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modelnet_dataset.py
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modelnet_dataset.py
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'''
ModelNet dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points.
'''
import os
import os.path
import json
import numpy as np
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'utils'))
import provider
def pc_normalize(pc):
l = pc.shape[0]
centroid = np.mean(pc, axis=0)
pc = pc - centroid
m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
pc = pc / m
return pc
class ModelNetDataset():
def __init__(self, root, batch_size = 32, npoints = 1024, split='train', normalize=True, normal_channel=False, modelnet10=False, cache_size=15000, shuffle=None):
self.root = root
self.batch_size = batch_size
self.npoints = npoints
self.normalize = normalize
if modelnet10:
self.catfile = os.path.join(self.root, 'modelnet10_shape_names.txt')
else:
self.catfile = os.path.join(self.root, 'shape_names.txt')
self.cat = [line.rstrip() for line in open(self.catfile)]
self.classes = dict(zip(self.cat, range(len(self.cat))))
self.normal_channel = normal_channel
shape_ids = {}
if modelnet10:
shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_train.txt'))]
shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_test.txt'))]
else:
shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_train.txt'))]
shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_test.txt'))]
assert(split=='train' or split=='test')
shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]]
# list of (shape_name, shape_txt_file_path) tuple
self.datapath = [(shape_names[i], os.path.join(self.root, shape_names[i], shape_ids[split][i])+'.txt') for i in range(len(shape_ids[split]))]
self.cache_size = cache_size # how many data points to cache in memory
self.cache = {} # from index to (point_set, cls) tuple
if shuffle is None:
if split == 'train': self.shuffle = True
else: self.shuffle = False
else:
self.shuffle = shuffle
self.reset()
def _augment_batch_data(self, batch_data):
if self.normal_channel:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def _get_item(self, index):
if index in self.cache:
point_set, cls = self.cache[index]
else:
fn = self.datapath[index]
cls = self.classes[self.datapath[index][0]]
cls = np.array([cls]).astype(np.int32)
point_set = np.loadtxt(fn[1],delimiter=',').astype(np.float32)
# Take the first npoints
point_set = point_set[0:self.npoints,:]
if self.normalize:
point_set[:,0:3] = pc_normalize(point_set[:,0:3])
if not self.normal_channel:
point_set = point_set[:,0:3]
if len(self.cache) < self.cache_size:
self.cache[index] = (point_set, cls)
return point_set, cls
def __getitem__(self, index):
return self._get_item(index)
def __len__(self):
return len(self.datapath)
def num_channel(self):
if self.normal_channel:
return 6
else:
return 3
def reset(self):
self.idxs = np.arange(0, len(self.datapath))
if self.shuffle:
np.random.shuffle(self.idxs)
self.num_batches = (len(self.datapath)+self.batch_size-1) // self.batch_size
self.batch_idx = 0
def has_next_batch(self):
return self.batch_idx < self.num_batches
def next_batch(self, augment=False):
''' returned dimension may be smaller than self.batch_size '''
start_idx = self.batch_idx * self.batch_size
end_idx = min((self.batch_idx+1) * self.batch_size, len(self.datapath))
bsize = end_idx - start_idx
batch_data = np.zeros((bsize, self.npoints, self.num_channel()))
batch_label = np.zeros((bsize), dtype=np.int32)
for i in range(bsize):
ps,cls = self._get_item(self.idxs[i+start_idx])
batch_data[i] = ps
batch_label[i] = cls
self.batch_idx += 1
if augment: batch_data = self._augment_batch_data(batch_data)
return batch_data, batch_label
if __name__ == '__main__':
d = ModelNetDataset(root = '../data/modelnet40_normal_resampled', split='test')
print(d.shuffle)
print(len(d))
import time
tic = time.time()
for i in range(10):
ps, cls = d[i]
print(time.time() - tic)
print(ps.shape, type(ps), cls)
print(d.has_next_batch())
ps_batch, cls_batch = d.next_batch(True)
print(ps_batch.shape)
print(cls_batch.shape)