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data_stanford.py
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data_stanford.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
def load_example_data(h5_path: str):
f = h5py.File(h5_path)
pc = f['pc'][:100]
return pc
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 consistent_pc(pc1, pc2):
num_points, dim = pc1.shape # 1024,3
Id_1 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
Id_2 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
pc_xyz1 = Id_1 @ pc1 # 1024,3
pc_xyz2 = Id_2 @ pc2 # 1024,3
I_gt = Id_1 @ Id_2.T # src = id1*id2^T*tgt = Ig*tgt # 1024,1024
return pc_xyz1, pc_xyz2, I_gt
def random_partial_sample(pc1, pc2, num_sample):
num_points, dim = pc1.shape # 1024,3
Id_1 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
Id_2 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
pc_xyz1 = Id_1 @ pc1 # 1024,3
pc_xyz2 = Id_2 @ pc2 # 1024,3
I_gt = Id_1 @ Id_2.T # src = id1*id2^T*tgt = Ig*tgt # 1024,1024
select1 = np.random.permutation(num_points)[:num_sample]
select2 = np.random.permutation(num_points)[:num_sample]
pointcloud1 = pc_xyz1[select1] # num_sample,3
pointcloud2 = pc_xyz2[select2] # num_sample,3
I_gt_temp = I_gt[select1].T
I_gt = I_gt_temp[select2].T
return pointcloud1, pointcloud2, I_gt
def farthest_partial_sample(pointcloud1, pointcloud2, num_subsampled_points=768):
num_points = pointcloud1.shape[0]
Id_1 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
Id_2 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
pc_xyz1 = Id_1 @ pointcloud1 # 1024,3
pc_xyz2 = Id_2 @ pointcloud2 # 1024,3
I_gt = Id_1 @ Id_2.T # src = id1*id2^T*tgt = Ig*tgt # 1024,1024
nbrs1 = NearestNeighbors(n_neighbors=num_subsampled_points, algorithm='auto',
metric=lambda x, y: minkowski(x, y)).fit(pc_xyz1)
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)).fit(pc_xyz2)
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,))
pointcloud1 = pc_xyz1[idx1] # num_sample,3
pointcloud2 = pc_xyz2[idx2] # num_sample,3
I_gt_temp = I_gt[idx1].T
I_gt = I_gt_temp[idx2].T
return pointcloud1, pointcloud2, I_gt
def both_partial_sample(pc1, pc2, num_sample1, num_sample2):
num_points, dim = pc1.shape # 1024,3
Id_1 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
Id_2 = np.eye(num_points)[np.random.permutation(num_points)] # 1024,1024
pc_xyz1 = Id_1 @ pc1 # 1024,3
pc_xyz2 = Id_2 @ pc2 # 1024,3
I_gt = Id_1 @ Id_2.T # src = id1*id2^T*tgt = Ig*tgt # 1024,1024
select1 = np.random.permutation(num_points)[:num_sample1]
select2 = np.random.permutation(num_points)[:num_sample1]
pointcloud1 = pc_xyz1[select1] # num_sample,3
pointcloud2 = pc_xyz2[select2] # num_sample,3
I_gt_temp = I_gt[select1].T
I_gt = I_gt_temp[select2].T
nbrs1 = NearestNeighbors(n_neighbors=num_sample2, algorithm='auto',
metric=lambda x, y: minkowski(x, y)).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_sample2,))
nbrs2 = NearestNeighbors(n_neighbors=num_sample2, algorithm='auto',
metric=lambda x, y: minkowski(x, y)).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_sample2,))
pointcloud1 = pointcloud1[idx1]
pointcloud2 = pointcloud2[idx2]
I_gt_temp = I_gt[idx1].T
I_gt = I_gt_temp[idx2].T
return pointcloud1, pointcloud2, I_gt
class Stanford(Dataset):
def __init__(self, partition ='train', select_ = 'bunny', num_points=1024, factor=4):
if select_ == 'bunny':
file_path = './data/bunny_zhangzhiyuan_entire.h5'
elif select_ == 'dragon':
file_path = './data/dragon_zhangzhiyuan.h5'
elif select_ == 'drill':
file_path = './data/drill_zhangzhiyuan.h5'
elif select_ == 'happy':
file_path = './data/happy_zhangzhiyuan.h5'
pc = load_example_data(file_path) #source: 500*1024*3
if partition == 'train':
self.pc = pc[:80]
else:
self.pc = pc[80:]
#print(self.pc.shape)
self.num_points = num_points
self.factor = factor
self.partition = partition
self.gaussian_noise = False
def __getitem__(self, item):
pointcloud = self.pc[item][:self.num_points]
partial = False
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]])
R_ab = Rx.dot(Ry).dot(Rz)
R_ba = R_ab.T
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 = pointcloud1.T
pointcloud2 = pointcloud2.T
if self.gaussian_noise:
pointcloud1 = jitter_pointcloud(pointcloud1)
pointcloud2 = jitter_pointcloud(pointcloud2)
if partial:
partial_format = 'random'
num_partial_point_first = 896
num_partial_point = 768
if partial_format == 'random':
pointcloud1, pointcloud2, I_gt = random_partial_sample(pointcloud1, pointcloud2, num_partial_point)
if partial_format == 'farthest':
pointcloud1, pointcloud2, I_gt = farthest_partial_sample(pointcloud1, pointcloud2, num_partial_point)
if partial_format == 'both':
pointcloud1, pointcloud2, I_gt = both_partial_sample(pointcloud1, pointcloud2, num_partial_point_first, num_partial_point)
else:
pointcloud1, pointcloud2, I_gt = consistent_pc(pointcloud1, pointcloud2)
return pointcloud1.T.astype('float32'), pointcloud2.T.astype('float32'), R_ab.astype('float32'), \
translation_ab.astype('float32'), I_gt.astype('float32')
def __len__(self):
return self.pc.shape[0]
if __name__ == '__main__':
train = ModelNet40(1024)
test = ModelNet40(1024, 'test')
for data in train:
print(len(data))
break