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modelnet40_dataset_orig.py
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'''
ModelNet dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points.
Edited by Mariam Ahmed
'''
import os
import os.path
import json
import numpy as np
import tensorflow as tf
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
sys.path.append('/home/mariam/pointnet2/tf_ops/sampling')
import provider
from numpy.linalg import inv
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection
from tf_grouping import query_ball_point, group_point, knn_point
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import NearestNeighbors
from plot_cube import plot_cube
from numpy import linalg as LA
from scipy.spatial import distance
from sklearn.cluster import KMeans
from scipy.interpolate import griddata
import random
import math
from random import randint
from numpy import linspace, meshgrid
import matplotlib.cm as cm
import random
from PIL import Image
from scipy.misc import imsave
from scipy.spatial.distance import cdist
total_count = [0 for _ in range(40)]
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
def denormalize(pc):
#pc = pc[:,0].values.reshape(-1,1)
#norm_output = norm_output.reshape(-1,1)
X_std = (pc - (-1)) / (1 - (-1))
norm_output = X_std * (3.142 - (-3.142)) + (-3.142)
return norm_output
def hex_to_rgb(value):
#value = value.lstrip('#')
lv = len(value)
return tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3))
def normalize_rot(pc):
#pc = pc[:,0].values.reshape(-1,1)
#norm_output = norm_output.reshape(-1,1)
X_std = (pc - (-3.142)) / (3.142 - (-3.142))
norm_output = X_std * (1 - (-1)) + (-1)
return norm_output
def quaternion_to_euler(x, y, z, w):
t0 = +2.0 * (w * x + y * z)
t1 = +1.0 - 2.0 * (x * x + y * y)
roll = math.atan2(t0, t1)
t2 = +2.0 * (w * y - z * x)
t2 = +1.0 if t2 > +1.0 else t2
t2 = -1.0 if t2 < -1.0 else t2
pitch = math.asin(t2)
t3 = +2.0 * (w * z + x * y)
t4 = +1.0 - 2.0 * (y * y + z * z)
yaw = math.atan2(t3, t4)
return [yaw, pitch, roll]
def mat2quat(M):
# Qyx refers to the contribution of the y input vector component to
# the x output vector component. Qyx is therefore the same as
# M[0,1]. The notation is from the Wikipedia article.
Qxx, Qyx, Qzx, Qxy, Qyy, Qzy, Qxz, Qyz, Qzz = M.flat
# Fill only lower half of symmetric matrix
K = np.array([
[Qxx - Qyy - Qzz, 0, 0, 0 ],
[Qyx + Qxy, Qyy - Qxx - Qzz, 0, 0 ],
[Qzx + Qxz, Qzy + Qyz, Qzz - Qxx - Qyy, 0 ],
[Qyz - Qzy, Qzx - Qxz, Qxy - Qyx, Qxx + Qyy + Qzz]]
) / 3.0
# Use Hermitian eigenvectors, values for speed
vals, vecs = np.linalg.eigh(K)
# Select largest eigenvector, reorder to w,x,y,z quaternion
q = vecs[[3, 0, 1, 2], np.argmax(vals)]
# Prefer quaternion with positive w
# (q * -1 corresponds to same rotation as q)
if q[0] < 0:
q *= -1
return q
def rotz(z):
mat = np.zeros((4,4),dtype = np.float32)
mat[0,0:4] = [np.cos(z), -np.sin(z), 0, 0]
mat[1,0:4] = [np.sin(z), np.cos(z), 0, 0]
mat[2,0:4] = [0, 0, 1, 0]
mat[3,0:4] = [0, 0, 0, 1]
return mat
def rotx(z):
mat = np.zeros((4,4),dtype = np.float32)
mat[0,0:4] = [1, 0, 0, 0]
mat[1,0:4] = [0, np.cos(z), -np.sin(z), 0]
mat[2,0:4] = [0, np.sin(z), np.cos(z), 0]
mat[3,0:4] = [0, 0, 0, 1]
return mat
def find_index(datapath,name,num,pose):
des_id = name +"_"+num+"_"+pose
with open(datapath) as f:
scene_id = f.readlines()
for i in range (0,len(scene_id)):
ind = scene_id[i].split(",")
if(des_id == ind[0]):
#x, y, z, offset_w, offset_l, offset_h, offset_theta
pose = [float(ind[1]),float(ind[2]),float(ind[3]),float(ind[4]),float(ind[5]),float(ind[6]),float(ind[7])]
label = int(ind[9])
break
return (pose,label)
class ModelNetDataset():
def __init__(self, root, batch_size = 32, npoints = 1024, split='train', normalize=True, normal_channel=False, modelnet10=True, cache_size=15000, shuffle=None):
self.root = root
self.batch_size = batch_size
self.npoints = npoints
self.normalize = normalize
self.classfile = os.path.join(self.root, 'shape_names.txt')
self.catclss = [line.rstrip() for line in open(self.classfile)]
self.classes1 = dict(zip(self.catclss, range(len(self.catclss))))
self.normal_channel = normal_channel
shape_ids = {}
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_full_test.txt'))]
assert(split=='train' or split=='test')
if (split=='train'):
shape_names = ['_'.join(x.split('_')[0:-2]) for x in shape_ids[split]]
else:
shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]]#-2
shape_pose = ['_'.join(x.split('_')[-1:]) for x in shape_ids[split]]
shape_num = ['_'.join(x.split('_')[-2:-1]) for x in shape_ids[split]]
shape_trans = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]]
if (split=='train'):
self.datapath = [(shape_names[i],shape_num[i],shape_pose[i], os.path.join(self.root, shape_names[i], shape_ids[split][i])+'.txt') for i in range(len(shape_ids[split]))]
else:
self.datapath = [(shape_names[i],shape_num[i],shape_pose[i], os.path.join("/media/mariam/Seagate Backup Plus Drive/ModelNet/modelnet40_normal_resampled", shape_names[i], shape_ids[split][i])+'.txt') for i in range(len(shape_ids[split]))]
self.cache_size = cache_size
self.cache = {}
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):
rotated_data = provider.rotate_point_cloud(batch_data)
return provider.shuffle_points(rotated_data)
def _augment_batch_data_pose(self, batch_data,batch_label):
#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,rot_label = provider.rotate_point_cloud_with_label(batch_data,denormalize(batch_label))
rot_label[:,0:3] = normalize_rot(rot_label[:,0:3])
return rotated_data,rot_label
def _get_item(self, index,data='train'): #modify this
if index in self.cache:
point,name,bb_param,bb_label,bb_edge3 = self.cache[index]
else:
if data == 'train':
#Load class
name = self.classes1[self.datapath[index][0]]
name = np.array([name]).astype(np.int32)
#Load points from file
fn = self.datapath[index]
point = np.loadtxt(fn[3],delimiter=',').astype(np.float32)
pose_file = "/home/mariam/pointnet2/data/ModelNet40/"+self.datapath[index][0]+"/" + self.datapath[index][0]+ "_"+self.datapath[index][1]+".txt"
f = open(pose_file,"r")
i = 0
arr = []
arrays = np.zeros((486,4), dtype=np.float32)
for line in f:
arr = np.array(line.split(),dtype=np.float32)
arrays[i,0:4] = arr[0:4]
i = i +1
#Apply transformation
trans = arrays[(int(self.datapath[index][2]))*3:(int(self.datapath[index][2]))*3+3,3]
trans = np.transpose(trans)
rot_mat = arrays[int(self.datapath[index][2])*3:(int(self.datapath[index][2]))*3+3,0:3]
T = np.zeros((4,4),dtype = np.float32)
data = np.zeros((1024,4),dtype = np.float32)
size = point[:,0].size
if(size>1024):
data[0:1024,0:3] = point[0:1024,0:3]
else:
less = 1024-size
data[0:size,0:3] = point[:,0:3]
ind = np.random.choice(point.shape[0], less, replace=True)
data[size:1024,0:3] = point[ind[:],0:3]
point = data[:,0:3]
T[0:3,0:3] = rot_mat
T[3,0:3] = 0
T[0:3,3] = trans
T[3,3] = 1
rot_inv = inv(rot_mat)
data[:,3] = 1
ps_trans = np.transpose(np.matmul(inv(T),np.transpose(data)))
quat = mat2quat(T[0:3,0:3])
euler = quaternion_to_euler(quat[1],quat[2],quat[3],quat[0])
rand_head = random.uniform(-0.175, +0.175)
z_mat = rotz(euler[0]+rand_head)#heading change will be +/- 10 degrees
#Rotate data around z axis
pcd = np.transpose(np.matmul(z_mat,np.transpose(ps_trans)))
point = pcd[:,0:3]
if(name==1 or name==2):
point[:,0] = point[:,0]*0.5
point[:,2] = point[:,2]*0.5
if(name==3):
point[:,0] = point[:,0]*0.8
point[:,1] = point[:,1]*0.4
point[:,1] = point[:,2]*0.4
if(name==4):
point[:,0] = point[:,0]*0.5
point[:,1] = point[:,1]*0.2
point[:,2] = point[:,2]*0.5
if(name==5):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.4
if(name==6 ):
point[:,2] = point[:,2]*0.5
if(name==7):
point[:,1] = point[:,1]*0.5
point[:,2] = point[:,2]*0.5
if(name==8):
point[:,0] = point[:,0]*0.4
point[:,2] = point[:,2]*0.6
if(name==10):
point[:,0] = point[:,0]*0.6
point[:,1] = point[:,1]*0.6
point[:,2] = point[:,2]*0.8
if(name==11):
point[:,0] = point[:,0]*0.1
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.8
if(name==12):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.6
point[:,2] = point[:,2]*0.6
if(name==13):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.1
if(name==14):
point[:,0] = point[:,0]*0.4
point[:,2] = point[:,2]*0.6
if(name==15):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]
if(name==16):
point[:,0] = point[:,0]*0.3
point[:,2] = point[:,2]*0.2
if(name==19):
point[:,0] = point[:,0]*0.5
point[:,1] = point[:,1]*0.6
if(name==20):
point[:,0] = point[:,0]*0.8
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.4
if(name==21):
point[:,0] = point[:,0]*0.6
point[:,1] = point[:,1]*0.4
if(name==22):
point[:,0] = point[:,0]*0.6
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.6
if(name==23):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.7
if(name==24):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.8
if(name==25):
point[:,0] = point[:,0]*0.6
point[:,1] = point[:,1]*0.6
point[:,2] = point[:,2]*0.7
if(name==27):
point[:,0] = point[:,0]*0.1
point[:,1] = point[:,1]*0.1
point[:,2] = point[:,2]*0.05
if(name==28):
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.7
if(name==29):
point[:,0] = point[:,0]*0.8
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.4
if(name==30):
point[:,0] = point[:,0]*0.6
point[:,1] = point[:,1]*0.6
point[:,2] = point[:,2]*0.6
if(name==31):
point[:,0] = point[:,0]*0.6
point[:,1] = point[:,1]*0.2
point[:,2] = point[:,2]*0.6
if(name==32):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.75
if(name==33):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.8
point[:,2] = point[:,2]*0.5
if(name==34):
point[:,0] = point[:,0]*0.3
point[:,1] = point[:,1]*0.8
point[:,2] = point[:,2]*0.4
if(name==35):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.8
point[:,2] = point[:,2]*0.7
if(name==36):
point[:,1] = point[:,1]*0.4
point[:,2] = point[:,2]*0.4
if(name==37):
point[:,0] = point[:,0]*0.8
point[:,1] = point[:,1]*0.8
point[:,2] = point[:,2]*0.8
if(name==38):
point[:,0] = point[:,0]*0.4
point[:,1] = point[:,1]*0.2
point[:,2] = point[:,2]*0.75
if(name==39):
point[:,0] = point[:,0]*0.2
point[:,1] = point[:,1]*0.6
point[:,2] = point[:,2]*0.8
#Find Edge points
nbrs = NearestNeighbors(n_neighbors=10, algorithm='ball_tree').fit(point)
distances, indices = nbrs.kneighbors(point)
centroids = np.mean(point[indices[:,:],0:3],axis=1)
dist = np.sqrt(np.sum((point[:,0:3]-centroids)**2,axis=1))
edges = np.zeros((1024,3), dtype=np.float32)
binary_edges = np.zeros((1024,4), dtype=np.float32)
#edges[:,0] = 1*(dist[:]>0.015)
edges[:,0] = 1*(dist[:]>0.02)
binary_edges[:,0:3] = point[:,:]
binary_edges[:,3] = edges[:,0]
ind = np.where(edges[:,0]==1)[0]
edges[ind,0:3] = point[ind,0:3]
#point = binary_edges
bb_edge = binary_edges[:,3]
#Convert Edge to 2D maps
x = np.arange(-1.0, 1.0, 0.0625)
y = np.arange(-1.0, 1.0, 0.0625)
#x, y = np.meshgrid(x, y)
p = np.zeros((1, 2))
grds_xy = np.zeros((32,32))
for l in range (0,32):
for t in range (0,32):
p[0,0] = x[l]
p[0,1] = y[t]
d = min(np.squeeze(cdist(p,binary_edges[:,0:2])))
if(d<0.1):
grds_xy[l,t] = 1
grds_yz = np.zeros((32,32))
for l in range (0,32):
for t in range (0,32):
p[0,0] = x[l]
p[0,1] = y[t]
d = min(np.squeeze(cdist(p,binary_edges[:,1:3])))
if(d<0.1):
grds_yz[l,t] = 1
grds_xz = np.zeros((32,32))
e = np.zeros((1024,2))
e[:,0] = binary_edges[:,0]
e[:,1] = binary_edges[:,2]
for l in range (0,32):
for t in range (0,32):
p[0,0] = x[l]
p[0,1] = y[t]
d = min(np.squeeze(cdist(p,e)))
if(d<0.1):
grds_xz[l,t] = 1
bb_edge3 = np.zeros((32,32,3))
bb_edge3[:,:,0] = grds_xy
bb_edge3[:,:,1] = grds_yz
bb_edge3[:,:,2] = grds_xz
'''
# plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(edges[0:1024,0], edges[0:1024,1], edges[0:1024,2],color='yellow')
plt.show()
if(total_count[int(name)]==0):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(point[:,0], point[:,1],point[:,2],color='red')
ax.scatter(edges[0:1024,0], edges[0:1024,1],edges[0:1024,2],color='yellow')
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.scatter(edges[0:1024,0], edges[0:1024,1],color='red')
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.scatter(edges[0:1024,1], edges[0:1024,2],color='red')
fig3 = plt.figure()
ax3 = fig3.add_subplot(111)
ax3.scatter(edges[0:1024,0], edges[0:1024,2],color='red')
print(name)
ax.set_xlabel('x-axis')
ax.set_ylabel('y-axis')
ax1.set_xlabel('x-axis')
ax1.set_ylabel('y-axis')
plt.show()
total_count[int(name)] += 1
'''
#Load pose data and anchor label from dictionary
bb_path = "/home/mariam/pointnet2/data/ModelNet40/gt_"+self.datapath[index][0]+"_one.txt"
[bb_param,bb_label] = find_index(bb_path,self.datapath[index][0],self.datapath[index][1],self.datapath[index][2])
bb_param[6] += rand_head
else:
#Load class
name = self.classes1[self.datapath[index][0]]
name = np.array([name]).astype(np.int32)
#Load points from file
fn = self.datapath[index]
point = np.loadtxt(fn[3],delimiter=',').astype(np.float32)
data = np.zeros((1024,4), dtype=np.float32)
size = point[:,0].size
if(size>1024):
ind = np.random.choice(point.shape[0], 1024, replace=False)
data[0:1024,0:3] = point[ind[:],0:3]
else:
less = 1024-size
data[0:size,0:3] = point[:,0:3]
ind = np.random.choice(point.shape[0], less, replace=True)
data[size:1024,0:3] = point[ind[:],0:3]
point = data[:,0:3]
if(name == 18):
x_mat = rotx(-0.57)
data[:,3] = 1
pcd = np.transpose(np.matmul(x_mat,np.transpose(data)))
point = pcd[:,0:3]
else:
x_mat = rotx(1.57)
data[:,3] = 1
pcd = np.transpose(np.matmul(x_mat,np.transpose(data)))
point = pcd[:,0:3]
#Find Edge points
nbrs = NearestNeighbors(n_neighbors=100, algorithm='ball_tree').fit(point)
distances, indices = nbrs.kneighbors(point)
centroids = np.mean(point[indices[:,:],0:3],axis=1)
dist = np.sqrt(np.sum((point[:,0:3]-centroids)**2,axis=1))
edges = np.zeros((len(point),3), dtype=np.float32)
binary_edges = np.zeros((len(point),4), dtype=np.float32)
edges[:,0] = 1*(dist[:]>0.035)
binary_edges[:,0:3] = point[:,:]
binary_edges[:,3] = edges[:,0]
#point = binary_edges
ind = np.where(edges[:,0]==1)[0]
edges[ind,0:3] = point[ind,0:3]
bb_edge = binary_edges[:,3]
#Convert Edge to 2D maps
x = np.arange(-1.0, 1.0, 0.0625)
y = np.arange(-1.0, 1.0, 0.0625)
#x, y = np.meshgrid(x, y)
p = np.zeros((1, 2))
grds_xy = np.zeros((32,32))
for l in range (0,32):
for t in range (0,32):
p[0,0] = x[l]
p[0,1] = y[t]
d = min(np.squeeze(cdist(p,binary_edges[:,0:2])))
if(d<0.1):
grds_xy[l,t] = 1
grds_yz = np.zeros((32,32))
for l in range (0,32):
for t in range (0,32):
p[0,0] = x[l]
p[0,1] = y[t]
d = min(np.squeeze(cdist(p,binary_edges[:,1:3])))
if(d<0.1):
grds_yz[l,t] = 1
grds_xz = np.zeros((32,32))
e = np.zeros((1024,2))
e[:,0] = binary_edges[:,0]
e[:,1] = binary_edges[:,2]
for l in range (0,32):
for t in range (0,32):
p[0,0] = x[l]
p[0,1] = y[t]
d = min(np.squeeze(cdist(p,e)))
if(d<0.1):
grds_xz[l,t] = 1
bb_edge3 = np.zeros((32,32,3))
bb_edge3[:,:,0] = grds_xy
bb_edge3[:,:,1] = grds_yz
bb_edge3[:,:,2] = grds_xz
'''
imsave('edge_xy.png', grds_xy)
imsave('edge_yz.png', grds_yz)
imsave('edge_xz.png', grds_xz)
if(total_count[int(name)]==0):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(point[:,0], point[:,1],point[:,2],color='red')
ax.scatter(edges[0:1024,0], edges[0:1024,1],edges[0:1024,2],color='yellow')
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.scatter(edges[0:1024,0], edges[0:1024,1],color='red')
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.scatter(edges[0:1024,1], edges[0:1024,2],color='red')
fig3 = plt.figure()
ax3 = fig3.add_subplot(111)
ax3.scatter(edges[0:1024,0], edges[0:1024,2],color='red')
print(name)
ax.set_xlabel('x-axis')
ax.set_ylabel('y-axis')
ax1.set_xlabel('x-axis')
ax1.set_ylabel('y-axis')
plt.show()
total_count[int(name)] += 1
'''
#Padding the additional pose parameters
bb_param = [0,0,0,0,0,0,0]
bb_label = 0
if len(self.cache) < self.cache_size:
self.cache[index] = (point,name,bb_param,bb_label,bb_edge3)
return point, name,bb_param,bb_label,bb_edge3
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 6
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,data='train'):#modify this
''' 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, 3))
batch_edge = np.zeros((bsize, 32,32,3))
batch_pose = np.zeros((bsize,7), dtype=np.float32)
batch_center = np.zeros((bsize,3), dtype=np.float32)
batch_id = np.empty([bsize,2],dtype="<U12")
batch_class = np.zeros((bsize), dtype=np.int32)
batch_anchor = np.zeros((bsize), dtype=np.int32)
for i in range(bsize):
ps,name,bb,anchor,edge = self._get_item(self.idxs[i+start_idx],data)#cls,cen,ids,
batch_data[i] = ps
batch_pose[i] = bb
#batch_center[i] = cen
#batch_id[i] = ids
batch_class[i] = name
batch_anchor[i] = anchor
batch_edge[i,...] = edge
self.batch_idx += 1
if augment: batch_data,batch_pose = self._augment_batch_data_pose(batch_data,batch_pose)
return batch_data,batch_class,batch_pose,batch_anchor,batch_edge
if __name__ == '__main__':
d = ModelNetDataset(root = '../data/modelnet10_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, cen_batch = d.next_batch(True)
print(ps_batch.shape)
print(cls_batch.shape)