
# pointcloud = jitter_pointcloud(pointcloud)
if self.partition != 'train':
np.random.seed(item)
anglex = np.random.uniform() * np.pi / self.factor
angley = np.random.uniform() * np.pi / self.factor
anglez = np.random.uniform() * np.pi / self.factor
cosx = np.cos(anglex)
cosy = np.cos(angley)
cosz = np.cos(anglez)
sinx = np.sin(anglex)
siny = np.sin(angley)
sinz = np.sin(anglez)
Rx = np.array([[1, 0, 0],
[0, cosx, -sinx],
[0, sinx, cosx]])
Ry = np.array([[cosy, 0, siny],
[0, 1, 0],
[-siny, 0, cosy]])
Rz = np.array([[cosz, -sinz, 0],
[sinz, cosz, 0],
[0, 0, 1]])
# 生成旋转矩阵
R_ab = Rx.dot(Ry).dot(Rz)
R_ba = R_ab.T
# 生成平移向量
translation_ab = np.array([np.random.uniform(-0.5, 0.5), np.random.uniform(-0.5, 0.5),
np.random.uniform(-0.5, 0.5)])
translation_ba = -R_ba.dot(translation_ab)
pointcloud1 = pointcloud.T
rotation_ab = Rotation.from_euler('zyx', [anglez, angley, anglex])
pointcloud2 = rotation_ab.apply(pointcloud1.T).T + np.expand_dims(translation_ab, axis=1)
euler_ab = np.asarray([anglez, angley, anglex])
euler_ba = -euler_ab[::-1]
pointcloud1 = np.random.permutation(pointcloud1.T).T
pointcloud2 = np.random.permutation(pointcloud2.T).T
if self.gaussian_noise:
pointcloud1 = jitter_pointcloud(pointcloud1)
pointcloud2 = jitter_pointcloud(pointcloud2)
if self.subsampled:
pointcloud1, pointcloud2 = farthest_subsample_points(pointcloud1, pointcloud2,
num_subsampled_points=self.num_subsampled_points)
return pointcloud1.astype('float32'), pointcloud2.astype('float32'), R_ab.astype('float32'), \
translation_ab.astype('float32'), R_ba.astype('float32'), translation_ba.astype('float32'), \
euler_ab.astype('float32'), euler_ba.astype('float32')
def __len__(self):
return self.data.shape[0]
class SceneflowDataset(Dataset):
def __init__(self, npoints=2048, root='data_preprocessing/data_processed_maxcut_35_both_mask_20k_2k', partition='train'):
self.npoints = npoints
self.partition = partition
self.root = root
if self.partition=='train':
self.datapath = glob.glob(os.path.join(self.root, 'TRAIN*.npz'))
else:
self.datapath = glob.glob(os.path.join(self.root, 'TEST*.npz'))
self.cache = {}
self.cache_size = 30000
###### deal with one bad datapoint with nan value
self.datapath = [d for d in self.datapath if 'TRAIN_C_0140_left_0006-0' not in d]
######
print(self.partition, ': ',len(self.datapath))
def __getitem__(self, index):
if index in self.cache:
pos1, pos2, color1, color2, flow, mask1 = self.cache[index]
else:
fn = self.datapath[index]
with open(fn, 'rb') as fp:
data = np.load(fp)
pos1 = data['points1'].astype('float32')
pos2 = data['points2'].astype('float32')
color1 = data['color1'].astype('float32')
color2 = data['color2'].astype('float32')
flow = data['flow'].astype('float32')
mask1 = data['valid_mask1']
if len(self.cache) < self.cache_size:
self.cache[index] = (pos1, pos2, color1, color2, flow, mask1)
if self.partition == 'train':
n1 = pos1.shape[0]
sample_idx1 = np.random.choice(n1, self.npoints, replace=False)
n2 = pos2.shape[0]
sample_idx2 = np.random.choice(n2, self.npoints, replace=False)
pos1 = pos1[sample_idx1, :]
pos2 = pos2[sample_idx2, :]
color1 = color1[sample_idx1, :]
color2 = color2[sample_idx2, :]
flow = flow[sample_idx1, :]
mask1 = mask1[sample_idx1]
else:
pos1 = pos1[:self.npoints, :]
pos2 = pos2[:self.npoints, :]
color1 = color1[:self.npoints, :]
color2 = color2[:self.npoints, :]
flow = flow[:self.npoints, :]
mask1 = mask1[:self.npoints]
pos1_center = np.mean(pos1, 0)
pos1 -= pos1_center
pos2 -= pos1_center
return pos1, pos2, color1, color2, flow, mask1
def __len__(self):
return len(self.datapath)
if __name__ == '__main__':
train = ModelNet40(1024)
test = ModelNet40(1024, 'test')
for data in train:
print(data[0].shape)
break