pointnet++代码逐行解析（四）——— modelnetdataloader

继续巩固PointNet++代码的实现这篇博客，把代码逐行注释一遍！
pointnet++的所有代码和数据集都在github上，Pytorch代码：https://github.com/yanx27/Pointnet2_pytorch
data_utils中的modelnetdataloader部分的python代码注释如下：

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# 导入第三方库
import numpy as np
import warnings
import os
from torch.utils.data import Dataset
warnings.filterwarnings('ignore')


# 将数据归一化
def pc_normalize(pc):
    # 计算pc簇的中心点，新的中心点每一个特征的值，是该簇所有数据在该特征的平均值
    centroid = np.mean(pc, axis=0)
    # 3D数据簇减去中心得到到中心的绝对距离
    pc = pc - centroid
    # 取到最大距离
    m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
    # 将数据归一化
    pc = pc / m
    return pc


# 最远点采样
def farthest_point_sample(point, npoint):
    """
    Input:
        xyz: pointcloud data, [N, D]
        npoint: number of samples
    Return:
        centroids: sampled pointcloud index, [npoint, D]
    """

    N, D = point.shape
    xyz = point[:, :3]

    # 先随机初始化一个centroids矩阵，
    # 后面用于存储npoint个采样点的索引位置
    centroids = np.zeros((npoint,))

    # 利用distance矩阵记录某个样本中所有点到某一个点的距离
    distance = np.ones((N,)) * 1e10  # 初值给个比较大的值，后面会迭代更新

    # 利用farthest表示当前最远的点，也是随机初始化，范围为0~N
    farthest = np.random.randint(0, N)

    # 直到采样点达到npoint，否则进行如下迭代
    for i in range(npoint):

        # 设当前的采样点centroids为当前的最远点farthest；
        centroids[i] = farthest

        # 取出这个中心点centroid的坐标
        centroid = xyz[farthest, :]

        # 求出所有点到这个farthest点的欧式距离，存在dist矩阵中
        dist = np.sum((xyz - centroid) ** 2, -1)

        # 建立一个mask，如果dist中的元素小于distance矩阵中保存的距离值，
        # 则更新distance中的对应值，
        # 即记录某个样本中每个点距离所有已出现的采样点的最小距离
        mask = dist < distance
        distance[mask] = dist[mask]

        # 最后从distance矩阵取出最远的点为farthest，继续下一轮迭代
        farthest = np.argmax(distance, -1)

    point = point[centroids.astype(np.int32)]

    # 返回结果是npoint个采样点在原始点云中的索引
    return point


# 加载数据集
class ModelNetDataLoader(Dataset):

    def __init__(self, root,  npoint=1024, split='train', uniform=False, normal_channel=True, cache_size=15000):
        self.root = root       # 数据集根目录
        self.npoints = npoint  # 对原始数据集下采样至1024个点
        self.uniform = uniform  # 是否归一化
        self.catfile = os.path.join(self.root, 'modelnet40_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 = {}

        # 将数据集划分为训练集和测试集
        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]]

        # (shape_name, shape_txt_file_path) 元组列表
        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]))]
        print('The size of %s data is %d' % (split, len(self.datapath)))

        # 在内存中缓存数据点的大小
        self.cache_size = cache_size  # 核函数cache缓存大小，默认设置为15000
        self.cache = {}  # 从索引到(point_set, cls) 元组

    def __len__(self):
        return len(self.datapath)

    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)
            if self.uniform:
                point_set = farthest_point_sample(point_set, self.npoints)
            else:
                point_set = point_set[0:self.npoints,:]

            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  # 返回点云集及其分类

    def __getitem__(self, index):
        return self._get_item(index)


if __name__ == '__main__':  # 测试模型导入是否无误
    # 导入第三方库
    import torch
    # 导入数据
    data = ModelNetDataLoader('/data/modelnet40_normal_resampled/', split='train', uniform=False, normal_channel=True,)

    DataLoader = torch.utils.data.DataLoader(data, batch_size=12, shuffle=True)

    for point, label in DataLoader:
        print(point.shape)
        print(label.shape)

对比每一行的输出就会明白代码：

标签：set,point,++,self,pointnet,pc,shape,np,逐行
来源： https://blog.csdn.net/weixin_45947476/article/details/121437265