provider.py内包含:1.数据集modelnet下载 2.数据集扰动旋转等操作 3.数据文件信息加载操作。
9个函数定义,结构图如下
代码与自己理解的注释
import os
import sys
import numpy as np
import h5py
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
# Download dataset for point cloud classification
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 shuffle_data(data, labels):
""" Shuffle data and labels.
Input:
data: B,N,... numpy array
label: B,... numpy array
Return:
shuffled data, label and shuffle indices
"""
idx = np.arange(len(labels))
#arange创建等差数列,0到最大值,也就是labels的编号
np.random.shuffle(idx)#随机打乱idx
return data[idx, ...], labels[idx], idx
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)
#根据batch_data的矩阵结构,构造一个元素都是0的矩阵
for k in range(batch_data.shape[0]):
rotation_angle = np.random.uniform() * 2 * np.pi#随机的一个转角
cosval = np.cos(rotation_angle)
sinval = np.sin(rotation_angle)
rotation_matrix = np.array([[cosval, 0, sinval],
[0, 1, 0],
[-sinval, 0, cosval]])#旋转矩阵
shape_pc = batch_data[k, ...]
rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
#一个shape_pc内切成无数个3元素的数组
return rotated_data
def rotate_point_cloud_by_angle(batch_data, rotation_angle):
""" Rotate the point cloud along up direction with certain angle.
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 range(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 jitter_point_cloud(batch_data, sigma=0.01, clip=0.05):
""" Randomly jitter points. jittering is per point.
Input:
BxNx3 array, original batch of point clouds
Return:
BxNx3 array, jittered batch of point clouds
"""
B, N, C = batch_data.shape
assert(clip > 0)
jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip)
#正负clip间的正态分布加到batch_data
jittered_data += batch_data
return jittered_data
def getDataFiles(list_filename):
return [line.rstrip() for line in open(list_filename)]
def load_h5(h5_filename):
f = h5py.File(h5_filename)
data = f['data'][:]
label = f['label'][:]
return (data, label)
def loadDataFile(filename):
return load_h5(filename)
def load_h5_data_label_seg(h5_filename):
f = h5py.File(h5_filename)
data = f['data'][:]
label = f['label'][:]
seg = f['pid'][:]
return (data, label, seg)
def loadDataFile_with_seg(filename):
return load_h5_data_label_seg(filename)