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part_dataset.py
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part_dataset.py
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'''
Dataset for shapenet part segmentaion.
'''
import os
import os.path
import json
import numpy as np
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
def pc_normalize(pc):
""" pc: NxC, return NxC """
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
def rotate_point_cloud(batch_data):
""" Randomly rotate the point clouds to augument the dataset
rotation is per shape based along up direction
Input:
BxNx3 array, original batch of point clouds
Return:
BxNx3 array, rotated batch of point clouds
"""
rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
for k in xrange(batch_data.shape[0]):
rotation_angle = np.random.uniform() * 2 * np.pi
cosval = np.cos(rotation_angle)
sinval = np.sin(rotation_angle)
rotation_matrix = np.array([[cosval, 0, sinval],
[0, 1, 0],
[-sinval, 0, cosval]])
shape_pc = batch_data[k, ...]
rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
return rotated_data
def add_noise_coordinates(batch_data, std=0.01):
noise = np.random.normal(loc=0.0, scale=std, size=batch_data.shape)
return batch_data + noise
def add_noise_nullify_random(batch_data, rat=0.1):
indices = np.random.choice(np.arange(batch_data.shape[1]), replace=False,
size=int(batch_data.shape[1] * rat))
noised_batch = np.copy(batch_data)
noised_batch[:, indices] = 0
return noised_batch
def add_noise_nullify_sector(batch_data, rat=0.1):
noised_batch = np.copy(batch_data)
sector_centers = np.random.uniform(-1., 1, (batch_data.shape[0], 1, 3))
diffs = noised_batch - np.repeat(sector_centers, batch_data.shape[1], axis=1)
dists = np.linalg.norm(diffs, axis=2)
quantiles = np.quantile(dists, rat, axis=1)
quantiles = np.repeat(quantiles[:, np.newaxis], 2048, axis=1)
noised_batch[np.where(dists < quantiles)] = 0
return noised_batch
def add_noise_everywhere(batch_data, rat=0.1):
noised_batch = np.copy(batch_data)
indices = np.random.choice(np.arange(noised_batch.shape[1]), replace=False,
size=int(noised_batch.shape[1] * rat))
noised_batch[:, indices] = np.random.uniform(-1., 1., size=(noised_batch.shape[0], indices.size, 3))
return noised_batch
def add_noise(batch_data, rat=0.1, proportion=(1./3, 1./3, 1./3)):
noise_type = np.random.choice(3, p=proportion)
if noise_type == 0:
return add_noise_nullify_random(batch_data, rat)
elif noise_type == 1:
return add_noise_nullify_sector(batch_data, rat)
elif noise_type == 2:
return add_noise_everywhere(batch_data, rat)
class PartDataset():
def __init__(self, root, npoints = 2500, classification = False, class_choice = None, split='train', normalize=True):
self.npoints = npoints
self.root = root
self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
self.cat = {}
self.classification = classification
self.normalize = normalize
with open(self.catfile, 'r') as f:
for line in f:
ls = line.strip().split()
self.cat[ls[0]] = ls[1]
if class_choice is not None:
self.cat = {k:v for k,v in self.cat.items() if k in class_choice}
self.meta = {}
with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f:
train_ids = set([str(d.split('/')[2]) for d in json.load(f)])
with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f:
val_ids = set([str(d.split('/')[2]) for d in json.load(f)])
with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f:
test_ids = set([str(d.split('/')[2]) for d in json.load(f)])
for item in self.cat:
self.meta[item] = []
dir_point = os.path.join(self.root, self.cat[item], 'points')
dir_seg = os.path.join(self.root, self.cat[item], 'points_label')
fns = sorted(os.listdir(dir_point))
if split=='trainval':
fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))]
elif split=='train':
fns = [fn for fn in fns if fn[0:-4] in train_ids]
elif split=='val':
fns = [fn for fn in fns if fn[0:-4] in val_ids]
elif split=='test':
fns = [fn for fn in fns if fn[0:-4] in test_ids]
else:
print('Unknown split: %s. Exiting..'%(split))
exit(-1)
for fn in fns:
token = (os.path.splitext(os.path.basename(fn))[0])
self.meta[item].append((os.path.join(dir_point, token + '.pts'), os.path.join(dir_seg, token + '.seg')))
self.datapath = []
for item in self.cat:
for fn in self.meta[item]:
self.datapath.append((item, fn[0], fn[1]))
self.classes = dict(zip(self.cat, range(len(self.cat))))
self.num_seg_classes = 0
if not self.classification:
for i in range(len(self.datapath)/50):
l = len(np.unique(np.loadtxt(self.datapath[i][-1]).astype(np.uint8)))
if l > self.num_seg_classes:
self.num_seg_classes = l
self.cache = {} # from index to (point_set, cls, seg) tuple
self.cache_size = 18000
def __getitem__(self, index):
if index in self.cache:
point_set, seg, 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]).astype(np.float32)
if self.normalize:
point_set = pc_normalize(point_set)
seg = np.loadtxt(fn[2]).astype(np.int64) - 1
if len(self.cache) < self.cache_size:
self.cache[index] = (point_set, seg, cls)
choice = np.random.choice(len(seg), self.npoints, replace=True)
#resample
point_set = point_set[choice, :]
seg = seg[choice]
if self.classification:
return point_set, cls
else:
return point_set, seg
def __len__(self):
return len(self.datapath)
if __name__ == '__main__':
d = PartDataset(root = os.path.join(BASE_DIR, 'data/shapenetcore_partanno_segmentation_benchmark_v0'), class_choice = ['Chair'], split='trainval')
print(len(d))
import time
tic = time.time()
i = 100
ps, seg = d[i]
print np.max(seg), np.min(seg)
print(time.time() - tic)
print(ps.shape, type(ps), seg.shape,type(seg))
sys.path.append('utils')
import show3d_balls
show3d_balls.showpoints(ps, ballradius=8)
d = PartDataset(root = os.path.join(BASE_DIR, 'data/shapenetcore_partanno_segmentation_benchmark_v0'), classification = True)
print(len(d))
ps, cls = d[0]
print(ps.shape, type(ps), cls.shape,type(cls))