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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
+
+
+# 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, 'data')
+    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
+
+
+class ModelNet40(Dataset):
+    def __init__(self, num_points, 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
+        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]])
+        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.T.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
+
+        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]
+
+
+if __name__ == '__main__':
+    train = ModelNet40(1024)
+    test = ModelNet40(1024, 'test')
+    for data in train:
+        print(len(data))
+        break